The status of golden eagles (Aquila chrysaetos) in coastal southern California is unclear. To address this knowledge gap, the U.S. Geological Survey (USGS) in collaboration with local, State, and other Federal agencies began a multi-year survey and tracking program of golden eagles to address questions regarding habitat use, movement behavior, nest occupancy, genetic population structure, and human impacts on eagles. Golden eagle trapping and tracking efforts began in October 2014 and continued until early March 2015. During the first trapping season that focused on San Diego County, we captured 13 golden eagles (8 females and 5 males). During the second trapping season that began in November 2015, we focused on trapping sites in San Diego, Orange, and western Riverside Counties. By February 23, 2016, we captured an additional 14 golden eagles (7 females and 7 males). In this report, biotelemetry data were collected between November 22, 2014, and February 23, 2016. The location data for eagles ranged as far north as San Luis Obispo, California, and as far south as La Paz, Baja California, Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds994","collaboration":"Prepared for San Diego Association of Governments (SANDAG), California Department of Fish and Wildlife, Bureau of Land Management, and U.S. Fish and Wildlife Service","usgsCitation":"Tracey, J.A., Madden, M.C., Sebes, J.B., Bloom, P.H., Katzner, T.E., and Fisher, R.N., 2016, Biotelemetry data for golden eagles (Aquila chrysaetos) captured in coastal southern California, November 2014–February 2016: U.S. Geological Survey Data Series 994, 32 p., https://dx.doi.org/10.3133/ds994.","productDescription":"iv, 32 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073516","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":320383,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0994/ds994.pdf","text":"Report","size":"19.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 994"},{"id":320382,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0994/coverthb.jpg"}],"country":"Mexico, United States","state":"Baja California, Baja Californa Sur, California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.83886718750001,\n 34.470335121217495\n ],\n [\n -120.36621093749999,\n 34.939985151560435\n ],\n [\n -120.52001953124999,\n 35.35321610123821\n ],\n [\n -120.34423828125,\n 35.65729624809628\n ],\n [\n -119.53125,\n 35.496456056584165\n ],\n [\n -118.6083984375,\n 35.08395557927643\n ],\n [\n -117.39990234375,\n 34.74161249883172\n ],\n [\n -116.82861328125001,\n 34.57895241036945\n ],\n [\n -116.03759765625,\n 34.45221847282654\n ],\n [\n -115.72998046875,\n 33.925129700072\n ],\n [\n -115.37841796874999,\n 33.284619968887704\n ],\n [\n -115.400390625,\n 32.75032260780972\n ],\n [\n -115.31249999999999,\n 32.15701248607008\n ],\n [\n -114.9609375,\n 31.541089879585808\n ],\n [\n -114.85107421875,\n 30.977609093348686\n ],\n [\n -114.6533203125,\n 30.751277776257812\n ],\n [\n -114.63134765625001,\n 30.183121842195515\n ],\n [\n -114.27978515625,\n 29.859701442126756\n ],\n [\n -113.6865234375,\n 29.305561325527698\n ],\n [\n -113.35693359375,\n 28.9023972285585\n ],\n [\n -113.22509765625,\n 28.767659105691255\n ],\n [\n -112.9833984375,\n 28.401064827220896\n ],\n [\n -112.87353515625,\n 28.459033019728043\n ],\n [\n -112.82958984375,\n 28.130127737874005\n ],\n [\n -112.60986328125,\n 27.664068965384516\n ],\n [\n -112.32421875,\n 27.46928747369202\n ],\n [\n -112.08251953125,\n 27.176469131898898\n ],\n [\n -111.8408203125,\n 26.941659545381516\n ],\n [\n -111.533203125,\n 26.70635985763354\n ],\n [\n -111.4013671875,\n 26.23430203240673\n ],\n [\n -111.3134765625,\n 25.918526162075153\n ],\n [\n -111.09374999999999,\n 25.522614647623293\n ],\n [\n -110.72021484375,\n 25.045792240303445\n ],\n [\n -110.58837890625,\n 25.025884063244828\n ],\n [\n -110.72021484375,\n 24.746831298412058\n ],\n [\n -110.61035156249999,\n 24.347096633808512\n ],\n [\n -110.36865234374999,\n 24.226928664976377\n ],\n [\n -110.1708984375,\n 23.926013033021203\n ],\n [\n -110.478515625,\n 23.765236889758672\n ],\n [\n -110.80810546875,\n 24.126701958681682\n ],\n [\n -111.09374999999999,\n 24.587090339209634\n ],\n [\n -111.357421875,\n 25.18505888358067\n ],\n [\n -111.533203125,\n 25.62171595984575\n ],\n [\n -112.0166015625,\n 25.958044673317843\n ],\n [\n -112.412109375,\n 26.175158990178133\n ],\n [\n -113.00537109375,\n 27.196014383173306\n ],\n [\n -113.53271484375,\n 27.858503954841247\n ],\n [\n -113.75244140624999,\n 28.285033294640684\n ],\n [\n -114.10400390625,\n 28.536274512989916\n ],\n [\n -114.43359375,\n 28.748396571187406\n ],\n [\n -114.71923828124999,\n 29.113775395114416\n ],\n [\n -115.42236328124999,\n 30.164126343161097\n ],\n [\n -115.59814453125001,\n 30.619004797647808\n ],\n [\n -116.21337890625,\n 31.29732799140429\n ],\n [\n -116.69677734375,\n 31.840232667909365\n ],\n [\n -116.96044921875,\n 32.02670629333614\n ],\n [\n -117.13623046874999,\n 32.36140331527543\n ],\n [\n -117.333984375,\n 32.97180377635759\n ],\n [\n -117.44384765625,\n 33.22949814144951\n ],\n [\n -117.83935546874999,\n 33.578014746143985\n ],\n [\n -118.125,\n 33.76088200086917\n ],\n [\n -118.43261718749999,\n 33.797408767572485\n ],\n [\n -118.67431640625,\n 33.925129700072\n ],\n [\n -118.95996093749999,\n 33.99802726234877\n ],\n [\n -119.3115234375,\n 34.08906131584996\n ],\n [\n -119.42138671875,\n 34.21634468843465\n ],\n [\n -119.64111328125,\n 34.34343606848294\n ],\n [\n -119.83886718750001,\n 34.470335121217495\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State Unviersity Drive East
Sacramento, CA 95819
http://www.werc.usgs.gov/
The Shiprock Disposal Site is the location of the former Navajo Mill (Mill), a uranium ore-processing facility, located on a terrace overlooking the San Juan River in the town of Shiprock, New Mexico. Following the closure of the Mill, all tailings and associated materials were encapsulated in a disposal cell built on top of the former Mill and tailings piles. The milling operations, conducted at the site from 1954 to 1968, created radioactive tailings and process-related wastes that are now found in the groundwater. Elevated concentrations of constituents of concern—ammonium, manganese, nitrate, selenium, strontium, sulfate, and uranium—have also been measured in groundwater seeps in the nearby Many Devils Wash arroyo, leading to the inference that these constituents originated from the Mill. These constituents have also been reported in groundwater that is associated with Mancos Shale, the bedrock that underlies the site. The objective of this report is to increase understanding of the source of water and solutes to the groundwater beneath Many Devils Wash and to establish the background concentrations for groundwater that is in contact with the Mancos Shale at the site. This report presents evidence on three working hypotheses: (1) the water and solutes in Many Devils Wash originated from the operations at the former Mill, (2) groundwater in deep aquifers is upwelling under artesian pressure to recharge the shallow groundwater beneath Many Devils Wash, and (3) the groundwater beneath Many Devils Wash originates as precipitation that infiltrates into the shallow aquifer system and discharges to Many Devils Wash in a series of springs on the east side of the wash. The solute concentrations in the shallow groundwater of Many Devils Wash would result from the interaction of the water and the Mancos Shale if the source of water was upwelling from deep aquifers or precipitation.
In order to compare the groundwater from various wells to groundwater that has been affected by Mill activities, a classification system was developed to determine which wells were most likely to have been affected. Affects to groundwater by the Mill were determined by using the reported uranium alpha activity ratios measured in groundwater samples, along with the concentration of the uranium and the location of the wells relative to the Mill. Activity ratios of 1.2 or less were determined to be the most reliable indicator of Mill-affected groundwater. Wells with samples that had a reported activity ratio of 1.2 or less were classified as Mill affected. To compare groundwater with background water-quality, data from groundwater seeps and springs in the Upper Eagle Nest Arroyo and Salt Creek Wash, located north of the San Juan River, are also presented and analyzed.
Based on groundwater elevations and tritium concentrations measured in wells located between the disposal cell and Many Devils Wash, Mill water is not likely to reach Many Devils Wash. The tritium concentrations also indicate that groundwater from the Mill has not substantially affected Many Devils Wash in the past. Upwelling from deep aquifers was also determined to be an unlikely source, primarily by comparing the composition of the stable isotopes of water in the shallow groundwater with those reported in groundwater samples from the deeper aquifers. The stable-isotope compositions of the shallow groundwater around the site are enriched relative to the San Juan River and local meteoric lines, which suggests that most of the shallow groundwater has been influenced by evaporation and therefore was recharged at the surface. Several observations indicate that focused recharge is the likely source of groundwater in the area of Many Devils Wash. The visible erosional features in Many Devils Wash provide evidence of piping and groundwater sapping, and the distribution and type of vegetation in Many Devils Wash suggest that the focused recharge of precipitation is occurring. The estimated recharge from precipitation was calculated to be 0.0008 inches per year (in/yr) by using the mass-balance approach from reported seep discharge and 0.0011 in/yr using the chloride mass-balance approach.
A conceptual model of groundwater quality beneath Many Devils Wash is presented to explain the source of solutes in the groundwater beneath Many Devils Wash. The major-ion concentrations and geochemical evolution in the groundwater beneath Many Devils Wash and across the study area support the conceptual model that the underlying Mancos Shale is the source of solutes. Differences in the major-ion composition between groundwater samples collected around the site, result from the degree of weathering to the Mancos Shale. The cation distribution appears to be an indicator of effects from the Mill, with samples from the Mill-affected wells largely having a calcium/magnesium-sulfate composition that resembles the reported compositions of more weathered shale; however, that composition could change if the Mill-processed water flowed into areas where the Mancos Shale was less weathered. On the basis of the widespread presence of uranium in the Mancos Shale and the distribution of aqueous uranium in the analog sites and other sites in the region, it appears likely that uranium in the groundwater of Many Devils Wash is naturally sourced from the Mancos Shale.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165031","collaboration":"Prepared in cooperation with the Navajo Nation Environmental Protection Agency","usgsCitation":"Robertson, A.J., Ranalli, A.J., Austin, S.A., and Lawlis, B.R., 2016, The source of groundwater and solutes to Many Devils Wash at a former uranium mill site in Shiprock, New Mexico: U.S. Geological Survey Scientific Investigations Report 2016–5031, 54 p., https://dx.doi.org/10.3133/sir20165031.","productDescription":"x, 54 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-051391","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":320372,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5031/coverthb.jpg"},{"id":320373,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5031/sir20165031.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5031"}],"country":"United States","state":"New Mexico","otherGeospatial":"Shiprock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.74404907226562,\n 36.84775766525783\n ],\n [\n -108.74267578125,\n 36.70806354647625\n ],\n [\n -108.54766845703125,\n 36.71356812817935\n ],\n [\n -108.56552124023438,\n 36.869733528373395\n ],\n [\n -108.74542236328125,\n 36.87742358748459\n ],\n [\n -108.74404907226562,\n 36.84775766525783\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montgomery Blvd. NE
Suite 400
Albuquerque, NM 87109
http://nm.water.usgs.gov/
The One Health initiative is a global effort fostering interdisciplinary collaborations to address challenges in human, animal, and environmental health. While One Health has received considerable press, its benefits remain unclear because its effects have not been quantitatively described. We systematically surveyed the published literature and used social network analysis to measure interdisciplinarity in One Health studies constructing dynamic pathogen transmission models. The number of publications fulfilling our search criteria increased by 14.6% per year, which is faster than growth rates for life sciences as a whole and for most biology subdisciplines. Surveyed publications clustered into three communities: one used by ecologists, one used by veterinarians, and a third diverse-authorship community used by population biologists, mathematicians, epidemiologists, and experts in human health. Overlap between these communities increased through time in terms of author number, diversity of co-author affiliations, and diversity of citations. However, communities continue to differ in the systems studied, questions asked, and methods employed. While the infectious disease research community has made significant progress toward integrating its participating disciplines, some segregation—especially along the veterinary/ecological research interface—remains.
","language":"English","publisher":"Public library of science","doi":"10.1371/journal.pbio.1002448","usgsCitation":"Manlove, K., Walker, J.G., Craft, M.E., Huyvaert, K., Joseph, M.B., Miller, R.S., Nol, P., Patyk, K.A., O’Brian, D., Walsh, D.P., and Cross, P.C., 2016, “One Health” or three? Publication silos among the One Health disciplines: PLoS Biology, v. 14, no. 4, e1002448; 14 p., https://doi.org/10.1371/journal.pbio.1002448.","productDescription":"e1002448; 14 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072536","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":471055,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pbio.1002448","text":"Publisher Index Page"},{"id":321692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-21","publicationStatus":"PW","scienceBaseUri":"5746ccc8e4b07e28b662dd7b","contributors":{"authors":[{"text":"Manlove, Kezia","contributorId":68204,"corporation":false,"usgs":true,"family":"Manlove","given":"Kezia","affiliations":[],"preferred":false,"id":625715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Josephine G","contributorId":168371,"corporation":false,"usgs":false,"family":"Walker","given":"Josephine","email":"","middleInitial":"G","affiliations":[{"id":7172,"text":"University of Bristol, U.K. and University of Oregon, Eugene","active":true,"usgs":false}],"preferred":false,"id":625716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Craft, Meggan E.","contributorId":168372,"corporation":false,"usgs":false,"family":"Craft","given":"Meggan","email":"","middleInitial":"E.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":625717,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huyvaert, Kathryn P.","contributorId":73906,"corporation":false,"usgs":true,"family":"Huyvaert","given":"Kathryn P.","affiliations":[],"preferred":false,"id":625718,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Joseph, Maxwell B.","contributorId":39678,"corporation":false,"usgs":true,"family":"Joseph","given":"Maxwell","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":625719,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Ryan S.","contributorId":49005,"corporation":false,"usgs":false,"family":"Miller","given":"Ryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":625720,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nol, Pauline","contributorId":34053,"corporation":false,"usgs":false,"family":"Nol","given":"Pauline","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":625721,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Patyk, Kelly A.","contributorId":139696,"corporation":false,"usgs":false,"family":"Patyk","given":"Kelly","email":"","middleInitial":"A.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":625722,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"O’Brian, Daniel","contributorId":168373,"corporation":false,"usgs":false,"family":"O’Brian","given":"Daniel","email":"","affiliations":[{"id":7024,"text":"Michigan Department of Natural Resources, Fisheries Research Station","active":true,"usgs":false}],"preferred":false,"id":625723,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Walsh, Daniel P. 0000-0002-7772-2445 dwalsh@usgs.gov","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":4758,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"dwalsh@usgs.gov","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":625724,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":625714,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70170459,"text":"70170459 - 2016 - Wintering Sandhill Crane exposure to wind energy development in the central and southern Great Plains, USA","interactions":[],"lastModifiedDate":"2016-04-21T10:33:43","indexId":"70170459","displayToPublicDate":"2016-04-21T11:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Wintering Sandhill Crane exposure to wind energy development in the central and southern Great Plains, USA","docAbstract":"Numerous wind energy projects have been constructed in the central and southern Great Plains, USA, the main wintering area for midcontinental Sandhill Cranes (Grus canadensis). In an initial assessment of the potential risks of wind towers to cranes, we estimated spatial overlap, investigated potential avoidance behavior, and determined the habitat associations of cranes. We used data from cranes marked with platform transmitting terminals (PTTs) with and without global positioning system (GPS) capabilities. We estimated the wintering distributions of PTT-marked cranes prior to the construction of wind towers, which we compared with current tower locations. Based on this analysis, we found 7% spatial overlap between the distributions of cranes and towers. When we looked at individually marked cranes, we found that 52% would have occurred within 10 km of a tower at some point during winter. Using data from cranes marked after tower construction, we found a potential indication of avoidance behavior, whereby GPS-marked cranes generally used areas slightly more distant from existing wind towers than would be expected by chance. Results from a habitat selection model suggested that distances between crane locations and towers may have been driven more by habitat selection than by avoidance, as most wind towers were constructed in locations not often selected by wintering cranes. Our findings of modest regional overlap and that few towers have been placed in preferred crane habitat suggest that the current distribution of wind towers may be of low risk to the continued persistence of wintering midcontinental Sandhill Cranes in the central and southern Great Plains.
","language":"English","publisher":"Cooper Ornithological Society","doi":"10.1650/CONDOR-15-99.1","usgsCitation":"Pearse, A.T., Brandt, D.A., and Krapu, G., 2016, Wintering Sandhill Crane exposure to wind energy development in the central and southern Great Plains, USA: The Condor, v. 118, no. 2, p. 391-401, https://doi.org/10.1650/CONDOR-15-99.1.","productDescription":"11 p.","startPage":"391","endPage":"401","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061835","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1650/condor-15-99.1","text":"Publisher Index Page"},{"id":320368,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, New Mexico, Oklahoma, 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Center","active":true,"usgs":true}],"preferred":true,"id":627300,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krapu, Gary 0000-0001-8482-6130 gkrapu@usgs.gov","orcid":"https://orcid.org/0000-0001-8482-6130","contributorId":168791,"corporation":false,"usgs":true,"family":"Krapu","given":"Gary","email":"gkrapu@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":627301,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170396,"text":"70170396 - 2016 - Does prescribed fire promote resistance to drought in low elevation forests of the Sierra Nevada, California, USA?","interactions":[],"lastModifiedDate":"2020-12-18T15:02:15.452384","indexId":"70170396","displayToPublicDate":"2016-04-21T11:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Does prescribed fire promote resistance to drought in low elevation forests of the Sierra Nevada, California, USA?","docAbstract":"Prescribed fire is a primary tool used to restore western forests following more than a century of fire exclusion, reducing fire hazard by removing dead and live fuels (small trees and shrubs). It is commonly assumed that the reduced forest density following prescribed fire also reduces competition for resources among the remaining trees, so that the remaining trees are more resistant (more likely to survive) in the face of additional stressors, such as drought. Yet this proposition remains largely untested, so that managers do not have the basic information to evaluate whether prescribed fire may help forests adapt to a future of more frequent and severe drought.
During the third year of drought, in 2014, we surveyed 9950 trees in 38 burned and 18 unburned mixed conifer forest plots at low elevation (<2100 m a.s.l.) in Kings Canyon, Sequoia, and Yosemite national parks in California, USA. Fire had occurred in the burned plots from 6 yr to 28 yr before our survey. After accounting for differences in individual tree diameter, common conifer species found in the burned plots had significantly reduced probability of mortality compared to unburned plots during the drought. Stand density (stems ha-1) was significantly lower in burned versus unburned sites, supporting the idea that reduced competition may be responsible for the differential drought mortality response. At the time of writing, we are not sure if burned stands will maintain lower tree mortality probabilities in the face of the continued, severe drought of 2015. Future work should aim to better identify drought response mechanisms and how these may vary across other forest types and regions, particularly in other areas experiencing severe drought in the Sierra Nevada and on the Colorado Plateau.
The U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On September 2-3, 2012, the USGS conducted an oblique aerial photographic survey along the Alabama, Mississippi, and Louisiana barrier islands aboard a Cessna 172 (aircraft) at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was flown to collect post-Hurricane Isaac data for assessing incremental changes in the beach and nearshore area since the last survey, flown in September 2008 (central Louisiana barrier islands) and June 2011 (Dauphin Island, Alabama, to Breton Island, Louisiana), and the data can be used in the assessment of future coastal change.
The photographs provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML files were created using the photographic navigation files. These KML file(s) can be found in the kml folder.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds988","usgsCitation":"Morgan, K.L.M., and Westphal, K.A., 2016, Post-Hurricane Isaac coastal oblique aerial photographs collected along the Alabama, Mississippi, and Louisiana barrier islands, September 2–3, 2012: U.S. Geological Survey Data Series 988, https://dx.doi.org/10.3133/ds988.\n","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2012-09-02","ipdsId":"IP-072305","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":319988,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0988/index.html","text":"Report ","linkFileType":{"id":5,"text":"html"},"description":"DS 988"}],"country":"United States","state":"Alabama, Mississippi, Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.175537109375,\n 29.0273547804184\n ],\n [\n -91.175537109375,\n 30.5717205651999\n ],\n [\n -88.0224609375,\n 30.5717205651999\n ],\n [\n -88.0224609375,\n 29.0273547804184\n ],\n [\n -91.175537109375,\n 29.0273547804184\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov
Coastal wetland plants are adapted to varying degrees of inundation. However, functional relationships between inundation and productivity are poorly characterized for most species. Determining species-specific tolerances to inundation is necessary to evaluate sea-level rise (SLR) effects on future marsh plant community composition, quantify organic matter inputs to marsh accretion, and inform predictive modeling of tidal wetland persistence. In 2 macrotidal estuaries in the northeast Pacific we grew 5 common species in experimental mesocosms across a gradient of tidal elevations to assess effects on growth. We also tested whether species abundance distributions along elevation gradients in adjacent marshes matched productivity profiles in the mesocosms. We found parabolic relationships between inundation and total plant biomass and shoot counts in Spartina foliosa and Bolboschoenus maritimus in California, USA, and in Carex lyngbyei in Oregon, USA, with maximum total plant biomass occurring at 38, 28, and 15% time submerged, respectively. However, biomass of Salicornia pacifica and Juncus balticus declined monotonically with increasing inundation. Inundation effects on the ratio of belowground to aboveground biomass varied inconsistently among species. In comparisons of field distributions with mesocosm results, B. maritimus, C. lyngbyei and J. balticus were abundant in marshes at or above elevations corresponding with their maximum productivity; however, S. foliosa and S. pacifica were frequently abundant at lower elevations corresponding with sub-optimal productivity. Our findings show species-level differences in how marsh plant growth may respond to future SLR and highlight the sensitivity of high marsh species such as S. pacifica and J. balticus to increases in flooding.
\n","language":"English","publisher":"Inter Research","publisherLocation":"Oldendorf/Luhe, Germany","doi":"10.3354/meps11683","usgsCitation":"Janousek, C., Buffington, K., Thorne, K.M., Guntenspergen, G.R., Takekawa, J.Y., and Dugger, B., 2016, Potential effects of sea-level rise on plant productivity: Species-specific responses in northeast Pacific tidal marshes: Marine Ecology Progress Series, v. 548, p. 111-125, https://doi.org/10.3354/meps11683.","productDescription":"15 p.","startPage":"111","endPage":"125","numberOfPages":"15","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067489","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps11683","text":"Publisher Index Page"},{"id":320394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","city":"Petaluma, Siletz","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.9645767211914,\n 44.68915916393589\n ],\n [\n -123.9645767211914,\n 44.752828833304385\n ],\n [\n -123.87067794799805,\n 44.752828833304385\n ],\n [\n -123.87067794799805,\n 44.68915916393589\n ],\n [\n -123.9645767211914,\n 44.68915916393589\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.82577514648438,\n 38.15021749945389\n ],\n [\n -122.82577514648438,\n 38.35942628215571\n ],\n [\n -122.48519897460936,\n 38.35942628215571\n ],\n [\n -122.48519897460936,\n 38.15021749945389\n ],\n [\n -122.82577514648438,\n 38.15021749945389\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"548","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"571b4b33e4b071321fe31cc4","contributors":{"authors":[{"text":"Janousek, Christopher 0000-0003-2124-6715 cjanousek@usgs.gov","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":150053,"corporation":false,"usgs":true,"family":"Janousek","given":"Christopher","email":"cjanousek@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":627443,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":627444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, Karen M. 0000-0002-1381-0657 kthorne@usgs.gov","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":4191,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen","email":"kthorne@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":627442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":627445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Takekawa, John Y. 0000-0003-0217-5907 john_takekawa@usgs.gov","orcid":"https://orcid.org/0000-0003-0217-5907","contributorId":176168,"corporation":false,"usgs":true,"family":"Takekawa","given":"John","email":"john_takekawa@usgs.gov","middleInitial":"Y.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":627446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dugger, Bruce D.","contributorId":81236,"corporation":false,"usgs":true,"family":"Dugger","given":"Bruce D.","affiliations":[],"preferred":false,"id":627447,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70170882,"text":"70170882 - 2016 - The Point Sal–Point Piedras Blancas correlation and the problem of slip on the San Gregorio–Hosgri fault, central California Coast Ranges","interactions":[],"lastModifiedDate":"2016-06-16T11:06:22","indexId":"70170882","displayToPublicDate":"2016-04-20T17:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"The Point Sal–Point Piedras Blancas correlation and the problem of slip on the San Gregorio–Hosgri fault, central California Coast Ranges","docAbstract":"
Existing models for large-magnitude, right-lateral slip on the San Gregorio–Hosgri fault system imply much more deformation of the onshore block in the Santa Maria basin than is supported by geologic data. This problem is resolved by a model in which dextral slip on this fault system increases gradually from 0–10 km near Point Arguello to ∼150 km at Cape San Martin, but such a model requires abandoning the cross-fault tie between Point Sal and Point Piedras Blancas, which requires 90–100 km of right-lateral slip on the southern Hosgri fault. We collected stratigraphic and detrital zircon data from Miocene clastic rocks overlying Jurassic basement at both localities to determine if either section contained unique characteristics that could establish how far apart they were in the early Miocene. Our data indicate that these basins formed in the early Miocene during a period of widespread transtensional basin formation in the central Coast Ranges, and they filled with sediment derived from nearby pre-Cenozoic basement rocks. Although detrital zircon data do not indicate a unique source component in either section, they establish the maximum depositional age of the previously undated Point Piedras Blancas section to be 18 Ma. We also show that detrital zircon trace-element data can be used to discriminate between zircons of oceanic crust and arc affinity of the same age, a potentially useful tool in future studies of the California Coast Ranges. Overall, we find no characteristics in the stratigraphy and provenance of the Point Sal and Point Piedras Blancas sections that are sufficiently unique to prove whether they were far apart or close together in the early Miocene, making them of questionable utility as piercing points.
","language":"English","publisher":"Geological Society of America","publisherLocation":"Washington, D.C.","doi":"10.1130/GES01289.1","usgsCitation":"Colgan, J.P., and Stanley, R.G., 2016, The Point Sal–Point Piedras Blancas correlation and the problem of slip on the San Gregorio–Hosgri fault, central California Coast Ranges: Geosphere, v. 12, no. 3, p. 971-984, https://doi.org/10.1130/GES01289.1.","productDescription":"14 p.","startPage":"971","endPage":"984","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061433","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":471059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges01289.1","text":"Publisher Index Page"},{"id":321036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-20","publicationStatus":"PW","scienceBaseUri":"572dc05be4b0dae0d5d8f2d8","contributors":{"authors":[{"text":"Colgan, Joseph P. 0000-0001-6671-1436 jcolgan@usgs.gov","orcid":"https://orcid.org/0000-0001-6671-1436","contributorId":1649,"corporation":false,"usgs":true,"family":"Colgan","given":"Joseph","email":"jcolgan@usgs.gov","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":628909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanley, Richard G. 0000-0001-6192-8783 rstanley@usgs.gov","orcid":"https://orcid.org/0000-0001-6192-8783","contributorId":1832,"corporation":false,"usgs":true,"family":"Stanley","given":"Richard","email":"rstanley@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":628910,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170379,"text":"70170379 - 2016 - Assessing atmospheric concentration of polychlorinated biphenyls (PCBs) by evergreen Rhododendron maximum next to a contaminated stream","interactions":[],"lastModifiedDate":"2016-08-26T14:37:05","indexId":"70170379","displayToPublicDate":"2016-04-20T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Assessing atmospheric concentration of polychlorinated biphenyls (PCBs) by evergreen Rhododendron maximum next to a contaminated stream","docAbstract":"Conifers are often used as an “air passive sampler”, but few studies have focused on the implication of broadleaf evergreens to monitor atmospheric semivolatile organic compounds such as polychlorinated biphenyls (PCBs). In this study, we used Rhododendron maximum (rhododendron) growing next to a contaminated stream to assess atmospheric PCB concentrations. The study area was located in a rural setting and approximately 2 km downstream of a former Sangamo-Weston (S-W) plant. Leaves from the same mature shrubs were collected in late fall 2010, and winter and spring 2011. PCBs were detected in the collected leaves suggesting that rhododendron can be used as air passive samplers in rural areas where active sampling is impractical. Estimated ΣPCB (47 congeners) concentrations in the atmosphere decreased from fall 2010 to spring 2011 with concentration means at 3990, 2850, and 931 pg m-3 in fall 2010, winter 2011, and spring 2011, respectively. These results indicate that the atmospheric concentrations at this location continue to be high despite termination of active discharge from the former S-W plant. Leaves had a consistent pattern of high concentrations of tetra- and penta-CBs similar to the congener distribution in polyethylene (PE) passive samplers deployed in the water column suggesting that volatilized PCBs from the stream were the primary source of contaminants in rhododendron leaves.
","language":"English","publisher":"Elsevier Science","doi":"10.1002/etc.3404","usgsCitation":"Dang, V.D., Walters, D., and Lee, C.M., 2016, Assessing atmospheric concentration of polychlorinated biphenyls (PCBs) by evergreen Rhododendron maximum next to a contaminated stream: Environmental Toxicology and Chemistry, v. 35, no. 9, p. 2192-2198, https://doi.org/10.1002/etc.3404.","productDescription":"7 p.","startPage":"2192","endPage":"2198","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068484","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","city":"Pickens","otherGeospatial":"Town Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.73447513580322,\n 34.88663500030197\n ],\n [\n -82.73447513580322,\n 34.89346406486655\n ],\n [\n -82.71533489227295,\n 34.89346406486655\n ],\n [\n -82.71533489227295,\n 34.88663500030197\n ],\n [\n -82.73447513580322,\n 34.88663500030197\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-02-17","publicationStatus":"PW","scienceBaseUri":"57189a1ae4b0ef3b7caaf76f","contributors":{"authors":[{"text":"Dang, Viet D.","contributorId":168701,"corporation":false,"usgs":false,"family":"Dang","given":"Viet","email":"","middleInitial":"D.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":627038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, David 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":147135,"corporation":false,"usgs":true,"family":"Walters","given":"David","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":627037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lee, Cindy M.","contributorId":168702,"corporation":false,"usgs":false,"family":"Lee","given":"Cindy","email":"","middleInitial":"M.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":627039,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170766,"text":"70170766 - 2016 - Biogeographical history and coalescent species delimitation of Pacific island skinks (Squamata: Scincidae: Emoia cyanura species group)","interactions":[],"lastModifiedDate":"2016-09-28T16:26:11","indexId":"70170766","displayToPublicDate":"2016-04-20T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2193,"text":"Journal of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Biogeographical history and coalescent species delimitation of Pacific island skinks (Squamata: Scincidae: Emoia cyanura species group)","docAbstract":"A prevailing hypothesis for how Pacific islands organisms have obtained their extant distributions is that of a stepping-stone model, in which populations originate from Papua New Guinea in the western Pacific and gradually disperse eastward. Here, we test this model using a spatiotemporal framework for Emoia cyanura and E. impar, two species within the Emoia cyanura species group (ECSG; Family: Scincidae). We further assess species limits within the group, utilizing novel coalescent methods.
\nPacific Islands.
\nWe obtained DNA sequence data from one mitochondrial and three nuclear markers for 117 individuals, representing seven of the nine species within the ECSG. These data were analysed for concordance with the stepping-stone model using estimation of population structure, divergence dates, and historical biogeographical range. To assess hypotheses of independent lineages within each widespread species, we also employed the Bayesian Phylogenetics & Phylogeography (BPP) program to define operational taxonomic units in *BEAST.
\nPopulation structure analyses consistently found individuals from western island groups representing divergent populations, with central and eastern populations demonstrating minimal genetic variation. Phylogenetic hypotheses support a western origin for E. cyanura and E. impar, while biogeographical and divergence time estimations predict a recent and rapid expansion out of the western Pacific. The BPP and *BEAST analyses found evidence for five independent lineages within E. impar and five independent lineages within E. cyanura/E. pseudocyanura.
\nIn contrast to the expectations of a stepping-stone model, E. cyanura and E. impar each exhibit the genetic signature of a rapid radiation during the mid to late Pleistocene, with evidence for newly identified lineages, mainly on western islands. Of these recovered lineages, we propose three to be elevated to species status. These findings expand our understanding of endemic Pacific biota, which are subject to conservation threats from human impacts and climate change.
","language":"English","publisher":"Blackwell Scientific Publications","doi":"10.1111/jbi.12772","usgsCitation":"Klein, E., Harris, R., Fisher, R.N., and Reeder, T., 2016, Biogeographical history and coalescent species delimitation of Pacific island skinks (Squamata: Scincidae: Emoia cyanura species group): Journal of Biogeography, v. 43, no. 10, p. 1917-1929, https://doi.org/10.1111/jbi.12772.","productDescription":"13 p.","startPage":"1917","endPage":"1929","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071494","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":320843,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Pacific islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 191.7333984375,\n 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PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-20","publicationStatus":"PW","scienceBaseUri":"57287a2be4b0b13d391865b1","contributors":{"authors":[{"text":"Klein, Elaine","contributorId":169068,"corporation":false,"usgs":false,"family":"Klein","given":"Elaine","email":"","affiliations":[{"id":25403,"text":"U Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":628335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Rebecca","contributorId":169069,"corporation":false,"usgs":false,"family":"Harris","given":"Rebecca","email":"","affiliations":[{"id":25404,"text":"U of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":628336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":628334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reeder, Tod","contributorId":169070,"corporation":false,"usgs":false,"family":"Reeder","given":"Tod","affiliations":[{"id":25405,"text":"San Diego State U.","active":true,"usgs":false}],"preferred":false,"id":628337,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170417,"text":"70170417 - 2016 - Multi-scale connectivity and graph theory highlight critical areas for conservation under climate change","interactions":[],"lastModifiedDate":"2016-06-16T10:57:00","indexId":"70170417","displayToPublicDate":"2016-04-20T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Multi-scale connectivity and graph theory highlight critical areas for conservation under climate change","docAbstract":"Conservation planning and biodiversity management require information on landscape connectivity across a range of spatial scales from individual home ranges to large regions. Reduction in landscape connectivity due changes in land-use or development is expected to act synergistically with alterations to habitat mosaic configuration arising from climate change. We illustrate a multi-scale connectivity framework to aid habitat conservation prioritization in the context of changing land use and climate. Our approach, which builds upon the strengths of multiple landscape connectivity methods including graph theory, circuit theory and least-cost path analysis, is here applied to the conservation planning requirements of the Mohave ground squirrel. The distribution of this California threatened species, as for numerous other desert species, overlaps with the proposed placement of several utility-scale renewable energy developments in the American Southwest. Our approach uses information derived at three spatial scales to forecast potential changes in habitat connectivity under various scenarios of energy development and climate change. By disentangling the potential effects of habitat loss and fragmentation across multiple scales, we identify priority conservation areas for both core habitat and critical corridor or stepping stone habitats. This approach is a first step toward applying graph theory to analyze habitat connectivity for species with continuously-distributed habitat, and should be applicable across a broad range of taxa.
","language":"English","publisher":"Ecological Society of America","doi":"10.1890/15-0925","usgsCitation":"Dilts, T.E., Weisberg, P.J., Leitner, P., Matocq, M.D., Inman, R.D., Nussear, K.E., and Esque, T., 2016, Multi-scale connectivity and graph theory highlight critical areas for conservation under climate change: Ecological Applications, v. 26, no. 4, p. 1223-1237, https://doi.org/10.1890/15-0925.","productDescription":"5 p.","startPage":"1223","endPage":"1237","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072705","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":320341,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"4","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-08","publicationStatus":"PW","scienceBaseUri":"57189a1be4b0ef3b7caaf78e","chorus":{"doi":"10.1890/15-0925","url":"http://dx.doi.org/10.1890/15-0925","publisher":"Wiley-Blackwell","authors":"Dilts Thomas E., Weisberg Peter J., Leitner Philip, Matocq Marjorie D., Inman Richard D., Nussear Kenneth E., Esque Todd C.","journalName":"Ecological Applications","publicationDate":"6/2016","auditedOn":"11/8/2016"},"contributors":{"authors":[{"text":"Dilts, Thomas E.","contributorId":36833,"corporation":false,"usgs":true,"family":"Dilts","given":"Thomas","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":627183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weisberg, Peter J.","contributorId":33631,"corporation":false,"usgs":true,"family":"Weisberg","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":627184,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leitner, Phillip","contributorId":168764,"corporation":false,"usgs":false,"family":"Leitner","given":"Phillip","email":"","affiliations":[{"id":25357,"text":"CSU Stanislaus ESRP","active":true,"usgs":false}],"preferred":false,"id":627185,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matocq, Marjorie D.","contributorId":25482,"corporation":false,"usgs":true,"family":"Matocq","given":"Marjorie","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":627186,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Inman, Richard D. rdinman@usgs.gov","contributorId":3316,"corporation":false,"usgs":true,"family":"Inman","given":"Richard","email":"rdinman@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":627187,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nussear, Ken E.","contributorId":103596,"corporation":false,"usgs":true,"family":"Nussear","given":"Ken","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":627188,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Esque, Todd C. 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":168763,"corporation":false,"usgs":true,"family":"Esque","given":"Todd C.","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":627182,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70162080,"text":"70162080 - 2016 - Effects of lek count protocols on greater sage-grouse population trend estimates","interactions":[],"lastModifiedDate":"2017-12-27T15:01:11","indexId":"70162080","displayToPublicDate":"2016-04-20T16:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Effects of lek count protocols on greater sage-grouse population trend estimates","docAbstract":"Annual counts of males displaying at lek sites are an important tool for monitoring greater sage-grouse populations (Centrocercus urophasianus), but seasonal and diurnal variation in lek attendance may increase variance and bias of trend analyses. Recommendations for protocols to reduce observation error have called for restricting lek counts to within 30 minutes of sunrise, but this may limit the number of lek counts available for analysis, particularly from years before monitoring was widely standardized. Reducing the temporal window for conducting lek counts also may constrain the ability of agencies to monitor leks efficiently. We used lek count data collected across Wyoming during 1995−2014 to investigate the effect of lek counts conducted between 30 minutes before and 30, 60, or 90 minutes after sunrise on population trend estimates. We also evaluated trends across scales relevant to management, including statewide, within Working Group Areas and Core Areas, and for individual leks. To further evaluate accuracy and precision of trend estimates from lek count protocols, we used simulations based on a lek attendance model and compared simulated and estimated values of annual rate of change in population size (λ) from scenarios of varying numbers of leks, lek count timing, and count frequency (counts/lek/year). We found that restricting analyses to counts conducted within 30 minutes of sunrise generally did not improve precision of population trend estimates, although differences among timings increased as the number of leks and count frequency decreased. Lek attendance declined >30 minutes after sunrise, but simulations indicated that including lek counts conducted up to 90 minutes after sunrise can increase the number of leks monitored compared to trend estimates based on counts conducted within 30 minutes of sunrise. This increase in leks monitored resulted in greater precision of estimates without reducing accuracy. Increasing count frequency also improved precision. These results suggest that the current distribution of count timings available in lek count databases such as that of Wyoming (conducted up to 90 minutes after sunrise) can be used to estimate sage-grouse population trends without reducing precision or accuracy relative to trends from counts conducted within 30 minutes of sunrise. However, only 10% of all Wyoming counts in our sample (1995−2014) were conducted 61−90 minutes after sunrise, and further increasing this percentage may still bias trend estimates because of declining lek attendance.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.1050","usgsCitation":"Monroe, A., Edmunds, D.R., and Aldridge, C.L., 2016, Effects of lek count protocols on greater sage-grouse population trend estimates: Journal of Wildlife Management, v. 80, no. 4, p. 667-678, https://doi.org/10.1002/jwmg.1050.","productDescription":"12 p.","startPage":"667","endPage":"678","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068601","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-17","publicationStatus":"PW","scienceBaseUri":"57189a1be4b0ef3b7caaf785","contributors":{"authors":[{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588478,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":588479,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":588480,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70174896,"text":"70174896 - 2016 - Effect of cysteine and humic acids on bioavailability of Ag from Ag nanoparticles to a freshwater snail","interactions":[],"lastModifiedDate":"2016-07-25T11:55:44","indexId":"70174896","displayToPublicDate":"2016-04-20T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5146,"text":"NanoImpact","active":true,"publicationSubtype":{"id":10}},"title":"Effect of cysteine and humic acids on bioavailability of Ag from Ag nanoparticles to a freshwater snail","docAbstract":"Metal-based engineered nanoparticles (NPs) will undergo transformations that will affect their bioavailability, toxicity and ecological risk when released to the environment, including interactions with dissolved organic material. The purpose of this paper is to determine how interactions with two different types of organic material affect the bioavailability of silver nanoparticles (AgNPs). Silver uptake rates by the pond snail Lymnaea stagnalis were determined after exposure to 25 nmol l-1 of Ag as PVP AgNPs, PEG AgNPs or AgNO3, in the presence of either Suwannee River humic acid or cysteine, a high-affinity thiol-rich organic ligand. Total uptake rate of Ag from the two NPs was either increased or not strongly affected in the presence of 1 – 10 mg 1-1 humic acid. Humic substances contain relatively few strong ligands for Ag explaining their limited effects on Ag uptake rate. In contrast, Ag uptake rate was substantially reduced by cysteine. Three components of uptake from the AgNPs were quantified in the presence of cysteine using a biodynamic modeling approach: uptake of dissolved Ag released by the AgNPs, uptake of a polymer or large (>3kD) Ag-cysteine complex and uptake of the nanoparticle itself. Addition of 1:1 Ag:cysteine reduced concentrations of dissolved Ag, which contributed to, but did not fully explain the reductions in uptake. A bioavailable Ag-cysteine complex (> 3kD) appeared to be the dominant avenue of uptake from both PVP AgNPs and PEG AgNPs in the presence of cysteine. Quantifying the different avenues of uptake sets the stage for studies to assess toxicity unique to NPs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.impact.2016.06.006","usgsCitation":"Luoma, S.N., Stoiber, T., Croteau, M.N., Romer, I., Merrifeild, R., and Lead, J., 2016, Effect of cysteine and humic acids on bioavailability of Ag from Ag nanoparticles to a freshwater snail: NanoImpact, v. 2, p. 61-69, https://doi.org/10.1016/j.impact.2016.06.006.","productDescription":"8 p.","startPage":"61","endPage":"69","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073881","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":325469,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5790a17ee4b030378fb47425","contributors":{"authors":[{"text":"Luoma, Samuel N. 0000-0001-5443-5091 snluoma@usgs.gov","orcid":"https://orcid.org/0000-0001-5443-5091","contributorId":2287,"corporation":false,"usgs":true,"family":"Luoma","given":"Samuel","email":"snluoma@usgs.gov","middleInitial":"N.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":643033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoiber, Tasha","contributorId":173022,"corporation":false,"usgs":false,"family":"Stoiber","given":"Tasha","email":"","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":643034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":643032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romer, Isabelle","contributorId":173025,"corporation":false,"usgs":false,"family":"Romer","given":"Isabelle","email":"","affiliations":[{"id":27144,"text":"University of Birmingham, UK","active":true,"usgs":false}],"preferred":false,"id":643037,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merrifeild, Ruth","contributorId":173023,"corporation":false,"usgs":false,"family":"Merrifeild","given":"Ruth","email":"","affiliations":[{"id":27143,"text":"University of South Carolina, Columbia, SC","active":true,"usgs":false}],"preferred":false,"id":643035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lead, Jamie","contributorId":173024,"corporation":false,"usgs":false,"family":"Lead","given":"Jamie","affiliations":[{"id":27143,"text":"University of South Carolina, Columbia, SC","active":true,"usgs":false}],"preferred":false,"id":643036,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70175115,"text":"70175115 - 2016 - Identification and distribution of the Olympic Shrew (Eulipotyphla: Soricidae), Sorex rohweri Rausch et al., 2007 in Oregon and Washington, based on USNM specimens","interactions":[],"lastModifiedDate":"2017-07-05T10:00:36","indexId":"70175115","displayToPublicDate":"2016-04-20T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3147,"text":"Proceedings of the Biological Society of Washington","active":true,"publicationSubtype":{"id":10}},"title":"Identification and distribution of the Olympic Shrew (Eulipotyphla: Soricidae), Sorex rohweri Rausch et al., 2007 in Oregon and Washington, based on USNM specimens","docAbstract":"Review of specimens of long-tailed shrews (Mammalia, Soricidae, Sorex) from the northwestern United States in the National Museum of Natural History (USNM), Washington, DC, has revealed the presence of the Olympic Shrew, Sorex rohweri Rausch et al., 2007, in the Coastal Range west of the Willamette Valley in Oregon. This determination nearly doubles the documented distribution for this species and increases the species diversity of soricids in Oregon to eleven. Sorex rohweri is relatively uncommon, but it occurs in a variety of forest successional stages and even clear cuts, as long as there is nearby forest and trees are allowed to regenerate. All USNM specimens from Washington formerly identified as S. cinereus streatori Merriam, 1895 are instead referable to the Olympic Shrew. The distribution of S. c. streatori is thereby restricted to the Pacific coasts of British Columbia north of the lower Frasier River and south central Alaska. Our study highlights the importance of taking and preserving high-quality voucher specimens in a collection where they are readily available for re-study.
","language":"English","publisher":"Biological Society of Washington","doi":"10.2988/0006-324X-129.Q2.84","usgsCitation":"Woodman, N., and Fisher, R.D., 2016, Identification and distribution of the Olympic Shrew (Eulipotyphla: Soricidae), Sorex rohweri Rausch et al., 2007 in Oregon and Washington, based on USNM specimens: Proceedings of the Biological Society of Washington, v. 129, no. 1, p. 84-102, https://doi.org/10.2988/0006-324X-129.Q2.84.","productDescription":"18 p.","startPage":"84","endPage":"102","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072372","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":325840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.30230712890625,\n 44.1289994645142\n ],\n [\n -123.09906005859375,\n 45.51212126820532\n ],\n [\n -122.794189453125,\n 45.4890202453737\n ],\n [\n -122.47283935546874,\n 45.50249699389712\n ],\n [\n -123.03039550781249,\n 44.071800467511565\n ],\n [\n -123.30230712890625,\n 44.1289994645142\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"129","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-17","publicationStatus":"PW","scienceBaseUri":"579c7e2be4b0589fa1ca11f6","contributors":{"authors":[{"text":"Woodman, Neal 0000-0003-2689-7373 nwoodman@usgs.gov","orcid":"https://orcid.org/0000-0003-2689-7373","contributorId":3547,"corporation":false,"usgs":true,"family":"Woodman","given":"Neal","email":"nwoodman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":643964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, Robert D. 0000-0002-2956-3240 rdfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":3913,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rdfisher@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":644020,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70175897,"text":"70175897 - 2016 - Using NDVI to measure precipitation in semi-arid landscapes","interactions":[],"lastModifiedDate":"2017-02-08T11:18:50","indexId":"70175897","displayToPublicDate":"2016-04-20T14:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Using NDVI to measure precipitation in semi-arid landscapes","docAbstract":"Measuring precipitation in semi-arid landscapes is important for understanding the processes related to rainfall and run-off; however, measuring precipitation accurately can often be challenging especially within remote regions where precipitation instruments are scarce. Typically, rain-gauges are sparsely distributed and research comparing rain-gauge and RADAR precipitation estimates reveal that RADAR data are often misleading, especially for monsoon season convective storms. This study investigates an alternative way to map the spatial and temporal variation of precipitation inputs along ephemeral stream channels using Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper imagery. NDVI values from 26 years of pre- and post-monsoon season Landsat imagery were derived across Yuma Proving Ground (YPG), a region covering 3,367 km2 of semiarid landscapes in southwestern Arizona, USA. The change in NDVI from a pre-to post-monsoon season image along ephemeral stream channels explained 73% of the variance in annual monsoonal precipitation totals from a nearby rain-gauge. In addition, large seasonal changes in NDVI along channels were useful in determining when and where flow events have occurred.
","language":"English","publisher":"Academic Press","doi":"10.1016/j.jaridenv.2016.04.004","usgsCitation":"Birtwhistle, A.N., Laituri, M., Bledsoe, B., and Friedman, J.M., 2016, Using NDVI to measure precipitation in semi-arid landscapes: Journal of Arid Environments, v. 131, p. 15-24, https://doi.org/10.1016/j.jaridenv.2016.04.004.","productDescription":"10 p.","startPage":"15","endPage":"24","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070425","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471060,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2016.04.004","text":"Publisher Index Page"},{"id":438618,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZK5DSB","text":"USGS data release","linkHelpText":"Mean of the Top Ten Percent of NDVI Values in the Yuma Proving Ground during Monsoon Season, 1986-2011"},{"id":327106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":334957,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7ZK5DSB","text":"Mean of the top ten percent of NDVI values in the Yuma Proving Ground during monsoon season, 1986-2011"}],"country":"United States","state":"Arizona","otherGeospatial":"Sonoran Desert, Yuma Proving Ground","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.49676513671875,\n 33.55741786324217\n ],\n [\n -114.23858642578125,\n 33.56199537293026\n ],\n [\n -114.2083740234375,\n 32.95797741405949\n ],\n [\n -113.9666748046875,\n 32.94875863715422\n ],\n [\n -113.9501953125,\n 33.0316929997831\n ],\n [\n -113.74420166015624,\n 33.04320549616557\n ],\n [\n -113.7469482421875,\n 33.33511774753217\n ],\n [\n -113.587646484375,\n 33.33741240611175\n ],\n [\n -113.56842041015625,\n 32.930318199070534\n ],\n [\n -113.90625,\n 32.76880048488168\n ],\n [\n -114.466552734375,\n 32.7295304134847\n ],\n [\n -114.466552734375,\n 32.85421076375023\n ],\n [\n -114.46105957031249,\n 32.877280526855394\n ],\n [\n -114.44183349609375,\n 32.89342578969231\n ],\n [\n -114.46380615234375,\n 32.92109653816924\n ],\n [\n -114.46380615234375,\n 32.94414888814148\n ],\n [\n -114.46105957031249,\n 32.9648908656902\n ],\n [\n -114.46929931640624,\n 32.98562797456918\n ],\n [\n -114.49127197265625,\n 32.98562797456918\n ],\n [\n -114.49676513671875,\n 33.00636021320537\n ],\n [\n -114.52972412109375,\n 33.02939031998959\n ],\n [\n -114.55169677734375,\n 33.038600678138195\n ],\n [\n -114.576416015625,\n 33.04090311724091\n ],\n [\n -114.60113525390625,\n 33.050112271849656\n ],\n [\n -114.5928955078125,\n 33.4955977448657\n ],\n [\n -114.49676513671875,\n 33.55741786324217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"131","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b82deee4b03fd6b7da3a5e","contributors":{"authors":[{"text":"Birtwhistle, Amy N.","contributorId":173888,"corporation":false,"usgs":false,"family":"Birtwhistle","given":"Amy","email":"","middleInitial":"N.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Laituri, Melinda","contributorId":173889,"corporation":false,"usgs":false,"family":"Laituri","given":"Melinda","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bledsoe, Brian","contributorId":173890,"corporation":false,"usgs":false,"family":"Bledsoe","given":"Brian","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":646519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":646516,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70169300,"text":"fs20163017 - 2016 - Coal-tar-based pavement sealcoat—Potential concerns for human health and aquatic life","interactions":[],"lastModifiedDate":"2017-06-30T10:18:22","indexId":"fs20163017","displayToPublicDate":"2016-04-20T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3017","title":"Coal-tar-based pavement sealcoat—Potential concerns for human health and aquatic life","docAbstract":"Sealcoat is the black, viscous liquid sprayed or painted on many asphalt parking lots, driveways, and playgrounds to protect and enhance the appearance of the underlying asphalt. Studies by the U.S. Geological Survey (USGS), academic institutions, and State and local agencies have identified coal-tar-based pavement sealcoat as a major source of polycyclic aromatic hydrocarbon (PAH) contamination in urban and suburban areas and a potential concern for human health and aquatic life.
\nKey Findings:
\nHuman Health Concerns—As coal-tar-based sealcoat ages, it wears into small particles with high levels of PAHs that can be tracked into homes and incorporated into house dust. For people who live adjacent to coal-tar-sealcoated pavement, ingestion of PAH-contaminated house dust and soil results in an elevated potential cancer risk, particularly for young children. Exposure to PAHs, especially early in childhood, has been linked by health professionals to an increased risk of lung, skin, bladder, and respiratory cancers.
\nAquatic Life Concerns—Runoff from coal-tar-sealcoated pavement, even runoff collected more than 3 months after sealcoat application, is acutely toxic to fathead minnows and water fleas, two species commonly used to assess toxicity to aquatic life. Exposure to even highly diluted runoff from coal-tar-sealcoated pavement can cause DNA damage and impair DNA repair. These findings demonstrate that coal-tar-sealcoat runoff can remain a risk to aquatic life for months after application.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163017","usgsCitation":"Mahler, B.J., Woodside, M.D., and VanMetre, P.C., 2016, Coal-tar-based pavement sealcoat—Potential concerns for human health and aquatic life: U.S. Geological Survey Fact Sheet 2016–3017, 6 p., https://dx.doi.org/10.3133/fs20163017.","productDescription":"6 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067036","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":320231,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3017/coverthb.jpg"},{"id":320232,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3017/fs20163017.pdf","text":"Report","size":"6.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3017"}],"contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754
http://tx.usgs.gov/sealcoat.html
Methylmercury contamination of the environment is an important issue globally, and birds are useful bioindicators for mercury monitoring programs. The available data on mercury contamination of birds in western North America were synthesized. Original data from multiple databases were obtained and a literature review was conducted to obtain additional mercury concentrations. In total, 29219 original bird mercury concentrations from 225 species were compiled, and an additional 1712 mean mercury concentrations, representing 19998 individuals and 176 species, from 200 publications were obtained. To make mercury data comparable across bird tissues, published equations of tissue mercury correlations were used to convert all mercury concentrations into blood-equivalent mercury concentrations. Blood-equivalent mercury concentrations differed among species, foraging guilds, habitat types, locations, and ecoregions. Piscivores and carnivores exhibited the greatest mercury concentrations, whereas herbivores and granivores exhibited the lowest mercury concentrations. Bird mercury concentrations were greatest in ocean and salt marsh habitats and lowest in terrestrial habitats. Bird mercury concentrations were above toxicity benchmarks in many areas throughout western North America, and multiple hotspots were identified. Additionally, published toxicity benchmarks established in multiple tissues were summarized and translated into a common blood-equivalent mercury concentration. Overall, 66% of birds sampled in western North American exceeded a blood-equivalent mercury concentration of 0.2 μg/g wet weight (ww; above background levels), which is the lowest-observed effect level, 28% exceeded 1.0 μg/g ww (moderate risk), 8% exceeded 3.0 μg/g ww (high risk), and 4% exceeded 4.0 μg/g ww (severe risk). Mercury monitoring programs should sample bird tissues, such as adult blood and eggs, that are most-easily translated into tissues with well-developed toxicity benchmarks and that are directly relevant to bird reproduction. Results indicate that mercury contamination of birds is prevalent in many areas throughout western North America, and large-scale ecological attributes are important factors influencing bird mercury concentrations.
\nUnstable hybrid swarms that arise following the introduction of non-native species can overwhelm native congeners, yet the stability of invasive hybrid swarms has not been well documented over time. Here we examine genetic variation and clinal stability across a recently formed hybrid swarm involving native blacktail shiner (Cyprinella venusta) and non-native red shiner (C. lutrensis) in the Upper Coosa River basin, which is widely considered to be a global hotspot of aquatic biodiversity. Examination of phenotypic, multilocus genotypic, and mitochondrial haplotype variability between 2005 and 2011 revealed that the proportion of hybrids has increased over time, with more than a third of all sampled individuals exhibiting admixture in the final year of sampling. Comparisons of clines over time indicated that the hybrid swarm has been rapidly progressing upstream, but at a declining and slower pace than rates estimated from historical collection records. Clinal comparisons also showed that the hybrid swarm has been expanding and contracting over time. Additionally, we documented the presence of red shiner and hybrids farther downstream than prior studies have detected, which suggests that congeners in the Coosa River basin, including all remaining populations of the threatened blue shiner (Cyprinella caerulea), are at greater risk than previously thought.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.12371","usgsCitation":"Glotzbecker, G.J., Walters, D., and Blum, M.J., 2016, Rapid movement and instability of an invasive hybrid swarm: Evolutionary Applications, v. 9, no. 6, p. 741-755, https://doi.org/10.1111/eva.12371.","productDescription":"15 p.","startPage":"741","endPage":"755","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068483","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471062,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.12371","text":"Publisher Index Page"},{"id":320335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Coosa River basin, Weiss Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.5340576171875,\n 34.268566186749894\n ],\n [\n -85.50521850585938,\n 34.250405862125\n ],\n [\n -85.4791259765625,\n 34.229970811273084\n ],\n [\n -85.46127319335938,\n 34.20953080048952\n ],\n [\n -85.45578002929688,\n 34.16977214177208\n ],\n [\n -85.54229736328125,\n 34.12203701907784\n ],\n [\n -85.61370849609375,\n 34.09474769880029\n ],\n [\n -85.6439208984375,\n 34.127721186043985\n ],\n [\n -85.69335937499999,\n 34.14136162745489\n ],\n [\n -85.72494506835936,\n 34.116352469972746\n ],\n [\n -85.73181152343749,\n 34.076549928891744\n ],\n [\n -85.78674316406249,\n 34.03900467904445\n ],\n [\n -85.8087158203125,\n 34.0219331594475\n ],\n [\n -85.88012695312499,\n 33.996888718429396\n ],\n [\n -85.91995239257812,\n 34.00144280255186\n ],\n [\n -85.92819213867188,\n 34.05152161016494\n ],\n [\n -85.91995239257812,\n 34.102707993174874\n ],\n [\n -85.91445922851562,\n 34.16068181711789\n ],\n [\n -85.84579467773438,\n 34.20612364990816\n ],\n [\n -85.77850341796875,\n 34.26062152714047\n ],\n [\n -85.70846557617186,\n 34.29239566218292\n ],\n [\n -85.64804077148438,\n 34.30827822549175\n ],\n [\n -85.61920166015625,\n 34.30600947175969\n ],\n [\n -85.58761596679688,\n 34.301471780404476\n ],\n [\n -85.5560302734375,\n 34.28558793000448\n ],\n [\n -85.5340576171875,\n 34.268566186749894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-27","publicationStatus":"PW","scienceBaseUri":"57189a1be4b0ef3b7caaf797","contributors":{"authors":[{"text":"Glotzbecker, Gregory J.","contributorId":168704,"corporation":false,"usgs":false,"family":"Glotzbecker","given":"Gregory","email":"","middleInitial":"J.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":627046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, David 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":147135,"corporation":false,"usgs":true,"family":"Walters","given":"David","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":627045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blum, Michael J.","contributorId":168705,"corporation":false,"usgs":false,"family":"Blum","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":627047,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70169137,"text":"sir20165032 - 2016 - Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013","interactions":[],"lastModifiedDate":"2016-04-20T12:57:06","indexId":"sir20165032","displayToPublicDate":"2016-04-20T09:30:00","publicationYear":"2016","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":"2016-5032","title":"Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013","docAbstract":"The U.S. Geological Survey, in cooperation with the City of Lubbock and the Texas State Soil and Water Conservation Board, developed and calibrated a Soil and Water Assessment Tool watershed model of the Double Mountain Fork Brazos River watershed in western Texas to simulate monthly mean streamflow and to evaluate the effects of brush management on water yields in the watershed, particularly to Lake Alan Henry, for calendar years 1994–2013. Model simulations were done to quantify the possible change in water yield of individual subbasins in the Double Mountain Fork Brazos River watershed as a result of the replacement of shrubland (brush) with grassland. The simulation results will serve as a tool for resource managers to guide brush-management efforts.
\nThe model was calibrated from 1994 through 2008 and validated from 2009 through 2013 with streamflow data collected at the U.S. Geological Survey streamflow-gaging station 08079600 Double Mountain Fork Brazos River at Justiceburg, Texas (hereinafter referred to as the “Justiceburg gage”). Simulated monthly mean streamflow showed agreement with measured monthly mean streamflow for the 1994–2013 study period: the percentage bias was +6, the coefficient of determination was 0.73, and the Nash–Sutcliffe coefficient of model efficiency was 0.71.
\nThe calibrated watershed model was used to perform brush-management simulations. The National Land Cover Database 2006, which was the land-cover data used to develop the watershed model, was modified to simulate shrubland replacement with grassland in each of the 35 model subbasins. After replacement of shrubland with grassland in areas with land slope less than 20 percent and excluding riparian areas, the modeled 20-year (1994 through 2013) water yields to Lake Alan Henry increased by 114,000 acre-feet or about 5,700 acre-feet per year. In terms of the increase in water yield per acre of shrubland replaced with grassland, the average annual increase in water yield was 17,300 gallons per acre. Within the modeled subbasins, the increase in average annual water yield ranged from 5,850 to 34,400 gallons per acre of shrubland replaced with grassland. Subbasins downstream from the Justiceburg gage had a higher average annual increase in water yield (21,700 gallons per acre) than subbasins upstream from the streamflow-gaging station (16,800 gallons per acre).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165032","collaboration":"Prepared in cooperation with the City of Lubbock and the Texas State Soil and Water Conservation Board","usgsCitation":"Harwell, G.R., Stengel, V.G., and Bumgarner, J.R., 2016, Simulation of streamflow and the effects of brush management on water yields in the Double Mountain Fork Brazos River watershed, western Texas 1994–2013: U.S. Geological Survey Scientific Investigations Report 2016–5032, 39 p., https://dx.doi.org/10.3133/sir20165032.","productDescription":"Report: viii, 39 p.; Precipitation and Temperature Data","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072426","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":320033,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5032/sir20165032_data.zip","text":"Precipitation and Temperature Data","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016–5032 Precipitation and Temperature Data"},{"id":319869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5032/coverthb.jpg"},{"id":319870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5032/sir20165032.pdf","text":"Report","size":"4.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5032"}],"country":"United States","state":"Texas","otherGeospatial":"Double Mountain Fork Brazos River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.7,\n 33.165\n ],\n [\n -101.7,\n 32.835\n ],\n [\n -100.9,\n 32.835\n ],\n [\n -100.9,\n 33.165\n ],\n [\n -101.7,\n 33.165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4733
This report describes a method for estimating ecologically relevant low-flow metrics that quantify late‑season streamflow regime for ungaged sites in the upper Grande Ronde River Basin, Oregon. The analysis presented here focuses on sites sampled by the Columbia River Inter‑Tribal Fish Commission as part of their efforts to monitor habitat restoration to benefit spring Chinook salmon recovery in the basin. Streamflow data were provided by the U.S. Geological Survey and the Oregon Water Resources Department. Specific guidance was provided for selection of streamgages, development of probabilistic frequency distributions for annual 7-day low-flow events, and regionalization of the frequency curves based on multivariate analysis of watershed characteristics. Evaluation of the uncertainty associated with the various components of this protocol indicates that the results are reliable for the intended purpose of hydrologic classification to support ecological analysis of factors contributing to juvenile salmon success. They should not be considered suitable for more standard water-resource evaluations that require greater precision, especially those focused on management and forecasting of extreme low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20165041","collaboration":"Prepared in cooperation with Columbia River Inter-Tribal Fish Commission","usgsCitation":"Kelly, V.J., and White, Seth, 2016, A method for characterizing late-season low-flow regime in the upper Grand Ronde River Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2016–5041, 41 p.,\nhttps://dx.doi.org/10.3133/sir20165041.","productDescription":"Report: vi, 41 p.; Appendixes A-F","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059110","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":320198,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5041/sir20165041.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5041 Report PDF"},{"id":320199,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5041/sir20165041_appendixes.xlsx","text":"Appendixes A-F","size":"75 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5041 Appendixes"},{"id":320197,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5041/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Grande Ronde River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115,\n 44\n ],\n [\n -115,\n 47\n ],\n [\n -119,\n 47\n ],\n [\n -119,\n 44\n ],\n [\n -115,\n 44\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
http://or.water.usgs.gov
Many barrier islands in the United States are eroding and losing elevation substantively because of storm surge, waves, and sea-level changes. This is particularly true for the deltaic barrier system in Louisiana. Breton Island is near the mouth of the Mississippi River at the southern end of the Chandeleur barrier island chain in southeast Louisiana. This report expands on previous geomorphic studies of Breton Island by incorporating additional historic and recent datasets. Multiple analyses focus on longand short-term shoreline change, as well as episodic events and anthropogenic modification. Analyses periods include long term (1869–2014), long-term historic (1869–1950), post-Mississippi River-Gulf Outlet (1950–2014), pre/post-Hurricane Katrina (2004–5), and recent (2005–14). In addition to shoreline change, barrier island geomorphology is evaluated using island area, elevation, and sediment volume change. In the long term (1869–2014), Breton Island was affected by landward transgression, island narrowing, and elevation loss. Major storm events exacerbated the long-term trends. In the recent period (2005–14), Breton Island eroded at a slower rate than in the long-term and gained area and total sediment volume. The recent accretion is likely because of the lack of major storms since Hurricane Katrina in 2005.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161039","usgsCitation":"Terrano, J.F., Flocks, J.G., and Smith, K.E.L., 2016, Analysis of shoreline and geomorphic change for Breton Island, Louisiana, from 1869 to 2014: U.S. Geological Survey Open-File Report 2016–1039, 34 p.,\nhttps://dx.doi.org/10.3133/ofr20161039.","productDescription":"Report: viii, 34 p.; Data Releases","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070444","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320056,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1039/coverthb.jpg"},{"id":320057,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1039/ofr20161039.pdf","text":"Report","size":"3.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1039"},{"id":324797,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F70G3H6G","text":"USGS data release - Topobathymetric Lidar Survey of Breton and Gosier Islands, Louisiana, January 16 and 18, 2014"},{"id":324798,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7XS5SGM","text":"USGS data release - A GIS Compilation of Vector Shorelines and Associated Shoreline Change Data for Breton Island, Louisiana: 1869–2014"}],"country":"United States","state":"Louisiana","otherGeospatial":"Breton Island, Breton National Wildlife Refuge, Chandeleur barrier island chain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.17087554931639,\n 29.50729642400116\n ],\n [\n -89.17757034301758,\n 29.506698839472033\n ],\n [\n -89.18306350708008,\n 29.503262659979747\n ],\n [\n -89.18684005737305,\n 29.49579230227246\n ],\n [\n -89.18478012084961,\n 29.493849919009545\n ],\n [\n -89.1840934753418,\n 29.492206334848714\n ],\n [\n -89.17928695678711,\n 29.492804004901785\n ],\n [\n -89.17671203613281,\n 29.49235575269256\n ],\n [\n -89.17722702026367,\n 29.48862024048175\n ],\n [\n -89.18169021606444,\n 29.483390291999466\n ],\n [\n -89.18254852294922,\n 29.477861195816843\n ],\n [\n -89.1789436340332,\n 29.473676814427723\n ],\n [\n -89.1734504699707,\n 29.473527369040777\n ],\n [\n -89.16847229003906,\n 29.478907264175373\n ],\n [\n -89.1650390625,\n 29.48742484748479\n ],\n [\n -89.16435241699219,\n 29.497585238377603\n ],\n [\n -89.16521072387695,\n 29.501918036260868\n ],\n [\n -89.1653823852539,\n 29.506101251415647\n ],\n [\n -89.16778564453125,\n 29.508043399702284\n ],\n [\n -89.17087554931639,\n 29.50729642400116\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
The Working Group on Utah Earthquake Probabilities has assessed the probability of large earthquakes in the Wasatch Front region. There is a 43 percent probability of one or more magnitude 6.75 or greater earthquakes and a 57 percent probability of one or more magnitude 6.0 or greater earthquakes in the region in the next 50 years. These results highlight the threat of large earthquakes in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163019","collaboration":"Prepared in cooperation with the Utah Geological Survey, URS Corporation, University of Utah Seismograph Stations, and University of Utah.","usgsCitation":"DuRoss, C.B., 2016, Earthquake forecast for the Wasatch Front region of the Intermountain West: U.S. Geological Survey Fact Sheet 2016–3019, 2 p., https://doi.dx.org/10.3133/fs20163019. \n","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073668","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":320043,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3019/fs20163019.pdf","text":"Report","size":"6.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3019"},{"id":320042,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3019/coverthb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Wasatch Front Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.26904296874999,\n 39.21948715423953\n ],\n [\n -113.26904296874999,\n 42.187829010590825\n ],\n [\n -110.8465576171875,\n 42.187829010590825\n ],\n [\n -110.8465576171875,\n 39.21948715423953\n ],\n [\n -113.26904296874999,\n 39.21948715423953\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Geologic Hazards Science Center
Box 25046, Mail Stop 966
Denver, CO 80225
http://geohazards.cr.usgs.gov/
","tableOfContents":"In 2013, Landsat 8 began adding high quality, global, moderate-resolution imagery to the more than 40-year archive of Landsat imagery. To assess the potential effects of the availability of Landsat 8 imagery on users and their work, the U.S. Geological Survey (USGS) Land Remote Sensing Program (LRS) initiated a survey of Landsat users. The objectives of the survey were to
\n1. Characterize various Landsat user groups, such as United States (U.S.) and international users and Landsat 8 and non-Landsat 8 users;
2. Identify any differences among user groups in uses and preferences;
3. Measure the importance of and satisfaction with Landsat 8 attributes;
4. Assess the importance to users of the frequency of usable imagery; and
5. Determine any challenges in using Landsat 8.
The online survey was sent to 51,617 Landsat users registered with USGS in May 2014. Almost 13,000 people responded to the survey for a response rate of 25 percent (n = 12,966). Current Landsat users (users who had used Landsat in their work in the year prior to the survey) composed 89 percent of the sample (n = 11,549) and past Landsat users composed 11 percent (n = 1,417). The results reported here apply to current Landsat users registered with the USGS Earth Resources Observation and Science (EROS) Center.
\nUsers from 161 countries responded to the survey. Of those, 19 percent were citizens or permanent residents of the United States and 81 percent resided in other countries. More than 70 percent of current users had used Landsat 8 in the year prior to the survey. The majority of Landsat 8 users (65 percent) were established users who used Landsat imagery regularly both before and after Landsat 8 imagery became available. The average current Landsat user was male, 36 years old, and highly educated, with 9 years of experience using satellite imagery or geographic information system (GIS) software. Landsat 8 users had, on average, two more years of experience than non-Landsat 8 users. Users were employed predominantly by academic institutions (65 percent), followed by private businesses (13 percent), Federal governments (10 percent), State and local governments (6 percent), and nonprofit organizations (6 percent).
\nOf the Landsat imagery obtained in the past year by current users, on average 31 percent came from a Landsat 8 sensor. An equivalent amount came from the Landsat 7 ETM+ sensor (33 percent); slightly less came from Landsats 4 and 5 TM sensors (27 percent). Much less came from Landsats 1 through 5 MSS sensors (5 percent). Overall, more than a third of users’ work used Landsat imagery (38 percent). Of this work, on average, 37 percent of the work was operational. Landsat 8 users considered a greater proportion of their work operational than non-Landsat 8 users (39 percent compared with 29 percent). Environmental sciences and management were the most commonly selected primary applications (selected by 42 percent of users). Land use/land cover (23 percent) was the second most commonly selected primary application, followed by education (12 percent), agriculture (9 percent), and planning and development (6 percent).
\nLandsat 8 users were asked to rank the importance of certain attributes in determining whether to use Landsat 8 imagery in their work. The archive was ranked most important, followed by cost, spatial resolution, extent of coverage, data quality, and frequency of revisit. Users were asked how satisfied they were with these same attributes as they currently apply to Landsat 8 imagery. On average, users were most satisfied with lack of cost, extent of coverage, data quality, and the archive, but they were satisfied with all attributes.
\nUsers were asked how often they needed Landsat imagery to meet various requirements for their primary application. The survey question specifically asked how often users needed usable imagery, which differs from how often they would like the Landsat satellites to acquire an image. Users were asked to identify their needed frequency of usable imagery for the following levels:
\n1. Threshold level—the minimum frequency of usable imagery needed to be of any value to their primary application.
2. Breakthrough level—the frequency of usable imagery that would result in a significant improvement for their primary application of the imagery.
3. Target level—the frequency of usable imagery that would only provide a limited additional increase in the expected performance for their primary application.
To meet the threshold level, three-quarters of users needed usable imagery every 17 days or less frequently. At the breakthrough level, two-thirds of users (64 percent) needed a usable image every 5–16 days. The current constellation of two satellites (Landsat 7 and 8) is capable of meeting the threshold and breakthrough needs of most users at least some of the time, but a single satellite would be highly unlikely to do so. Two-fifths of users (40 percent) felt that usable imagery provided every 4 days or more frequently would meet their target level which the current Landsat constellation cannot provide. Landsat 8 users were significantly more likely than non-Landsat 8 users to need usable imagery more frequently to meet their target levels. Additionally, U.S. Landsat 8 users were significantly more likely than other Landsat users to need usable imagery more frequently in order meet both their breakthrough and target levels.
\nTo explore the effect of the availability of Landsat 8 imagery on Landsat imagery use in general, established users (those who had consistently used Landsat imagery both before and after Landsat 8 imagery became available) using Landsat 8 imagery were asked about changes in the amount of Landsat imagery they used. The majority of established users using Landsat 8 imagery (60 percent) reported an average increase of 51 percent in the number of scenes obtained after Landsat 8 imagery became available. Landsat 8 users were asked if they had encountered challenges in using Landsat 8 whereas non-Landsat 8 users were asked if such challenges had played a role in why they were not using Landsat 8 imagery. Although many users did not encounter challenges when using or trying to use Landsat 8 data, slightly less than 30 percent did encounter issues with processing the data to a usable point. The most common issue reported was not being able to create or have access to a surface reflectance corrected product. Other challenges were related to the file sizes of images being too large to download, store, or analyze. There were no statistically significant differences between Landsat 8 and non-Landsat 8 users in terms of challenges encountered when using or trying to use the imagery, which indicates that users were not unduly discouraged by the challenges they may have encountered. When asked about potential consequences of not using Landsat 8, more than half of the non-Landsat 8 users did not report detrimental effects on their work from not using the imagery. Of those who did report detrimental effects, decreased quality of work, decreased scope of work, and increased time spent on work were the most common.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161032","usgsCitation":"Miller, H.M., 2016, Users and uses of Landsat 8 satellite imagery—2014 survey results: U.S. Geological Survey Open-File Report 2016–1032, 27 p., https://dx.doi/org/10.3133/ofr20161032.","productDescription":"vi, 27 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069774","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1032/ofr20161032.pdf","text":"Report","size":"2.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1032"},{"id":320058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1032/coverthb.jpg"}],"contact":"Center Director, USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Box 25046, MS-939
Fort Collins, CO 80526-8118
Concentrations of H2O and CO2 in olivine-hosted melt inclusions can be used to estimate crystallization depths for the olivine host. However, the original dissolved CO2concentration of melt inclusions at the time of trapping can be difficult to measure directly because in many cases substantial CO2 is transferred to shrinkage bubbles that form during post-entrapment cooling and crystallization. To investigate this problem, we heated olivine from the 1959 Kīlauea Iki and 1960 Kapoho (Hawai‘i) eruptions in a 1-atm furnace to temperatures above the melt inclusion trapping temperature to redissolve the CO2 in shrinkage bubbles. The measured CO2 concentrations of the experimentally rehomogenized inclusions (⩽590 ppm for Kīlauea Iki [n=10]; ⩽880 ppm for Kapoho, with one inclusion at 1863 ppm [n=38]) overlap with values for naturally quenched inclusions from the same samples, but experimentally rehomogenized inclusions have higher within-sample median CO2 values than naturally quenched inclusions, indicating at least partial dissolution of CO2 from the vapor bubble during heating. Comparison of our data with predictions from modeling of vapor bubble formation and published Raman data on the density of CO2 in the vapor bubbles suggests that 55-85% of the dissolved CO2 in the melt inclusions at the time of trapping was lost to post-entrapment shrinkage bubbles. Our results combined with the Raman data demonstrate that olivine from the early part of the Kīlauea Iki eruption crystallized at <6 km depth, with the majority of olivine in the 1-3 km depth range. These depths are consistent with the interpretation that the Kīlauea Iki magma was supplied from Kīlauea’s summit magma reservoir (∼2-5 km depth). In contrast, olivine from Kapoho, which was the rift zone extension of the Kīlauea Iki eruption, crystallized over a much wider range of depths (∼1-16 km). The wider depth range requires magma transport during the Kapoho eruption from deep beneath the summit region and/or from deep beneath Kīlauea’s east rift zone. The deeply derived olivine crystals and their host magma mixed with stored, more evolved magma in the rift zone, and the mixture was later erupted at Kapoho.
\nThe Florida Everglades freshwater landscape exhibits a distribution of islands covered by woody vegetation and bordered by marshes and wet prairies. Known as “tree islands”, these ecogeomorphic features can be found in few other low gradient, nutrient limited freshwater wetlands. In the last few decades, however, a large percentage of tree islands have either shrank or disappeared in apparent response to altered water depths and other stressors associated with human impacts on the Everglades. Because the processes determining the formation and spatial organization of tree islands remain poorly understood, it is still unclear what controls the sensitivity of these landscapes to altered conditions. We hypothesize that positive feedbacks between woody plants and soil accretion are crucial to emergence and decline of tree islands. Likewise, positive feedbacks between phosphorus (P) accumulation and trees explain the P enrichment commonly observed in tree island soils. Here, we develop a spatially-explicit model of tree island formation and evolution, which accounts for these positive feedbacks (facilitation) as well as for long range competition and fire dynamics. It is found that tree island patterns form within a range of parameter values consistent with field data. Simulated impacts of reduced water levels, increased intensity of drought, and increased frequency of dry season/soil consuming fires on these feedback mechanisms result in the decline and disappearance of tree islands on the landscape.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.ecocom.2016.03.007","usgsCitation":"Carr, J., D’Odorico, P., Engel, V.C., and Redwine, J., 2016, Tree island pattern formation in the Florida Everglades: Ecological Complexity, v. 26, p. 37-44, https://doi.org/10.1016/j.ecocom.2016.03.007.","productDescription":"8 p.","startPage":"37","endPage":"44","numberOfPages":"8","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064593","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471064,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/7vh3x450","text":"External Repository"},{"id":320122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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