The Maumee River watershed in the Laurentian Great Lakes Basin has been impacted by decades of pollution and habitat modification due to human settlement and development. As such, the lower 35 km of the Maumee River and several smaller adjacent watersheds comprising over 2000 km2 were designated the Maumee Area of Concern (AOC) under the revised Great Lakes Water Quality Agreement in 1987. As part of pre-rehabilitation assessments in the Maumee AOC, we assessed fish and invertebrate communities in river km 24–11 of the Maumee River to identify: 1) areas that exhibit the highest biodiversity, 2) habitat characteristics associated with high biodiversity areas, 3) areas in need of protection from further degradation, and 4) areas that could feasibly be rehabilitated to increase biodiversity. Based on benthic trawl data, shallow water habitats surrounding large island complexes had the highest fish diversity and catch per unit effort (CPUE). Electrofishing displayed similar fish diversity and CPUE patterns across habitat types early in the study but yielded no discernable fish diversity or CPUE patterns towards the end of our study. Although highly variable among study sites, macroinvertebrate density was greatest in shallow water habitats <2.5 m and around large island complexes. Our results provide valuable baseline data that could act as a foundation for developing rehabilitation strategies in the lower Maumee River and for assessing the effectiveness of future aquatic habitat rehabilitation projects. In addition to increasing in-channel habitat, watershed-scale improvements of water quality might be necessary to ensure rehabilitation strategies are successful.
Habitat loss is a main contributor to fish fauna declines in the southwestern USA. Several studies have defined stream-specific habitat conditions that support the growth and survival of native fish in Arizona to inform stream restoration efforts, yet general habitat use of most individual species across the region is not established. Therefore, we evaluated habitat use of four native fishes, Speckled Dace Rhinichthys osculus, Sonora Sucker Catostomus insignis, Desert Sucker Catostomus clarkii, and Longfin Dace Agosia chrysogaster, across three Arizona streams through the development of habitat suitability criteria (HSC). We developed both stream-specific and generalized HSC for each species. Generalized HSC were calculated as the combination of stream-specific HSC for each species. We then assessed the utility of generalized HSC through transferability among study streams. Also, past HSC studies have not considered the occurrence of nonnative species, so we tested whether the presence of nonnative fishes influenced native fish habitat use through logistic regression models. Fish and habitat data were collected along the Mogollon Rim in Arizona during the 2017 summer field season at base flow conditions. We established minimum microhabitat use for four native Arizona fish species through developing HSC. Most generalized criteria did not transfer among study streams due to variation in habitat availability and fish community structure. Logistic regression analysis showed that the presence of nonnative fishes was inversely related to the presence of two native fish species, which could have influenced habitat use of both species. The lack of transferability across streams as demonstrated in this study confirms that only HSC developed in the stream of interest or in similar undegraded streams with comparable fish communities should be used for restoration efforts. For projects to restore native fishes in streams where nonnative competitors will not dominate, the least degraded similar streams without coexisting nonnative fishes can guide restoration efforts.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10575","usgsCitation":"Nemec, Z.C., Lee, L.N., and Bonar, S.A., 2021, Development and evaluation of habitat suitability criteria for native fishes in three Arizona streams: North American Journal of Fisheries Management, v. 41, no. 3, p. 661-677, https://doi.org/10.1002/nafm.10575.","productDescription":"17 p.","startPage":"661","endPage":"677","ipdsId":"IP-125478","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Blue River, Eagle Creek, Tonto Creek, Verde River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.26904296874999,\n 32.82421110161336\n ],\n [\n -109.072265625,\n 32.82421110161336\n ],\n [\n -109.072265625,\n 35.40696093270201\n ],\n [\n -113.26904296874999,\n 35.40696093270201\n ],\n [\n -113.26904296874999,\n 32.82421110161336\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Nemec, Zach C.","contributorId":288222,"corporation":false,"usgs":false,"family":"Nemec","given":"Zach","email":"","middleInitial":"C.","affiliations":[{"id":56363,"text":"uaz","active":true,"usgs":false}],"preferred":false,"id":837576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Larissa N.","contributorId":288223,"corporation":false,"usgs":false,"family":"Lee","given":"Larissa","email":"","middleInitial":"N.","affiliations":[{"id":56363,"text":"uaz","active":true,"usgs":false}],"preferred":false,"id":837577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837575,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229418,"text":"70229418 - 2021 - Minimal stratigraphic evidence for coseismic coastal subsidence during 2000 yr of megathrust earthquakes at the central Cascadia subduction zone","interactions":[],"lastModifiedDate":"2022-03-07T14:55:02.548792","indexId":"70229418","displayToPublicDate":"2022-03-07T08:43:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10266,"text":"Geosphere (Geological Society of America)","active":true,"publicationSubtype":{"id":10}},"title":"Minimal stratigraphic evidence for coseismic coastal subsidence during 2000 yr of megathrust earthquakes at the central Cascadia subduction zone","docAbstract":"Lithology and microfossil biostratigraphy beneath the marshes of a central Oregon estuary limit geophysical models of Cascadia megathrust rupture during successive earthquakes by ruling out >0.5 m of coseismic coastal subsidence for the past 2000 yr. Although the stratigraphy in cores and outcrops includes as many as 12 peat-mud contacts, like those commonly inferred to record subsidence during megathrust earthquakes, mapping, qualitative diatom analysis, foraminiferal transfer function analysis, and 14C dating of the contacts failed to confirm that any contacts formed through subsidence during great earthquakes. Based on the youngest peat-mud contact’s distinctness, >400 m distribution, ∼0.6 m depth, and overlying probable tsunami deposit, we attribute it to the great 1700 CE Cascadia earthquake and(or) its accompanying tsunami. Minimal changes in diatom assemblages from below the contact to above its probable tsunami deposit suggest that the lower of several foraminiferal transfer function reconstructions of coseismic subsidence across the contact (0.1–0.5 m) is most accurate. The more limited stratigraphic extent and minimal changes in lithology, foraminifera, and(or) diatom assemblages across the other 11 peat-mud contacts are insufficient to distinguish them from contacts formed through small, gradual, or localized changes in tide levels during river floods, storm surges, and gradual sea-level rise. Although no data preclude any contacts from being synchronous with a megathrust earthquake, the evidence is equally consistent with all contacts recording relative sea-level changes below the ∼0.5 m detection threshold for distinguishing coseismic from nonseismic changes.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02254.1","usgsCitation":"Nelson, A., Hawkes, A.D., Sawai, Y., Hotron, B.P., Witter, R., Bradley, L., and Cahill, N., 2021, Minimal stratigraphic evidence for coseismic coastal subsidence during 2000 yr of megathrust earthquakes at the central Cascadia subduction zone: Geosphere (Geological Society of America), v. 17, p. 171-200, https://doi.org/10.1130/GES02254.1.","productDescription":"30 p.","startPage":"171","endPage":"200","ipdsId":"IP-120011","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":449915,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02254.1","text":"Publisher Index Page"},{"id":396786,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","otherGeospatial":"central Cascadia subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -132.275390625,\n 38.34165619279595\n ],\n [\n -122.08007812499999,\n 38.34165619279595\n ],\n [\n -122.08007812499999,\n 52.64306343665892\n ],\n [\n -132.275390625,\n 52.64306343665892\n ],\n [\n -132.275390625,\n 38.34165619279595\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationDate":"2020-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Alan 0000-0001-7117-7098","orcid":"https://orcid.org/0000-0001-7117-7098","contributorId":216700,"corporation":false,"usgs":true,"family":"Nelson","given":"Alan","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":837343,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawkes, Andrea D.","contributorId":192811,"corporation":false,"usgs":false,"family":"Hawkes","given":"Andrea","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":837344,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sawai, Yuki","contributorId":127509,"corporation":false,"usgs":false,"family":"Sawai","given":"Yuki","email":"","affiliations":[{"id":6981,"text":"National Institute of Advanced Industrial Science and Technology, AIST, Japan","active":true,"usgs":false}],"preferred":false,"id":837345,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hotron, Ben P.","contributorId":288083,"corporation":false,"usgs":false,"family":"Hotron","given":"Ben","email":"","middleInitial":"P.","affiliations":[{"id":61708,"text":"Earth Observatory of Singapore and Asian School of the Environment, Nanyang Technological University, 639798, Singapore","active":true,"usgs":false}],"preferred":false,"id":837346,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":4528,"corporation":false,"usgs":true,"family":"Witter","given":"Robert C.","email":"rwitter@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":837347,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bradley, Lee-Ann","contributorId":193406,"corporation":false,"usgs":false,"family":"Bradley","given":"Lee-Ann","affiliations":[],"preferred":false,"id":837348,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cahill, Niamh","contributorId":150754,"corporation":false,"usgs":false,"family":"Cahill","given":"Niamh","email":"","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false},{"id":18091,"text":"University College Dublin","active":true,"usgs":false}],"preferred":false,"id":837349,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229054,"text":"70229054 - 2021 - Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands","interactions":[],"lastModifiedDate":"2022-02-28T15:54:29.873881","indexId":"70229054","displayToPublicDate":"2022-02-28T09:41:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands","docAbstract":"Forests are often affected by management that could influence demographics of breeding and post-breeding birds that reside within. Numerous studies have focused on immediate effects from management on wildlife soon after forestry treatment (e.g., 0–5 years), however, fewer studies have examined changes in focal species abundance over longer durations as a forest regenerates after disturbance. We examined how forest management influenced 18 conifer-associated birds during breeding and post-breeding over the forest regeneration period in a landscape dominated by forestry. To achieve this, we paired avian detection data from point count surveys in lowland conifer and mixed-wood forests with Bayesian distance-removal models and an information-theoretic framework. We estimated abundance and associations with seven common forestry treatment categories applied at the stand scale, years-since-harvest (YSH; 5–120+), and seven vegetation variables measured within stands. Forestry treatment categories and YSH were poor predictors of abundance, and none of the 14 species with good-fitting models had associations with these covariates. Twelve of 13 species with good-fitting models had important associations between abundance and vegetation variables. All vegetation variables were associated with abundance of some species, irrespective of the forestry treatment in which the site occurred, including spruce-fir tree composition (seven species), tree basal area (six species), midstory cover (five species), live crown ratio (three species), shrub cover (three species), tree diameter at breast height (two species), and shrub composition (one species). In a companion study, several species assemblages were associated with vegetation variables (i.e., spruce-fir tree composition, tree basal area, and tree diameter at breast height) that varied with YSH and forestry treatments, suggesting that some forestry treatments may indirectly influence avian abundance when certain vegetation outcomes are achieved. Our results suggest that managers should target species-specific vegetation outcomes rather than more broadly categorized forestry treatment types when managing for individual focal species because of large variations in vegetative outcomes across stands within a forest treatment category. Our study informs management and conservation of biodiversity in regions such as the Atlantic Northern Forest where commercial forestry is the dominant human land use.","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119708","usgsCitation":"Rolek, B.W., Harrison, D.J., Linden, D.W., Loftin, C., and Wood, P.B., 2021, Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands: Forest Ecology and Management, v. 502, p. 1-15, https://doi.org/10.1016/j.foreco.2021.119708.","productDescription":"119708, 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-123856","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":449917,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119708","text":"Publisher Index 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J.","contributorId":210902,"corporation":false,"usgs":false,"family":"Harrison","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":836368,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Linden, Daniel W.","contributorId":171466,"corporation":false,"usgs":false,"family":"Linden","given":"Daniel","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":836369,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836365,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wood, Petra B. 0000-0002-8575-1705 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management and the basis for Lake Erie’s Fish Community Goals and Objectives (FCOs) developed in 2020 (Francis et al. 2020). The 2021 USGS Lake Erie Biological Station annual report is responsive to these FCOs and the U.S. Geological Survey (USGS) obligations via a Memorandum of Understanding (MOU) in 2004 with the GLFC Council of Lake Committees (CLC) to provide scientific information in support of fishery management. Goals for the USGS Great Lakes Deepwater Fish Assessment and Ecological Studies were to monitor long-term changes in the fish community and population dynamics of key fishes of interest to management agencies (MOU 2004). Specific to Lake Erie, expectations of the MOU were sustained investigations of native percids, forage (prey) fish populations, and Lake Trout. Additionally, this work was conducted under the authority of the Great Lakes Fishery Research Authorization Act of 2019.","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Dufour, M.R., Hilling, C.D., Keretz, K.R., Kraus, R.T., Oldham, R.C., Roberts, J., and Schmitt, J., 2021, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2021, 17 p.","productDescription":"17 p.","ipdsId":"IP-138896","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":425702,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"http://www.glfc.org/"},{"id":425722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.94328472654291,\n 42.45303920377259\n ],\n [\n -83.94328472654291,\n 41.175748153693036\n ],\n [\n -82.10856792966806,\n 41.175748153693036\n ],\n [\n -82.10856792966806,\n 42.45303920377259\n ],\n [\n -83.94328472654291,\n 42.45303920377259\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keretz, Kevin R. 0000-0002-4808-8350 kkeretz@usgs.gov","orcid":"https://orcid.org/0000-0002-4808-8350","contributorId":5859,"corporation":false,"usgs":true,"family":"Keretz","given":"Kevin","email":"kkeretz@usgs.gov","middleInitial":"R.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":894892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraus, Richard T. 0000-0001-5280-6530 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0001-5280-6530","contributorId":334185,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oldham, Richard Cole 0000-0002-2331-7612","orcid":"https://orcid.org/0000-0002-2331-7612","contributorId":294345,"corporation":false,"usgs":true,"family":"Oldham","given":"Richard","email":"","middleInitial":"Cole","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894898,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":894897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":894894,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227786,"text":"70227786 - 2021 - Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization","interactions":[],"lastModifiedDate":"2022-01-31T15:46:01.075703","indexId":"70227786","displayToPublicDate":"2022-01-31T09:32:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10069,"text":"Frontiers in Soil Science","active":true,"publicationSubtype":{"id":10}},"title":"Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization","docAbstract":"Wetland soil stocks are important global repositories of carbon (C) but are difficult to quantify and model due to varying sampling protocols, and geomorphic/spatio-temporal discontinuity. Merging scales of soil-survey spatial extents with wetland-specific point-based data offers an explicit, empirical and updatable improvement for regional and continental scale soil C stock assessments. Agency-collected (U.S. Department of Agriculture, U.S. Environmental Protection Agency) and community-contributed soil datasets were compared for representativeness and bias, with the goal of producing a harmonized national map of wetland soil C stocks with error quantification for wetland areas of the conterminous United States (CONUS) identified by the USGS National Landcover Change Dataset (NLCD). This allowed application of an empirical predictive model of SOC density to be applied across the entire CONUS using relational %OC distribution alone. A broken-stick quantile-regression model identified %OC with its relatively high analytical confidence as a key predictor of SOC density in soil segments; soils less than 6%OC (hereafter, mineral wetland soils, 85% of the dataset) had a strong linear relationship of %OC to SOC density (RMSE = 0.0059, ~4% mean RMSE) and soils greater than 6%OC (organic wetland soils, 15% of the dataset) had virtually no predictive relationship of %OC to SOC density (RMSE = 0.0348 g C cm-3, ~56% mean RMSE). Disaggregation by vegetation type (woody v. emergent herbaceous), or region did not alter the breakpoint significantly (6% OC) nor improve model accuracies for inland and tidal wetlands. Similarly, SOC stocks in tidal wetlands were related to %OC, but without a mappable product for disaggregation to improve accuracy by soil class, region or depth. Our layered, harmonized CONUS wetland soil maps have now revised wetland SOC stock estimates downward by 24% (9.5 vs. 12.5Pg C) with the overestimation being entirely an issue of inland, organic wetland soils, (35% lower than SSURGO-derived SOC stocks). Further, SSURGO underestimated soil carbon stocks at depth, as modeled wetland SOC stocks for organic-rich soils showed significant preservation downcore in the NWCA dataset (<3% loss between 0-30 cm and 30-100 cm depths) in contrast to mineral-rich soils (37% downcore stock loss). Future CONUS wetland soil C assessments will benefit from focused attention on improved organic wetland soil measurements, land history, and spatial representativeness.","language":"English","publisher":"Frontiers Media","doi":"10.3389/fsoil.2021.706701","usgsCitation":"Uhran, B.R., Windham-Myers, L., Bliss, N.B., Nahlik, A.M., Sundquist, E.T., and Stagg, C.L., 2021, Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization: Frontiers in Soil Science, v. 1, p. 1-16, https://doi.org/10.3389/fsoil.2021.706701.","productDescription":"706701, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-123603","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience 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,{"id":70222383,"text":"sir20215068 - 2021 - Precipitation-driven flood-inundation mapping of the Little Blue River at Grandview, Missouri","interactions":[],"lastModifiedDate":"2026-04-02T14:16:23.467047","indexId":"sir20215068","displayToPublicDate":"2022-01-07T13:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5068","displayTitle":"Precipitation-Driven Flood-Inundation Mapping of the Little Blue River at Grandview, Missouri","title":"Precipitation-driven flood-inundation mapping of the Little Blue River at Grandview, Missouri","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the City of Grandview, Missouri, assessed flooding of the Little Blue River at Grandview resulting from varying precipitation magnitudes and durations and expected land-cover changes. The precipitation scenarios were used to develop a library of flood-inundation maps that included a 3.5-mile reach of the Little Blue River and tributaries within and adjacent to the city.
A hydrologic model of the upper Little Blue River Basin and a hydraulic model of a selected study reach of the Little Blue River and tributaries were constructed to assess streamflow magnitudes associated with simulated precipitation amounts and the resulting flood-inundation conditions. The U.S. Army Corps of Engineers Hydrologic Engineering Center-Hydrologic Modeling System (HEC–HMS; version 4.4.1) was used to simulate the amount of streamflow produced from a range of rain events. The Hydrologic Engineering Center-River Analysis System (HEC–RAS; version 5.0.7) was then used to construct a steady-state hydraulic model to map resulting areas of flood inundation.
Both models were calibrated to the May 28, 2020, high-flow event that produced a peak streamflow approximating a 10-percent annual exceedance probability (10-year flood-frequency recurrence interval) at the Little Blue River at Grandview streamgage (USGS station 06893750). The calibrated HEC–HMS model was used to simulate streamflows from design rainfall events of 1- to 8-hour durations and ranging from a 100- to 0.2-percent annual exceedance probability. Flood-inundation maps were produced for USGS streamflow stages of 17.0 feet (ft), or near bankfull, to 23.0 ft, or a stage exceeding the 0.2-percent annual exceedance interval flood, using the HEC–RAS model. The consequence of each precipitation duration-frequency value was represented by a 1-ft increment inundation map based on the generated peak streamflow from that rainfall event and the corresponding stage at the reference USGS streamgage.
Four scenarios were developed with the HEC–HMS hydrologic model: (1) current (2016) land cover, normal antecedent soil-moisture conditions; (2) current land cover, wet antecedent soil-moisture conditions; (3) future land cover, normal antecedent soil-moisture conditions; and (4) future land cover, wet antecedent soil-moisture conditions. The future land-cover condition was estimated based on anticipated development in the basin. All precipitation scenarios were input into each of the four land-cover antecedent moisture conditions and then assigned to a resulting flood-inundation map based on the generated peak flow and corresponding stage at the reference streamgage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215068","collaboration":"Prepared in cooperation with City of Grandview, Missouri","usgsCitation":"Heimann, D.C., Voss, J.D., and Rydlund, P.H., Jr., 2021, Precipitation-driven flood-inundation mapping of the Little Blue River at Grandview, Missouri (ver. 1.1, January 2022): U.S. Geological Survey Scientific Investigations Report 2021–5068, 19 p., https://doi.org/10.3133/sir20215068.","productDescription":"Report: viii, 19 p.; 2 Data Releases; Dataset","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-127298","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501949,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111580.htm","linkFileType":{"id":5,"text":"html"}},{"id":394027,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5068/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021–5068 version history"},{"id":387436,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":387435,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JLOM4K","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial data and hydraulic-model archive for evaluation of flood-inundation maps developed for a reach of the Little Blue River at Grandview, Missouri"},{"id":387434,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9G68JQQ","text":"USGS data release","description":"USGS data release","linkHelpText":"Archive of hydrologic models used to generate flood peaks based on selected precipitation durations and recurrence intervals for the Little Blue River Basin, Grandview, Missouri"},{"id":387433,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5068/sir20215068.pdf","text":"Report","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5068"},{"id":387432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5068/coverthb2.jpg"}],"country":"United States","state":"Missouri","city":"Grandview","otherGeospatial":"Little Blue 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1.0: July 2021; Version 1.1: January 2022","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, Missouri 65401
Statistical modeling of water-quality data collected at the Sacramento River at Freeport and San Joaquin River near Vernalis, California, USA, was used to examine trends in concentrations and loads of various forms of dissolved and particulate nitrogen and phosphorus that entered the Sacramento–San Joaquin River Delta (Delta) from upstream sources between 1970 and 2019. Ammonium concentrations and loads decreased at the Sacramento River site from the mid-1970s through 1990 because of the consolidation of wastewater treatment and continuously reduced from the mid-1970s to 2019 at the San Joaquin River site. Current ammonium concentrations are mostly below 4 µM (0.056 mg N L–1) at both sites, a concentration above which reductions in phytoplankton productivity or changes in algal species composition may occur. The Sacramento River at Freeport site is located upstream of the Sacramento Regional County Sanitation District’s treatment facility’s discharge point; nutrient water quality there is representative of upstream sources. Inorganic nitrogen (nitrate plus ammonium) concentrations and loading differed at both sites. At the Sacramento River location, concentrations decrease in the summer agricultural season, reducing the molar ratios of nitrogen to phosphorus.
In contrast, inorganic nitrogen concentrations increase in the San Joaquin River during the agricultural season as a result of irrigation runoff, increasing the molar ratio of nitrogen to phosphorus. This increase suggests a possible nitrogen limitation in the northern Delta and a phosphorus limitation in the southern Delta, as indicated by the molar ratios of bioavailable nitrogen to bioavailable phosphorus. Planned upgrades to the Sacramento Regional Wastewater Treatment Plant (SRWTP) will reduce inorganic nitrogen inputs to the northern Delta. Consequently, the supply of bioavailable nitrogen throughout the upper estuary should diminish. Source modeling of nitrogen and phosphorus identifies agriculture, atmospheric deposition, and wastewater effluent as sources of total nitrogen in the Central Valley. In contrast, geologic sources, agriculture, and wastewater discharge are the primary sources of phosphorus.
","language":"English","publisher":"University of California","doi":"10.15447/sfews.2021v19iss4art6","usgsCitation":"Saleh, D., and Domagalski, J.L., 2021, Concentrations, loads, and associated trends of nutrients entering the Sacramento-San Joaquin Delta, California: San Francisco Estuary and Watershed Science, v. 19, no. 4, p. 1-25, https://doi.org/10.15447/sfews.2021v19iss4art6.","productDescription":"6, 25 p.","startPage":"1","endPage":"25","ipdsId":"IP-114557","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":449932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2021v19iss4art6","text":"Publisher Index Page"},{"id":393859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Freeport, Vernalis","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.629150390625,\n 37.23470197166817\n ],\n [\n -119.0643310546875,\n 37.23470197166817\n ],\n [\n -119.0643310546875,\n 39.11727568585598\n ],\n [\n -123.629150390625,\n 39.11727568585598\n ],\n [\n -123.629150390625,\n 37.23470197166817\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Saleh, Dina 0000-0002-1406-9303 dsaleh@usgs.gov","orcid":"https://orcid.org/0000-0002-1406-9303","contributorId":939,"corporation":false,"usgs":true,"family":"Saleh","given":"Dina","email":"dsaleh@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Domagalski, Joseph L. 0000-0002-6032-757X joed@usgs.gov","orcid":"https://orcid.org/0000-0002-6032-757X","contributorId":1330,"corporation":false,"usgs":true,"family":"Domagalski","given":"Joseph","email":"joed@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829997,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227203,"text":"70227203 - 2021 - Dominant Sonoran Desert plant species have divergent phenological responses to climate change","interactions":[],"lastModifiedDate":"2022-01-04T14:31:38.457341","indexId":"70227203","displayToPublicDate":"2022-01-04T08:19:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9976,"text":"Madroño - A West American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Dominant Sonoran Desert plant species have divergent phenological responses to climate change","docAbstract":"The southwestern U.S. is a global hotspot of climate change. Models project that temperatures will continue to rise through the end of the 21st century, accompanied by significant changes to the hydrological cycle. Within the Sonoran Desert, a limited number of studies have documented climate change impacts on the phenology of native plant species. Much of this phenological work to understand climate change impacts to phenology builds on research conducted nearly three decades ago to define flowering triggers and developmental requirements for native keystone Sonoran Desert woody species. Here we expand on the drivers and explore recent phenological trends for six species using a unique 36-year observational data set. We use statistical models to determine which aspects of climate influence the probability of flowering, and how flowering time may respond to climate change. We move beyond traditional models of phenology by incorporating different metrics of moisture availability in addition to temperature, weather, and climate at several time scales, including daily, weekly, seasonal, and antecedent conditions. Our results provide evidence of a trend towards earlier flowering (on the order of 1–4 days per decade) for five of the six species analyzed, and no trend for one species. The species we evaluated had contrasting phenological responses to different aspects of climate, suggesting individualistic changes in phenology and the potential of divergent plant community flowering patterns under future climate change. Understanding recent changes in flowering phenology and their climatic triggers is important to anticipating whether plant species can attract pollinators, reproduce, and persist within the community under continued climate change.
Land disposal of sewage wastewater through septic systems and cesspools is a major cause of elevated concentrations of nitrogen in the shallow coastal aquifers of southern New England. The discharge of nitrogen from these sources at the coast is affecting the environmental health of coastal saltwater bodies. In response, local, State, and Federal agencies are considering expensive actions to mitigate these effects, including installing municipal sewer systems. To increase the understanding of the effects of municipal sewering on groundwater quality discharging to coastal surface waters, a network of multilevel monitoring wells was established in a densely developed coastal neighborhood on the Maravista peninsula, Falmouth, Massachusetts, which was undergoing conversion from onsite septic disposal to municipal sewering.
The geohydrology of the study area on the peninsula is generally characterized as consisting of fine to coarse, well-sorted sands containing 2.9 to 9.3 meters of fresh groundwater and a flow system characterized by a groundwater divide slightly west of the center of the peninsula. The magnitude of hydraulic gradients at the water table is gently sloping, ranging from 0.000032 to 0.00059, and affected by daily and bimonthly tidal fluctuations from adjacent coastal ponds. On the western side of the divide, upgradient from Little Pond, average linear groundwater velocities and traveltimes along shallow flow paths, estimated from observed hydraulic gradients and estimated aquifer hydraulic conductivity and effective porosity, range from 0.076 to 0.094 meters per day and 7.8 to 9.7 years, respectively.
The groundwater monitoring network consists of 14 profile sites on the peninsula that each include a multilevel sampler for water-quality data collection and a shallow monitoring well for groundwater-level measurements. The study area encompasses about 230 residences that transitioned from onsite septic disposal to municipal sewering between spring 2017 and summer 2019. An additional multilevel sampler that was in a residential coastal setting but not undergoing sewering also was sampled periodically as a reference site.
Elevated nitrogen, as compared to typical uncontaminated, fresh groundwater in the Cape Cod aquifer, predominately as nitrate, was measured in 15 water-quality profiles at nitrate concentrations as great as 26.2 milligrams per liter as nitrogen (n=749; mean and median values were 5.1 and 4.1 milligrams per liter as nitrogen, respectively). At all 14 profile sites and the reference profile site on a nearby peninsula, wastewater effects were denoted by increased nitrate, boron, and specific conductance, and by decreased pH and dissolved oxygen. The highest concentrations of nitrate typically occurred in the deepest one-half of the freshwater zone and in intervals of suboxic and oxic groundwater.
Thickness-weighted mean and maximum nitrate concentrations, and total nitrate mass from four sampling rounds, provided a metric to evaluate expected changes at the 14 profile sites on the peninsula. Nitrate concentrations varied moderately by site between sampling rounds through both the presewering (June 2016 and April 2017) and transitional periods (April 2018 and June 2019). Nitrate concentrations greater than the U.S. Environmental Protection Agency maximum contaminant level for nitrate in drinking water (10 milligrams per liter as nitrogen), were detected at 9 of the 14 profile sites and at the reference site. The average of the mean thickness-weighted nitrate concentrations for the four full sampling rounds was greater than 5.0 milligrams per liter as nitrogen at 8 sites (7 profile sites and the reference site) and greater than 8 milligrams per liter as nitrogen at 3 profile sites. The total nitrate mass per square meter of land area at each profile site ranged from 1,830 to 36,800 milligrams per square meter. Nitrate mass flux, across a 500-meter-long section upgradient from Little Pond and covering about 15 percent of the total pond shoreline length, ranged from 124.3 to 192.6 kilograms per year for the four full sampling rounds under three groundwater-flow conditions.
The expected improvements in groundwater quality in the freshwater zone should be characterized by decreases in concentrations of dissolved total and inorganic nitrogen and common ions such as boron, chloride, and fluoride. A statistical analysis using the Regional Kendall test for sampling points grouped in specific depth ranges confirmed that water-quality changes were statistically significant in at least one depth group during the 3-year sampling period (nitrate: −0.76 milligram per liter per year; specific conductance: −12.1 microsiemens per centimeter at 25 degrees Celsius per year; dissolved oxygen: 0.82 milligram per liter per year); however, the rate at which the water-quality improvements will result in decreases in nitrate mass loads to the coastal ponds primarily depends on groundwater traveltimes and the rate of flushing of wastewater constituents from the aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215130","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency’s Southeast New England Program","usgsCitation":"McCobb, T.D., Barbaro, J.R., LeBlanc, D.R., and Belaval, M., 2021, Evaluating the effects of replacing septic systems with municipal sewers on groundwater quality in a densely developed coastal neighborhood, Falmouth, Massachusetts, 2016–19: U.S. Geological Survey Scientific Investigations Report 2021–5130, 39 p., https://doi.org/10.3133/sir20215130.","productDescription":"Report viii, 39 p.; Data Release; Dataset","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126300","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":393105,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5130/images/"},{"id":393103,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393102,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GEMMN6","text":"USGS data release","linkHelpText":"Baseline groundwater-quality data from a densely developed coastal neighborhood, Falmouth, Massachusetts (2016–2020) (ver. 3.0, April 2021)"},{"id":393101,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5130/sir20215130.pdf","text":"Report","size":"8.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5130"},{"id":393100,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5130/coverthb.jpg"},{"id":393104,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5130/sir20215130.XML"}],"country":"United States","state":"Massachusetts","city":"Falmouth","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.65788269042969,\n 41.52245918082221\n ],\n [\n -70.39627075195312,\n 41.52245918082221\n ],\n [\n -70.39627075195312,\n 41.725205507257016\n ],\n [\n -70.65788269042969,\n 41.725205507257016\n ],\n [\n -70.65788269042969,\n 41.52245918082221\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Earthquake-induced secondary ground failure hazards, such as liquefaction and landslides, result in catastrophic building and infrastructure damage as well as human fatalities. To facilitate emergency responses and mitigate losses, the U.S. Geological Survey provides a rapid hazard estimation system for earthquake-triggered landslides and liquefaction using geospatial susceptibility proxies and ShakeMap ground motion estimates. However, the resolution and accuracy of these models are often limited by coarse-granularity and large uncertainties of available geospatial features provided at a regional scale. Recently, with the advancement of remote sensing technologies, synthetic aperture radar (SAR) images are captured and analyzed to obtain a rapid estimate of earthquake-induced correlation changes between pre- and post-event images. These correlation changes indicate ground failures and building damage t, showing the potential to provide supplementary information for rapid hazard and loss estimation. However, the exact causes of changes in satellite images are not directly ascertained by the DPM alone. For example, changes could be due to building damage, landslides, liquefaction, noise or any combination thereof. More importantly, the occurrence and intensity of landslides, liquefaction, and building damages are spatially correlated, which makes it yet more challenging to distinguish the sources of any such changes.
In this study, we develop a generalized causal graph-based Bayesian Network that models the physical interdependencies between geospatial features, seismic ground failures and building damage, as well as DPMs. Geospatial features provide physical insights for estimating ground failure occurrence while DPMs contain event-specific surface change observations. This physics-informed causal graph incorporate these variables with complex physical relationships in one holistic Bayesian updating scheme to effectively fuse information from both geospatial models and remote sensing data. This framework is scalable and flexible enough to deal with highly complex multi-hazard combinations. We then develop a stochastic variational inference algorithm to jointly update the intractable posterior probabilities of unobserved landslides, liquefaction, and building damage at different locations efficiently. In addition, a local graphical model pruning algorithm is presented to reduce the computational cost of large-scale seismic ground failure estimation. We apply this framework to September 2018 Hokkaido Iburi-Tobu, Japan (M6.6) earthquake and January 2020 Southwest Puerto Rico (M6.4) earthquake to evaluate the performance of our algorithm
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 17th World Conference on Earthquake Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"The 17th World Conference on Earthquake Engineering","conferenceDate":"September 27-October 2, 2021","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan","usgsCitation":"Xu, S., Dimasaka, J., Wald, D.J., and Noh, H., 2021, Bayesian updating of seismic ground failure estimates via causal graphical models and satellite imagery, in Proceedings of the 17th World Conference on Earthquake Engineering, Sendai, Japan, September 27-October 2, 2021, 12 p.","productDescription":"12 p.","ipdsId":"IP-127995","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":426079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421116,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, S.","contributorId":330153,"corporation":false,"usgs":false,"family":"Xu","given":"S.","affiliations":[{"id":78827,"text":"State University of New York at Stony Brook","active":true,"usgs":false}],"preferred":false,"id":884126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dimasaka, J.","contributorId":330154,"corporation":false,"usgs":false,"family":"Dimasaka","given":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":884127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":884128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, H.","contributorId":330155,"corporation":false,"usgs":false,"family":"Noh","given":"H.","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":884129,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225677,"text":"70225677 - 2021 - Multi-period response spectra","interactions":[],"lastModifiedDate":"2022-04-18T16:30:04.442385","indexId":"70225677","displayToPublicDate":"2021-12-31T11:29:28","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"title":"Multi-period response spectra","docAbstract":"Multi-period response spectra (MPRS) are incorporated in the development of seismic design ground motions in the 2020 edition of the NEHRP Recommended Seismic Provisions for New Buildings and Other Structures (2020 NEHRP Provisions) and are approved for adoption in the American Society of Civil Engineers (ASCE) Standard, Minimum Design Loads and Associated Criteria for Buildings and Other Structures (ASCE/SEI 7-22). MPRS are incorporated in these design regulations because it was discovered that the standard spectral shape based on two periods and one reference site class was substantially understating spectral response in moderately long period structures located on soft soil sites where ground motion hazard is dominated by large magnitude events. These are the motions that are relevant to tall buildings in the Los Angeles region and of interest to the Los Angeles Tall Buildings Seismic Design Council (LATBSDC). The MPRS incorporation updated Chapters 11, 20, 21, and 22 of the 2020 NEHRP Provisions (a.k.a. FEMA P-2082); changes are described in detail in the commentary of FEMA P-2082. The MPRS also influenced the development of the 2018 U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) for the conterminous U.S. because valid ground motion models for all periods and site classes of interest were required. FEMA P-2082 is complemented by the FEMA P-2078 technical report that provides a procedure for approximating MPRS outside of the conterminous U.S. This paper presents a condensed version of the relevant sections of FEMA P-2082 and FEMA P-2078 that would interest the LATBSDC.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2021 Los Angeles tall buildings confrerence","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2021 Los Angeles Tall Buildings Conference","conferenceDate":"Nov 12, 2021","conferenceLocation":"Los Angeles, CA","language":"English","publisher":"Los Angeles Tall Buildings Structural Design Council","usgsCitation":"Rezaeian, S., Luco, N., and Kircher, C.A., 2021, Multi-period response spectra, in Proceedings of the 2021 Los Angeles tall buildings confrerence, Los Angeles, CA, Nov 12, 2021, p. 110-129.","productDescription":"20 p.","startPage":"110","endPage":"129","ipdsId":"IP-134522","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":398946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398945,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.latallbuildings.org/past-conference-proceedings"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":826188,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":826189,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kircher, C. A.","contributorId":194952,"corporation":false,"usgs":false,"family":"Kircher","given":"C.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":826190,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240352,"text":"70240352 - 2021 - A desert tortoise-common raven viable conflict threshold","interactions":[],"lastModifiedDate":"2023-02-06T16:05:37.397759","indexId":"70240352","displayToPublicDate":"2021-12-31T10:03:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"A desert tortoise-common raven viable conflict threshold","docAbstract":"Since 1966, common raven (Corvus corax; raven) abundance has increased throughout much of this species’ Holarctic distribution, fueled by an ever-expanding supply of anthropogenic resource subsidies (e.g., water, food, shelter, and nesting substrate) to ecoregion specific raven population carrying capacities. Consequently, ravens are implicated in declines of both avian and reptilian species of conservation concern, including the California (USA) endangered and federally threatened Mojave desert tortoise (Gopherus agassizii; desert tortoise). While ravens are a natural predator of desert tortoises, the inter-generational stability of desert tortoise populations is expected to be compromised as annual juvenile survival is suppressed below 0.77 through a combination of raven depredation and other sources of mortality. To estimate the extent to which raven depredation suppresses desert tortoise recruitment within the Mojave Desert of California, we collected data from 274 variable-radius point counts, 78 desert tortoise decoy stations, and 8 control stations during the spring of 2020. Additionally, we complied a geodatabase of previously active raven nests, observed between 2013 and 2020. Raven density estimates from 4 monitoring areas ranged between 0.63 (eastern most) and 2.44 (western most) raven km-2 (95% CI: 0.35–1.14 and 1.33–4.48, respectively). We used a Bayesian shared frailty model to estimate the effects of raven density and distance to the nearest previously active raven nest on the annual “survival” of juvenile desert tortoise decoys (75-mm Midline Carapace Length), which we then converted into survival estimates for 0- to 10-year-old desert tortoises by adjusting exposure to reflect natural activity patterns. At the 1.72-km median distance from the nearest previously active raven nest, the estimated annual survival of desert tortoises decreased as raven density increased, ranging among conservation areas from 0.774 (eastern most) to 0.733 (western most). Accordingly, our model predicts that desert tortoise populations exposed to raven densities in excess of 0.89 raven km-2, at a distance
","language":"English","publisher":"Berryman Institute","doi":"10.26077/eeca-1eec","usgsCitation":"Holcomb, K.L., Coates, P.S., Prochazka, B.G., Shields, T., and Boarman, W., 2021, A desert tortoise-common raven viable conflict threshold: Human–Wildlife Interactions, v. 15, no. 3, p. 405-421, https://doi.org/10.26077/eeca-1eec.","productDescription":"17 p.","startPage":"405","endPage":"421","ipdsId":"IP-130973","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Basin & Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.97303916756042,\n 35.71726205140463\n ],\n [\n -117.97303916756042,\n 34.34636579137755\n ],\n [\n -114.99857556861961,\n 34.34636579137755\n ],\n [\n -114.99857556861961,\n 35.71726205140463\n ],\n [\n -117.97303916756042,\n 35.71726205140463\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holcomb, Kerry L.","contributorId":296962,"corporation":false,"usgs":false,"family":"Holcomb","given":"Kerry","email":"","middleInitial":"L.","affiliations":[{"id":64256,"text":"U.S. Fish and Wildlife Service, Carlsbad Fish and Wildlife Office, 777 East Tahquitz Canyon Way, Suite 208, Palm Springs, California, 92262, USA","active":true,"usgs":false}],"preferred":false,"id":863528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shields, Timothy","contributorId":296963,"corporation":false,"usgs":false,"family":"Shields","given":"Timothy","affiliations":[{"id":64257,"text":"Hardshell Labs, Inc., P.O. Box 362, Haines, Alaska, 99827, USA","active":true,"usgs":false}],"preferred":false,"id":863531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boarman, William I.","contributorId":302114,"corporation":false,"usgs":false,"family":"Boarman","given":"William I.","affiliations":[{"id":65416,"text":"Hardshell Labs","active":true,"usgs":false}],"preferred":false,"id":863532,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224527,"text":"70224527 - 2021 - Exploring probabilistic seismic risk assessment to monitor the Sendai Framework for Disaster Risk Reduction","interactions":[],"lastModifiedDate":"2024-02-28T16:23:59.120117","indexId":"70224527","displayToPublicDate":"2021-12-31T09:57:42","publicationYear":"2021","noYear":false,"publicationType":{"id":26,"text":"Extramural-Authored Publication Paper"},"publicationSubtype":{"id":31,"text":"Extramural-Authored Publication"},"title":"Exploring probabilistic seismic risk assessment to monitor the Sendai Framework for Disaster Risk Reduction","docAbstract":"The Sendai Framework for Disaster Risk Reduction (SFDRR) calls upon the systematic collection of damage and loss data between 2015 and 2030 to monitor a number of disaster indicators. These indicators include the number of deaths, number of injured people, number of people affected by disasters, and direct economic losses. These results can then be compared with previous periods in order to track progress in disaster risk reduction. However, there is an important limitation with such an approach when measuring disaster risk due to earthquakes. Even in countries with significant seismic risk, it is plausible to witness a 15 year period without any destructive earthquakes (e.g., Nicaragua, Haiti, Myanmar). This situation can lead to the perception that efficient measures are being undertaken to reduce the impact of earthquakes, when in reality the trend could be the opposite. An alternative approach to monitor the SFDRR indicators is through probabilistic risk models. These models allow the estimation of the indicators of the SFDRR probabilistically (e.g., average annual economic losses, average annual fatalities), which do not depend on the occurrence of destructive events during the period of interest. Although seismic activity can be assumed as stationary over several decades, in order to evaluate the evolution of the SFDRR over these time frames, the consistent updating of the risk model has to be considered in order to reflect the evolution and change of the built environment and its vulnerability, such as the introduction of new design regulations or the implementation of retrofitting campaigns. A comparison of the various risk indicators throughout time allows assessing whether the potential losses caused by earthquakes are decreasing or increasing, as well as where risk reduction measures should be prioritized. This study discusses how the global seismic risk model released in December 2018 by the Global Earthquake Model (GEM) Foundation and its partners can be explored to monitor the SFDRR, and more importantly, how it can be modified to assess which measures should be endorsed to respect the 2030 targets.
","conferenceTitle":"17th World Conference on Earthquake Engineering, 17WCEE","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Silva, V., Calderon, A., Costa, C., Dabbeek, J., Martins, L., Rao, A., Yepes-Estrada, C., Acevedo, A., Crowley, H., Journeay, M., and Pittore, M., 2021, Exploring probabilistic seismic risk assessment to monitor the Sendai Framework for Disaster Risk Reduction, 11 p.","productDescription":"11 p.","ipdsId":"IP-116586","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":425821,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/"},{"id":426074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":true,"publicationStatus":"PW","contributors":{"authors":[{"text":"Silva, V.","contributorId":211393,"corporation":false,"usgs":false,"family":"Silva","given":"V.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calderon, A.","contributorId":211395,"corporation":false,"usgs":false,"family":"Calderon","given":"A.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Costa, C.","contributorId":265967,"corporation":false,"usgs":false,"family":"Costa","given":"C.","affiliations":[{"id":54846,"text":"Global Earthquake Model Foundation","active":true,"usgs":false}],"preferred":false,"id":823881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dabbeek, J.","contributorId":211396,"corporation":false,"usgs":false,"family":"Dabbeek","given":"J.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martins, L.","contributorId":211398,"corporation":false,"usgs":false,"family":"Martins","given":"L.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823883,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rao, A.","contributorId":211399,"corporation":false,"usgs":false,"family":"Rao","given":"A.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823884,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yepes-Estrada, Catalina","contributorId":222353,"corporation":false,"usgs":false,"family":"Yepes-Estrada","given":"Catalina","email":"","affiliations":[{"id":40531,"text":"Global Earthquake Model Foundation, Pavia, Italy","active":true,"usgs":false}],"preferred":false,"id":823885,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Acevedo, A.","contributorId":211403,"corporation":false,"usgs":false,"family":"Acevedo","given":"A.","email":"","affiliations":[{"id":38244,"text":"Department of Civil Engineering, Universidad EAFIT, Medellin, Colombia","active":true,"usgs":false}],"preferred":false,"id":823886,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Crowley, H.","contributorId":211404,"corporation":false,"usgs":false,"family":"Crowley","given":"H.","email":"","affiliations":[{"id":38245,"text":"EUCENTRE Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823887,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Journeay, M.","contributorId":211405,"corporation":false,"usgs":false,"family":"Journeay","given":"M.","affiliations":[{"id":38246,"text":"Geological Survey of Canada, Vancouver Canada","active":true,"usgs":false}],"preferred":false,"id":823889,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pittore, M.","contributorId":211406,"corporation":false,"usgs":false,"family":"Pittore","given":"M.","affiliations":[{"id":38247,"text":"GFZ Potsdam Germany","active":true,"usgs":false}],"preferred":false,"id":823890,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70224525,"text":"70224525 - 2021 - Near real-time updating of pager loss estimates","interactions":[],"lastModifiedDate":"2024-02-21T15:52:50.673441","indexId":"70224525","displayToPublicDate":"2021-12-31T09:51:40","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Near real-time updating of pager loss estimates","docAbstract":"Initial alerts by PAGER (Prompt Assessment of Global Earthquakes for Response) within minutes following an earthquake include several uncertainties, mainly due to potential inaccuracies in location, depth, fault delineation, and shaking estimates. We enhance an updating framework by incorporating early reports of fatalities within the first 24 hours, or so, of an earthquake to update PAGER’s overall fatality estimates and its resulting alert level. Though initial loss reports by officials or the media are uncertain and often undercount the eventual reported impacts, their temporal evolution provides predictive constraints for the PAGER model. The proposed framework helps capture these in a systematic way to minimize potential large fluctuations in PAGER alerts as ShakeMap (the USGS product which estimates how an area is affected by an earthquake) gets updated in the early hours after an earthquake. The new framework also accounts for uncertainties associated with early fatality reports as well as PAGER model-related uncertainties in order to improve the overall impact forecast. This updating framework improves the loss estimate and alert level to the correct level within the first 24 hours even when the initial estimation from PAGER is assumed to be off by two levels of alert, which is plausible due to potential over- or under-estimation of the PAGER model. While test results are very encouraging, our future work aims at implementation of operational PAGER model updating, which entails additional challenges in acquiring useful data, estimating their credibility, and developing rigorously tested operational code and protocols","conferenceTitle":"17th World Conference on Earthquake Engineering, 17WCEE","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Engler, D., Jaiswal, K.S., Noh, H.Y., and Wald, D.J., 2021, Near real-time updating of pager loss estimates, 17th World Conference on Earthquake Engineering, 17WCEE, Sendai, Japan, September 13-18, 2020, 10 p.","productDescription":"10 p.","ipdsId":"IP-116417","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":425820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425817,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Engler, Davis 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265963,"corporation":false,"usgs":false,"family":"Engler","given":"Davis","affiliations":[{"id":27102,"text":"USGS student contractor","active":true,"usgs":false}],"preferred":false,"id":823867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":823869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229239,"text":"70229239 - 2021 - Numerical modelling of mine pollution to inform remediation decision-making in watersheds","interactions":[],"lastModifiedDate":"2022-03-03T15:20:56.513912","indexId":"70229239","displayToPublicDate":"2021-12-31T09:09:08","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Numerical modelling of mine pollution to inform remediation decision-making in watersheds","docAbstract":"Prioritisation of mine pollution sources for remediation is a key challenge facing environmental managers. This paper presents a numerical modelling methodology to evaluate potential improvements in stream water quality from remediation of important mine pollution sources. High spatial resolution synoptic sampling data from a Welsh watershed were used to calibrate the OTIS solute transport model. Simulation of mine pollution remediation scenarios using OTIS revealed decreases in stream Zn concentrations between 9% and 62% under mean streamflow conditions. Remediation scenarios under low streamflow conditions were less effective (<1% to 17% decrease in Zn concentrations), due to diffuse and metal-rich groundwater inflows.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of international mine water association 2021","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Mine Water Management for Future Generations","conferenceLocation":"Cardiff, Wales","language":"English","publisher":"ISI Thomson","usgsCitation":"Byrne, P., Onnis, P., Runkel, R.L., Frau, I., Lynch, S.F., Brown, A.M., Robertson, I., and Edwards, P., 2021, Numerical modelling of mine pollution to inform remediation decision-making in watersheds, in Proceedings of international mine water association 2021, Cardiff, Wales, p. 66-71.","productDescription":"6 p.","startPage":"66","endPage":"71","ipdsId":"IP-129772","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":396699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396694,"type":{"id":15,"text":"Index Page"},"url":"https://www.imwa.info/imwaconferencesandcongresses/proceedings/325-proceedings-2021.html"}],"country":"Wales","otherGeospatial":"Nant Cwmnewyddion watershed","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byrne, Patrick","contributorId":192845,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","affiliations":[],"preferred":false,"id":837016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Onnis, Patrizia","contributorId":209909,"corporation":false,"usgs":false,"family":"Onnis","given":"Patrizia","email":"","affiliations":[{"id":16820,"text":"University of Cagliari","active":true,"usgs":false}],"preferred":false,"id":837017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frau, Ilaria","contributorId":247580,"corporation":false,"usgs":false,"family":"Frau","given":"Ilaria","email":"","affiliations":[{"id":49583,"text":"Liverpool John Moores University","active":true,"usgs":false}],"preferred":false,"id":837019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lynch, Sarah F. 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This paper, the second of two parts, addresses processing the collected data to inform inputs for probabilistic bank erosion estimates in BSTEM. Measuring the intrinsic soil properties for BSTEM is discussed in part one. Soil critical shear stress and soil erodibility coefficients were calibrated by Unified Soil Classification soil type to observed erosion on the American River. Adjustments were made in the probability density functions for these parameters to reflect field-measured variability and carry forward the reduction in error achieved during calibration. The resulting calibrated values were tested at additional sites, validating the resulting critical shear stress and soil erodibility coefficient values and probability density functions for more robust probabilistic bank erosion estimates using BSTEM.","conferenceTitle":"10th International Conference on Scour and Erosion (ICSE-10)","conferenceDate":"October 18-20, 2021","language":"English","publisher":"ASCE","usgsCitation":"Rivas, T.M., AuBuchon, J., Shidlovskaya, A., Langendoen, E., Work, P.A., Livsey, D.N., Timchenko, A., Jemes, K., and Briaud, J., 2021, Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 2: Probabilistic numerical simulation of bank erosion, 10th International Conference on Scour and Erosion (ICSE-10), October 18-20, 2021, 10 p.","productDescription":"10 p.","ipdsId":"IP-116981","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":425819,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.issmge.org/publications/publication/risk-informed-levee-erosion-countermeasure-site-selection-and-design-in-the-sacramento-area-part-2-probabilistic-numerical-simulation-of-bank-erosion","linkFileType":{"id":5,"text":"html"}},{"id":425814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Sacramento","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62841736139654,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.61545545028389\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivas, Todd M.","contributorId":256771,"corporation":false,"usgs":false,"family":"Rivas","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":51857,"text":"Sacramento District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"AuBuchon, Jonathan","contributorId":256772,"corporation":false,"usgs":false,"family":"AuBuchon","given":"Jonathan","email":"","affiliations":[{"id":51859,"text":"Albuquerque District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shidlovskaya, Anna","contributorId":256773,"corporation":false,"usgs":false,"family":"Shidlovskaya","given":"Anna","email":"","affiliations":[{"id":51860,"text":"Department of Civil Engineering, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":813242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langendoen, Eddy J.","contributorId":256774,"corporation":false,"usgs":false,"family":"Langendoen","given":"Eddy J.","affiliations":[{"id":51861,"text":"USDA National Sedimentation Laboratory, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813243,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813244,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Timchenko, Anna","contributorId":334257,"corporation":false,"usgs":false,"family":"Timchenko","given":"Anna","email":"","affiliations":[],"preferred":false,"id":895117,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jemes, Kellie","contributorId":334259,"corporation":false,"usgs":false,"family":"Jemes","given":"Kellie","email":"","affiliations":[],"preferred":false,"id":895118,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Briaud, Jean-Louis","contributorId":334258,"corporation":false,"usgs":false,"family":"Briaud","given":"Jean-Louis","email":"","affiliations":[],"preferred":false,"id":895119,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70219211,"text":"70219211 - 2021 - Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing","interactions":[],"lastModifiedDate":"2024-02-21T14:39:05.70841","indexId":"70219211","displayToPublicDate":"2021-12-31T08:31:46","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing","docAbstract":"USACE partnered with the United States Department of Agriculture, Agricultural Research Service, United States Geological Survey, and Texas A&M University to evaluate the erodibility of the river banks and levees to inform probabilistic numerical simulations using the Bank Stability and Toe Erosion Model (BSTEM). This paper discusses the measurement of the intrinsic erosion and geotechnical properties of the soil for improved BSTEM results and is the first of two parts. Sampling and testing were conducted at select sites to obtain the soil stratigraphy and collect samples for estimation of engineering properties. Soil erodibility was measured using the Erosion Function Apparatus test, the Borehole Erosion Test, the Pocket Erodometer Test, and the mini-Jet Erosion Test. Critical evaluation of previously existing datasets and the collection of these new datasets helped to provide better definition of the range in erosion parameters for improved probabilistic erosion estimates using BSTEM for risk-informed levee erosion countermeasure site selection and design.","conferenceTitle":"10th International Conference on Scour and Erosion (ICSE-10)","conferenceDate":"October 18-20, 2021","language":"English","publisher":"ASCE","collaboration":"US Army Corps of Engineers","usgsCitation":"Rivas, T.M., AuBuchon, J., Shidlovskaya, A., Langendoen, E., Work, P.A., Livsey, D.N., Timchenko, A., and Briaud, J., 2021, Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing, 10th International Conference on Scour and Erosion (ICSE-10), October 18-20, 2021, 11 p.","productDescription":"11 p.","ipdsId":"IP-117504","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":425813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425812,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.issmge.org/publications/publication/risk-informed-levee-erosion-countermeasure-site-selection-and-design-in-the-sacramento-area-part-2-probabilistic-numerical-simulation-of-bank-erosion","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","city":"Sacramento","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62841736139654,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.61545545028389\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivas, Todd M.","contributorId":256771,"corporation":false,"usgs":false,"family":"Rivas","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":51857,"text":"Sacramento District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"AuBuchon, Jonathan","contributorId":256772,"corporation":false,"usgs":false,"family":"AuBuchon","given":"Jonathan","email":"","affiliations":[{"id":51859,"text":"Albuquerque District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shidlovskaya, Anna","contributorId":256773,"corporation":false,"usgs":false,"family":"Shidlovskaya","given":"Anna","email":"","affiliations":[{"id":51860,"text":"Department of Civil Engineering, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":813236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langendoen, Eddy J.","contributorId":256774,"corporation":false,"usgs":false,"family":"Langendoen","given":"Eddy J.","affiliations":[{"id":51861,"text":"USDA National Sedimentation Laboratory, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Timchenko, Anna","contributorId":334257,"corporation":false,"usgs":false,"family":"Timchenko","given":"Anna","email":"","affiliations":[],"preferred":false,"id":895115,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Briaud, Jean-Louis","contributorId":334258,"corporation":false,"usgs":false,"family":"Briaud","given":"Jean-Louis","email":"","affiliations":[],"preferred":false,"id":895116,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70241610,"text":"70241610 - 2021 - Estimating trends of common raven populations in North America, 1966—2018","interactions":[],"lastModifiedDate":"2024-09-11T16:27:03.40755","indexId":"70241610","displayToPublicDate":"2021-12-31T08:28:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1914,"text":"Human-Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Estimating trends of common raven populations in North America, 1966—2018","docAbstract":"Over the last half century, common raven (Corvus corax; raven) populations have increased in abundance across much of North America. Ravens are generalist predators known to depredate the eggs and young of several sensitive species. Quantifying raven population increases at multiple spatial scales across North America will help wildlife resource managers identify areas where population increases present the greatest risk to species conservation. We used a hierarchical Bayesian modeling approach to analyze trends of standardized raven counts from 1966 to 2018 using Breeding Bird Survey data within each Level I and II ecoregion of the United States and Canada. We also compared raven abundance within and outside the distributions of 9 sensitive or endangered species. Although we found substantial evidence that raven populations have increased across North America, populations varied in growth rates and relative abundances among regions. We found 73% of Level I (11/15) and II (25/34) ecoregions demonstrated positive annual population growth rates ranging from 0.2–9.4%. We found higher raven abundance inside versus outside the distributions of 7 of the 9 sensitive species included in our analysis. Gunnison sage-grouse (Centrocercus minimus) had the highest discrepancy, with 293% more ravens within compared to outside of their range, followed by greater sandhill crane (Antigone canadensis tabida; 280%), and greater sage-grouse (C. urophasianus; 204%). Only 2 species, least tern (Sternula antillarum) and piping plover (Charadrius melodus), indicated lower raven abundance within relative to outside their distributions. Our findings will help wildlife resource managers identify regional trends in abundance of ravens and anticipate which sensitive species are at greatest risk from elevated raven populations. Future research directed at identifying the underlying regional drivers of these trends could help elucidate the most appropriate and responsive management actions and, thereby, guide the development of raven population management plans to mitigate impacts to sensitive species.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/c27f-e335","usgsCitation":"Harju, S.M., Coates, P.S., Dettenmaier, S.J., Dinkins, J.B., Jackson, P.J., and Chenaille, M.P., 2021, Estimating trends of common raven populations in North America, 1966—2018: Human-Wildlife Interactions, v. 15, no. 3, p. 248-269, https://doi.org/10.26077/c27f-e335.","productDescription":"22 p.","startPage":"248","endPage":"269","ipdsId":"IP-130935","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436079,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99CNYHP","text":"USGS data release","linkHelpText":"Trend Estimates of Common Raven Populations in the United States and Canada, 1966 - 2018"},{"id":414699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United 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Seth M. 0000-0003-0444-7881","orcid":"https://orcid.org/0000-0003-0444-7881","contributorId":238889,"corporation":false,"usgs":false,"family":"Harju","given":"Seth","email":"","middleInitial":"M.","affiliations":[{"id":47817,"text":"Heron Ecological","active":true,"usgs":false}],"preferred":false,"id":867486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dettenmaier, Seth J. 0000-0001-6325-8808","orcid":"https://orcid.org/0000-0001-6325-8808","contributorId":302087,"corporation":false,"usgs":true,"family":"Dettenmaier","given":"Seth","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867488,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dinkins, Jonathan B.","contributorId":177565,"corporation":false,"usgs":false,"family":"Dinkins","given":"Jonathan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":867489,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":867490,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chenaille, Michael P. 0000-0003-3387-7899 mchenaille@usgs.gov","orcid":"https://orcid.org/0000-0003-3387-7899","contributorId":194661,"corporation":false,"usgs":true,"family":"Chenaille","given":"Michael","email":"mchenaille@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867491,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259394,"text":"70259394 - 2021 - Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019","interactions":[],"lastModifiedDate":"2024-10-07T14:00:24.63834","indexId":"70259394","displayToPublicDate":"2021-12-31T08:26:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18741,"text":"Incasciences, Revista del Instituto Geológico, Minero y Metalúrgico","active":true,"publicationSubtype":{"id":10}},"title":"Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019","docAbstract":"Using a volcanic monitoring data set from Ubinas volcano, we applied a process-based model of pre-eruptive seismic patterns to the 2019 eruptive episode with the goal of identifying possible seismic precursors in order to help forecast future eruptions. This conceptual model, based on geologic processes, is divided into four seismicity stages: Stage 1. Characterized by the occurrence of deep seismicity associated with deep intrusion(s); Stage 2. Occurrence of distal volcano – tectonic seismicity in response to magma intrusion(s) into the upper crustal reservoir; Stage 3. Dominated by seismicity associated with vent – clearing; and Stage 4. Corresponding to the occurrence of repetitive seismicity related with final magma ascent. \nIn the 2019 eruptive episode, we identified the last three stages: seismicity associated with the intrusion of new magma (Stage 2), seismicity associated with an opened and vent – clearing inside of the volcanic system (Stage 3) and repetitive seismicity that suggested the magma ascent towards shallower depths (Stage 4), however, no surficial lava was observed. Because Ubinas is an active system with frequent eruptions, Stage 2 was very brief; however, we were still able to identify the transition from phreatomagmatic to magmatic activity.\nThe model allows us to provide a process-based interpretation to the volcanic monitoring observations from Ubinas volcano. Additionally, this model will aid in future assessment of unrest and contribute to eruption forecasting.","language":"English","publisher":"Instituto Geológico, Minero y Metalúrgico","usgsCitation":"Ortega, M.A., McCausland, W., White, R., Anccasi, R.M., and Ccallata, B., 2021, Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019: Incasciences, Revista del Instituto Geológico, Minero y Metalúrgico, v. 1, no. 1, p. 53-61.","productDescription":"9 p.","startPage":"53","endPage":"61","ipdsId":"IP-122050","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","otherGeospatial":"Ubinas Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.92669701072916,\n -16.328296011041587\n ],\n [\n -70.92669701072916,\n -16.37059339221304\n ],\n [\n -70.87807952769546,\n -16.37059339221304\n ],\n [\n -70.87807952769546,\n -16.328296011041587\n ],\n [\n -70.92669701072916,\n -16.328296011041587\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ortega, Mayra A.","contributorId":344962,"corporation":false,"usgs":false,"family":"Ortega","given":"Mayra","email":"","middleInitial":"A.","affiliations":[{"id":82443,"text":"INGEMMET (Peru)","active":true,"usgs":false}],"preferred":false,"id":915137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCausland, Wendy 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":344963,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Randall A. 0000-0003-4074-8577","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":344964,"corporation":false,"usgs":false,"family":"White","given":"Randall A.","affiliations":[{"id":82444,"text":"none, retired USGS","active":true,"usgs":false}],"preferred":false,"id":915139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anccasi, Rosa M.","contributorId":344965,"corporation":false,"usgs":false,"family":"Anccasi","given":"Rosa","email":"","middleInitial":"M.","affiliations":[{"id":82443,"text":"INGEMMET (Peru)","active":true,"usgs":false}],"preferred":false,"id":915140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ccallata, Beto","contributorId":190928,"corporation":false,"usgs":false,"family":"Ccallata","given":"Beto","email":"","affiliations":[],"preferred":false,"id":915141,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240348,"text":"70240348 - 2021 - Evaluating common raven take for greater sage-grouse in Oregon’s Baker County Priority Conservation Area and Great Basin Region","interactions":[],"lastModifiedDate":"2023-02-06T14:48:20.040945","indexId":"70240348","displayToPublicDate":"2021-12-31T08:24:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating common raven take for greater sage-grouse in Oregon’s Baker County Priority Conservation Area and Great Basin Region","docAbstract":"The common raven (Corvus corax; raven) is a nest predator of species of conservation concern, such as the greater sage-grouse (Centrocercus urophasianus). Reducing raven abundance by take requires authorization under the Migratory Bird Treaty Act. To support U.S. Fish and Wildlife Service’s take decisions (e.g., those that authorize killing a specified proportion or number of individuals annually in a defined area), including the most recent one for Oregon’s Baker County Priority Area for Conservation (PAC), we modeled raven population dynamics under hypothetical scenarios with take rates ranging from below to above the maximum sustained yield (MSY; i.e., trmsy= 0.01-0.60). We fit a Bayesian state-space logistic model to estimate abundance based on the Breeding Bird Survey route-level count data for the PAC during 1997-2019 and Great Basin Region (GBR) during 1968-2019. We predicted abundance for 2019-2030 and evaluated potential take levels (PTL) for the PAC and GBR. Abundance averaged 682 (SE = 93) for the PAC during 1997-2019 and 333,027 (SE = 20,504) for the GBR during 1968-2019. With take rates between 0.41 and 0.60, predicted abundance averaged 308 (SD = 405) for the PAC and 142,258 (SD = 53,474) for the GBR during 2019-2030. With management factor F = 0.75-2 for takes ranging from below to above the MSY, the PTL 50th percentiles were 150-401 yr-1 for the PAC and 60,457-161,219 yr-1 for the GBR. Our modeling framework is flexible and can be part of a comprehensive management strategy for ravens in the western United States.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/mft7-3s49","usgsCitation":"Rivera-Milan, F.F., Coates, P.S., Cupples, J.B., Greenfield, M., and Devers, P.K., 2021, Evaluating common raven take for greater sage-grouse in Oregon’s Baker County Priority Conservation Area and Great Basin Region: Human–Wildlife Interactions, v. 15, no. 3, p. 544-555, https://doi.org/10.26077/mft7-3s49.","productDescription":"12 p.","startPage":"544","endPage":"555","ipdsId":"IP-130939","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","county":"Baker County","otherGeospatial":"Baker County Priority Area for Conservation, Great Basin region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.68951161920046,\n 34.86882693230382\n ],\n [\n -114.41790621700025,\n 36.01914680504717\n ],\n [\n -113.82020760748156,\n 37.035851159985356\n ],\n [\n -112.52378196216432,\n 38.12537531265528\n ],\n [\n -110.60534802832137,\n 40.207403813426794\n ],\n [\n -109.85711979438781,\n 40.96708218000899\n ],\n [\n -110.93379332696821,\n 42.70865552935243\n ],\n [\n -112.11378671967859,\n 43.905933672244174\n ],\n [\n -114.50077945797338,\n 44.03396589456605\n ],\n [\n -115.45854012963628,\n 43.83833640174953\n ],\n [\n -115.40704209937076,\n 43.018005042973016\n ],\n [\n -116.42329526100599,\n 43.68826560687998\n ],\n [\n -117.43582175960373,\n 44.15254599056391\n ],\n [\n -117.56411494928062,\n 45.51071863545178\n ],\n [\n -120.86148371385596,\n 45.196163484203765\n ],\n [\n -122.83048081173237,\n 44.614156463039876\n ],\n [\n -122.62369563732213,\n 42.15476186217535\n ],\n [\n -121.34389073677045,\n 39.29239163753837\n ],\n [\n -119.00109543128349,\n 37.18611006397258\n ],\n [\n -114.62870331939041,\n 34.87238166990903\n ],\n [\n -114.68951161920046,\n 34.86882693230382\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivera-Milan, Frank F.","contributorId":302112,"corporation":false,"usgs":false,"family":"Rivera-Milan","given":"Frank","email":"","middleInitial":"F.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":863518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cupples, Jacqueline B.","contributorId":289741,"corporation":false,"usgs":false,"family":"Cupples","given":"Jacqueline","email":"","middleInitial":"B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":863520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greenfield, Michael","contributorId":224657,"corporation":false,"usgs":false,"family":"Greenfield","given":"Michael","affiliations":[{"id":40903,"text":"Greenfield Geotechnical, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":863521,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Devers, Patrick K.","contributorId":167173,"corporation":false,"usgs":false,"family":"Devers","given":"Patrick","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":863522,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227374,"text":"70227374 - 2021 - Geologic map of the Middendorf quadrangle, Chesterfield County, South Carolina","interactions":[],"lastModifiedDate":"2023-03-13T14:40:41.012899","indexId":"70227374","displayToPublicDate":"2021-12-31T07:21:51","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":13452,"text":"South Carolina Geological Survey Geologic Quadrangle Map","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"GQM-56","title":"Geologic map of the Middendorf quadrangle, Chesterfield County, South Carolina","docAbstract":"The Middendorf 7.5-minute quadrangle is located entirely within the Carolina Sandhills region of the upper Atlantic Coastal Plain province in Chesterfield County, South Carolina. The Carolina Sandhills, which has been recognized as a separate region for a long time (e.g., McGee, 1890, 1891; Holmes, 1893), extends from central North Carolina across South Carolina to the western border of Georgia along the updip (inland) margin of the Atlantic Coastal Plain province. In Chesterfield County, the Carolina Sandhills form a relatively high plateau that is bounded to the west by Paleozoic metamorphic rocks of the Piedmont province. This plateau is bounded to the east by the east-facing Orangeburg Scarp, which is interpreted as a shoreline formed by wave erosion during a middle Pliocene time of high sea level (Dowsett and Cronin, 1990).
Digital Elevation Models (DEMs) of the Middendorf quadrangle derived from lidar point cloud data reveal a landscape incised by creeks and streams. The highest elevation in the Middendorf quadrangle is 596 ft (182 m) on top of a sandhill in the northwest quadrant of the quadrangle, whereas the lowest elevation is 230 ft (70 m) in the floodplain of Big Black Creek on the southern margin of the quadrangle. Most of the landscape is covered by a mantle of unconsolidated sand that is mapped as the Quaternary Pinehurst Formation. At many locations, the unconsolidated sand is <2 m thick and forms a sand sheet of low relief. In areas of higher elevation, however, the unconsolidated sand can be up to 10 m thick and forms subdued hills (degraded dunes) of up to 6 m relief with steeper sides on the east and southeast. Many of these subdued hills (degraded dunes) are present in the area of closed depressions in the southwest corner of the map. Outcrops within the quadrangle are not common, and are limited mostly to a few exposures of sandstone and clay of the Cretaceous Middendorf Formation in a few road cuts, railroad cuts, and borrow pits as well as some slopes and roadside ditches.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"South Carolina Geological Survey Geologic Quadrangle Map (GQM)","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"South Carolina Geological Survey","usgsCitation":"Swezey, C.S., Fitzwater, B.A., and Whittecar, G.R., 2021, Geologic map of the Middendorf quadrangle, Chesterfield County, South Carolina: South Carolina Geological Survey Geologic Quadrangle Map GQM-56, 2 Plates: 30.00 x 32.50 inches or smaller.","productDescription":"2 Plates: 30.00 x 32.50 inches or smaller","ipdsId":"IP-082619","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":394243,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394224,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.dnr.sc.gov/geology/publications.html"}],"country":"United States","state":"South Carolina","county":"Chesterfield County","otherGeospatial":"Middendorf quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.25,\n 34.5\n ],\n [\n -80.125,\n 34.5\n ],\n [\n -80.125,\n 34.625\n ],\n [\n -80.25,\n 34.625\n ],\n [\n -80.25,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swezey, Christopher S. 0000-0003-4019-9264 cswezey@usgs.gov","orcid":"https://orcid.org/0000-0003-4019-9264","contributorId":173033,"corporation":false,"usgs":true,"family":"Swezey","given":"Christopher","email":"cswezey@usgs.gov","middleInitial":"S.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":830646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzwater, Bradley A.","contributorId":177211,"corporation":false,"usgs":false,"family":"Fitzwater","given":"Bradley","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":830647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whittecar, G. Richard","contributorId":177212,"corporation":false,"usgs":false,"family":"Whittecar","given":"G.","email":"","middleInitial":"Richard","affiliations":[],"preferred":false,"id":830648,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229403,"text":"70229403 - 2021 - Revising the marine range of the endangered black-capped petrel Pterodroma hasitata: occurrence in the northern Gulf of Mexico and exposure to conservation threats","interactions":[],"lastModifiedDate":"2022-03-07T12:58:13.997591","indexId":"70229403","displayToPublicDate":"2021-12-31T06:56:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Revising the marine range of the endangered black-capped petrel Pterodroma hasitata: occurrence in the northern Gulf of Mexico and exposure to conservation threats","docAbstract":"The black-capped petrel Pterodroma hasitata is an Endangered seabird endemic to the western North Atlantic. Although estimated at ~1000 breeding pairs, only ~100 nests have been located at 2 sites in Haiti and 3 sites in the Dominican Republic. At sea, the species primarily occupies waters of the western Gulf Stream in the Atlantic and the Caribbean Sea. Due to limited data, there is currently no consensus on the geographic marine range of the species although no current proposed ranges include the Gulf of Mexico. Here, we report on observations of black-capped petrels during 2 vessel-based survey efforts throughout the northern Gulf of Mexico from 2010-2011 and 2017-2019. During 558 d and ~54700 km of surveys, we tallied 40 black-capped petrels. Most observations occurred in the eastern Gulf, although birds were observed over much of the east-west and north-south footprint of the survey area. Predictive models indicated that habitat suitability for black-capped petrels was highest in areas associated with dynamic waters of the Loop Current. We used the extent of occurrence and area of occupancy concepts to delimit the geographic range of the species within the northern Gulf. We suggest that the marine range for black-capped petrels be modified to include the northern Gulf of Mexico, recognizing that distribution may be more clumped in the eastern Gulf and that occurrence in the southern Gulf remains unknown due to a lack of surveys there. To date, however, it remains unclear which nesting areas are linked to the Gulf of Mexico.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.1101/2021.01.19.427288","usgsCitation":"Jodice, P.G., Michael, P., Gleason, J., Haney, J., and Satge, Y., 2021, Revising the marine range of the endangered black-capped petrel Pterodroma hasitata: occurrence in the northern Gulf of Mexico and exposure to conservation threats: Endangered Species Research, v. 46, p. 49-65, https://doi.org/10.1101/2021.01.19.427288.","productDescription":"17 p.","startPage":"49","endPage":"65","ipdsId":"IP-124873","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":449960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1101/2021.01.19.427288","text":"Publisher Index Page"},{"id":396779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.96484375,\n 25.799891182088334\n ],\n [\n -80.771484375,\n 25.799891182088334\n ],\n [\n -80.771484375,\n 31.42866311735861\n ],\n [\n -98.96484375,\n 31.42866311735861\n ],\n [\n -98.96484375,\n 25.799891182088334\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":837280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michael, P.E.","contributorId":288015,"corporation":false,"usgs":false,"family":"Michael","given":"P.E.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":837281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gleason, J.S.","contributorId":288017,"corporation":false,"usgs":false,"family":"Gleason","given":"J.S.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":837282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haney, J.C.","contributorId":288019,"corporation":false,"usgs":false,"family":"Haney","given":"J.C.","email":"","affiliations":[{"id":61685,"text":"Terra Mar Applied Sciences","active":true,"usgs":false}],"preferred":false,"id":837283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Satge, Y.G.","contributorId":279816,"corporation":false,"usgs":false,"family":"Satge","given":"Y.G.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":837284,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230359,"text":"70230359 - 2021 - Can we prove that an undetected species is absent? Evaluating whether brown treesnakes are established on the island of Saipan using surveillance and expert opinion","interactions":[],"lastModifiedDate":"2022-04-08T11:55:38.460524","indexId":"70230359","displayToPublicDate":"2021-12-31T06:52:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Can we prove that an undetected species is absent? Evaluating whether brown treesnakes are established on the island of Saipan using surveillance and expert opinion","docAbstract":"Detection of invasive species and decisions centered around early detection and rapid response (EDRR) are notorious challenges for decision makers. Detection probability is low for cryptic species, resources are limited, and ecological harm (especially for island ecosystems) can result from failure to remove invasive species due to inadequate or delayed surveillance efforts. Due to the proximity to the U.S. territory of Guam and inter-island traffic, the Commonwealth of the Northern Mariana Islands (CNMI) is at high risk of colonization by the invasive and cryptic brown treesnake (Boiga irregularis; BTS). There have been numerous reports of snakes and 7 confirmed specimens secured at ports of entry on the island of Saipan in the CNMI over the last four decades, raising the possibility that a population might be established. Establishment of BTS on Saipan is a major concern, as evidenced by the ecological and economic disruption that occurred on Guam. We evaluated the possibility of a small localized population on Saipan using evidence from surveillance efforts in 1999, 2007, 2009, 2016, and 2018, and from results of expert assessment of the credibility of non-confirmed reports of snakes for the period 1982–2013. For active surveillance efforts, we use a Poisson-based model to estimate the 95% probability of at least one snake being detected at a stated density given the level of sampling effort and detection probability. Based on this collective evidence we conclude there is a low probability that Saipan currently has an incipient population of BTS. However, with the continued presence of BTS on Guam, continuing commercial and military transportation in the region, and relief shipments responding to increased storm intensity, Saipan remains highly vulnerable to accidental introductions. Effective surveillance remains a crucial element for detection of any species, but this may be particularly true for a cryptic snake that is difficult to control once established. |