Major flooding occurred June 30–July 1, 2018, in the Fourmile Creek Basin in central Iowa after thunderstorm activity over the region. The largest recorded 24-hour precipitation total at a National Oceanic and Atmospheric Administration weather station was 8.72 inches in Ankeny, Iowa, and 7.54 inches in Des Moines, Iowa. A maximum peak-of-record discharge of 10,000 cubic feet per second was recorded at U.S. Geological Survey streamgage 05485605, Fourmile Creek near Ankeny, Iowa, on July 1, 2018, with an annual exceedance probability of less than 0.2 percent. A maximum peak-of-record discharge of 12,000 cubic feet per second also was recorded at U.S. Geological Survey streamgage 05485640, Fourmile Creek at Des Moines, Iowa, on July 1, 2018, with an annual exceedance-probability range of 0.5–1 percent. High-water mark elevations were surveyed at 11 locations along Fourmile Creek between State Highway 163 in Pleasant Hill, Iowa, and U.S. Route 69 near Alleman, Iowa, a distance of 21.0 river miles. The high-water marks were used to develop a flood profile for Fourmile Creek.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211044","collaboration":"Prepared in cooperation with the Iowa Department of Transportation and the Iowa Highway Research Board (Project HR–140)","usgsCitation":"O’Shea, P.S., Vegrzyn, J.C., and Barnes, K.K., 2021, Flood of June 30–July 1, 2018, in the Fourmile Creek Basin, near Ankeny, Iowa: U.S. Geological Survey Open-File Report 2021–1044, 18 p., https://doi.org/10.3133/ofr20211044.","productDescription":"Report: vi, 18 p.; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-111452","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385727,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1044/coverthb.jpg"},{"id":385791,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1044/images"},{"id":385790,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1044/ofr20211044.xml"},{"id":385730,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":385729,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XZGOG3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Peak-flow frequency analysis for two selected streamgages in the Fourmile Creek Basin in central Iowa, based on data through water year 2018"},{"id":385728,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1044/ofr20211044.pdf","text":"Report","size":"1.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1044"}],"country":"United States","state":"Iowa","city":"Ankeny","otherGeospatial":"Fourmile Creek basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.548583984375,\n 41.86956082699455\n ],\n [\n -93.75457763671874,\n 41.933954896061636\n ],\n [\n -93.63784790039061,\n 41.71187978193456\n ],\n [\n -93.51699829101562,\n 41.54867239252432\n ],\n [\n -93.4002685546875,\n 41.57641597789266\n ],\n [\n -93.548583984375,\n 41.86956082699455\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The federally threatened American crocodile (Crocodylus acutus) is a flagship species and ecological indicator of hydrologic restoration in the Florida Everglades. We conducted a long-term capture-recapture study on the South Florida population of American crocodiles from 1978 to 2015 to evaluate the effects of restoration efforts to more historic hydrologic conditions. The study produced 10,040 crocodile capture events of 9,865 individuals and more than 90% of captures were of hatchlings. Body condition and growth rates of crocodiles were highly age-structured with younger crocodiles presenting with the poorest body condition and highest growth rates. Mean crocodile body condition in this study was 2.14±0.35 SD across the South Florida population. Crocodiles exposed to hypersaline conditions (> 40 psu) during the dry season maintained lower body condition scores and reduced growth rate by 13% after one year, by 24% after five years, and by 29% after ten years. Estimated hatchling survival for the South Florida population was 25% increasing with ontogeny and reaching near 90% survival at year six. Hatchling survival was 34% in NE Florida Bay relative to a 69% hatchling survival at Crocodile Lake National Wildlife Refuge and 53% in Flamingo area of Everglades National Park. Hypersaline conditions negatively affected survival, growth and body condition and was most pronounced in NE Florida Bay, where the hydrologic conditions have been most disturbed. The American crocodile, a long-lived animal, with relatively slow growth rate provides an excellent model system to measure the effects of altered hydropatterns in the Everglades landscape. These results illustrate the need for continued long-term monitoring to assess system-wide restoration outcomes and inform resource managers.
Ongoing climate change and human conversion of forests to other land uses alter regional evapotranspiration dynamics and, consequently, impact associated hydrological systems in Amazonia. We studied the effects of drought and fragmentation on forest evapotranspiration using the surface energy balance-based model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) for a fragmented forest landscape in Brazil's Amazonian state of Rondônia.
Dry season (June-August) forest evapotranspiration estimates were produced for the 2009-2011 period that encompassed the 2010 drought event, one of the extreme droughts in the Amazon. METRIC evapotranspiration data were analyzed in relation to climate (monthly precipitation and cumulative water deficit) and forest fragmentation (edge distance from 100m to 1000m from forest edge and edge density). During the dry season of 2009, pre-drought, forest evapotranspiration did not fall below 110mm/month. However, the 2010 drought year showed a drastic decline in evapotranspiration by 32%, to 75mm/month, from July to August. In 2011, evapotranspiration rates were still depressed with August rates dropping as low as 85mm/month. Forest evapotranspiration dynamics were driven mainly by precipitation and corresponding water deficits in the drier years (2010 and 2011), although evapotranspiration deficits along the edges of forest fragments were locally significant, at the landscape scale. The forests near edges (to 100m) had progressively lower evapotranspiration levels than interior forests as dry seasons progressed and these differences were greatest in the 2010 drought year, reaching almost 5%.
Our results suggest that during the driest months, fragmentation exacerbated both the rate and extent of evapotranspiration reductions over forest areas up to 100m from edges, equivalent to ~20% of the forested landscape in our study area.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2021.108446","usgsCitation":"Numata, I., Khand, K.B., Kjaersgaard, J., Cochrane, M.A., and Silva, S.S., 2021, Forest evapotranspiration dynamics over a fragmented forest landscape under drought in southwestern Amazonia: Agricultural and Forest Meteorology, v. 306, 108446, 9 p., https://doi.org/10.1016/j.agrformet.2021.108446.","productDescription":"108446, 9 p.","ipdsId":"IP-122348","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452208,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2021.108446","text":"Publisher Index Page"},{"id":385833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","state":"Rondonia","otherGeospatial":"Amazon Rain Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.97265625,\n -2.4162756547063857\n ],\n [\n -56.6455078125,\n -2.4162756547063857\n ],\n [\n -56.6455078125,\n 6.18424616128059\n ],\n [\n -66.97265625,\n 6.18424616128059\n ],\n [\n -66.97265625,\n -2.4162756547063857\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"306","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Numata, Izaya","contributorId":219508,"corporation":false,"usgs":false,"family":"Numata","given":"Izaya","email":"","affiliations":[],"preferred":false,"id":816235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Khand, Kul Bikram 0000-0002-1593-1508","orcid":"https://orcid.org/0000-0002-1593-1508","contributorId":242921,"corporation":false,"usgs":true,"family":"Khand","given":"Kul","email":"","middleInitial":"Bikram","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":816236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kjaersgaard, Jeppe","contributorId":258261,"corporation":false,"usgs":false,"family":"Kjaersgaard","given":"Jeppe","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":816237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cochrane, Mark A.","contributorId":20884,"corporation":false,"usgs":false,"family":"Cochrane","given":"Mark","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":816238,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silva, Sonaira S.","contributorId":258262,"corporation":false,"usgs":false,"family":"Silva","given":"Sonaira","email":"","middleInitial":"S.","affiliations":[{"id":52266,"text":"Federal University of Acre","active":true,"usgs":false}],"preferred":false,"id":816239,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220617,"text":"70220617 - 2021 - Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models","interactions":[],"lastModifiedDate":"2021-06-30T18:58:48.8679","indexId":"70220617","displayToPublicDate":"2021-05-19T06:41:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8605,"text":"JGR-Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models","docAbstract":"Re-examination of previously published dissolved iron time-series data from Ocean Station Papa in the central Gulf of Alaska (GoA) reveals 33-70% increases in the dissolved iron inventories occurring between September and February of successive years, implying a source of Fe to this region during autumn or early winter. Because I can virtually rule out many possible iron sources at this time of year, I suggest Alaskan glacial dust is the likely iron source. Large plumes of such dust are known to be generated regularly in the autumn by anomalous offshore winds and channelled through mountain gaps, simultaneously from several locations spanning ∼1000 km of the northern Gulf of Alaska coastline. Large dust flux events occur when below-freezing, low-humidity air temperatures persist for many days during the autumn. I suggest that existing state-of-the-art global dust models fail to reproduce this Alaskan dust flux because the model spatial resolution is too coarse to resolve the high winds through the narrow mountain gaps that generate the dust. Future work that could help to confirm this Fe source to the central GoA includes time-series profiles of iron concentrations, and ancillary information from sensor-equipped profiling floats. If this mechanism of Fe supply to the central GoA were confirmed, it would imply this Alaskan dust is transported ≥ 1100 km from the coast, more than twice as far as has been visually documented from satellite observations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006323","usgsCitation":"Crusius, J., 2021, Dissolved Fe supply to the central Gulf of Alaska is inferred to be derived from Alaskan glacial dust that is not resolved by dust transport models: JGR-Biogeosciences, v. 126, e2021JG006323, 13 p., https://doi.org/10.1029/2021JG006323.","productDescription":"e2021JG006323, 13 p.","ipdsId":"IP-102176","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":385832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.28125,\n 51.39920565355378\n ],\n [\n -131.1328125,\n 51.39920565355378\n ],\n [\n -131.1328125,\n 59.5343180010956\n ],\n [\n -153.28125,\n 59.5343180010956\n ],\n [\n -153.28125,\n 51.39920565355378\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Crusius, John 0000-0003-2554-0831 jcrusius@usgs.gov","orcid":"https://orcid.org/0000-0003-2554-0831","contributorId":2155,"corporation":false,"usgs":true,"family":"Crusius","given":"John","email":"jcrusius@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":816240,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70220525,"text":"ofr20211043 - 2021 - Dynamics of endangered sucker populations in Clear Lake Reservoir, California","interactions":[],"lastModifiedDate":"2021-05-19T12:01:59.893466","indexId":"ofr20211043","displayToPublicDate":"2021-05-18T16:18:22","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1043","displayTitle":"Dynamics of Endangered Sucker Populations in Clear Lake Reservoir, California","title":"Dynamics of endangered sucker populations in Clear Lake Reservoir, California","docAbstract":"In collaboration with the Bureau of Reclamation, the U.S. Geological Survey began a consistent monitoring program for endangered Lost River suckers (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Clear Lake Reservoir, California, in fall 2004. The program was intended to improve understanding of the Clear Lake Reservoir populations because they are important to recovery efforts for these species. We report results from the ongoing program and include sampling efforts through fall 2019. We summarize catches and passive integrated transponder (PIT) tagging efforts from trammel net sampling in the fall seasons (September–October each year) and detections of PIT-tagged suckers on remote antennas in the spring in each year from 2006 to 2019. We also combine the data from physical captures and remote detections in capture-recapture models to provide estimates of annual survival for suckers in the reservoir.
A lack of genetic distinctiveness between shortnose suckers and Klamath largescale suckers (Catostomus snyderi) in the Lost River subbasin, including Clear Lake Reservoir, is a likely cause of past difficulty in identification of these species. Field identification can be subjective for many captured individuals, and very few individuals were identified as Klamath largescale suckers in the most recent years of our monitoring program. For this report, we combine individuals that were identified as either shortnose sucker (SNS) or Klamath largescale sucker (KLS) into a single “SNS-KLS” group for most analyses. Identification of Lost River suckers (LRS) is based on external morphological characteristics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211043","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Hewitt, D.A., Hayes, B.S., Harris, A.C., Janney, E.C., Kelsey, C.M., Perry, R.W., and Burdick, S.M., 2021, Dynamics of endangered sucker populations in Clear Lake Reservoir, California: U.S. Geological Survey Open-File Report 2021–1043, 59 p., https://doi.org/10.3133/ofr20211043.","productDescription":"v, 59 p.","onlineOnly":"Y","ipdsId":"IP-108970","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":385709,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1043/coverthb.jpg"},{"id":385710,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1043/ofr20211043.pdf","text":"Report","size":"12.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1043"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake Reservoir","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.25747680664064,\n 41.78360106648078\n ],\n [\n -121.01852416992186,\n 41.78360106648078\n ],\n [\n -121.01852416992186,\n 41.96663812286332\n ],\n [\n -121.25747680664064,\n 41.96663812286332\n ],\n [\n -121.25747680664064,\n 41.78360106648078\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
In 2015–16, a comparison study of stream sediment collection techniques was done for a U.S. Geological Survey streamgage on the Nemadji River near South Superior, Wisconsin (U.S. Geological Survey station number 04024430) to provide an adjustment factor for comparing suspended-sediment rating curves for two historical periods 1973–86 and 2006–16. During 1973–1986, the U.S. Geological Survey used the equal-width-increment technique to collect suspended-sediment concentration data (EWI SSC). The Wisconsin Department of Natural Resources and Minnesota Pollution Control Agency collected grab samples for total suspended solids (grab TSS) concentration starting in 2006 and continuing beyond 2016. In addition to the comparison study of suspended-sediment concentrations, bedload and bed material samples were collected in 2015–16, and the modified Einstein procedure was run to further characterize total sediment loads. The 2015–16 study indicated that the EWI SSC and grab TSS concentrations were different, but not as much as expected, especially on the high end where grab TSS concentrations were sometimes higher than EWI SSC concentrations, possibly due to a combination of a high percentage of fines in suspension and higher concentrations in the center of the channel than the margins. The 2015–16 measured bedload made up a small percentage of total sediment load, and bedload and streambed particle sizes are 90 to 100 percent sand sized or smaller. The relative proportion of measured bedload to total load decreased with increased streamflow, and for streamflows greater than 1,800 cubic feet per second, the suspended load made up 98 percent of the total load. Calculated 2015–16 instantaneous total sediment loads from the modified Einstein procedure were up to 70 percent of the measured loads for flows less than 1,000 cubic feet per second and near or more than 100 percent for flows greater than 1,000 cubic feet per second. The sediment rating curve developed for the 2006–16 adjusted grab TSS data had a similar slope but a lower intercept than its 1973–86 EWI SSC counterpart, indicating that for a given streamflow, suspended-sediment concentrations were lower for 2006–16 compared to 1973–86. The negative offset equates to estimates of annual suspended-sediment loads in 2006–16 being on average 87 percent of the 1973–86 loads. Over the period 2009–16, annual suspended-sediment loads ranged from a low of about 21,000 tons per year in 2015 to a high of 167,000 tons per year in 2012 with a mean of 85,000 tons per year. However, reductions in suspended-sediment concentrations are likely obscured by large loads during years with flooding.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211003","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources","usgsCitation":"Fitzpatrick, F.A., 2021, Sediment characteristics of northwestern Wisconsin’s Nemadji River, 1973–2016: U.S. Geological Survey Open-File Report 2021–1003, 27 p., https://doi.org/10.3133/ofr20211003.","productDescription":"Report: viii, 27 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-085024","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385361,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FX0X6Y","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selected sediment data and results from regression models, modified Einstein Procedure, and loads estimation for the Nemadji River, 1973–2016"},{"id":385360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1003/ofr20211003.pdf","text":"Report","size":"5.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1003"},{"id":385359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1003/coverthb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"Nemadji River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.55157470703125,\n 46.38672781370433\n ],\n [\n -92.01599121093749,\n 46.38672781370433\n ],\n [\n -92.01599121093749,\n 46.65697731621612\n ],\n [\n -92.55157470703125,\n 46.65697731621612\n ],\n [\n -92.55157470703125,\n 46.38672781370433\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Baker River (Washington, USA) sockeye salmon (Oncorhynchus nerka) are a recovering Puget Sound stock that are aided by trap-and-haul and hatchery programs to mitigate for the presence of a high head dam. The relative contribution of hatchery and natural adults to overall production of smolts and recruits is unknown. The ability to identify three different sockeye production groups (natural production, artificial incubation, and artificial spawning beach) within the Baker system is crucial to moving forward with management goals. The examination of otoliths was proposed as a technical tool for improved understanding and management of Baker sockeye rebuilding efforts. Otoliths were chosen as they provide a chronological record on an individual fish basis and have been shown to identify fish origin through both otolith microstructure and chemistry.
The goal of this pilot project was to determine the feasibility of assigning sockeye to their production source based on otolith analysis. A variety of methods were employed and compared for accuracy of group assignment. The maximum overall accuracy capable of attainment was 88.57 percent, however complete confidence (100 percent) in the separation of natural production from artificial production was reached through the analysis of trace elements alone. Some segregation of the two artificial production groups was reached through analysis of a few specific trace elements (magnesium, manganese, and zinc). This confidence in assignment for the artificial production groups was aided by a two-step process of combining trace elements with microstructure. The Sr isotope ratios supported the trace element findings but did not help to boost the overall level of confidence in the separation of production groups. Based upon the results from this preliminary investigation, one could choose a statistically sound, efficient, and cost-effective use of otoliths as a tool for discriminating between the sockeye production groups of the Baker Lake system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211032","collaboration":"A Report to the Upper Skagit Indian Tribe per agreement # 17WNYD00SIT5545","usgsCitation":"Larsen, K.A., Wetzel, L.A., Stenberg, K.D., and Lind-Null, A.M., 2021, Investigation of otolith microstructure and composition for identification of rearing strategies and associated Baker Lake sockeye salmon (Oncorhynchus nerka) smolt production, Washington, 2016–17: U.S. Geological Survey Open-File Report 2021–1032, 15 p., https://doi.org/10.3133/ofr20211032.","productDescription":"vii, 16 p.","onlineOnly":"Y","ipdsId":"IP-091287","costCenters":[{"id":654,"text":"Western Fisheries Research 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\"}}]}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Integrating remote sensing into assessments of carbon stocks and fluxes has advanced our understanding of how global change affects landscapes and our capacity to support decision making about forest management. However, there remains a lack of detailed and actionable analyses conducted across widely ranging environmental conditions that are appropriate for tactical planning. We used airborne laser scanning data and multi-source satellite imagery to estimate forest aboveground carbon density and gross primary production, and to map forest cover across the main Hawaiian Islands. We used these measures to develop the Carbon Sequestration Potential Index (CSPI), which identifies where the potential for carbon sequestration following afforestation would be highest within a complex landscape of 304 management units. Variation in CSPI was high across islands and between ecosystems, with low values for cool, dry and largely intact forest systems and high values for warm, wet and largely non-forested systems. The CSPI provided a rapid, spatially-explicit and actionable assessment of Hawaiian forest reserves, which can help stewardship agencies contribute to state carbon neutrality goals through climate-smart and science-driven prescriptions that encompass conservation to restoration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119343","usgsCitation":"Pascual, A., Giardina, C.P., Selmants, P., Laramee, L.J., and Asner, G.P., 2021, A new remote sensing-based Carbon Sequestration Potential Index (CSPI): A tool to support land carbon management: Forest Ecology and Management, v. 494, 119343, 10 p., https://doi.org/10.1016/j.foreco.2021.119343.","productDescription":"119343, 10 p.","ipdsId":"IP-125868","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":452211,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119343","text":"Publisher Index Page"},{"id":386868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.45654296875,\n 18.8335153964335\n ],\n [\n -154.632568359375,\n 19.580493479202527\n ],\n [\n -155.928955078125,\n 20.910134481692683\n ],\n [\n -156.895751953125,\n 21.320080964008206\n ],\n [\n -157.96142578124997,\n 21.790107059807873\n ],\n [\n -159.59838867187497,\n 22.370396344320053\n ],\n [\n -159.9169921875,\n 22.085639901650328\n ],\n [\n -158.192138671875,\n 21.135745255030603\n ],\n [\n -157.005615234375,\n 20.64306554672647\n ],\n [\n -156.478271484375,\n 19.921712747556207\n ],\n [\n -156.07177734375,\n 18.8335153964335\n ],\n [\n -155.45654296875,\n 18.8335153964335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"494","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pascual, Adrian","contributorId":260677,"corporation":false,"usgs":false,"family":"Pascual","given":"Adrian","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":818464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giardina, Christian P. 0000-0002-3431-5073","orcid":"https://orcid.org/0000-0002-3431-5073","contributorId":182695,"corporation":false,"usgs":false,"family":"Giardina","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":818465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selmants, Paul 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":818466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laramee, Leah J","contributorId":260678,"corporation":false,"usgs":false,"family":"Laramee","given":"Leah","email":"","middleInitial":"J","affiliations":[{"id":52640,"text":"Dept. of Land and Natural Resources, State of Hawaii","active":true,"usgs":false}],"preferred":false,"id":818467,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":818468,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263925,"text":"70263925 - 2021 - Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning","interactions":[],"lastModifiedDate":"2025-02-28T16:13:52.073178","indexId":"70263925","displayToPublicDate":"2021-05-18T10:10:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning","docAbstract":"Earthquake early warning (EEW) systems can quickly identify the beginning of a significant earthquake rupture, but the first seconds of seismic data have not been found to predict the final rupture length. We present two approaches for estimating probabilities of rupture length given the rupture initiation from an EEW system. In the first approach, bends and steps on the fault are interpreted as physical mechanisms for rupture arrest. Arrest probability relations are developed from empirical observations and depend on bend angle and step size. Probability of arrest compounds serially with increasing rupture length as bends or steps are encountered. In the second approach, time‐independent rates among ruptures from the Uniform California Earthquake Rupture Forecast, Version 3 (UCERF3), are interpreted to apply to the time‐dependent condition in which rupture grows from a known starting point. Length probabilities from a Gutenberg–Richter magnitude–frequency relation provide a reference of comparison. We illustrate the new approach using the discretized fault model for California developed for UCERF3. For the case of rupture initiating on the southeast end of the San Andreas fault we find the geometric complexity of the Mill Creek section impedes most ruptures, and only ∼5% are predicted to reach to San Bernardino on the eastern edge of the greater Los Angeles region. Conditional probabilities of length can be precompiled in this manner for any initiation point on the fault system and thus are of potential value in seismic hazard and EEW applications.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200370","usgsCitation":"Biasi, G., and Wesnousky, S.G., 2021, Rupture passing probabilities at fault bends and steps, with application to rupture length probabilities for earthquake early warning: Bulletin of the Seismological Society of America, v. 111, no. 4, p. 2235-2247, https://doi.org/10.1785/0120200370.","productDescription":"13 p.","startPage":"2235","endPage":"2247","ipdsId":"IP-116890","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"111","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929127,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wesnousky, Steven G.","contributorId":193416,"corporation":false,"usgs":false,"family":"Wesnousky","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":33746,"text":"Center for Neotectonic Studies, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":929128,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70229234,"text":"70229234 - 2021 - Rediscovery and genetic confirmation of the Threeridge Mussel, Amblema plicata (Say, 1817) (Bivalvia, Unionidae), in the Choctawhatchee River, Florida, USA","interactions":[],"lastModifiedDate":"2022-03-03T16:08:43.422797","indexId":"70229234","displayToPublicDate":"2021-05-18T10:04:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1208,"text":"Check List","active":true,"publicationSubtype":{"id":10}},"title":"Rediscovery and genetic confirmation of the Threeridge Mussel, Amblema plicata (Say, 1817) (Bivalvia, Unionidae), in the Choctawhatchee River, Florida, USA","docAbstract":"Recent freshwater mussel research has resulted in rediscovery of several species presumed extinct. We report the rediscovery of Amblema plicata (Say, 1817) in 2019 from the Choctawhatchee River, Florida, USA. Amblema plicata has not been reported in the Choctawhatchee river basin since 1958, more than 61 years ago. This species was collected during the long-term monitoring of freshwater mussels in Florida streams. We provide genetic confirmation of our voucher identification using a DNA barcoding approach and discuss potential risks to A. plicata populations in the Choctawhatchee river basin.
","language":"English","publisher":"Pensoft","doi":"10.15560/17.3.783","usgsCitation":"Patterson, L.N., Geda, S.R., and Johnson, N., 2021, Rediscovery and genetic confirmation of the Threeridge Mussel, Amblema plicata (Say, 1817) (Bivalvia, Unionidae), in the Choctawhatchee River, Florida, USA: Check List, v. 17, no. 3, p. 783-790, https://doi.org/10.15560/17.3.783.","productDescription":"8 p.","startPage":"783","endPage":"790","ipdsId":"IP-124667","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452215,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15560/17.3.783","text":"Publisher Index Page"},{"id":396705,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Choctawhatchee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.10671997070311,\n 30.359841397025537\n ],\n [\n -85.79635620117188,\n 30.359841397025537\n ],\n [\n -85.79635620117188,\n 30.782547981939047\n ],\n [\n -86.10671997070311,\n 30.782547981939047\n ],\n [\n -86.10671997070311,\n 30.359841397025537\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Patterson, Lauren N.","contributorId":287676,"corporation":false,"usgs":false,"family":"Patterson","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":837003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Geda, Susan R.","contributorId":287678,"corporation":false,"usgs":false,"family":"Geda","given":"Susan","email":"","middleInitial":"R.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":837004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":837005,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224285,"text":"70224285 - 2021 - Identifying chemicals and mixtures of potential biological concern detected in passive samplers from Great Lakes tributaries using high-throughput data and biological pathways","interactions":[],"lastModifiedDate":"2021-09-20T12:59:52.651404","indexId":"70224285","displayToPublicDate":"2021-05-18T07:57:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Identifying chemicals and mixtures of potential biological concern detected in passive samplers from Great Lakes tributaries using high-throughput data and biological pathways","docAbstract":"Waterborne contaminants were monitored in 69 tributaries of the Laurentian Great Lakes in 2010 and 2014 using semipermeable membrane devices (SPMDs) and polar organic chemical integrative samplers (POCIS). A risk-based screening approach was used to prioritize chemicals and chemical mixtures, identify sites at greatest risk for biological impacts, and identify potential hazards to monitor at those sites. Analyses included 185 chemicals (143 detected) including polycyclic aromatic hydrocarbons (PAHs), legacy and current-use pesticides, fire retardants, pharmaceuticals, and fragrances. Hazard quotients were calculated by dividing detected concentrations by biological effect concentrations reported in the ECOTOX Knowledgebase (toxicity quotients) or ToxCast database (exposure–activity ratios [EARs]). Mixture effects were estimated by summation of EAR values for chemicals that influence ToxCast assays with common gene targets. Nineteen chemicals—atrazine, N,N-diethyltoluamide, di(2-ethylhexyl)phthalate, dl-menthol, galaxolide, p-tert-octylphenol, 3 organochlorine pesticides, 3 PAHs, 4 pharmaceuticals, and 3 phosphate flame retardants—had toxicity quotients >0.1 or EARs for individual chemicals >10–3 at 10% or more of the sites monitored. An additional 4 chemicals (tributyl phosphate, triethyl citrate, benz[a]anthracene, and benzo[b]fluoranthene) were present in mixtures with EARs >10–3. To evaluate potential apical effects and biological endpoints to monitor in exposed wildlife, in vitro bioactivity data were compared to adverse outcome pathway gene ontology information. Endpoints and effects associated with endocrine disruption, alterations in xenobiotic metabolism, and potentially neuronal development would be relevant to monitor at the priority sites. The EAR threshold exceedance for many chemical classes was correlated with urban land cover and wastewater effluent influence, whereas herbicides and fire retardants were also correlated to agricultural land cover. Environ Toxicol Chem 2021;40:2165–2182. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Increasing the resilience of coastal communities while decreasing the risk to them are key to the continued inhabitance and sustainability of these areas. Low-lying coral reef-lined islands are experiencing storm wave-driven flood events that currently strike with little to no warning. These events are occurring more frequently and with increasing severity. There is a need along the world’s coral reef-lined coasts for a tool that can forecast the timing and severity of wave-driven flooding events. Without this tool, coastal communities are vulnerable to:
loss of life from drowning • loss of, and damage to, property and infrastructure • decreasing viability of communities via loss of, and damage to crops, fishing (via decreased water quality and wave-damaged reefs), and freshwater resources • reduction of livable land due to increased erosion and salt intrusion. The currently available tools were developed for sandy shorelines and do not accurately predict wave-driven flooding on reef-lined coasts, leaving inhabitants without accurate and timely warnings. In addition, the flood models that do exist for reef-lined coasts have only been implemented on a small number of areas throughout the world because running these models is costly and requires a high level of computing power. Using these existing models and techniques to generate high-resolution forecasts for wave-driven flooding for all reef-lined coasts would cost approximately US$1 billion. To remedy this issue, an international team associated with the GEO Blue Planet initiative is working to develop a wave-driven flood-forecasting early-warning system (EWS) for coral reef-lined coasts known as WaveFoRCE. The system aims to provide all nations and people living on a coral reef-lined coast anywhere in the world with an up to 7.5-day forecast of storm wave-driven flood events.
","largerWorkType":{"id":25,"text":"Newsletter"},"largerWorkTitle":"Environment Coastal & Offshore (ECO)","language":"English","publisher":"United Nations","usgsCitation":"Skirving, W., Storlazzi, C.D., and Smail, E.A., 2021, Wave-driven flood-forecasting on reef-lined coasts early warning system (WaveFoRCE), p. 144-147.","productDescription":"4 p.","startPage":"144","endPage":"147","ipdsId":"IP-127710","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":386977,"type":{"id":15,"text":"Index Page"},"url":"https://www.oceandecade.org/news/128/ECO-Magazine--special-digital-issue-on-the-Ocean-Decade-May-2021"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skirving, William","contributorId":224303,"corporation":false,"usgs":false,"family":"Skirving","given":"William","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smail, Emily A","contributorId":217219,"corporation":false,"usgs":false,"family":"Smail","given":"Emily","email":"","middleInitial":"A","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818747,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220611,"text":"70220611 - 2021 - Pilot-scale expanded assessment of inorganic and organic tapwater exposures and predicted effects in Puerto Rico, USA","interactions":[],"lastModifiedDate":"2021-06-01T17:49:41.562885","indexId":"70220611","displayToPublicDate":"2021-05-18T06:57:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1523,"text":"Environment International","active":true,"publicationSubtype":{"id":10}},"title":"Pilot-scale expanded assessment of inorganic and organic tapwater exposures and predicted effects in Puerto Rico, USA","docAbstract":"A pilot-scale expanded target assessment of mixtures of inorganic and organic contaminants in point-of-consumption drinking water (tapwater, TW) was conducted in Puerto Rico (PR) to continue to inform TW exposures and corresponding estimations of cumulative human-health risks across the US. In August 2018, a spatial synoptic pilot assessment of than 524 organic, 37 inorganic, and select microbiological contaminant indicators was conducted in 14 locations (7 home; 7 commercial) across PR. A follow-up 3-day temporal assessment of TW variability was conducted in December 2018 at two of the synoptic locations (1 home, 1 commercial) and included daily pre- and post-flush samples. Concentrations of regulated and unregulated TW contaminants were used to calculate cumulative in vitro bioactivity ratios and Hazard Indices (HI) based on existing human-health benchmarks. Synoptic results confirmed that human exposures to inorganic and organic contaminant mixtures, which are rarely monitored together in drinking water at the point of consumption, occurred across PR and consisted of elevated concentrations of inorganic contaminants (e.g., lead, copper), disinfection byproducts (DBP), and to a lesser extent per/polyfluoroalkyl substances (PFAS) and phthalates. Exceedances of human-health benchmarks in every synoptic TW sample support further investigation of the potential cumulative risk to vulnerable populations in PR and emphasize the importance of continued broad characterization of drinking-water exposures at the tap with analytical capabilities that better represent the complexity of both inorganic and organic contaminant mixtures known to occur in ambient source waters. Such health-based monitoring data are essential to support public engagement in source water sustainability and treatment and to inform consumer point-of-use treatment decision making in PR and throughout the US.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.147721","usgsCitation":"Bradley, P., Padilla, I.Y., Romanok, K., Smalling, K., Focazio, M.J., Breitmeyer, S.E., Cardon, M.C., Conley, J.M., Evans, N., Givens, C.E., Gray, J., Gray, L., Hartig, P.C., Hladik, M.L., Higgins, C.P., Iwanowicz, L., Lane, R.F., Loftin, K.A., McCleskey, R., McDonough, C.A., Medlock-Kakaley, E., Meppelink, S.M., Weis, C.P., and Wilson, V.S., 2021, Pilot-scale expanded assessment of inorganic and organic tapwater exposures and predicted effects in Puerto Rico, USA: Environment International, v. 788, 147721, 14 p., https://doi.org/10.1016/j.scitotenv.2021.147721.","productDescription":"147721, 14 p.","ipdsId":"IP-110491","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":452219,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://europepmc.org/pmc/articles/PMC8504685","text":"Publisher Index Page"},{"id":436359,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EQS5CS","text":"USGS data release","linkHelpText":"Target-Chemical Concentration Results of Mixed-Organic/Inorganic Chemical Exposures in Puerto Rico Tapwater, 2017 to 2018"},{"id":385835,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3681640625,\n 17.727758609852284\n ],\n [\n -65.5224609375,\n 17.727758609852284\n ],\n [\n -65.5224609375,\n 18.625424540701264\n ],\n [\n -67.3681640625,\n 18.625424540701264\n ],\n [\n -67.3681640625,\n 17.727758609852284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"788","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Padilla, Ingrid Y. 0000-0001-8460-1679","orcid":"https://orcid.org/0000-0001-8460-1679","contributorId":258259,"corporation":false,"usgs":false,"family":"Padilla","given":"Ingrid","email":"","middleInitial":"Y.","affiliations":[{"id":52264,"text":"University of Puerto Rico-Mayaguez","active":true,"usgs":false}],"preferred":false,"id":816178,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816176,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smalling, Kelly 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":221234,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816177,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Focazio, Michael J. 0000-0003-0967-5576 mfocazio@usgs.gov","orcid":"https://orcid.org/0000-0003-0967-5576","contributorId":1276,"corporation":false,"usgs":true,"family":"Focazio","given":"Michael","email":"mfocazio@usgs.gov","middleInitial":"J.","affiliations":[{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true}],"preferred":true,"id":816179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breitmeyer, Sara E. 0000-0003-0609-1559 sbreitmeyer@usgs.gov","orcid":"https://orcid.org/0000-0003-0609-1559","contributorId":172622,"corporation":false,"usgs":true,"family":"Breitmeyer","given":"Sara","email":"sbreitmeyer@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":816180,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cardon, Mary C.","contributorId":190792,"corporation":false,"usgs":false,"family":"Cardon","given":"Mary","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":816181,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Conley, Justin M.","contributorId":184086,"corporation":false,"usgs":false,"family":"Conley","given":"Justin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":816182,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Evans, Nicola","contributorId":184087,"corporation":false,"usgs":false,"family":"Evans","given":"Nicola","email":"","affiliations":[],"preferred":false,"id":816183,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Givens, Carrie E. 0000-0003-2543-9610","orcid":"https://orcid.org/0000-0003-2543-9610","contributorId":247691,"corporation":false,"usgs":true,"family":"Givens","given":"Carrie","middleInitial":"E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816184,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gray, James L. 0000-0002-0807-5635","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":202726,"corporation":false,"usgs":true,"family":"Gray","given":"James L.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":816185,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Gray, L. Earl","contributorId":193147,"corporation":false,"usgs":false,"family":"Gray","given":"L. 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Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. 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The geochronology of this coastal system is based on uranium series, radiocarbon, amino acid racemization (AAR), and optically stimulated luminescence (OSL) methods. We report over 600 mollusk AAR results from 93 sites between northeastern North Carolina and the central New Jersey shelf, representing samples from both onshore cores or outcrops, sub-barrier and offshore cores, and transported shells from barrier island beaches. AAR age estimates are constrained by paired 14C analyses on specific shells and associated U-series coral ages from onshore sites. AAR data from offshore cores are interpreted in the context of detailed seismic stratigraphy. The distribution of Pleistocene-age shells on the island beaches is linked to the distribution of inner shelf or sub-barrier source units. Age mixing over a range of time-scales (~1 ka to ~100 ka) is identified by AAR results from onshore, beach, and shelf collections, often contributing insights into the processes forming individual barrier islands. The regional aminostratigraphic framework identifies a widespread late Pleistocene (Marine Isotope Stage 5) aminozone, with isolated records of middle and early Pleistocene deposition. AAR results provide age estimates for the timing of formation of the three major paleochannels that underlie the Delmarva Peninsula: Persimmon Point paleochannel ≥800 ka; Exmore paleochannel ~400–500 ka (MIS 12); and Eastville paleochannel > 125 ka (MIS 6). The results demonstrate the value of synthesizing abundant AAR chronologic data across various coastal environments, integrating multiple distinct geologic studies. The ages and elevations of the Quaternary units are important for current hypotheses about relative sea-level history and crustal dynamics in the region, which was likely influenced by the Laurentide ice sheet, the margin just ~400 km to the north.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quageo.2021.101177","usgsCitation":"Wehmiller, J., Brothers, L.L., Ramsey, K., Foster, D.S., Mattheus, C., Hein, C., and Shawler, J.L., 2021, Molluscan aminostratigraphy of the US Mid-Atlantic Quaternary coastal system: Implications for onshore-offshore correlation, paleochannel and barrier island evolution, and local late Quaternary sea-level history: Quaternary Geochronology, v. 66, 101177, 34 p., https://doi.org/10.1016/j.quageo.2021.101177.","productDescription":"101177, 34 p.","ipdsId":"IP-122893","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":452222,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quageo.2021.101177","text":"Publisher Index Page"},{"id":386336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland, Virginia","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.365966796875,\n 36.914764288955936\n ],\n [\n -74.8828125,\n 36.914764288955936\n ],\n [\n -74.8828125,\n 39.791654835253425\n ],\n [\n -76.365966796875,\n 39.791654835253425\n ],\n [\n -76.365966796875,\n 36.914764288955936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"66","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wehmiller, John","contributorId":260088,"corporation":false,"usgs":false,"family":"Wehmiller","given":"John","affiliations":[{"id":52500,"text":"University of Delaware, Newark DE","active":true,"usgs":false}],"preferred":false,"id":817215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Laura L. 0000-0003-2986-5166 lbrothers@usgs.gov","orcid":"https://orcid.org/0000-0003-2986-5166","contributorId":176698,"corporation":false,"usgs":true,"family":"Brothers","given":"Laura","email":"lbrothers@usgs.gov","middleInitial":"L.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":817216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, Kelvin","contributorId":260089,"corporation":false,"usgs":false,"family":"Ramsey","given":"Kelvin","email":"","affiliations":[{"id":52502,"text":"Geological Survey, University of Delaware, Newark DE","active":true,"usgs":false}],"preferred":false,"id":817217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foster, David S. 0000-0003-1205-0884 dfoster@usgs.gov","orcid":"https://orcid.org/0000-0003-1205-0884","contributorId":1320,"corporation":false,"usgs":true,"family":"Foster","given":"David","email":"dfoster@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":817218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mattheus, C.R.","contributorId":260090,"corporation":false,"usgs":false,"family":"Mattheus","given":"C.R.","email":"","affiliations":[{"id":52504,"text":"Illinois Geological Survey, DGS","active":true,"usgs":false}],"preferred":false,"id":817219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hein, Christopher","contributorId":214093,"corporation":false,"usgs":false,"family":"Hein","given":"Christopher","affiliations":[{"id":18865,"text":"VIMS","active":true,"usgs":false}],"preferred":false,"id":817220,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shawler, Justin L.","contributorId":256701,"corporation":false,"usgs":false,"family":"Shawler","given":"Justin","email":"","middleInitial":"L.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":817221,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230080,"text":"70230080 - 2021 - Quantifying eruptive and background seismicity, deformation, degassing, and thermal emissions at volcanoes in the United States during 1978–2020","interactions":[],"lastModifiedDate":"2022-03-28T11:44:20.391333","indexId":"70230080","displayToPublicDate":"2021-05-18T06:40:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying eruptive and background seismicity, deformation, degassing, and thermal emissions at volcanoes in the United States during 1978–2020","docAbstract":"An important aspect of volcanic hazard assessment is determination of the level and character of background activity at a volcano so that deviations from background (called unrest) can be identified. Here, we compile the instrumentally recorded eruptive and noneruptive activity for 161 US volcanoes between 1978 and 2020. We combine monitoring data from four techniques: seismicity, ground deformation, degassing, and thermal emissions. To previous work, we add the first comprehensive survey of US volcanoes using medium-spatial resolution satellite thermal observations, newly available field surveys of degassing, and new compilations of seismic and deformation data. We report previously undocumented thermal activity at 30 volcanoes using data from the spaceborne ASTER sensor during 2000–2020. To facilitate comparison of activity levels for all US volcanoes, we assign a numerical classification of the Activity Intensity Level for each monitoring technique, with the highest ranking corresponding to an eruption. There are 96 US volcanoes (59%) with at least one type of detected activity, but this represents a lower bound: For example, there are 12 volcanoes where degassing has been observed but has not yet been quantified. We identify dozens of volcanoes where volcanic activity is only measured by satellite (45% of all thermal observations), and other volcanoes where only ground-based sensors have detected activity (e.g., all seismic and 62% of measured degassing observations). Our compilation provides a baseline against which future measurements can be compared, demonstrates the need for both ground-based and remote observations, and serves as a guide for prioritizing future monitoring efforts.
The St. Clair and Detroit rivers historically supported abundant fish populations. However, like many river systems, these rivers have been greatly altered through the creation of navigation channels and other anthropogenic disturbances, resulting in the loss of fish and wildlife habitat and declines in native fish populations. To ameliorate this environmental degradation, artificial fish spawning reefs were constructed in the St. Clair and Detroit rivers. One native species to potentially benefit from artificial reefs is the Northern Madtom Noturus stigmosus, a small ictalurid that is listed as endangered in the state of Michigan and the province of Ontario. Between 2016 and 2018, artificial reefs and nearby control sites were sampled in the St. Clair and Detroit rivers to compare the number of Northern Madtoms. In total, 171 Northern Madtoms were captured in 1,848 minnow traps with one of four bait types: cheese, dog food, worms, or control (no bait). Baited minnow traps successfully captured Northern Madtoms in the fast-flowing, deep water of the St. Clair–Detroit River system, and catch rates were significantly higher when traps were baited with worms. The number of Northern Madtoms captured was lower in the Detroit River than in the St. Clair River and increased with increasing water temperature and turbidity. Artificial reefs constructed in the St. Clair–Detroit River system are providing habitat for Northern Madtoms; however, use did not differ between reef sites and nearby control sites. This work provides insight regarding sampling strategies to target Northern Madtoms in large-river systems and highlights the importance of incorporating a temporal sampling strategy into survey design.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10614","usgsCitation":"Johnson, J., Chiotti, J., Briggs, A.S., Boase, J., Hessenauer, J., and Roseman, E., 2021, Northern Madtom use of artificial reefs in the St. Clair–Detroit River System: North American Journal of Fisheries Management, v. 41, no. S1, p. S42-S53, https://doi.org/10.1002/nafm.10614.","productDescription":"12 p.","startPage":"S42","endPage":"S53","ipdsId":"IP-119659","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":452226,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10614","text":"Publisher Index Page"},{"id":386094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Detroit River, St Clair River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.07861328125,\n 42.037054301883806\n ],\n [\n -82.94677734375,\n 42.28950073090457\n ],\n [\n -82.4359130859375,\n 42.27730877423709\n ],\n [\n -82.3590087890625,\n 42.532844281713125\n ],\n [\n -82.4853515625,\n 42.56926437219384\n ],\n [\n -82.353515625,\n 43.01669737169671\n ],\n [\n -82.4359130859375,\n 43.04881979669318\n ],\n [\n -82.5677490234375,\n 42.70665956351041\n ],\n [\n -82.7764892578125,\n 42.73894375124377\n ],\n [\n -82.9742431640625,\n 42.55712670332118\n ],\n [\n -82.9742431640625,\n 42.382894009614034\n ],\n [\n -83.177490234375,\n 42.3016903282445\n ],\n [\n -83.2928466796875,\n 42.08191667830631\n ],\n [\n -83.22143554687499,\n 41.97582726102573\n ],\n [\n -83.07861328125,\n 42.037054301883806\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"S1","noUsgsAuthors":false,"publicationDate":"2021-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Jennifer","contributorId":258148,"corporation":false,"usgs":false,"family":"Johnson","given":"Jennifer","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":815839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiotti, Justin A.","contributorId":26629,"corporation":false,"usgs":false,"family":"Chiotti","given":"Justin A.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":815840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Andrew S 0000-0002-0268-9310","orcid":"https://orcid.org/0000-0002-0268-9310","contributorId":215596,"corporation":false,"usgs":false,"family":"Briggs","given":"Andrew","email":"","middleInitial":"S","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boase, James C.","contributorId":38077,"corporation":false,"usgs":false,"family":"Boase","given":"James C.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":815842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hessenauer, Jan-Michael","contributorId":257795,"corporation":false,"usgs":false,"family":"Hessenauer","given":"Jan-Michael","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815844,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221150,"text":"70221150 - 2021 - Aeolian sediments in paleowetland deposits of the Las Vegas Formation","interactions":[],"lastModifiedDate":"2022-01-06T17:13:04.657899","indexId":"70221150","displayToPublicDate":"2021-05-17T08:21:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Aeolian sediments in paleowetland deposits of the Las Vegas Formation","docAbstract":"The Las Vegas Formation (LVF) is a well-characterized sequence of groundwater discharge (GWD) deposits exposed in and around the Las Vegas Valley in southern Nevada. Nearly monolithologic bedrock surrounds the valley, which provides an excellent opportunity to test the hypothesis that GWD deposits include an aeolian component. Mineralogical data indicate that the LVF sediments are dominated by carbonate minerals, similar to the local bedrock, but silicate minerals are also present. The median particle size is ~35 μm, consistent with modern dust in the region, and magnetic properties contrast strongly with local bedrock, implying an extralocal origin. By combining geochemical data from the LVF sediments and modern dust, we found that an average of ~25% of the LVF deposits were introduced by aeolian processes. The remainder consists primarily of authigenic groundwater carbonate as well as minor amounts of alluvial material and soil carbonate. Our data also show that the aeolian sediments accumulated in spring ecosystems in the Las Vegas Valley in a manner that was independent of both time and the specific hydrologic environment. These results have broad implications for investigations of GWD deposits located elsewhere in the southwestern U.S. and worldwide.
We used a 12-yr data set of benthic cover (2005–2017), spanning two bleaching events, to assess changes in benthic cover and coral community composition along 21 islands within Gulf of Mannar (GoM), southeast India. Overall, between 2005 and 2017 reefs had a simultaneous decrease in relative coral cover (avg. = − 36%) and increase in algal cover (avg. = + 45%). Changes in benthic cover were not consistent among islands, ranging from − 34 to + 5% for coral cover and from − 0.3 to + 50% for algae. There was a spatial gradient in coral mortality, which increased among islands from west to east. However, there was a disconnect between coral loss and subsequent increases in algae. Algal cover increased more on islands in west GoM where coral loss was minimal. Environmental co-factors (coral cover, percent bleaching, degree heating weeks, fish densities, Chl-a, pollution) explained > 50% of the benthic cover responses to successive bleaching. Coral survival was favored on islands with higher fish densities and chlorophyll-a levels, and increases in algal cover were associated with higher measures of pollution from terrestrial runoff. Coral morphotypes differed in their response following successive bleaching resulting in changes in the relative abundance of different coral morphotypes. Existing climate projections (RCP8.5) indicate a 22-yr gap in the onset of annual severe bleaching (ASB) for reefs in the east versus west GoM, and ASB was ameliorated for all reefs under the RCP4.5 projections. There is limited knowledge of the resilience of GoM reefs, and this study identifies coral morphotypes and reefs that are most likely to recover or decline from successive bleaching, in the context of forecasts of the frequency of future bleaching events in GoM.
Coastal salt marshes, which provide valuable ecosystem services such as flood mitigation and carbon sequestration, are threatened by rising sea level. In response, these ecosystems migrate landward, converting available upland into salt marsh. In the coastal-plain surrounding Chesapeake Bay, United States, conversion of coastal forest to salt marsh is well-documented and may offset salt marsh loss due to sea level rise, sediment deficits, and wave erosion. Land slope at the marsh-forest boundary is an important factor determining migration likelihood, however, the standard method of using field measurements to assess slope across the marsh-forest boundary is impractical on the scale of an estuary. Therefore, we developed a general slope quantification method that uses high resolution elevation data and a repurposed shoreline analysis tool to determine slope along the marsh-forest boundary for the entire Chesapeake Bay coastal-plain and find that less than 3% of transects have a slope value less than 1%; these low slope environments offer more favorable conditions for forest to marsh conversion. Then, we combine the bay-wide slope and elevation data with inundation modeling from Hurricane Isabel to determine likelihood of coastal forest conversion to salt marsh. This method can be applied to local and estuary-scale research to support management decisions regarding which upland forested areas are more critical to preserve as available space for marsh migration.
Riparian ecosystems provide critical habitat for many species, yet assessment of vegetation condition at local scales is difficult to measure when considering large areas over long time periods. We present a framework to map and monitor two deciduous cover types, upland and riparian, occupying a small fraction of an expansive, mountainous landscape in north-central Wyoming. Initially, we developed broad-scale predictions of predominant woody vegetation types by integrating Landsat data into species distribution models and combining subsequent outputs into a synthesis map. Then, we evaluated a 35-year Landsat time series (1985–2019) using the Mann-Kendall test to identify significant trends in the condition of upland and riparian deciduous vegetation and assessed the rate and direction of change using the Theil-Sen estimator. Finally, we used plot level data to assess the utility of the framework to detect bottom-up controls (ungulate browse pressure and management actions) on vegetation condition. The synthesis map had an overall correct classification rate of 87% and field data indicated deciduous vegetation within 45 m of coniferous forest faces increased pressure of conifer expansion. The trend assessment identified consistent patterns operating at the landscape scale across both upland and riparian deciduous vegetation; a predominant greening trend was observed for 12 years followed by a 9-year browning trend, before switching back to a greening trend for the last 13 years of the study. Our results indicate trends are driven by the climate of the measurement period at the landscape scale. Although we did not find conclusive evidence to establish a strong link between browse pressure and satellite data, we highlight examples where prevailing trends can be overridden by local disturbance or management intervention. This framework is transferable to other understudied riparian environments throughout western North America to provide insight on ecohydrological processes and assess global and local stressors across broad spatiotemporal scales.
Binary regression models are commonly used in disciplines such as epidemiology and ecology to determine how spatial covariates influence individuals. In many studies, binary data are shared in a spatially aggregated form to protect privacy. For example, rather than reporting the location and result for each individual that was tested for a disease, researchers may report that a disease was detected or not detected within geopolitical units. Often, the spatial aggregation process obscures the values of response variables, spatial covariates, and locations of each individual, which makes recovering individual-level inference difficult. We show that applying a series of transformations, including a change of support, to a bivariate point process model allows researchers to recover individual-level inference for spatial covariates from spatially aggregated binary data. The series of transformations preserves the convenient interpretation of desirable binary regression models that are commonly applied to individual-level data. Using a simulation experiment, we compare the performance of our proposed method under varying types of spatial aggregation against the performance of standard approaches using the original individual-level data. We illustrate our method by modeling individual-level probability of infection using a data set that has been aggregated to protect an at-risk and endangered species of bats. Our simulation experiment and data illustration demonstrate the utility of the proposed method when access to original non-aggregated data is impractical or prohibited.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.spasta.2021.100514","usgsCitation":"Walker, N., Hefley, T.J., Ballmann, A., Russell, R., and Walsh, D.P., 2021, Recovering individual-level spatial inference from aggregated binary data: Spatial Statistics, v. 44, 100514, 14 p.; Data release, https://doi.org/10.1016/j.spasta.2021.100514.","productDescription":"100514, 14 p.; Data release","ipdsId":"IP-118748","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":452237,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://arxiv.org/abs/2004.12013","text":"Publisher Index Page"},{"id":386279,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418318,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XUPDIB","text":"USGS data release","description":"USGS data release","linkHelpText":"Pseudogymnoascus destructans detections by US county (2008-2012)"}],"country":"United States","otherGeospatial":"Northeast and Midwest United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.20703125,\n 36.87962060502676\n ],\n [\n -66.26953125,\n 36.87962060502676\n ],\n [\n -66.26953125,\n 49.15296965617042\n ],\n [\n -97.20703125,\n 49.15296965617042\n ],\n [\n -97.20703125,\n 36.87962060502676\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Nelson","contributorId":259320,"corporation":false,"usgs":false,"family":"Walker","given":"Nelson","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":817117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":817118,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ballmann, Anne 0000-0002-0380-056X aballmann@usgs.gov","orcid":"https://orcid.org/0000-0002-0380-056X","contributorId":140319,"corporation":false,"usgs":true,"family":"Ballmann","given":"Anne","email":"aballmann@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":817119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":817120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":817121,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220612,"text":"70220612 - 2021 - Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest","interactions":[],"lastModifiedDate":"2021-12-10T16:26:48.54244","indexId":"70220612","displayToPublicDate":"2021-05-17T06:49:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"Oxygen isotopes in terrestrial gastropod shells track Quaternary climate change in the American Southwest","docAbstract":"Recent studies have shown the oxygen isotopic composition (δ18O) of modern terrestrial gastropod shells is determined largely by the δ18O of precipitation. This implies that fossil shells could be used to reconstruct the δ18O of paleo-precipitation as long as the isotopic system, including the hydrologic pathways of the local watershed and the gastropod systematics, is well understood. In this study, we measured the δ18O values of 456 individual gastropod shells collected from paleowetland deposits in the San Pedro Valley, Arizona that range in age from ca. 29.1 to 9.8 ka. Isotopic differences of up to 2‰ were identified among the four taxa analyzed (Succineidae, Pupilla hebes, Gastrocopta tappaniana, and Vallonia gracilicosta), with Succineidae shells yielding the highest values and V. gracilicosta shells exhibiting the lowest values. We used these data to construct a composite isotopic record that incorporates these taxonomic offsets, and found shell δ18O values increased by ~4‰ between the last glacial maximum and early Holocene, which is similar to the magnitude, direction, and rate of isotopic change recorded by speleothems in the region. These results suggest the terrestrial gastropods analyzed here may be used as a proxy for past climate in a manner that is complementary to speleothems, but potentially with much greater spatial coverage.
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In the northeastern United States, much of the reduction has been attributed to winter tick (Dermacentor albipictus) infestations. Winter ticks are fairly immobile throughout all life stages, and therefore their distribution patterns at any given time are shaped largely by the occurrence of moose across the landscape during the peak of two critical time periods: fall questing (when ticks latch onto moose) and spring drop-off (when engorged female ticks detach from moose). We used recent land cover and lidar data within a dynamic occupancy modeling framework to estimate first-order habitat selection (use vs. non-use) of female moose (n = 74) during the tick questing and drop-off periods. Patch extinction and colonization rates between the fall questing and spring drop-off periods were strongly influenced by habitat and elevation, but these effects were diminished during the fall questing period when moose were more active across the landscape. From the fall questing period to the spring drop-off period, patches where colonization was high and extinction was low had higher proportions of young (shrub/forage) mixed forest at higher elevations. Further, we evaluated the fitness consequences of habitat selection by adult females during the fall questing period, when females and their calves acquire ticks. We compared Resource Selection Functions (RSF) for five females that successfully reared a calf to age 1 with five females whose calves perished due to ticks. Adult female moose whose offspring perished selected habitats in the fall that spatially coincided with areas of high occupancy probability during the spring tick drop-off period. In contrast, adult female moose whose offspring survived selected areas where the probability of occupancy during the spring drop-off was low; at present, natural selection may favor female adults who do not select the same habitats in fall as in spring. Our model coefficients and mapped results define “hotspots” that are likely encouraging the deleterious effects of the tick-moose cycle. These findings fill knowledge gaps about moose habitat selection that may improve the effectiveness of management aimed at reversing declining population trends.