Planetary science is the study of planets, moons, irregular bodies such as asteroids and the processes that create and modify them. Like terrestrial sciences, planetary science research is heavily dependent on collecting, processing and archiving large quantities of spatial data to support a range of activities. To address the complexity of storing, discovering, accessing, and utilizing spatial data, the terrestrial research community has developed conceptual Spatial Data Infrastructure (SDI) models and cyberinfrastructures. The needs that these systems seek to address for terrestrial spatial data users are similar to the needs of the planetary science community: spatial data should just work for the non-spatial expert. Here we discuss a path towards a Planetary Spatial Data Infrastructure (PSDI) solution that fulfills this primary need. We first explore the linkage between SDI models and cyberinfrastructures, then describe the gaps in current PSDI concepts, and discuss the overlap between terrestrial SDIs and a new, conceptual PSDI that best serves the needs of the planetary science community.
","language":"English","publisher":"MDPI","doi":"10.3390/ijgi6060181","usgsCitation":"Laura, J., Hare, T.M., Gaddis, L.R., Fergason, R.L., Skinner, Hagerty, J., and Archinal, B., 2017, Towards a planetary spatial data infrastructure: ISPRS International Journal of Geo-Information , v. 6, no. 6, 181, 18 p., https://doi.org/10.3390/ijgi6060181.","productDescription":"181, 18 p.","ipdsId":"IP-087170","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":469739,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijgi6060181","text":"Publisher Index Page"},{"id":371916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaddis, Lisa R. 0000-0001-9953-5483 lgaddis@usgs.gov","orcid":"https://orcid.org/0000-0001-9953-5483","contributorId":2817,"corporation":false,"usgs":true,"family":"Gaddis","given":"Lisa","email":"lgaddis@usgs.gov","middleInitial":"R.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fergason, Robin L. 0000-0002-2044-1714","orcid":"https://orcid.org/0000-0002-2044-1714","contributorId":206167,"corporation":false,"usgs":true,"family":"Fergason","given":"Robin","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skinner, Jr. 0000-0002-3644-7010","orcid":"https://orcid.org/0000-0002-3644-7010","contributorId":222125,"corporation":false,"usgs":true,"family":"Skinner","suffix":"Jr.","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hagerty, Justin 0000-0003-3800-7948 jhagerty@usgs.gov","orcid":"https://orcid.org/0000-0003-3800-7948","contributorId":911,"corporation":false,"usgs":true,"family":"Hagerty","given":"Justin","email":"jhagerty@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Archinal, Brent A. 0000-0002-6654-0742","orcid":"https://orcid.org/0000-0002-6654-0742","contributorId":206341,"corporation":false,"usgs":true,"family":"Archinal","given":"Brent A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":781276,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188641,"text":"ofr20171075 - 2017 - Structured decision making for conservation of bull trout (Salvelinus confluentus) in Long Creek, Klamath River Basin, south-central Oregon","interactions":[],"lastModifiedDate":"2017-11-22T12:09:14","indexId":"ofr20171075","displayToPublicDate":"2017-06-21T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1075","displayTitle":"Structured decision making for conservation of bull trout (Salvelinus confluentus) in Long Creek, Klamath River Basin, south-central Oregon","title":"Structured decision making for conservation of bull trout (Salvelinus confluentus) in Long Creek, Klamath River Basin, south-central Oregon","docAbstract":"With the decline of bull trout (Salvelinus confluentus), managers face multiple, and sometimes contradictory, management alternatives for species recovery. Moreover, effective decision-making involves all stakeholders influenced by the decisions (such as Tribal, State, Federal, private, and non-governmental organizations) because they represent diverse objectives, jurisdictions, policy mandates, and opinions of the best management strategy. The process of structured decision making is explicitly designed to address these elements of the decision making process. Here we report on an application of structured decision making to a population of bull trout believed threatened by high densities of nonnative brook trout (S. fontinalis) and habitat fragmentation in Long Creek, a tributary to the Sycan River in the Klamath River Basin, south-central Oregon. This involved engaging stakeholders to identify (1) their fundamental objectives for the conservation of bull trout, (2) feasible management alternatives to achieve their objectives, and (3) biological information and assumptions to incorporate in a decision model. Model simulations suggested an overarching theme among the top decision alternatives, which was a need to simultaneously control brook trout and ensure that the migratory tactic of bull trout can be expressed. More specifically, the optimal management decision, based on the estimated adult abundance at year 10, was to combine the eradication of brook trout from Long Creek with improvement of downstream conditions (for example, connectivity or habitat conditions). Other top decisions included these actions independently, as well as electrofishing removal of brook trout. In contrast, translocating bull trout to a different stream or installing a barrier to prevent upstream spread of brook trout had minimal or negative effects on the bull trout population. Moreover, sensitivity analyses suggested that these actions were consistently identified as optimal across a large range of parameter values. Taken together, these results support the conclusion that management actions focused on controlling brook trout and enhancing migrant bull trout are more likely to yield more adult bull trout within the 10-year time frame specified by stakeholders.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171075","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Benjamin, J.R., McDonnell, Kevin, Dunham, J.B., Brignon, W.R., and Peterson, J.T., 2017, Structured decision making for conservation of bull trout (Salvelinus confluentus) in Long Creek, Klamath River Basin, south-central Oregon: U.S. Geological Survey Open-Report 2017–1075, 32 p., https://doi.org/10.3133/ofr20171075.","productDescription":"iv, 32 p.","onlineOnly":"Y","ipdsId":"IP-086486","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":342727,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1075/ofr20171075.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1075"},{"id":342726,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1075/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Long Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.23001098632812,\n 42.707668698664335\n ],\n [\n -120.86196899414061,\n 42.707668698664335\n ],\n [\n -120.86196899414061,\n 42.94737618460479\n ],\n [\n -121.23001098632812,\n 42.94737618460479\n ],\n [\n -121.23001098632812,\n 42.707668698664335\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Declining sea-ice extent is currently amplifying climate warming in the Arctic. Instrumental records at high latitudes are too short-term to provide sufficient historical context for these trends, so paleoclimate archives are needed to better understand the functioning of the sea ice-albedo feedback. Here we use the oxygen isotope values of wood cellulose in living and sub-fossil willow shrubs (δ18Owc) (Salix spp.) that have been radiocarbon-dated (14C) to produce a multi-millennial record of climatic change on Alaska's North Slope during the Pleistocene-Holocene transition (13,500–7500 calibrated 14C years before present; 13.5–7.5 ka). We first analyzed the spatial and temporal patterns of δ18Owc in living willows growing at upland sites and found that over the last 30 years δ18Owc values in individual growth rings correlate with local summer temperature and inter-annual variations in summer sea-ice extent. Deglacial δ18Owcvalues from 145 samples of subfossil willows clearly record the Allerød warm period (∼13.2 ka), the Younger Dryas cold period (12.9–11.7 ka), and the Holocene Thermal Maximum (11.7–9.0 ka). The magnitudes of isotopic changes over these rapid climate oscillations were ∼4.5‰, which is about 60% of the differences in δ18Owc between those willows growing during the last glacial period and today. Modeling of isotope-precipitation relationships based on Rayleigh distillation processes suggests that during the Younger Dryas these large shifts in δ18Owc values were caused by interactions between local temperature and changes in evaporative moisture sources, the latter controlled by seaice extent in the Arctic Ocean and Bering Sea. Based on these results and on the effects that sea-ice have on climate today, we infer that ocean-derived feedbacks amplified temperature changes and enhanced precipitation in coastal regions of Arctic Alaska during warm times in the past. Today, isotope values in willows on the North Slope of Alaska are similar to those growing during the warmest times of the Pleistocene-Holocene transition, which were times of widespread permafrost thaw and striking ecological changes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2017.05.012","usgsCitation":"Gaglioti, B.V., Mann, D.H., Wooller, M.J., Jones, B.M., Wiles, G.C., Groves, P., Kunz, M.L., Baughman, C., and Reanier, R.E., 2017, Younger-Dryas cooling and sea-ice feedbacks were prominent features of the Pleistocene-Holocene transition in Arctic Alaska: Quaternary Science Reviews, v. 169, p. 330-343, https://doi.org/10.1016/j.quascirev.2017.05.012.","productDescription":"14 p.","startPage":"330","endPage":"343","ipdsId":"IP-084358","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":469740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2017.05.012","text":"Publisher Index Page"},{"id":342699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -164.1796875,\n 67.33986082559095\n ],\n [\n -142.03125,\n 67.33986082559095\n ],\n [\n -142.03125,\n 71.63599288330609\n ],\n [\n -164.1796875,\n 71.63599288330609\n ],\n [\n -164.1796875,\n 67.33986082559095\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"169","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"594b85b2e4b062508e382b70","contributors":{"authors":[{"text":"Gaglioti, Benjamin V. 0000-0003-0591-5253 bgaglioti@usgs.gov","orcid":"https://orcid.org/0000-0003-0591-5253","contributorId":4521,"corporation":false,"usgs":true,"family":"Gaglioti","given":"Benjamin","email":"bgaglioti@usgs.gov","middleInitial":"V.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":698793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mann, Daniel H.","contributorId":67010,"corporation":false,"usgs":true,"family":"Mann","given":"Daniel","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":698794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wooller, Matthew J.","contributorId":192799,"corporation":false,"usgs":false,"family":"Wooller","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":698795,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":698792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiles, Gregory C.","contributorId":39278,"corporation":false,"usgs":true,"family":"Wiles","given":"Gregory","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":698796,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Groves, Pamela","contributorId":193132,"corporation":false,"usgs":false,"family":"Groves","given":"Pamela","email":"","affiliations":[],"preferred":false,"id":698797,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kunz, Michael L.","contributorId":42157,"corporation":false,"usgs":true,"family":"Kunz","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":698798,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":698799,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reanier, Richard E.","contributorId":77850,"corporation":false,"usgs":true,"family":"Reanier","given":"Richard","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":698800,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70188279,"text":"70188279 - 2017 - Widespread occurrence and potential for biodegradation of bioactive contaminants in Congaree National Park, USA","interactions":[],"lastModifiedDate":"2018-09-13T13:51:56","indexId":"70188279","displayToPublicDate":"2017-06-21T00:00:00","publicationYear":"2017","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":"Widespread occurrence and potential for biodegradation of bioactive contaminants in Congaree National Park, USA","docAbstract":"Organic contaminants with designed molecular bioactivity, such as pesticides and pharmaceuticals, originate from human and agricultural sources, occur frequently in surface waters, and threaten the structure and function of aquatic and terrestrial ecosystems. Congaree National Park in South Carolina (USA) is a vulnerable park unit due to its location downstream of multiple urban and agricultural contaminant sources and its hydrologic setting, being composed almost entirely of floodplain and aquatic environments. Seventy-two water and sediment samples were collected from 16 sites in Congaree National Park during 2013 to 2015, and analyzed for 199 and 81 targeted organic contaminants, respectively. More than half of these water and sediment analytes were not detected or potentially had natural sources. Pharmaceutical contaminants were detected (49 total) frequently in water throughout Congaree National Park, with higher detection frequencies and concentrations at Congaree and Wateree River sites, downstream from major urban areas. Forty-seven organic wastewater indicator chemicals were detected in water, and 36 were detected in sediment, of which approximately half are distinctly anthropogenic. Endogenous sterols and hormones, which may originate from humans or wildlife, were detected in water and sediment samples throughout Congaree National Park, but synthetic hormones were detected only once, suggesting a comparatively low risk of adverse impacts. Assessment of the biodegradation potentials of 8 14C-radiolabeled model contaminants indicated poor potentials for some contaminants, particularly under anaerobic sediments conditions.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.3873","usgsCitation":"Bradley, P.M., Battaglin, W.A., Clark, J.M., Henning, F., Hladik, M., Iwanowicz, L.R., Journey, C.A., Riley, J.W., and Romanok, K.M., 2017, Widespread occurrence and potential for biodegradation of bioactive contaminants in Congaree National Park, USA: Environmental Toxicology and Chemistry, v. 36, no. 11, p. 3045-3056, https://doi.org/10.1002/etc.3873.","productDescription":"12 p.","startPage":"3045","endPage":"3056","ipdsId":"IP-079959","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":342730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Congaree National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.86709976196289,\n 33.81138754703431\n ],\n [\n -80.86898803710938,\n 33.81010387680115\n ],\n [\n -80.86727142333983,\n 33.80739384305862\n ],\n [\n -80.86641311645508,\n 33.8052542821026\n ],\n [\n -80.86555480957031,\n 33.801545583049325\n ],\n [\n -80.86126327514648,\n 33.80126029184948\n ],\n [\n -80.85662841796875,\n 33.80083235326659\n ],\n [\n -80.85371017456055,\n 33.798977928014544\n ],\n [\n -80.85147857666014,\n 33.79783672327325\n ],\n [\n -80.84272384643555,\n 33.79498364485254\n ],\n [\n -80.83585739135742,\n 33.792843773631844\n ],\n [\n -80.83499908447266,\n 33.790846512234154\n ],\n [\n -80.83568572998047,\n 33.78799320084192\n ],\n [\n -80.83568572998047,\n 33.7864238390465\n ],\n [\n -80.83414077758789,\n 33.784711775193855\n ],\n [\n -80.83139419555664,\n 33.78442642789061\n ],\n [\n -80.82864761352539,\n 33.78456910166107\n ],\n [\n -80.82590103149414,\n 33.78599582629231\n ],\n [\n -80.82298278808594,\n 33.78727985813685\n ],\n [\n -80.82195281982422,\n 33.78571048326767\n ],\n [\n -80.82298278808594,\n 33.78442642789061\n ],\n [\n -80.82435607910156,\n 33.78371305547283\n ],\n [\n -80.82538604736328,\n 33.78114486556651\n ],\n [\n -80.8293342590332,\n 33.77886196544357\n ],\n [\n -80.83002090454102,\n 33.77700706429133\n ],\n [\n -80.82693099975586,\n 33.77529481219232\n ],\n [\n -80.8212661743164,\n 33.774010600658606\n ],\n [\n -80.81954956054688,\n 33.77543750117441\n ],\n [\n -80.81680297851562,\n 33.77786317750661\n ],\n [\n -80.81680297851562,\n 33.780288785150866\n ],\n [\n -80.81422805786133,\n 33.781002186091435\n ],\n [\n -80.81045150756836,\n 33.77843391489665\n ],\n [\n -80.8095932006836,\n 33.77700706429133\n ],\n [\n -80.80821990966797,\n 33.77372521770351\n ],\n [\n -80.80581665039062,\n 33.77358252586945\n ],\n [\n -80.80255508422852,\n 33.772583676376705\n ],\n [\n -80.79671859741211,\n 33.770015152780125\n ],\n [\n -80.79036712646484,\n 33.76915896113718\n ],\n [\n -80.78607559204102,\n 33.76915896113718\n ],\n [\n -80.78298568725586,\n 33.76830276093902\n ],\n [\n -80.78161239624023,\n 33.76587681391894\n ],\n [\n -80.78418731689453,\n 33.764307046898494\n ],\n [\n -80.78710556030273,\n 33.7624518306226\n ],\n [\n -80.78538894653319,\n 33.760453860491914\n ],\n [\n -80.782470703125,\n 33.76088200086917\n ],\n [\n -80.7795524597168,\n 33.7614528513789\n ],\n [\n -80.77783584594725,\n 33.763165380096595\n ],\n [\n -80.77611923217773,\n 33.76616222301528\n ],\n [\n -80.77474594116211,\n 33.76858816195565\n ],\n [\n -80.7718276977539,\n 33.771299424203335\n ],\n [\n -80.76925277709961,\n 33.77172751039999\n ],\n [\n -80.76787948608398,\n 33.77087133586779\n ],\n [\n -80.76822280883789,\n 33.76958705802806\n ],\n [\n -80.76873779296875,\n 33.76758925423873\n ],\n [\n -80.76770782470703,\n 33.766733038356286\n ],\n [\n -80.76633453369139,\n 33.76644763116106\n ],\n [\n -80.76410293579102,\n 33.766733038356286\n ],\n [\n -80.76358795166016,\n 33.76915896113718\n ],\n [\n -80.76238632202147,\n 33.77072863928061\n ],\n [\n -80.76101303100586,\n 33.77244098264139\n ],\n [\n -80.75963973999023,\n 33.774010600658606\n ],\n [\n -80.75723648071289,\n 33.77358252586945\n ],\n [\n -80.75689315795898,\n 33.77215559445779\n ],\n [\n -80.75689315795898,\n 33.769872454767096\n ],\n [\n -80.75603485107422,\n 33.76830276093902\n ],\n [\n -80.75603485107422,\n 33.76644763116106\n ],\n [\n -80.75603485107422,\n 33.76459246122269\n ],\n [\n -80.75414657592772,\n 33.763593506929496\n ],\n [\n -80.7505416870117,\n 33.76444975417943\n ],\n [\n -80.74796676635742,\n 33.763593506929496\n ],\n [\n -80.74728012084961,\n 33.760739287647716\n ],\n [\n -80.74504852294922,\n 33.761880986766215\n ],\n [\n -80.74384689331055,\n 33.76302267067703\n ],\n [\n -80.74453353881836,\n 33.76630492720698\n ],\n [\n -80.74813842773438,\n 33.76744655218575\n ],\n [\n -80.74831008911133,\n 33.7694443593021\n ],\n [\n -80.74882507324219,\n 33.771299424203335\n ],\n [\n -80.75071334838867,\n 33.77229828866843\n ],\n [\n -80.74848175048828,\n 33.77429598266308\n ],\n [\n -80.74504852294922,\n 33.77629363007593\n ],\n [\n -80.7436752319336,\n 33.775009433515145\n ],\n [\n -80.74041366577148,\n 33.77315444894136\n ],\n [\n -80.74007034301758,\n 33.76758925423873\n ],\n [\n -80.738525390625,\n 33.76758925423873\n ],\n [\n -80.73543548583984,\n 33.767018444600986\n ],\n [\n -80.7356071472168,\n 33.765305992874715\n ],\n [\n -80.73801040649414,\n 33.76402163162377\n ],\n [\n -80.74041366577148,\n 33.76102471385305\n ],\n [\n -80.74127197265625,\n 33.75831312652787\n ],\n [\n -80.73904037475585,\n 33.75602961803962\n ],\n [\n -80.7356071472168,\n 33.75831312652787\n ],\n [\n -80.73251724243164,\n 33.76031114655761\n ],\n [\n -80.72925567626953,\n 33.76059657418862\n ],\n [\n -80.7278823852539,\n 33.7613101391079\n ],\n [\n -80.7278823852539,\n 33.76302267067703\n ],\n [\n -80.72719573974608,\n 33.76573410901434\n ],\n [\n -80.72616577148438,\n 33.76858816195565\n ],\n [\n -80.72341918945312,\n 33.76830276093902\n ],\n [\n -80.72135925292969,\n 33.767018444600986\n ],\n [\n -80.72050094604492,\n 33.763450798222834\n ],\n [\n -80.72015762329102,\n 33.7624518306226\n ],\n [\n -80.71586608886719,\n 33.760025717976134\n ],\n [\n -80.71483612060547,\n 33.76031114655761\n ],\n [\n -80.71432113647461,\n 33.76102471385305\n ],\n [\n -80.7139778137207,\n 33.76287996101986\n ],\n [\n -80.71329116821289,\n 33.76616222301528\n ],\n [\n -80.71294784545898,\n 33.767874657631765\n ],\n [\n -80.71294784545898,\n 33.771442119839875\n ],\n [\n -80.71088790893555,\n 33.77229828866843\n ],\n [\n -80.70831298828125,\n 33.7688735620217\n ],\n [\n -80.70470809936523,\n 33.76687574159746\n ],\n [\n -80.70316314697266,\n 33.76402163162377\n ],\n [\n -80.70556640625,\n 33.76173827520807\n ],\n [\n -80.7059097290039,\n 33.76059657418862\n ],\n [\n -80.70127487182617,\n 33.7580276912933\n ],\n [\n -80.69664001464844,\n 33.75660050086414\n ],\n [\n -80.69389343261719,\n 33.75688594085081\n ],\n [\n -80.68599700927734,\n 33.75674322097627\n ],\n [\n -80.68582534790039,\n 33.75774225510831\n ],\n [\n -80.68016052246092,\n 33.76102471385305\n ],\n [\n -80.6781005859375,\n 33.763165380096595\n ],\n [\n -80.67466735839844,\n 33.7624518306226\n ],\n [\n -80.67260742187499,\n 33.76402163162377\n ],\n [\n -80.67123413085938,\n 33.767303849895136\n ],\n [\n -80.6667709350586,\n 33.76801735897184\n ],\n [\n -80.66368103027344,\n 33.77072863928061\n ],\n [\n -80.6587028503418,\n 33.77286906313435\n ],\n [\n -80.65664291381836,\n 33.773011756156684\n ],\n [\n -80.65235137939453,\n 33.77201290000953\n ],\n [\n -80.65217971801758,\n 33.76958705802806\n ],\n [\n -80.65441131591797,\n 33.767303849895136\n ],\n [\n -80.6568145751953,\n 33.76587681391894\n ],\n [\n -80.65664291381836,\n 33.763878923629974\n ],\n [\n -80.6568145751953,\n 33.762023698086736\n ],\n [\n -80.6535530090332,\n 33.7597402884442\n ],\n [\n -80.64926147460936,\n 33.7580276912933\n ],\n [\n -80.64737319946289,\n 33.7564577805144\n ],\n [\n -80.64651489257812,\n 33.753317872708074\n ],\n [\n -80.64325332641602,\n 33.752176059540126\n ],\n [\n -80.64033508300781,\n 33.75146241858857\n ],\n [\n -80.63913345336914,\n 33.74946419232578\n ],\n [\n -80.6393051147461,\n 33.7470377121195\n ],\n [\n -80.63604354858398,\n 33.74461116325586\n ],\n [\n -80.6315803527832,\n 33.743469234152094\n ],\n [\n -80.62557220458983,\n 33.744753903324835\n ],\n [\n -80.62145233154297,\n 33.74575307715579\n ],\n [\n -80.61904907226562,\n 33.744468422949346\n ],\n [\n -80.61870574951172,\n 33.74561033874977\n ],\n [\n -80.61990737915039,\n 33.74732318394263\n ],\n [\n -80.61922073364258,\n 33.748465061732375\n ],\n [\n -80.61750411987305,\n 33.74860779538701\n ],\n [\n -80.61647415161133,\n 33.747608654815494\n ],\n [\n -80.61424255371094,\n 33.74732318394263\n ],\n [\n -80.6147575378418,\n 33.74917872762981\n ],\n [\n -80.61595916748047,\n 33.75060604160645\n ],\n [\n -80.61201095581055,\n 33.75060604160645\n ],\n [\n -80.60995101928711,\n 33.751890603872255\n ],\n [\n -80.60806274414062,\n 33.75374604872583\n ],\n [\n -80.60754776000977,\n 33.755173286674825\n ],\n [\n -80.60943603515625,\n 33.754745117784864\n ],\n [\n -80.61201095581055,\n 33.75488784098578\n ],\n [\n -80.6096076965332,\n 33.75688594085081\n ],\n [\n -80.6063461303711,\n 33.75717137988705\n ],\n [\n -80.60394287109375,\n 33.757456817972894\n ],\n [\n -80.60291290283203,\n 33.75931214236421\n ],\n [\n -80.606689453125,\n 33.758598560812004\n ],\n [\n -80.61012268066405,\n 33.762594540992644\n ],\n [\n -80.6070327758789,\n 33.76216640916964\n ],\n [\n -80.60274124145508,\n 33.7624518306226\n ],\n [\n -80.60308456420898,\n 33.763593506929496\n ],\n [\n -80.60548782348633,\n 33.76402163162377\n ],\n [\n -80.60565948486328,\n 33.76616222301528\n ],\n [\n -80.6022262573242,\n 33.76687574159746\n ],\n [\n -80.6011962890625,\n 33.769872454767096\n ],\n [\n -80.60565948486328,\n 33.771299424203335\n ],\n [\n -80.60239791870117,\n 33.77215559445779\n ],\n [\n -80.6205940246582,\n 33.779147331286474\n ],\n [\n -80.62488555908203,\n 33.78071682642825\n ],\n [\n -80.62660217285156,\n 33.783142353260104\n ],\n [\n -80.62917709350585,\n 33.784854448488915\n ],\n [\n -80.63381195068358,\n 33.7855678113988\n ],\n [\n -80.6367301940918,\n 33.784711775193855\n ],\n [\n -80.63621520996094,\n 33.7864238390465\n ],\n [\n -80.63690185546875,\n 33.788849204243554\n ],\n [\n -80.63896179199219,\n 33.789990528800494\n ],\n [\n -80.64393997192383,\n 33.792843773631844\n ],\n [\n -80.64496994018553,\n 33.794555674887555\n ],\n [\n -80.64857482910156,\n 33.794555674887555\n ],\n [\n -80.65011978149414,\n 33.794127702782966\n ],\n [\n -80.65114974975586,\n 33.792701113648654\n ],\n [\n -80.65475463867188,\n 33.790846512234154\n ],\n [\n -80.65492630004883,\n 33.788563870727174\n ],\n [\n -80.65732955932617,\n 33.78628116836614\n ],\n [\n -80.65973281860352,\n 33.78571048326767\n ],\n [\n -80.66247940063477,\n 33.78442642789061\n ],\n [\n -80.6674575805664,\n 33.783142353260104\n ],\n [\n -80.67312240600586,\n 33.78128754480386\n ],\n [\n -80.67758560180664,\n 33.782000937425444\n ],\n [\n -80.70213317871094,\n 33.80725120732596\n ],\n [\n -80.70419311523438,\n 33.81067439928266\n ],\n [\n -80.71191787719727,\n 33.814953196620415\n ],\n [\n -80.71809768676758,\n 33.81823346297344\n ],\n [\n -80.72427749633789,\n 33.81210068884124\n ],\n [\n -80.72479248046875,\n 33.80996124558631\n ],\n [\n -80.7223892211914,\n 33.80867755395255\n ],\n [\n -80.71998596191406,\n 33.80725120732596\n ],\n [\n -80.72067260742188,\n 33.80596747503222\n ],\n [\n -80.72359085083008,\n 33.8052542821026\n ],\n [\n -80.72839736938477,\n 33.80568219857369\n ],\n [\n -80.73234558105469,\n 33.80582483692183\n ],\n [\n -80.73732376098633,\n 33.807821748829916\n ],\n [\n -80.73783874511719,\n 33.81481057349054\n ],\n [\n -80.74470520019531,\n 33.81352695462067\n ],\n [\n -80.74951171875,\n 33.81352695462067\n ],\n [\n -80.75397491455078,\n 33.813384329112786\n ],\n [\n -80.7575798034668,\n 33.81452532651738\n ],\n [\n -80.76547622680664,\n 33.816664655630305\n ],\n [\n -80.76908111572266,\n 33.81780560927904\n ],\n [\n -80.77371597290039,\n 33.81980224154123\n ],\n [\n -80.77457427978516,\n 33.81694989546935\n ],\n [\n -80.7769775390625,\n 33.81637941484004\n ],\n [\n -80.78109741210938,\n 33.81680727566873\n ],\n [\n -80.78470230102539,\n 33.818661314527525\n ],\n [\n -80.78624725341797,\n 33.819659626496865\n ],\n [\n -80.79036712646484,\n 33.82065792681319\n ],\n [\n -80.7905387878418,\n 33.82222666090873\n ],\n [\n -80.79277038574219,\n 33.82379536622731\n ],\n [\n -80.79483032226562,\n 33.8252214369992\n ],\n [\n -80.79654693603516,\n 33.828786509874305\n ],\n [\n -80.79654693603516,\n 33.831781056208825\n ],\n [\n -80.79568862915039,\n 33.8339199536547\n ],\n [\n -80.82229614257812,\n 33.83449031726809\n ],\n [\n -80.82710266113281,\n 33.84290273847109\n ],\n [\n -80.82881927490233,\n 33.84447106439623\n ],\n [\n -80.82950592041016,\n 33.842760161959596\n ],\n [\n -80.82916259765625,\n 33.83805500362334\n ],\n [\n -80.83087921142578,\n 33.835773621482865\n ],\n [\n -80.83602905273436,\n 33.83506067707558\n ],\n [\n -80.83911895751953,\n 33.834775497647584\n ],\n [\n -80.84152221679688,\n 33.83420513593713\n ],\n [\n -80.84495544433594,\n 33.831923651036924\n ],\n [\n -80.84735870361328,\n 33.83249402797066\n ],\n [\n -80.8542251586914,\n 33.83605879758069\n ],\n [\n -80.85371017456055,\n 33.837056906429986\n ],\n [\n -80.85559844970703,\n 33.8387679230536\n ],\n [\n -80.85800170898438,\n 33.8387679230536\n ],\n [\n -80.85851669311523,\n 33.84104922526587\n ],\n [\n -80.86126327514648,\n 33.84233243099772\n ],\n [\n -80.86212158203125,\n 33.839053089160544\n ],\n [\n -80.86349487304688,\n 33.83805500362334\n ],\n [\n -80.86727142333983,\n 33.82436579739006\n ],\n [\n -80.86830139160156,\n 33.82436579739006\n ],\n [\n -80.86881637573242,\n 33.81752037229372\n ],\n [\n -80.86503982543945,\n 33.818803931236594\n ],\n [\n -80.86212158203125,\n 33.81837608039595\n ],\n [\n -80.86126327514648,\n 33.816664655630305\n ],\n [\n -80.86177825927734,\n 33.81424007859304\n ],\n [\n -80.86332321166991,\n 33.812671198006534\n ],\n [\n -80.86709976196289,\n 33.81138754703431\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"11","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-21","publicationStatus":"PW","scienceBaseUri":"594b85b3e4b062508e382b7a","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606 pbradley@usgs.gov","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":361,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul","email":"pbradley@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Battaglin, William A. 0000-0001-7287-7096 wbattagl@usgs.gov","orcid":"https://orcid.org/0000-0001-7287-7096","contributorId":1527,"corporation":false,"usgs":true,"family":"Battaglin","given":"William","email":"wbattagl@usgs.gov","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Jimmy M. 0000-0002-3138-5738 jmclark@usgs.gov","orcid":"https://orcid.org/0000-0002-3138-5738","contributorId":4773,"corporation":false,"usgs":true,"family":"Clark","given":"Jimmy","email":"jmclark@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697065,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Henning, Frank 0000-0002-3477-3875","orcid":"https://orcid.org/0000-0002-3477-3875","contributorId":192615,"corporation":false,"usgs":false,"family":"Henning","given":"Frank","email":"","affiliations":[],"preferred":false,"id":697066,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":189904,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":697070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178 liwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":190787,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke","email":"liwanowicz@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":697063,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Journey, Celeste A. 0000-0002-2284-5851 cjourney@usgs.gov","orcid":"https://orcid.org/0000-0002-2284-5851","contributorId":189681,"corporation":false,"usgs":true,"family":"Journey","given":"Celeste","email":"cjourney@usgs.gov","middleInitial":"A.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697069,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Riley, Jeffrey W. 0000-0001-5525-3134 jriley@usgs.gov","orcid":"https://orcid.org/0000-0001-5525-3134","contributorId":3605,"corporation":false,"usgs":true,"family":"Riley","given":"Jeffrey","email":"jriley@usgs.gov","middleInitial":"W.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697067,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Romanok, Kristin M. 0000-0002-8472-8765 kromanok@usgs.gov","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":189680,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin","email":"kromanok@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":697068,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70186976,"text":"ds1046 - 2017 - The physical characteristics of the sediments on and surrounding Dauphin Island, Alabama","interactions":[],"lastModifiedDate":"2017-06-20T12:58:19","indexId":"ds1046","displayToPublicDate":"2017-06-20T10:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1046","title":"The physical characteristics of the sediments on and surrounding Dauphin Island, Alabama","docAbstract":"Scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center collected 303 surface sediment samples from Dauphin Island, Alabama, and the surrounding water bodies in August 2015. These sediments were processed to determine physical characteristics such as organic content, bulk density, and grain-size. The environments where the sediments were collected include high and low salt marshes, washover deposits, dunes, beaches, sheltered bays, and open water. Sampling by the USGS was part of a larger study to assess the feasibility and sustainability of proposed restoration efforts for Dauphin Island, Alabama, and assess the island’s resilience to rising sea level and storm events. The data presented in this publication can be used by modelers to attempt validation of hindcast models and create predictive forecast models for both baseline conditions and storms. This study was funded by the National Fish and Wildlife Foundation, via the Gulf Environmental Benefit Fund.
This report serves as an archive for sedimentological data derived from surface sediments. Downloadable data are available as Excel spreadsheets, JPEG files, and formal Federal Geographic Data Committee metadata.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1046","usgsCitation":"Ellis, A.M., Marot, M.E., Smith, C.G., and Wheaton, C.J., 2017, The physical characteristics of the sediments on and surrounding Dauphin Island, Alabama: U.S. Geological Survey Data Series 1046, https://doi.org/10.3133/ds1046.\n\n","productDescription":"HTML document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078883","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":342208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1046","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1046"},{"id":342207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1046/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":" Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.37127685546875,\n 30.25076353594852\n ],\n [\n -88.34518432617188,\n 30.209234673510014\n ],\n [\n -88.29299926757812,\n 30.20211367909724\n ],\n [\n -88.18450927734375,\n 30.206861065952626\n ],\n [\n -88.08837890625,\n 30.212794977500614\n ],\n [\n -88.04580688476562,\n 30.244831915307145\n ],\n [\n -88.05816650390625,\n 30.278044377800153\n ],\n [\n -88.0609130859375,\n 30.295832146790442\n ],\n [\n -88.08151245117188,\n 30.324285866937423\n ],\n [\n -88.165283203125,\n 30.312431154103745\n ],\n [\n -88.29711914062499,\n 30.292274851024256\n ],\n [\n -88.36715698242186,\n 30.28634573802957\n ],\n [\n -88.37127685546875,\n 30.25076353594852\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
After Hurricane Sandy made landfall along the northeastern Atlantic coast of the United States on October 29, 2012, the U.S. Geological Survey (USGS) carried out scientific investigations to assist with protecting coastal communities and resources from future flooding. The work included development and implementation of the Surge, Wave, and Tide Hydrodynamics (SWaTH) network consisting of more than 900 monitoring stations. The SWaTH network was designed to greatly improve the collection and timely dissemination of information related to storm surge and coastal flooding. The network provides a significant enhancement to USGS data-collection capabilities in the region impacted by Hurricane Sandy and represents a new strategy for observing and monitoring coastal storms, which should result in improved understanding, prediction, and warning of storm-surge impacts and lead to more resilient coastal communities.
As innovative as it is, SWaTH evolved from previous USGS efforts to collect storm-surge data needed by others to improve storm-surge modeling, warning, and mitigation. This report discusses the development and implementation of the SWaTH network, and some of the regional stories associated with the landfall of Hurricane Sandy, as well as some previous events that informed the SWaTH development effort. Additional discussions on the mechanics of inundation and how the USGS is working with partners to help protect coastal communities from future storm impacts are also included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1431","isbn":"ISBN 978-1-4113-4149-4","usgsCitation":"Verdi, R.J., Lotspeich, R.R., Robbins, J.C., Busciolano, R.J., Mullaney, J.R., Massey, A.J., Banks, W.S., Roland, M.A., Jenter, H.L., Peppler, M.C., Suro, T.P., Schubert, C.E., and Nardi, M.R., 2017, The surge, wave, and tide hydrodynamics (SWaTH) network of the U.S. Geological Survey—Past and future implementation of storm-response monitoring, data collection, and data delivery: U.S. Geological Survey Circular 1431, 35 p., https://dx.doi.org/10.3133/cir1431.","productDescription":"iv, 35 p. ","numberOfPages":"44","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068589","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":342624,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1431/coverthb.jpg"},{"id":342625,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1431/cir1431.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIRC 1431"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Rhode Island, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.72802734375,\n 34.016241889667015\n ],\n [\n -78.44238281249999,\n 33.779147331286474\n ],\n [\n -78.189697265625,\n 33.8247936182649\n ],\n [\n -77.947998046875,\n 33.742612777346885\n ],\n [\n -77.76123046875,\n 33.90689555128866\n ],\n [\n -77.640380859375,\n 34.17090836352573\n ],\n [\n -77.442626953125,\n 34.379712580462204\n ],\n [\n -76.75048828125,\n 34.615126683462194\n ],\n [\n -76.431884765625,\n 34.58799745550482\n ],\n [\n -76.13525390624999,\n 34.831841149828655\n ],\n [\n -75.8056640625,\n 35.02099970111467\n ],\n [\n -75.4541015625,\n 35.191766965947394\n ],\n [\n -75.223388671875,\n 35.43381992014202\n ],\n [\n -75.38818359375,\n 35.99578538642032\n ],\n [\n -75.531005859375,\n 36.2354121683998\n ],\n [\n -75.706787109375,\n 36.4477991295848\n ],\n [\n -75.82763671875,\n 37.020098201368114\n ],\n [\n -75.333251953125,\n 37.666429212090605\n ],\n [\n -74.915771484375,\n 38.47939467327645\n ],\n [\n -74.915771484375,\n 38.7283759182398\n ],\n [\n -74.7509765625,\n 38.976492485539396\n ],\n [\n -74.520263671875,\n 39.155622393423215\n ],\n [\n -74.11376953125,\n 39.47860556892209\n ],\n [\n -73.992919921875,\n 39.825413103424786\n ],\n [\n -73.80615234375,\n 40.136890695345905\n ],\n [\n -73.839111328125,\n 40.455307212131494\n ],\n [\n -73.377685546875,\n 40.538851525354666\n ],\n [\n -72.257080078125,\n 40.81380923056958\n ],\n [\n -71.69677734375,\n 41.03793062246529\n ],\n [\n -70.894775390625,\n 41.19518982948959\n ],\n [\n -70.521240234375,\n 41.1290213474951\n ],\n [\n -69.90600585937499,\n 41.19518982948959\n ],\n [\n -69.78515625,\n 41.590796851056005\n ],\n [\n -69.708251953125,\n 41.95131994679697\n ],\n [\n -69.949951171875,\n 42.16340342422401\n ],\n [\n -70.29052734375,\n 42.00848901572399\n ],\n [\n -70.543212890625,\n 42.13082130188811\n ],\n [\n -70.57617187499999,\n 42.309815415686664\n ],\n [\n -70.51025390625,\n 42.60970621339408\n ],\n [\n -70.51025390625,\n 42.78733853171998\n ],\n [\n -70.675048828125,\n 42.85180609584705\n ],\n [\n -70.455322265625,\n 43.1090040242731\n ],\n [\n -70.33447265624999,\n 43.34914966389313\n ],\n [\n -70.081787109375,\n 43.54058479482877\n ],\n [\n -69.818115234375,\n 43.65197548731187\n ],\n [\n -69.3017578125,\n 43.75522505306928\n ],\n [\n -68.675537109375,\n 43.92163712834673\n ],\n [\n -68.115234375,\n 44.02442151965934\n ],\n [\n -67.115478515625,\n 44.33170718680922\n ],\n [\n -66.81884765625,\n 44.512176171071054\n ],\n [\n -66.5771484375,\n 44.69989765840318\n ],\n [\n -66.97265625,\n 44.902577996288876\n ],\n [\n -67.08251953125,\n 45.058001435398275\n ],\n [\n -67.181396484375,\n 45.19752230305682\n ],\n [\n -67.4560546875,\n 45.13555516012536\n ],\n [\n -67.47802734375,\n 45.298075138707965\n ],\n [\n -67.401123046875,\n 45.42158812329091\n ],\n [\n -67.5,\n 45.48324350868221\n ],\n [\n -67.357177734375,\n 45.56021795715051\n ],\n [\n -67.467041015625,\n 45.62940492064501\n ],\n [\n -67.5439453125,\n 45.590978249451936\n ],\n [\n -67.7197265625,\n 45.67548217560647\n ],\n [\n -67.818603515625,\n 45.72152152227954\n ],\n [\n -67.73071289062499,\n 45.84410779560204\n ],\n [\n -67.752685546875,\n 45.98932892799953\n ],\n [\n -70.0048828125,\n 44.77793589631623\n ],\n [\n -70.565185546875,\n 44.37098696297173\n ],\n [\n -71.4990234375,\n 43.46089378008257\n ],\n [\n -71.9384765625,\n 42.633958722673135\n ],\n [\n -72.24609375,\n 42.05745022024682\n ],\n [\n -72.828369140625,\n 41.713930073371294\n ],\n [\n -73.45458984375,\n 41.53325414281322\n ],\n [\n -74.256591796875,\n 41.21998578493921\n ],\n [\n -75.025634765625,\n 40.6306300839918\n ],\n [\n -75.78369140625,\n 40.32141999593439\n ],\n [\n -76.83837890625,\n 38.8824811975508\n ],\n [\n -76.9482421875,\n 38.26406296833961\n ],\n [\n -77.025146484375,\n 36.58024660149866\n ],\n [\n -77.05810546875,\n 36.03133177633187\n ],\n [\n -78.85986328125,\n 34.67839374011646\n ],\n [\n -79.178466796875,\n 34.397844946449865\n ],\n [\n -78.72802734375,\n 34.016241889667015\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Office of Surface Water
U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Lithium-cesium-tantalum (LCT) pegmatites comprise a compositionally defined subset of granitic pegmatites. The major minerals are quartz, potassium feldspar, albite, and muscovite; typical accessory minerals include biotite, garnet, tourmaline, and apatite. The principal lithium ore minerals are spodumene, petalite, and lepidolite; cesium mostly comes from pollucite; and tantalum mostly comes from columbite-tantalite. Tin ore as cassiterite and beryllium ore as beryl also occur in LCT pegmatites, as do a number of gemstones and high-value museum specimens of rare minerals. Individual crystals in LCT pegmatites can be enormous: the largest spodumene was 14 meters long, the largest beryl was 18 meters long, and the largest potassium feldspar was 49 meters long.
Lithium-cesium-tantalum pegmatites account for about one-fourth of the world’s lithium production, most of the tantalum production, and all of the cesium production. Giant deposits include Tanco in Canada, Greenbushes in Australia, and Bikita in Zimbabwe. The largest lithium pegmatite in the United States, at King’s Mountain, North Carolina, is no longer being mined although large reserves of lithium remain. Depending on size and attitude of the pegmatite, a variety of mining techniques are used, including artisanal surface mining, open-pit surface mining, small underground workings, and large underground operations using room-and-pillar design. In favorable circumstances, what would otherwise be gangue minerals (quartz, potassium feldspar, albite, and muscovite) can be mined along with lithium and (or) tantalum as coproducts.
Most LCT pegmatites are hosted in metamorphosed supracrustal rocks in the upper greenschist to lower amphibolite facies. Lithium-cesium-tantalum pegmatite intrusions generally are emplaced late during orogeny, with emplacement being controlled by pre-existing structures. Typically, they crop out near evolved, peraluminous granites and leucogranites from which they are inferred to be derived by fractional crystallization. In cases where a parental granite pluton is not exposed, one is inferred to lie at depth. Lithium-cesium-tantalum LCT pegmatite melts are enriched in fluxing components including H2O, F, P, and B, which depress the solidus temperature, lower the density, and increase rates of ionic diffusion. This, in turn, enables pegmatites to form thin dikes and massive crystals despite having a felsic composition and temperatures that are significantly lower than ordinary granitic melts. Lithium-cesium-tantalum pegmatites crystallized at remarkably low temperatures (about 350–550 °C) in a remarkably short time (days to years).
Lithium-cesium-tantalum pegmatites form in orogenic hinterlands as products of plate convergence. Most formed during collisional orogeny (for example, Kings Mountain district, North Carolina). Specific causes of LCT pegmatite-related magmatism could include: ordinary arc processes; over thickening of continental crust during collision or subduction; slab breakoff during or after collision; slab delamination before, during, or after collision; and late collisional extensional collapse and consequent decompression melting. Lithium-cesium-tantalum pegmatite deposits are present in all continents including Antarctica and in rocks spanning 3 billion years of Earth history. The global age distribution of LCT pegmatites is similar to those of common pegmatites, orogenic granites, and detrital zircons. Peak times of LCT pegmatite genesis at about 2640, 1800, 960, 485, and 310 Ma (million years before present) correspond to times of collisional orogeny and supercontinent assembly. Between these pulses were long intervals when few or no LCT pegmatites formed. These minima overlap with supercontinent tenures at ca. 2450–2225, 1625–1000, 875–725, and 250–200 Ma.
Exploration and assessment for LCT pegmatites are guided by a number of observations. In frontier areas where exploration has been minimal at best, the key first-order criteria are an orogenic hinterland setting, appropriate regional metamorphic grades, and the presence of evolved granites and common granitic pegmatites. New LCT pegmatites are most likely to be found near known deposits. Pegmatites tend to show a regional mineralogical and geochemical zoning pattern with respect to the inferred parental granite, with the greatest enrichment in the more distal pegmatites. Mineral-chemical trends in common pegmatites that can point toward an evolved LCT pegmatite include: increasing rubidium in potassium feldspar, increasing lithium in white mica, increasing manganese in garnet, and increasing tantalum and manganese in columbite-tantalite. Most LCT pegmatite bodies show a distinctive internal zonation featuring four zones: border, wall, intermediate (where lithium, cesium, and tantalum are generally concentrated), and core. This zonation is expressed both in cross section and map view; thus, what may appear to be a common pegmatite may instead be the edge of a mineralized body.
Neither lithium-cesium-tantalum pegmatites nor their parental granites are likely to cause serious environmental concerns. Soils and country rock surrounding a LCT pegmatite, as well as waste from mining operations, may be enriched in characteristic elements relative to global average soil and bedrock values. These elements may include lithium, cesium, tantalum, beryllium, boron, fluorine, phosphorus, manganese, gallium, rubidium, niobium, tin, and hafnium. Among this suite of elements, however, the only ones that might present a concern for environmental health are beryllium and fluorine, which are included in the U.S. Environmental Protection Agency drinking-water regulations with maximum contaminant levels of 4 micrograms per liter and 4 milligrams per liter, respectively.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit model for resource assessment (Scientific Investigations Report 2010-5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070O","usgsCitation":"Bradley, D.C., McCauley, A.D., and Stillings, L.M., 2017, Mineral-deposit model for lithium-cesium-tantalum pegmatites: U.S. Geological Survey Scientific Investigations Report 2010–5070–O, 48 p., https://doi.org/10.3133/sir20105070O.","productDescription":"v, 48 p.","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-055446","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":342538,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/o/sir20105070o.pdf","text":"Report","size":"3.80 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010–5070–O"},{"id":342537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5070/o/coverthb.jpg"}],"contact":"Director, Central Mineral and Environmental Resources Science Center
U.S. Geological Survey
Box 25046, MS–973
Denver, CO 80225
Groundwater quality in the 112-square-mile Bear Valley and Lake Arrowhead Watershed (BEAR) study unit was investigated as part of the Priority Basin Project (PBP) of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit comprises two study areas (Bear Valley and Lake Arrowhead Watershed) in southern California in San Bernardino County. The GAMA-PBP is conducted by the California State Water Resources Control Board (SWRCB) in cooperation with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory.
The GAMA BEAR study was designed to provide a spatially balanced, robust assessment of the quality of untreated (raw) groundwater from the primary aquifer systems in the two study areas of the BEAR study unit. The assessment is based on water-quality collected by the USGS from 38 sites (27 grid and 11 understanding) during 2010 and on water-quality data from the SWRCB-Division of Drinking Water (DDW) database. The primary aquifer system is defined by springs and the perforation intervals of wells listed in the SWRCB-DDW water-quality database for the BEAR study unit.
This study included two types of assessments: (1) a status assessment, which characterized the status of the quality of the groundwater resource as of 2010 by using data from samples analyzed for volatile organic compounds, pesticides, and naturally present inorganic constituents, such as major ions and trace elements, and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting the groundwater quality. The assessments were intended to characterize the quality of groundwater resources in the primary aquifer system of the BEAR study unit, not the treated drinking water delivered to consumers. Bear Valley study area and the Lake Arrowhead Watershed study area were also compared statistically on the basis of water-quality results and factors potentially affecting the groundwater quality.
Relative concentrations (RCs), which are sample concentration of a particular constituent divided by its associated health- or aesthetic-based benchmark concentrations, were used for evaluating the groundwater quality for those constituents that have Federal or California regulatory or non-regulatory benchmarks for drinking-water quality. An RC greater than 1.0 indicates a concentration greater than a benchmark. Organic (volatile organic compounds and pesticides) and special-interest (perchlorate) constituent RCs were classified as “high” (RC greater than 1.0), “moderate” (RC less than or equal to 1.0 and greater than 0.1), or “low” (RC less than or equal to 0.1). For inorganic (radioactive, trace element, major ion, and nutrient) constituents, the boundary between low and moderate RCs was set at 0.5.
Aquifer-scale proportion was used as the primary metric in the status assessment for evaluating groundwater quality at the study-unit scale or for its component areas. High aquifer-scale proportion was defined as the percentage of the area of the primary aquifer system with a RC greater than 1.0 for a particular constituent or class of constituents; the percentage is based on area rather than volume. Moderate and low aquifer-scale proportions were defined as the percentage of the primary aquifer system with moderate and low RCs, respectively. A spatially weighted statistical approach was used to evaluate aquifer-scale proportions for individual constituents and classes of constituents.
The status assessment for the Bear Valley study area found that inorganic constituents with health-based benchmarks were detected at high RCs in 9.0 percent of the primary aquifer system and at moderate RCs in 13 percent. The high RCs of inorganic constituents primarily reflected high aquifer-scale proportions of fluoride (in 5.4 percent of the primary aquifer system) and arsenic (3.6 percent). The RCs of organic constituents with health-based benchmarks were high in 1.0 percent of the primary aquifer system, moderate in 8.1 percent, and low in 70 percent. Organic constituents were detected in 79 percent of the primary aquifer system. Two groups of organic constituents and two individual organic constituents were detected at frequencies greater than 10 percent of samples from the USGS grid sites: trihalomethanes (THMs), solvents, methyl tert-butyl ether (MTBE), and simazine. The special-interest constituent perchlorate was detected in 93 percent of the primary aquifer system; it was detected at moderate RCs in 7.1 percent and at low RCs in 86 percent.
The status assessment in the Lake Arrowhead Watershed study area showed that inorganic constituents with human-health benchmarks were detected at high RCs in 25 percent of the primary aquifer system and at moderate RCs in 41 percent. The high aquifer-scale proportion of inorganic constituents primarily reflected high aquifer-scale proportions of radon‑222 (in 62 percent of the primary aquifer system) and uranium (26 percent). RCs of organic constituents with health-based benchmarks were moderate in 7.7 percent of the primary aquifer system and low in 46 percent. Organic constituents were detected in 54 percent of the primary aquifer system. The only organic constituents that were detected at frequencies greater than 10 percent of samples from the USGS grid sites were THMs. Perchlorate was detected in 62 percent of the primary aquifer system at uniformly low RCs.
The second component of this study, the understanding assessment, identified the natural and human factors that could have affected the groundwater quality in the BEAR study unit by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were land use (including density of septic tanks and leaking or formerly leaking underground fuel tanks), site type, aquifer lithology, well construction (well depth and depth to the top-of-perforated interval), elevation, aridity index, groundwater-age distribution, and oxidation-reduction condition (including pH and dissolved oxygen concentration). Results of the statistical evaluations were used to explain the distribution of constituents in groundwater of the BEAR study unit.
In the Bear Valley study area, high and moderate RCs of fluoride were found in sites known to be influenced by hydrothermic conditions or that had high concentrations of fluoride historically. The high RC of arsenic can likely be attributed to desorption of arsenic from aquifer sediments saturated in old groundwater with high pH under reducing conditions. The THMs were detected more frequently at USGS grid sites that were wells, part of a large urban water system, and surrounded by urban land use. Solvents, MTBE, and simazine were all detected more frequently at USGS grid sites that were wells with a greater urban percentage of surrounding land use and that accessed older groundwater than other USGS grid sites. Comparison between the observed and predicted detection frequencies of perchlorate at USGS grid sites indicated that anthropogenic sources could have contributed to low levels of perchlorate in the groundwater of the Bear Valley study area.
In the Lake Arrowhead Watershed study area, high and moderate RCs of radon-222 and uranium can be attributed to older groundwater from the granitic fractured-rock primary aquifer system. Low RCs of THMs were detected at USGS grid sites that were wells and part of small water systems. The similarities between the observed and predicted detection frequencies of perchlorate in samples from USGS grid sites indicated that the source and distribution of perchlorate were most likely attributable to precipitation (rain and snow), with minimal, if any, contribution from anthropogenic sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175043","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Mathany, T.M., and Burton, C.A., 2017, Status and understanding of groundwater quality in the Bear Valley and Lake Arrowhead Watershed Study Unit, 2010: California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2017–5043, 71 p., https://doi.org/10.3133/sir20175043.","productDescription":"xii, 71 p.","onlineOnly":"Y","ipdsId":"IP-051454","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":342654,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5043/coverthb2.jpg"},{"id":342655,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5043/sir20175043.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5043"},{"id":342683,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20173037","text":"Fact Sheet 2017-3037"}],"country":"United States","state":"California","otherGeospatial":"Bear Valley and Lake Arrowhead Watershed study unit","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -116.98001861572266, 34.24245948736849 ], [ -116.96250915527344, 34.237350707417534 ], [ -116.95632934570312, 34.23564771187119 ], [ -116.94774627685547, 34.235363875931114 ], [ -116.93950653076172, 34.23564771187119 ], [ -116.91856384277342, 34.23195777001729 ], [ -116.9161605834961, 34.23479620118046 ], [ -116.91753387451172, 34.23820219227295 ], [ -116.91650390625, 34.240756595166985 ], [ -116.91169738769531, 34.239621314560885 ], [ -116.9110107421875, 34.237634536659534 ], [ -116.9058609008789, 34.237350707417534 ], [ -116.89453125, 34.23451236236987 ], [ -116.89281463623047, 34.23394468187816 ], [ -116.87839508056639, 34.232241617439286 ], [ -116.87015533447266, 34.22911924317382 ], [ -116.85916900634766, 34.225996753139725 ], [ -116.84371948242186, 34.22344190246092 ], [ -116.82964324951172, 34.219183645793215 ], [ -116.82174682617188, 34.21748028285746 ], [ -116.81213378906249, 34.21719638568665 ], [ -116.80732727050781, 34.219183645793215 ], [ -116.80526733398438, 34.222022507488326 ], [ -116.80423736572264, 34.22372577858536 ], [ -116.7959976196289, 34.225145144857166 ], [ -116.78913116455077, 34.225996753139725 ], [ -116.78260803222658, 34.226564487211114 ], [ -116.77745819091797, 34.226280620653775 ], [ -116.77505493164062, 34.226280620653775 ], [ -116.7675018310547, 34.225996753139725 ], [ -116.76441192626953, 34.225996753139725 ], [ -116.76441192626953, 34.22400965375313 ], [ -116.75926208496094, 34.22003531434658 ], [ -116.75273895263672, 34.21861586197447 ], [ -116.75033569335936, 34.21549298257326 ], [ -116.7458724975586, 34.21464126810258 ], [ -116.74415588378906, 34.21293781333383 ], [ -116.74243927001952, 34.21095040591389 ], [ -116.7355728149414, 34.20669151790316 ], [ -116.73110961914061, 34.20697545046596 ], [ -116.73145294189453, 34.210382566613696 ], [ -116.73522949218751, 34.214357361365884 ], [ -116.74209594726562, 34.21719638568665 ], [ -116.74930572509766, 34.22259026834739 ], [ -116.75067901611328, 34.228835385227214 ], [ -116.75411224365233, 34.23394468187816 ], [ -116.76200866699217, 34.23366084019708 ], [ -116.76956176757812, 34.23252546390448 ], [ -116.77539825439455, 34.232241617439286 ], [ -116.78020477294922, 34.231673921638496 ], [ -116.78707122802733, 34.23082237076133 ], [ -116.79840087890626, 34.22798380564679 ], [ -116.8021774291992, 34.23025466539262 ], [ -116.80046081542969, 34.23479620118046 ], [ -116.79050445556642, 34.23905366851639 ], [ -116.78604125976562, 34.240472776450844 ], [ -116.77677154541016, 34.24132422972854 ], [ -116.77093505859375, 34.242175674393906 ], [ -116.76612854003905, 34.24387853788724 ], [ -116.76681518554689, 34.24614896895508 ], [ -116.77196502685545, 34.24728416152137 ], [ -116.7795181274414, 34.24870313069657 ], [ -116.78672790527344, 34.250405862125 ], [ -116.80252075195312, 34.25324367127495 ], [ -116.80595397949219, 34.256081384716566 ], [ -116.8124771118164, 34.260905277906076 ], [ -116.806640625, 34.263742732916505 ], [ -116.8021774291992, 34.263742732916505 ], [ -116.7959976196289, 34.26317524957206 ], [ -116.78810119628906, 34.261756524459805 ], [ -116.78295135498047, 34.26289150646404 ], [ -116.77574157714844, 34.265445159976366 ], [ -116.77745819091797, 34.26686382286875 ], [ -116.78226470947266, 34.27083595165 ], [ -116.78741455078125, 34.27253823511796 ], [ -116.78878784179688, 34.26941735576717 ], [ -116.79153442382812, 34.26913363371872 ], [ -116.79565429687499, 34.275375297643876 ], [ -116.79737091064453, 34.27849595584549 ], [ -116.80011749267577, 34.28360223756049 ], [ -116.80767059326172, 34.28785723534703 ], [ -116.8128204345703, 34.293813870295985 ], [ -116.81900024414062, 34.29721747208954 ], [ -116.82518005371092, 34.29551568843025 ], [ -116.82792663574217, 34.29267930572073 ], [ -116.82518005371092, 34.29097743012992 ], [ -116.82140350341798, 34.28785723534703 ], [ -116.81900024414062, 34.286722590335955 ], [ -116.81694030761719, 34.28218385709024 ], [ -116.81728363037108, 34.27849595584549 ], [ -116.81900024414062, 34.275375297643876 ], [ -116.8231201171875, 34.274807892797554 ], [ -116.82929992675783, 34.274524188938365 ], [ -116.83376312255858, 34.27594269866068 ], [ -116.83616638183594, 34.27594269866068 ], [ -116.8447494506836, 34.27565899863098 ], [ -116.84955596923827, 34.274524188938365 ], [ -116.8567657470703, 34.27197081112463 ], [ -116.8612289428711, 34.272821945678636 ], [ -116.86912536621094, 34.27055223438805 ], [ -116.87496185302734, 34.26941735576717 ], [ -116.87736511230469, 34.267147552575445 ], [ -116.88491821289062, 34.26459395075398 ], [ -116.89350128173827, 34.265445159976366 ], [ -116.89350128173827, 34.267147552575445 ], [ -116.89968109130861, 34.27197081112463 ], [ -116.90723419189453, 34.27310565528197 ], [ -116.90757751464845, 34.268566186749894 ], [ -116.91272735595702, 34.267998735951934 ], [ -116.92920684814452, 34.27111966795463 ], [ -116.93641662597655, 34.27679379300553 ], [ -116.94225311279295, 34.270268516168784 ], [ -116.94740295410156, 34.26743128132487 ], [ -116.94774627685547, 34.264026473152875 ], [ -116.94740295410156, 34.261756524459805 ], [ -116.94362640380858, 34.26005402273768 ], [ -116.94534301757812, 34.25835148655734 ], [ -116.95152282714842, 34.2586352449803 ], [ -116.95804595947264, 34.255230080733675 ], [ -116.96491241455078, 34.25012207594618 ], [ -116.97212219238281, 34.24728416152137 ], [ -116.98001861572266, 34.24245948736849 ] ] ] } }, { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -117.01915740966795, 34.254378768136796 ], [ -117.02293395996094, 34.26487768811868 ], [ -117.03014373779297, 34.26913363371872 ], [ -117.037353515625, 34.2750915956994 ], [ -117.0486831665039, 34.2750915956994 ], [ -117.05863952636717, 34.2742404841218 ], [ -117.06996917724608, 34.2742404841218 ], [ -117.07923889160158, 34.275375297643876 ], [ -117.0867919921875, 34.27395677834786 ], [ -117.0929718017578, 34.27111966795463 ], [ -117.09743499755858, 34.26913363371872 ], [ -117.103271484375, 34.27310565528197 ], [ -117.11151123046875, 34.274807892797554 ], [ -117.12112426757811, 34.2750915956994 ], [ -117.12249755859374, 34.27877964630122 ], [ -117.12284088134766, 34.280481768929675 ], [ -117.12421417236328, 34.2819001781238 ], [ -117.12421417236328, 34.28502059409368 ], [ -117.1307373046875, 34.28984282725109 ], [ -117.1413803100586, 34.294948419549335 ], [ -117.15030670166016, 34.294948419549335 ], [ -117.16197967529295, 34.294097509045734 ], [ -117.16918945312499, 34.290693780846574 ], [ -117.17124938964842, 34.28785723534703 ], [ -117.17777252197264, 34.28331856338139 ], [ -117.18017578125, 34.27736118444846 ], [ -117.18292236328124, 34.277077489205695 ], [ -117.18910217285156, 34.28388591078211 ], [ -117.19287872314452, 34.289275520066276 ], [ -117.19596862792969, 34.29324658992357 ], [ -117.19940185546874, 34.29750109934765 ], [ -117.20729827880858, 34.301471780404476 ], [ -117.21038818359375, 34.30345705054134 ], [ -117.22103118896483, 34.302322616209494 ], [ -117.23133087158203, 34.30090455174585 ], [ -117.23682403564452, 34.30033731925641 ], [ -117.24300384521486, 34.298919221273074 ], [ -117.24746704101561, 34.30374065672995 ], [ -117.26119995117186, 34.30430786623395 ], [ -117.2690963745117, 34.302322616209494 ], [ -117.27699279785155, 34.30033731925641 ], [ -117.28111267089845, 34.296650214700406 ], [ -117.28763580322264, 34.29239566218292 ], [ -117.29347229003905, 34.289559174137445 ], [ -117.29553222656249, 34.28502059409368 ], [ -117.30274200439452, 34.27736118444846 ], [ -117.30583190917969, 34.279914398549934 ], [ -117.30823516845703, 34.28445325435288 ], [ -117.31647491455077, 34.28842455210738 ], [ -117.32402801513672, 34.288991865037524 ], [ -117.33329772949219, 34.28757357553056 ], [ -117.34119415283203, 34.28388591078211 ], [ -117.3460006713867, 34.27849595584549 ], [ -117.35252380371094, 34.27225452359997 ], [ -117.3556137084961, 34.267147552575445 ], [ -117.35836029052734, 34.26232401737632 ], [ -117.35733032226561, 34.25154099726973 ], [ -117.35424041748047, 34.25154099726973 ], [ -117.35149383544923, 34.25125721491913 ], [ -117.35149383544923, 34.24841933877555 ], [ -117.34840393066406, 34.24529756448196 ], [ -117.34325408935547, 34.24728416152137 ], [ -117.33844757080078, 34.249838288810324 ], [ -117.33535766601562, 34.25409499535699 ], [ -117.33158111572264, 34.25551384968508 ], [ -117.32986450195312, 34.254378768136796 ], [ -117.33055114746094, 34.249838288810324 ], [ -117.3281478881836, 34.24501376107695 ], [ -117.3278045654297, 34.242175674393906 ], [ -117.32402801513672, 34.237634536659534 ], [ -117.31613159179688, 34.2350800390342 ], [ -117.3068618774414, 34.23422852260242 ], [ -117.30239868164062, 34.23422852260242 ], [ -117.30033874511719, 34.235363875931114 ], [ -117.29724884033203, 34.236215380880736 ], [ -117.29209899902342, 34.23479620118046 ], [ -117.2845458984375, 34.231673921638496 ], [ -117.27905273437499, 34.23309315396439 ], [ -117.26978302001952, 34.22940310016367 ], [ -117.25399017333983, 34.22940310016367 ], [ -117.24712371826172, 34.22940310016367 ], [ -117.2406005859375, 34.229970811273084 ], [ -117.23133087158203, 34.229970811273084 ], [ -117.22652435302736, 34.22798380564679 ], [ -117.21519470214842, 34.22968695619676 ], [ -117.20317840576172, 34.23025466539262 ], [ -117.19390869140625, 34.23252546390448 ], [ -117.18807220458984, 34.23479620118046 ], [ -117.17811584472656, 34.23422852260242 ], [ -117.16815948486328, 34.23337699755914 ], [ -117.16403961181642, 34.23195777001729 ], [ -117.15511322021484, 34.23451236236987 ], [ -117.14653015136719, 34.23451236236987 ], [ -117.13966369628905, 34.23309315396439 ], [ -117.13794708251953, 34.232241617439286 ], [ -117.13451385498047, 34.22798380564679 ], [ -117.13211059570312, 34.2242935279642 ], [ -117.13451385498047, 34.219467536267636 ], [ -117.12421417236328, 34.21293781333383 ], [ -117.12215423583984, 34.20754331272217 ], [ -117.1200942993164, 34.20555577808725 ], [ -117.11185455322264, 34.20300030754922 ], [ -117.10121154785156, 34.20271636159618 ], [ -117.09709167480469, 34.20271636159618 ], [ -117.09674835205077, 34.201012665793264 ], [ -117.08988189697266, 34.19561740194418 ], [ -117.08473205566406, 34.193345608618024 ], [ -117.07820892333984, 34.189937803874194 ], [ -117.07717895507811, 34.19362958613085 ], [ -117.06893920898438, 34.19618534071253 ], [ -117.05932617187499, 34.19646930866224 ], [ -117.04971313476562, 34.19504945935049 ], [ -117.04113006591797, 34.19874101783143 ], [ -117.03323364257812, 34.20186451799857 ], [ -117.03392028808594, 34.20697545046596 ], [ -117.03460693359375, 34.21236998742515 ], [ -117.03460693359375, 34.218048074329204 ], [ -117.02945709228516, 34.222022507488326 ], [ -117.02430725097656, 34.22287414734192 ], [ -117.01950073242188, 34.2223063883962 ], [ -117.01881408691406, 34.22684835281174 ], [ -117.02121734619139, 34.22940310016367 ], [ -117.02739715576172, 34.2350800390342 ], [ -117.03357696533203, 34.24160804557394 ], [ -117.03186035156251, 34.24501376107695 ], [ -117.02842712402342, 34.2492707116675 ], [ -117.02396392822267, 34.25324367127495 ], [ -117.01915740966795, 34.254378768136796 ] ] ] } } ] }","contact":"California Water Science Center
California GAMA
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
We studied how drought and an associated stressor, wildfire, influenced stream flow permanence and thermal regimes in a Great Basin stream network. We quantified these responses by collecting information with a spatially extensive network of data loggers. To understand the effects of wildfire specifically, we used data from 4 additional sites that were installed prior to a 2012 fire that burned nearly the entire watershed. Within the sampled network 73 reaches were classified as perennial, yet only 51 contained surface water during logger installation in 2014. Among the sites with pre-fire temperature data, we observed 2–4 °C increases in maximum daily stream temperature relative to an unburned control in the month following the fire; effects (elevated up to 6.6 °C) appeared to persist for at least one year. When observed August mean temperatures in 2015 (the peak of regionally severe drought) were compared to those predicted by a regional stream temperature model, we observed deviations of −2.1°-3.5°. The model under-predicted and over-predicted August mean by > 1 °C in 54% and 10% of sites, respectively, and deviance from predicted was negatively associated with elevation. Combined drought and post-fire conditions appeared to greatly restrict thermally-suitable habitat for Lahontan cutthroat trout (Oncorhynchus clarkii henshawi).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2017.05.008","usgsCitation":"Schultz, L., Heck, M., Hockman-Wert, D., Allai, T., Wenger, S., Cook, and Dunham, J.B., 2017, Spatial and temporal variability in the effects of wildfire and drought on thermal habitat for a desert trout: Journal of Arid Environments, v. 145, p. 60-68, https://doi.org/10.1016/j.jaridenv.2017.05.008.","productDescription":"9 p.","startPage":"60","endPage":"68","ipdsId":"IP-083354","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":438296,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8QFV","text":"USGS data release","linkHelpText":"Stream temperature data from Willow-Whitehorse and Little Blitzen watersheds, southeast Oregon, 2011-2015"},{"id":342751,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willow-Whitehorse Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.62213134765626,\n 42.04521345501039\n ],\n [\n -117.14447021484375,\n 42.04521345501039\n ],\n [\n -117.14447021484375,\n 43.113014204188914\n ],\n [\n -118.62213134765626,\n 43.113014204188914\n ],\n [\n -118.62213134765626,\n 42.04521345501039\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"594cd73fe4b062508e3951d8","contributors":{"authors":[{"text":"Schultz, Luke 0000-0002-6751-4626 lschultz@usgs.gov","orcid":"https://orcid.org/0000-0002-6751-4626","contributorId":193171,"corporation":false,"usgs":true,"family":"Schultz","given":"Luke","email":"lschultz@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":698993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heck, Michael 0000-0001-8858-7325 mheck@usgs.gov","orcid":"https://orcid.org/0000-0001-8858-7325","contributorId":4796,"corporation":false,"usgs":true,"family":"Heck","given":"Michael","email":"mheck@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hockman-Wert, David 0000-0003-2436-6237 dhockman-wert@usgs.gov","orcid":"https://orcid.org/0000-0003-2436-6237","contributorId":3891,"corporation":false,"usgs":true,"family":"Hockman-Wert","given":"David","email":"dhockman-wert@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allai, T","contributorId":193172,"corporation":false,"usgs":false,"family":"Allai","given":"T","affiliations":[],"preferred":false,"id":698996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":698997,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cook","contributorId":193173,"corporation":false,"usgs":false,"family":"Cook","email":"","affiliations":[],"preferred":false,"id":698998,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":698992,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70192350,"text":"70192350 - 2017 - Earthquake source properties from instrumented laboratory stick-slip","interactions":[],"lastModifiedDate":"2017-10-25T11:49:49","indexId":"70192350","displayToPublicDate":"2017-06-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"8","title":"Earthquake source properties from instrumented laboratory stick-slip","docAbstract":"Stick-slip experiments were performed to determine the influence of the testing apparatus on source properties, develop methods to relate stick-slip to natural earthquakes and examine the hypothesis of McGarr [2012] that the product of stiffness, k, and slip duration, Δt, is scale-independent and the same order as for earthquakes. The experiments use the double-direct shear geometry, Sierra White granite at 2 MPa normal stress and a remote slip rate of 0.2 µm/sec. To determine apparatus effects, disc springs were added to the loading column to vary k. Duration, slip, slip rate, and stress drop decrease with increasing k, consistent with a spring-block slider model. However, neither for the data nor model is kΔt constant; this results from varying stiffness at fixed scale.
In contrast, additional analysis of laboratory stick-slip studies from a range of standard testing apparatuses is consistent with McGarr's hypothesis. kΔt is scale-independent, similar to that of earthquakes, equivalent to the ratio of static stress drop to average slip velocity, and similar to the ratio of shear modulus to wavespeed of rock. These properties result from conducting experiments over a range of sample sizes, using rock samples with the same elastic properties as the Earth, and scale-independent design practices.
The U.S. Geological Survey (USGS) serves the Nation by providing reliable scientific information and tools to build resilience in communities exposed to subduction zone earthquakes, tsunamis, landslides, and volcanic eruptions. Improving the application of USGS science to successfully reduce risk from these events relies on whole community efforts, with continuing partnerships among scientists and stakeholders, including researchers from universities, other government labs and private industry, land-use planners, engineers, policy-makers, emergency managers and responders, business owners, insurance providers, the media, and the general public.
Motivated by recent technological advances and increased awareness of our growing vulnerability to subduction-zone hazards, the USGS is uniquely positioned to take a major step forward in the science it conducts and products it provides, building on its tradition of using long-term monitoring and research to develop effective products for hazard mitigation. This science plan provides a blueprint both for prioritizing USGS science activities and for delineating USGS interests and potential participation in subduction zone science supported by its partners.
The activities in this plan address many USGS stakeholder needs:
USGS Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road
Mail Stop 977
Menlo Park, CA 94025
(650) 329-4668
Migrations of springtime Alewife Alosa pseudoharengus and Blueback Herring A. aestivalis, collectively referred to as river herring, are monitored in many rivers along the Atlantic coast to estimate population sizes. While these estimates give an indication of annual differences in the number of returning adults, links to the subsequent timing and duration of spawning and freshwater juvenile productivity remain equivocal. In this study, we captured juvenile river herring at night in 20 coastal Massachusetts lakes using a purse seine and extracted otoliths to derive daily fish ages and back-calculate spawn dates. Estimates of spawning dates were compared with fishway counts of migrating adults to assess differences in migration timing and the timing and duration of spawning. We observed a distinct delay between the beginning of the adult migration run and the start of spawning, ranging from 7 to 28 d across the 20 lakes. Spawning continued 13–48 d after adults stopped migrating into freshwater, further demonstrating a pronounced delay in spawning following migration. Across the study sites the duration of spawning (43–76 d) was longer but not related to the duration of migration (29–66 d). The extended spawning period is consistent with recent studies suggesting that Alewives are indeterminate spawners. The long duration in freshwater provides the opportunity for top-down (i.e., predation on zooplankton) and bottom-up (i.e., food for avian, fish, and other predators) effects, with implications for freshwater food webs and nutrient cycling. General patterns of spawn timing and duration can be incorporated into population models and used to estimate temporal changes in productivity associated with variable timing and density of spawning river herring in lakes.
","language":"English","publisher":"Informa UK ","doi":"10.1080/00028487.2017.1341851","usgsCitation":"Rosset, J., Roy, A.H., Gahagan, B.I., Whiteley, A.R., Armstrong, M., Sheppard, J.J., and Jordaan, A., 2017, Temporal patterns of migration and spawning of river herring in coastal Massachusetts: Transactions of the American Fisheries Society, v. 146, no. 6, p. 1101-1114, https://doi.org/10.1080/00028487.2017.1341851.","productDescription":"14 p.","startPage":"1101","endPage":"1114","ipdsId":"IP-083403","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.17218017578125,\n 41.58463401188338\n ],\n [\n -69.89776611328124,\n 41.58463401188338\n ],\n [\n -69.89776611328124,\n 42.72280375732727\n ],\n [\n -71.17218017578125,\n 42.72280375732727\n ],\n [\n -71.17218017578125,\n 41.58463401188338\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"146","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-19","publicationStatus":"PW","scienceBaseUri":"5a0425b7e4b0dc0b45b45352","contributors":{"authors":[{"text":"Rosset, Julianne","contributorId":197446,"corporation":false,"usgs":false,"family":"Rosset","given":"Julianne","email":"","affiliations":[],"preferred":false,"id":721270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gahagan, Benjamin I.","contributorId":200168,"corporation":false,"usgs":false,"family":"Gahagan","given":"Benjamin","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":721271,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whiteley, Andrew R.","contributorId":52072,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":721272,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Armstrong, Michael P.","contributorId":200170,"corporation":false,"usgs":false,"family":"Armstrong","given":"Michael P.","affiliations":[],"preferred":false,"id":721273,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sheppard, John J.","contributorId":200171,"corporation":false,"usgs":false,"family":"Sheppard","given":"John","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":721274,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jordaan, Adrian","contributorId":197449,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[],"preferred":false,"id":721275,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188627,"text":"70188627 - 2017 - Biological soil crusts: Diminutive communities of potential global importance","interactions":[],"lastModifiedDate":"2017-06-19T13:09:30","indexId":"70188627","displayToPublicDate":"2017-06-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1701,"text":"Frontiers in Ecology and the Environment","active":true,"publicationSubtype":{"id":10}},"title":"Biological soil crusts: Diminutive communities of potential global importance","docAbstract":"Biological soil crusts (biocrusts) are widespread, diverse communities of cyanobacteria, fungi, lichens, and mosses living on soil surfaces, primarily in drylands. Biocrusts can locally govern primary production, soil fertility, hydrology, and surface energy balance, with considerable variation in these functions across alternate community states. Further, these communities have been implicated in Earth system functioning via potential influences on global biogeochemistry and climate. Biocrusts are easily destroyed by disturbances and appear to be exceptionally vulnerable to warming temperatures and altered precipitation inputs, signaling possible losses of dryland functions with global change. Despite these concerns, we lack sufficient spatiotemporal data on biocrust function, cover, and community structure to confidently assess their ecological roles across the extensive dryland biome. Here, we present the case for cross-scale research and restoration efforts coupled with remote-sensing and modeling approaches that improve our collective understanding of biocrust responses to global change and the ecological roles of these diminutive communities at global scales.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.1469","usgsCitation":"Ferrenberg, S., Tucker, C., and Reed, S.C., 2017, Biological soil crusts: Diminutive communities of potential global importance: Frontiers in Ecology and the Environment, v. 15, no. 3, p. 160-167, https://doi.org/10.1002/fee.1469.","productDescription":"8 p.","startPage":"160","endPage":"167","ipdsId":"IP-079877","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469743,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1401283","text":"Publisher Index Page"},{"id":342642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-13","publicationStatus":"PW","scienceBaseUri":"5948e2a4e4b062508e354c67","contributors":{"authors":[{"text":"Ferrenberg, Scott 0000-0002-3542-0334 sferrenberg@usgs.gov","orcid":"https://orcid.org/0000-0002-3542-0334","contributorId":147684,"corporation":false,"usgs":true,"family":"Ferrenberg","given":"Scott","email":"sferrenberg@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tucker, Colin 0000-0002-4539-7780 ctucker@usgs.gov","orcid":"https://orcid.org/0000-0002-4539-7780","contributorId":167487,"corporation":false,"usgs":true,"family":"Tucker","given":"Colin","email":"ctucker@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698664,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70186812,"text":"sir20175028 - 2017 - Geophysics- and geochemistry-based assessment of the geochemical characteristics and groundwater-flow system of the U.S. part of the Mesilla Basin/Conejos-Médanos aquifer system in Doña Ana County, New Mexico, and El Paso County, Texas, 2010–12","interactions":[],"lastModifiedDate":"2017-06-23T10:09:56","indexId":"sir20175028","displayToPublicDate":"2017-06-16T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5028","title":"Geophysics- and geochemistry-based assessment of the geochemical characteristics and groundwater-flow system of the U.S. part of the Mesilla Basin/Conejos-Médanos aquifer system in Doña Ana County, New Mexico, and El Paso County, Texas, 2010–12","docAbstract":"One of the largest rechargeable groundwater systems by total available volume in the Rio Grande/Río Bravo Basin (hereinafter referred to as the “Rio Grande”) region of the United States and Mexico, the Mesilla Basin/Conejos-Médanos aquifer system, supplies water for irrigation as well as for cities of El Paso, Texas; Las Cruces, New Mexico; and Ciudad Juárez, Chihuahua, Mexico. The U.S. Geological Survey in cooperation with the Bureau of Reclamation assessed the groundwater resources in the Mesilla Basin and surrounding areas in Doña Ana County, N. Mex., and El Paso County, Tex., by using a combination of geophysical and geochemical methods. The study area consists of approximately 1,400 square miles in Doña Ana County, N. Mex., and 100 square miles in El Paso County, Tex. The Mesilla Basin composes most of the study area and can be divided into three parts: the Mesilla Valley, the West Mesa, and the East Bench. The Mesilla Valley is the part of the Mesilla Basin that was incised by the Rio Grande between Selden Canyon to the north and by a narrow valley (about 4 miles wide) to the southeast near El Paso, Tex., named the Paso del Norte, which is sometimes referred to in the literature as the “El Paso Narrows.”
Previously published geophysical data for the study area were compiled and these data were augmented by collecting additional geophysical and geochemical data. Geophysical resistivity measurements from previously published helicopter frequency domain electromagnetic data, previously published direct-current resistivity soundings, and newly collected (2012) time-domain electromagnetic soundings were used in the study to detect spatial changes in the electrical properties of the subsurface, which reflect changes that occur within the hydrogeology. The geochemistry of the groundwater system was evaluated by analyzing groundwater samples collected in November 2010 for physicochemical properties, major ions, trace elements, nutrients, pesticides (reported but not used in the assessment), and environmental tracers. The data obtained from these samples (with the exception of the pesticide data) were used to gain insights into processes controlling the groundwater movement through the groundwater system in the study area. Results from the geophysical and geochemical assessments facilitated the interpretation of the geochemical characteristics of the groundwater sources and geochemical groups within the groundwater system.
The groundwater-flow system in the study area consists primarily of the Mesilla Basin aquifer system, which can be divided into four hydrogeologic units by using an informal classification scheme based on basin-fill stratigraphy and sedimentology with an emphasis on aquifer characteristics. The four hydrogeologic units are (1) the Rio Grande alluvium, which is the shallow aquifer of the Mesilla Basin within the confines of the Mesilla Valley, and the three hydrogeologic units that compose the Santa Fe Group: (2) the lower part of the Santa Fe Group, which is the least productive zone, (3) the middle part of the Santa Fe Group, which is the primary water-bearing hydrogeologic unit in the basin and is generally saturated, and (4) the upper part of the Santa Fe Group, which is the most productive water-bearing unit within the Santa Fe Group but is only partially saturated in the north and largely unsaturated in the south and western parts of the Mesilla Basin.
The helicopter frequency domain electromagnetic survey results indicated that approximately half of the resistivity values were less than 10 ohm-meters at depths of 50 and 100 feet with a transition where the resistivity values changed from relatively high values (greater than 20 ohm-meters) to relatively low resistivity values (less than 10 ohm-meters) near Vado, New Mexico. Slightly more than 25 percent of the gridded resistivity values from the three-dimensional grid of the combined inverse modeling results of the direct-current resistivity and time-domain electromagnetic soundings were equal to or less than 10 ohm-meters with large regions of low resistivity becoming apparent in the southernmost part of the study area near the Paso Del Norte where these low resistivity features are spatially the widest at or below the top of the bedrock. These low resistivity values might represent clayey deposits, sediments composed largely of sand and gravel saturated with saline water, or both. Historical dissolved-solids-concentration data within the surface geophysical subset area of the study area were compiled and compared to the inverse modeling results of the combined direct-current resistivity and time-domain soundings; this comparison was done to strengthen the interpretation made from the combined inverse modeling results that the low resistivity features were representative of sand and gravel deposits saturated with saline water and not clayey deposits.
Water-level altitudes within the Rio Grande alluvium generally decreased from north to south, with a west to east decrease in water-level altitudes near Las Cruces, New Mexico, as a result of groundwater pumping. Groundwater flow within the Santa Fe Group is more complex than the groundwater flow within the Rio Grande alluvium because of the larger lateral and vertical extent of the Santa Fe Group compared to the Rio Grande alluvium. Groundwater from the Organ Mountains flows directly south towards the Paso del Norte. Groundwater from the Robledo Mountains, the Rough and Ready Hills, and the Sleeping Lady Hills generally flows to the southeast. Groundwater flowing near the north end of the midbasin uplift generally continues east towards the Rio Grande and then flows south on the east side of the midbasin uplift. Groundwater flowing near the west side of the midbasin uplift generally continues south parallel to the faults that make up the midbasin uplift and then flows east towards the Paso del Norte when it reaches the south end of the midbasin uplift. Groundwater from the Aden Hills and the East and West Potrillo Mountains flows to the south end of the midbasin uplift and then continues east towards the Paso del Norte. Throughout most of the Mesilla Valley, the vertical hydraulic gradient was downward because the water-level altitude in the Rio Grande alluvium was higher than it was in the Santa Fe Group, but in some areas (typically in the middle and southern parts of the Mesilla Valley), the vertical hydraulic gradient was substantially reduced or even reversed to an upward hydraulic gradient.
The geochemistry data indicate that there was a complex system of multiple geochemical endmembers and mixing between these endmembers with recharge to the Rio Grande alluvium and Santa Fe Group composed mostly of seepage from the Rio Grande, inflows from deeper or neighboring water systems, and mountain-front recharge. Five distinct geochemical groups were identified in the Mesilla Basin study area: (1) ancestral Rio Grande (pre-Pleistocene) geochemical group, (2) modern Rio Grande (Pleistocene to present) geochemical group, (3) mountain-front geochemical group, (4) deep groundwater upwelling geochemical group, and (5) unknown freshwater geochemical group. The ancestral Rio Grande groundwater was water that recharged into the system as seepage losses from the ancestral Rio Grande; this groundwater generally flows from north to south-southeast towards the Paso del Norte. Groundwater on the west side of the midbasin uplift generally flows south until it reaches the southern part of the study area; from the southern part of the study area, the groundwater flows east towards the Paso del Norte. Groundwater on the east side of the midbasin uplift flows south-southeast towards the Paso del Norte where it mixes with groundwater from the modern Rio Grande, uplifted areas in the west, and the deep saline source. The water type of the modern Rio Grande geochemical group ranged from calcium-sulfate water type in the northern part of the study area to sodium-chloride-sulfate water type in the southern part of the study area; from north to south there was a substantial increase in specific conductance, strontium-87/strontium-86 ratio, potassium, and the trace metals of iron and lithium, changing the water chemistry such that it became similar to the water chemistry of the deep groundwater upwelling geochemical group. From age-dating results, water in the modern Rio Grande geochemical group was recharged to the Rio Grande alluvium within the past 10 years. The mountain-front geochemical group was generally old water (apparent age was greater than 10,000 carbon-14 years before present) that was somewhat mineralized and has relatively high concentrations of fluoride and silica, which might indicate longer exposure to volcanic and siliciclastic rocks or aluminosilicate minerals. There were five different locations of recharge determined from the groundwater geochemistry within the mountain-front geochemical group, all having a slightly different geochemical signature: (1) the Rough and Ready Hills, Robledo Mountains, and the Sleeping Lady Hills, (2) the Doña Ana Mountains, (3) the Aden Hills and West Potrillo Mountains, (4) the East Potrillo Mountains, and (5) the Sierra Juárez in Mexico. The groundwater from the Rough and Ready Hills, Robledo Mountains, the Sleeping Lady Hills, and the Doña Ana Mountains generally flows toward the Rio Grande and eventually mixes together and with the modern Rio Grande groundwater. The groundwater originating from the Aden Hills and East and West Potrillo Mountains generally flows east to southeast at a slow rate and eventually mixes and continues east, where it mixes with groundwater from the ancestral Rio Grande geochemical group and with the groundwater from the Sierra Juárez. The groundwater from the Sierra Juárez flows north and then east towards the Paso del Norte where it mixes with groundwater from the uplifted areas in the west, ancestral and modern Rio Grande groundwater, and the upwelling groundwater from a deep saline source. The deep groundwater upwelling geochemical group had the highest concentrations of bicarbonate, potassium, silica, aluminum, iron, and lithium within the study area, indicating that it had been in contact with carbonate and siliciclastic rocks for a much longer period of time and at higher temperatures compared to the other geochemical groups, and was most likely ancient marine groundwater originating from the Paleozoic and Cretaceous carbonate rocks which was upwelling into the Mesilla Basin aquifer system in the southeastern part of the study area through the extensive fault systems. Direct-current resistivity and time-domain electromagnetic soundings support the interpretation of ancient marine groundwater upwelling into the Mesilla Basin aquifer system, as do the analytical results from wells, and the helicopter frequency domain electromagnetic data collected along the Rio Grande. The hydrogen-2/hydrogen-1 ratio and oxygen-18/oxygen-16 ratio isotopic results for samples in the unknown freshwater geochemical group did not plot on the Rio Grande evaporation line, indicating this group did not have a Rio Grande signature (that is, there was no isotopic evidence of a component of Rio Grande water) and it also had the lowest mineralized content of any geochemical group in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175028","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Teeple, A.P., 2017, Geophysics- and geochemistry-based assessment of the geochemical characteristics and groundwater-flow system of the U.S. part of the Mesilla Basin/Conejos-Médanos aquifer system in Doña Ana County, New Mexico, and El Paso County, Texas, 2010–12: U.S. Geological Survey Scientific Investigations Report 2017–5028, 183 p., https://doi.org/10.3133/sir20175028.","productDescription":"Report: x, 183 p.; 2 Figures; Project Sites, Read Me","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070474","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":438297,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PV6HJ3","text":"USGS data release","linkHelpText":"Time-Domain Electromagnetic Data Used in the Assessment of the U.S. Part of the Mesilla Basin/Conejos-Mdanos Aquifer System in Doa Ana County, New Mexico, and El Paso County, Texas"},{"id":342582,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5028/sir20175028.pdf","text":"Report","size":"16.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5028"},{"id":342585,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2017/5028/sir20175028_readme_figures14_17.pdf","size":"890 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5028 Read Me"},{"id":342586,"rank":6,"type":{"id":18,"text":"Project Site"},"url":"https://water.usgs.gov/ogw/","text":"Office of Groundwater"},{"id":342587,"rank":7,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/science/mission-areas/water/national-water-quality-program?qt-programs_l2_landing_page=0#qt-programs_l2_landing_page","text":"National Water-Quality Assessment Program"},{"id":342581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5028/coverthb.jpg"},{"id":342584,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5028/sir20175028_figure17.pdf","text":"Figure 17","size":"23.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5028 Figure 17"},{"id":342583,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5028/sir20175028_figure14.pdf","text":"Figure 14","size":"22.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5028 Figure 14"}],"country":"United States","state":"New Mexico, Texas","county":"Doña Ana County, El Paso County","otherGeospatial":"Mesilla Basin/Conejos-Médanos Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.25,\n 31.65\n ],\n [\n -106.375,\n 31.65\n ],\n [\n -106.375,\n 32.625\n ],\n [\n -107.25,\n 32.625\n ],\n [\n -107.25,\n 31.65\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Here, I present a database of >160 finite fault models for all earthquakes of M 7.5 and above since 1990, created using a consistent modeling approach. The use of a common approach facilitates easier comparisons between models, and reduces uncertainties that arise when comparing models generated by different authors, data sets and modeling techniques.
I use this database to verify published scaling relationships, and for the first time show a clear and intriguing relationship between maximum potency (the product of slip and area) and average potency for a given earthquake. This relationship implies that earthquakes do not reach the potential size given by the tectonic load of a fault (sometimes called “moment deficit,” calculated via a plate rate over time since the last earthquake, multiplied by geodetic fault coupling). Instead, average potency (or slip) scales with but is less than maximum potency (dictated by tectonic loading). Importantly, this relationship facilitates a more accurate assessment of maximum earthquake size for a given fault segment, and thus has implications for long-term hazard assessments. The relationship also suggests earthquake cycles may not completely reset after a large earthquake, and thus repeat rates of such events may appear shorter than is expected from tectonic loading. This in turn may help explain the phenomenon of “earthquake super-cycles” observed in some global subduction zones.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2017.04.003","usgsCitation":"Hayes, G.P., 2017, The finite, kinematic rupture properties of great-sized earthquakes since 1990: Earth and Planetary Science Letters, v. 468, p. 94-100, https://doi.org/10.1016/j.epsl.2017.04.003.","productDescription":"7 p.","startPage":"94","endPage":"100","ipdsId":"IP-085877","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":342594,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"468","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5944ee10e4b062508e3335d6","contributors":{"authors":[{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698456,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70188568,"text":"70188568 - 2017 - Climate change may restrict dryland forest regeneration in the 21st century","interactions":[],"lastModifiedDate":"2017-06-15T13:40:23","indexId":"70188568","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change may restrict dryland forest regeneration in the 21st century","docAbstract":"The persistence and geographic expansion of dryland forests in the 21st century will be influenced by how climate change supports the demographic processes associated with tree regeneration. Yet, the way that climate change may alter regeneration is unclear. We developed a quantitative framework that estimates forest regeneration potential (RP) as a function of key environmental conditions for ponderosa pine, a key dryland forest species. We integrated meteorological data and climate projections for 47 ponderosa pine forest sites across the western United States, and evaluated RP using an ecosystem water balance model. Our primary goal was to contrast conditions supporting regeneration among historical, mid-21st century and late-21st century time frames. Future climatic conditions supported 50% higher RP in 2020–2059 relative to 1910–2014. As temperatures increased more substantially in 2060–2099, seedling survival decreased, RP declined by 50%, and the frequency of years with very low RP increased from 25% to 58%. Thus, climate change may initially support higher RP and increase the likelihood of successful regeneration events, yet will ultimately reduce average RP and the frequency of years with moderate climate support of regeneration. Our results suggest that climate change alone may begin to restrict the persistence and expansion of dryland forests by limiting seedling survival in the late 21st century.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1791","usgsCitation":"Petrie, M., Bradford, J.B., Hubbard, R., Lauenroth, W., Andrews, C.M., and Schlaepfer, D., 2017, Climate change may restrict dryland forest regeneration in the 21st century: Ecology, v. 98, no. 6, p. 1548-1559, https://doi.org/10.1002/ecy.1791.","productDescription":"12 p.","startPage":"1548","endPage":"1559","ipdsId":"IP-081499","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":342558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.408203125,\n 26.27371402440643\n ],\n [\n -96.328125,\n 26.27371402440643\n ],\n [\n -96.328125,\n 51.12421275782688\n ],\n [\n -128.408203125,\n 51.12421275782688\n ],\n [\n -128.408203125,\n 26.27371402440643\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"98","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-30","publicationStatus":"PW","scienceBaseUri":"59439c91e4b062508e31a97e","contributors":{"authors":[{"text":"Petrie, M.D.","contributorId":192983,"corporation":false,"usgs":false,"family":"Petrie","given":"M.D.","email":"","affiliations":[],"preferred":false,"id":698374,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hubbard, R.M.","contributorId":167015,"corporation":false,"usgs":false,"family":"Hubbard","given":"R.M.","email":"","affiliations":[{"id":24595,"text":"USDA Forest Service, Fort Collins CO","active":true,"usgs":false}],"preferred":false,"id":698375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauenroth, W.K.","contributorId":192984,"corporation":false,"usgs":false,"family":"Lauenroth","given":"W.K.","email":"","affiliations":[],"preferred":false,"id":698376,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698377,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schlaepfer, D.R.","contributorId":140421,"corporation":false,"usgs":false,"family":"Schlaepfer","given":"D.R.","email":"","affiliations":[{"id":13488,"text":"Dept. of Botany, University of Wyoming, 1000 E. UNIVersity Avenue, Laramie, WY 82070","active":true,"usgs":false}],"preferred":false,"id":698378,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188555,"text":"70188555 - 2017 - Response of bird community structure to habitat management in piñon-juniper woodland-sagebrush ecotones","interactions":[],"lastModifiedDate":"2017-11-22T16:50:56","indexId":"70188555","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Response of bird community structure to habitat management in piñon-juniper woodland-sagebrush ecotones","docAbstract":"Piñon (Pinus spp.) and juniper (Juniperus spp.) woodlands have been expanding their range across the intermountain western United States into landscapes dominated by sagebrush (Artemisia spp.) shrublands. Management actions using prescribed fire and mechanical cutting to reduce woodland cover and control expansion provided opportunities to understand how environmental structure and changes due to these treatments influence bird communities in piñon-juniper systems. We surveyed 43 species of birds and measured vegetation for 1–3 years prior to treatment and 6–7 years post-treatment at 13 locations across Oregon, California, Idaho, Nevada, and Utah. We used structural equation modeling to develop and statistically test our conceptual model that the current bird assembly at a site is structured primarily by the previous bird community with additional drivers from current and surrounding habitat conditions as well as external regional bird dynamics. Treatment reduced woodland cover by >5% at 80 of 378 survey sites. However, habitat change achieved by treatment was highly variable because actual disturbance differed widely in extent and intensity. Biological inertia in the bird community was the strongest single driver; 72% of the variation in the bird assemblage was explained by the community that existed seven years earlier. Greater net reduction in woodlands resulted in slight shifts in the bird community to one having ecotone or shrubland affinities. However, the overall influence of woodland changes from treatment were relatively small and were buffered by other extrinsic factors. Regional bird dynamics did not significantly influence the structure of local bird communities at our sites. Our results suggest that bird communities in piñon-juniper woodlands can be highly stable when management treatments are conducted in areas with more advanced woodland development and at the level of disturbance measured in our study.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2017.06.017","usgsCitation":"Knick, S.T., Hanser, S.E., Grace, J.B., Hollenbeck, J.P., and Leu, M., 2017, Response of bird community structure to habitat management in piñon-juniper woodland-sagebrush ecotones: Forest Ecology and Management, v. 400, p. 256-268, https://doi.org/10.1016/j.foreco.2017.06.017.","productDescription":"13 p.","startPage":"256","endPage":"268","ipdsId":"IP-083953","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469746,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2017.06.017","text":"Publisher Index Page"},{"id":342543,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.53076171875,\n 36.20882309283712\n ],\n [\n -112.5,\n 36.20882309283712\n ],\n [\n -112.5,\n 46.28622391806706\n ],\n [\n -121.53076171875,\n 46.28622391806706\n ],\n [\n -121.53076171875,\n 36.20882309283712\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"400","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59439c92e4b062508e31a98b","contributors":{"authors":[{"text":"Knick, Steven T. 0000-0003-4025-1704 steve_knick@usgs.gov","orcid":"https://orcid.org/0000-0003-4025-1704","contributorId":159,"corporation":false,"usgs":true,"family":"Knick","given":"Steven","email":"steve_knick@usgs.gov","middleInitial":"T.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanser, Steve E. 0000-0002-4430-2073 shanser@usgs.gov","orcid":"https://orcid.org/0000-0002-4430-2073","contributorId":152523,"corporation":false,"usgs":true,"family":"Hanser","given":"Steve","email":"shanser@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":698328,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grace, James B. 0000-0001-6374-4726 gracej@usgs.gov","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":884,"corporation":false,"usgs":true,"family":"Grace","given":"James","email":"gracej@usgs.gov","middleInitial":"B.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":698327,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hollenbeck, Jeff P. 0000-0001-6481-5354 jhollenbeck@usgs.gov","orcid":"https://orcid.org/0000-0001-6481-5354","contributorId":5130,"corporation":false,"usgs":true,"family":"Hollenbeck","given":"Jeff","email":"jhollenbeck@usgs.gov","middleInitial":"P.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":698329,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leu, Matthias","contributorId":68393,"corporation":false,"usgs":true,"family":"Leu","given":"Matthias","affiliations":[],"preferred":false,"id":698333,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70188351,"text":"ofr20171069 - 2017 - Inter-annual variability in apparent relative production, survival, and growth of juvenile Lost River and shortnose suckers in Upper Klamath Lake, Oregon, 2001–15","interactions":[],"lastModifiedDate":"2017-06-16T08:26:47","indexId":"ofr20171069","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1069","title":"Inter-annual variability in apparent relative production, survival, and growth of juvenile Lost River and shortnose suckers in Upper Klamath Lake, Oregon, 2001–15","docAbstract":"Populations of the once abundant Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) of the Upper Klamath Basin, decreased so substantially throughout the 20th century that they were listed under the Endangered Species Act in 1988. Major landscape alterations, deterioration of water quality, and competition with and predation by exotic species are listed as primary causes of the decreases in populations. Upper Klamath Lake populations are decreasing because fish lost due to adult mortality, which is relatively low for adult Lost River suckers and variable for adult shortnose suckers, are not replaced by new young adult suckers recruiting into known adult spawning aggregations. Catch-at-age and size data indicate that most adult suckers presently in Upper Klamath Lake spawning populations were hatched around 1991. While, a lack of egg production and emigration of young fish (especially larvae) may contribute, catch-at-length and age data indicate high mortality during the first summer or winter of life may be the primary limitation to the recruitment of young adults. The causes of juvenile sucker mortality are unknown.
We compiled and analyzed catch, length, age, and species data on juvenile suckers from Upper Klamath Lake from eight prior studies conducted from 2001 to 2015 to examine annual variation in apparent production, survival, and growth of young suckers. We used a combination of qualitative assessments, general linear models, and linear regression to make inferences about annual differences in juvenile sucker dynamics. The intent of this exercise is to provide information that can be compared to annual variability in environmental conditions with the hopes of understanding what drives juvenile sucker population dynamics.
Age-0 Lost River suckers generally grew faster than age-0 shortnose suckers, but the difference in growth rates between the two species varied among years. This unsynchronized annual variation in daily growth may be an indication that environmental conditions are affecting growth rates of these species in different ways.
The combined evidence outlined in this report and in Simon and others (2012) indicates that years of relatively high age-0 sucker production occurred in the late 1990s through at least 2000, in 2006, and in 2011. Our analysis of annual age-0 sucker catch per unit effort (CPUE), which accounted for zero inflated data and annual variation in sampling gears and locations, indicated that 2006 had the greatest apparent relative production of age-0 suckers ≥ 45 mm standard length (SL) during the time period examined. Midsummer trap net effort by the U.S. Geological Survey (USGS) was too sparse to examine age-0 sucker CPUE from 2011 to 2013. Relatively frequent catches of age-1 suckers in 2001, 2007, and 2012 corroborated relatively high CPUE for age-0 suckers during 1999–2000, 2006, and 2011, as reported by USGS or Simon and others (2012).
There were several indications in the data that juvenile sucker survival is low from at least midsummer of the first year of life through mid-September of the second year of life. Our estimated index of relative apparent age-0 sucker late-summer survival, which accounted for zero inflated data and variations in sampling gears and locations, was higher in 2009 than in 2004. Our index of apparent age-0 sucker mortality for all other years from 2001 to 2015 was similar among years. Seventy-five percent of age-1 suckers were captured prior to July 17 each year. In 2007, the one year with substantial age-1 sucker summertime catches, the proportion of nets to capture age-1 suckers decreased from July to mid-September. Maximum annual age-2+ sucker CPUE was 0.02 fish per net, 10,000 times less than the maximum annual age-0 sucker CPUE.
Analysis of species data indicated that juvenile Lost River suckers may have greater apparent mortality than shortnose suckers. Lost River suckers made up a smaller proportion of age-0 suckers captured in July each year than would be expected, based on the abundance of adult Lost River suckers relative to shortnose suckers, and higher Lost River than shortnose sucker fecundity. The proportion of age-0 suckers captured that were Lost River suckers decreased from July to September in several years. Only 14 percent of age-1 or older juvenile suckers identified to species over the 15-year time period were Lost River suckers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171069","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Burdick, S.M., and Martin, B.A., 2017, Inter-annual variability in apparent relative production, survival, and growth of juvenile Lost River and shortnose suckers in Upper Klamath Lake, Oregon, 2001–15: U.S. Geological Survey Open-File Report 2017–1069, 55 p., https://doi.org/10.3133/ofr20171069.","productDescription":"Report: vi, 55 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-082248","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":342516,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1069/ofr20171069.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1069"},{"id":342517,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZC812F","text":"USGS data release","description":"USGS data release","linkHelpText":"Data for trap net captured juvenile Lost River and shortnose suckers from Upper Klamath Lake, Oregon"},{"id":342515,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1069/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.14,\n 42.20\n ],\n [\n -121.7,\n 42.20\n ],\n [\n -121.7,\n 42.64\n ],\n [\n -122.14,\n 42.64\n ],\n [\n -122.14,\n 42.20\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
Forest vegetation classification and structure measurements are fundamental steps for planning, monitoring, and evaluating large-scale forest changes including restoration treatments. High spatial and spectral resolution remote sensing data are critically needed to classify vegetation and measure their 3-dimensional (3D) canopy structure at the level of individual species. Here we test high-resolution lidar, hyperspectral, and multispectral data collected from unmanned aerial vehicles (UAV) and demonstrate a lidar-hyperspectral image fusion method in treated and control forests with varying tree density and canopy cover as well as in an ecotone environment to represent a gradient of vegetation and topography in northern Arizona, U.S.A. The fusion performs better (88% overall accuracy) than either data type alone, particularly for species with similar spectral signatures, but different canopy sizes. The lidar data provides estimates of individual tree height (R2 = 0.90; RMSE = 2.3 m) and crown diameter (R2 = 0.72; RMSE = 0.71 m) as well as total tree canopy cover (R2 = 0.87; RMSE = 9.5%) and tree density (R2 = 0.77; RMSE = 0.69 trees/cell) in 10 m cells across thin only, burn only, thin-and-burn, and control treatments, where tree cover and density ranged between 22 and 50% and 1–3.5 trees/cell, respectively. The lidar data also produces highly accurate digital elevation model (DEM) (R2 = 0.92; RMSE = 0.75 m). In comparison, 3D data derived from the multispectral data via structure-from-motion produced lower correlations with field-measured variables, especially in dense and structurally complex forests. The lidar, hyperspectral, and multispectral sensors, and the methods demonstrated here can be widely applied across a gradient of vegetation and topography for monitoring landscapes undergoing large-scale changes such as the forests in the southwestern U.S.A.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2017.04.007","usgsCitation":"Sankey, T.T., Donager, J., McVay, J.L., and Sankey, J.B., 2017, UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA: Remote Sensing of Environment, v. 195, p. 30-43, https://doi.org/10.1016/j.rse.2017.04.007.","productDescription":"14 p.","startPage":"30","endPage":"43","ipdsId":"IP-073648","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":346176,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","volume":"195","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59ce0a2be4b05fe04cc0210a","contributors":{"authors":[{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":711500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Donager, Jonathon","contributorId":196772,"corporation":false,"usgs":false,"family":"Donager","given":"Jonathon","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":711501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McVay, Jason L.","contributorId":196235,"corporation":false,"usgs":false,"family":"McVay","given":"Jason","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":711502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":711499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188552,"text":"70188552 - 2017 - Integrating count and detection–nondetection data to model population dynamics","interactions":[],"lastModifiedDate":"2017-12-04T12:26:31","indexId":"70188552","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating count and detection–nondetection data to model population dynamics","docAbstract":"There is increasing need for methods that integrate multiple data types into a single analytical framework as the spatial and temporal scale of ecological research expands. Current work on this topic primarily focuses on combining capture–recapture data from marked individuals with other data types into integrated population models. Yet, studies of species distributions and trends often rely on data from unmarked individuals across broad scales where local abundance and environmental variables may vary. We present a modeling framework for integrating detection–nondetection and count data into a single analysis to estimate population dynamics, abundance, and individual detection probabilities during sampling. Our dynamic population model assumes that site-specific abundance can change over time according to survival of individuals and gains through reproduction and immigration. The observation process for each data type is modeled by assuming that every individual present at a site has an equal probability of being detected during sampling processes. We examine our modeling approach through a series of simulations illustrating the relative value of count vs. detection–nondetection data under a variety of parameter values and survey configurations. We also provide an empirical example of the model by combining long-term detection–nondetection data (1995–2014) with newly collected count data (2015–2016) from a growing population of Barred Owl (Strix varia) in the Pacific Northwest to examine the factors influencing population abundance over time. Our model provides a foundation for incorporating unmarked data within a single framework, even in cases where sampling processes yield different detection probabilities. This approach will be useful for survey design and to researchers interested in incorporating historical or citizen science data into analyses focused on understanding how demographic rates drive population abundance.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1831","usgsCitation":"Zipkin, E.F., Rossman, S., Yackulic, C.B., Wiens, D., Thorson, J.T., Davis, R.J., and Grant, E., 2017, Integrating count and detection–nondetection data to model population dynamics: Ecology, v. 98, no. 6, p. 1640-1650, https://doi.org/10.1002/ecy.1831.","productDescription":"11 p.","startPage":"1640","endPage":"1650","ipdsId":"IP-075390","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":438298,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7348J9M","text":"USGS data release","linkHelpText":"Count and detection-nondetection survey data of barred owls (Strix varia) in historical breeding territories of Northern Spotted Owls (Strix occidentalis caurina) in the Oregon Coast Range, 1995-2016"},{"id":342540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"98","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-11","publicationStatus":"PW","scienceBaseUri":"59439c92e4b062508e31a993","contributors":{"authors":[{"text":"Zipkin, Elise F. 0000-0003-4155-6139","orcid":"https://orcid.org/0000-0003-4155-6139","contributorId":192755,"corporation":false,"usgs":false,"family":"Zipkin","given":"Elise","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":698309,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rossman, Sam","contributorId":8759,"corporation":false,"usgs":false,"family":"Rossman","given":"Sam","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":698310,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":698311,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiens, David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":167538,"corporation":false,"usgs":true,"family":"Wiens","given":"David","email":"jwiens@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":698312,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorson, James T.","contributorId":146580,"corporation":false,"usgs":false,"family":"Thorson","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":698313,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davis, Raymond J.","contributorId":150574,"corporation":false,"usgs":false,"family":"Davis","given":"Raymond","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":698314,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Grant, Evan H. Campbell 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":167017,"corporation":false,"usgs":true,"family":"Grant","given":"Evan H. Campbell","email":"ehgrant@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":698308,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188474,"text":"70188474 - 2017 - Relationship between water and aragonite barium concentrations in aquaria reared juvenile corals","interactions":[],"lastModifiedDate":"2017-06-13T12:25:06","indexId":"70188474","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Relationship between water and aragonite barium concentrations in aquaria reared juvenile corals","docAbstract":"Coral barium to calcium (Ba/Ca) ratios have been used to reconstruct records of upwelling, river and groundwater discharge, and sediment and dust input to the coastal ocean. However, this proxy has not yet been explicitly tested to determine if Ba inclusion in the coral skeleton is directly proportional to seawater Ba concentration and to further determine how additional factors such as temperature and calcification rate control coral Ba/Ca ratios. We measured the inclusion of Ba within aquaria reared juvenile corals (Favia fragum) at three temperatures (∼27.7, 24.6 and 22.5 °C) and three seawater Ba concentrations (73, 230 and 450 nmol kg−1). Coral polyps were settled on tiles conditioned with encrusting coralline algae, which complicated chemical analysis of the coral skeletal material grown during the aquaria experiments. We utilized Sr/Ca ratios of encrusting coralline algae (as low as 3.4 mmol mol−1) to correct coral Ba/Ca for this contamination, which was determined to be 26 ± 11% using a two end member mixing model. Notably, there was a large range in Ba/Ca across all treatments, however, we found that Ba inclusion was linear across the full concentration range. The temperature sensitivity of the distribution coefficient is within the range of previously reported values. Finally, calcification rate, which displayed large variability, was not correlated to the distribution coefficient. The observed temperature dependence predicts a change in coral Ba/Ca ratios of 1.1 μmol mol−1 from 20 to 28 °C for typical coastal ocean Ba concentrations of 50 nmol kg−1. Given the linear uptake of Ba by corals observed in this study, coral proxy records that demonstrate peaks of 10–25 μmol mol−1 would require coastal seawater Ba of between 60 and 145 nmol kg−1. Further validation of the coral Ba/Ca proxy requires evaluation of changes in seawater chemistry associated with the environmental perturbation recorded by the coral as well as verification of these results for Porites species, which are widely used in paleo reconstructions.
","language":"English","publisher":"Geochemical Society","doi":"10.1016/j.gca.2017.04.006","usgsCitation":"Gonneea Eagle, M., Cohen, A.L., DeCarlo, T.M., and Charette, M.A., 2017, Relationship between water and aragonite barium concentrations in aquaria reared juvenile corals: Geochimica et Cosmochimica Acta, v. 209, p. 123-134, https://doi.org/10.1016/j.gca.2017.04.006.","productDescription":"12 p.","startPage":"123","endPage":"134","ipdsId":"IP-079452","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469749,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/9163","text":"External Repository"},{"id":342424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"209","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5965b1c8e4b0d1f9f05b37b0","contributors":{"authors":[{"text":"Gonneea Eagle, Meagan 0000-0001-5072-2755 mgonneea@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":174590,"corporation":false,"usgs":true,"family":"Gonneea Eagle","given":"Meagan","email":"mgonneea@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":697917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cohen, Anne L.","contributorId":190716,"corporation":false,"usgs":false,"family":"Cohen","given":"Anne","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":697918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeCarlo, Thomas M.","contributorId":190720,"corporation":false,"usgs":false,"family":"DeCarlo","given":"Thomas","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":697919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Charette, Matthew A.","contributorId":92355,"corporation":false,"usgs":true,"family":"Charette","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":697920,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190239,"text":"70190239 - 2017 - A genetic signature of the evolution of loss of flight in the Galapagos cormorant","interactions":[],"lastModifiedDate":"2018-04-24T14:39:03","indexId":"70190239","displayToPublicDate":"2017-06-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"A genetic signature of the evolution of loss of flight in the Galapagos cormorant","docAbstract":"INTRODUCTION
Changes in the size and proportion of limbs and other structures have played a key role in the evolution of species. One common class of limb modification is recurrent wing reduction and loss of flight in birds. Indeed, Darwin used the occurrence of flightless birds as an argument in favor of his theory of natural selection. Loss of flight has evolved repeatedly and is found among 26 families of birds in 17 different orders. Despite the frequency of these modifications, we have a limited understanding of their underpinnings at the genetic and molecular levels.
RATIONALE
To better understand the evolution of changes in limb size, we studied a classic case of recent loss of flight in the Galapagos cormorant (Phalacrocorax harrisi). Cormorants are large water birds that live in coastal areas or near lakes, and P. harrisi is the only flightless cormorant among approximately 40 extant species. The entire population is distributed along the coastlines of Isabela and Fernandina islands in the Galapagos archipelago. P. harrisi has a pair of short wings, which are smaller than those of any other cormorant. The extreme reduction of the wings and pectoral skeleton observed in P. harrisi is an attractive model for studying the evolution of loss of flight because it occurred very recently; phylogenetic evidence suggests that P. harrisi diverged from its flighted relatives within the past 2 million years. We developed a comparative and predictive genomics approach that uses the genome sequences of P. harrisi and its flighted relatives to find candidate genetic variants that likely contributed to the evolution of loss of flight.
RESULTS
We sequenced and de novo assembled the whole genomes of P. harrisi and three closely related flighted cormorant species. We identified thousands of coding variants exclusive to P. harrisi and classified them according to their probability of altering protein function based on conservation. Variants most likely to alter protein function were significantly enriched in genes mutated in human skeletal ciliopathies, including Ofd1, Evc, Wdr34, and Ift122. We carried out experiments in Caenorhabditis elegans to confirm that a missense variant present in the Galapagos cormorant IFT122 protein is sufficient to affect ciliary function. The primary cilium is essential for Hedgehog (Hh) signaling in vertebrates, and individuals affected by ciliopathies have small limbs and ribcages, mirroring the phenotype of P. harrisi. We also identified a 4–amino acid deletion in the regulatory domain of Cux1, a highly conserved transcription factor that has been experimentally shown to regulate limb growth in chicken. The four missing amino acids are perfectly conserved in all birds and mammals sequenced to date. We tested the consequences of this deletion in a chondrogenic cell line and showed that it impairs the ability of CUX1 to transcriptionally up-regulate cilia-related genes (some of which contain function-altering variants in P. harrisi) and to promote chondrogenic differentiation. Finally, we show that positive selection may have played a role in the fixation of the variants associated with loss of flight in P. harrisi.
CONCLUSION
Our results indicate that the combined effect of variants in genes necessary for the correct transcriptional regulation and function of the primary cilium likely contributed to the evolution of highly reduced wings and other skeletal adaptations associated with loss of flight in P. harrisi. Our approach may be generally useful for identification of variants underlying evolutionary novelty from genomes of closely related species.
The Bushy Park Reservoir is a relatively shallow impoundment in a semi-tropical climate and is the principal water supply for the 400,000 people of the city of Charleston, South Carolina, and the surrounding areas including the Bushy Park Industrial Complex. Although there is an adequate supply of freshwater in the reservoir, taste-and-odor water-quality issues are a concern. The U.S. Geological Survey conducted an investigation in cooperation with the Charleston Water System to study the hydrology and hydrodynamics of the Bushy Park Reservoir to identify factors affecting water-quality conditions. Specifically, five areas for monitoring and (or) analysis were addressed: (1) hydrologic monitoring of the reservoir to establish a water budget, (2) flow monitoring in the tunnels to compute flow from Bushy Park Reservoir and at critical distribution junctions, (3) water-quality sampling, profiling, and continuous monitoring to identify the causes of taste-and-odor occurrence, (4) technical evaluation of appropriate hydrodynamic and water-quality simulation models for the reservoir, and (5) preliminary evaluation of alternative reservoir operations scenarios.
This report describes the hydrodynamic and hydrologic data collected from 2013 to 2015 to support the application and calibration of a three-dimensional hydrodynamic model and the water-quality monitoring and analysis to gain insight into the principal causes of the Bushy Park Reservoir taste-and-odor episodes. The existing U.S. Geological Survey real-time network on the West Branch of the Cooper River was augmented with a tidal flow gage on Durham Canal Back River, and Foster Creek. The Charleston Water System intake structure was instrumented to collect water-level, water temperature (top and bottom probes), specific conductance (top and bottom probes), wind speed and direction, and photosynthetically active radiation data. In addition to the gages attached to fixed structures, four bottom-mounted velocity profilers were deployed at six locations over different periods. The deployment period for the velocity profiler ranged from 2 weeks to 4 months. During the investigation, tidal cycle (13-hour) streamflow measurements were made at 30-minute intervals at five locations.
The Williams Station is a coal-fired powerplant that withdraws water from Bushy Park Reservoir for cooling purposes. The magnitude of the withdrawal (approximately 550 million gallons per day) is the major factor controlling the circulation in the reservoir. The net flow in Durham Canal to the reservoir is comparable to the withdrawal rates of the powerplant. When the Williams Station is not withdrawing water, the net flow in Durham Canal quickly goes to zero or reverses with a net flow away from the reservoir and to the Cooper River. Plan views of the velocity vectors for the tidal cycle streamflow measurements and rose diagram of the velocity profilers created with the Williams Station withdrawing and not withdrawing water show substantial effects of the distribution of magnitude and direction of the water velocities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175050","collaboration":"Prepared in cooperation with Charleston Water System","usgsCitation":"Conrads, P.A., Petkewich, M.D., Falls, W.F., and Lanier, T.H., 2017, Hydrologic characterization of Bushy Park Reservoir, South Carolina, 2013–15: U.S. Geological Survey Scientific Investigations Report 2017–5050, 83 p., https://doi.org/10.3133/sir20175050.","productDescription":"Report: ix, 83 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077941","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":342449,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GB2274","text":"USGS data release","description":"USGS data release","linkHelpText":"Hydrodynamic data of Bushy Park Reservoir, South Carolina 2013–15"},{"id":342447,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5050/coverthb.jpg"},{"id":342448,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5050/sir20175050.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5050"}],"country":"United States","state":"South Carolina","otherGeospatial":"Santee-Cooper River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.5,\n 32.68\n ],\n [\n -79.7,\n 32.68\n ],\n [\n -79.7,\n 33.56\n ],\n [\n -80.5,\n 33.56\n ],\n [\n -80.5,\n 32.68\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210