A disconnect between scientific research and environmental management communities can be a detriment to both. In the case of Great Lakes coastal ecosystems, which are inherently complex and subject to uncertain effects of future climatic, environmental, and anthropogenic drivers, greater collaboration could be beneficial to their sustainability. We capture the challenges and opportunities identified by a scientist/decision-maker co-production workshop focused on the future environmental quality of Great Lakes coastal wetlands. We explain our path through the stakeholder workshop process, our challenges in translating meeting outcomes into actionable items, and lessons learned to bridge gaps between scientists and decision-makers. Additionally, we determine topics and directions identified by decision-makers that can be modeled with existing technologies and others that require further research. These topics may be incorporated into future research efforts and could serve as a shortlist of research priorities that were identified by decision-makers working with coastal wetland issues. Based on lessons learned during and after the workshop, we provide suggestions for bridging the gap between researchers and decision-makers, including sustained engagement between these groups and improved interaction through the beginning, duration, and end of research and/or management efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.04.018","usgsCitation":"Weinstein, C.B., Bourgeau-Chavez, L., Martin, S.L., Currie, W.S., Grantham, K., Hamlin, Q.F., Hyndman, D.W., Kowalski, K., Martina, J.P., and Pearsall, D., 2021, Enhancing Great Lakes coastal ecosystems research by initiating engagement between scientists and decision-makers: Journal of Great Lakes Research, v. 47, no. 4, p. 1235-1240, https://doi.org/10.1016/j.jglr.2021.04.018.","productDescription":"6 p.","startPage":"1235","endPage":"1240","ipdsId":"IP-125315","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":387990,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5419921875,\n 44.08758502824516\n ],\n [\n -77.3876953125,\n 44.33956524809713\n ],\n [\n -78.92578124999999,\n 44.08758502824516\n ],\n [\n -82.353515625,\n 42.58544425738491\n ],\n [\n -81.123046875,\n 43.866218006556394\n ],\n [\n -81.123046875,\n 44.37098696297173\n ],\n [\n -79.5849609375,\n 44.5278427984555\n ],\n [\n -79.62890625,\n 45.089035564831036\n ],\n [\n -81.1669921875,\n 46.28622391806706\n ],\n [\n -84.0673828125,\n 46.649436163350245\n ],\n [\n -85.517578125,\n 48.22467264956519\n ],\n [\n -87.0556640625,\n 49.18170338770663\n ],\n [\n -90.3076171875,\n 48.60385760823255\n ],\n [\n -92.94433593749999,\n 46.76996843356982\n ],\n [\n -91.7578125,\n 46.40756396630067\n ],\n [\n -88.72558593749999,\n 46.9502622421856\n ],\n [\n -88.330078125,\n 46.558860303117164\n ],\n [\n -86.8359375,\n 46.28622391806706\n ],\n [\n -85.517578125,\n 46.5286346952717\n ],\n [\n -85.0341796875,\n 46.164614496897094\n ],\n [\n -87.099609375,\n 45.920587344733654\n ],\n [\n -88.41796875,\n 44.5278427984555\n ],\n [\n -87.890625,\n 44.33956524809713\n ],\n [\n -87.978515625,\n 43.004647127794435\n ],\n [\n -87.71484375,\n 41.77131167976407\n ],\n [\n -86.572265625,\n 41.705728515237524\n ],\n [\n -85.8251953125,\n 43.068887774169625\n ],\n [\n -85.9130859375,\n 44.43377984606822\n ],\n [\n -84.72656249999999,\n 45.460130637921004\n ],\n [\n -83.8037109375,\n 44.933696389694674\n ],\n [\n -83.935546875,\n 43.77109381775651\n ],\n [\n -83.84765625,\n 43.29320031385282\n ],\n [\n -83.056640625,\n 43.89789239125797\n ],\n [\n -82.79296874999999,\n 42.94033923363181\n ],\n [\n -83.583984375,\n 41.77131167976407\n ],\n [\n -83.056640625,\n 41.07935114946899\n ],\n [\n -81.2109375,\n 41.376808565702355\n ],\n [\n -78.31054687499999,\n 42.65012181368022\n ],\n [\n -76.2451171875,\n 43.48481212891603\n ],\n [\n -75.5419921875,\n 44.08758502824516\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weinstein, Charlotte B.","contributorId":260518,"corporation":false,"usgs":false,"family":"Weinstein","given":"Charlotte","email":"","middleInitial":"B.","affiliations":[{"id":34530,"text":"Michigan Tech Research Institute","active":true,"usgs":false}],"preferred":false,"id":821246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bourgeau-Chavez, Laura 0000-0001-7127-279X","orcid":"https://orcid.org/0000-0001-7127-279X","contributorId":220963,"corporation":false,"usgs":false,"family":"Bourgeau-Chavez","given":"Laura","email":"","affiliations":[{"id":34530,"text":"Michigan Tech Research Institute","active":true,"usgs":false}],"preferred":false,"id":821247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, S. L.","contributorId":264243,"corporation":false,"usgs":false,"family":"Martin","given":"S.","email":"","middleInitial":"L.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":821248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Currie, W. S.","contributorId":264245,"corporation":false,"usgs":false,"family":"Currie","given":"W.","email":"","middleInitial":"S.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":821249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grantham, K.","contributorId":264247,"corporation":false,"usgs":false,"family":"Grantham","given":"K.","email":"","affiliations":[{"id":54411,"text":"Southeast Michigan Council of Governments","active":true,"usgs":false}],"preferred":false,"id":821250,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hamlin, Q. F.","contributorId":264248,"corporation":false,"usgs":false,"family":"Hamlin","given":"Q.","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":821251,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hyndman, David W","contributorId":264249,"corporation":false,"usgs":false,"family":"Hyndman","given":"David","email":"","middleInitial":"W","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":821252,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":821253,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Martina, J. P.","contributorId":264250,"corporation":false,"usgs":false,"family":"Martina","given":"J.","email":"","middleInitial":"P.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":821254,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pearsall, D.","contributorId":264252,"corporation":false,"usgs":false,"family":"Pearsall","given":"D.","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":821255,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70223137,"text":"70223137 - 2021 - Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation","interactions":[],"lastModifiedDate":"2021-09-21T13:11:40.385044","indexId":"70223137","displayToPublicDate":"2021-05-27T07:58:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3735,"text":"Weather and Forecasting","active":true,"publicationSubtype":{"id":10}},"title":"Impact of SST and surface waves on Hurricane Florence (2018): A coupled modeling investigation","docAbstract":"Hurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir-Simpson Category 4 to Category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical modeling experiments in coupled and uncoupled configurations to examine the impact of sea surface temperature (SST) and ocean waves on storm characteristics. In addition to experiments using a fully coupled atmosphere-ocean-wave model, we introduced the capability of the atmospheric model to modulate wind stress and surface fluxes by oceanwaves through data from an uncoupled wave model. We examined these experiments by comparing track, intensity, strength, SST, storm structure, wave height, surface roughness, heat fluxes, and precipitation in order to determine the impacts of resolving ocean conditions with varying degrees of coupling. We found differences in the storm’s intensity and strength, with the best correlation coefficient of intensity (r=0.89) and strength (r=0.95) coming from the fully-coupled simulations. Further analysis into surface roughness parameterizations added to the atmospheric model revealed differences in the spatial distribution and magnitude of the largest roughness lengths. Adding ocean andwave features to the model further modified the fluxes due to more realistic cooling beneath the stormwhich in turn modified the precipitation field. Our experiments highlight significant differences in how air-sea processes impact hurricane modeling. The storm characteristics of track, intensity, strength, and precipitation at landfall are crucial to predictability and forecasting of future landfalling hurricanes.
Forests are a significant CO2 sink. However, CO2 sequestration in forests is radiatively offset by emissions of nitrous oxide (N2O), a potent greenhouse gas, from forest soils. Reforestation, an important strategy for mitigating climate change, has focused on maximizing CO2 sequestration in plant biomass without integrating N2O emissions from soils. Although nitrogen (N)-fixing trees are often recommended for reforestation because of their rapid growth on N-poor soil, they can stimulate significant N2O emissions from soils. Here, we first used a field experiment to show that a N-fixing tree (Robinia pseudoacacia) initially mitigated climate change more than a non-fixing tree (Betula nigra). We then used our field data to parameterize a theoretical model to investigate these effects over time. Under lower N supply, N-fixers continued to mitigate climate change more than non-fixers by overcoming N limitation of plant growth. However, under higher N supply, N-fixers ultimately mitigated climate change less than non-fixers by enriching soil N and stimulating N2O emissions from soils. These results have implications for reforestation, suggesting that N-fixing trees are more effective at mitigating climate change at lower N supply, whereas non-fixing trees are more effective at mitigating climate change at higher N supply.
","language":"English","publisher":"Wiley","doi":"10.1002/ecy.3414","usgsCitation":"Kou-Giesbrecht, S., Funk, J.L., Perakis, S.S., Wolf, A.A., and Menge, D., 2021, N supply mediates the radiative balance of N2O emissions and CO2 sequestration driven by N-fixing vs. non-fixing trees: Ecology, v. 102, no. 8, e03414, 8 p., https://doi.org/10.1002/ecy.3414.","productDescription":"e03414, 8 p.","ipdsId":"IP-123004","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":452122,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3414","text":"Publisher Index Page"},{"id":387284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Black Rock Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0584945678711,\n 41.3791271230665\n ],\n [\n -73.99017333984375,\n 41.3791271230665\n ],\n [\n -73.99017333984375,\n 41.41737138589576\n ],\n [\n -74.0584945678711,\n 41.41737138589576\n ],\n [\n -74.0584945678711,\n 41.3791271230665\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Kou-Giesbrecht, Sian 0000-0002-4086-0561","orcid":"https://orcid.org/0000-0002-4086-0561","contributorId":261258,"corporation":false,"usgs":false,"family":"Kou-Giesbrecht","given":"Sian","email":"","affiliations":[{"id":52786,"text":"Columbia U","active":true,"usgs":false}],"preferred":false,"id":819602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Jennifer L.","contributorId":260668,"corporation":false,"usgs":false,"family":"Funk","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":819603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":819604,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolf, Amelia A.","contributorId":190685,"corporation":false,"usgs":false,"family":"Wolf","given":"Amelia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":819605,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Menge, Duncan 0000-0003-4736-9844","orcid":"https://orcid.org/0000-0003-4736-9844","contributorId":241126,"corporation":false,"usgs":false,"family":"Menge","given":"Duncan","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":819606,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220727,"text":"sir20215028 - 2021 - Flow characteristics and salinity patterns in tidal rivers within the northern Ten Thousand Islands, southwest Florida, water years 2007–19","interactions":[],"lastModifiedDate":"2021-05-27T11:52:45.64841","indexId":"sir20215028","displayToPublicDate":"2021-05-26T13:37:00","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":"2021-5028","displayTitle":"Flow Characteristics and Salinity Patterns in Tidal Rivers Within the Northern Ten Thousand Islands, Southwest Florida, Water Years 2007–19","title":"Flow characteristics and salinity patterns in tidal rivers within the northern Ten Thousand Islands, southwest Florida, water years 2007–19","docAbstract":"Freshwater flow to the Ten Thousand Islands (TTI) estuary has been altered by the construction of the Tamiami Trail and construction of features in the now defunct Southern Golden Gate Estates development. This development included four associated canals that combine into the Faka Union Canal, which discharges into the TTI estuary. The Picayune Strand Restoration Project (PSRP) was initiated in 2007 to improve freshwater delivery to the TTI estuary by removing hundreds of miles of roads, emplacing hundreds of canal plugs, removing exotic vegetation, and constructing three pump stations. Quantifying the tributary flows and salinity patterns prior to, during, and after the restoration is essential to assessing the effectiveness of upstream restoration efforts. The U.S. Geological Survey, in cooperation with U.S. Army Corps of Engineers, initiated an ongoing study in 2006 to assess flow and salinity patterns in the TTI estuary. This is the second report by the U.S. Geological Survey describing flow characteristics and salinity patterns in the TTI area as part of the PSRP. This report describes flow characteristics and salinity patterns for the monitoring stations at Faka Union River, Pumpkin River, and East River and includes an assessment of salinity data from the Faka Union Boundary and Blackwater River water-quality stations for water years 2007–19. A water year is defined as the 12-month period from October 1 for any given year to September 30 of the following year.
Annual and monthly variations in flow and salinity are often related to variations in rainfall with high and low annual flows (and below average and above average salinities) typically occurring during years with high and low annual rainfall, respectively. Monthly flows typically begin increasing in June and peak in September. Over the study period, positive trends in rainfall-adjusted monthly flow were detected at Faka Union River and East River, whereas no significant trend in flow was detected at Pumpkin River. Faka Union River is the largest contributor of freshwater to the TTI estuary, providing over 80 percent of the annual freshwater inflow to the estuary. The Faka Union Canal is expected to be the largest contributor of freshwater because until the PSRP is completed, the Faka Union Canal receives substantial drainage from multiple canals, which is not the case for Pumpkin and East Rivers. East River was the second largest contributor, followed by Pumpkin River. East River is downstream of the Fakahatchee Stand, which is a larger contributing area than the current contributing area for Pumpkin River. Monthly mean salinities were lowest at Faka Union River and East River, indicating that they received a greater amount of freshwater than the stations to the west. Negative trends in rainfall-adjusted salinity monthly means were observed at all monitoring stations during the study period. Increased trends in flow and decreased trends in salinity are attributed to increases in flow from upstream canals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215028","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Booth, A.C., and Knight, T.M., 2021, Flow characteristics and salinity patterns in tidal rivers within the northern Ten Thousand Islands, southwest Florida, water years 2007–19: U.S. Geological Survey Scientific Investigations Report 2021–5028, 21 p., https://doi.org/10.3133/sir20215028.","productDescription":"vii, 21 p.","numberOfPages":"34","ipdsId":"IP-122818","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":385935,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5028/images"},{"id":385934,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5028/sir20215028.pdf","text":"Report","size":"4.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5028"},{"id":385933,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5028/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Northern Ten Thousand Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.67304992675781,\n 25.852426562716428\n ],\n [\n -81.47941589355469,\n 25.852426562716428\n ],\n [\n -81.47941589355469,\n 25.972243398901558\n ],\n [\n -81.67304992675781,\n 25.972243398901558\n ],\n [\n -81.67304992675781,\n 25.852426562716428\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center (CFWSC)
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria. Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107822","usgsCitation":"Coffer, M.M., Schaeffer, B., Salls, W.B., Urquhart, E., Loftin, K.A., Stumpf, R.P., Werdell, P.J., and Darling, J., 2021, Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales: Ecological Indicators, v. 128, 107822, 12 p., https://doi.org/10.1016/j.ecolind.2021.107822.","productDescription":"107822, 12 p.","ipdsId":"IP-126524","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":452125,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107822","text":"Publisher Index Page"},{"id":387135,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coffer, Megan M. 0000-0003-3188-4729","orcid":"https://orcid.org/0000-0003-3188-4729","contributorId":260857,"corporation":false,"usgs":false,"family":"Coffer","given":"Megan","email":"","middleInitial":"M.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaeffer, Blake 0000-0001-9794-3977","orcid":"https://orcid.org/0000-0001-9794-3977","contributorId":245603,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Salls, Wilson B. 0000-0001-7505-0828","orcid":"https://orcid.org/0000-0001-7505-0828","contributorId":260858,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","email":"","middleInitial":"B.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urquhart, Erin 0000-0001-7141-9499","orcid":"https://orcid.org/0000-0001-7141-9499","contributorId":260859,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","email":"","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818983,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":818984,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stumpf, Richard P. 0000-0001-5531-6860","orcid":"https://orcid.org/0000-0001-5531-6860","contributorId":222357,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard","email":"","middleInitial":"P.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818985,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werdell, P. Jeremy 0000-0002-3592-0152","orcid":"https://orcid.org/0000-0002-3592-0152","contributorId":222358,"corporation":false,"usgs":false,"family":"Werdell","given":"P.","email":"","middleInitial":"Jeremy","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":818986,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Darling, John A. 0000-0002-4776-9533","orcid":"https://orcid.org/0000-0002-4776-9533","contributorId":260860,"corporation":false,"usgs":false,"family":"Darling","given":"John A.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":818987,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221214,"text":"70221214 - 2021 - Seismic wave propagation and basin amplification in the Wasatch Front, Utah","interactions":[],"lastModifiedDate":"2021-11-01T15:26:45.377797","indexId":"70221214","displayToPublicDate":"2021-05-26T08:16:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Seismic wave propagation and basin amplification in the Wasatch Front, Utah","docAbstract":"Ground‐motion analysis of more than 3000 records from 59 earthquakes, including records from the March 2020
The Mississippi embayment encompasses about 100,000 square miles and covers parts of eight States. In 2016, the U.S. Geological Survey began updating previous work for a part of the embayment known as the Mississippi Alluvial Plain to support informed water use and agricultural policy in the region. Groundwater, water use, economic, and other related models are being combined with field surveys and observations to create a quantitative framework for evaluating regional groundwater withdrawals and their effects on long-term water availability in the Mississippi Alluvial Plain.
As part of this effort, the U.S. Geological Survey’s Soil-Water-Balance code (version 2.0) is being used to model potential groundwater recharge and irrigation water use, as necessary inputs to the long-term groundwater modeling efforts. The Soil-Water-Balance code is designed to estimate the distribution and timing of net infiltration leaving the root zone. Soil-Water-Balance makes use of gridded datasets of elevation, soils, land use (including specific crop types), and daily weather datasets to calculate other components of the root-zone water balance, including soil moisture, reference, actual evapotranspiration, snowfall, snowmelt, and canopy interception. Parameters on plant height and growing-season water needs are used to estimate crop-water demand and potential irrigation water use.
This report documents the initial construction, calibration, and application of a Soil-Water-Balance model of the Mississippi Embayment Regional Aquifer Study area for simulations running from 1915 to 2017. Further refinements of the model calibration for an expanded model area are planned.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211008","programNote":"Water Availability and Use Science Program","usgsCitation":"Westenbroek, S.M., Nielsen, M.G., and Ladd, D.E., 2021, Initial estimates of net infiltration and irrigation from a soil-water-balance model of the Mississippi Embayment Regional Aquifer Study Area: U.S. Geological Survey Open-File Report 2021-1008, 29 p., https://doi.org/10.3133/ofr20211008.","productDescription":"Report: v, 29 p.; 2 Data Releases","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-108908","costCenters":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385921,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U484X5","text":"USGS data release","description":"USGS data release","linkHelpText":"OFR 2021–1008 MODEL OUTPUT—Soil-Water-Balance net infiltration and irrigation water use output datasets for the Mississippi Embayment Regional Aquifer System, 1915 to 2018"},{"id":385920,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98PBR8O","text":"USGS data release","description":"USGS data release","linkHelpText":"OFR 2021–1008 MODEL ARCHIVE—Soil-Water-Balance model developed to simulate net infiltration and irrigation water use for the Mississippi Embayment Regional Aquifer System, 1915 to 2018"},{"id":385919,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1008/ofr20211008.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1008"},{"id":385918,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1008/coverthb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Embayment Regional Aquifer Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.6044921875,\n 37.16031654673677\n ],\n [\n -90.4833984375,\n 36.527294814546245\n ],\n [\n -91.2744140625,\n 35.71083783530009\n ],\n [\n -91.7138671875,\n 35.31736632923788\n ],\n [\n -92.4169921875,\n 35.02999636902566\n ],\n [\n -93.3837890625,\n 34.59704151614417\n ],\n [\n -94.0869140625,\n 33.578014746143985\n ],\n [\n -94.1748046875,\n 32.69486597787505\n ],\n [\n -93.6474609375,\n 31.690781806136822\n ],\n [\n -92.94433593749999,\n 31.50362930577303\n ],\n [\n -91.93359375,\n 31.42866311735861\n ],\n [\n -91.14257812499999,\n 31.541089879585808\n ],\n [\n -89.7802734375,\n 31.316101383495624\n ],\n [\n -88.5498046875,\n 30.86451022625836\n ],\n [\n -87.3193359375,\n 31.203404950917395\n ],\n [\n -87.5390625,\n 31.80289258670676\n ],\n [\n -88.5498046875,\n 32.69486597787505\n ],\n [\n -88.505859375,\n 33.8339199536547\n ],\n [\n -88.5498046875,\n 34.59704151614417\n ],\n [\n -88.3740234375,\n 35.60371874069731\n ],\n [\n -88.5498046875,\n 36.94989178681327\n ],\n [\n -89.6044921875,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Global conservation policy and action have largely neglected protecting and monitoring genetic diversity—one of the three main pillars of biodiversity. Genetic diversity (diversity within species) underlies species’ adaptation and survival, ecosystem resilience, and societal innovation. The low priority given to genetic diversity has largely been due to knowledge gaps in key areas, including the importance of genetic diversity and the trends in genetic diversity change; the perceived high expense and low availability and the scattered nature of genetic data; and complicated concepts and information that are inaccessible to policymakers. However, numerous recent advances in knowledge, technology, databases, practice, and capacity have now set the stage for better integration of genetic diversity in policy instruments and conservation efforts. We review these developments and explore how they can support improved consideration of genetic diversity in global conservation policy commitments and enable countries to monitor, report on, and take action to maintain or restore genetic diversity.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/biosci/biab054","usgsCitation":"Hoban, S.M., Bruford, M.W., Funk, W., Galbusera, P., Griffith, M.P., Grueber, C.E., Heuertz, M., Hunter, M., Hvilsom, C., Stroil, B., Kershaw, F., Khoury, C.K., Laikre, L., Lopes-Fernandes, M., MacDonald, A.J., Mergeay, J., Meek, M., Mittan, C., Mukassabi, T.A., O'Brien, D., Ogden, R., Palma-Silva, C., Ramakrishnan, U., Segelbacher, G., Shaw, R.E., Sjogren-Gulve, P., Velickovic, N., and Vernesi, C., 2021, Global commitments to conserving and monitoring genetic diversity are now necessary and feasible: BioScience, biab054, 13 p., https://doi.org/10.1093/biosci/biab054.","productDescription":"biab054, 13 p.","ipdsId":"IP-123824","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":452136,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biab054","text":"Publisher Index Page"},{"id":387073,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoban, Sean M. 0000-0002-0348-8449","orcid":"https://orcid.org/0000-0002-0348-8449","contributorId":206582,"corporation":false,"usgs":false,"family":"Hoban","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":818910,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bruford, Michael W.","contributorId":190769,"corporation":false,"usgs":false,"family":"Bruford","given":"Michael","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":818911,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Funk, W. Chris 0000-0002-9254-6718","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":189580,"corporation":false,"usgs":false,"family":"Funk","given":"W. Chris","affiliations":[],"preferred":false,"id":818912,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Galbusera, Peter","contributorId":260827,"corporation":false,"usgs":false,"family":"Galbusera","given":"Peter","email":"","affiliations":[{"id":52682,"text":"Royal Zoological Society of Antwerp, Centre for Research and Conservation (CRC), Antwerp, Belgium","active":true,"usgs":false}],"preferred":false,"id":818913,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Griffith, M. Patrick","contributorId":260828,"corporation":false,"usgs":false,"family":"Griffith","given":"M.","email":"","middleInitial":"Patrick","affiliations":[{"id":52683,"text":"Montgomery Botanical Center, Coral Gables","active":true,"usgs":false}],"preferred":false,"id":818914,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grueber, Catherine E.","contributorId":239927,"corporation":false,"usgs":false,"family":"Grueber","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":48055,"text":"School of Life and Environmental Sciences, Faculty of Science, The University of Sydney","active":true,"usgs":false}],"preferred":false,"id":818915,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heuertz, Myriam","contributorId":239920,"corporation":false,"usgs":false,"family":"Heuertz","given":"Myriam","email":"","affiliations":[{"id":48049,"text":"INRAE, Univ. Bordeaux","active":true,"usgs":false}],"preferred":false,"id":818916,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214958,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":818917,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hvilsom, Christina","contributorId":260829,"corporation":false,"usgs":false,"family":"Hvilsom","given":"Christina","email":"","affiliations":[{"id":52684,"text":"Copenhagen Zoo, Frederiksberg, Denmark","active":true,"usgs":false}],"preferred":false,"id":818918,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stroil, Belma Kalamujic","contributorId":260830,"corporation":false,"usgs":false,"family":"Stroil","given":"Belma Kalamujic","affiliations":[{"id":52685,"text":"University of Sarajevo-Institute for Genetic Engineering and Biotechnology, Laboratory for Molecular Genetics of Natural Resources, Sarajevo, Bosnia and Herzegovina","active":true,"usgs":false}],"preferred":false,"id":818919,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kershaw, Francine","contributorId":260831,"corporation":false,"usgs":false,"family":"Kershaw","given":"Francine","email":"","affiliations":[{"id":52686,"text":"Natural Resources Defense Council, New York","active":true,"usgs":false}],"preferred":false,"id":818920,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Khoury, Colin K.","contributorId":260832,"corporation":false,"usgs":false,"family":"Khoury","given":"Colin","email":"","middleInitial":"K.","affiliations":[{"id":52687,"text":"International Center for Tropical Agriculture (CIAT), Cali, Colombia and Saint Louis University, St. Louis, Missouri","active":true,"usgs":false}],"preferred":false,"id":818921,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Laikre, Linda","contributorId":198139,"corporation":false,"usgs":false,"family":"Laikre","given":"Linda","email":"","affiliations":[],"preferred":false,"id":818922,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lopes-Fernandes, Magarida","contributorId":260833,"corporation":false,"usgs":false,"family":"Lopes-Fernandes","given":"Magarida","affiliations":[{"id":48048,"text":"Instituto da Conservação da Natureza e das Florestas, IP, Lisbon, Portugal","active":true,"usgs":false}],"preferred":false,"id":818923,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"MacDonald, Anna J.","contributorId":260834,"corporation":false,"usgs":false,"family":"MacDonald","given":"Anna","email":"","middleInitial":"J.","affiliations":[{"id":52688,"text":"The Australian National University, John Curtin School of Medical Research and Research School of Biology, Canberra, Australia","active":true,"usgs":false}],"preferred":false,"id":818924,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Mergeay, Joachim","contributorId":239929,"corporation":false,"usgs":false,"family":"Mergeay","given":"Joachim","email":"","affiliations":[{"id":48057,"text":"Research Institute for Nature and Forest, Aquatic Ecology, Evolution and Conservation, KULeuven","active":true,"usgs":false}],"preferred":false,"id":818925,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Meek, Mariah","contributorId":260835,"corporation":false,"usgs":false,"family":"Meek","given":"Mariah","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":818926,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Mittan, Cinnamon","contributorId":260836,"corporation":false,"usgs":false,"family":"Mittan","given":"Cinnamon","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":818927,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Mukassabi, Tarek A.","contributorId":260837,"corporation":false,"usgs":false,"family":"Mukassabi","given":"Tarek","email":"","middleInitial":"A.","affiliations":[{"id":52689,"text":"University of Benghazi, Department of Botany, Faculty of Sciences, Benghazi, Libya","active":true,"usgs":false}],"preferred":false,"id":818928,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"O'Brien, David","contributorId":192192,"corporation":false,"usgs":false,"family":"O'Brien","given":"David","affiliations":[],"preferred":false,"id":818929,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Ogden, Rob","contributorId":239611,"corporation":false,"usgs":false,"family":"Ogden","given":"Rob","email":"","affiliations":[{"id":47931,"text":"Royal (Dick) School of Veterinary Studies & the Roslin Institute, University of Edinburgh","active":true,"usgs":false}],"preferred":false,"id":818930,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Palma-Silva, Clarisse","contributorId":239931,"corporation":false,"usgs":false,"family":"Palma-Silva","given":"Clarisse","email":"","affiliations":[{"id":48061,"text":"Department of Plant Science, Institute of Biology, University of Campinas","active":true,"usgs":false}],"preferred":false,"id":818931,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Ramakrishnan, Uma","contributorId":197653,"corporation":false,"usgs":false,"family":"Ramakrishnan","given":"Uma","email":"","affiliations":[],"preferred":false,"id":818932,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":818933,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Shaw, Robyn E.","contributorId":260838,"corporation":false,"usgs":false,"family":"Shaw","given":"Robyn","email":"","middleInitial":"E.","affiliations":[{"id":52690,"text":"Environmental and Conservation Sciences, Murdoch University, Perth, Australia","active":true,"usgs":false}],"preferred":false,"id":818934,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Sjogren-Gulve, Per","contributorId":239921,"corporation":false,"usgs":false,"family":"Sjogren-Gulve","given":"Per","email":"","affiliations":[{"id":48050,"text":"The Wildlife Analysis Unit, The Swedish Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":818935,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Velickovic, Nevena","contributorId":260839,"corporation":false,"usgs":false,"family":"Velickovic","given":"Nevena","email":"","affiliations":[{"id":52691,"text":"University of Novi Sad, Faculty of Sciences, Department of Biology and Ecology, Novi Sad, Serbia","active":true,"usgs":false}],"preferred":false,"id":818936,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Vernesi, Cristiano","contributorId":239922,"corporation":false,"usgs":false,"family":"Vernesi","given":"Cristiano","email":"","affiliations":[{"id":48051,"text":"Dept. of Sustainable Agroecosystems and Bioresources, Research and Innovation Centre - Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":818937,"contributorType":{"id":1,"text":"Authors"},"rank":28}]}} ,{"id":70227526,"text":"70227526 - 2021 - Does type, quantity, and location of habitat matter for fish diversity in a Great Plains riverscape?","interactions":[],"lastModifiedDate":"2022-01-20T12:50:09.006233","indexId":"70227526","displayToPublicDate":"2021-05-26T06:45:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Does type, quantity, and location of habitat matter for fish diversity in a Great Plains riverscape?","docAbstract":"Fisheries professionals frequently measure habitat type and amount, but less often measure the importance of where those habitats are located and in what combinations. We address this challenge by testing whether the individual and combined type, quantity, and location of habitat affects fish diversity in the upper Neosho River basin, Kansas, as a different approach to measuring habitat heterogeneity. Habitat type mattered in that species richness increased in areas of higher riffle density. Furthermore, variation within habitat type also influenced fish diversity; specifically, slower, shallower riffles had more species of fish. The spatial arrangement (i.e., impact of neighbor habitats) influenced fish diversity patterns in that riffle–run and riffle–glide pairings altered riffle habitat characteristics. The study illustrates a useful approach by measuring the type, amount, and arrangement of habitats to assess fish populations and could be adapted to other stream ecosystems.
Groundwater quality in the northern Sierra Nevada foothills region of California was investigated as part of California State Water Resources Control Board (SWRCB) Groundwater Ambient Monitoring Assessment Priority Basin Project (GAMA-PBP). The region was divided into two study units: the Yuba-Bear watersheds (YBW) study unit and the American-Cosumnes-Mokelumne watersheds (ACMW) study unit. The GAMA-PBP made a spatially unbiased assessment of aquifer systems used for domestic drinking-water supply in the study region, which are predominantly composed of fractured, hard-rock aquifers of varying lithology. These assessments characterized the quality of raw groundwater to evaluate ambient conditions in the domestic-supply aquifer and not the quality of treated drinking water.
The study included three components: (1) a status assessment, which characterized the quality of groundwater resources used for domestic drinking-water supply in the YBW and ACMW study units; (2) an understanding assessment, which evaluated natural and anthropogenic explanatory factors that could potentially affect groundwater quality in the study region; and (3) a comparative assessment between the groundwater resources used for domestic and public drinking-water supply in the study region.
The status assessment was based on data collected by the GAMA-PBP from 74 sites in the YBW study unit during 2015–16 and 67 sites in the ACMW study unit from 2016 to 2017. To contextualize water-quality results, concentrations of water-quality constituents in ambient groundwater were compared to regulatory and non-regulatory benchmarks typically used by the State of California and Federal agencies as health-based or aesthetic standards for public drinking water. The status assessment used a grid-based method to estimate proportions of groundwater resources with concentrations approaching or exceeding benchmark thresholds. This method provides spatially unbiased results and allows inter-comparability with similar groundwater-quality assessments.
Inorganic constituents with health-based benchmarks were present at high relative concentration (RC), meaning they exceeded the benchmark threshold, in 5.4 and 10 percent of domestic-supply aquifer systems in the YBW and ACMW study units, respectively. Inorganic constituents with aesthetic-based benchmarks were detected at high-RCs in 20 and 28 percent of the YBW and ACMW study units, respectively. The inorganic constituents present at high RC were arsenic, barium, boron, molybdenum, strontium, nitrate, adjusted gross-alpha particle activity, chloride, total dissolved solids, specific conductance, iron, manganese, and hardness. Groundwater samples were tested for presence or absence of three microbial indicators (total coliform, Escherichia coli, and Enterococci). At least one microbial indicator was present in 26 and 28 percent of the YBW and ACMW study units, respectively. At least one organic constituent was detected in 30 and 42 percent of the YBW and ACMW study units, respectively. Organic constituents were not present at high RC, but tetrachloroethene (PCE), trichloroethene (TCE), and toluene were detected in the YBW study unit at moderate RC (between the benchmark concentration and one-tenth of the benchmark concentration). Methyl tert-butyl ether (MTBE) and chloroform were present at low RC (less than one-tenth of the benchmark concentration) in the YBW and ACMW study units with detection frequencies greater than 10 percent. Perchlorate, a constituent of special interest, was detected in 31 and 41 percent of the YBW and ACMW study units, respectively, at either low or moderate RCs.
Relations among select water-quality constituents and potential explanatory factors were evaluated using statistical and graphical approaches. Nitrate, microbial indicators, and perchlorate were all correlated to elevation-dependent variables relating to climate, land use, and recharge condition. Isotopic and dissolved noble-gas tracers indicated these water-quality constituents are associated with recharge conditions associated with irrigation during the summer dry-season, which is common in areas of rural-residential or agricultural land uses. Higher concentrations of iron and manganese were primarily associated with anoxic groundwater in aquifers of metasedimentary lithology. Increased hardness was primarily associated with anoxic groundwater in aquifers of mafic-ultramafic or metavolcanics lithologies at lower elevations in the study region in the Melones fault zone. Chloroform and MTBE were associated with shallow groundwater (wells depths less than 130 m) under oxic and anoxic redox conditions, respectively.
The comparative assessment evaluated differences between the aquifer systems used for domestic- and public-supply in study region based on (1) well-construction characteristics, and (2) water quality. Analysis of over 60,000 well-completion reports in the study region showed that although domestic-supply wells span the deepest depth zones in regional aquifers, median depths for public-supply wells were significantly greater than those of domestic-supply wells in both study units. Water-quality data from more than 300 public-supply wells in the study region were assessed using a spatially weighted method for calculation aquifer-scale proportions and compared with the domestic-supply assessment results. Detections of inorganic constituents at high RC and detection frequencies for organic constituents were generally similar between the domestic- and public-supply aquifer systems in both study units, with a few notable exceptions in the ACMW study unit: nitrate was greater for the public- compared to domestic-supply aquifer system and both manganese, hardness, and MTBE were greater in the domestic- compared to public-supply aquifer system. These differences are likely related to contrasting land uses, aquifer lithologies, landscape positions, and depths characterizing domestic- and public-supply wells in the ACMW study unit.
Overall, fewer samples from domestic-supply wells in the northern Sierra Nevada foothills exceeded health-based benchmarks compared to aesthetic-based benchmarks for groundwater quality. Exceedences of health-based benchmarks were primarily caused by nitrate and coliform bacteria, which were associated with recharge from diverted surface water used primarily for irrigation. Exceedences of aesthetic-based benchmarks were primarily caused by iron, managanese, and hardness, which were associated with geologic factors. Regional irrigation practices and aquifer lithology can affect groundwater quality in fractured-rock aquifers in the northern Sierra Nevada foothills used for domestic drinking-water supply.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215019","collaboration":"Prepared in cooperation with the California State Water Resources Control BoardDirector,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Approximately 2 million California residents depend on groundwater from domestic wells for their drinking-water supply. The State of California, in collaboration with the U.S. Geological Survey, created the Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP) to assess the quality of groundwater used for domestic supply throughout the state and determine regional vulnerabilities to drinking-water resources. Many rural households in the northern Sierra Nevada foothills (hereafter referred to as “the foothills”) use domestic wells that pump water from fractured-bedrock aquifers. In the foothills, complicated and varied regional bedrock geology can cause substantial variation in groundwater chemistry and quality over relatively short distances. This factsheet presents findings from the GAMA-PBP assessment that highlight influences of geologic factors on groundwater quality in the foothills.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213013","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Levy, Z.F. and Fram, M.S., 2021, Geologic influences on the quality of groundwater used for domestic supply in the northern Sierra Nevada Foothills: U.S. Geological Survey Fact Sheet 2021-3013, 4 p., https://doi.org/10.3133/fs20213013.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-117979","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":385799,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215019","text":"Scientific Investigations Report 2021-5019","linkHelpText":"- Status and Understanding of Groundwater Quality in the Northern Sierra Nevada Foothills Domestic-Supply Aquifer Study Units, 2015–17: California GAMA Priority Basin Project"},{"id":385797,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2021/3013/fs20213013.xml"},{"id":385796,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3013/fs20213013.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":385795,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3013/covrthb.jpg"},{"id":385798,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2021/3013/images"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada foothills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.81640624999999,\n 37.56199695314352\n ],\n [\n -119.06982421874999,\n 37.56199695314352\n ],\n [\n -119.06982421874999,\n 39.70718665682654\n ],\n [\n -121.81640624999999,\n 39.70718665682654\n ],\n [\n -121.81640624999999,\n 37.56199695314352\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"GAMA Project Chief
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
GAMA Program Unit Chief
State Water Resources Control Board
Division of Water Quality
PO Box 2231, Sacramento, CA 95812
Telephone number: (916) 341-5855
Per- and polyfluoroalkyl substances (PFAS) are a family of human-made chemicals that can persist in the environment. In 2019, the California State Water Resources Control Board’s Groundwater Ambient Monitoring and Assessment Priority Basin Project (GAMA-PBP) added PFAS to the projects’ on-going assessments of the quality of groundwater used for drinking-water supplies. This fact sheet describes the GAMA-PBP plans for sampling public-supply and domestic wells across California for PFAS and presents preliminary results for data collected in 2019–20.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213028","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Kent, R.H., 2021, Sampling for Per- and Polyfluoroalkyl Substances (PFAS) by the Groundwater Ambient Monitoring and Assessment Priority Basin Project: U.S. Geological Survey Fact Sheet 2021-3028, 4 p., https://doi.org/10.3133/fs20213028.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-124312","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":436338,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92IPRJD","text":"USGS data release","linkHelpText":"Data sets for: Sampling for Per- and Polyfluoroalkyl Substances (PFAS) by the Groundwater Ambient Monitoring and Assessment Priority Basin Project 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\"}}]}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey, in cooperation with the Muskingum Watershed Conservancy District and the city of Rittman, Ohio, did a study to provide data to update and expand parts of two Federal Emergency Management Agency Flood Insurance Studies. The study consisted of hydrologic and hydraulic analyses for selected reaches of four streams (Chippewa Creek, Little Chippewa Creek, Styx River, and the unnamed tributary to Styx River) near the city of Rittman in Wayne and Medina Counties, Ohio. The study covered 36.2 miles of stream reaches.
Instantaneous peak streamflows for floods with 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities were estimated using historical streamflow data from three U.S. Geological Survey streamgages and regional flood-frequency regression equations. The flood-frequency estimates were then used in a Hydrologic Engineering Center River Analysis System step-backwater model to determine water-surface profiles; flood-inundation boundaries for the 10-, 4-, 2-, 1-, and 0.2-percent and 1-percent plus annual exceedance probabilities; and a regulatory floodway for the study reaches. Model inputs included cross sections derived from a digital elevation model supplemented with field surveys of open-channel cross sections and hydraulic structures, field estimates of Manning’s roughness values, and flood estimates determined from regional regression equations and historical streamflow data. Flood-inundation boundaries were mapped for each stream reach for the 1- and 0.2-percent annual exceedance probability floods and a regulatory floodway. All data used in the creation of the flood-inundation boundaries are available through a U.S. Geological Survey data release (Ostheimer, 2021) and will be submitted to the Federal Emergency Management Agency for inclusion in updated Flood Insurance Studies for Wayne and Medina Counties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215040","collaboration":"Prepared in cooperation with the city of Rittman and the Muskingum Watershed Conservancy District","usgsCitation":"Ostheimer, C.J., 2021, Hydrologic and hydraulic analyses of selected streams near the city of Rittman in Wayne and Medina Counties, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5040, 30 p., https://doi.org/10.3133/sir20215040.","productDescription":"Report: iv, 30 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-117425","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":385929,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W6ROMC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial data sets and hydraulic models for selected streams near Rittman in Wayne and Medina Counties, Ohio"},{"id":385927,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5040/coverthb.jpg"},{"id":385956,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5040/images"},{"id":385928,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5040/sir20215040.pdf","text":"Report","size":"7.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5040"}],"country":"United States","state":"Ohio","county":"Wayne County, Medina County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.6845,41.2772],[-81.6885,40.9887],[-81.6477,40.9884],[-81.648,40.9145],[-81.6483,40.7371],[-81.6491,40.6681],[-82.126,40.6682],[-82.1266,40.778],[-82.1292,40.9921],[-82.1736,40.9922],[-82.1722,41.0435],[-82.1714,41.0639],[-82.1699,41.1251],[-82.1699,41.1369],[-82.0741,41.1362],[-82.0725,41.2001],[-81.9736,41.1998],[-81.9724,41.2747],[-81.8777,41.2747],[-81.7848,41.2765],[-81.6845,41.2772]]]},\"properties\":{\"name\":\"Medina\",\"state\":\"OH\"}}]}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
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6460 Busch Blvd., Suite 100
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The lower Las Vegas Wash represents the terminal surface drainage for the Las Vegas Valley in southern Nevada. In 1997, high concentrations of perchlorate were found in seeps contributing to discharge in this area and traced to an industrial byproduct from manufacturing operations in the mid-1900s at the nearby Basic Magnesium, Incorporated, plant. The discovery prompted a water-resources investigation by the Nevada Department of Environmental Protection (NDEP) to develop an understanding of the nearby groundwater flow system and the dynamics associated with surface-water flow in the Wash. In 2016, the U.S. Geological Survey was tasked with evaluating surface-water discharge in the lower Las Vegas Wash near locations where perchlorate concentrations from the groundwater system had been detected. Results of this study will assist NDEP with identifying areas of groundwater and surface-water interaction and help guide future cleanup and monitoring efforts.
Streamflow discharge is evaluated along a 4-mile section of the lower Las Vegas Wash (referred to as the Wash) and used to describe surface-water and groundwater interactions between the Wash channel and bank sediments. Continuous discharge data were collected during a 2-year period (2016–18) at 5 gaging stations along the Wash. Additionally, multiple discrete measurements between gaging stations were collected during 4 synoptic sampling events between 2016 and 2018.
A diurnal discharge pattern, controlled by upstream treated wastewater releases, provided high- and low-discharge markers that are used to compute downstream time-lags of peak and minimum flows. Computed time-lags are used to establish travel times between measurement sites, and difference in upstream and time-lagged downstream hydrographs are used to compute increases (gain) or decreases (loss) in discharge between gaging stations or between gaging stations and discrete measurements. Tributary surface-water inflows to the lower Las Vegas Wash from wastewater discharge, remediation efforts, and periodic flooding from rainfall runoff are included in computing differences in discharge. Differences between discharge data from delineated reaches are used to define locations of daily, monthly, and yearly streamflow gains from or losses to adjacent bank sediments. Construction of additional channel-stabilization weirs have occurred since the completion of this study and the associated change to streamflow dynamics may limit study results to the period analyzed; however, methods and processes described in this report can be used in future evaluations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215034","collaboration":"Prepared in cooperation with the Nevada Division of Environmental Protection","usgsCitation":"Wilson, J.W., 2021, Discharge data collection and analysis and implications for surface-water/groundwater interactions in the lower Las Vegas Wash, Clark County, Nevada, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5034, 25 p., https://doi.org/10.3133/sir20215034.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-091622","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":385836,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UQCOSM","linkHelpText":"Trace of the Lower Las Vegas Wash Study Area, 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Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Shallow areas of drawdown reservoirs are often devoid of adequate fish habitat due to degradation associated with unnatural and relatively invariable cycles of exposure and flooding. One method of enhancing fish habitat in these areas is to sow exposed shorelines with agricultural plants to provide structure once flooded. It remains unclear if some plants may be more suitable than others to provide effective fish habitat. To determine the fish habitat potential of various crops, we performed a replicated tank experiment evaluating the selection of agricultural plants by prey and predator fishes with and without the presence of the other. We submerged diverse treatments of potted plants in outdoor mesocosms stocked with prey and/or predator fish and monitored selection of plant species, stem density, and stem height over 0.5-h trials. Prey fish selected the densest vegetation, and selection was accentuated when a predator was present. Predators selected the second highest stem density and were more active when prey were present. Prey schooling was increased by predation risk, suggesting that cover was insufficient to outweigh the advantages of increased group size. Our data indicate that the perception of cover quality is reciprocally context dependent on predator–prey interactions for both predator and prey. Applications of the two most selected plant treatments in this study could enhance structural habitat for both predator and prey fishes in reservoirs, adding to their already reliable functionality as supplemental forage crops for terrestrial wildlife.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-083","usgsCitation":"Coppola, G., Miranda, L.E., Colvin, M., Hatcher, H., and Lashley, M., 2021, Selection of habitat-enhancing plants depends on predator-prey interactions: Journal of Fish and Wildlife Management, v. 12, no. 2, p. 294-307, https://doi.org/10.3996/JFWM-20-083.","productDescription":"14 p.","startPage":"294","endPage":"307","ipdsId":"IP-121514","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":452137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-083","text":"Publisher Index Page"},{"id":396556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Coppola, G.","contributorId":265335,"corporation":false,"usgs":false,"family":"Coppola","given":"G.","email":"","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":836399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":836400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Colvin, M. E.","contributorId":265334,"corporation":false,"usgs":false,"family":"Colvin","given":"M. E.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":836401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hatcher, H. R.","contributorId":265333,"corporation":false,"usgs":false,"family":"Hatcher","given":"H. R.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":836402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lashley, M. A.","contributorId":265336,"corporation":false,"usgs":false,"family":"Lashley","given":"M. A.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":836403,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227050,"text":"70227050 - 2021 - Demographic rate variability of Bighead and Silver Carps along an invasion gradient","interactions":[],"lastModifiedDate":"2021-12-28T15:04:26.047659","indexId":"70227050","displayToPublicDate":"2021-05-25T08:57:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Demographic rate variability of Bighead and Silver Carps along an invasion gradient","docAbstract":"Invasive Bighead Carp Hypophthalmichthys nobilis and Silver Carp H. molitrix have infested and caused largescale ecological and economic damage to the Illinois, Mississippi, and Ohio rivers. We compiled demographic data from 42,995 fish from 23 pools in the Illinois, Mississippi, and Ohio rivers, which universities and management agencies previously collected as part of management, monitoring, and research activities. We used this data set to test whether demographic rates (length–weight relations including body condition, mortality, growth curves, and female maturity curves) varied among subpopulations across a gradient of invasion status. We found that length–weight relations and growth curves varied among subpopulations, whereas maturity curves did not. Our findings demonstrated spatial variability in demographic rates for Bighead and Silver carp across a broad geographic area in relation to invasion status and river conditions. Herein, we provide general subpopulation management options and present different hypotheses to explain the observed spatial variability in demographic rates.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-070","usgsCitation":"Erickson, R.A., Kallis, J.L., Coulter, A.A., Coulter, D.P., MacNamara, R., Lamer, J.T., Bouska, W.W., Irons, K.S., Solomon, L.E., Stump, A.J., Weber, M.J., Brey, M.K., Sullivan, C., Sass, G.G., Garvey, J.E., and Glover, D.C., 2021, Demographic rate variability of Bighead and Silver Carps along an invasion gradient: Fisheries Research, v. 12, no. 2, p. 338-353, https://doi.org/10.3996/JFWM-20-070.","productDescription":"16 p.","startPage":"338","endPage":"353","ipdsId":"IP-103929","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":452139,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-070","text":"Publisher Index Page"},{"id":436341,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q6SUML","text":"USGS data release","linkHelpText":"Demographic variability of two invasive species along an invasion gradient: Bighead and silver carps in the Illinois, Ohio, and Mississippi rivers, USA Software Release"},{"id":436340,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IAOZ8G","text":"USGS data release","linkHelpText":"Bighead and silver carp individual fish data from the Mississippi, Ohio, and Illinois rivers from 1997 to 2018"},{"id":436339,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ABPDIR","text":"USGS data release","linkHelpText":"Carp Demographic Rates"},{"id":393501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Kentucky, West Virginia","otherGeospatial":"Illinois River, Mississippi River, Ohio River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.845703125,\n 36.56260003738545\n ],\n [\n -80.88134765625,\n 36.56260003738545\n ],\n [\n -80.88134765625,\n 42.374778361114195\n ],\n [\n -91.845703125,\n 42.374778361114195\n ],\n [\n -91.845703125,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":829378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kallis, Jahn L.","contributorId":205603,"corporation":false,"usgs":false,"family":"Kallis","given":"Jahn","email":"","middleInitial":"L.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":829379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coulter, Alison A.","contributorId":90992,"corporation":false,"usgs":false,"family":"Coulter","given":"Alison","email":"","middleInitial":"A.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false},{"id":26877,"text":"Southern Illinois University, Carbondale, IL","active":true,"usgs":false}],"preferred":false,"id":829380,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coulter, David P.","contributorId":205629,"corporation":false,"usgs":false,"family":"Coulter","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":829381,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"MacNamara, Ruairi","contributorId":270484,"corporation":false,"usgs":false,"family":"MacNamara","given":"Ruairi","email":"","affiliations":[{"id":13660,"text":"Hubbs-Sea World Research Institute","active":true,"usgs":false}],"preferred":false,"id":829382,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lamer, James T. 0000-0003-1155-1548","orcid":"https://orcid.org/0000-0003-1155-1548","contributorId":196307,"corporation":false,"usgs":false,"family":"Lamer","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":48847,"text":"Illinois River Biological Station, Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":829383,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bouska, Wesley W.","contributorId":143724,"corporation":false,"usgs":false,"family":"Bouska","given":"Wesley","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":829384,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Irons, Kevin S.","contributorId":270485,"corporation":false,"usgs":false,"family":"Irons","given":"Kevin","email":"","middleInitial":"S.","affiliations":[{"id":33955,"text":"Illinois Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829385,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Solomon, Levi E.","contributorId":194776,"corporation":false,"usgs":false,"family":"Solomon","given":"Levi","middleInitial":"E.","affiliations":[],"preferred":false,"id":829386,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stump, Andrew J.","contributorId":270486,"corporation":false,"usgs":false,"family":"Stump","given":"Andrew","email":"","middleInitial":"J.","affiliations":[{"id":53972,"text":"Kentucky Department of Fish and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":829387,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Weber, Michael J. 0000-0003-0430-3087","orcid":"https://orcid.org/0000-0003-0430-3087","contributorId":210835,"corporation":false,"usgs":false,"family":"Weber","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":829388,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":829389,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sullivan, Christopher J.","contributorId":265442,"corporation":false,"usgs":false,"family":"Sullivan","given":"Christopher J.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":829390,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sass, Greg G.","contributorId":207135,"corporation":false,"usgs":false,"family":"Sass","given":"Greg","email":"","middleInitial":"G.","affiliations":[{"id":16117,"text":"Wisconsin DNR","active":true,"usgs":false}],"preferred":false,"id":829391,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Garvey, James E.","contributorId":178007,"corporation":false,"usgs":false,"family":"Garvey","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":829392,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Glover, David C.","contributorId":178006,"corporation":false,"usgs":false,"family":"Glover","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":829393,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70230073,"text":"70230073 - 2021 - Onset and evolution of Kilauea’s 2018 flank eruption and summit collapse from continuous gravity","interactions":[],"lastModifiedDate":"2022-03-28T13:19:34.266653","indexId":"70230073","displayToPublicDate":"2021-05-25T08:14:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Onset and evolution of Kīlauea's 2018 flank eruption and summit collapse from continuous gravity","title":"Onset and evolution of Kilauea’s 2018 flank eruption and summit collapse from continuous gravity","docAbstract":"Prior to the 2018 lower East Rift Zone (ERZ) eruption and summit collapse of Kīlauea Volcano, Hawai‘i, continuous gravimeters operated on the vent rims of ongoing eruptions at both the summit and Pu‘u ‘Ō‘ō. These instruments captured the onset of the 2018 lower ERZ eruption and the effects of lava withdrawal from both locales, providing constraints on the timing and style of activity and the physical properties of the lava lakes at both locations. At the summit, combining gravity, lava level, and a three-dimensional model of the vent indicates that the upper ∼200 m of the lava lake had a density of about 1700 kg m−3, slightly greater than estimates from 2011–2015 and possibly indicating a gradual densification over time. At Pu‘u ‘Ō‘ō, gravity and vent geometry were used to model both the density and the rate of crater collapse, which was unknown owing to a lack of visual observations. Results suggest the withdrawal of at least 11×106 m3 of lava over the course of two hours, and a material density of 1800–1900 kg m−3. In addition, gravity data at Pu‘u ‘Ō‘ō captured a transient decrease and increase about an hour prior to crater collapse and that was probably related to a small, short-lived fissure eruption on the west flank of the cone and possibly to dike intrusion beneath Pu‘u ‘Ō‘ō. The fissure was the first event in the subsequent cascade that ultimately led to the extrusion of over 1 km3 of lava from lower ERZ vents, collapse of the summit caldera floor by more than 500 m, and the destruction of over 700 homes and other structures. These results emphasize the importance of continuous gravity in operational monitoring of active volcanoes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2021.117003","usgsCitation":"Poland, M., Carbone, D., and Patrick, M.R., 2021, Onset and evolution of Kilauea’s 2018 flank eruption and summit collapse from continuous gravity: Earth and Planetary Science Letters, v. 567, 117003, 12 p., https://doi.org/10.1016/j.epsl.2021.117003.","productDescription":"117003, 12 p.","ipdsId":"IP-123201","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":452142,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2021.117003","text":"Publisher Index Page"},{"id":436343,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99QB29I","text":"USGS data release","linkHelpText":"Crater geometry data for Puʻuʻōʻō, on Kīlauea Volcano’s East Rift Zone, in May 2018"},{"id":436342,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PP5LX1","text":"USGS data release","linkHelpText":"Continuous gravity data from K?lauea Volcano, Hawai?i"},{"id":397686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.40985107421875,\n 19.158141038187704\n ],\n [\n -154.78912353515625,\n 19.158141038187704\n ],\n [\n -154.78912353515625,\n 19.557202031700292\n ],\n [\n -155.40985107421875,\n 19.557202031700292\n ],\n [\n -155.40985107421875,\n 19.158141038187704\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"567","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Poland, Michael 0000-0001-5240-6123","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":49920,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":838947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carbone, Daniele","contributorId":124561,"corporation":false,"usgs":false,"family":"Carbone","given":"Daniele","email":"","affiliations":[{"id":5113,"text":"INGV","active":true,"usgs":false}],"preferred":false,"id":838948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":838949,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221158,"text":"70221158 - 2021 - Synthesizing and analyzing long-term monitoring data: A greater sage-grouse case study","interactions":[],"lastModifiedDate":"2021-06-07T11:51:59.056332","indexId":"70221158","displayToPublicDate":"2021-05-25T07:40:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1457,"text":"Ecological Informatics","active":true,"publicationSubtype":{"id":10}},"title":"Synthesizing and analyzing long-term monitoring data: A greater sage-grouse case study","docAbstract":"Long-term monitoring of natural resources is imperative for increasing the understanding of ecosystem processes, services, and how to manage those ecosystems to maintain or improve function. Challenges with using these data may occur because methods of monitoring changed over time, multiple organizations collect and manage data differently, and monetary resources fluctuate, affecting many aspects of data. Because many species respond to changes in habitat conditions and predator-prey relationships across different spatial scales that span management boundaries, greater efforts for collaborating are essential. We demonstrate the challenges and methods for standardizing greater sage-grouse (Centrocercus urophasianus) long-term monitoring data across the species range in the western United States to inform population modeling needs identified by the Western Association of Fish and Wildlife Agencies. We used automated and repeatable methods of standardizing data via custom open-source software (grsg_lekdb) to improve the scientific integrity of future sage-grouse population assessments within and among states. Data standardization included reconciling uses of different terminology and expunging unusable data, resulting in the removal of 26% of data records due to database insertion errors and modifications to >1 million values to correct formatting and typing errors. Our approaches maximized the inclusion of usable data and identified data that could inform detection probabilities, population trends, and monitoring guidelines. Using sage-grouse databases as an example, we identified the importance of data management and how quality assurance and quality control measures can improve the usefulness of these data for future research needs. Our methods of using informatics and concluding recommendations can support similar endeavors of flora and fauna monitoring programs, whether those efforts are to use existing data or support new monitoring programs.
=
The western pond turtle (WPT) was formerly considered a single species (Actinemys or Emys marmorata) that ranged from southern British Columbia, Canada to Baja California, México. More recently it was divided into a northern and a southern species. WPTs are found primarily in streams that drain into the Pacific Ocean, although scattered populations exist in endorheic drainages of the Great Basin and Mojave deserts. Populations in the Mojave Desert were long thought to be restricted to the Mojave River, but recently another population was documented in Piute Ponds, a terminal wetland complex associated with Amargosa Creek on Edwards Air Force Base. WPT fossils in the Mojave Desert are known from the Miocene to the Pleistocene. Recently, Pleistocene fossils have been found as far into the desert as Salt Springs, just south of Death Valley. The oldest fossil records suggest that WPTs were present in wetlands and drainages of the geological feature known as the Mojave block prior to the uplift of the Sierra Nevada Range about 8 Ma and prior to the ~ 3 Ma uplift of the Transverse Ranges. Archaeological records document use of turtles by Native Americans for food and cultural purposes 1,000 or more years ago at the Cronese Lakes on the lower Mojave River and Oro Grande on the upper river. The first modern publication documenting their presence in the Mojave River was 1861. Museum specimens were collected as early as 1937. These fossil and early literature records support the indigenous status of WPTs to the Mojave River. However, mtDNA-based genetic evidence shows that Mojave River turtles share an identical haplotype with turtles on the California coast. Limited nuclear data show some minor differences. Overdraft of water from the Mojave River for municipal and agricultural uses, urban development, and saltcedar expansion are threats to the continued survival of WPTs in the Mojave River.
A multiweek standardized sampling regime during 2004–2016 in a 60-km reach of the Upper Missouri River assessed reproduction and catch rates for Sturgeon Chub Macrhybopsis gelida and Sicklefin Chub Macrhybopsis meeki. We sampled age-0 Macrhybopsis (primarily Sturgeon Chubs, but potentially including Sicklefin Chubs) all years to indicate successful reproduction, but noted an inverse correlation of catch per unit area (CPUA) with year. There was an inverse correlation for CPUA of age-1+ Sturgeon Chubs with year. There was no correlation for CPUA of age-1+ Sicklefin Chubs with year, but we noted a depression in CPUA during 2010 and 2012. The study reach includes restoration directives for federally endangered Pallid Sturgeon Scaphirhynchus albus, with 245,000 hatchery-origin Pallid Sturgeon (HOPS) stocked since 1998 to supplement the declining wild stock. Pallid Sturgeon longer than 350 mm fork length transition to piscivory and are known to prey on Sturgeon Chubs and Sicklefin Chubs. We examined the hypothesis that mass additions of HOPS to the existing predator community could have population-level effects on the two chub species. Population modeling for the stocked HOPS through time yielded estimates of nearly 1,300 piscivore-sized HOPS in 2004, an increase to 26,000 HOPS in 2012, and decreasing numbers through 2016 (14,500). A negative correlation between HOPS abundance and age-0 Macrhybopsis CPUA had the best support among other candidate variables (discharge, water temperature, catch rates of Sauger Sander canadensis). We found an inverse correlation for CPUA of age-1+ Sturgeon Chubs and estimated HOPS abundance, and there was also evidence of an inverse association between age-1+ Sicklefin Chub CPUA and HOPS in the study area. Results for a 60-km reach of the Upper Missouri River suggest declining CPUA for age-0 Macrhybopsis and Sturgeon Chubs during 2004–2016 and modest recovery of Sicklefin Chubs after 2012. Although causative factors driving CPUA changes through time are not known, correlative analyses suggest that large numbers of HOPS added to the Missouri River predator community potentially influence CPUA of Sturgeon Chubs and Sicklefin Chubs in the study area. Testing this hypothesis will require expanded quantification of chub populations and HOPS numbers through time.
Eruptive column models are powerful tools for investigating the transport of volcanic gas and ash, reconstructing past explosive eruptions, and simulating future hazards. However, the evaluation of these models is challenging as it requires independent estimates of the main model inputs (e.g. mass eruption rate) and outputs (e.g. column height). There exists no database of independently estimated eruption source parameters (ESPs) that is extensive, standardized, maintained, and consensus-based. This paper introduces the Independent Volcanic Eruption Source Parameter Archive (IVESPA, ivespa.co.uk), a community effort endorsed by the International Association of Volcanology and Chemistry of the Earth’s Interior (IAVCEI) Commission on Tephra Hazard Modelling. We compiled data for 134 explosive eruptive events, spanning the 1902-2016 period, with independent estimates of: i) total erupted mass of fall deposits; ii) duration; iii) eruption column height; and iv) atmospheric conditions. Crucially, we distinguish plume top versus umbrella spreading height, and the height of ash versus sulphur dioxide injection. All parameter values provided have been vetted independently by at least two experts. Uncertainties are quantified systematically, including flags to describe the degree of interpretation of the literature required for each estimate. IVESPA also includes a range of additional parameters such as total grain size distribution, eruption style, morphology of the plume (weak versus strong), and mass contribution from pyroclastic density currents, where available. We discuss the future developments and potential applications of IVESPA and make recommendations for reporting ESPs to maximize their usability across different applications. IVESPA covers an unprecedented range of ESPs and can therefore be used to evaluate and develop eruptive column models across a wide range of conditions using a standardized dataset.
Sin Nombre virus (SNV) is a zoonotic virus that is highly pathogenic to humans. The deer mouse, Peromyscus maniculatus, is the primary host of SNV, and SNV prevalence in P. maniculatus is an important indicator of human disease risk. Because the California Channel Islands contain permanent human settlements, receive hundreds of thousands of visitors each year, and can have extremely high densities of P. maniculatus, surveillance for SNV in island P. maniculatus is important for understanding the human risk of zoonotic disease. Despite the importance of surveillance on these heavily utilized islands, SNV prevalence (i.e. the proportion of P. maniculatus that test positive to antibodies to SNV) has not been examined in the last 13–27 years. We present data on 1,610 mice sampled for four consecutive years (2014–2017) on five of the California Channel Islands: East Anacapa, Santa Barbara, Santa Catalina, San Nicolas, and San Clemente. Despite historical data indicating SNV-positive mice on San Clemente and Santa Catalina, we detected no SNV-positive mice on these islands, suggesting very low prevalence or possible loss of SNV. Islands historically free of SNV (East Anacapa, Santa Barbara, and San Nicolas) remained free of SNV, suggesting that rates of pathogen introduction from other islands and/or the mainland are low. Although continued surveillance is warranted to determine whether SNV establishes on these islands, our work helps inform current human disease risk in these locations and suggests that SNV prevalence on these islands is currently very low.
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