Translocations of North American prairie-grouse (genus Tympanuchus) present a conservation paradox wherein they are performed to augment, restore, or reintroduce populations, but translocated individuals exhibit a diminished ability to contribute to population restoration. For reintroduced populations without immigration, persistence can only be achieved through reproductive contributions by translocated individuals and their progeny. Due to the disruptive nature of translocation (e.g., physiological chronic stress), progeny produced at restoration sites may outperform founder populations in terms of demographics, but this hypothesis has yet to be tested. We reintroduced Columbian Sharp-tailed Grouse (T. phasianellus columbianus; CSTG) to north central Nevada from 2013 to 2017 and used integrated population models (IPMs) to evaluate the process of population establishment and estimate latent contributions of progeny hatched at the restoration site to population rate of change (
This paper investigates applicability of cassiterite to dating ore deposits in a wide age range. We report in situ LA-ICPMS U-Pb and Pb-Pb dating results (n = 15) of cassiterite from six ore deposits in Russia ranging in age from ~1.85 Ga to 93 Ma. The two oldest deposits dated at ~1.83–1.86 Ga are rare metal Vishnyakovskoe located in the East Sayan pegmatite belt and tin deposits within the Tuyukan ore region in the Baikal folded region. Rare metal skarn deposits of Pitkäranta ore field in the Ladoga region, Fennoscandian Shield are dated at ~1.54 Ga. Cassiterite from the Mokhovoe porphyry tin deposit located in western Transbaikalia is 810 ± 20 Ma. The youngest cassiterite was dated from the deposits Valkumei (Russian North East, 108 ± 2 Ma) and Merek (Russian Far East, 93 ± 2 Ma). Three methods of age calculations, including 208Pb/206Pb-207Pb/206Pb inverse isochron age, Tera-Wasserburg Concordia lower intercept age, and 207Pb-corrected 206Pb*/238U age were used and the comparison of the results is discussed. In all cases, the dated cassiterite from the ore deposits agreed, within error, with the established period of magmatism of the associated granitic rock
","language":"English","publisher":"MDPI","doi":"10.3390/min11111166","usgsCitation":"Neymark, L., Larin, A.M., and Moscati, R.J., 2021, Pb-Pb and U-Pb dating of cassiterite by in situ LA-ICPMS: Examples spanning ~1.85 Ga to ~100 Ma in Russia and implications for dating Proterozoic to Phanerozoic tin deposits.: Minerals, v. 11, 1166, 30 p., https://doi.org/10.3390/min11111166.","productDescription":"1166, 30 p.","ipdsId":"IP-132675","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":450377,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min11111166","text":"Publisher Index Page"},{"id":436137,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HK0RL5","text":"USGS data release","linkHelpText":"Pb-Pb and U-Pb data of Proterozoic to Phanerozoic cassiterite deposits in Russia"},{"id":391681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"Chuya-Kodar complex, East Sayan belt, Merek Greisen tin ore deposit, Mokhovoe Porphyry tin deposit, Pitkaranta Mining District, Valkumei silicate-sulfide vein tin deposit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 97.5,\n 52\n ],\n [\n 104,\n 52\n ],\n [\n 104,\n 55.37286814115173\n ],\n [\n 97.5,\n 55.37286814115173\n ],\n [\n 97.5,\n 52\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 112.5,\n 58\n ],\n [\n 115,\n 58\n ],\n [\n 115,\n 59\n ],\n [\n 112.5,\n 59\n ],\n [\n 112.5,\n 58\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 31,\n 61\n ],\n [\n 33,\n 61\n ],\n [\n 33,\n 62\n ],\n [\n 31,\n 62\n ],\n [\n 31,\n 61\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 114.5,\n 55.333\n ],\n [\n 115,\n 55.333\n ],\n [\n 115,\n 56\n ],\n [\n 114.5,\n 56\n ],\n [\n 114.5,\n 55.333\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 170,\n 68.75\n ],\n [\n 173,\n 68.75\n ],\n [\n 173,\n 70\n ],\n [\n 170,\n 70\n ],\n [\n 170,\n 68.75\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 130,\n 42\n ],\n [\n 142,\n 42\n ],\n [\n 142,\n 52\n ],\n [\n 130,\n 52\n ],\n [\n 130,\n 42\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2021-10-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Neymark, Leonid A. 0000-0003-4190-0278 lneymark@usgs.gov","orcid":"https://orcid.org/0000-0003-4190-0278","contributorId":140338,"corporation":false,"usgs":true,"family":"Neymark","given":"Leonid A.","email":"lneymark@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":826692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larin, Anatoly M. 0000-0001-5677-7415","orcid":"https://orcid.org/0000-0001-5677-7415","contributorId":268799,"corporation":false,"usgs":false,"family":"Larin","given":"Anatoly","email":"","middleInitial":"M.","affiliations":[{"id":55670,"text":"IPGG, RAS","active":true,"usgs":false}],"preferred":false,"id":826693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moscati, Richard J. 0000-0002-0818-4401 rmoscati@usgs.gov","orcid":"https://orcid.org/0000-0002-0818-4401","contributorId":2462,"corporation":false,"usgs":true,"family":"Moscati","given":"Richard","email":"rmoscati@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":826694,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238588,"text":"70238588 - 2021 - Novel insights into the genetic population connectivity of transient whale sharks (Rhincodon typus) in Pacific Panama provide crucial data for conservation efforts","interactions":[],"lastModifiedDate":"2022-12-01T13:17:51.909243","indexId":"70238588","displayToPublicDate":"2021-10-22T06:55:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Novel insights into the genetic population connectivity of transient whale sharks (Rhincodon typus) in Pacific Panama provide crucial data for conservation efforts","title":"Novel insights into the genetic population connectivity of transient whale sharks (Rhincodon typus) in Pacific Panama provide crucial data for conservation efforts","docAbstract":"The whale shark (Rhincodon typus) is an endangered and highly migratory species, of which solitary individuals or aggregations are observed in oceans worldwide and for which conservation efforts are hindered by a lack of comprehensive data on genetic population connectivity. Tissue samples were collected from wandering whale sharks in Pacific Panama to determine genetic diversity, phylogeographic origin, and possible global and local connectivity patterns using a 700–800 bp fragment of the mitochondrial control region gene. Genetic diversity among samples was high, with five new haplotypes and nine polymorphic sites identified among the 15 sequences. Haplotype diversity (Hd = 0.83) and nucleotide diversity (π = 0.00516) were similar to those reported in other studies. Our sequences, in particular haplotypes PTY1 and PTY2, were similar to those previously reported in the Arabian Gulf and the Western Indian Ocean populations (a novel occurrence in the latter case). Haplotypes PTY3, PTY4, and PTY5 were similar to populations in Mexico and the Gulf of California. In contrast, the only populations to which our Panamanian sequences were genetically dissimilar were those from the Atlantic Ocean. The absence of reference sequences in GenBank from southern sites in the Eastern Tropical Pacific, such as Galapagos (Ecuador), Gorgona and Malpelo Islands (Colombia), and Coco Island (Costa Rica), reduced our capacity to genetically define regional patterns. Genetic differentiation and connectivity were also assessed using an analysis of molecular variance (AMOVA), which showed a similar population structure (five groups) to the neighbor-joining tree. Other population features based on neutrality tests, such as Tajima’s D and Fu’s Fs statistics, showed positive values for Panama of 0.79 and 1.61, respectively. Positive values of these statistics indicate a lack of evidence for population expansion among the sampled individuals. Our results agree with previous reports suggesting that whale sharks can travel over long distances and that transboundary conservation measures may be effective for species protection.
Alluvial aquifers in Iowa have more wells with nitrate exceeding drinking-water standards than other aquifers; are susceptible to contamination by organic contaminants; and have high concentrations of naturally occurring iron and manganese in depositional areas that contain abundant organic matter. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, Iowa, studied the Cedar River alluvial aquifer in Linn County, Iowa, from 1990 to 2019 to understand the effect of municipal pumping on spatial and temporal hydrologic and water-quality variability. The Cedar River alluvial aquifer is the source of water for the city of Cedar Rapids, Iowa. Withdrawal of large quantities of water for municipal and industrial supply has altered the normal flow of water in the alluvial aquifer. Pumping induces flow from the Cedar River and the underlying bedrock aquifer into the alluvial aquifer.
Water quality in the alluvial aquifer varies along the Cedar River. Changes in nitrate, ammonia, manganese, and iron in the alluvial aquifer are seen as the upstream free-flowing reach of the Cedar River transitions to a partially regulated downstream reach, likely because of differences in reduction-oxidation conditions in the aquifer, which are controlled by infiltration from the Cedar River under normal conditions and when wells are being pumped. Nitrate, normally found in oxygenated environments, had the highest concentrations in the most upstream wells in the Seminole well field and the lowest concentrations in the most downstream wells in the East well field. In contrast, ammonia, manganese, and iron, normally found in greatest abundance in anoxic (reducing) conditions, had the greatest concentrations in the most downstream wells. Additionally, dissolved nitrate plus nitrite nitrogen concentrations in wells were substantially less and manganese concentrations were greater in production wells near backwater wetlands in contrast to wells near the Cedar River.
Temporal variability in water quality in the alluvial aquifer was driven by pumping that increased flow from the Cedar River into the alluvial aquifer and ultimately led to changes in reduction-oxidation conditions of the aquifer. Increasing dissolved nitrate plus nitrite nitrogen concentrations in the Cedar River from 1990 to 2019 were mirrored in the alluvial aquifer. Anoxic conditions are prevalent in the alluvial aquifer next to the Cedar River when the aquifer is not under pumping stress. However, production well pumping caused induced infiltration of oxygenated river water into the aquifer resulting in increased dissolved nitrate plus nitrite nitrogen concentrations and pesticides and decreased naturally occurring dissolved iron and manganese.
Hydrologic and water-quality conditions in the Cedar River alluvial aquifer from 1990 to 2019 provide baseline conditions needed to evaluate the effects of current and future nutrient reduction efforts and land-use changes in the Cedar River Basin on water quality of the Cedar River alluvial aquifer and its source water, the Cedar River. This summary and analysis provide information that can assist the City of Cedar Rapids Utilities Water Department in managing groundwater resources, and provides information that could be used develop a groundwater-quality model to characterize variability over larger areas of the alluvial aquifer, allowing water providers to plan for future water needs of their users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215110","usgsCitation":"Kalkhoff, S.J., 2021, Hydrologic and water-quality conditions in the Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019: U.S. Geological Survey Scientific Investigations Report 2021–5110, 61 p., https://doi.org/10.3133/sir20215110.","productDescription":"Report: ix, 61 p.; Data Release; Dataset","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-121189","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5110/coverthb.jpg"},{"id":390748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5110/sir20215110.pdf","text":"Report","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5110"},{"id":390749,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z7VKOU","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Hydrologic and water quality data from the Cedar River and Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019"},{"id":390750,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Iowa","county":"Linn County","otherGeospatial":"Cedar River Alluvial Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.3649,42.2964],[-91.3651,42.2082],[-91.3653,42.1215],[-91.3661,42.0343],[-91.3669,41.948],[-91.3677,41.8603],[-91.4836,41.8608],[-91.5989,41.8612],[-91.716,41.862],[-91.8318,41.8617],[-91.8329,41.9485],[-91.8338,42.0366],[-91.8342,42.1242],[-91.8328,42.2087],[-91.8319,42.2987],[-91.7153,42.2971],[-91.5969,42.2959],[-91.4809,42.296],[-91.3649,42.2964]]]},\"properties\":{\"name\":\"Linn\",\"state\":\"IA\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Arizona pronghorn (Antilocapra americana) population numbers have declined over the last century due to unregulated-harvest, population fragmentation, urban expansion, and habitat loss. Captive breeding, reintroductions, and translocations have helped to curb decline and boost population numbers of the endangered Sonoran subspecies (A. a. sonoriensis). To assess the effect of on-going management actions on the Sonoran subspecies, we collected multi-locus genotype data and performed tests of genetic differentiation and population structure in comparison to the non-endangered American subspecies (A. a. americana). We provide updated estimates of genetic diversity and relatedness to serve as a benchmark for future management toward further recovery of Sonoran pronghorn. Management actions have upheld distinction between the two subspecies in Arizona and stemmed further genetic diversity loss while avoiding an increase in inbreeding within the captive-bred Sonoran population.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.498","usgsCitation":"Vaughn, E.E., and Culver, M., 2021, Genetic diversity and structure in Arizona pronghorn following conservation efforts: Conservation Science and Practice, v. 3, no. 10, e498, 9 p., https://doi.org/10.1111/csp2.498.","productDescription":"e498, 9 p.","ipdsId":"IP-119192","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489011,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.498","text":"Publisher Index Page"},{"id":396451,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Cabeza Prieta National Wildlife Refuge.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.96279907226562,\n 32.3822809650579\n ],\n [\n -112.97378540039062,\n 32.507445513754526\n ],\n [\n -113.13858032226562,\n 32.50860363229596\n ],\n [\n -113.14544677734375,\n 32.42402179265739\n ],\n [\n -113.66180419921875,\n 32.41590703229392\n ],\n [\n -113.75930786132811,\n 32.227904590766364\n ],\n [\n -113.51348876953125,\n 32.113985463263816\n ],\n [\n -113.40225219726562,\n 32.09071916431268\n ],\n [\n -113.29513549804688,\n 32.10351636222566\n ],\n [\n -113.27728271484374,\n 32.10467965495091\n ],\n [\n -113.21548461914062,\n 32.13724583390058\n ],\n [\n -113.14544677734375,\n 32.098863043145876\n ],\n [\n -113.08227539062499,\n 32.127942397192314\n ],\n [\n -113.08639526367188,\n 32.20582936513577\n ],\n [\n -112.994384765625,\n 32.20234331330286\n ],\n [\n -113.03146362304688,\n 32.287132632616384\n ],\n [\n -113.04519653320312,\n 32.288293580436644\n ],\n [\n -113.05755615234375,\n 32.36952297435149\n ],\n [\n -113.06716918945312,\n 32.377641904110355\n ],\n [\n -113.06442260742188,\n 32.397356268013105\n ],\n [\n -113.03695678710938,\n 32.397356268013105\n ],\n [\n -113.01223754882812,\n 32.38344069307763\n ],\n [\n -112.96279907226562,\n 32.3822809650579\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Vaughn, Erin E.","contributorId":196145,"corporation":false,"usgs":false,"family":"Vaughn","given":"Erin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":835910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":197693,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":835909,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236986,"text":"70236986 - 2021 - Collaborative recorded data based response studies of four tall buildings in California","interactions":[],"lastModifiedDate":"2024-02-22T17:43:36.060459","indexId":"70236986","displayToPublicDate":"2021-10-21T11:35:22","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Collaborative recorded data based response studies of four tall buildings in California","docAbstract":"Seismic instrumentation, recorded earthquake responses, and collaborative studies of the response records from four tall California buildings are summarized in this summary paper. These buildings include the tallest San Francisco building, the 61-story Salesforce Tower, and the tallest California building, the 73-story Wilshire Grand Tower, as well as a 51-story residential building in Los Angeles and a 24-story government building in San Diego. Various system identification methods are used to analyze the largest earthquake response records retrieved from seismic arrays installed in each of the four buildings. Significant structural dynamics characteristics (fundamental periods and critical damping percentages) are extracted. In general, critical damping percentages for the first mode are <2.5%, consistent with recent studies and recommendations.","largerWorkTitle":"SMIP21 seminar proceedings","language":"English","publisher":"California Department of conservation","usgsCitation":"Daniel Swensen, and Celebi, M., 2021, Collaborative recorded data based response studies of four tall buildings in California, in SMIP21 seminar proceedings, p. 38-48.","productDescription":"11 p.","startPage":"38","endPage":"48","ipdsId":"IP-134253","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":425881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425880,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.conservation.ca.gov/cgs/pages/program-smi/seminar/smip21_toc.aspx","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","city":"Los Angeles, San Diego, San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.27793927666661,\n 32.81175349960459\n ],\n [\n -117.27793927666661,\n 32.66751317352421\n ],\n [\n -116.98543987874795,\n 32.66751317352421\n ],\n [\n -116.98543987874795,\n 32.81175349960459\n ],\n [\n -117.27793927666661,\n 32.81175349960459\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.32851723322638,\n 34.09004541303398\n ],\n [\n -118.32851723322638,\n 33.99086081996401\n ],\n [\n -118.21551894814657,\n 33.99086081996401\n ],\n [\n -118.21551894814657,\n 34.09004541303398\n ],\n [\n -118.32851723322638,\n 34.09004541303398\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.43823029376674,\n 37.80524238604413\n ],\n [\n -122.43823029376674,\n 37.74678153015232\n ],\n [\n -122.38226917764973,\n 37.74678153015232\n ],\n [\n -122.38226917764973,\n 37.80524238604413\n ],\n [\n -122.43823029376674,\n 37.80524238604413\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Daniel Swensen","contributorId":296942,"corporation":false,"usgs":false,"family":"Daniel Swensen","affiliations":[{"id":64249,"text":"CSMIP","active":true,"usgs":false}],"preferred":false,"id":852925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852926,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70262586,"text":"70262586 - 2021 - ShakeAlert® earthquake warning: The challenge of transforming ground motion into protective actions","interactions":[],"lastModifiedDate":"2025-01-24T15:14:50.673249","indexId":"70262586","displayToPublicDate":"2021-10-21T09:09:37","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"ShakeAlert® earthquake warning: The challenge of transforming ground motion into protective actions","docAbstract":"The USGS ShakeAlert® earthquake early warning (EEW) system is operational and providing public alerting in three West Coast states: California, Washington, and Oregon. Since 2006 the USGS has pursued a strategy of incrementally developing and rolling out EEW for increasingly larger areas and uses. As funding from federal and state budgets grew the system became more capable, detection methods were developed and improved, core network sensor stations were built or upgraded, and partners were enlisted to deliver alerts and implement protective actions. In the fall of 2018, the system became sufficiently functional to publicly declare it “open for business” in all three states for use by licensed partners to alert personnel in limited settings and take automated machine-to-machine actions. State-wide public alerting began in California in October of 2019, expanded to Oregon in March of 2021, and to Washington in May of 2021. Today millions of people can receive ShakeAlert-powered EEW through a variety of delivery methods and dozens of machine-to-machine protective systems are in place in transportation systems, utilities, fire stations, schools, hospitals, and public and private buildings. The ShakeAlert System implementation plan calls for a supporting network of 1,675 seismic stations. 1,129 (73%) have been completed and the rest should be done by 2025.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SMIP 2021 seminar on utilization of strong-motion data","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"California Department of Conservation","usgsCitation":"Given, D.D., and West Coast ShakeAlert Project Team, 2021, ShakeAlert® earthquake warning: The challenge of transforming ground motion into protective actions, in Proceedings of SMIP 2021 seminar on utilization of strong-motion data, p. 70-76.","productDescription":"7 p.","startPage":"70","endPage":"76","ipdsId":"IP-133383","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":480811,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.conservation.ca.gov/cgs/pages/program-smi/seminar/smip21_toc.aspx"},{"id":481135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Given, Douglas D. 0000-0002-3277-5121 doug@usgs.gov","orcid":"https://orcid.org/0000-0002-3277-5121","contributorId":201870,"corporation":false,"usgs":true,"family":"Given","given":"Douglas","email":"doug@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"West Coast ShakeAlert Project Team","contributorId":349872,"corporation":false,"usgs":false,"family":"West Coast ShakeAlert Project Team","affiliations":[],"preferred":false,"id":925004,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224935,"text":"sir20205100 - 2021 - Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies","interactions":[],"lastModifiedDate":"2023-03-03T15:45:09.446861","indexId":"sir20205100","displayToPublicDate":"2021-10-21T08:45:00","publicationYear":"2021","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":"2020-5100","displayTitle":"Hydrology and Water Quality of the Great Dismal Swamp, Virginia and North Carolina, and Implications for Hydrologic-Management Goals and Strategies","title":"Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies","docAbstract":"The Great Dismal Swamp is a peat wetland in the Coastal Plain of southeastern Virginia and northeastern North Carolina. Timber harvesting and the construction of ditches to drain the swamp and facilitate the harvesting are collectively implicated in changes that altered the wetland forests, caused subsidence and decomposition of the peat, and increased the risk of fire. In response to these changes, managers have implemented strategies to control water levels and rewet the swamp using a network of 64 adjustable-height, water-control structures on the ditches. Rewetting the swamp is intended to re-establish the original wetland-forest types, reduce the risk of fire, reduce subsidence and decomposition of the peat, enhance peat accretion, and reduce the risk of fire. Knowledge of responses of the swamp to hydrologic controls, however, is critical to developing and implementing effective management goals and strategies. Because the 2008 South One fire reemphasized the need for this knowledge, the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service began studies in 2009 to identify critical hydrologic controls and responses to these controls.
These studies identified water sources, topography, the two-layered hydraulic characteristics of the peat, the absence of peat in some areas, the ditch and road network, water-control structures on the ditches, the Dismal Swamp Canal and associated infrastructure, and wetland forests as the primary hydrologic controls. Precipitation is the only water source across much of the swamp. The eastward flow of streams and groundwater from the Isle of Wight Plain, across the Suffolk scarp, and into the swamp are additional water sources to the western part of the swamp. Vertical differences in the hydraulic characteristics of the peat reflect an upper peat having a high hydraulic conductivity and specific yield overlying a lower peat and sand having lower hydraulic conductivity and specific yield. The upper peat forms the main aquifer for the storage, flow, and release of water from the swamp. Maintaining water in the upper peat is critical to water availability to the wetland forests because of these properties.
Groundwater flows from the swamp into the ditches and the Dismal Swamp Canal where it discharges into nearby streams. Discharge typically is to the closest ditch except where a spoil-pile road that impedes flow intervenes between the swamp and the ditch. When groundwater levels in a ditch are about 2 feet lower than levels in the other three ditches surrounding a part of the swamp, however, most groundwater typically discharges to the ditch having the lower level. This occurs even if a spoil-pile road intervenes between the swamp and the ditch having the lower level. Flow to a single ditch shifts watershed boundaries and groundwater divides toward the ditches having higher water levels and demonstrates how flow and discharge are controlled by ditch water levels. Consequently, managing water levels based on these and other hydrologic controls and responses is critical to achieving management objectives.
The chemistry of water across the swamp shows the effects of the peat. Dissolved organic carbon concentrations in the groundwater are among the highest reported globally, ranging from 55 to 195 milligrams per liter. The pH of groundwater and ditch water is commonly less than 4.0 standard units because of organic acids. A relation between the pH and specific conductance of groundwater and ditch water reflects water sources, flow paths, and the chemical evolution, as waters from the different sources mix and flow along the paths.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205100","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Speiran, G.K., and Wurster, F.C., 2021, Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies: U.S. Geological Survey Scientific Investigations Report 2020-5100, 104 p., https://doi.org/10.3133/sir20205100.","productDescription":"xii, 104 p.","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108950","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":436139,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZVW9C8","text":"USGS data release","linkHelpText":"Hydrologic, water-quality, fire, forest-cover, and other data, the Great Dismal Swamp, Virginia and North Carolina"},{"id":390256,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205100/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2020-5100"},{"id":390255,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5100/images"},{"id":390252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5100/coverthb.jpg"},{"id":390253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5100/sir20205100.pdf","text":"Report","size":"20.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5100"},{"id":390254,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5100/sir20205100.XML"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Great Dismal Swamp","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.651611328125,\n 36.575835338491736\n ],\n [\n -76.65710449218749,\n 36.41244153535644\n ],\n [\n -76.5142822265625,\n 36.32397712011261\n ],\n [\n -76.3714599609375,\n 36.36822190085109\n ],\n [\n -76.25061035156251,\n 36.4345419190089\n ],\n [\n -76.2835693359375,\n 36.85325222344016\n ],\n [\n -76.4483642578125,\n 36.87522650673951\n ],\n [\n -76.61865234374999,\n 36.84006462037767\n ],\n [\n -76.651611328125,\n 36.575835338491736\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Virginia and West Virginia Water Science Center
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This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030H","usgsCitation":"Ramaseri Chandra, S.N., Christopherson, J., Anderson, C., Stensaas, G.L., and Kim, M., 2021, System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor (ver. 1.2, December 2024), chap. H of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 20 p., https://doi.org/10.3133/ofr20211030H.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126659","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":433262,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/h/versionHist.txt","text":"Version History","size":"2.07 KB","linkFileType":{"id":2,"text":"txt"}},{"id":390427,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/h/images"},{"id":390426,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.xml","size":"75.7 kB","linkFileType":{"id":8,"text":"xml"}},{"id":390425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.pdf","text":"Report","size":"3.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–H"},{"id":390424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/h/coverthb4.jpg"},{"id":464526,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030H/full"}],"edition":"Version 1.0: October 21, 2021; Version 1.1: August 29, 2024; Version 1.2: December 2, 2024","contact":"Director, Earth Resources Observation and Science Center
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47914 252nd Street
Sioux Falls, SD 57198
Many applications in wildlife management require knowledge of the sex of individual animals. The Yuma Ridgway's rail Rallus obsoletus yumanensis is an endangered marsh bird with monomorphic plumage and secretive behaviors, thereby complicating sex determination in field studies. We collected morphometric measurements from 270 adult Yuma Ridgway's rails and quantified the plumage and mandible color of 91 of those individuals throughout their geographic range to evaluate intersexual differences in morphology and coloration. We genetically sexed a subset of adult Yuma Ridgway's rails (n = 101) and used these individuals to determine the optimal combination of measurements (based on discriminant function analyses) to distinguish between sexes. Males averaged significantly larger than females in all measurements, and the optimal discriminant function contained whole leg, culmen, and tail measurements and classified correctly 97.8% (95% CI: 92.5–100.0%) of genetically sexed individuals. We used two additional functions that classified correctly ≥ 95.6% of genetically sexed Yuma Ridgway's rails to assign sex to individuals with missing measurements. These simple models provide managers and researchers with a practical tool to determine the sex of Yuma Ridgway's rails based on morphometric measurements. Although color measurements were not in the most accurate discriminant functions, we quantified subtle intersexual differences in the color of mandibles and greater coverts of Yuma Ridgway's rails. These results document sex-specific patterns in coloration that allow future researchers to test hypotheses to determine the mechanisms underlying sex-based differences in plumage coloration.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-095","usgsCitation":"Conway, C.J., Harrity, E.J., and Michael, L.E., 2021, Sexual dimorphism in morphology and plumage of endangered Yuma Ridgway’s Rails: A model for documenting sex: Journal of Fish and Wildlife Management, v. 12, no. 2, p. 464-474, https://doi.org/10.3996/JFWM-20-095.","productDescription":"11 p.","startPage":"464","endPage":"474","ipdsId":"IP-126314","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450384,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-095","text":"Publisher Index Page"},{"id":397003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.8505859375,\n 32.38923910985902\n ],\n [\n -111.29150390625,\n 32.38923910985902\n ],\n [\n -111.29150390625,\n 36.62434536776987\n ],\n [\n -116.8505859375,\n 36.62434536776987\n ],\n [\n -116.8505859375,\n 32.38923910985902\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrity, E. J.","contributorId":288332,"corporation":false,"usgs":false,"family":"Harrity","given":"E.","email":"","middleInitial":"J.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":837765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michael, L. E.","contributorId":288333,"corporation":false,"usgs":false,"family":"Michael","given":"L.","email":"","middleInitial":"E.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":837766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229718,"text":"70229718 - 2021 - Diets and stable isotope signatures of native and nonnative Leucisid fishes advances our understanding of the Yellowstone Lake food web","interactions":[],"lastModifiedDate":"2022-03-16T16:44:09.597649","indexId":"70229718","displayToPublicDate":"2021-10-20T11:39:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6476,"text":"Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Diets and stable isotope signatures of native and nonnative Leucisid fishes advances our understanding of the Yellowstone Lake food web","docAbstract":"(1) Many forage fishes, such as Leucisids (minnows) have depauperate studies on diet composition or stable isotope signatures, as these fishes are often only viewed as food for higher trophic levels. The need exists to understand and document the diet and stable isotope signatures of Leucisids (redside shiner, longnose dace, lake chub) in relation to the community ecology and food-web dynamics in Yellowstone Lake, especially given an invasive piscivore introduction and potential future effects of climate change on the Yellowstone Lake ecosystem. (2) Diet data collected during summer of 2020 were analyzed by species using proportion by number, frequency of occurrence, and mean proportion by weight, and diet overlap was compared using Schoener’s index (D). Stable isotope (δ15N and δ13C) values were estimated by collecting tissue during the summer of 2020 by species, and isotopic overlap was compared using 40% Bayesian ellipses. (3) Nonnative redside shiners and lake chub had similar diets, and native longnose dace diet differed from the nonnative Leucisids. Diet overlap was also higher between the nonnative Leucisids, and insignificant when comparing native and nonnative Leucisids. No evidence existed to suggest a difference in δ15N signatures among the species. Longnose dace had a mean δ13C signature of −15.65, indicating an decreased reliance on pelagic prey compared to nonnative Leucisids. Nonnative redside shiners and lake chub shared 95% of isotopic niche space, but stable isotope overlap was <25% for comparisons between native longnose dace and the nonnative Leucisids. (4) This study established the diet composition and stable isotope signatures of Leusicids residing in Yellowstone Lake, thus expanding our knowledge of Leucisid feeding patterns and ecology in relation to the native and nonnative species in the ecosystem. We also expand upon our knowledge of Leucisids in North America. Additionally, quantifying minnow diets can provide a baseline for understanding food web response to invasive suppression management actions.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes6040051","usgsCitation":"Glassic, H., Guy, C.S., and Koel, T., 2021, Diets and stable isotope signatures of native and nonnative Leucisid fishes advances our understanding of the Yellowstone Lake food web: Fishes, v. 6, no. 4, 51, 10 p., https://doi.org/10.3390/fishes6040051.","productDescription":"51, 10 p.","ipdsId":"IP-130868","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450387,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes6040051","text":"Publisher Index Page"},{"id":397183,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Lake Yellowstone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58837890625,\n 44.23929609118664\n ],\n [\n -110.15,\n 44.23929609118664\n ],\n [\n -110.15,\n 44.60806814444478\n ],\n [\n -110.58837890625,\n 44.60806814444478\n ],\n [\n -110.58837890625,\n 44.23929609118664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Glassic, Hayley C.","contributorId":288563,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":838086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":838085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koel, Todd M.","contributorId":288564,"corporation":false,"usgs":false,"family":"Koel","given":"Todd M.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":838087,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225534,"text":"pp1867G - 2021 - A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","interactions":[{"subject":{"id":70225534,"text":"pp1867G - 2021 - A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","indexId":"pp1867G","publicationYear":"2021","noYear":false,"chapter":"G","displayTitle":"A Decade of Geodetic Change at Kīlauea’s Summit— Observations, Interpretations, and Unanswered Questions from Studies of the 2008–2018 Halema‘uma‘u Eruption","title":"A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":1}],"isPartOf":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"lastModifiedDate":"2024-06-26T15:54:23.569219","indexId":"pp1867G","displayToPublicDate":"2021-10-20T10:42:24","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1867","chapter":"G","displayTitle":"A Decade of Geodetic Change at Kīlauea’s Summit— Observations, Interpretations, and Unanswered Questions from Studies of the 2008–2018 Halema‘uma‘u Eruption","title":"A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","docAbstract":"On March 19, 2008, a small explosion heralded the onset of an extraordinary eruption at the summit of Kīlauea Volcano. The following 10 years provided unprecedented access to an actively circulating lava lake located within a region monitored by numerous geodetic tools, including Global Navigation Satellite System (GNSS), interferometric synthetic aperture radar (InSAR), tilt, and gravity. These datasets revealed a range of processes occurring on widely different timescales. Over years, pressure change within the summit magmatic system, determined from ground deformation and lava-lake surface height, seems to have been governed by broad variations in the rate of magma supply from the mantle to the volcano’s shallow magmatic system, as well as changes in the efficiency of East Rift Zone (ERZ) magma transport and eruption. Over weeks to months, intrusions at the summit and along the ERZ, where new eruptive vents commonly formed and intrusions were primed by extension from south-flank motion, were a result of short-term increases in magma supply or waning lava effusion from the ERZ. Waning lava effusion caused magma to back up behind the ERZ eruptive vent all the way to the summit. ERZ intrusions and eruptions caused rapid depressurization of the summit magmatic system, whereas summit intrusions resulted in complex deformation patterns as magma moved to and from two main sub-caldera storage areas. Over hours to days, pressure changes were caused by episodic deflation-inflation (DI) events and possibly small summit intrusions, and deformation of the rim of the summit eruptive vent revealed instabilities that indicated an increased potential for collapse and minor explosive activity. Finally, over timescales of minutes to hours, gas pistoning, summit explosions, very-long-period seismic events, and even the airborne eruptive plume had clear manifestations in geodetic datasets, providing insights into the causes and consequences of those processes. The diversity and quantity of geodetic observations shed important light on this exceptional and well-documented decade-long summit eruption and its accompanying phenomena, yet numerous questions remain about the causal mechanisms, physical processes, and magmatic conditions associated with eruptive and intrusive activity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1867G","usgsCitation":"Poland, M.P., Miklius, A., Johanson, I.A., and Anderson, K.R., 2021, A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption, chap. G of Patrick, M., Orr, T., Swanson, D., and Houghton, B., eds., The 2008–2018 summit lava lake at Kīlauea Volcano, Hawaiʻi: U.S. Geological Survey Professional Paper 1867, 29 p., https://doi.org/10.3133/pp1867G.","productDescription":"vi, 29 p.","numberOfPages":"29","onlineOnly":"N","ipdsId":"IP-123914","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":390677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1867/g/pp1867g.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1867/g/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.32539367675778,\n 19.37334071336406\n ],\n [\n -155.20797729492188,\n 19.37334071336406\n ],\n [\n -155.20797729492188,\n 19.454938719968595\n ],\n [\n -155.32539367675778,\n 19.454938719968595\n ],\n [\n -155.32539367675778,\n 19.37334071336406\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact HVO
Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue
Suite A-8
Hilo, HI 96720
Increases in nitrogen applications to the land surface since the 1950s have led to a cascade of negative environmental impacts, including degradation of drinking water supplies, nutrient enrichment of aquatic ecosystems and contributions to global climate change. In this study, groundwater, streambed porewater, and stream sampling were used to establish trends in nitrate concentrations and how redox gradients influence nitrate transport across diverse glacial terranes. Decadal sampling has found that elevated nitrate concentrations in shallow groundwater beneath cropland have been sustained for decades. Redox gradients established in the saturated zone using dissolved O2, iron, nitrate and excess N2 from denitrification suggest that nitrate-bearing zones are thin in glacial terranes dominated by fine materials. These thin nitrate-bearing zones lead to suboxic, low nitrate streambed porewater and limit the contributions of nitrate to streams from slow-flow groundwater. In contrast, thick oxic zones in more coarse-grained glacial terranes allow nitrate to reach deeper groundwater, resulting in streambed porewater with elevated nitrate concentrations and causing a large portion of stream nitrate to be derived from slow-flow groundwater. Groundwater age tracer data indicate that denitrification occurs more quickly in the terrane dominated by fine material than in the more coarse-grained terrane. The quicker depletion of nitrate in the more fine-grained terrane suggests that the thinner oxic zone in this terrane is due, in part, to the greater availability and reactivity of electron donors in this terrane than in the more coarse-grained terrane. Groundwater age tracer data and hydrograph separation analysis suggest that saturated zone lag times between when changes in land use practices occur and when changes in stream water are fully observed may vary widely across hydrogeologic settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.150200","usgsCitation":"Tesoriero, A.J., Stratton, L., and Miller, M., 2021, Influence of redox gradients on nitrate transport from the landscape to groundwater and streams: Science of the Total Environment, v. 800, p. 1-12, https://doi.org/10.1016/j.scitotenv.2021.150200.","productDescription":"150200, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-123707","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":436140,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WVKF1K","text":"USGS data release","linkHelpText":"Dissolved Gas Modeling Results for Groundwater Samples Collected in the Western Lake Michigan Drainages and Eastern Iowa Basins Study Areas of the United States: 2007, 2017"},{"id":390680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, Wisconsin","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.658203125,\n 40.59727063442024\n ],\n [\n -86.220703125,\n 40.59727063442024\n ],\n [\n -86.220703125,\n 46.649436163350245\n ],\n [\n -94.658203125,\n 46.649436163350245\n ],\n [\n -94.658203125,\n 40.59727063442024\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"800","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stratton, Laurel E. 0000-0001-8567-8619","orcid":"https://orcid.org/0000-0001-8567-8619","contributorId":215056,"corporation":false,"usgs":true,"family":"Stratton","given":"Laurel E.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":825389,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225518,"text":"70225518 - 2021 - Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA","interactions":[],"lastModifiedDate":"2021-10-20T15:36:49.819571","indexId":"70225518","displayToPublicDate":"2021-10-20T10:26:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA","docAbstract":"Study Region: Rocky Mountains, United States
Study Focus: Groundwater-flow modeling requires estimates of hydraulic properties, namely hydraulic conductivity. Hydraulic conductivity values commonly vary over orders of magnitudes however and estimation may require extensive field campaigns applying slug or pumping tests. As an alternative, specific-capacity tests can be used to estimate hydraulic properties for large areas when benchmarked with slug or pumping tests. This study combined aquifer testing with specific capacity data to estimate hydraulic properties in a large alluvial aquifer.
New hydrological insights for region: In the Wet Mountain Valley, Colorado, both slug tests and pumping tests were conducted, resulting in a likely range of hydraulic-conductivity values. Aquifer-testing results were related to specific-capacity data, a more spatially distributed dataset, to expand the area of aquifer characterization beyond the distribution of wells included in aquifer testing. Specific-capacity data were used in two ways: (1) a regression was built between specific-capacity values and transmissivity derived from aquifer testing; and (2) an iterative method was utilized to estimate transmissivity from specific capacity at all sites (including sites lacking aquifer tests). Study results indicate that there is a statistically significant difference between hydraulic-conductivity values estimated using the two approaches and that the regression method yields systematically greater values. These results indicate that careful consideration of methods that use specific capacity for extrapolating aquifer properties is warranted as bias could be introduced depending on the applied methodology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100949","usgsCitation":"Newman, C.P., Kisfalusi, Z.D., and Holmberg, M.J., 2021, Assessing specific-capacity data and short-term aquifer testing to estimate hydraulic properties in alluvial aquifers of the Rocky Mountains, Colorado, USA: Journal of Hydrology: Regional Studies, v. 38, p. 1-20, https://doi.org/10.1016/j.ejrh.2021.100949.","productDescription":"100949, 20 p.","startPage":"1","endPage":"20","ipdsId":"IP-109533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":450390,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100949","text":"Publisher Index Page"},{"id":436141,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W7DHLY","text":"USGS data release","linkHelpText":"Water-level and well-discharge data related to aquifer testing in Wet Mountain Valley, Colorado, 2019"},{"id":390678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains, Wet Mountain Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.85052490234375,\n 38.31149091244452\n ],\n [\n -105.50033569335938,\n 37.79676317682161\n ],\n [\n -105.08010864257812,\n 37.95394377350263\n ],\n [\n -105.47012329101562,\n 38.449286817153556\n ],\n [\n -105.85052490234375,\n 38.31149091244452\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825390,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kisfalusi, Zachary D. 0000-0001-6016-3213","orcid":"https://orcid.org/0000-0001-6016-3213","contributorId":222422,"corporation":false,"usgs":true,"family":"Kisfalusi","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825391,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmberg, Michael J. 0000-0002-1316-0412 mholmber@usgs.gov","orcid":"https://orcid.org/0000-0002-1316-0412","contributorId":190084,"corporation":false,"usgs":true,"family":"Holmberg","given":"Michael","email":"mholmber@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825482,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224031,"text":"ofr20211089 - 2021 - Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","interactions":[],"lastModifiedDate":"2021-10-20T14:18:57.158711","indexId":"ofr20211089","displayToPublicDate":"2021-10-20T10:20:00","publicationYear":"2021","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":"2021-1089","displayTitle":"Managed Aquifer Recharge Suitability—Regional Screening and Case Studies in Jordan and Lebanon","title":"Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon","docAbstract":"The U.S. Geological Survey, at the request of the U.S. Agency for International Development, led a 5-year regional project to develop and apply methods for water availability and suitability mapping for managed aquifer recharge (MAR) in the Middle East and North Africa region. A regional model of surface runoff for the period from 1984 to 2015 was developed to characterize water availability using remote sensing data on climate, vegetation, and topography in Jordan, Lebanon, and surrounding areas. Surface runoff was accumulated to characterize potential streamflow available for MAR and these data were combined with land surface slope to prepare a regional screening map of MAR suitability, illustrating suitability mapping concepts and methods. The application of the methods is demonstrated by the evaluation of water availability and suitability for potential MAR in study areas in Jordan and Lebanon. Locations suitable for MAR are present in both Jordan and Lebanon, but limitations exist in both countries, related primarily to water availability in Jordan and land areas of suitable terrain in Lebanon. An additional feasibility study including field investigations would likely provide decision makers with essential information for further development of the use of MAR in Jordan, Lebanon, and the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211089","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Goode, D.J., ed., 2021, Managed aquifer recharge suitability—Regional screening and case studies in Jordan and Lebanon: U.S. Geological Survey Open-File Report 2021–1089, 87 p., https://doi.org/10.3133/ofr20211089.","productDescription":"Report: xi, 87 p.; 2 Data Releases","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124064","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":436143,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":390660,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9WDQ4VF","text":"USGS data release","linkHelpText":"- Regional screening for managed aquifer recharge suitability in Jordan, Lebanon, and surrounding areas"},{"id":389216,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971ZVHF","text":"USGS data release","linkHelpText":"Assembly of satellite-based rainfall datasets in situ data and rainfall climatology contours for the MENA region"},{"id":389217,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TXLT1X","text":"USGS data release","linkHelpText":"Modeling accumulated surface runoff and water availability for aquifer storage and recovery in the MENA region from 1984–2015"},{"id":389215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1089/ofr20211089.pdf","text":"Report","size":"22.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1089"},{"id":389214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1089/coverthb.jpg"}],"country":"Jordan, Lebanon","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[35.54567,32.39399],[35.71992,32.70919],[36.83406,32.31294],[38.79234,33.37869],[39.19547,32.16101],[39.00489,32.01022],[37.00217,31.50841],[37.99885,30.5085],[37.66812,30.33867],[37.50358,30.00378],[36.74053,29.86528],[36.50121,29.50525],[36.06894,29.19749],[34.95604,29.35655],[34.9226,29.50133],[35.42092,31.10007],[35.39756,31.48909],[35.54525,31.7825],[35.54567,32.39399]]],[[[35.8211,33.27743],[35.5528,33.26427],[35.46071,33.08904],[35.12605,33.0909],[35.48221,33.90545],[35.97959,34.61006],[35.9984,34.64491],[36.44819,34.59394],[36.61175,34.20179],[36.06646,33.82491],[35.8211,33.27743]]]]},\"properties\":{\"name\":\"Jordan\"}}]}","contact":"U.S. Geological Survey
Office of International Programs
917 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
directoroip@usgs.gov
Habitat loss from land-use change is one of the top causes of declines in wildlife species of concern. As such, it is critical to assess and reassess habitat suitability as land cover and anthropogenic features change for both monitoring and developing current information to inform management decisions. However, there are obstacles that must be overcome to develop consistent assessments through time. A range-wide lek habitat suitability model for the lesser prairie-chicken (Tympanuchus pallidicinctus), currently under review by the U. S. Fish and Wildlife Service for potential listing under the Endangered Species Act) was published in 2016. This model was based on lek data from 2002 to 2012, land cover data ranging from 2001 to 2013, and anthropogenic features from circa 2011, and has been used to help guide lesser prairie-chicken management and anthropogenic development actions. We created a second iteration model based on new lek surveys (2015 to 2019) and updated predictor layers (2016 land cover and cleaned/ updated anthropogenic data) to evaluate changes in lek suitability and to quantify current range-wide habitat suitability. Only three of 11 predictor variables were directly comparable between the iterations, making it difficult to directly assess what predicted changes resulted from changes in model inputs versus actual landscape change. The second iteration model showed a similar positive relationship with land cover and negative with anthropogenic features to the first iteration, but exhibited more variation among candidate models. Range-wide, more suitable habitat was predicted in the second iteration. The Shinnery Oak Ecoregion, however, exhibited a loss in predicted suitable habitat which could be due to predictor source changes. Iterated models such as this are important to ensure current information is being used in conservation and development decisions.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0256633","usgsCitation":"Jarnevich, C.S., Belamaric, P.N., Fricke, K., Houts, M., Rossi, L., Beauprez, G.M., Cooper, B., and Martin, R., 2021, Challenges in updating habitat suitability models: An example with the lesser prairie-chicken: PLoSOne, v. 16, no. 9, e0256633, 19 p., https://doi.org/10.1371/journal.pone.0256633.","productDescription":"e0256633, 19 p.","ipdsId":"IP-120427","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450394,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0256633","text":"Publisher Index Page"},{"id":436145,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MS0QR0","text":"USGS data release","linkHelpText":"Second Iteration of Range Wide Lesser Prairie Chicken Lek Habitat Suitability in 2019, Predicted in Southern Great Plains"},{"id":390673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.64453124999999,\n 31.98944183792288\n ],\n [\n -101.2060546875,\n 31.98944183792288\n ],\n [\n -101.22802734375,\n 34.34343606848294\n ],\n [\n -99.97558593749999,\n 34.361576287484176\n ],\n [\n -96.328125,\n 36.98500309285596\n ],\n [\n -96.328125,\n 40.01078714046552\n ],\n [\n -102.10693359375,\n 40.027614437486655\n ],\n [\n -102.12890625,\n 39.62261494094297\n ],\n [\n -104.0625,\n 39.67337039176558\n ],\n [\n -103.11767578124999,\n 37.03763967977139\n ],\n [\n -103.07373046875,\n 36.4566360115962\n ],\n [\n -105.62255859375,\n 34.65128519895413\n ],\n [\n -105.64453124999999,\n 31.98944183792288\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":825400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belamaric, Pairsa Nicole 0000-0001-7529-0370","orcid":"https://orcid.org/0000-0001-7529-0370","contributorId":267846,"corporation":false,"usgs":true,"family":"Belamaric","given":"Pairsa","email":"","middleInitial":"Nicole","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":47756,"text":"Student contractor to the U.S. Geological Survey Fort Collins Science Center","active":true,"usgs":false}],"preferred":true,"id":825401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fricke, Kent","contributorId":267847,"corporation":false,"usgs":false,"family":"Fricke","given":"Kent","affiliations":[{"id":40289,"text":"Kansas Department of Wildlife, Parks, and Tourism","active":true,"usgs":false}],"preferred":false,"id":825402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Houts, Mike","contributorId":267848,"corporation":false,"usgs":false,"family":"Houts","given":"Mike","email":"","affiliations":[{"id":33109,"text":"Kansas Biological Survey, Lawrence, KS","active":true,"usgs":false},{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":825403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rossi, Liza","contributorId":267849,"corporation":false,"usgs":false,"family":"Rossi","given":"Liza","email":"","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":825404,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beauprez, Grant M.","contributorId":172889,"corporation":false,"usgs":false,"family":"Beauprez","given":"Grant","email":"","middleInitial":"M.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":825405,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cooper, Brett","contributorId":267850,"corporation":false,"usgs":false,"family":"Cooper","given":"Brett","email":"","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":825406,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Russell","contributorId":267876,"corporation":false,"usgs":false,"family":"Martin","given":"Russell","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":825471,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229154,"text":"70229154 - 2021 - Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas","interactions":[],"lastModifiedDate":"2022-03-01T15:14:30.323759","indexId":"70229154","displayToPublicDate":"2021-10-20T09:14:18","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas","docAbstract":"A concentrated animal feeding operation (CAFO) established in Newton County, Arkansas, near Big Creek, a tributary of the Buffalo National River, raised concern about potential degradation of water quality in the karst watershed. In this study, isotopic tools were combined with standard geochemical approaches to characterize nutrient sources and dynamics in the Big Creek watershed. An isotopic and geochemical reference database of potential nutrient sources in the Big Creek watershed was constructed based on samples collected from representative potential sources. Nutrient sources and stream samples were analyzed for delta (δ)15N-NO3, δ18O NO3, and a suite of selected dissolved ions. Data provide evidence of modification of potential local nutrient source signatures by nitrification, atmospheric deposition, evaporation, and denitrification. Samples taken from the CAFO waste pond, a septic system, field and parking lot runoff, fertilizer, and hog manure exhibited different δ15N-NO3 and δ18O-NO3 values as compared to stream samples. Stream δ15N-NO3 and δ18O-NO3 values cannot be explained by direct input of any one of these potential sources without modification of the isotopic composition by mixing or fractionation. Big Creek nitrate isotope values (-3.4 per mil [‰] to 6.7‰ δ15N-NO3 and -7.6 to 9.1‰ δ18O-NO3) were similar to values expected from nitrification of nitrogen stored in soils sampled in the watershed (2.8 to 7.6‰ δ15N-NO3 and 3.4 to 4.8‰ δ18O-NO3).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"conferenceTitle":"2020 KIG workshop","conferenceDate":"October 19-20, 2021","conferenceLocation":"Online","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Sokolosky, K., and Hays, P.D., 2021, Stable isotope and geochemical characterization of nutrient sources and surface water near a confined animal feeding operation in the Big Creek watershed of northwest Arkansas, in U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021, v. 8, Online, October 19-20, 2021, p. 54-63.","productDescription":"10 p.","startPage":"54","endPage":"63","ipdsId":"IP-117025","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":396600,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/publication/sir20205019"},{"id":396602,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","county":"Newton County","otherGeospatial":"Big Creek, Buffalo National River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.6307373046875,\n 36.50522086338427\n ],\n [\n -94.4549560546875,\n 35.40696093270201\n ],\n [\n -91.7578125,\n 35.420391545750746\n ],\n [\n -91.768798828125,\n 36.50522086338427\n ],\n [\n -94.6307373046875,\n 36.50522086338427\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Kuniansky, Eve L. 0000-0002-5581-0225 elkunian@usgs.gov","orcid":"https://orcid.org/0000-0002-5581-0225","contributorId":932,"corporation":false,"usgs":true,"family":"Kuniansky","given":"Eve","email":"elkunian@usgs.gov","middleInitial":"L.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":836807,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Spangler, Lawrence E. 0000-0003-3928-8809 spangler@usgs.gov","orcid":"https://orcid.org/0000-0003-3928-8809","contributorId":973,"corporation":false,"usgs":true,"family":"Spangler","given":"Lawrence","email":"spangler@usgs.gov","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836808,"contributorType":{"id":2,"text":"Editors"},"rank":2}],"authors":[{"text":"Sokolosky, Kelly","contributorId":287479,"corporation":false,"usgs":false,"family":"Sokolosky","given":"Kelly","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":836794,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hays, Phillip D. 0000-0001-5491-9272 pdhays@usgs.gov","orcid":"https://orcid.org/0000-0001-5491-9272","contributorId":4145,"corporation":false,"usgs":true,"family":"Hays","given":"Phillip","email":"pdhays@usgs.gov","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":836795,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225524,"text":"70225524 - 2021 - Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","interactions":[],"lastModifiedDate":"2023-11-08T16:34:39.150126","indexId":"70225524","displayToPublicDate":"2021-10-20T08:25:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning","docAbstract":"Study region: The study was conducted in the Northern Atlantic Coastal Plain aquifer system, eastern USA, an important water supply in a densely populated region.
Study focus: Manganese (Mn), an emerging health concern and common nuisance contaminant in drinking water, is mapped and modeled using the XGBoost machine learning method, predictions of pH and redox conditions from previous models, and other explanatory variables that describe the groundwater flow system and surface characteristics. Methods to address the imbalanced occurrence of elevated and low Mn concentrations are compared and used to more accurately predict concentrations of interest for human health and drinking water quality.
New hydrological insights for the region: Elevated Mn concentrations were more likely in shallow groundwater, close to recharge areas and in topographically low areas where soil or unsaturated processes influence groundwater quality. Predicted concentrations greater than the health threshold of 300 micrograms per liter extended across 17 % of the surficial aquifer area, but across <1% of the areas of underlying aquifers. pH and variables related to flow-system position and near-surface processes were more important predictors than the probability of low dissolved oxygen (DO). Mapped variable influence (SHAP values) showed that both pH and DO variables were related to hydrogeologic conditions. Class weights, which improved the predictive ability for elevated Mn without altering the data, was the preferred method to address class imbalance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2021.100925","usgsCitation":"DeSimone, L.A., and Ransom, K.M., 2021, Manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA—Modeling regional occurrence with pH, redox, and machine learning: Journal of Hydrology: Regional Studies, v. 37, 100925, 20 p., https://doi.org/10.1016/j.ejrh.2021.100925.","productDescription":"100925, 20 p.","ipdsId":"IP-126500","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":450397,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2021.100925","text":"Publisher Index Page"},{"id":436146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M64CD1","text":"USGS data release","linkHelpText":"Data used to model and map manganese in the Northern Atlantic Coastal Plain aquifer system, eastern USA"},{"id":390662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New Jersey, New York, North Carolina, Pennsylvania, Virginia","city":"Baltimore, New York, Philadelphia, Richmond, Washington D.C.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.1142578125,\n 41.22824901518529\n ],\n [\n -73.63037109375,\n 40.94671366508002\n ],\n [\n -75.816650390625,\n 39.78321267821705\n ],\n [\n -78.321533203125,\n 38.30718056188316\n ],\n [\n -78.079833984375,\n 35.200744801724014\n ],\n [\n -77.069091796875,\n 35.06597313798418\n ],\n [\n -76.80541992187499,\n 34.94899072578227\n ],\n [\n -76.475830078125,\n 35.092945313732635\n ],\n [\n -76.387939453125,\n 35.34425514918409\n ],\n [\n -76.036376953125,\n 35.29943548054545\n ],\n [\n -75.509033203125,\n 35.84453450421662\n ],\n [\n -75.882568359375,\n 36.43012234551576\n ],\n [\n -75.904541015625,\n 37.055177106660814\n ],\n [\n -75.860595703125,\n 37.28279464911045\n ],\n [\n -75.201416015625,\n 38.07404145941957\n ],\n [\n -74.893798828125,\n 38.496593518947584\n ],\n [\n -75.289306640625,\n 39.16414104768742\n ],\n [\n -74.94873046875,\n 39.138581990583525\n ],\n [\n -75.069580078125,\n 38.976492485539396\n ],\n [\n -74.87182617187499,\n 38.865374851611634\n ],\n [\n -74.124755859375,\n 39.715638134796336\n ],\n [\n -73.883056640625,\n 40.38839687388361\n ],\n [\n -74.058837890625,\n 40.50544628405211\n ],\n [\n -71.74072265625,\n 40.98819156349393\n ],\n [\n -72.1142578125,\n 41.22824901518529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeSimone, Leslie A. 0000-0003-0774-9607 ldesimon@usgs.gov","orcid":"https://orcid.org/0000-0003-0774-9607","contributorId":195635,"corporation":false,"usgs":true,"family":"DeSimone","given":"Leslie","email":"ldesimon@usgs.gov","middleInitial":"A.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825413,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225698,"text":"70225698 - 2021 - Hierarchical clustering for paired watershed experiments: Case study in southeastern Arizona, U.S.A.","interactions":[],"lastModifiedDate":"2021-11-03T12:50:00.117476","indexId":"70225698","displayToPublicDate":"2021-10-20T07:46:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Hierarchical clustering for paired watershed experiments: Case study in southeastern Arizona, U.S.A.","docAbstract":"Understanding the rates and patterns of tidal wetland elevation changes relative to sea-level is essential for understanding the extent of potential wetland loss over the coming years. Using an enhanced and more flexible modeling framework of an ecosystem model (WARMER-2), we explored sea-level rise (SLR) impacts on wetland elevations and carbon sequestration rates through 2100 by considering plant community transitions, salinity effects on productivity, and changes in sediment availability. We incorporated local experimental results for plant productivity relative to inundation and salinity into a species transition model, as well as site-level estimates of organic matter decomposition. The revised modeling framework includes an improved calibration scheme that more accurately reconstructs soil profiles and incorporates parameter uncertainty through Monte Carlo simulations. Using WARMER-2, we evaluated elevation change in three tidal wetlands in the San Francisco Bay Estuary, CA, USA along an estuarine tidal and salinity gradient with varying scenarios of SLR, salinization, and changes in sediment availability. We also tested the sensitivity of marsh elevation and carbon accumulation rates to different plant productivity functions. Wetland elevation at all three sites was sensitive to changes in sediment availability, but sites with greater initial elevations or space for upland transgression persisted longer under higher SLR rates than sites at lower elevations. Using a multi-species wetland vegetation transition model for organic matter contribution to accretion, WARMER-2 projected increased elevations relative to sea levels (resilience) and higher rates of carbon accumulation when compared with projections assuming no future change in vegetation with SLR. A threshold analysis revealed that all three wetland sites were likely to eventually transition to an unvegetated state with SLR rates above 7 mm/yr. Our results show the utility in incorporating additional estuary-specific parameters to bolster confidence in model projections. The new WARMER-2 modeling framework is widely applicable to other tidal wetland ecosystems and can assist in teasing apart important drivers of wetland elevation change under SLR.
Historically, Cisco Coregonus artedi and Lake Whitefish Coregonus clupeaformis were abundant throughout the Laurentian Great Lakes, but overharvest, habitat degradation, and interactions with exotic species caused most populations to collapse by the mid-1900s. Strict commercial fishery regulations and improved environmental and ecological conditions allowed Cisco to partially recover only in Lake Superior, whereas Lake Whitefish recovered in all the upper Great Lakes (Superior, Michigan, and Huron). The differential responses of Cisco and Lake Whitefish to improved environmental and ecological conditions in lakes Michigan and Huron have led to questions about potential negative interactions between these species. To provide context for fishery managers, we tested for positive and negative correlations between historical (1929–1970) Cisco and Lake Whitefish commercial gill net catch per effort (CPE; kg/km of net) at a variety of spatial scales in Michigan waters of the upper Great Lakes. The three best-fit spatial models—LAKEWIDE, REGIONAL 10, and SIMPLE—all had similar levels of support (scaled second-order Akaike Information Criterion < 3.0), and we used these models to determine whether there was a significant correlation between Cisco and Lake Whitefish CPE (positive and negative). There was either no correlation between Cisco and Lake Whitefish CPE or a positive correlation for most (12 of 13) pairwise (Cisco–Lake Whitefish) comparisons. We identified no strong positive or negative correlations in the lakewide (LAKEWIDE) or reduced (SIMPLE) models. In the regional model (REGIONAL 10), we identified strong and positive correlations between Cisco and Lake Whitefish CPE in two regions (ρ = 0.59–0.71) and a weak negative correlation in one region (ρ = −0.45). Collectively, our findings suggest that Cisco and Lake Whitefish CPE were largely independent of each other; thus, these species likely did not interact to the detriment of one another in Michigan waters of the upper Great Lakes during 1929–1970.
Shorebird use of horseshoe crab (Limulus polyphemus) eggs as food items has been well documented along the Atlantic coast of the United States at northeastern stopover sites such as the Delaware Bay. However, the relationship between migratory shorebirds and horseshoe crab eggs has not been well studied in the South Atlantic Bight. The objective of our study was to assess the spatial and temporal overlap between the density of horseshoe crab eggs and the relative abundance of foraging shorebirds during spring migration in the Cape Romain-Santee Delta Region (CRSD), South Carolina, USA. The CRSD is a site of international importance for shorebirds that supports ∼100,000 shorebirds annually. We also sought to determine if horseshoe crab eggs were present in the diets of shorebirds at these sites. We monitored study plots between March and June 2015–2016 at predicted horseshoe crab spawning sites on beaches throughout Cape Romain National Wildlife Refuge. We conducted weekly shorebird surveys and collected core samples of beach substrate twice per month to measure densities of horseshoe crab eggs. We found a positive correlation between number of foraging shorebirds and horseshoe crab eggs for both years. In a molecular analysis of shorebird fecal samples, 95% of the samples tested contained DNA from horseshoe crab eggs. The spatial and temporal overlap between shorebirds and horseshoe crab eggs, and the dietary analysis of fecal samples, suggest that there are areas of localized horseshoe crab spawning that shorebirds can utilize as a food source during spring in Cape Romain National Wildlife Refuge.
","language":"English","publisher":"The Wilson Ornithological Society","doi":"10.1676/21-00009","usgsCitation":"Takahashi, F., Sanders, F., and Jodice, P.G., 2021, Spatial and temporal overlap between foraging shorebirds and spawning horseshoe crabs (Limulus polyphemus) in the Cape Romain-Santee Delta Region of the U.S. Atlantic coast: Wilson Journal of Ornithology, v. 133, no. 1, p. 58-72, https://doi.org/10.1676/21-00009.","productDescription":"15 p.","startPage":"58","endPage":"72","ipdsId":"IP-124095","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":450407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1676/21-00009","text":"Publisher Index Page"},{"id":396758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Cape Romain National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.63027954101562,\n 32.88881315761995\n ],\n [\n -79.5794677734375,\n 32.90466807419695\n ],\n [\n -79.57191467285156,\n 32.931182680502246\n ],\n [\n -79.48986053466797,\n 33.00866349457558\n ],\n [\n -79.47235107421875,\n 33.005208549965474\n ],\n [\n -79.46102142333984,\n 33.00981511270531\n ],\n [\n -79.42531585693358,\n 33.01499720819903\n ],\n [\n -79.4036865234375,\n 33.01643662512605\n ],\n [\n -79.40059661865234,\n 33.00779977111022\n ],\n [\n -79.37828063964842,\n 33.00808767987181\n ],\n [\n -79.36248779296874,\n 33.00779977111022\n ],\n [\n -79.35081481933594,\n 33.03370779524476\n ],\n [\n -79.34154510498047,\n 33.04752229444311\n ],\n [\n -79.33502197265625,\n 33.066801409996835\n ],\n [\n -79.34154510498047,\n 33.071117051436254\n ],\n [\n -79.35047149658203,\n 33.05183888113547\n ],\n [\n -79.3597412109375,\n 33.05529199813852\n ],\n [\n -79.36866760253906,\n 33.05040004241502\n ],\n [\n -79.37210083007812,\n 33.06565053653404\n ],\n [\n -79.38514709472656,\n 33.067952268409094\n ],\n [\n -79.39098358154297,\n 33.073130945006625\n ],\n [\n -79.39544677734375,\n 33.07284324874736\n ],\n [\n -79.3927001953125,\n 33.07830931680089\n ],\n [\n -79.3985366821289,\n 33.08952807659452\n ],\n [\n -79.3978500366211,\n 33.09240445113745\n ],\n [\n -79.39132690429688,\n 33.09326734515043\n ],\n [\n -79.3927001953125,\n 33.099307366092084\n ],\n [\n -79.38995361328125,\n 33.109660722085074\n ],\n [\n -79.4036865234375,\n 33.099307366092084\n ],\n [\n -79.44557189941406,\n 33.07974769931395\n ],\n [\n -79.46136474609375,\n 33.07773395720986\n ],\n [\n -79.49432373046875,\n 33.06248555690915\n ],\n [\n -79.54032897949219,\n 33.03975190521167\n ],\n [\n -79.55509185791016,\n 33.042629907076225\n ],\n [\n -79.58702087402344,\n 33.016724505691684\n ],\n [\n -79.6343994140625,\n 32.967771310880366\n ],\n [\n -79.65019226074219,\n 32.94501323438982\n ],\n [\n -79.64229583740234,\n 32.936657533381286\n ],\n [\n -79.6395492553711,\n 32.92167292013293\n ],\n [\n -79.63027954101562,\n 32.91129747043409\n ],\n [\n -79.6343994140625,\n 32.90178557318628\n ],\n [\n -79.63027954101562,\n 32.88881315761995\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Takahashi, F.","contributorId":287942,"corporation":false,"usgs":false,"family":"Takahashi","given":"F.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":837222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders, F.","contributorId":287943,"corporation":false,"usgs":false,"family":"Sanders","given":"F.","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":837223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837224,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229677,"text":"70229677 - 2021 - Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distribution","interactions":[],"lastModifiedDate":"2022-09-02T16:41:05.254001","indexId":"70229677","displayToPublicDate":"2021-10-19T06:26:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distribution","docAbstract":"Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.
Karst hydrogeologic systems represent challenging and unique conditions to scientists attempting to study groundwater flow and contaminant transport. Karst terrains are characterized by distinct and beautiful landscapes, caverns, and springs, and many of the exceptional karst areas are designated as national or state parks. The range and complexity of landforms and groundwater flow systems associated with karst terrains are enormous, perhaps more than any other type of aquifer.
The U.S. Geological Survey (USGS) Karst Interest Group (KIG), formed in 2000, is a loosely knit, grassroots organization of USGS and non-USGS scientists and researchers devoted to fostering better communication among scientists working on, or interested in, karst aquifers. The primary mission of the KIG is to encourage and support interdisciplinary collaboration and technology transfer among scientists working in karst areas. To accomplish its mission, the KIG has organized a series of workshops. To date (2021), eight KIG workshops, including the workshop documented in this report, have been held. This workshop is the first virtual workshop. The abstracts and extended abstracts provide a snapshot in time of past and current karst-related studies. The field trip guide is included in the proceedings volume even though the field trip will not occur in person.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205019","usgsCitation":"Kuniansky, E.L., and Spangler, L.E., eds., 2021, U.S. Geological Survey Karst Interest Group Proceedings, October 19–20, 2021: U.S. Geological Survey Scientific Investigations Report 2020–5019, 147 p., https://doi.org/10.3133/sir20205019.","productDescription":"iv, 147 p.","numberOfPages":"147","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114317","costCenters":[{"id":448,"text":"National Water Availability and Use Program","active":false,"usgs":true}],"links":[{"id":390521,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5019/coverthb.jpg"},{"id":390522,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5019/sir20205019.pdf","text":"Report","size":"9.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5019"}],"contact":"Water Mission Area
U.S. Geological Survey
1770 Corporate Drive
Suite 500
Norcross, GA 30093
https://www.usgs.gov/mission-areas/water-resources/science/karst-aquifers