Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.
This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (Rana pipiens) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.
Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.
STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.
While the R. pipiens populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01515-8","usgsCitation":"Waraniak, J.M., Mushet, D., and Stockwell, C.A., 2022, Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region: Landscape Ecology, v. 37, p. 2877-2893, https://doi.org/10.1007/s10980-022-01515-8.","productDescription":"17 p.","startPage":"2877","endPage":"2893","ipdsId":"IP-137156","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446618,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-022-01515-8","text":"Publisher Index Page"},{"id":406585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.39257812499999,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 45.920587344733654\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Waraniak, Justin M.","contributorId":211882,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","email":"","middleInitial":"M.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":851527,"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":851528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockwell, Craig A.","contributorId":194252,"corporation":false,"usgs":false,"family":"Stockwell","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":851529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255280,"text":"70255280 - 2022 - Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake","interactions":[],"lastModifiedDate":"2024-06-17T13:49:07.864148","indexId":"70255280","displayToPublicDate":"2022-08-25T08:43:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake","docAbstract":"Wildlife distributions are often subdivided into discrete conservation units to aid in implementing management and conservation objectives. Habitat suitability models, resistance surfaces, and resistant kernels provide tools for delineating spatially explicit conservation units but guidelines for parameterizing resistant kernels are generally lacking.
We used the federally threatened eastern indigo snake (Drymarchon couperi) as a case study for calibrating resistant kernels using observed movement data and resistance surfaces to help delineate habitat-based conservation units.
We simulated eastern indigo snake movements under different resistance surface and resistant kernel parameterizations and selected the scenario that produced simulated movement distances that best approximated the maximum observed annual movement distance. We used our calibrated resistant kernel to model range-wide connectivity and compared delineated conservation units to Euclidean distance-based population units from the recent eastern indigo snake species status assessment (SSA).
We identified a total of 255 eastern indigo snake conservation units, with numerous large (2500–5000 ha of suitable habitat) conservation units across the eastern indigo snake distribution. There was substantial variation in the degree of overlap with the SSA population units likely reflecting the spatial heterogeneity in habitat suitability and landscape resistance.
Our calibration approach is widely applicable to other systems for parameterizing biologically meaningful resistant kernels. Our conservation units can be used to prioritize future eastern indigo snake conservation efforts, identify areas where more survey work is needed, or identify small, isolated populations with high extinction risks.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01510-z","usgsCitation":"Bauder, J.M., Chandler, H.C., Elmore, M., and Jenkins, C.L., 2022, Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake: Landscape Ecology, v. 37, https://doi.org/10.1007/s10980-022-01510-z.","productDescription":"15 p.","startPage":"2533","ipdsId":"IP-137585","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467167,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/666095","text":"External Repository"},{"id":430270,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.6589463070196,\n 30.27164925540076\n ],\n [\n -87.4471664851995,\n 30.273991912380097\n ],\n [\n -87.243062061103,\n 30.25688691395483\n ],\n [\n -86.504722900934,\n 30.288280599826876\n ],\n [\n -85.8821985085937,\n 30.127920150613292\n ],\n [\n -85.45848132490356,\n 29.678163497359677\n ],\n [\n -85.18012336836138,\n 29.52298729685313\n ],\n [\n -84.431494016169,\n 29.8435460527122\n ],\n [\n -84.27785036727468,\n 29.98120848065058\n ],\n [\n -83.89226362456542,\n 29.960791456317352\n ],\n [\n -83.47317674842255,\n 29.509171366635385\n ],\n [\n -83.08833538532544,\n 29.1434267271204\n ],\n [\n -82.82094989668451,\n 29.0956993019621\n ],\n [\n -82.67858009554807,\n 28.666898644234394\n ],\n [\n -82.84220852464915,\n 27.892907277971844\n ],\n [\n -81.9139493773393,\n 26.026944742093463\n ],\n [\n -81.3608497502143,\n 25.677695288573233\n ],\n [\n -81.20240783558897,\n 25.110332177132477\n ],\n [\n -80.5049186418957,\n 25.163605849744997\n ],\n [\n -81.39546331741505,\n 24.792875468134326\n ],\n [\n -81.84017458395616,\n 24.611130518564238\n ],\n [\n -81.49095266413781,\n 24.52625187147568\n ],\n [\n -80.30964454840066,\n 25.01219197845387\n ],\n [\n -80.28167788112444,\n 25.441483917895624\n ],\n [\n -80.07107634394508,\n 25.88314239044695\n ],\n [\n -80.01314206035136,\n 26.51492387439309\n ],\n [\n -80.110778674335,\n 27.072566624838785\n ],\n [\n -80.54296836001978,\n 28.439325701479135\n ],\n [\n -80.92465488637467,\n 29.033168269780845\n ],\n [\n -81.40440957377771,\n 30.780806652102896\n ],\n [\n -80.97885233616921,\n 32.04045515787071\n ],\n [\n -81.28694625263563,\n 32.58791480990446\n ],\n [\n -81.99407467041935,\n 32.551271523349655\n ],\n [\n -87.68936380825409,\n 31.462681042444245\n ],\n [\n -87.8537521440345,\n 31.229156643229587\n ],\n [\n -87.97481408751028,\n 30.53533344322976\n ],\n [\n -87.6589463070196,\n 30.27164925540076\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","edition":"2519","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":904088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandler, H. C.","contributorId":339318,"corporation":false,"usgs":false,"family":"Chandler","given":"H.","email":"","middleInitial":"C.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":904089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elmore, M.","contributorId":339320,"corporation":false,"usgs":false,"family":"Elmore","given":"M.","email":"","affiliations":[{"id":81289,"text":"Georgia Ecological Services","active":true,"usgs":false}],"preferred":false,"id":904090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, C. L.","contributorId":339321,"corporation":false,"usgs":false,"family":"Jenkins","given":"C.","email":"","middleInitial":"L.","affiliations":[{"id":13223,"text":"The Orianne Society","active":true,"usgs":false}],"preferred":false,"id":904091,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70235807,"text":"70235807 - 2022 - Magnetotelluric investigations of the Kīlauea Volcano, Hawaii","interactions":[],"lastModifiedDate":"2022-08-22T14:35:53.522926","indexId":"70235807","displayToPublicDate":"2022-08-22T09:35:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Magnetotelluric investigations of the Kīlauea Volcano, Hawaii","docAbstract":"In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the U.S. Geological Survey (USGS) Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kīlauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kīlauea and the East and Southwest Rift zones, which has implications for understanding Kīlauea's plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. In total, data were acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone, where electrical contact resistances are on the order of 100 kΩ. A three-dimensional (3D) MT model study was done to guide interpretation of the observed MT measurements. Synthetic modeling demonstrates that conductive bodies in the upper 3 km can be spatially resolved where MT station sampling is good. Resistivity anomalies in the 3D inversions have a high degree of spatial correlation with previously published seismic velocity anomalies beneath Kīlauea. Melt fractions between 0.096 and 0.117 are calculated for the Kīlauea and Puʻuʻōʻō low resistivity anomalies, respectively.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024418","usgsCitation":"Hoversten, G., Gasperikova, E., Mackie, R., Myer, D., Kauahikaua, J.P., Newman, G.A., and Cuevas, N., 2022, Magnetotelluric investigations of the Kīlauea Volcano, Hawaii: Journal of Geophysical Research: Solid Earth, v. 127, no. 8, e2022JB024418, 24 p., https://doi.org/10.1029/2022JB024418.","productDescription":"e2022JB024418, 24 p.","ipdsId":"IP-135899","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446701,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022jb024418","text":"External Repository"},{"id":405386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.29131889343262,\n 19.396901484778134\n ],\n [\n -155.28496742248535,\n 19.39892544698541\n ],\n [\n -155.2786159515381,\n 19.399087362874425\n ],\n [\n -155.2730369567871,\n 19.39827778181811\n ],\n [\n -155.2676296234131,\n 19.400949384016776\n ],\n [\n -155.26385307312012,\n 19.403944764615613\n ],\n [\n -155.25887489318848,\n 19.40629245679785\n ],\n [\n -155.2511501312256,\n 19.4109877394962\n ],\n [\n -155.24694442749023,\n 19.408882974361152\n ],\n [\n -155.24042129516602,\n 19.408478208711944\n ],\n [\n -155.23784637451172,\n 19.410906787494763\n ],\n [\n -155.23836135864258,\n 19.414306736852605\n ],\n [\n -155.24102210998535,\n 19.414306736852605\n ],\n [\n -155.2434253692627,\n 19.418758943963656\n ],\n [\n -155.24943351745605,\n 19.42353481237166\n ],\n [\n -155.25372505187985,\n 19.427662992293143\n ],\n [\n -155.25853157043457,\n 19.4298484568423\n ],\n [\n -155.26239395141602,\n 19.43081976498137\n ],\n [\n -155.2672004699707,\n 19.430576938491143\n ],\n [\n -155.2708911895752,\n 19.4313863587134\n ],\n [\n -155.27406692504883,\n 19.432033891986865\n ],\n [\n -155.2799892425537,\n 19.429605628899985\n ],\n [\n -155.28857231140134,\n 19.42013505603468\n ],\n [\n -155.29492378234863,\n 19.416492381036047\n ],\n [\n -155.2946662902832,\n 19.413416280797698\n ],\n [\n -155.29646873474118,\n 19.410663931248664\n ],\n [\n -155.29698371887207,\n 19.407021044033193\n ],\n [\n -155.29131889343262,\n 19.396901484778134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoversten, G.M.","contributorId":295409,"corporation":false,"usgs":false,"family":"Hoversten","given":"G.M.","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":849391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gasperikova, Erika","contributorId":193561,"corporation":false,"usgs":false,"family":"Gasperikova","given":"Erika","affiliations":[],"preferred":false,"id":849392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mackie, Randall","contributorId":295410,"corporation":false,"usgs":false,"family":"Mackie","given":"Randall","email":"","affiliations":[{"id":63861,"text":"CGG Multiphysics","active":true,"usgs":false}],"preferred":false,"id":849393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Myer, David","contributorId":206497,"corporation":false,"usgs":false,"family":"Myer","given":"David","email":"","affiliations":[],"preferred":false,"id":849394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":849395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newman, Greg A.","contributorId":295412,"corporation":false,"usgs":false,"family":"Newman","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":63862,"text":"Lawrence Berkeley National Laboratory, Sandia National Laboratory","active":true,"usgs":false}],"preferred":false,"id":849396,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cuevas, Nestor","contributorId":295414,"corporation":false,"usgs":false,"family":"Cuevas","given":"Nestor","email":"","affiliations":[{"id":63864,"text":"Electromagnetic Instruments","active":true,"usgs":false}],"preferred":false,"id":849397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"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
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
Stream morphology is affected by changes on the surrounding landscape. Understanding the effects of urbanization on stream morphology is a critical factor for land managers to maintain and improve vulnerable stream corridors in urbanizing landscapes. Stormwater practices are used in urban landscapes to manage runoff volumes and peak flows, potentially mitigating alterations to the flow regime that drive changes in channel morphology. However, there remains a paucity of long-term studies assessing watershed-scale relationships between urbanization and effects on stream morphology where green stormwater infrastructure exists in high densities. This paper evaluates the geomorphic changes across four headwater catchments in the Chesapeake Bay Watershed over the course of >10 yr and relates these changes to urban development. Annual cross-sectional surveys conducted from 2002 to 2019 in one forested catchment, one agricultural catchment, and two treatment catchments were used to understand the relationship between urbanization and changes in stream morphology. Six cross-sectional geomorphic metrics were calculated and compared with development timelines and high flow events. A channel evolution model was then used to understand the status of morphologic stability at sites within the study. Results suggest downstream environments in developing areas are more impacted during early phases of suburban construction. Channel change during construction could be a result of sediment and erosion control efforts' limitations on preventing and controlling overland sediment mobilization or of increased discharge causing widening and thus bank-derived sediment to move to the streambed. Results demonstrate that geomorphic metrics are highly variable within a small area and are not always accurate representations of broader landscape changes but rather of the more localized environment at a specific stream segment. Despite a high density of stormwater management facilities in urban catchments, substantial alterations to cross sections were found at multiple locations in each catchment including the controls.
Studying the movements of organisms that live underground for at least a portion of their life history is challenging, given the state of current technology. Passive integrated transponders (PIT tags) provide a way to individually identify and, more recently, study the movement of smaller animals, including those that make subterranean movements. However, there are widespread assumptions of the use of PIT tags that remain problematic. We tested the effects of PIT-tag implantation on growth and survival, along with the effects of electromagnetic fields for reading PIT tags on behavior, of the smallest salamander that has been PIT-tagged: the Red-Backed Salamander. We found no effect of PIT tags on growth or survival. Using a mesocosm experiment, we also found that electromagnetic effects associated with reading PIT tags, had no effect on salamander behavior. Further, we describe a novel PIT antenna and soil mesocosm experimental arena for studying belowground movements of woodland salamanders. Collectively, these studies suggest that the use of PIT tags do not influence the growth, survival, or behavior of Red-Backed Salamanders. Given the challenges of studying salamanders that live underground and the impending changes in climate and landscapes, this research suggests that PIT tags remain a viable tool for studying the movement ecology of salamanders under global change.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/20-006","usgsCitation":"Sterrett, S., Dubreuil, T.L., O'Donnell, M.J., Brand, A., and Campbell Grant, E.H., 2022, Testing assumptions in the use of PIT tags to study movement of Plethodon salamanders: Journal of Herpetology, v. 56, no. 2, p. 146-152, https://doi.org/10.1670/20-006.","productDescription":"7 p.","startPage":"146","endPage":"152","ipdsId":"IP-115968","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":405456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sterrett, Sean C 0000-0003-1356-2785","orcid":"https://orcid.org/0000-0003-1356-2785","contributorId":242972,"corporation":false,"usgs":false,"family":"Sterrett","given":"Sean C","affiliations":[{"id":38445,"text":"Monmouth University","active":true,"usgs":false}],"preferred":false,"id":849501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dubreuil, Todd L. 0000-0003-0189-4336 tdubreuil@usgs.gov","orcid":"https://orcid.org/0000-0003-0189-4336","contributorId":5552,"corporation":false,"usgs":true,"family":"Dubreuil","given":"Todd","email":"tdubreuil@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":849549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":295467,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":849504,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brand, Adrianne 0000-0003-2664-0041","orcid":"https://orcid.org/0000-0003-2664-0041","contributorId":295466,"corporation":false,"usgs":true,"family":"Brand","given":"Adrianne","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":849503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70235889,"text":"70235889 - 2022 - Winter severity affects occupancy of spring- and summer-breeding anurans across the eastern United States","interactions":[],"lastModifiedDate":"2022-09-27T16:58:22.989788","indexId":"70235889","displayToPublicDate":"2022-08-09T06:40:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Winter severity affects occupancy of spring- and summer-breeding anurans across the eastern United States","docAbstract":"Climate change is an increasingly important driver of biodiversity loss. The ectothermic nature of amphibians may make them particularly sensitive to changes in temperature and precipitation regimes, adding to declines from other threats. While active season environmental conditions can influence growth and survival, effects of variation in winter conditions on population dynamics are less well-studied. Given that extreme winter temperatures can influence amphibian survival and fitness, we expected that increased winter severity—as measured by variability in winter temperatures and snow cover—would be associated with decreased occupancy, and that populations that experience more severe winters would have the largest sensitivities and show the greatest declines.
Eastern United States.
2001–2015.
Anurans.
We used large-scale citizen science data from the eastern half of the United States, a diverse biogeographic and climatic region, to assess how variation in winter severity influenced occupancy dynamics (i.e. presence or absence of species across sites and years) of 11 spring and summer breeding anuran species.
Most species had increased occupancy in years with greater than average snow cover and warmer than average mean winter temperatures. Surprisingly, climatic conditions in winter affected occupancy dynamics of species with varying life history characteristics, including both spring and summer breeding species, those that overwinter under the soil, and those that overwinter in ponds and stream beds. For two wide-ranging species (Lithobates catesbeianus and Lithobates clamitans), colder winter temperatures reduced occupancy more at northern latitudes, while the association between days of snow cover and latitude was equivocal.
As the climate continues to change, expected reductions in snowpack may reduce occupancy of already declining anuran populations, while milder winters may improve overwinter survival for some species. The contradictory impacts of temperature and snow cover illustrate the importance of considering multi-dimensional impacts of climate change on anuran populations.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13620","usgsCitation":"Weiskopf, S.R., Shiklomanov, A.N., Thompson, L., Wheedleton, S., and Campbell Grant, E.H., 2022, Winter severity affects occupancy of spring- and summer-breeding anurans across the eastern United States: Diversity and Distributions, v. 28, no. 10, p. 2187-2199, https://doi.org/10.1111/ddi.13620.","productDescription":"13 p.","startPage":"2187","endPage":"2199","ipdsId":"IP-127531","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":446854,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13620","text":"Publisher Index Page"},{"id":405526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.28320312499999,\n 25.24469595130604\n ],\n [\n -66.88476562499999,\n 25.24469595130604\n ],\n [\n -66.88476562499999,\n 49.26780455063753\n ],\n [\n -100.28320312499999,\n 49.26780455063753\n ],\n [\n -100.28320312499999,\n 25.24469595130604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiskopf, Sarah R. 0000-0002-5933-8191","orcid":"https://orcid.org/0000-0002-5933-8191","contributorId":207699,"corporation":false,"usgs":true,"family":"Weiskopf","given":"Sarah","email":"","middleInitial":"R.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":849614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shiklomanov, Alexey N. 0000-0003-4022-5979","orcid":"https://orcid.org/0000-0003-4022-5979","contributorId":245541,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexey","email":"","middleInitial":"N.","affiliations":[{"id":49218,"text":"Boston University Department of Earth and Environment","active":true,"usgs":false}],"preferred":false,"id":849615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":849616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wheedleton, Sarah","contributorId":295508,"corporation":false,"usgs":false,"family":"Wheedleton","given":"Sarah","email":"","affiliations":[{"id":63897,"text":"Smithsonian Conservation Commons","active":true,"usgs":false}],"preferred":false,"id":849617,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849618,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239138,"text":"70239138 - 2022 - RNA-seq reveals potential gene biomarkers in fathead minnows (Pimephales promelas) for exposure to treated wastewater effluent","interactions":[],"lastModifiedDate":"2022-12-29T13:16:42.17673","indexId":"70239138","displayToPublicDate":"2022-08-08T07:09:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9161,"text":"Environmental Science: Processes & Impacts","active":true,"publicationSubtype":{"id":10}},"displayTitle":"RNA-seq reveals potential gene biomarkers in fathead minnows (Pimephales promelas) for exposure to treated wastewater effluent","title":"RNA-seq reveals potential gene biomarkers in fathead minnows (Pimephales promelas) for exposure to treated wastewater effluent","docAbstract":"Discharged wastewater treatment plant (WWTP) effluent greatly contributes to the generation of complex mixtures of contaminants of emerging concern (CECs) in aquatic environments which often contain neuropharmaceuticals and other emerging contaminants that may impact neurological function. However, there is a paucity of knowledge on the neurological impacts of these exposures to aquatic organisms. In this study, caged fathead minnows (Pimephales promelas) were exposed in situ in a temperate-region effluent-dominated stream (i.e., Muddy Creek) in Coralville, Iowa, USA upstream and downstream of a WWTP effluent outfall. The pharmaceutical composition of Muddy Creek was recently characterized by our team and revealed many compounds there were at a low microgram to high nanogram per liter concentration. Total RNA sequencing analysis on brain tissues revealed 280 gene isoforms that were significantly differentially expressed in male fish and 293 gene isoforms in female fish between the upstream and downstream site. Only 66 (13%) of such gene isoforms overlapped amongst male and female fish, demonstrating sex-dependent impacts on neuronal gene expression. By using a systems biology approach paired with functional enrichment analyses, we identified several potential novel gene biomarkers for treated effluent exposure that could be used to expand monitoring of environmental effects with respect to complex CEC mixtures. Lastly, when comparing the results of this study to those that relied on a single-compound approach, there was relatively little overlap in terms of gene-specific effects. This discovery brings into question the application of single-compound exposures in accurately characterizing environmental risks of complex mixtures and for gene biomarker identification.
The lesser prairie-chicken (Tympanuchus pallidicinctus) is a species of prairie grouse that occupies grassland ecosystems in the Southern and Central High Plains of the Great Plains. Reduced abundance and occupied ranges have led to increased conservation efforts throughout the species’ range. Habitat loss is considered the predominant cause of these declines. In the Southern High Plains of Texas and New Mexico, lesser prairie-chicken habitat corresponds to the Sand Shinnery Oak Prairie Ecoregion, which is comprised of a mixture of sand shinnery oak (Quercus havardii)-dominated grasslands, sand sagebrush (Artemisia filifolia)-dominated grasslands, and mixed grasslands. In sand shinnery oak–grassland communities, conversion to row-crop agriculture, continuous unmanaged livestock grazing, restriction of natural fire, invasive plant species (e.g., mesquite (Prosopis spp.)), extensive use of herbicides, energy development, and a variety of other factors have also negatively affected ecosystem extent and function. We integrated historical maps and remote sensing-derived information to measure trends in the extent and geographical distribution of sand shinnery oak prairies in eastern New Mexico and northwest Texas. Potential lesser prairie-chicken habitat was reduced by 56% from a potential of 43,258 km2 to 18,908 km2 in ~115 years (since pre-settlement). Our assessment indicated both mixed grasslands and sand shinnery oak-dominated grasslands were transformed from large parcels of existing vegetation communities to urban settlements, row crops, roads, and industrial land uses by the 1970s. Currently, potential habitat is highly fragmented and restricted to isolated locations in Texas and New Mexico, with an increasing dominance in mixed grasslands, especially in the southeastern portion of the lesser prairie-chicken range. Sand shinnery oak-dominated grasslands have been declining rapidly, from 69% of its potential extent in 1985, 65% in 1995, 54% in 2005, to 42% in 2015. Mixed grasslands drastically declined to 50% of its potential distribution by 1985. Since then, it has been stable until the 2005–2015 period when it declined to 45% of its potential extent. Based on the 2015 assessment, the current potential habitat for lesser prairie chicken is estimated at 18,908 km2 (1,890,800 ha or 4.6 million acres), where 13,126 km2 corresponds to mixed grasslands and 5782 km2 corresponds to sand shinnery oak-dominated grasslands.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14153780","usgsCitation":"Portillo-Quintero, C., Grisham, B., Haukos, D.A., Boal, C.W., Christian A. Hagen, Wan, Z., Subedi, M., and Menkiti, N., 2022, Trends of lesser prairie-chicken habitat extent and distribution on the Southern High Plains: Remote Sensing, v. 14, no. 15, 3780, 21 p., https://doi.org/10.3390/rs14153780.","productDescription":"3780, 21 p.","ipdsId":"IP-134001","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":446881,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14153780","text":"Publisher Index Page"},{"id":429754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.1173828305202,\n 34.97867213600597\n ],\n [\n -105.1173828305202,\n 31.262556094417107\n ],\n [\n -101.82148439302048,\n 31.262556094417107\n ],\n [\n -101.82148439302048,\n 34.97867213600597\n ],\n [\n -105.1173828305202,\n 34.97867213600597\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"15","noUsgsAuthors":false,"publicationDate":"2022-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Portillo-Quintero, Carlos","contributorId":198384,"corporation":false,"usgs":false,"family":"Portillo-Quintero","given":"Carlos","email":"","affiliations":[],"preferred":false,"id":902666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grisham, Blake","contributorId":337771,"corporation":false,"usgs":false,"family":"Grisham","given":"Blake","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":902870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","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":902665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":902671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christian A. Hagen","contributorId":217299,"corporation":false,"usgs":false,"family":"Christian A. Hagen","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":902871,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wan, Zhanming","contributorId":211684,"corporation":false,"usgs":false,"family":"Wan","given":"Zhanming","email":"","affiliations":[],"preferred":false,"id":902669,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Subedi, Mukti","contributorId":337996,"corporation":false,"usgs":false,"family":"Subedi","given":"Mukti","email":"","affiliations":[],"preferred":false,"id":902872,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Menkiti, Nwasinachi","contributorId":337772,"corporation":false,"usgs":false,"family":"Menkiti","given":"Nwasinachi","email":"","affiliations":[{"id":40367,"text":"Utah Valley University","active":true,"usgs":false}],"preferred":false,"id":902670,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70234234,"text":"70234234 - 2022 - Freshwater unionid mussels threatened by predation of Round Goby (Neogobius melanostomus)","interactions":[],"lastModifiedDate":"2022-08-04T14:05:53.114941","indexId":"70234234","displayToPublicDate":"2022-08-04T08:57:15","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}},"displayTitle":"Freshwater unionid mussels threatened by predation of Round Goby (Neogobius melanostomus)","title":"Freshwater unionid mussels threatened by predation of Round Goby (Neogobius melanostomus)","docAbstract":"Indigenous freshwater mussels (Unionidae) are integral to riverine ecosystems, playing a pivotal role in aquatic food webs and providing ecological services. With populations on the decline worldwide, freshwater mussels are of conservation concern. In this study, we explore the propensity of the invasive Round Goby (Neogobius melanostomus) fish to prey upon indigenous freshwater mussels. First, we conducted lab experiments where Round Gobies were given the opportunity to feed on juvenile unionid mussels and macroinvertebrates, revealing rates and preferences of consumption. Several Round Gobies consumed whole freshwater mussels during these experiments, as confirmed by mussel counts and x-ray images of the fishes. Next, we investigated Round Gobies collected from stream habitats of the French Creek watershed, which is renowned for its unique and rich aquatic biodiversity. We developed a novel DNA metabarcoding method to identify the specific species of mussels consumed by Round Goby and provide a new database of DNA gene sequences for 25 indigenous unionid mussel species. Several of the fishes sampled had consumed indigenous mussels, including the Elktoe (non-endangered), Creeper (non-endangered), Long Solid (state endangered), and Rayed Bean (federally endangered) species. The invasive Round Goby poses a growing threat to unionid mussels, including species of conservation concern. The introduction of the invasive Round Goby to freshwaters of North America is shaping ecosystem transitions within the aquatic critical zone having widespread implications for conservation and management.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-022-16385-y","usgsCitation":"Clark, K., Iwanowicz, D.D., Iwanowicz, L., Mueller, S., Wisor, J., Bradshaw-Wilson, C., Schill, W., Stauffer, J.R., and Boyer, E.W., 2022, Freshwater unionid mussels threatened by predation of Round Goby (Neogobius melanostomus): Scientific Reports, v. 12, 12859, 11 p., https://doi.org/10.1038/s41598-022-16385-y.","productDescription":"12859, 11 p.","ipdsId":"IP-137170","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446924,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-16385-y","text":"Publisher Index Page"},{"id":404822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, New York, Pennsylvania, West Virginia","otherGeospatial":"Allegheny River Basin, Monongahela River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.815673828125,\n 38.37611542403604\n ],\n [\n -79.70581054687499,\n 38.634036452919226\n ],\n [\n -78.99169921875,\n 39.01064750994083\n ],\n [\n -78.607177734375,\n 39.47860556892209\n ],\n [\n -78.64013671875,\n 39.985538414809746\n ],\n [\n -78.2666015625,\n 40.64730356252251\n ],\n [\n -77.34374999999999,\n 41.21998578493921\n ],\n [\n -77.2119140625,\n 42.27730877423709\n ],\n [\n -77.662353515625,\n 42.771211138625894\n ],\n [\n -78.914794921875,\n 42.78733853171998\n ],\n [\n -80.518798828125,\n 41.9921602333763\n ],\n [\n -80.13427734374999,\n 41.15384235711447\n ],\n [\n -80.628662109375,\n 38.77978137804918\n ],\n [\n -80.035400390625,\n 38.40194908237822\n ],\n [\n -79.815673828125,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Clark, Kyle","contributorId":294537,"corporation":false,"usgs":false,"family":"Clark","given":"Kyle","email":"","affiliations":[{"id":63593,"text":"Penn State University, Pennsylvania Fish & Boat Commission","active":true,"usgs":false}],"preferred":false,"id":848276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwanowicz, Deborah D. 0000-0002-9613-8594 diwanowicz@usgs.gov","orcid":"https://orcid.org/0000-0002-9613-8594","contributorId":2253,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Deborah","email":"diwanowicz@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":79382,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848277,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mueller, Sara","contributorId":294538,"corporation":false,"usgs":false,"family":"Mueller","given":"Sara","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":848279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wisor, Joshua","contributorId":294539,"corporation":false,"usgs":false,"family":"Wisor","given":"Joshua","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":848280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bradshaw-Wilson, Casey","contributorId":294540,"corporation":false,"usgs":false,"family":"Bradshaw-Wilson","given":"Casey","email":"","affiliations":[{"id":63066,"text":"Allegheny College","active":true,"usgs":false}],"preferred":false,"id":848281,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schill, W. Bane 0000-0002-9217-984X","orcid":"https://orcid.org/0000-0002-9217-984X","contributorId":213903,"corporation":false,"usgs":true,"family":"Schill","given":"W. Bane","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":848282,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stauffer, Jay R. Jr.","contributorId":119700,"corporation":false,"usgs":false,"family":"Stauffer","given":"Jay","suffix":"Jr.","email":"","middleInitial":"R.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":848283,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":848284,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70235849,"text":"70235849 - 2022 - Living with wildfire in Grand County, Colorado: 2021 data report","interactions":[],"lastModifiedDate":"2022-08-23T14:44:06.307743","indexId":"70235849","displayToPublicDate":"2022-08-01T09:41:02","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":72,"text":"Research Note","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"RMRS-RN-94","title":"Living with wildfire in Grand County, Colorado: 2021 data report","docAbstract":"Wildfire affects hundreds of wildland-urban interface communities each year, and yet most communities lack data reflecting the conditions before an event. This study was conducted before the devastating 2020 East Troublesome Fire1, which spread across 193,812 acres and resulted in two lives lost and 366 homes and 214 other structures burned. The fire’s dramatic run threatened over 7,000 structures and led to a mandatory evacuation of over 35,000 people in Grand and Larimer Counties. The data reported here serve as baseline data to aid in understanding the parcel and social conditions before the fire. This report presents results from WiRē Rapid Wildfire Risk Assessment (WiRē RA) data, collected from 1,162 private residential properties in six communities in five fire protection districts (FPDs), the majority (72%) of which were characterized as high, very high, or extreme risk.
This report also presents results from household surveys sent to homeowners in the study area. Household survey respondents underestimated their risk compared to the conditions observed through the professional risk assessment. Respondents consistently overestimated the amount of defensible space and the distance from their homes to nonvegetative combustibles. Respondents also overestimated the availability of driveway clearance that would enable access for response vehicles and for safe passing of residents evacuating and responders arriving to their homes.
This chapter introduces seismic monitoring of structural systems for buildings and begins with a historical background of this topic in the United States. After providing the historical context, the chapter reviews common seismic instrumentation issues such as utilization of data, code versus extensive instrumentation, free-field instrumentation, record synchronization requirements and more. Recent developments in damage detection is examined including damage detection based on changes in natural frequencies, permanent deformations, and interstory drift. Finally, applications in Europe, the Middle East, and Japan of seismic monitoring of structural systems for buildings are discussed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Sensor technologies for civil infrastructures: Applications in structural health monitoring","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-08-102706-6.00004-0","usgsCitation":"Celebi, M., and Kaya, Y., 2022, Seismic monitoring solutions for buildings, chap. 3 of Sensor technologies for civil infrastructures: Applications in structural health monitoring, v. 2, p. 63-101, https://doi.org/10.1016/B978-0-08-102706-6.00004-0.","productDescription":"39 p.","startPage":"63","endPage":"101","ipdsId":"IP-113785","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":406964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852254,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaya, Yavuz","contributorId":296700,"corporation":false,"usgs":false,"family":"Kaya","given":"Yavuz","email":"","affiliations":[{"id":64148,"text":"BC Ministry of Transportation and Infrastructure","active":true,"usgs":false}],"preferred":false,"id":852255,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70234181,"text":"70234181 - 2022 - Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle","interactions":[],"lastModifiedDate":"2022-08-03T12:09:42.114227","indexId":"70234181","displayToPublicDate":"2022-07-29T07:05:04","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":"Spatiotemporal changes in influenza A virus prevalence among wild waterfowl inhabiting the continental United States throughout the annual cycle","docAbstract":"Avian influenza viruses can pose serious risks to agricultural production, human health, and wildlife. An understanding of viruses in wild reservoir species across time and space is important to informing surveillance programs, risk models, and potential population impacts for vulnerable species. Although it is recognized that influenza A virus prevalence peaks in reservoir waterfowl in late summer through autumn, temporal and spatial variation across species has not been fully characterized. We combined two large influenza databases for North America and applied spatiotemporal models to explore patterns in prevalence throughout the annual cycle and across the continental United States for 30 waterfowl species. Peaks in prevalence in late summer through autumn were pronounced for dabbling ducks in the genera Anas and Spatula, but not Mareca. Spatially, areas of high prevalence appeared to be related to regional duck density, with highest predicted prevalence found across the upper Midwest during early fall, though further study is needed. We documented elevated prevalence in late winter and early spring, particularly in the Mississippi Alluvial Valley. Our results suggest that spatiotemporal variation in prevalence outside autumn staging areas may also represent a dynamic parameter to be considered in IAV ecology and associated risks.
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water (Burow and Belitz, 2014). The stream-valley aquifers constitute one of the important aquifer systems being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223037","collaboration":"National Water-Quality Assessment Project","programNote":"National Water Quality Program","usgsCitation":"Kingsbury, J.A., 2022, Groundwater quality in selected Stream Valley aquifers, eastern United States: U.S. Geological Survey Fact Sheet 2022-3037, 4 p., https://doi.org/10.3133/fs20223037.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-135420","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":404450,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3037/covrthb.jpg"},{"id":404451,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3037/fs20223037.pdf","text":"Report","size":"3.41 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":404452,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3037/fs20223037.xml"},{"id":404453,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3037/images"},{"id":501492,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113350.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Indiana, Kentucky, Missouri, New York, Ohio, Pennsylvania, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.2529296875,\n 36.59788913307022\n ],\n [\n -87.890625,\n 36.94989178681327\n ],\n [\n -85.517578125,\n 37.71859032558816\n ],\n [\n -83.54003906250001,\n 38.13455657705411\n ],\n [\n -82.44140625,\n 37.92686760148135\n ],\n [\n -80.85937499999999,\n 38.30718056188316\n ],\n [\n -80.1123046875,\n 38.75408327579141\n ],\n [\n -78.92578124999999,\n 39.977120098439634\n ],\n [\n -77.82714843749999,\n 40.713955826286046\n ],\n [\n -76.4208984375,\n 41.409775832009565\n ],\n [\n -76.4208984375,\n 41.902277040963696\n ],\n [\n -76.728515625,\n 42.58544425738491\n ],\n [\n -77.82714843749999,\n 42.58544425738491\n ],\n [\n -78.486328125,\n 41.96765920367816\n ],\n [\n -79.89257812499999,\n 41.27780646738183\n ],\n [\n -81.650390625,\n 40.91351257612758\n ],\n [\n -83.14453125,\n 40.44694705960048\n ],\n [\n -84.375,\n 39.9434364619742\n ],\n [\n -86.484375,\n 39.33429742980725\n ],\n [\n -88.11035156249999,\n 38.58252615935333\n ],\n [\n -88.9453125,\n 38.13455657705411\n ],\n [\n -89.8681640625,\n 37.37015718405753\n ],\n [\n -89.736328125,\n 36.70365959719456\n ],\n [\n -89.2529296875,\n 36.59788913307022\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Chief Scientist
National Water-Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192-0002
Geological reservoir characterization is essential for accurate evaluation of gas production performance from gas hydrate reservoirs. Particularly, the understanding of reservoir architecture and heterogeneity is of great importance since these are considered as major controls on fluid hydrodynamic and thermodynamic conditions. This study deals with well log and three-dimensional (3-D) vertical seismic profile (VSP) data acquired from the Hydrate-01 Stratigraphic Test Well within the 7-11-12 prospect, Prudhoe Bay Unit, Alaska North Slope and reports on the results of geological/geophysical evaluation related to the geological structure and reservoir properties of the 7-11-12 prospect. The structural trends of the target reservoirs, based on well correlations, are mostly consistent with the predrill prediction using the surface seismic data, and infer the existence of subseismic faults cutting through the Hydrate-01 well. The 3-D VSP data confirm a down-to-the-east normal fault that offsets the reservoir units across the Hydrate-01 well, which is concordant with the well identification of the same fault, and indicate a northeast-dipping relay structure associated with the overstepping normal faults. The edge enhancement attribute associated with discontinuity generated from the 3-D VSP data shows small faults/fractures, possibly as part of a complex fault network within the imaged normal fault system. These results reveal that the 3-D VSP data provide detailed structural information that is not present from the surface seismic data. The Hydrate-01 well log data confirm the occurrence of gas hydrate at high saturation in the two targeted sand units (B1 and D1 sands), and the comparison to a nearby pre-existing well (7-11-12 well) shows the same general trend in gas hydrate saturation as a map of seismic impedance generated from surface seismic data. The well log data also suggest that the base of gas hydrate occurrence in the Hydrate-01 and 7-11-12 wells is almost aligned at the same depth in both of the targeted B1 and D1 sand reservoirs. Especially for the D1 sand in the Hydrate-01 well, the resistivity logs show a sharp transition from high gas hydrate saturation to fully water-saturated within the D1 sand, suggesting a common gas hydrate/water contact. The results of this study will be used to construct the geological models needed for reservoir simulation studies and they can provide important insights into the geological factors that control the occurrence of gas hydrate on the Alaska North Slope.
New lithologic and detrital zircon (DZ) U-Pb data from Devonian–Triassic strata on St. Lawrence Island in the Bering Sea and from the western Brooks Range of Alaska suggest affinities between these two areas. The Brooks Range constitutes part of the Arctic Alaska–Chukotka microplate, but the tectonic and paleogeographic affinities of St. Lawrence Island are unknown or at best speculative. Strata on St. Lawrence Island form a Devonian–Triassic carbonate succession and a Mississippian(?)–Triassic clastic succession that are subdivided according to three distinctive DZ age distributions. The Devonian–Triassic carbonate succession has Mississippian-age quartz arenite beds with Silurian, Cambrian, Neoproterozoic, and Mesoproterozoic DZ age modes, and it exhibits similar age distributions and lithologic and biostratigraphic characteristics as Mississippian-age Utukok Formation strata in the Kelly River allochthon of the western Brooks Range. Consistent late Neoproterozoic, Cambrian, and Silurian ages in each of the Mississippian-age units suggest efficient mixing of the DZ prior to deposition, and derivation from strata exposed by the pre-Mississippian unconformity and/or Endicott Group strata that postdate the unconformity. The Mississippian(?)–Triassic clastic succession is subdivided into feldspathic and graywacke subunits. The feldspathic subunit has a unimodal DZ age mode at 2.06 Ga, identical to Nuka Formation strata in the Nuka Ridge allochthon of the western Brooks Range, and it records a distinctive depositional episode related to late Paleozoic juxtaposition of a Paleoproterozoic terrane along the most distal parts of the Arctic Alaska–Chukotka microplate. The graywacke subunit has Triassic maximum depositional ages and abundant late Paleozoic grains, likely sourced from fringing arcs and/or continent-scale paleorivers draining Eurasia, and it has similar age distributions to Triassic strata from the Lisburne Peninsula (northwestern Alaska), Chukotka and Wrangel Island (eastern Russia), and the northern Sverdrup Basin (Canadian Arctic), but, unlike the Devonian–Triassic carbonate succession and feldspathic subunit of the Mississippian(?)–Triassic clastic succession, it has no obvious analogue in the western Brooks Range allochthon stack. These correlations establish St. Lawrence Island as conclusively belonging to the Arctic Alaska–Chukotka microplate, thus enhancing our understanding of the circum-Arctic region in late Paleozoic–Triassic time.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02490.1","usgsCitation":"Amato, J.M., Dumoulin, J.A., Gottlieb, E.S., and Moore, T.E., 2022, Detrital zircon ages from upper Paleozoic–Triassic clastic strata on St. Lawrence Island, Alaska: An enigmatic component of the Arctic Alaska–Chukotka microplate: Geosphere, v. 18, no. 5, p. 1492-1523, https://doi.org/10.1130/GES02490.1.","productDescription":"32 p.","startPage":"1492","endPage":"1523","ipdsId":"IP-134575","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":447026,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02490.1","text":"Publisher Index Page"},{"id":435756,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PILIK","text":"USGS data release","linkHelpText":"Location Data for Petrographic Samples and Isotopic and Age Data from Detrital Zircon Grains from Selected Rock Samples from St. Lawrence Island and the Western Brooks Range, Alaska"},{"id":408489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"St. Lawrence Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.968994140625,\n 62.88520467163244\n ],\n [\n -168.50830078125,\n 62.88520467163244\n ],\n [\n -168.50830078125,\n 63.86487567533106\n ],\n [\n -171.968994140625,\n 63.86487567533106\n ],\n [\n -171.968994140625,\n 62.88520467163244\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Amato, Jeffrey M.","contributorId":247883,"corporation":false,"usgs":false,"family":"Amato","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[{"id":49682,"text":"Dept of Geolgical Sciences, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":854897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":854898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gottlieb, Eric S. 0000-0002-4904-9492","orcid":"https://orcid.org/0000-0002-4904-9492","contributorId":291239,"corporation":false,"usgs":false,"family":"Gottlieb","given":"Eric","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":854899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Thomas E. 0000-0002-0878-0457 tmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-0878-0457","contributorId":127538,"corporation":false,"usgs":true,"family":"Moore","given":"Thomas","email":"tmoore@usgs.gov","middleInitial":"E.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854900,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70233533,"text":"sir20225060 - 2022 - Trends in groundwater levels, and orthophosphate and nitrate concentrations in the Middle Snake River Region, south-central Idaho","interactions":[],"lastModifiedDate":"2026-04-23T16:44:54.255731","indexId":"sir20225060","displayToPublicDate":"2022-07-22T09:58:04","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5060","displayTitle":"Trends in Groundwater Levels, and Orthophosphate and Nitrate Concentrations in the Middle Snake River Region, South-Central Idaho","title":"Trends in groundwater levels, and orthophosphate and nitrate concentrations in the Middle Snake River Region, south-central Idaho","docAbstract":"The U.S. Geological Survey (USGS) evaluated nitrate and orthophosphate concentrations in groundwater for temporal trends (monotonic and step trends) for the middle Snake River region (Cassia, Gooding, Jerome, Lincoln, Minidoka, and Twin Falls Counties) in south-central Idaho using the Regional Kendall test (monotonic trends) and the Wilcoxon signed rank test (step trends). The study evaluated two trend periods: 2000–09 and 2010–19/20. The study area was divided into six hydrogeologic zones (HZs) that had similar geologic and hydrologic characteristics and that correlated with county boundaries where possible. Two well networks sampled by the USGS National Water Quality Program within the HZs were also evaluated.
The northern Gooding County HZ had statistically significant increasing nitrate concentration trends for both the monotonic and step trends in the early trend period, while the Cassia and Jerome/Southern Gooding County HZs only had one of the statistical tests with statistically significant increasing nitrate concentrations. The Minidoka County HZ had conflicting results between the two statistical tests for the early time period with a statistically significant increasing monotonic trend in nitrate concentration and a statistically significant decreasing step trend. The differing results between these two statistical tests indicates the significance of concentration data during the middle of the time period. Both the Lincoln and Twin Falls County HZs did not have statistically significant trends for either test during either time period as well as the Northern Gooding County HZ for the latter time period. The Minidoka County HZ had statistically significant nitrate trends for both tests in the latter time period along with one of the trend tests for the Cassia and Jerome/Southern Gooding County HZ. Most of the nitrate concentration trend rates are low from 0.01 to 0.12 milligram per liter per year (mg/L/year) with the northern Gooding County HZ having the highest trend rate during the early time period of 0.28 mg/L/year for the step trend and 0.55 mg/L/year for the monotonic trend.
All the HZs and both well networks had statistically significant increasing orthophosphate-concentrations trends in groundwater for the early time period except for the Lincoln County HZ and the step-trend for the Minidoka County HZ. Orthophosphate concentration trend rates for the early period were low, ranging from 0.001 to 0.015 mg/L/year. Only two HZs and the well networks had enough orthophosphate concentration data available in the latter time period to do statistical analysis. The two HZs (Minidoka and Southern Gooding/Jerome County) both have decreasing orthophosphate concentration trends, with only the monotonic trend for the Southern Gooding/Jerome County HZ being statistically significant at 90 percent with a rate of −0.001 mg/L/year.
Groundwater levels in two well networks in the eastern Snake River Plain aquifer were also evaluated for trends (monotonic and step), with both networks having statistically significant declining groundwater levels for the 1993–2009 trend period. The latter trend period (2010–20) had statistically significant declining groundwater levels for the A&B well network and statistically significant increasing groundwater levels for the Jerome/Gooding well network, which is downgradient from an aquifer recharge area.
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Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
From the flat, rich soil of western Tennessee to the Appalachian Mountains in the east, and rolling hills in between, “the Volunteer State” enjoys a wealth of natural resources.
The Tennessee, Cumberland, and Mississippi Rivers supply economically crucial navigation routes, along with recreation for residents and visitors. Additionally, 14 million acres of hardwood and softwood forests cover roughly one-half of the State, contributing an estimated $24 billion and nearly 100,000 jobs to Tennessee’s economy. Within a span of more than 400 miles, the State’s diverse agricultural products include cotton, corn, soybeans, poultry, horses, cattle, goats, hay, vegetables, nursery crops, and tobacco.
Energy production is important to Tennessee and the region, and power sources range from coal and nuclear to hydroelectric sources. Tourism also is a key industry, and music attractions and historical sites are balanced by natural features such as the Great Smoky Mountains National Park, which recorded 14.1 million visits and ranked second for most visited National Park Service site in the United States in 2021.
Landsat imagery’s broad geographic scale and rich historical archive have proven useful to land managers and State agencies for monitoring natural resources. Here are several ways Landsat has benefited Tennessee.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Rhode Island is an oasis of natural calm surrounded by heavily urbanized East Coast areas, which may explain why the smallest State in the United States is such a popular tourist destination for residents of New York, Pennsylvania, and New Jersey, or perhaps its popularity is a measure of the Ocean State’s abundant wildlife and picturesque views. Although small in land area, Rhode Island claims the largest estuary in New England in the 147-square-mile Narragansett Bay. Locals and visitors feast on clams caught in the bay, trek to glimpse shorebirds, or boat to 1 of 30 islands.
As with any coastal State, the natural wonders of Rhode Island face threats related to sea level rise and warming ocean temperatures. State agencies also work to fend off foes like the invasive Lymantria dispar (Linnaeus, 1758; spongy moth) and protect the forests that cover more than one-half of Rhode Island.
The U.S. Geological Survey Landsat Program, with 50 years of recurring Earth observations from space, offers a unique and freely available public data source for the study of land and coastal change across Rhode Island and the United States. Here are just a few of the ways Landsat imagery has been used to benefit the State.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
At its widest point, a mere 80 miles separate the eastern and western borders of New Hampshire. Its northern and southern borders are just 175 miles apart. Even so, few States can boast as much rugged natural beauty per mile as the Nation’s fifth smallest.
Nestled within New Hampshire are 93 State parks teeming with moose, Ursus americanus (Pallas, 1780; black bears), coyotes, beavers, river otters, and foxes. The largest section of White Mountain National Forest cuts across north-central New Hampshire, drawing visitors to its lakes, streams, mountain peaks, and hardwood forests. New Hampshire also is home to Lake Winnipesaukee, the State’s largest lake, notable for its floating post offices, the annual “ice-out” contest that sees residents vying to guess the date its surface ice dissipates, and its supporting role in films such as “On Golden Pond” and “What About Bob?” However, the scenic forests of New Hampshire face challenges in the form of invasive species such as Lymantria dispar (Linnaeus, 1758; spongy moth), Adelges piceae (balsam woolly adelgid), and Agrilus planipennis (emerald ash borer). In recent years, New Hampshire’s lakes and streams have seen more cyanobacterial blooms as well.
The U.S. Geological Survey Landsat Program offers a consistent, reliable, and historically unmatched source of Earth observations that can aid in the mapping, monitoring, and management of New Hampshire’s land and water resources. Here are a few ways Landsat data have been used in the Granite State.
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Hampshire\",\"nation\":\"USA \"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The northern and central parts of the Tug Hill glacial aquifer consist of a 29-mile-long, crescent-shaped, mixture of glaciofluvial, glaciolacustrine, and recent alluvial deposits of predominantly sand and gravel on the western side of the Tug Hill Plateau in Jefferson and Oswego Counties in north-central New York. Approximately 11,400 people are supplied by groundwater that is withdrawn from municipal and nonmunicipal wells in the northern and central parts of the aquifer. In addition, many farms, several industries, and a large New York State fish hatchery also rely on the water from the aquifer.
In the early 2000s, anticipated developmental pressures from potential new industries (including a proposed water-bottling plant in the central part of the Tug Hill glacial aquifer) and expansion of the Fort Drum military base north of Watertown (with the projected increase in population extending into the northern part of the aquifer) prompted the Tug Hill Commission, local municipal officials, and representatives from the New York State Department of Environmental Conservation to initiate a geohydrologic study with the U.S. Geological Survey. The information from this study is intended to help the state, counties, and local communities make sound policy decisions about their use of this large groundwater resource.
The northern part of the Tug Hill glacial aquifer is a combination of glaciofluvial outwash and alluvial sand and gravel in the Sandy Creek Valley northeast of Adams, New York, and mostly glaciolacustrine beach and deltaic sand or sand and gravel north and south of the village of Adams. The southern and eastern areas of the central part of the aquifer are composed mostly of glaciofluvial sediments such as kames, kame moraines, and kame terraces, whereas most of the western areas of the central part are composed mostly of glaciolacustrine sediments such as deltaic sand and beach sand and gravel.
The northern and central parts of the aquifer are unconfined. Recharge to the northern and central parts of the aquifer is from three main sources: (1) precipitation that falls directly onto the aquifer; (2) unchannelized runoff (overland flow) and groundwater from till and bedrock in the Tug Hill Plateau that seeps into the eastern side of the aquifer; and (3) streams that drain the Tug Hill Plateau and flow across and lose water to the aquifer. Groundwater discharges to springs, seeps, headwaters of streams, and wetlands in the middle area of the central part of the aquifer and along the entire western boundary of the northern and central parts of the aquifer; pumping wells; artificial ditches; and deeply incised streams in the northern and central parts of the aquifer. The groundwater discharge to such streams is critical in supporting the salmonid fishery in the central part of the aquifer.
Groundwater levels were measured on July 17, 2014, at 22 wells throughout the northern and central parts of the aquifer. Water-table contours were drawn on the basis of the measured July 2014 water levels, historical water-level data, and surface-water levels where surface water in the channels was expected to be hydraulically connected to the groundwater system. The water table generally slopes from east to west throughout the northern and central parts of the aquifer; this slope also indicates that the direction of groundwater flow is generally from east to west.
Water-quality samples were collected from 23 stream sites during base-flow conditions, and groundwater-quality and other types of environmental samples were collected from 20 wells in the northern and central parts of the Tug Hill glacial aquifer. The results of the sampling indicate that surface water and groundwater are generally of good quality.
Comparison of the median concentration values of major ions in groundwater samples indicated that hardness in the northern part of the aquifer was about twice as great, and concentrations of calcium and sodium were more than three times as great, as in the central part of the aquifer. As was the case with surface water, the much greater median concentrations in groundwater of calcium, hardness, and alkalinity in the northern part of the aquifer are due to the dissolution of limestone that underlies most of that area and to the high-carbonate content of the clasts in the sand and gravel. There was little to no difference among the median values for bromide, fluoride, silica, and iron in the two parts of the aquifer. Concentrations of most other major ions were slightly greater in the northern part than in the central part of the Tug Hill glacial aquifer, except for magnesium, whose concentration was greater in the central part. Median concentrations of nutrients were generally greatest in surface water and groundwater in the northern part of the aquifer.
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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Kansas seems synonymous with agriculture, and rightly so—87 percent of Kansas land is devoted to it. As a key contributor to the State’s economy, agriculture makes Kansas one of the top producers of wheat, grain sorghum, and cattle in the country, but the State at the geographic center of the conterminous United States contains much more than fields and pastures.
Deciduous woodlands sprawl throughout the east. Tallgrass prairie—the only extensive stand remaining in the country—covers the east-central Flint Hills with more than 500 species of plants, many of them wildflowers, including the Sunflower State’s nickname inspiration. Near the center of Kansas, Cheyenne Bottoms—the largest marsh in the interior United States at 41,000 acres—welcomes migrating birds, including the endangered Grus americana (Linnaeus, 1758; whooping crane), by the thousands in the spring and fall. To the south, the inland saltwater marshes of Quivira National Wildlife Refuge attract many more.
Farther west, chalk outcroppings like Castle Rock and Monument Rocks rise above the landscape as fossil-bearing remnants of a sea floor from millions of years ago. Oil and natural gas fields exist throughout the State. After two University of Kansas professors discovered helium in a sample from one natural gas well in 1905, Kansas became a substantial supplier. The Landsat Program provides tools for monitoring and managing our conservation lands and their many resources. Here are several ways Landsat helps Kansas.
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Per- and polyfluoroalkyl substances (PFAS) are widely observed anthropogenic compounds found in water supplies worldwide and increasingly linked with adverse health effects in humans. In 2019, the West Virginia Legislature recognized the contamination risk to public source-water supplies posed by PFAS and passed a resolution that required a statewide PFAS study. The purpose of the resolution was to understand the occurrence and distribution of PFAS contamination throughout the State’s rivers, lakes, and groundwater aquifers. The U.S. Geological Survey has worked in cooperation with the West Virginia Department of Environmental Protection and West Virginia Department of Health and Human Resources to collect raw-water samples at 279 public-water systems across West Virginia. Public-water systems sampled for this study were identified by the West Virginia Department of Health and Human Resources and included all community water systems in the State and all daycares and schools that operate their own water systems.
Raw source water was sampled for both groundwater and surface-water sites at the first available tap in the public-water system, prior to any treatment. One hundred and seventy-three samples were collected from groundwater sources and 106 samples were collected from surface-water sources. Parameters collected at the time of sampling included pH, specific conductance, water temperature, dissolved oxygen, turbidity, and alkalinity. PFAS was analyzed at all 279 sites, major ions and trace elements were analyzed at 272 sites, and nutrients were analyzed at 270 sites.
The type of source water used for public supply in West Virginia is generally dependent on geology with more groundwater sites sampled in high-yield aquifers such as karst and alluvium. Surface-water sites were more evenly distributed throughout the State and are often the only source used in areas underlain by lower-yielding fractured-rock aquifers. Twenty-four percent of the sites sampled for this study had at least 1 PFAS detected, 47 of which were in groundwater sources and 20 in surface-water sources. Five sites exceeded the U.S. Environmental Protection Agency’s health advisory for combined perfluorooctanoate and perfluorooctanesulfonate concentrations of 70 nanograms per liter. These sites were located in highly susceptible karst and alluvial groundwater aquifers on the east and west sides of the State.
Higher PFAS concentrations were more commonly found in groundwater than surface-water sources, and high concentrations and PFAS detections were generally concentrated in the Ohio River Valley and West Virginia’s eastern panhandle. PFAS was rarely detected in groundwater sites in fractured-rock aquifers and abandoned underground coal-mine aquifers in the Appalachian Plateaus Physiographic Province had very little PFAS detected. These data represent a baseline summary of source water in West Virginia. Additional studies may be needed to understand exposure to private homeowners with domestic-water sources, variability of PFAS concentrations over time, and PFAS in finished drinking water as evaluated by current and future drinking-water regulations.
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Virginia\",\"nation\":\"USA \"}}]}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Several species of fish in large lakes and marine environments have a pelagic larval stage, and are subject to variable transport that can ultimately regulate survival and recruitment success. Alewife, Alosa pseudoharengus, are subject to transport by complex coastal currents during their pelagic larval stage (~ 30 d). We assessed backward-trajectory simulations, consisting of a Lagrangian particle dispersion model linked to the Finite Volume Community Ocean Model, to estimate likely hatch locations of aged larval alewife collected from locations on both the eastern and western sides of Lake Michigan during July 2015. We used four deployments of three satellite-tracked drifter buoys in coastal waters to assess model skill in estimating the origin of a drifter from its final location. We found that the trajectories of drifters varied greatly, depending on wind events and associated coastal transport processes, including upwelling/downwelling and coastal jet currents. In 2 of 12 cases, the backward trajectory simulations failed to predict the drifter origin, associated with transport of 170 km in a narrow coastal jet current. In the remaining 10 cases, the known drifter origin was within 3.5 km of the spatial patch of predicted possible origins for a scenario of horizontal diffusivity (188 m2 s−1) consistent with the offshore model grid resolution. Modeled backward trajectories estimated that alewife originated from the same side of the lake where they were collected, within ~ 100 km of the collection site. Our paper demonstrates the utility of hydrodynamic models to estimate a region of origin for aged larval fish.