Larval fishes are sensitive to abiotic conditions and provide a direct measure of spawning success. The St. Clair – Detroit River System, a Laurentian Great Lakes connecting channel with a history of environmental degradation, has undergone improvements in habitat and water quality since the 1970s. We compared 2006–2015 ichthyoplankton community data with those collected prior to remediation efforts (1977–1978) to identify patterns in spatial and temporal variability. Both assemblages exhibited a predictable phenology, with taxa from the subfamily Coregoninae dominant in early spring followed by families Osmeridae, Percidae, and Moronidae (May–June) and Cyprinidae and Clupeidae (June–August). While higher densities of larval fish were found in the Detroit River, greater taxa richness and Shannon diversity were observed in the St. Clair River. System wide, 14 new taxa were observed in the 2000s study period. In addition, relative densities of two nonnative species, alewife (Alosa pseudoharengus) and rainbow smelt (Osmerus mordax), declined since the 1970s. Increased larval fish richness and decreased densities of nonnative taxa in the 2000s are consistent with improvements to environmental conditions.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2017-0511","usgsCitation":"Taaja R. Tucker, Roseman, E.F., DeBruyne, R.L., Jeremy J. Pritt, Bennion, D., Hondorp, D.W., and Boase, J.C., 2019, Long-term assessment of ichthyoplankton in a large North American river system reveals changes in fish community dynamics: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 12, p. 2255-2270, https://doi.org/10.1139/cjfas-2017-0511.","productDescription":"16 p.","startPage":"2255","endPage":"2270","ipdsId":"IP-092433","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":468132,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2017-0511","text":"Publisher Index Page"},{"id":365334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Detroit River, Lake St Claire, St Claire River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.5675048828125,\n 41.89409955811395\n ],\n [\n -82.353515625,\n 41.89409955811395\n ],\n [\n -82.353515625,\n 43.0287452513488\n ],\n [\n -83.5675048828125,\n 43.0287452513488\n ],\n [\n -83.5675048828125,\n 41.89409955811395\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"12","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taaja R. 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Pritt","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":765616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennion, David 0000-0003-4927-4195 dbennion@usgs.gov","orcid":"https://orcid.org/0000-0003-4927-4195","contributorId":149533,"corporation":false,"usgs":true,"family":"Bennion","given":"David","email":"dbennion@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765617,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765618,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boase, James C.","contributorId":216809,"corporation":false,"usgs":false,"family":"Boase","given":"James","email":"","middleInitial":"C.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":765619,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204202,"text":"70204202 - 2019 - Evidence of repeated long-distance movements by lake charr Salvelinus namaycush in Lake Huron","interactions":[],"lastModifiedDate":"2019-07-12T08:59:40","indexId":"70204202","displayToPublicDate":"2018-02-20T15:27:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evidence of repeated long-distance movements by lake charr Salvelinus namaycush in Lake Huron","title":"Evidence of repeated long-distance movements by lake charr Salvelinus namaycush in Lake Huron","docAbstract":"Movements and dispersal distances of acoustically-tagged adult lake charr Salvelinus namaycush were estimated based on detections at acoustic receivers in Lake Huron during 2010–2014. Most lake charr were detected only at receivers proximate to their release location or were not detected at all, but 3–9% of tagged lake charr were detected at receivers located over 100 km from their release location. Several fish made extensive repeated migrations within the lake, some at the scale of the entire main basin. Our observations show that some lake charr individuals repeat a similar pattern each year of moving long distances, and some fish were observed to show annual fidelity to presumed foraging sites in the spring at a spatial scale of approximately 200 km. Our telemetry-based estimates were minimum estimates of dispersal, as the placement of receivers within Lake Huron was not optimal for detection of lake charr and did not cover the majority of the lake. Further study of long-distance movement in lake charr is necessary to fully understand the implications of this behavior to lake charr ecology, population dynamics, and management in the Great Lakes.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-018-0714-6","usgsCitation":"Riley, S., Binder, T., Taaja R. Tucker, and Krueger, C.C., 2019, Evidence of repeated long-distance movements by lake charr Salvelinus namaycush in Lake Huron: Environmental Biology of Fishes, v. 101, no. 4, p. 531-545, https://doi.org/10.1007/s10641-018-0714-6.","productDescription":"15 p.","startPage":"531","endPage":"545","ipdsId":"IP-087084","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":365490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.7705078125,\n 42.85180609584705\n ],\n [\n -79.5849609375,\n 42.85180609584705\n ],\n [\n -79.5849609375,\n 46.430285240839964\n ],\n [\n -84.7705078125,\n 46.430285240839964\n ],\n [\n -84.7705078125,\n 42.85180609584705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"101","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Riley, Stephen 0000-0002-8968-8416 sriley@usgs.gov","orcid":"https://orcid.org/0000-0002-8968-8416","contributorId":169479,"corporation":false,"usgs":true,"family":"Riley","given":"Stephen","email":"sriley@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":765965,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Binder, Tom","contributorId":166711,"corporation":false,"usgs":false,"family":"Binder","given":"Tom","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":765966,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taaja R. Tucker","contributorId":169481,"corporation":false,"usgs":false,"family":"Taaja R. Tucker","affiliations":[{"id":25527,"text":"CSS-Dynamac","active":true,"usgs":false}],"preferred":false,"id":765967,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krueger, Charles C.","contributorId":169487,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":765968,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204367,"text":"70204367 - 2019 - Seasonal trophic variation of yellow perch exceeds spatial variation in a large lake basin","interactions":[],"lastModifiedDate":"2019-12-22T14:50:00","indexId":"70204367","displayToPublicDate":"2018-02-10T12:35:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal trophic variation of yellow perch exceeds spatial variation in a large lake basin","docAbstract":"Trophic structuring of complex food webs may vary at multiple spatial and temporal scales, both in terms of direct trophic connections and underlying energy pathways that support production. In large freshwater systems, the prey and primary producers that support individual higher-order consumers may vary across seasons and habitats due to differences in food availability, predator consumption patterns, seasonal succession of organisms at lower trophic levels, and heterogeneous nutrient inputs. We examined spatial and temporal variation in stomach contents, fatty acids, and stable isotopes of yellow perch (Perca flavescens) across seasons and across sites spanning approximately 200 km in Lake Erie's Central Basin (LECB). Stomach contents provided a short-term index of trophic patterns, while biochemical markers (fatty acids and stable isotopes) provided a more temporally integrated description of underlying energy pathways and trophic links. We found limited spatial variation of biochemical indicators and documented seasonal variation for all three trophic indicators, especially fatty acid profiles. Differences in stomach contents were driven by relative chironomid consumption, the most abundant prey resource, while fatty acid profiles were predominantly influenced by seasonal fluctuations in C22:6n-3 (DHA). Seasonal trends were evident in δ13C and δ15N; however, they varied within a narrow range of values. Our findings suggest that adult yellow perch in LECB showed little differentiation in resource use across space in 2014, but their diets and biochemical compositions varied seasonally.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2018.01.007","usgsCitation":"Hrycik, A.R., Collingsworth, P.D., Rogers, M.W., Guffey, S.C., and Hook, T.O., 2019, Seasonal trophic variation of yellow perch exceeds spatial variation in a large lake basin: Journal of Great Lakes Research, v. 44, no. 2, p. 299-310, https://doi.org/10.1016/j.jglr.2018.01.007.","productDescription":"12 p.","startPage":"299","endPage":"310","ipdsId":"IP-082056","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":365800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.69384765625,\n 41.80407814427234\n ],\n [\n -83.408203125,\n 41.343824581185686\n ],\n [\n -82.77099609375,\n 41.21172151054787\n ],\n [\n -81.7822265625,\n 41.1455697310095\n ],\n [\n -80.068359375,\n 41.95131994679697\n ],\n [\n -78.837890625,\n 42.65012181368022\n ],\n [\n -78.68408203124999,\n 43.03677585761058\n ],\n [\n -80.771484375,\n 42.85985981506279\n ],\n [\n -83.03466796874999,\n 42.17968819665961\n ],\n [\n -83.69384765625,\n 41.80407814427234\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hrycik, Allison R. 0000-0002-0870-3398","orcid":"https://orcid.org/0000-0002-0870-3398","contributorId":217379,"corporation":false,"usgs":false,"family":"Hrycik","given":"Allison","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":766675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collingsworth, Paris D.","contributorId":145526,"corporation":false,"usgs":false,"family":"Collingsworth","given":"Paris","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":766676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":766551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guffey, Samuel C.","contributorId":217380,"corporation":false,"usgs":false,"family":"Guffey","given":"Samuel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":766677,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hook, Tomas O.","contributorId":150480,"corporation":false,"usgs":false,"family":"Hook","given":"Tomas","email":"","middleInitial":"O.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":766678,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215340,"text":"70215340 - 2019 - Construction of probabilistic event trees for eruption forecasting at Sinabung volcano, Indonesia 2013–14","interactions":[],"lastModifiedDate":"2020-10-15T19:27:16.575269","indexId":"70215340","displayToPublicDate":"2018-02-08T13:57:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Construction of probabilistic event trees for eruption forecasting at Sinabung volcano, Indonesia 2013–14","docAbstract":"Eruptions of Sinabung volcano, Indonesia have been ongoing since 2013. Since that time, the character of eruptions has changed, from phreatic to phreatomagmatic to magmatic explosive eruptions, and from production of a lava dome that collapsed to a subsequent thick lava flow that slowly ceased to be active, and later, to a new lava dome. As the eruption progressed, event trees were constructed to forecast eruptive behavior six times, with forecast windows that ranged from 2 weeks to 1 year: November 7–10, December 12–14, and December 27, 2013; and January 9–10, May 13, and October 7, 2014. These event trees were successful in helping to frame the forecast scenarios, to collate current monitoring information, and to document outstanding questions and unknowns. The highest probability forecasts closely matched outcomes of eruption size (including extrusion of the first dome), production of pyroclastic density currents, and pyroclastic density current runout distances. Events assigned low probabilities also occurred, including total collapse of the lava dome in January 2014 and production of a small blast pyroclastic density current in February 2014.
Accurate assessment of abundance forms a central challenge in population ecology and wildlife management. Many statistical techniques have been developed to estimate population sizes because populations change over time and space and to correct for the bias resulting from animals that are present in a study area but not observed. The mobility of individuals makes it difficult to design sampling procedures that account for movement into and out of areas with fixed jurisdictional boundaries. Aerial surveys are the gold standard used to obtain data of large mobile species in geographic regions with harsh terrain, but these surveys can be prohibitively expensive and dangerous. Estimating abundance with ground‐based census methods have practical advantages, but it can be difficult to simultaneously account for temporary emigration and observer error to avoid biased results. Contemporary research in population ecology increasingly relies on telemetry observations of the states and locations of individuals to gain insight on vital rates, animal movements, and population abundance. Analytical models that use observations of movements to improve estimates of abundance have not been developed. Here we build upon existing multi‐state mark–recapture methods using a hierarchical N‐mixture model with multiple sources of data, including telemetry data on locations of individuals, to improve estimates of population sizes. We used a state‐space approach to model animal movements to approximate the number of marked animals present within the study area at any observation period, thereby accounting for a frequently changing number of marked individuals. We illustrate the approach using data on a population of elk (Cervus elaphus nelsoni) in Northern Colorado, USA. We demonstrate substantial improvement compared to existing abundance estimation methods and corroborate our results from the ground based surveys with estimates from aerial surveys during the same seasons. We develop a hierarchical Bayesian N‐mixture model using multiple sources of data on abundance, movement and survival to estimate the population size of a mobile species that uses remote conservation areas. The model improves accuracy of inference relative to previous methods for estimating abundance of open populations.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1692","usgsCitation":"Ketz, A.C., Johnson, T.L., Monello, R.J., Mack, J.A., George, J.L., Hooten, M., Kraft, B.R., Wild, M.A., and Hobbs, N.T., 2019, Estimating abundance of an open population with an N-mixture model using auxiliary data on animal movements: Ecological Applications, v. 28, no. 3, p. 816-825, https://doi.org/10.1002/eap.1692.","productDescription":"10 p.","startPage":"816","endPage":"825","ipdsId":"IP-082132","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":365802,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Ketz, Alison C.","contributorId":217310,"corporation":false,"usgs":false,"family":"Ketz","given":"Alison","email":"","middleInitial":"C.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":766559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Therese L.","contributorId":217311,"corporation":false,"usgs":false,"family":"Johnson","given":"Therese","email":"","middleInitial":"L.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monello, Ryan J.","contributorId":217312,"corporation":false,"usgs":false,"family":"Monello","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mack, John A.","contributorId":217313,"corporation":false,"usgs":false,"family":"Mack","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"George, Janet L.","contributorId":217314,"corporation":false,"usgs":false,"family":"George","given":"Janet","email":"","middleInitial":"L.","affiliations":[{"id":36246,"text":"CPW","active":true,"usgs":false}],"preferred":false,"id":766563,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":766558,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kraft, Benjamin R.","contributorId":217315,"corporation":false,"usgs":false,"family":"Kraft","given":"Benjamin","email":"","middleInitial":"R.","affiliations":[{"id":36246,"text":"CPW","active":true,"usgs":false}],"preferred":false,"id":766564,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wild, Margaret A.","contributorId":217316,"corporation":false,"usgs":false,"family":"Wild","given":"Margaret","email":"","middleInitial":"A.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":766565,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hobbs, N. Thompson","contributorId":217317,"corporation":false,"usgs":false,"family":"Hobbs","given":"N.","email":"","middleInitial":"Thompson","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":766566,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70204674,"text":"70204674 - 2019 - Phenotypic plasticity and climate change: Can polar bears respond to longer Arctic summers with an adaptive fast?","interactions":[],"lastModifiedDate":"2019-08-13T07:07:15","indexId":"70204674","displayToPublicDate":"2018-02-01T12:47:52","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Phenotypic plasticity and climate change: Can polar bears respond to longer Arctic summers with an adaptive fast?","docAbstract":"Plasticity in the physiological and behavioural responses of animals to prolonged food shortages may determine the persistence of species under climate warming. This is particularly applicable for species that can “adaptively fast” by conserving protein to protect organ function while catabolizing endogenous tissues. Some Ursids, including polar bears (Ursus maritimus), adaptively fast during winter hibernation—and it has been suggested that polar bears also employ this strategy during summer. We captured 57 adult female polar bears in the Southern Beaufort Sea (SBS) during summer 2008 and 2009 and measured blood variables that indicate feeding, regular fasting, and adaptive fasting. We also assessed tissue δ13C and δ15N to infer diet, and body condition via mass and length. We found that bears on shore maintained lipid and protein stores by scavenging on bowhead whale (Balaena mysticetus) carcasses from human harvest, while those that followed the retreating sea ice beyond the continental shelf were food deprived. They had low ratios of blood urea to creatinine (U:C), normally associated with adaptive fasting. However, they also exhibited low albumin and glucose (indicative of protein loss) and elevated alanine aminotransferase and ghrelin (which fall during adaptive fasting). Thus, the ~ 70% of the SBS subpopulation that spends summer on the ice experiences more of a regular, rather than adaptive, fast. This fast will lengthen as summer ice declines. The resulting protein loss prior to winter could be a mechanism driving the reported correlation between summer ice and polar bear reproduction and survival in the SBS.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-017-4023-0","usgsCitation":"Whiteman, J.P., Harlow, H.J., Durner, G.M., Regher, E.V., Amstrup, S.C., and Ben-David, M., 2019, Phenotypic plasticity and climate change: Can polar bears respond to longer Arctic summers with an adaptive fast?: Oecologia, v. 186, no. 2, p. 369-381, https://doi.org/10.1007/s00442-017-4023-0.","productDescription":"13 p.","startPage":"369","endPage":"381","ipdsId":"IP-073266","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":366390,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"186","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Whiteman, John P.","contributorId":194427,"corporation":false,"usgs":false,"family":"Whiteman","given":"John","email":"","middleInitial":"P.","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":768025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harlow, Henry J.","contributorId":195844,"corporation":false,"usgs":false,"family":"Harlow","given":"Henry","email":"","middleInitial":"J.","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":768026,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":768024,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Regher, Eric V","contributorId":140838,"corporation":false,"usgs":false,"family":"Regher","given":"Eric","email":"","middleInitial":"V","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":768027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amstrup, Steven C.","contributorId":67034,"corporation":false,"usgs":false,"family":"Amstrup","given":"Steven","email":"","middleInitial":"C.","affiliations":[{"id":13182,"text":"Polar Bears International","active":true,"usgs":false}],"preferred":false,"id":768028,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ben-David, Merav","contributorId":190901,"corporation":false,"usgs":false,"family":"Ben-David","given":"Merav","email":"","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":768029,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194829,"text":"sir20185003 - 2019 - Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California","interactions":[],"lastModifiedDate":"2019-02-04T09:40:36","indexId":"sir20185003","displayToPublicDate":"2018-02-01T00:00:00","publicationYear":"2019","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":"2018-5003","title":"Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California","docAbstract":"Beginning in the 1970s, Alameda County Water District began infiltrating imported water through ponds in repurposed gravel quarries at the Quarry Lakes Regional Park, in the Niles Cone groundwater subbasin, to recharge groundwater and to minimize intrusion of saline, San Francisco Bay water into freshwater aquifers. Hydraulic connection between distinct aquifers underlying Quarry Lakes allows water to recharge the upper aquifer system to depths of 400 feet below land surface, and the Deep aquifer to depths of more than 650 feet. Previous studies of the Niles Cone and southern East Bay Plain groundwater subbasins suggested that these two subbasins may be hydraulically connected. Characterization of storage capacities and hydraulic properties of the complex aquifers and the structural and stratigraphic controls on groundwater movement aids in optimal storage and recovery of recharged water and provides information on the ability of aquifers shared by different water management agencies to fulfill competing storage and extraction demands. The movement of recharge water through the Niles Cone groundwater subbasin from Quarry Lakes and the possible hydraulic connection between the Niles Cone and the southern East Bay Plain groundwater subbasins were investigated using interferometric synthetic aperture radar (InSAR), water-chemistry, and isotopic data, including tritium/helium-3, helium-4, and carbon-14 age-dating techniques.
InSAR data collected during refilling of the Quarry Lakes recharge ponds show corresponding ground-surface displacement. Maximum uplift was about 0.8 inches, reasonable for elastic expansion of sedimentary materials experiencing an increase in hydraulic head that resulted from pond refilling. Sodium concentrations increase while calcium and magnesium concentrations in groundwater decrease along groundwater flowpaths from the Niles Cone groundwater subbasin through the Deep aquifer to the northwest toward the southern East Bay Plain groundwater subbasin. Residual effects of pre-1970s intrusion of saline water from San Francisco Bay, including high chloride concentrations in groundwater, are evident in parts of the Niles Cone subbasin. Noble gas recharge temperatures indicate two primary recharge sources (Quarry Lakes and Alameda Creek) in the Niles Cone groundwater subbasin. Although recharge at Quarry Lakes affects hydraulic heads as far as the transition zone between the Niles Cone and East Bay Plain groundwater subbasins (about 5 miles), the effect of recharged water on water quality is only apparent in wells near (less than 2 miles) recharge sources. Groundwater chemistry from upper aquifer system wells near Quarry Lakes showed an evaporated signal (less negative oxygen and hydrogen isotopic values) relative to surrounding groundwater and a tritium concentration (2 tritium units) consistent with recently recharged water from a surface-water impoundment.
Uncorrected carbon-14 activities measured in water sampled from wells in the Niles Cone groundwater subbasin range from 16 to 100 percent modern carbon (pmC). The geochemical reaction modeling software NETPATH was used to interpret carbon-14 ages along a flowpath from Quarry Lakes toward the East Bay Plain groundwater subbasin. Model results indicate that changes in groundwater chemistry are controlled by cation exchange on clay minerals and weathering of primary silicate minerals. Old groundwater (lower carbon-14 activities) is characterized by high dissolved silica and pH. Interpreted carbon-14 ages ranged from 830 to more than 7,000 years before present and are less than helium-4 ages that range from 2,000 to greater than 11,000 years before present. The average horizontal groundwater velocity along the studied flowpath, as calculated using interpreted carbon-14 ages, through the Deep aquifer of the Niles Cone groundwater subbasin is between 3 and 12 feet per year. The groundwater velocity decreases near the boundary of the transition zone to the southern East Bay Plain groundwater subbasin to about 0.5 feet per year. These changes may result from water recharged from different sources converging in flowpaths north of the transition zone, or a boundary to flow between the Niles Cone and southern East Bay Plain groundwater subbasins, likely owing to changes in lithology caused by depositional patterns.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185003","collaboration":"Prepared in cooperation with the East Bay Municipal Utility District, City of Hayward, and Alameda County Water District","usgsCitation":"Teague, Nick, Izbicki, John, Borchers, Jim, Kulongoski, Justin, and Jurgens, Bryant, 2018, Hydrogeologic controls and geochemical indicators of groundwater movement in the Niles Cone and southern East Bay Plain groundwater subbasins, Alameda County, California (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2018–5003, 62 p., https://doi.org/10.3133/sir20185003.","productDescription":"x, 62 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-043410","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":360934,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2018/5003/versionHist.txt"},{"id":351228,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5003/coverthb.jpg"},{"id":351229,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5003/sir20185003_v1.1.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5003"}],"country":"United States","state":"California","county":"Alameda County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.3333,\n 37.5\n ],\n [\n -121.9167,\n 37.5\n ],\n [\n -121.9167,\n 37.8333\n ],\n [\n -122.3333,\n 37.8333\n ],\n [\n -122.3333,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Ver. 1.0: February 2018; Ver. 1.1: February 2019","contact":"Director,
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
We investigated the direct and indirect influence of tides on net ecosystem exchange (NEE) of carbon dioxide (CO2) in a temperate brackish tidal marsh. NEE displayed a tidally driven pattern with obvious characteristics at the multiday scale, with greater net CO2uptake during spring tides than neap tides. Based on the relative mutual information between NEE and biophysical variables, this was driven by a combination of higher water table depth (WTD), cooler air temperature, and lower vapor pressure deficit (VPD) during spring tides relative to neap tides, as the fortnightly tidal cycle not only influenced water levels but also strongly modulated water and air temperature and VPD. Tides also influenced NEE at shorter timescales, with a reduction in nighttime fluxes during growing season spring tides when the higher of the two semidiurnal tides caused inundation at the site. WTD significantly influenced ecosystem respiration (Reco), with lower Reco during spring tides than neap tides. While WTD did not appear to affect ecosystem photosynthesis (gross ecosystem production, GPP) directly, the impact of tides on temperature and VPD influenced GPP, with higher daily light‐use efficiency and photosynthetic activity during spring tides than neap tides when temperature and VPD were lower. The strong direct and indirect influence of tides on NEE across the diel and multiday timescales has important implications for modeling NEE in tidal wetlands and can help inform the timing and frequency of chamber measurements as annual or seasonal net CO2 uptake may be underestimated if measurements are only taken during nonflooded periods.
","language":"English","publisher":"Wiley","doi":"10.1002/2017JG004048","usgsCitation":"Knox, S., Windham-Myers, L., Frank Anderson, Sturtevant, C., and Bergamaschi, B.A., 2019, Direct and indirect effects of tides on ecosystem-scale CO2 exchange in a brackish tidal marsh in Northern California: Journal of Geophysical Research: Biogeosciences, v. 123, no. 3, p. 787-806, https://doi.org/10.1002/2017JG004048.","productDescription":"20 p.","startPage":"787","endPage":"806","ipdsId":"IP-094185","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":365630,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay National Estuarine Research Reserve, Suisun Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.2235870361328,\n 38.065932950547484\n ],\n [\n -122.22427368164064,\n 38.05944549633448\n ],\n [\n -122.1906280517578,\n 38.053498158026564\n ],\n [\n -122.17758178710939,\n 38.03619406237626\n ],\n [\n -122.15217590332031,\n 38.02267239225365\n ],\n [\n -122.13294982910156,\n 38.028622234587964\n ],\n [\n -122.11509704589845,\n 38.038357297980816\n ],\n [\n -122.09587097167967,\n 38.04322434446539\n ],\n [\n -122.08694458007812,\n 38.04755033643351\n ],\n [\n -122.0684051513672,\n 38.05403884511629\n ],\n [\n -122.04574584960938,\n 38.05566088242076\n ],\n [\n -122.02583312988281,\n 38.058364198044636\n ],\n [\n -121.99493408203125,\n 38.05403884511629\n ],\n [\n -121.96609497070312,\n 38.04755033643351\n ],\n [\n -121.94892883300781,\n 38.050794662666895\n ],\n [\n -121.93450927734375,\n 38.05457952821193\n ],\n [\n -121.9207763671875,\n 38.059986139487975\n ],\n [\n -121.91734313964844,\n 38.07025760046315\n ],\n [\n -121.91116333007811,\n 38.067554724225275\n ],\n [\n -121.90910339355467,\n 38.077284611299554\n ],\n [\n -121.91665649414062,\n 38.08268954483802\n ],\n [\n -121.9379425048828,\n 38.07944663265489\n ],\n [\n -121.95236206054688,\n 38.077284611299554\n ],\n [\n -121.96060180664062,\n 38.07674409597339\n ],\n [\n -121.97158813476561,\n 38.07458199472\n ],\n [\n -121.97845458984375,\n 38.07998712800633\n ],\n [\n -122.00042724609374,\n 38.08647276060778\n ],\n [\n -122.02308654785156,\n 38.09674050228651\n ],\n [\n -122.00111389160155,\n 38.10268432504872\n ],\n [\n -121.98875427246092,\n 38.11024849111732\n ],\n [\n -121.9921875,\n 38.131856078273124\n ],\n [\n -122.00935363769531,\n 38.1399572748485\n ],\n [\n -122.03407287597655,\n 38.13725697591337\n ],\n [\n -122.06085205078125,\n 38.135096664819784\n ],\n [\n -122.06634521484374,\n 38.12159327165922\n ],\n [\n -122.06634521484374,\n 38.109708219514644\n ],\n [\n -122.11990356445312,\n 38.05944549633448\n ],\n [\n -122.13020324707033,\n 38.04538737239996\n ],\n [\n -122.135009765625,\n 38.04268357749736\n ],\n [\n -122.15011596679688,\n 38.04322434446539\n ],\n [\n -122.16110229492186,\n 38.043765107439675\n ],\n [\n -122.17071533203125,\n 38.05403884511629\n ],\n [\n -122.17483520507811,\n 38.05944549633448\n ],\n [\n -122.1844482421875,\n 38.065932950547484\n ],\n [\n -122.18994140624999,\n 38.069176461951876\n ],\n [\n -122.1954345703125,\n 38.06052677864718\n ],\n [\n -122.20573425292967,\n 38.06809530746208\n ],\n [\n -122.2235870361328,\n 38.065932950547484\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Knox, Sara 0000-0003-2255-5835","orcid":"https://orcid.org/0000-0003-2255-5835","contributorId":216996,"corporation":false,"usgs":false,"family":"Knox","given":"Sara","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":766227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - 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2019 - Isotopic evidence that nitrogen enrichment intensifies nitrogen losses to the atmosphere from subtropical mangroves","interactions":[],"lastModifiedDate":"2019-08-15T11:54:06","indexId":"70202922","displayToPublicDate":"2018-01-08T11:31:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic evidence that nitrogen enrichment intensifies nitrogen losses to the atmosphere from subtropical mangroves","docAbstract":"Nitrogen (N) enrichment can have large effects on mangroves’ capacity to provide critical ecosystem services by affecting fundamental functions such as N cycling and primary productivity. However, our understanding of excess N input effects on N cycling in mangroves remains quite limited. To advance our understanding of how N enrichment via water or air pollution affects mangroves, we evaluated whether increasing N inputs would decrease biological N fixation (BNF), but intensify N dynamics and N losses to the atmosphere in these systems. We measured N concentrations in sediment and vegetation, rates of BNF in sediment and litter, and net sediment ammonification and nitrification rates. We also evaluated long-term integrated N dynamics and N losses to the atmosphere using the natural abundance of N stable isotopes (δ15N) in the sediment–plant system and in estuarine water. We performed these analyses at non-N-enriched and N-enriched (that is, polluted) fringe and basin mangroves in southeastern Brazil. The δ15N in the sediment–plant system was higher at N-enriched than non-N-enriched fringe sites, indicating increased N losses to the atmosphere from N-enriched sites. However, N concentrations in sediment and vegetation were similar or lower at N-enriched relative to non-N-enriched sites. BNF and net ammonification and nitrification rates were also similar between N-enriched and non-N-enriched sites. Excess N inputs intensified N losses to the atmosphere from mangroves, but N pools, BNF, and net ammonification and nitrification rates were not affected by N enrichment, likely because excess N was quickly lost from the system by direct denitrification and volatilization.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-018-0327-0","usgsCitation":"Reis, C.R., Reed, S.C., Oliveira, R.S., and Nardoto, G.B., 2019, Isotopic evidence that nitrogen enrichment intensifies nitrogen losses to the atmosphere from subtropical mangroves: Ecosystems, v. 22, no. 5, p. 1126-1144, https://doi.org/10.1007/s10021-018-0327-0.","productDescription":"19 p.","startPage":"1126","endPage":"1144","ipdsId":"IP-102196","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":362799,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"São Paulo","otherGeospatial":"Estuarine Lagunar Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -47.471923828125,\n -24.705043861733333\n ],\n 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]\n ]\n }\n }\n ]\n}","volume":"22","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Reis, Carla Roberta Goncalves","contributorId":214647,"corporation":false,"usgs":false,"family":"Reis","given":"Carla","email":"","middleInitial":"Roberta Goncalves","affiliations":[{"id":39103,"text":"Programa de Pós-Graduação em Ecologia, Instituto de Ciências Biológicas, Campus Darcy Ribeiro, Universidade de Brasília, 70910-900, Brasília, Distrito Federal, Brazil","active":true,"usgs":false}],"preferred":false,"id":760483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oliveira, Rafael Silva","contributorId":214648,"corporation":false,"usgs":false,"family":"Oliveira","given":"Rafael","email":"","middleInitial":"Silva","affiliations":[{"id":39104,"text":"Departamento de Biologia Vegetal, Rua Monteiro Lobato 255, Cidade Universitária Zeferino Vaz, Universidade Estadual de Campinas, 13083-862, Barão Geraldo, Campinas, São Paulo, Brazil","active":true,"usgs":false}],"preferred":false,"id":760484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nardoto, Gabriela Bielefeld","contributorId":214649,"corporation":false,"usgs":false,"family":"Nardoto","given":"Gabriela","email":"","middleInitial":"Bielefeld","affiliations":[{"id":39103,"text":"Programa de Pós-Graduação em Ecologia, Instituto de Ciências Biológicas, Campus Darcy Ribeiro, Universidade de Brasília, 70910-900, Brasília, Distrito Federal, Brazil","active":true,"usgs":false}],"preferred":false,"id":760485,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204760,"text":"70204760 - 2019 - Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King","interactions":[],"lastModifiedDate":"2019-09-03T08:16:10","indexId":"70204760","displayToPublicDate":"2017-12-31T12:46:33","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Understanding the genetic characteristics of Wild Brook Trout populations in North Carolina thanks to the guidance of Dr. Tim King","docAbstract":"We genotyped 7,588 brook trout representing 406 collections from across the State of North Carolina (Figure 1) at 12 microsatellite loci (King et al. 2012). The vast majority of
collections appeared to represent single populations, based on general conformance to HardyWeinberg equilibrium and limited evidence for linkage-disequilibrium. Allelic diversity was low to moderate relative to Brook Trout Salvelinus fontinalis populations endemic to higher latitudes. Effective population sizes varied widely among populations, but were often very small and indicate that many populations are at risk of losing diversity through genetic drift. Remarkable levels of genetic differentiation exist among populations, which suggests that little, if any, gene flow occurs among most populations. Analysis of molecular variance (AMOVA) revealed that a substantial portion of the observed genetic variation was attributed to differences among patches (44.8%), and there was some variation (11.2%) even among collections within a single patch. These results, taken in conjunction with high levels of genetic differentiation among populations, suggest that the fundamental unit of management for Brook Trout should be the
population. Interestingly, despite extensive stocking across the state, the vast majority of wild populations show limited evidence of introgression by northern origin hatchery strains. These results represent a valuable baseline for management and restoration efforts, and can be used to (a) select suitable donor streams for translocation efforts, (b) identify streams with low effective population sizes that may be vulnerable to extirpation, and (c) target stocking efforts into watersheds where extensive introgression has already occurred. All data associated with this manuscript has been publicly released (Kazyak et al. 2017).
Of the 24 species in the Auk (or Alcidae) family of seabirds living in the northern hemisphere, 22 reside within the North Pacific Ocean. These “penguins of the north” use their small wings to “fly” underwater, some to more than 200 meters, where they catch and eat a variety of small fish and invertebrates. In terms of sheer numbers (>65 million) and food consumption, the Auks dominate seabird communities on our continental shelves and they serve as indicators of the health of our ocean. If Auk populations are not all thriving, then we should be concerned about the status of the oceans, plankton and fish that normally sustain them. A few Auk “tribes” genera) are abundant and widespread (such as Uria murres and Aethia auklets), and some are rare and isolated such as Synthliboramphus murrelets, including the Japanese “Crested” Murrelet). Only 8 species of Auk breed in Japan, including species that have either widespread or isolated populations in the North Pacific. During the past century, most of these Auks have declined dramatically in Japan from many causes, including the introduction of predatory rats and cats to breeding islands, bycatch in fishing nets, alteration of food supplies by fishing and climate change, oil spills, and destruction of seabird nesting habitats. Widespread species such as the Common Murre and Tufted Puffin were once common in Japan but now breed in low numbers at only a few locations. Probably common in the past, small numbers of the widespread Ancient Murrelet were recently re-discovered breeding at Teuri Island, which is also home to the world’s largest colony of Rhinoceros Auklet, another widespread species. Though common throughout the North Pacific, Pigeon Guillemots, breed only in the southern Kuril Islands. Their population status is unknown, but they were never considered common in Japan. In contrast, Spectacled Guillemots are an example of an uncommon and isolated population of Auk. They nest along coasts of the Sea of Okhotsk and Sea of Japan, and populations have declined in recent decades. The Long-billed Murrelet has a similar distribution to Spectacled Guillemot, and once bred in Hokkaido, but populations appear to have been extirpated. The Japanese Murrelet has a very small world population, and breeds at only a few locations in southern Japan and the Republic of Korea. The international community of research and conservation biologists is greatly concerned about the ability of this species—probably the rarest of all Auks in the world— to maintain its population size. Owing to its small size and high metabolic demand, this species is especially vulnerable to any stress that increases its food requirements such as changing fish stocks, disturbance on feeding or wintering grounds, or changing ocean climate. Immediate management actions are needed to preserve Japanese Murrelets and other Auks in Japan, by such means as eradicating rats and cats on breeding islands, altering fishing gear to minimize bycatch, and reducing human disturbance to nesting habitats. More research and monitoring of Auk populations in Japan is needed to track population trends, and further identify factors responsible for declines. Interaction between governments and biologists at regional and international levels will be mutually beneficial as we all strive to conserve precious resources and biodiversity in the northwest Pacific, and particularly the Japanese islands.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Status and Monitoring of Rare and Threatened Japanese Crested Murrelet","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Marine Bird Restoration Group","usgsCitation":"Piatt, J.F., Nelson, S., and Carter, H.R., 2019, Monitoring and conservation of Japanese Murrelets and related seabirds in Japan, in Status and Monitoring of Rare and Threatened Japanese Crested Murrelet, p. 33-42.","startPage":"33","endPage":"42","ipdsId":"IP-090741","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":365028,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":365027,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://marinebird-restorationgroup.jimdo.com/app/download/11136230791/4_p33-42_Piatt.pdf?t=1510725322"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":761700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, S Kim","contributorId":205442,"corporation":false,"usgs":false,"family":"Nelson","given":"S Kim","affiliations":[{"id":37105,"text":"Oregon State Unversity","active":true,"usgs":false}],"preferred":false,"id":765061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter, Harry R.","contributorId":216125,"corporation":false,"usgs":false,"family":"Carter","given":"Harry","email":"","middleInitial":"R.","affiliations":[{"id":39369,"text":"Carter Biological Consulting","active":true,"usgs":false}],"preferred":false,"id":765062,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227758,"text":"70227758 - 2019 - Remaining populations of an upland stream fish persist in refugia defined by habitat features at multiple scales","interactions":[],"lastModifiedDate":"2022-01-28T13:29:15.856931","indexId":"70227758","displayToPublicDate":"2017-12-07T07:27:14","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Remaining populations of an upland stream fish persist in refugia defined by habitat features at multiple scales","docAbstract":"Conserving stream biota could require strategies that preserve habitats conveying resistance to ecological impacts of changing land use and climate. Retrospective analyses of species’ responses to anthropogenic disturbances can inform such strategies. We developed a hierarchical framework to contrast environmental conditions underlying persistence versus extirpation of an imperilled stream fish, Candy Darter (Etheostoma osburni), over decades of changing land use. The decline of E. osburni may broadly represent the challenge of conserving sensitive freshwater species in intensively used upland environments.
New River drainage, Appalachian Mountains, USA.
We surveyed fish and habitat in historically occupied sites to identify population refugia, and used multivariate and spatial analyses to address three questions: (a) what are the environmental correlates of refugia? (b) are the pathways by which land use impacts instream habitat constrained by catchment- and/or segment-scale features? and (c) are E. osburni distributional dynamics spatially structured and explained by fine sediment and warm stream temperatures?
We confirmed a recently localized distribution similar to other upland species, marked by at least seven extirpations from streams throughout E. osburni's southern range. Catchment-scale features primarily constrained land use and finer-scale habitat, leading to either extirpations or population-supporting refugia defined by features at multiple scales. Refugium habitats contained cooler temperatures and less fine sediment. Rare mismatches between persistence and habitat suitability were explained by network location, suggesting unmeasured environmental gradients and/or dispersal contributed to distributional dynamics.
We provided insight at multiple spatial scales into how aquatic species’ distributions become fragmented and localized. Our results demonstrate that natural landscape heterogeneity imparts spatially variable resistance of sensitive species to intensive land uses. By recognizing the scale-specific features that buffer populations from extirpation, conservation strategies could be tailored to protect naturally occurring refugium habitats and focus restoration in systems where such habitats are broadly lacking.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.12866","usgsCitation":"Dunn, C., and Angermeier, P.L., 2019, Remaining populations of an upland stream fish persist in refugia defined by habitat features at multiple scales: Diversity and Distributions, v. 25, no. 3, p. 385-399, https://doi.org/10.1111/ddi.12866.","productDescription":"15 p.","startPage":"385","endPage":"399","ipdsId":"IP-090855","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.12866","text":"Publisher Index Page"},{"id":395042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.123046875,\n 37.28279464911045\n ],\n [\n -78.20068359374999,\n 37.28279464911045\n ],\n [\n -78.20068359374999,\n 39.757879992021756\n ],\n [\n -81.123046875,\n 39.757879992021756\n ],\n [\n -81.123046875,\n 37.28279464911045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"3","noUsgsAuthors":false,"publicationDate":"2018-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Dunn, Corey G.","contributorId":272531,"corporation":false,"usgs":false,"family":"Dunn","given":"Corey G.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":832056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Angermeier, Paul L. 0000-0003-2864-170X biota@usgs.gov","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":166679,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul","email":"biota@usgs.gov","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832055,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70190431,"text":"sir20175093 - 2019 - Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam","interactions":[],"lastModifiedDate":"2019-12-30T14:46:50","indexId":"sir20175093","displayToPublicDate":"2017-12-01T00:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5093","title":"Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam","docAbstract":"In 2014, the U.S. Geological Survey, in cooperation with the U.S. Department of Defense’s Strategic Environmental Research and Development Program, initiated a project to evaluate the potential impacts of projected climate-change on Department of Defense installations that rely on Guam’s water resources. A major task of that project was to develop a watershed model of southern Guam and a water-balance model for the Fena Valley Reservoir. The southern Guam watershed model provides a physically based tool to estimate surface-water availability in southern Guam. The U.S. Geological Survey’s Precipitation Runoff Modeling System, PRMS-IV, was used to construct the watershed model. The PRMS-IV code simulates different parts of the hydrologic cycle based on a set of user-defined modules. The southern Guam watershed model was constructed by updating a watershed model for the Fena Valley watersheds, and expanding the modeled area to include all of southern Guam. The Fena Valley watershed model was combined with a previously developed, but recently updated and recalibrated Fena Valley Reservoir water-balance model.
Two important surface-water resources for the U.S. Navy and the citizens of Guam were modeled in this study; the extended model now includes the Ugum River watershed and improves upon the previous model of the Fena Valley watersheds. Surface water from the Ugum River watershed is diverted and treated for drinking water, and the Fena Valley watersheds feed the largest surface-water reservoir on Guam. The southern Guam watershed model performed “very good,” according to the criteria of Moriasi and others (2007), in the Ugum River watershed above Talofofo Falls with monthly Nash-Sutcliffe efficiency statistic values of 0.97 for the calibration period and 0.93 for the verification period (a value of 1.0 represents perfect model fit). In the Fena Valley watershed, monthly simulated streamflow volumes from the watershed model compared reasonably well with the measured values for the gaging stations on the Almagosa, Maulap, and Imong Rivers—tributaries to the Fena Valley Reservoir—with Nash-Sutcliffe efficiency values of 0.87 or higher. The southern Guam watershed model simulated the total volume of the critical dry season (January to May) streamflow for the entire simulation period within –0.54 percent at the Almagosa River, within 6.39 percent at the Maulap River, and within 6.06 percent at the Imong River.
The recalibrated water-balance model of the Fena Valley Reservoir generally simulated monthly reservoir storage volume with reasonable accuracy. For the calibration and verification periods, errors in end-of-month reservoir-storage volume ranged from 6.04 percent (284.6 acre-feet or 92.7 million gallons) to –5.70 percent (–240.8 acre-feet or –78.5 million gallons). Monthly simulation bias ranged from –0.48 percent for the calibration period to 0.87 percent for the verification period; relative error ranged from –0.60 to 0.88 percent for the calibration and verification periods, respectively. The small bias indicated that the model did not consistently overestimate or underestimate reservoir storage volume.
In the entirety of southern Guam, the watershed model has a “satisfactory” to “very good” rating when simulating monthly mean streamflow for all but one of the gaged watersheds during the verification period. The southern Guam watershed model uses a more sophisticated climate-distribution scheme than the older model to make use of the sparse climate data, as well as includes updated land-cover parameters and the capability to simulate closed depression areas.
The new Fena Valley Reservoir water-balance model is useful as an updated tool to forecast short-term changes in the surface-water resources of Guam. Furthermore, the now spatially complete southern Guam watershed model can be used to evaluate changes in streamflow and recharge owing to climate or land-cover changes. These are substantial improvements to the previous models of the Fena Valley watershed and Reservoir. Datasets associated with this report are available as a U.S. Geological Survey data release (Rosa and Hay, 2017; DOI:10.5066/F7HH6HV4).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175093","collaboration":"Prepared in cooperation with the U.S. Department of Defense Strategic Environmental Research and Development Program (SERDP)","usgsCitation":"Rosa, S.N., and Hay, L.E., 2019, Fena Valley Reservoir watershed and water-balance model updates and expansion of watershed modeling to southern Guam (ver. 1.1, February 2019): U.S. Geological Survey Scientific Investigations Report 2017–5093, 64 p., https://doi.org/10.3133/sir20175093.","productDescription":"Report: viii, 64 p.","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-081743","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":349631,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5093/coverthb2.jpg"},{"id":349632,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5093/sir20175093.pdf","text":"Report","size":"22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5093 v1.1"},{"id":361066,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5093/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2017-5093 Version History"}],"otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 144.6240234375,\n 13.230587802102518\n ],\n [\n 144.96047973632812,\n 13.230587802102518\n ],\n [\n 144.96047973632812,\n 13.652659349024093\n ],\n [\n 144.6240234375,\n 13.652659349024093\n ],\n [\n 144.6240234375,\n 13.230587802102518\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 2017; Version 1.1: February 2019","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network‐based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row‐standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals (Phoca vitulina) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row‐standardization. We include several take‐home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical models, and not only in explicit spatial settings, but also for more general connectivity models.
Salinity has a major effect on water users in the Colorado River Basin, estimated to cause almost $300 million per year in economic damages. The Colorado River Basin Salinity Control Program implements and manages projects to reduce salinity loads, investing millions of dollars per year in irrigation upgrades, canal projects, and other mitigation strategies. To inform and improve mitigation efforts, there is a need to better understand sources of salinity to streams and how salinity has changed over time. This study explores salinity in the baseflow fraction of streamflow, assessing whether groundwater is a significant contributor of dissolved solids to streams in the Upper Colorado River Basin (UCRB). Chemical hydrograph separation was used to estimate baseflow discharge and baseflow dissolved solids loads at stream gages (n = 69) across the UCRB. On average, it is estimated that 89% of dissolved solids loads originate from the baseflow fraction of streamflow, indicating that subsurface transport processes play a dominant role in delivering dissolved solids to streams in the UCRB. A statistical trend analysis using weighted regressions on time, discharge, and season was used to evaluate changes in baseflow dissolved solids loads in streams (n = 27) from 1986 to 2011. Decreasing trends in baseflow dissolved solids loads were observed at 63% of streams. At the three most downstream sites, Green River at Green River, UT, Colorado River at Cisco, UT, and the San Juan River near Bluff, UT, baseflow dissolved solids loads decreased by a combined 823,000 metric tons (mT), which is approximately 69% of projected basin‐scale decreases in total dissolved solids loads as a result of salinity control efforts. Decreasing trends in baseflow dissolved solids loads suggest that salinity mitigation projects, landscape changes, and/or climate are reducing dissolved solids transported to streams through the subsurface. Notably, the pace and extent of decreases in baseflow dissolved solids loads declined during the most recent decade; average decreasing loads during the 2000s (28,200 mT) were only 54% of average decreasing loads in the 1990s (51,700 mT).
A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, are starting points to develop higher-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as for assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015–2016.
","language":"English","publisher":"MDPI","doi":"10.3390/rs9101065","usgsCitation":"Xiong, J., Thenkabail, P.S., James C. Tilton, Gumma, M.K., Teluguntla, P.G., Oliphant, A., Congalton, R., Yadav, K., and Gorelick, N., 2019, Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 Data on Google Earth Engine: Remote Sensing, v. 9, no. 10, Article 1065: 27 p., https://doi.org/10.3390/rs9101065.","productDescription":"Article 1065: 27 p.","ipdsId":"IP-088538","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":468137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs9101065","text":"Publisher Index Page"},{"id":362333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Africa","volume":"9","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Xiong, Jun 0000-0002-2320-0780 jxiong@usgs.gov","orcid":"https://orcid.org/0000-0002-2320-0780","contributorId":5276,"corporation":false,"usgs":true,"family":"Xiong","given":"Jun","email":"jxiong@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760062,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James C. Tilton","contributorId":214483,"corporation":false,"usgs":false,"family":"James C. Tilton","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":760063,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gumma, Murali Krishna 0000-0002-3760-3935","orcid":"https://orcid.org/0000-0002-3760-3935","contributorId":192327,"corporation":false,"usgs":false,"family":"Gumma","given":"Murali","email":"","middleInitial":"Krishna","affiliations":[],"preferred":false,"id":760064,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teluguntla, Pardhasaradhi G. 0000-0001-8060-9841 pteluguntla@usgs.gov","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":5275,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","email":"pteluguntla@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760065,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":760066,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Congalton, Russell G.","contributorId":84646,"corporation":false,"usgs":true,"family":"Congalton","given":"Russell G.","affiliations":[],"preferred":false,"id":760067,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yadav, Kamini","contributorId":214487,"corporation":false,"usgs":false,"family":"Yadav","given":"Kamini","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":760068,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gorelick, Noel ","contributorId":214496,"corporation":false,"usgs":false,"family":"Gorelick","given":"Noel ","affiliations":[],"preferred":false,"id":760069,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70203515,"text":"70203515 - 2019 - Twenty years (1990–2010) of geodetic monitoring of Galeras volcano (Colombia) from continuous tilt measurements.","interactions":[],"lastModifiedDate":"2019-05-20T08:51:51","indexId":"70203515","displayToPublicDate":"2017-09-15T08:50:49","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Twenty years (1990–2010) of geodetic monitoring of Galeras volcano (Colombia) from continuous tilt measurements.","docAbstract":"Galeras - an andesitic stratovolcano part of the Galeras Volcanic Complex - is one of the most active volcanoes in Colombia. Historic activity is centered on a small-volume cone inside the youngest amphitheater, which breaches the west flank of the volcano. At least 30 confirmed eruption periods have been recorded in the past 480 years, with episodes of unrest ranging from weak fumarolic activity and ash emissions to larger explosive events. The most recent eruption periods, recorded instrumentally since 1988, have been characterized by minor explosive eruptions, and the emplacement of three crater domes and small pyroclastic flow deposits. In this paper, we discuss the evolution of volcanic activity using a 20-year-long record of tilt measurements. In particular, we focus on three episodes of unrest occurred in 1991, 2006 and 2008, when the deformation was clearly associated with shallow magma intrusions, and the emplacement and destruction of crater domes. The depth of the intrusions varied from a few hundred meters (August 2005) to two kilometers (January 2009), while the volume change ranged from 104 m3 (May–October 2009) to 106 m3 (January 2009). A comparison with seismic data indicates that the deformation sources were located within the cloud of hypocenters of the volcano-tectonic events. The lack of a clear correlation between the volume change (and depth) of the sources and the total SO2 flux could indicate that the unrest at Galeras was related to a larger intrusive event with only a small part of the magma erupted in the form of tephra and lava domes.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.03.026","usgsCitation":"Narvaez Medina, L., Arcos, D.F., and Battaglia, M., 2019, Twenty years (1990–2010) of geodetic monitoring of Galeras volcano (Colombia) from continuous tilt measurements.: Journal of Volcanology and Geothermal Research, v. 344, p. 232-245, https://doi.org/10.1016/j.jvolgeores.2017.03.026.","productDescription":"14 p.","startPage":"232","endPage":"245","ipdsId":"IP-077748","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2017.03.026","text":"Publisher Index Page"},{"id":364000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Colombia","otherGeospatial":"Galeras volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.49137878417967,\n 1.1342642839220822\n ],\n [\n -77.22908020019531,\n 1.1342642839220822\n ],\n [\n -77.22908020019531,\n 1.3100054779424755\n ],\n [\n -77.49137878417967,\n 1.3100054779424755\n ],\n [\n -77.49137878417967,\n 1.1342642839220822\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"344","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Narvaez Medina, Lourdes","contributorId":215678,"corporation":false,"usgs":false,"family":"Narvaez Medina","given":"Lourdes","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762960,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arcos, Dario F","contributorId":215679,"corporation":false,"usgs":false,"family":"Arcos","given":"Dario","email":"","middleInitial":"F","affiliations":[{"id":39304,"text":"Colomban Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762961,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Battaglia, Maurizio 0000-0003-4726-5287 mbattaglia@usgs.gov","orcid":"https://orcid.org/0000-0003-4726-5287","contributorId":204742,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":762959,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202783,"text":"70202783 - 2019 - MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types","interactions":[],"lastModifiedDate":"2019-03-26T11:03:41","indexId":"70202783","displayToPublicDate":"2017-09-01T11:02:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types","docAbstract":"Innovative, open, and rapid methods to map crop types over large areas are needed for long-term cropland monitoring. We developed two novel and automated decision tree classification approaches to map crop types across the conterminous United States (U.S.) using MODIS 250 m resolution data: 1) generalized, and 2) year-specific classification. The classification approaches use similarities and dissimilarities in crop type phenologyderived from NDVI time-series data for the two approaches. The year-specific approach uses the training samples from one year and classifies crop types for that year only, whereas the generalized classification approach uses above-average, average, and below-average precipitation years for training to produce crop type maps for one or multiple years more robustly. We produced annual crop type maps using the generalized classification approach for 2001–2014 and the year-specific approach for 2008, 2010, 2011 and 2012. The year-specific classification had overall accuracies > 78%, while the generalized classifier had accuracies > 75% for the conterminous U.S. for 2008, 2010, 2011, and 2012. The generalized classifier enables automated and routine crop type mapping without repeated and expensive ground sample collection year after year. The resulting crop type maps for years prior to 2007 are new and especially important for long-term cropland monitoring and food security analysis because no other map products are currently available for 2001–2007.
We evaluated the impact of predation on juvenile steelhead Oncorhynchus mykiss and yearling and subyearling Chinook Salmon O. tshawytscha by piscivorous waterbirds from 11 different breeding colonies in the Columbia River basin during 2012 and 2014. Fish were tagged with both acoustic tags and PIT tags and were tracked via a network of hydrophone arrays to estimate total smolt mortality (1 – survival) at various spatial and temporal scales during out‐migration. Recoveries of PIT tags on bird colonies, coupled with the last known detections of live fish passing hydrophone arrays, were used to estimate the impact of avian predation relative to total smolt mortality. Results indicated that avian predation was a substantial source of steelhead mortality, with predation probability (proportion of available fish consumed by birds) ranging from 0.06 to 0.28 for fish traveling through the lower Snake River and the lower and middle Columbia River. Predation probability estimates ranged from 0.03 to 0.09 for available tagged yearling Chinook Salmon and from 0.01 to 0.05 for subyearlings. Smolt predation by gulls Larusspp. was concentrated near hydroelectric dams, while predation by Caspian terns Hydroprogne caspia was concentrated within reservoirs. No concentrated areas of predation were identified for double‐crested cormorants Phalacrocorax auritus or American white pelicans Pelecanus erythrorhynchos. Comparisons of total smolt mortality relative to mortality from colonial waterbirds indicated that avian predation was one of the greatest sources of mortality for steelhead and yearling Chinook Salmon during out‐migration. In contrast, avian predation on subyearling Chinook Salmon was generally low and constituted a minor component of total mortality. Our results demonstrate that acoustic and PIT tag technologies can be combined to quantify where and when smolt mortality occurs and the fraction of mortality that is due to colonial waterbird predation relative to non‐avian mortality sources.
","language":"English","publisher":"Wiley","doi":"10.1080/00028487.2016.1150881","usgsCitation":"Evans, A.F., Payton, Q., Turecek, A., Cramer, B., Collis, K., Roby, D.D., Loschl, P.J., Sullivan, L., Skalski, Weiland, M., and Dotson, C., 2019, Avian predation on juvenile Salmonids: Spatial and temporal analysis based on acoustic and passive integrated transponder tags: Transactions of the American Fisheries Society, https://doi.org/10.1080/00028487.2016.1150881.","ipdsId":"IP-071908","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490058,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/dataset/Avian_Predation_on_Juvenile_Salmonids_Spatial_and_Temporal_Analysis_Based_on_Acoustic_and_Passive_Integrated_Transponder_Tags/3471605","text":"External Repository"},{"id":364261,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, Allen F.","contributorId":171691,"corporation":false,"usgs":false,"family":"Evans","given":"Allen","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":763477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Payton, Quinn","contributorId":149990,"corporation":false,"usgs":false,"family":"Payton","given":"Quinn","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":763478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turecek, Aaron aturecek@usgs.gov","contributorId":4940,"corporation":false,"usgs":true,"family":"Turecek","given":"Aaron","email":"aturecek@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":763479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cramer, Bradley D.","contributorId":51562,"corporation":false,"usgs":true,"family":"Cramer","given":"Bradley D.","affiliations":[],"preferred":false,"id":763480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collis, Ken","contributorId":149991,"corporation":false,"usgs":false,"family":"Collis","given":"Ken","email":"","affiliations":[{"id":17879,"text":"Real Time Research, Inc., 231 SW Scalehouse Loop, Suite 101, Bend, OR 97702","active":true,"usgs":false}],"preferred":false,"id":763481,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roby, Daniel D. 0000-0001-9844-0992 droby@usgs.gov","orcid":"https://orcid.org/0000-0001-9844-0992","contributorId":3702,"corporation":false,"usgs":true,"family":"Roby","given":"Daniel","email":"droby@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":763482,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loschl, Peter J.","contributorId":7195,"corporation":false,"usgs":true,"family":"Loschl","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":763483,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sullivan, Leah","contributorId":215942,"corporation":false,"usgs":false,"family":"Sullivan","given":"Leah","email":"","affiliations":[],"preferred":false,"id":763484,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Skalski, John","contributorId":120021,"corporation":false,"usgs":true,"family":"Skalski","suffix":"John","affiliations":[],"preferred":false,"id":763485,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weiland, Mark","contributorId":215944,"corporation":false,"usgs":false,"family":"Weiland","given":"Mark","email":"","affiliations":[],"preferred":false,"id":763486,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Dotson, Curtis","contributorId":215945,"corporation":false,"usgs":false,"family":"Dotson","given":"Curtis","email":"","affiliations":[],"preferred":false,"id":763487,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70203024,"text":"70203024 - 2019 - Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations","interactions":[],"lastModifiedDate":"2019-04-11T16:06:24","indexId":"70203024","displayToPublicDate":"2017-05-09T15:54:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations","docAbstract":"Water and sediment extracts samples were analyzed for extractable hydrocarbons by gas chromatography/mass spectrometry (GC/MS) using an Agilent (Agilent Technologies, Palo Alto, CA, USA) 7890 series GC and 5975 electron ionization (EI) mass selective detector (MSD) operated in scan mode. Agilent ChemStation software was used for data acquisition and analysis (version E.02.00.493 on GC/MS computer and version F.01.03.2357 on laptop for data workup). A 30 m x 250 m x 0.25 m HP-5MS column (95% dimethyl 5% diphenyl polydimethylsiloxane) was used for GC/MS under the following conditions: 1.0 L splitless injection, constant flow of 0.7 mL/min, solvent delay of 7.5 min, injector temperature of 280C, interface at 300C, temperature program of 50-150C at 7C/min, 150-230C at 6C/min, and 230-300C at 3C/min with mass scanned from 35-500 Da.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.02.016","usgsCitation":"Orem, W.H., Varonka, M.S., Crosby, L.M., Haase, K.B., Loftin, K.A., Hladik, M., Akob, D.M., Tatu, C., Mumford, A.C., Jaeschke, J.B., Bates, A.L., Schell, T., and Cozzarelli, I.M., 2019, Organic geochemistry and toxicology of a stream impacted by unconventional oil and gas wastewater disposal operations: Applied Geochemistry, v. 80, p. 155-167, https://doi.org/10.1016/j.apgeochem.2017.02.016.","productDescription":"13 p.","startPage":"155","endPage":"167","ipdsId":"IP-075085","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":436,"text":"National Research Program - 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Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":760839,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70204784,"text":"70204784 - 2019 - A 15-year catalog of more than 1 million low-frequency earthquakes: Tracking tremor and slip along the deep San Andreas Fault","interactions":[],"lastModifiedDate":"2019-08-16T06:58:39","indexId":"70204784","displayToPublicDate":"2017-05-01T06:56:43","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"A 15-year catalog of more than 1 million low-frequency earthquakes: Tracking tremor and slip along the deep San Andreas Fault","docAbstract":"Low-frequency earthquakes (LFEs) are small, rapidly recurring slip events that occur on the deep extensions of some major faults. Their collective activation is often observed as a semi-continuous signal known as tectonic (or non-volcanic) tremor. This manuscript presents a catalog of more than 1 million LFEs detected along the central San Andreas Fault from 2001-2016. These events have been detected via a multi-channel matched-filter search, cross-correlating waveform templates representing 88 different LFE families with continuous seismic data. Together, these source locations span nearly 150 km along the central San Andreas Fault, ranging in depth from ~16-30 km. \nThis accumulating catalog has been the source of numerous studies examining the behavior of these LFE sources and the inferred slip behavior of the deep fault. The relatively high temporal and spatial resolution of the catalog has provided new insights into properties such as tremor migration, recurrence, and triggering by static and dynamic stress perturbations. Collectively, these characteristics are inferred to reflect a very weak fault likely under near-lithostatic fluid pressure, yet the physical processes controlling the stuttering rupture observed as tremor and LFE signals remain poorly understood. This paper aims to document the LFE catalog assembly process and associated caveats, while also updating earlier observations and inferred physical constraints. The catalog itself accompanies this manuscript as part of the electronic supplement, with the goal of providing a useful resource for continued future investigations.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017JB014047","usgsCitation":"Shelly, D.R., 2019, A 15-year catalog of more than 1 million low-frequency earthquakes: Tracking tremor and slip along the deep San Andreas Fault: Journal of Geophysical Research, v. 122, no. 5, p. 3739-3753, https://doi.org/10.1002/2017JB014047.","productDescription":"15 p.","startPage":"3739","endPage":"3753","ipdsId":"IP-083653","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":366580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.95849609375,\n 35.79108281624994\n ],\n [\n -120.95947265624999,\n 35.79108281624994\n ],\n [\n -120.95947265624999,\n 39.9434364619742\n ],\n [\n -124.95849609375,\n 39.9434364619742\n ],\n [\n -124.95849609375,\n 35.79108281624994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":768470,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205836,"text":"70205836 - 2019 - Using a process-based model of pre-eruptive seismic patterns to forecast evolving eruptive styles at Sinabung Volcano, Indonesia","interactions":[],"lastModifiedDate":"2021-08-12T15:55:44.445534","indexId":"70205836","displayToPublicDate":"2017-04-09T07:43:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Using a process-based model of pre-eruptive seismic patterns to forecast evolving eruptive styles at Sinabung Volcano, Indonesia","docAbstract":"Most volcanoes worldwide are not monitored in real-time; for those that are, patterns of pre-eruptive earthquakes coupled with conceptual models of magma ascent enable short-term forecasting of eruption onset. Basic event locations, characterization of background seismicity, and recognition of changes in earthquake types and energy release are most important to successful eruption forecasting. During renewed activity at Sinabung volcano, Indonesia, this approach was used by the Center for Volcanology and Geological Hazards Mitigation (CVGHM) and the USGS Volcano Disaster Assistance Program to forecast eruption onset, identify changes in eruptive styles and raise or lower alert levels and extend or contract evacuation zones. After > 400 years of quiescence, Sinabung began erupting in August 2010. The volcano was unmonitored at the onset of these eruptions, which were phreatic, but soon after a monitoring network was installed by CVGHM. Increasing swarms of high-frequency volcano tectonic (VT) earthquakes were used to forecast continuing phreatic eruptions. Volcanic activity decreased in mid-September 2010, while additional intrusions at depth (inferred from continued distal VT swarms) continued through September 2013, when explosive phreatic eruptions recurred. Explosive eruptions were forecast based on increases in real-time seismic amplitude measurement (RSAM) and VT seismicity. Seismicity changed markedly in late November and early December 2013 with the occurrence of deep earthquakes and an overall transition from low-frequency (LF) dominated and irregular (in time and magnitude) earthquakes to more regular LF and hybrid seismicity – a transition that accompanied the continued rise, eventual emergence and growth of a lava dome in the summit crater. This lava dome was first observed on 18 December. In late December 2013 to early January 2014, the eruptive style changed again as additional ascending magma deformed the summit and the dome grew beyond the capacity of the summit crater, resulting in the en masse collapse of the lava dome (2 Mm3) on 11 January and the largest pyroclastic flow to date. The collapse was forecast on the basis of a several order of magnitude increase in RSAM, continued strong distal VT seismicity, an increase in proximal seismicity, and large-scale observed deformation of the summit area. Similarly, a later collapse of a second summit lava dome on 1 February 2014 was forecast on the basis of increased distal seismicity. Here, we demonstrate how a process-based volcano seismicity model was used in combination with real-time data to forecast the time and magnitude of eruptions, as well as changes in eruption style.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2017.04.004","usgsCitation":"McCausland, W.A., Gunawan, H., White, R.A., Indrastuti, N., Patria, C., Suparman, Y., Putra, A., Triastuty, H., and Hendrasto, M., 2019, Using a process-based model of pre-eruptive seismic patterns to forecast evolving eruptive styles at Sinabung Volcano, Indonesia: Journal of Volcanology and Geothermal Research, v. 382, p. 253-266, https://doi.org/10.1016/j.jvolgeores.2017.04.004.","productDescription":"14 p.","startPage":"253","endPage":"266","ipdsId":"IP-078320","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":468140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2017.04.004","text":"Publisher Index Page"},{"id":368082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sinabung Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 96.01226806640625,\n 3.9300201571114752\n ],\n [\n 97.174072265625,\n 3.9300201571114752\n ],\n [\n 97.174072265625,\n 5.019810836520885\n ],\n [\n 96.01226806640625,\n 5.019810836520885\n ],\n [\n 96.01226806640625,\n 3.9300201571114752\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"382","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCausland, Wendy A. 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":204380,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":772552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gunawan, Hendra","contributorId":194977,"corporation":false,"usgs":false,"family":"Gunawan","given":"Hendra","email":"","affiliations":[],"preferred":false,"id":772553,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Randall A. 0000-0003-4074-8577 rwhite@usgs.gov","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":1993,"corporation":false,"usgs":true,"family":"White","given":"Randall","email":"rwhite@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":772554,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Indrastuti, Novianti","contributorId":204389,"corporation":false,"usgs":false,"family":"Indrastuti","given":"Novianti","email":"","affiliations":[{"id":36928,"text":"Center for Volcanology and Geological Hazard Mitigation, Bandung, Indonesia","active":true,"usgs":false}],"preferred":false,"id":772555,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Patria, Cahya","contributorId":219559,"corporation":false,"usgs":false,"family":"Patria","given":"Cahya","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772558,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suparman, Yasa","contributorId":219560,"corporation":false,"usgs":false,"family":"Suparman","given":"Yasa","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772559,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Putra, Armen","contributorId":219561,"corporation":false,"usgs":false,"family":"Putra","given":"Armen","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772560,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Triastuty, Hetty","contributorId":219558,"corporation":false,"usgs":false,"family":"Triastuty","given":"Hetty","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772557,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hendrasto, Mochammad","contributorId":219557,"corporation":false,"usgs":false,"family":"Hendrasto","given":"Mochammad","email":"","affiliations":[{"id":40024,"text":"Center for Volcanology and Geologic Hazard Mitigation","active":true,"usgs":false}],"preferred":false,"id":772556,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ]}