Roadway deicing agents, including rock salt and brine containing NaCl, have had a profound impact on the water quality and aquatic health of rivers and streams in urbanized areas with temperate climates. Yet, few studies evaluate impacts to watersheds characterized by relatively low impervious surface cover (ISC; < 15 %). Here, we use long-term (1997-2019), monthly streamwater quality data combined with daily streamflow for six exurban and suburban watersheds in southeastern Pennsylvania to examine the relations among chloride (Cl−) concentrations and ISC. Both flow-normalized Cl− concentrations and ISC increased over time in each of the six watersheds, consistent with changes in watershed management (e.g., ISC, road salt application, etc.). The watersheds that experienced the greatest changes in percent ISC (e.g., agriculture replaced by residential and commercial development) experienced the greatest changes in flow-normalized Cl− concentrations. We also utilized a comprehensive mass-balance model (2011–2018) that indicated Cl− inputs exceeded the outputs for the study watersheds. Road salt applied to state roads, non-state roads, and other impervious surfaces accounted for the majority of Cl− inputs to the six watersheds. Furthermore, increasing Cl− concentrations during baseflow conditions confirm impacts to shallow groundwater. Although flow-normalized Cl− concentrations are below the U.S. Environmental Protection Agency's chronic threshold value for impacts to aquatic organisms, year-round exceedances may result before the end of this century based on current trends. Though reduced Cl− loading to streams may be achieved by limiting the expansion of impervious surfaces in exurban and suburban watersheds, changes in baseflow concentrations are likely to be gradual because of the accumulated Cl− in groundwater.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157933","usgsCitation":"Rossi, M., Kremer, P., Cravotta, C., Scheirer, K.E., and Goldsmith, S.T., 2022, Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds: Science of the Total Environment, v. 851, no. Part 2, 157933, 13 p., https://doi.org/10.1016/j.scitotenv.2022.157933.","productDescription":"157933, 13 p.","ipdsId":"IP-139821","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":446757,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.157933","text":"Publisher Index Page"},{"id":406679,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Berks County, Bucks County, Chester County, Delaware County, Lehigh County, Montgomery County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.6,\n 39.812755695478124\n ],\n [\n -74.9542236328125,\n 39.812755695478124\n ],\n [\n -74.9542236328125,\n 40.2\n ],\n [\n -75.6,\n 40.2\n ],\n [\n -75.6,\n 39.812755695478124\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"851","issue":"Part 2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rossi, Marissa L. 0000-0003-2341-0312","orcid":"https://orcid.org/0000-0003-2341-0312","contributorId":296518,"corporation":false,"usgs":false,"family":"Rossi","given":"Marissa L.","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":851695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kremer, Peleg","contributorId":296521,"corporation":false,"usgs":false,"family":"Kremer","given":"Peleg","email":"","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":851696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scheirer, Krista E.","contributorId":296524,"corporation":false,"usgs":false,"family":"Scheirer","given":"Krista","email":"","middleInitial":"E.","affiliations":[{"id":64093,"text":"Aqua Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":851698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldsmith, Steven T.","contributorId":193458,"corporation":false,"usgs":false,"family":"Goldsmith","given":"Steven","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":851699,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240257,"text":"70240257 - 2022 - Seismometer records of ground tilt induced by debris flows","interactions":[],"lastModifiedDate":"2023-02-02T15:41:51.102633","indexId":"70240257","displayToPublicDate":"2022-08-17T09:21:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Seismometer records of ground tilt induced by debris flows","docAbstract":"A change in surface loading causes the Earth’s surface to deform. Mass movements, such as debris flows, can cause a tilt large enough to be recorded by nearby instruments, but the signal is strongly dependent on the mass loading and subsurface parameters. Specifically designed sensors for such measurements (tiltmeters) are cumbersome to install. Alternatively, broadband seismometers record translational motion and also tilt signals, often at periods of tens to hundreds of seconds. Their horizontal components are thereby the most sensitive to tilt. In this study, we show how to obtain tilt caused by the passing by of debris flows from seismic measurements recorded within tens of meters of the flow and investigate the usefulness of this signal for flow characterization. We investigate the problem on three scales (1) large‐scale laboratory experiments at the U.S. Geological Survey debris‐flow flume, where broadband seismometers and tiltmeters were installed for six
The interaction of aseismic and seismic slip before and after an earthquake is fundamental for both earthquake nucleation and postseismic stress relaxation. However, it can be difficult to determine where and when aseismic slip occurs within the seismogenic zone because geodetic techniques are limited to detecting moderate to large slip amplitudes or long duration small slip amplitudes. Here, we use repeating earthquakes (earthquakes that re-rupture the same fault patch) as a proxy for aseismic slip during the 2011 Prague, Oklahoma earthquake sequence. We find that aseismic slip in the Prague earthquake sequence occurs both within the granitic basement and the overlying sedimentary rocks. The repeating earthquakes show that patches of aseismic slip are mostly located at fault intersections. These fault intersections hosted possible mainshock slip, abundant aftershocks, and afterslip. We estimate that ∼40% of the aftershocks are driven by afterslip. We interpret that aseismic slip occurs at fault intersections where stress heterogeneity creates patches of lower stress that are stable within a nonsteady state, rate-state framework.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024431","usgsCitation":"Okamoto, K., Savage, H., Cochran, E.S., and Keranen, K.M., 2022, Stress heterogeneity as a driver of aseismic slip during the 2011 Prague, Oklahoma aftershock sequence: Journal of Geophysical Research Solid Earth, v. 127, no. 8, e2022JB024431, 15 p., https://doi.org/10.1029/2022JB024431.","productDescription":"e2022JB024431, 15 p.","ipdsId":"IP-138835","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":446761,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jb024431","text":"Publisher Index Page"},{"id":406831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","city":"Prague","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.7,\n 35.44\n ],\n [\n -96.9,\n 35.44\n ],\n [\n -96.9,\n 35.59\n ],\n [\n -96.7,\n 35.59\n ],\n [\n -96.7,\n 35.44\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Okamoto, Kristina","contributorId":296586,"corporation":false,"usgs":false,"family":"Okamoto","given":"Kristina","email":"","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":851914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Savage, Heather M.","contributorId":296588,"corporation":false,"usgs":false,"family":"Savage","given":"Heather","middleInitial":"M.","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":851915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":851916,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keranen, Katie M.","contributorId":197630,"corporation":false,"usgs":false,"family":"Keranen","given":"Katie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":851917,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238479,"text":"70238479 - 2022 - Bet-hedging and best-bet strategies shape seed dormancy","interactions":[],"lastModifiedDate":"2022-11-28T12:43:22.457856","indexId":"70238479","displayToPublicDate":"2022-08-17T06:41:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2863,"text":"New Phytologist","active":true,"publicationSubtype":{"id":10}},"title":"Bet-hedging and best-bet strategies shape seed dormancy","docAbstract":"Seed dormancy (i.e. delayed germination even when conditions are favourable) is a key plant characteristic that occurs among many species worldwide. But, what selective pressures led to seed dormancy? A recent study provides a major analysis of the factors driving this trait at the global scale (Zhang et al., 2022). Using c. 12 000 species and 10 million records across the globe, they conclude that dormancy is a strategy for plants living under ‘seasonal/unpredictable’ environments; and suggest that bet-hedging could be the major mechanism behind the pattern. To reach their conclusions the authors relate the proportion of species with dormant seeds in a grid-cell global map against climate variables related to annual precipitation, temperature and seasonality. Then they showed that the most significant variables were those related to climate seasonality that they equate with unpredictable climates (although seasonal climates usually are highly predictable in their seasonal cycles).
","language":"English","publisher":"Wiley","doi":"10.1111/nph.18436","usgsCitation":"Pausas, J.G., Lamont, B.B., Keeley, J., and Bond, W.J., 2022, Bet-hedging and best-bet strategies shape seed dormancy: New Phytologist, v. 236, no. 4, p. 1232-1236, https://doi.org/10.1111/nph.18436.","productDescription":"5 p.","startPage":"1232","endPage":"1236","ipdsId":"IP-140651","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446766,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/nph.18436","text":"External Repository"},{"id":409667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"236","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pausas, Juli G.","contributorId":197439,"corporation":false,"usgs":false,"family":"Pausas","given":"Juli","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":857584,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamont, Byron B","contributorId":299345,"corporation":false,"usgs":false,"family":"Lamont","given":"Byron","email":"","middleInitial":"B","affiliations":[{"id":64817,"text":"Ecology Section, School of Life and Molecular Sciences, Curtin University, Perth, WA, 6845 Australia","active":true,"usgs":false}],"preferred":false,"id":857585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bond, William J.","contributorId":81621,"corporation":false,"usgs":false,"family":"Bond","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":857587,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235725,"text":"sim3493 - 2022 - Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California","interactions":[],"lastModifiedDate":"2026-04-01T15:26:29.678467","indexId":"sim3493","displayToPublicDate":"2022-08-16T12:19:22","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3493","displayTitle":"Colored Shaded-Relief Bathymetric Map and Surrounding Aerial Imagery of Whiskeytown Lake, California","title":"Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California","docAbstract":"The Carr wildfire began on July 23, 2018, and burned almost 300,000 acres (approximately half on Federal lands) in northern California during the subsequent 6-week period. Over 97 percent of the area within Whiskeytown National Recreation Area, California, burned during the 2018 Carr wildfire, including the entire landscape that surrounds and drains into Whiskeytown Lake. Shortly after the Carr wildfire ended, the U.S. Geological Survey began investigations into the landscape responses, such as changes in erosion and sediment deposition, that occurred after the fire. This study focused on the collection and processing of bathymetric data and onshore aerial imagery in and around Whiskeytown Lake, California, to support wildfire science after the fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3493","usgsCitation":"Dartnell, P., Logan, J.B., and East, A.E., 2022, Colored shaded-relief bathymetric map and surrounding aerial imagery of Whiskeytown Lake, California: U.S. Geological Survey Scientific Investigations Map 3493, scale 1:8,900, https://doi.org/10.3133/sim3493.","productDescription":"1 Sheet: 35.00 x 35.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-132510","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501934,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113395.htm","linkFileType":{"id":5,"text":"html"}},{"id":405195,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HEDYNT","text":"Bathymetry, topography and orthomosaic imagery for Whiskeytown Lake, northern California (ver. 2.0, July 2021)","description":"Logan, J.B., Dartnell, P., East, A.E., and Ritchie, A.C., 2020, Bathymetry, topography and orthomosaic imagery for Whiskeytown Lake, northern California (ver. 2.0, July 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9HEDYNT."},{"id":405194,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3493/sim3493.pdf","size":"25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3493"},{"id":405193,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3493/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Whiskeytown Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.63214111328125,\n 40.59257812608644\n ],\n [\n -122.51609802246092,\n 40.59257812608644\n ],\n [\n -122.51609802246092,\n 40.660066379630365\n ],\n [\n -122.63214111328125,\n 40.660066379630365\n ],\n [\n -122.63214111328125,\n 40.59257812608644\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Wetland ecosystems are diverse, productive habitats that are essential reservoirs of biodiversity. Not only are they home to numerous wetland-specialist species, but they also provide food, water, and shelter that support terrestrial wildlife populations. However, like observed patterns of biodiversity loss, wetland habitats have experienced widespread loss and degradation. In order to conserve and restore wetlands, and thereby the biodiversity they support, it is important to understand how biodiversity in wetland habitats is maintained. Habitat heterogeneity and connectivity are thought to be predominate drivers of wetland biodiversity. We quantified temporal coherence (i.e., spatial synchrony) of wetland invertebrate communities using intra-class correlations among 16 wetlands sampled continuously over 24 years to better understand the relative influences wetland heterogeneity (i.e., internal processes specific to individual wetlands and spatial connectivity and external processes occurring on the landscape) on wetland biodiversity. We found that while wetlands with different ponded-water regimes (temporarily ponded or permanently ponded) often hosted different invertebrate communities, temporal shifts in invertebrate composition were synchronous. We also found the relative importance of internal versus external forces in determining community assembly vary depending on a wetland’s hydrologic function and climate influences. Our results confirm that heterogeneity and spatial connectivity of wetland landscapes are important drivers of wetland biodiversity.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.897872","usgsCitation":"McLean, K., Mushet, D., and Sweetman, J.N., 2022, Temporal coherence patterns of prairie pothole wetlands indicate the importance of landscape linkages and wetland heterogeneity in maintaining biodiversity: Frontiers in Ecology and Evolution, v. 10, 897872, 16 p., https://doi.org/10.3389/fevo.2022.897872.","productDescription":"897872, 16 p.","ipdsId":"IP-123627","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.897872","text":"Publisher Index Page"},{"id":408491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Stutsman County","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1056,\n 47.0944\n ],\n [\n -99.088889,\n 47.0944\n ],\n [\n -99.088889,\n 47.1027\n ],\n [\n -99.1056,\n 47.1027\n ],\n [\n -99.1056,\n 47.0944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetman, Jon N. 0000-0002-9849-7355","orcid":"https://orcid.org/0000-0002-9849-7355","contributorId":221489,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","email":"","middleInitial":"N.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":854925,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247760,"text":"70247760 - 2022 - FY22 Technical Report: Evaluation of fish passage for assessment of invasive carp deterrents at locks in the upper Mississippi River","interactions":[],"lastModifiedDate":"2023-08-16T12:23:16.574239","indexId":"70247760","displayToPublicDate":"2022-08-16T07:22:33","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"FY22 Technical Report: Evaluation of fish passage for assessment of invasive carp deterrents at locks in the upper Mississippi River","docAbstract":"FY22 Technical Report for the project, \"Evaluation of fish passage for assessment of invasive carp deterrents at locks in the Upper Mississippi River.\" This document describes specific methods, highlights, and results that show how progress towards meeting objectives in a timely manner.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Annual Techical Report for the management and control of invasive carp, Mississippi River","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Upper Mississippi River Invasive Carp Partnership","collaboration":"Invasive Carp Regional Coordinating Committee (ICRCC); U.S. Army Corps of Engineers (USACE); U.S. Fish and Wildlife Service (USFWS); Illinois Natural History Survey (INHS); Iowa Department of Natural Resources (IA DNR); Missouri Department of Conservation (MO DNR); Illinois Department of Natural resources (IL DNR)","usgsCitation":"Fritts, A.K., Abner, J., Fritts, M.W., Lamer, J.T., and Cornish, M., 2022, FY22 Technical Report: Evaluation of fish passage for assessment of invasive carp deterrents at locks in the upper Mississippi River, 7 p.","productDescription":"7 p.","ipdsId":"IP-150717","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":419883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419864,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://micrarivers.org/wp-content/uploads/2023/06/2022-Deterrent-Technical-Report-USGS.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":880300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abner, Joshua","contributorId":291645,"corporation":false,"usgs":false,"family":"Abner","given":"Joshua","email":"","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":880301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fritts, Mark W.","contributorId":139239,"corporation":false,"usgs":false,"family":"Fritts","given":"Mark","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":880302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":880303,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cornish, Mark","contributorId":203379,"corporation":false,"usgs":false,"family":"Cornish","given":"Mark","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":880304,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235698,"text":"sim3489 - 2022 - Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:16:53.878209","indexId":"sim3489","displayToPublicDate":"2022-08-15T12:33:16","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3489","displayTitle":"Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars","title":"Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars","docAbstract":"The landscape in Mars Transverse Mercator (MTM) −10022 and −15022 quadrangles (lat −7.5° N. to −17.5° N. between long 335° E. and 340° E.) in Margaritifer Terra preserves a record of sedimentary and alluvial deposits, volcanic and tectonic structures, and erosional landforms that record a long and complex geologic and geomorphic history. MTM −10022 and −15022 quadrangles primarily encompass Morava Valles, the terminus of the Samara-Himera and Paraná-Loire valley networks, the broad catchment informally named Margaritifer basin, and Margaritifer Chaos. Morava Valles is the lowermost reach of the northward draining mesoscale outflow system that consists of Uzboi Vallis, Ladon Valles, and Morava Valles, was sourced from flow out of Argyre basin, and incises across and between the ancient Ladon and Holden impact basins. The broad-scale topography and surface relief within the map, including the topographic low occupied by Margaritifer basin, were largely shaped during the Noachian by the formation of the Holden, Ladon, and Ares impact basins and the Chryse trough. Multiple processes modified the ancient surface until the Late Noachian and resulted in the formation of the terra unit that forms the widely exposed surface. Later resurfacing associated with likely sedimentary and volcanic processes modified predominantly lower elevation surfaces and basins during the Late Noachian into at least the Hesperian. Sedimentary processes during the Late Noachian were dominated by fluvial incision of the Samara-Himera and Paraná-Loire valley networks and discharge related to the dissection of Morava Valles that drained Ladon basin. The history of geomorphic activity within Margaritifer basin was more complex and was likely dominated by the evolution of Morava Valles relative to the formation of the valley networks. The floor of Margaritifer basin preserves likely lacustrine plains related to sedimentation in water ponded during early discharge from Morava Valles, which were later embayed by volcanic plains. Crater densities and cross-cutting relations indicate Margaritifer basin evolved over a relatively short period of geologic time. The timing of the last drainage out of Morava Valles is not well constrained but could have occurred during the Hesperian. Structural collapse and the formation of the Margaritifer Chaos and other chaotic terrain formed by the release of subsurface water that may have been related to volcanic activity along the southern margin of Margaritifer basin. Final geomorphic events within the map region include the formation of Late Hesperian to perhaps Amazonian alluvial fans within some craters and isolated mass wasting on steep slopes. A final, variable veneer associated with locally occurring impacts and redistribution of fine-grained material by eolian processes resulted in the landscape observed today.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3489","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Wilson, S.A., Grant, J.A., and Williams, K.K., 2022, Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars: U.S. Geological Survey Scientific Investigations Map 3489, pamphlet 11 p., 1 sheet, scale 1:500,000, https://doi.org/10.3133/sim3489.","productDescription":"Report: iv, 11 p.; 1 Sheet: 45.73 x 54.01 inches; 2 Databases; Metadata; Read Me","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-118399","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":435729,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJZDWR","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3489 Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405424,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9JJZDWR","text":"Interactive map","linkHelpText":"- Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405141,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_supdata.zip","text":"Supplemental Data","size":"500 MB","linkFileType":{"id":6,"text":"zip"}},{"id":405140,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_sheet.pdf","text":"Map sheet","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405138,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_metadata.xml","size":"7 KB","linkFileType":{"id":8,"text":"xml"}},{"id":405137,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_pamphlet.pdf","text":"Pamphlet","size":"700 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":405136,"rank":2,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_gis.zip","text":"GIS Files","size":"55 MB","linkFileType":{"id":6,"text":"zip"}},{"id":405135,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3489/covrthb.jpg"},{"id":405139,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_readme.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}}],"otherGeospatial":"Margaritifer basin, Mars, Morava Valles basin","contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Birds that have adapted to urban landscapes often display changes in their behavioral responses to human disturbance. This habituation may result in a decreased wariness and secrecy near their nest and an increased inclination to engage in aggressive nest defenses. Aggressive defense of nests has been noted among Mississippi Kites (Ictinia mississippiensis), a raptor that has become a common and abundant urban nesting species across the Southern Great Plains. Defensive behaviors include swoops at, and occasional physical contact with, humans who venture close to nests. Previous research found aggressive responses by kites in 16–20% of experimental passes under nests. However, the type of human pedestrian activity around nests may influence the occurrence of aggressive responses. We assessed adult Mississippi Kite responses to experimental nest area intrusions during each week of the first four weeks of nestling development to explore increases or attenuation of aggressive behaviors. We also assessed responses in context of three types of pedestrian passes to nests. We conducted 84 trials consisting of 252 passes and found 90% elicited no response from the attending kite, 8% elicited a flight response, and 2% elicited an attack. There was no difference in response to pass type, but we found evidence that responsiveness may be associated with nestling age. Our results suggest aggression by Mississippi Kites is uncommon, but infrequent events that gain local attention may increase the perceived risk from the species due to the availability heuristic. Additionally, individual nesting pairs may tend toward aggression and become persistent nuisance birds that gain attention but are not representative of the majority of nesting pairs.
","language":"English","publisher":"The Raptor Research Foundation, Inc.","doi":"10.3356/JRR-21-74","usgsCitation":"Boal, C.W., Bibles, B., Pryor, M.M., and Skipper, B.R., 2022, Mississippi Kite nest defense: Is there an influence of nest phenology or human activity?: Journal of Raptor Research, v. 56, no. 3, p. 356-361, https://doi.org/10.3356/JRR-21-74.","productDescription":"6 p.","startPage":"356","endPage":"361","ipdsId":"IP-135185","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bibles, Brent D.","contributorId":341439,"corporation":false,"usgs":false,"family":"Bibles","given":"Brent D.","affiliations":[{"id":81739,"text":"Unity College","active":true,"usgs":false}],"preferred":false,"id":908634,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pryor, Mikayla M.","contributorId":341570,"corporation":false,"usgs":false,"family":"Pryor","given":"Mikayla","email":"","middleInitial":"M.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skipper, Ben R.","contributorId":198462,"corporation":false,"usgs":false,"family":"Skipper","given":"Ben","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":908633,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236669,"text":"70236669 - 2022 - Expression plasticity regulates intraspecific variation in the acclimatization potential of a reef-building coral","interactions":[],"lastModifiedDate":"2022-09-15T14:31:32.383153","indexId":"70236669","displayToPublicDate":"2022-08-15T09:21:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Expression plasticity regulates intraspecific variation in the acclimatization potential of a reef-building coral","docAbstract":"Phenotypic plasticity is an important ecological and evolutionary response for organisms experiencing environmental change, but the ubiquity of this capacity within coral species and across symbiont communities is unknown. We exposed ten genotypes of the reef-building coral Montipora capitata with divergent symbiont communities to four thermal pre-exposure profiles and quantified gene expression before stress testing 4 months later. Here we show two pre-exposure profiles significantly enhance thermal tolerance despite broadly different expression patterns and substantial variation in acclimatization potential based on coral genotype. There was no relationship between a genotype’s basal thermal sensitivity and ability to acquire heat tolerance, including in corals harboring naturally tolerant symbionts, which illustrates the potential for additive improvements in coral response to climate change. These results represent durable improvements from short-term stress hardening of reef-building corals and substantial cryptic complexity in the capacity for plasticity.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41467-022-32452-4","usgsCitation":"Drury, C., Dilworth, J., Majerova, E., Caruso, C., and Greer, J.B., 2022, Expression plasticity regulates intraspecific variation in the acclimatization potential of a reef-building coral: Nature Communications, v. 13, 4790, 9 p., https://doi.org/10.1038/s41467-022-32452-4.","productDescription":"4790, 9 p.","ipdsId":"IP-139687","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":446773,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-32452-4","text":"Publisher Index Page"},{"id":406755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2022-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Drury, Crawford","contributorId":296558,"corporation":false,"usgs":false,"family":"Drury","given":"Crawford","email":"","affiliations":[{"id":64096,"text":"Hawaiʻi Institute of Marine Biology, Kāneʻohe, HI","active":true,"usgs":false}],"preferred":false,"id":851817,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dilworth, Jenna","contributorId":296559,"corporation":false,"usgs":false,"family":"Dilworth","given":"Jenna","email":"","affiliations":[{"id":64096,"text":"Hawaiʻi Institute of Marine Biology, Kāneʻohe, HI","active":true,"usgs":false}],"preferred":false,"id":851818,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Majerova, Eva","contributorId":296560,"corporation":false,"usgs":false,"family":"Majerova","given":"Eva","email":"","affiliations":[{"id":64096,"text":"Hawaiʻi Institute of Marine Biology, Kāneʻohe, HI","active":true,"usgs":false}],"preferred":false,"id":851819,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caruso, Carlo","contributorId":296561,"corporation":false,"usgs":false,"family":"Caruso","given":"Carlo","email":"","affiliations":[{"id":64096,"text":"Hawaiʻi Institute of Marine Biology, Kāneʻohe, HI","active":true,"usgs":false}],"preferred":false,"id":851820,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Greer, Justin Blaine 0000-0001-6660-9976","orcid":"https://orcid.org/0000-0001-6660-9976","contributorId":265183,"corporation":false,"usgs":true,"family":"Greer","given":"Justin","email":"","middleInitial":"Blaine","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":851821,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235855,"text":"70235855 - 2022 - Vote-processing rules for combining control recommendations from multiple models","interactions":[],"lastModifiedDate":"2022-08-23T14:17:13.519962","indexId":"70235855","displayToPublicDate":"2022-08-15T09:13:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3047,"text":"Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Vote-processing rules for combining control recommendations from multiple models","docAbstract":"Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability.
","language":"English","publisher":"Royal Society Publishing","doi":"10.1098/rsta.2021.0314","usgsCitation":"Probert, W.J., Nicol, S., Ferrari, M.J., Li, S., Shea, K., Tildesley, M.J., and Runge, M.C., 2022, Vote-processing rules for combining control recommendations from multiple models: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 380, no. 2233, 20210314, 20 p., https://doi.org/10.1098/rsta.2021.0314.","productDescription":"20210314, 20 p.","ipdsId":"IP-142649","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446776,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsta.2021.0314","text":"Publisher Index Page"},{"id":405458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"380","issue":"2233","noUsgsAuthors":false,"publicationDate":"2022-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Probert, William J.M.","contributorId":295477,"corporation":false,"usgs":false,"family":"Probert","given":"William","email":"","middleInitial":"J.M.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":849532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicol, Sam","contributorId":171610,"corporation":false,"usgs":false,"family":"Nicol","given":"Sam","email":"","affiliations":[{"id":26927,"text":"CSIRO, Australia","active":true,"usgs":false}],"preferred":false,"id":849533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrari, Matthew J. 0000-0001-5251-8168","orcid":"https://orcid.org/0000-0001-5251-8168","contributorId":216186,"corporation":false,"usgs":false,"family":"Ferrari","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":849534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Shou-Li","contributorId":193644,"corporation":false,"usgs":false,"family":"Li","given":"Shou-Li","email":"","affiliations":[],"preferred":false,"id":849535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":849536,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tildesley, Michael J.","contributorId":126971,"corporation":false,"usgs":false,"family":"Tildesley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6620,"text":"University of Nottingham, School of Biology","active":true,"usgs":false}],"preferred":false,"id":849537,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849538,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254835,"text":"70254835 - 2022 - The abundance and persistence of Caprinae populations","interactions":[],"lastModifiedDate":"2024-06-11T12:17:07.60111","indexId":"70254835","displayToPublicDate":"2022-08-15T07:15:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"The abundance and persistence of Caprinae populations","docAbstract":"Stable or growing populations may go extinct when their sizes cannot withstand large swings in temporal variation and stochastic forces. Hence, the minimum abundance threshold defining when populations can persist without human intervention forms a key conservation parameter. We identify this threshold for many populations of Caprinae, typically threatened species lacking demographic data. Doing so helps triage conservation and management actions for threatened or harvested populations. Methodologically, we used population projection matrices and simulations, with starting abundance, recruitment, and adult female survival predicting future abundance, growth rate (λ), and population trend. We incorporated mean demographic rates representative of Caprinae populations and corresponding variances from desert bighorn sheep (Ovis canadensis nelsoni), as a proxy for Caprinae sharing similar life histories. We found a population’s minimum abundance resulting in ≤ 0.01 chance of quasi-extinction (QE; population ≤ 5 adult females) in 10 years and ≤ 0.10 QE in 30 years as 50 adult females, or 70 were translocation (removals) pursued. Discovering the threshold required 3 demographic parameters. We show, however, that monitoring populations’ relationships to this threshold requires only abundance and recruitment data. This applied approach avoids the logistical and cost hurdles in measuring female survival, making assays of population persistence more practical.
Mathematical modelling is used during disease outbreaks to compare control interventions. Using multiple models, the best method to combine model recommendations is unclear. Existing methods weight model projections, then rank control interventions using the combined projections, presuming model outputs are directly comparable. However, the way each model represents the epidemiological system will vary. We apply electoral vote-processing rules to combine model-generated rankings of interventions. Combining rankings of interventions, instead of combining model projections, avoids assuming that projections are comparable as all comparisons of projections are made within each model. We investigate four rules: First-past-the-post, Alternative Vote (AV), Coombs Method and Borda Count. We investigate rule sensitivity by including models that favour only one action or including those that rank interventions randomly. We investigate two case studies: the 2014 Ebola outbreak in West Africa (37 compartmental models) and a hypothetical foot-and-mouth disease outbreak in UK (four individual-based models). The Coombs Method was least susceptible to adding models that favoured a single action, Borda Count and AV were most susceptible to adding models that ranked interventions randomly. Each rule chose the same intervention as when ranking interventions by mean projections, suggesting that combining rankings provides similar recommendations with fewer assumptions about model comparability.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rsta.2021.0314","usgsCitation":"Probert, W.J., Nicol, S., Ferrari, M.J., Li, S., Shea, K., Tildesley, M.J., and Runge, M.C., 2022, Vote-processing rules for combining control recommendations from multiple models: Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, v. 380, no. 2233, 20210314, 20 p., https://doi.org/10.1098/rsta.2021.0314.","productDescription":"20210314, 20 p.","ipdsId":"IP-136798","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":467169,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsta.2021.0314","text":"Publisher Index Page"},{"id":405525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"380","issue":"2233","noUsgsAuthors":false,"publicationDate":"2022-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Probert, William JM","contributorId":295493,"corporation":false,"usgs":false,"family":"Probert","given":"William","email":"","middleInitial":"JM","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":849595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nicol, Sam","contributorId":171610,"corporation":false,"usgs":false,"family":"Nicol","given":"Sam","email":"","affiliations":[{"id":26927,"text":"CSIRO, Australia","active":true,"usgs":false}],"preferred":false,"id":849596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrari, Matthew J. 0000-0001-5251-8168","orcid":"https://orcid.org/0000-0001-5251-8168","contributorId":216186,"corporation":false,"usgs":false,"family":"Ferrari","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":849597,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Shou-Li","contributorId":193644,"corporation":false,"usgs":false,"family":"Li","given":"Shou-Li","email":"","affiliations":[],"preferred":false,"id":849598,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":849599,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tildesley, Michael J.","contributorId":126971,"corporation":false,"usgs":false,"family":"Tildesley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6620,"text":"University of Nottingham, School of Biology","active":true,"usgs":false}],"preferred":false,"id":849600,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849601,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256627,"text":"70256627 - 2022 - Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets","interactions":[],"lastModifiedDate":"2024-08-27T16:04:33.044121","indexId":"70256627","displayToPublicDate":"2022-08-13T10:55:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets","docAbstract":"The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14163937","usgsCitation":"Gonzalez Perez, A., Abd-Elrahman, A., Wilkinson, B., Johnson, D.J., and Carthy, R., 2022, Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets: Remote Sensing, v. 14, no. 16, 3937, 41 p., https://doi.org/10.3390/rs14163937.","productDescription":"3937, 41 p.","ipdsId":"IP-141836","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446787,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14163937","text":"Publisher Index Page"},{"id":433203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Wolf Branch Creek Coastal Nature Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.43286683042422,\n 27.759840853994277\n ],\n [\n -82.46741924846995,\n 27.759840853994277\n ],\n [\n -82.46741924846995,\n 27.737037233479754\n ],\n [\n -82.43286683042422,\n 27.737037233479754\n ],\n [\n -82.43286683042422,\n 27.759840853994277\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"16","noUsgsAuthors":false,"publicationDate":"2022-08-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Gonzalez Perez, Ali","contributorId":341416,"corporation":false,"usgs":false,"family":"Gonzalez Perez","given":"Ali","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908380,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abd-Elrahman, Amr","contributorId":341417,"corporation":false,"usgs":false,"family":"Abd-Elrahman","given":"Amr","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908381,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkinson, Benjamin","contributorId":239953,"corporation":false,"usgs":false,"family":"Wilkinson","given":"Benjamin","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":908382,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Daniel J.","contributorId":197828,"corporation":false,"usgs":false,"family":"Johnson","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":908383,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carthy, Raymond 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":219303,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908384,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247742,"text":"70247742 - 2022 - Brittle faulting at elevated temperature and vanishing effective stress","interactions":[],"lastModifiedDate":"2023-08-15T14:20:11.412815","indexId":"70247742","displayToPublicDate":"2022-08-13T09:12:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Brittle faulting at elevated temperature and vanishing effective stress","docAbstract":"If brittle fault strength depends only on friction, slip instability is discouraged at low effective normal stress, σ. Stress drop and the critical stiffness necessary for unstable sliding both vanish with σ; small earthquakes cannot occur. Very low σ is inferred in the source region of low-frequency earthquakes (LFEs) on the San Andreas fault (SAF). Moreover, if pore pressure, p, is undrained at low σ, then instabilities are prevented at all scales. This is due to dilatant strengthening which arises due to a dependence of porosity on strain rate. Dilatant strengthening is σ-independent and dominates at low σ. Undrained p is inferred over time scales of less than a few days for the SAF LFEs. Based on experiments that measure rapid contact overgrowth between 350 and 530°C at very low σ, fault failure controlled by time-dependent cementation is invoked as an explanation for the SAF LFEs. Because this “cohesion” is σ-independent, stress drops can occur at σ = 0. If in addition cohesion exceeds any dilatant strengthening during slip, cohesion dominates strength at low σ. Dilatancy measured in prior faulting and shear experiments indicate that at all stress levels steady-state porosity depends on σ in addition to strain rate. Moreover, porosity at low σ depends elastically on the confining and differential stresses. A model with these additional pore pressure effects, friction, and time-dependent cohesion, applied to the SAF LFEs produces stress drops, slip speeds, and durations that are consistent with the observations, when the shear-induced dilatancy is not extreme.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024335","usgsCitation":"Beeler, N.M., 2022, Brittle faulting at elevated temperature and vanishing effective stress: JGR Solid Earth, v. 127, no. 9, e2022JB024335, 30 p., https://doi.org/10.1029/2022JB024335.","productDescription":"e2022JB024335, 30 p.","ipdsId":"IP-132369","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":419812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Beeler, Nicholas M. 0000-0002-3397-8481 nbeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-3397-8481","contributorId":2682,"corporation":false,"usgs":true,"family":"Beeler","given":"Nicholas","email":"nbeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":880227,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70238501,"text":"70238501 - 2022 - Natural and anthropogenic landscape factors shape functional connectivity of an ecological specialist in urban Southern California","interactions":[],"lastModifiedDate":"2022-11-28T12:26:40.467915","indexId":"70238501","displayToPublicDate":"2022-08-13T06:21:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2774,"text":"Molecular Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Natural and anthropogenic landscape factors shape functional connectivity of an ecological specialist in urban Southern California","docAbstract":"Identifying how natural (i.e., unaltered by human activity) and anthropogenic landscape variables influence contemporary functional connectivity in terrestrial organisms can elucidate the genetic consequences of environmental change. We examine population genetic structure and functional connectivity among populations of a declining species, the Blainville's horned lizard (Phrynosoma blainvillii), in the urbanized landscape of the Greater Los Angeles Area in Southern California, USA. Using single nucleotide polymorphism data, we assessed genetic structure among populations occurring at the interface of two abutting evolutionary lineages, and at a fine scale among habitat fragments within the heavily urbanized area. Based on the ecology of P. blainvillii, we predicted which environmental variables influence population structure and gene flow and used gravity models to distinguish among hypotheses to best explain population connectivity. Our results show evidence of admixture between two evolutionary lineages and strong population genetic structure across small habitat fragments. We also show that topography, microclimate, and soil and vegetation types are important predictors of functional connectivity, and that anthropogenic disturbance, including recent fire history and urban development, are key factors impacting contemporary population dynamics. Examining how natural and anthropogenic sources of landscape variation affect contemporary population genetics is critical to understanding how to best manage sensitive species in a rapidly changing landscape.
Habitat loss due to increasing anthropogenic disturbance is the major driver for bird population declines across the globe. Within the Eastern Ghats of India, shrubland bird communities are threatened by shrinking of suitable habitats due to increased anthropogenic disturbance and climate change. The development of an effective habitat management strategy is hampered by the absence of data for this bird community. To address this knowledge gap, we examined foraging sites for 14 shrubland bird species, including three declining species, in three study areas representing the shrubland type of forest community in the Eastern Ghats. We recorded microhabitat features within an 11 m radius of observed foraging points and compared these data with similar data from random plots. We used chi-square to test the association between plant species and bird species for sites where they were observed foraging. We observed significant differences between foraging sites of all the study species and random plots, thus indicating selection for foraging habitat. Using linear discriminant analysis, we found that the microhabitat features important for the bird species were shrub density, vegetational height, vertical foliage stratification, grass height, and percent rock cover. Our results show that diet guild and foraging strata influence the foraging microhabitat selection of a species (e.g., ground-foraging species differed significantly from other species). Except for two species, all focal birds were associated with at least one plant species. The plant-bird association was based on foraging, structural, or behavioral preferences. Several key factors affecting foraging habitat such as shrub density can be actively managed at the local scale. Strategic and selective harvesting of forest products and a spatially and temporally controlled livestock grazing regime may allow regeneration of scrubland and create conditions favorable to birds.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9192","usgsCitation":"Deshwall, A., Stephenson, S., Panwar, P., DeGregorio, B.A., Kannan, R., and Willson, J., 2022, Foraging habitat selection of shrubland bird community in tropical dry forest: Ecology and Evolution, v. 12, no. 8, e9192, 12 p., https://doi.org/10.1002/ece3.9192.","productDescription":"e9192, 12 p.","ipdsId":"IP-119426","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":486945,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9192","text":"Publisher Index Page"},{"id":433371,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","state":"Andhra Pradesh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 79.37736382663809,\n 13.491137039228363\n ],\n [\n 78.55592471748383,\n 13.448724063058705\n ],\n [\n 78.57234419763398,\n 13.03288769532432\n ],\n [\n 79.44277297167974,\n 13.149403444594242\n ],\n [\n 79.37736382663809,\n 13.491137039228363\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Deshwall, A.","contributorId":341560,"corporation":false,"usgs":false,"family":"Deshwall","given":"A.","email":"","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":908618,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, S.L.","contributorId":341562,"corporation":false,"usgs":false,"family":"Stephenson","given":"S.L.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908620,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panwar, P.","contributorId":341564,"corporation":false,"usgs":false,"family":"Panwar","given":"P.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908622,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kannan, R.","contributorId":341561,"corporation":false,"usgs":false,"family":"Kannan","given":"R.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908619,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Willson, J.D.","contributorId":341563,"corporation":false,"usgs":false,"family":"Willson","given":"J.D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908621,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234573,"text":"70234573 - 2022 - Could Kı̄lauea's 2020 post caldera-forming eruption have been anticipated?","interactions":[],"lastModifiedDate":"2022-08-12T13:49:44.69867","indexId":"70234573","displayToPublicDate":"2022-08-12T08:37:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Could Kı̄lauea's 2020 post caldera-forming eruption have been anticipated?","docAbstract":"In 2018 Kīlauea volcano erupted a decade’s worth of basalt, given estimated magma supply rates, triggering caldera collapse. Yet, less than 2.5 years later Kīlauea re-erupted. At the 2018 eruption onset, pressure within the summit reservoir was ~20 MPa above magmastatic. By the onset of collapse this decreased by ~17 MPa. Analysis of magma surges at the 2018 fissures, following collapse events, implies excess pressure at the eruption end of only ~1 MPa. Given the new vent elevation, ∼11 − 12 MPa pressure increase was required to bring magma to the surface in December 2020. Analysis of GPS data between 8/2018 and 12/2020 shows there was a 73% probability that this condition was met at the onset of the 2020 eruption. Given a plausible range of possible vent elevations, there was a 40 to 88% probability of sufficient pressure to bring magma to the surface 100 days before the eruption.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL099270","usgsCitation":"Segall, P., Anderson, K.R., and Wang, T., 2022, Could Kı̄lauea's 2020 post caldera-forming eruption have been anticipated?: Geophysical Research Letters, v. 49, no. 15, e2022GL099270, 9 p., https://doi.org/10.1029/2022GL099270.","productDescription":"e2022GL099270, 9 p.","ipdsId":"IP-139845","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446792,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gl099270","text":"Publisher Index Page"},{"id":405114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kı̄lauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.29149055480957,\n 19.397144361572217\n ],\n [\n -155.29020309448242,\n 19.396658607621543\n ],\n [\n -155.28565406799316,\n 19.3992492786023\n ],\n [\n -155.2806758880615,\n 19.400058854824785\n ],\n [\n -155.27252197265625,\n 19.39803490671575\n ],\n [\n -155.26857376098633,\n 19.399654067217075\n ],\n [\n -155.26694297790527,\n 19.402163734150218\n ],\n [\n -155.26007652282715,\n 19.405240047251386\n ],\n [\n -155.25441169738767,\n 19.409611549990895\n ],\n [\n -155.25097846984863,\n 19.410178217668324\n ],\n [\n -155.24763107299805,\n 19.40839725546121\n ],\n [\n -155.24127960205078,\n 19.40928773900289\n ],\n [\n -155.23818969726562,\n 19.411716305695418\n ],\n [\n -155.2393054962158,\n 19.415035288178863\n ],\n [\n -155.2434253692627,\n 19.419325579756944\n ],\n [\n -155.24934768676758,\n 19.423291974992168\n ],\n [\n -155.25441169738767,\n 19.42806771016873\n ],\n [\n -155.25870323181152,\n 19.4303341116379\n ],\n [\n -155.26196479797363,\n 19.43081976498137\n ],\n [\n -155.26780128479004,\n 19.43041505396265\n ],\n [\n -155.27398109436035,\n 19.431791067311856\n ],\n [\n -155.27887344360352,\n 19.429929399409083\n ],\n [\n -155.28050422668457,\n 19.428391483743205\n ],\n [\n -155.28788566589355,\n 19.42078263415394\n ],\n [\n -155.29458045959473,\n 19.415844785823072\n ],\n [\n -155.29458045959473,\n 19.412849624405244\n ],\n [\n -155.29698371887207,\n 19.40977345524309\n ],\n [\n -155.2954387664795,\n 19.405240047251386\n ],\n [\n -155.29329299926758,\n 19.40192086484868\n ],\n [\n -155.29252052307126,\n 19.400544598624666\n ],\n [\n -155.29149055480957,\n 19.397144361572217\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"15","noUsgsAuthors":false,"publicationDate":"2022-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Segall, Paul","contributorId":223199,"corporation":false,"usgs":false,"family":"Segall","given":"Paul","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":848871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":848872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Taiyi","contributorId":241095,"corporation":false,"usgs":false,"family":"Wang","given":"Taiyi","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":848873,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70234571,"text":"70234571 - 2022 - Impacts of the ocean-atmosphere coupling into the very short range prediction system during the impact of Hurricane Matthew on Cuba","interactions":[],"lastModifiedDate":"2022-08-12T13:33:40.507397","indexId":"70234571","displayToPublicDate":"2022-08-12T08:23:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11602,"text":"Ciência e Natura","active":true,"publicationSubtype":{"id":10}},"title":"Impacts of the ocean-atmosphere coupling into the very short range prediction system during the impact of Hurricane Matthew on Cuba","docAbstract":"The main goal of this investigation is analyzing the impact of insert the ocean-atmosphere coupling into the very short range prediction system of Cuba. The ocean-atmosphere coupled components of the Coupled Ocean-Atmosphere-Wave-Sediment Transport Modeling System are used for this purpose and the hurricane Matthew is selected as study case. Two experiments are performed: first, using a dynamic sea surface temperature, updated daily in the atmospheric model WRF; and second using adynamic coupling between the atmospheric and an oceanic models. For the simulated track, the best results are obtained with the coupled system. The impact of coupling on the maximum wind velocities and minimum central pressure is studied. In the coupled system the sea surface temperature has more influence in the surface latent heat fluxes. Also, with this methodology the dry footprint and the behavior of the precipitation field in the presence of a hurricane are studied. This analysis shows that the hurricane acts like an open and self-sustained system in the numerical experiments. The highest differences in the precipitation simulations are in the significant convective area inside the hurricane.","language":"English","publisher":"Ciência e Natura","doi":"10.5902/2179460X66169","usgsCitation":"Vazquez Proveyer, L., Sierra Lorenzo, M., Cruz Rodriguez, R.C., and Warner, J.C., 2022, Impacts of the ocean-atmosphere coupling into the very short range prediction system during the impact of Hurricane Matthew on Cuba: Ciência e Natura, v. 44, no. 3, 24 p., https://doi.org/10.5902/2179460X66169.","productDescription":"24 p.","ipdsId":"IP-122772","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488977,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5902/2179460x66169","text":"Publisher Index Page"},{"id":405113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cuba","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-82.26815,23.18861],[-81.40446,23.11727],[-80.61877,23.10598],[-79.67952,22.7653],[-79.28149,22.3992],[-78.34743,22.51217],[-77.9933,22.27719],[-77.14642,21.65785],[-76.52382,21.20682],[-76.19462,21.22057],[-75.59822,21.01662],[-75.67106,20.73509],[-74.9339,20.69391],[-74.17802,20.28463],[-74.29665,20.05038],[-74.96159,19.92344],[-75.63468,19.87377],[-76.32366,19.95289],[-77.75548,19.85548],[-77.08511,20.41335],[-77.49265,20.67311],[-78.13729,20.73995],[-78.48283,21.02861],[-78.71987,21.59811],[-79.285,21.55918],[-80.21748,21.82732],[-80.51753,22.03708],[-81.82094,22.19206],[-82.16999,22.38711],[-81.795,22.63696],[-82.7759,22.68815],[-83.49446,22.16852],[-83.9088,22.15457],[-84.05215,21.91058],[-84.54703,21.80123],[-84.97491,21.89603],[-84.44706,22.20495],[-84.23036,22.56575],[-83.77824,22.78812],[-83.26755,22.98304],[-82.51044,23.07875],[-82.26815,23.18861]]]},\"properties\":{\"name\":\"Cuba\"}}]}","volume":"44","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Vazquez Proveyer, Liset","contributorId":293212,"corporation":false,"usgs":false,"family":"Vazquez Proveyer","given":"Liset","email":"","affiliations":[{"id":63246,"text":"Center for Atmospheric Physics, Institute of Meteorology, Casablanca, 10900, Havana, Cuba","active":true,"usgs":false}],"preferred":false,"id":848867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sierra Lorenzo, Maibys","contributorId":293213,"corporation":false,"usgs":false,"family":"Sierra Lorenzo","given":"Maibys","email":"","affiliations":[{"id":63246,"text":"Center for Atmospheric Physics, Institute of Meteorology, Casablanca, 10900, Havana, Cuba","active":true,"usgs":false}],"preferred":false,"id":848868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cruz Rodriguez, Roberto Carlos","contributorId":293214,"corporation":false,"usgs":false,"family":"Cruz Rodriguez","given":"Roberto","email":"","middleInitial":"Carlos","affiliations":[{"id":63247,"text":"Department of Atmospheric Physics, National Autonomous University of Mexico, Av. Universidad 3000, 04510, DF, Mexico","active":true,"usgs":false}],"preferred":false,"id":848869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":258015,"corporation":false,"usgs":true,"family":"Warner","given":"John","email":"jcwarner@usgs.gov","middleInitial":"C.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":848870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248350,"text":"70248350 - 2022 - Plague and trace metals in natural systems","interactions":[],"lastModifiedDate":"2023-09-08T12:53:03.970706","indexId":"70248350","displayToPublicDate":"2022-08-12T07:42:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16704,"text":"International Journal of Environmental Research and Public Health.","active":true,"publicationSubtype":{"id":10}},"title":"Plague and trace metals in natural systems","docAbstract":"All pathogenic organisms are exposed to abiotic influences such as the microclimates and chemical constituents of their environments. Even those pathogens that exist primarily within their hosts or vectors can be influenced directly or indirectly. Yersinia pestis, the flea-borne bacterium causing plague, is influenced by climate and its survival in soil suggests a potentially strong influence of soil chemistry. We summarize a series of controlled studies conducted over four decades in Russia by Dr. Evgeny Rotshild and his colleagues that investigated correlations between trace metals in soils, plants, and insects, and the detection of plague in free-ranging small mammals. Trace metal concentrations in plots where plague was detected were up to 20-fold higher or lower compared to associated control plots, and these differences were >2-fold in 22 of 38 comparisons. The results were statistically supported in eight studies involving seven host species in three families and two orders of small mammals. Plague tended to be positively associated with manganese and cobalt, and the plague association was negative for copper, zinc, and molybdenum. In additional studies, these investigators detected similar connections between pasturellosis and concentrations of some chemical elements. A One Health narrative should recognize that the chemistry of soil and water may facilitate or impede epidemics in humans and epizootics in non-human animals.
","language":"English","publisher":"MDPI","doi":"10.3390/ijerph19169979","usgsCitation":"Kosoy, M., and Biggins, D.E., 2022, Plague and trace metals in natural systems: International Journal of Environmental Research and Public Health., v. 19, no. 16, 9979, 16 p., https://doi.org/10.3390/ijerph19169979.","productDescription":"9979, 16 p.","ipdsId":"IP-139292","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446795,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijerph19169979","text":"Publisher Index Page"},{"id":420659,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan, Mongolia, Russia, Uzbekistan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 89.61243249363457,\n 48.466692874478724\n ],\n [\n 97.55135950387046,\n 47.62550521415008\n ],\n [\n 100.05863199276087,\n 45.61730314876178\n ],\n [\n 107.59703605851308,\n 45.61644272490017\n ],\n [\n 117.91939263544919,\n 50.016697469514526\n ],\n [\n 114.52594957787551,\n 53.226451135161994\n ],\n [\n 104.46048981621641,\n 53.650228892772446\n ],\n [\n 93.26601227091152,\n 53.58743226668929\n ],\n [\n 86.19871833540293,\n 50.60502492758772\n ],\n [\n 89.61243249363457,\n 48.466692874478724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 131.52445743282328,\n 42.959534327424876\n ],\n [\n 133.9963905892347,\n 42.029488763129535\n ],\n [\n 140.3914092267985,\n 47.95819400683317\n ],\n [\n 134.99148683585338,\n 48.88738814243581\n ],\n [\n 133.08363821719092,\n 44.63086350828141\n ],\n [\n 131.70164687710013,\n 44.96600756264411\n ],\n [\n 131.52445743282328,\n 42.959534327424876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 53.655114406350464,\n 45.481667787108734\n ],\n [\n 57.50335847359082,\n 46.82513492063478\n ],\n [\n 58.23209315029882,\n 50.60820394065402\n ],\n [\n 54.78716384083083,\n 50.839955137287575\n ],\n [\n 51.03936589302583,\n 51.86712489459745\n ],\n [\n 46.9304799930151,\n 50.38992855031401\n ],\n [\n 46.094105965124754,\n 48.256122327868354\n ],\n [\n 48.57953872080762,\n 45.92189885652772\n ],\n [\n 51.235842842518906,\n 46.87566419536793\n ],\n [\n 52.97267495391941,\n 46.82532952286226\n ],\n [\n 53.655114406350464,\n 45.481667787108734\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 61.93106513607361,\n 43.52165246683586\n ],\n [\n 60.98567078825775,\n 41.00558370742854\n ],\n [\n 62.64289594949247,\n 39.713860066117434\n ],\n [\n 64.25179698173943,\n 39.272687744425355\n ],\n [\n 66.9157819290491,\n 40.774219802330464\n ],\n [\n 65.46901711769192,\n 43.7560452565958\n ],\n [\n 61.93106513607361,\n 43.52165246683586\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"16","noUsgsAuthors":false,"publicationDate":"2022-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Kosoy, Michael","contributorId":329584,"corporation":false,"usgs":false,"family":"Kosoy","given":"Michael","email":"","affiliations":[{"id":78666,"text":"KB One Health, LLC","active":true,"usgs":false}],"preferred":false,"id":882651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":882652,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236729,"text":"70236729 - 2022 - Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption","interactions":[],"lastModifiedDate":"2022-09-16T12:22:53.172839","indexId":"70236729","displayToPublicDate":"2022-08-12T07:16:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7514,"text":"Journal of Geophysical Research - Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying large-scale surface change using SAR amplitude images: Crater morphology changes during the 2019-2020 Shishaldin Volcano eruption","docAbstract":"Morphological processes often induce meter-scale elevation changes. When a volcano erupts, tracking such processes provides insights into the style and evolution of eruptive activity and related hazards. Compared to optical remote-sensing products, synthetic aperture radar (SAR) observes surface change during inclement weather and at night. Differential SAR interferometry estimates phase change between SAR acquisitions and is commonly applied to quantify deformation. However, large deformation or other coherence loss can limit its use. We develop a new approach applicable when repeated digital elevation models (DEMs) cannot be otherwise retrieved. Assuming an isotropic radar cross-section, we estimate meter-scale vertical morphological change directly from SAR amplitude images via an optimization method that utilizes a high-quality DEM. We verify our implementation through simulation of a collapse feature that we modulate onto topography. We simulate radar effects and recover the simulated collapse. To validate our method, we estimate elevation changes from TerraSAR-X stripmap images for the 2011–2012 eruption of Mount Cleveland. Our results reproduce those from two previous studies; one that used the same dataset, and another based on thermal satellite data. By applying this method to the 2019–2020 eruption of Shishaldin Volcano, Alaska, we generate elevation change time series from dozens of co-registered TerraSAR-X high-resolution spotlight images. Our results quantify previously unresolved cone growth in November 2019, collapses associated with explosions in December–January, and further changes in crater elevations into spring 2020. This method can be used to track meter-scale morphology changes for ongoing eruptions with low latency as SAR imagery becomes available.
Glen Torridon (GT) is a recessive-trough feature on the northwestern slope of “Mt. Sharp” in Gale crater, Mars with the highest Fe-/Mg-phyllosilicates abundances detected by the Curiosity rover to date. Understanding the origin of these clay minerals and their relationship with diagenetic processes is critical for reconstructing the nature and habitability of past surface and subsurface environments in Gale crater. We aim to constrain the distribution and extent of diagenesis using compositional and morphological trends observed by visible-to-near infrared reflectance spectra in GT from Mastcam and ChemCam, supported by high-resolution images from the Mars Hand Lens Imager. Spectral features consistent with nontronite and fine-grained red hematite are ubiquitous throughout lower GT, and are strongest where diagenetic features are limited, suggesting that both were formed early, before burial. Diagenetic features increase in both abundance and diversity farther up-section, and we observe morphologic evidence for multiple episodes of diagenesis, with the edge of a diagenetic front partially preserved in the middle stratigraphic member, Knockfarril Hill. Near the contact between GT and the overlying Greenheugh pediment capping unit, we observe a lack of clay minerals with signatures consistent instead with coarse-grained gray hematite, likely formed through late-diagenetic alteration. We hypothesize that the sandstone-dominant Stimson formation acted as a conduit for diagenetic fluid flow into the area and that the clay-rich impermeable GT slowed the flow of those fluids, leading to enhanced alteration surrounding the clay-rich portions of GT, including within the nearby Vera Rubin ridge.
The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering South Oyster Bay, a shallow embayment on the southern shore of Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bay and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Analysis revealed that the most substantial source of fecal contamination to South Oyster Bay was stormwater, particularly during the summer months. The highest frequency of fecal coliform detections in source sites were under wet summer conditions, and the highest fecal coliform concentrations were under wet summer conditions at the Cedar Creek near Bay Place and Unqua Lake Culvert sites (more than 16,000 most probable number per 100 milliliters each). The human-associated Bacteroides marker was the most frequently detected microbial source tracking marker in South Oyster Bay (50 percent positive detections). The human marker was detected at least twice in all surface water source and receptor sites, except for the Massapequa Lake East Culvert source site that did not have any positive human marker detections. Canine contamination was prolific at source sites but was associated with low fecal coliform concentrations in the winter months. All detections of the canine-associated Bacteroides marker were in samples collected during the winter season and were associated with fecal coliform concentrations below the reporting limit, indicating that birds are not a persistent source of fecal coliform to South Oyster Bay. The absence of fecal coliform and human markers in groundwater samples collected throughout the larger study area indicates that water from cesspools or septic tanks do not contribute fecal coliform to the bay. Further, microbial source tracking markers were not detected in the sandy sediment collected at Zachs Bay. Based a classification scheme developed to convey the degree of fecal contamination to stakeholders and resource managers, the Cedar Creek near Bay Place and Unqua Lake Culvert sites were identified as locations that contribute substantial fecal contamination to South Oyster Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225082","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2022, Using microbial source tracking to identify fecal contamination sources in South Oyster Bay on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5082, 15 p., https://doi.org/10.3133/sir20225082.","productDescription":"Report: vi, 15 p.; Dataset","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130129","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":404962,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5082/coverthb.jpg"},{"id":404963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5082/sir20225082.pdf","text":"Report","size":"2.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5082"},{"id":404964,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":404965,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5082/sir20225082.XML"},{"id":404966,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5082/images/"},{"id":404967,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225082/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5082"},{"id":503402,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113394.htm","linkFileType":{"id":5,"text":"html"}},{"id":405037,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York"}],"country":"United States","state":"New York","otherGeospatial":"Long Island, South Oyster Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.50883483886719,\n 40.58606020705239\n ],\n [\n -73.37596893310547,\n 40.58606020705239\n ],\n [\n -73.37596893310547,\n 40.70016219564594\n ],\n [\n -73.50883483886719,\n 40.70016219564594\n ],\n [\n -73.50883483886719,\n 40.58606020705239\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349