{"pageNumber":"48","pageRowStart":"1175","pageSize":"25","recordCount":11004,"records":[{"id":70225608,"text":"70225608 - 2021 - Hydrogeology and simulation of groundwater flow in Columbia County, Wisconsin","interactions":[],"lastModifiedDate":"2021-10-27T16:48:33.308605","indexId":"70225608","displayToPublicDate":"2021-10-01T08:15:46","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5959,"text":"Wisconsin Geological and NaturalHistory Survey Bulletin","active":true,"publicationSubtype":{"id":2}},"title":"Hydrogeology and simulation of groundwater flow in Columbia County, Wisconsin","docAbstract":"This report describes the regional hydrogeology and groundwater resources of Columbia County, Wisconsin, and documents a regional groundwater flow model developed for the county. Regional hydrostratigraphic units include the unlithified aquifer, the upper bedrock aquifer, and the Elk Mound aquifer.\n\nThe unlithified aquifer consists of deposits that range in composition from sand and gravel outwash and stream deposits to silty, sandy till. This aquifer is less than 25 ft thick in much of eastern Columbia County, but consists of permeable sand and gravel extending to over 250 ft in depth in the Wisconsin River valley bottom. \n\nThe upper bedrock aquifer consists of Ordovician and upper Cambrian sedimentary formations, including sandstone, siltstone and dolomitic strata. The upper bedrock aquifer underlies the unlithified aquifer in eastern portions of the County, but is absent to the west, where these formations are largely eroded. The contact between the Tunnel City Group and Wonewoc Formation (Top of Elk Mound Group) forms the base of the upper bedrock aquifer. Bedding plane fractures are common to this aquifer, although only a portion of the observed fractures appear to be hydraulically active. The upper bedrock aquifer is a significant source of groundwater at a regional scale. Measurements of hydraulic head showed a difference of several feet across the bottom of this aquifer to the underlying Wonewoc sandstone, indicating that the basal facies of the Tunnel City Group functions as an aquitard separating the upper bedrock aquifer from the Elk Mound aquifer. Conditions vary considerably within this aquifer, depending on the local lithostratigraphy. For example, where present, the St. Lawrence Fm. and fine-grained intervals of the Tunnel City Group may be locally-extensive aquitards. \nThe Elk Mound aquifer consists of Cambrian sandstone of the Wonewoc, Eau Claire, and Mount Simon Formations. It is thin to absent in several locations but ranges up to 600 ft in thickness over much of southern Columbia County. The variation in thickness is due in large part to the irregular topography of the underlying Precambrian crystalline rock, which generally serves as the base of the groundwater system. In neighboring counties, a fine-grained facies within the Eau Claire Fm. acts as a regionally extensive aquitard, referred to as the Eau Claire aquitard. Much of the data collected and compiled for this study suggest that shale or dolomite within the Eau Claire Fm., which is the equivalent of the Eau Claire aquitard, occurs only within southwestern Columbia County. There is little to no evidence of the Eau Claire aquitard over most of the county. Where the dolomite and shale are absent, the Elk Mound aquifer is relatively homogenous and does not include a mappable aquitard. \nA three-dimensional steady-state flow model presented here represents long-term, average conditions in the regional groundwater system since about 1970. The model was constructed with the U.S. Geological Survey’s MODFLOW-NWT code; it has six layers with a uniform grid of 300 ft x 300 ft cells. Layers 1 and 2 simulate the unlithified aquifer and layer 3 represents the upper bedrock aquifer. The Elk Mound aquifer is simulated by layers 4, 5 and 6, representing the Wonewoc, Eau Claire, and Mount Simon Formations, respectively. The model extends beyond the boundaries of Columbia County to ensure that hydrologic conditions simulated within the County are consistent with regional conditions. \nRecharge to the groundwater flow model is based on results from a GIS-based soil-water-balance model. Recharge was simulated with the unsaturated zone flow (UZF) package in MODFLOW. This approach is particularly useful for quantifying groundwater discharge to riparian wetlands because UZF tracks recharge that would lead to the simulated water table exceeding the land surface (represented by the top of model layer 1) and reroutes it to nearby stream segments. The model includes pumping from 256 wells, and 178 of these are located within Columbia County. Pumping totaled about 28 million gallons per day (mgd) on average since 1970, with 7.2 mgd of the withdrawal from within the County. Model calibration was performed with the PEST parameter estimation code. Calibration targets included approximately 3,900 head measurements and 91 stream flow measurements. Four vertical-head differences across hydrogeologic units, calculated from data collected during packer testing in wells in Columbia County, were also used in model calibration. \n\nResults from the calibrated model provide a groundwater balance for the region. About 83 percent of groundwater originates as recharge to the water table, 12 percent comes from leakage from streams, and about 5 percent of the groundwater flows into the model domain from surrounding areas. About 95 percent of the simulated groundwater discharges to steams and other surface water features, about 3 percent flows across model boundaries to surrounding areas of the groundwater system, and pumping accounts for 2 percent of discharge. Simulated flow paths are relatively local, from recharge in upland areas to discharge in nearby streams and wetlands. \n\nThe model has many potential applications, including simulation of the effects of existing or proposed high-capacity wells, estimating the zone of contribution for these wells, and understanding relationships between surface water and groundwater. Future refinements to the model, such as incorporating new information about the extent and hydraulic characteristics of the Tunnel City Group, will improve its utility in understanding advective flow between the upper bedrock and Elk Mound aquifers. If seasonal or annual variations in the groundwater system are of interest, this steady-state model could be brought into a transient mode.","language":"English","publisher":"Wisconsin Geological and Natural History Survey","usgsCitation":"Gotkowitz, M., Leaf, A.T., and Sellwood, S.M., 2021, Hydrogeology and simulation of groundwater flow in Columbia County, Wisconsin: Wisconsin Geological and NaturalHistory Survey Bulletin, 51 p.","productDescription":"51 p.","ipdsId":"IP-101440","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391008,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391000,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.wisc.edu/catalog/publication/000985"}],"country":"United States","state":"Wisconsin","county":"Columbia County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.2453,43.643],[-89.127,43.6436],[-89.1271,43.6318],[-89.007,43.6332],[-89.0063,43.548],[-89.0044,43.4616],[-89.0038,43.3737],[-89.0088,43.3738],[-89.0094,43.286],[-89.1271,43.2827],[-89.246,43.2834],[-89.3624,43.2832],[-89.3617,43.2954],[-89.4819,43.2942],[-89.6008,43.2932],[-89.7209,43.2935],[-89.7235,43.2935],[-89.7292,43.3026],[-89.7279,43.3108],[-89.7254,43.3153],[-89.7229,43.3181],[-89.7185,43.3195],[-89.7129,43.3226],[-89.7078,43.3277],[-89.7028,43.3345],[-89.6909,43.3495],[-89.684,43.3573],[-89.6783,43.3586],[-89.6708,43.3582],[-89.6613,43.3577],[-89.6456,43.36],[-89.6311,43.3646],[-89.6166,43.371],[-89.6009,43.3806],[-89.6004,43.4688],[-89.5999,43.5544],[-89.6075,43.5603],[-89.6138,43.5626],[-89.6277,43.5617],[-89.6359,43.5603],[-89.6511,43.5621],[-89.658,43.5634],[-89.6643,43.5657],[-89.6707,43.5666],[-89.6783,43.5671],[-89.6877,43.5634],[-89.6934,43.5616],[-89.6991,43.562],[-89.706,43.5648],[-89.7187,43.5652],[-89.7288,43.5661],[-89.7351,43.5693],[-89.7364,43.5743],[-89.7326,43.5793],[-89.7288,43.5829],[-89.7244,43.587],[-89.7188,43.5929],[-89.7207,43.597],[-89.727,43.5979],[-89.7428,43.597],[-89.751,43.5997],[-89.7567,43.6029],[-89.7662,43.6029],[-89.7738,43.6092],[-89.7763,43.6161],[-89.7808,43.6215],[-89.7802,43.6274],[-89.7789,43.6343],[-89.784,43.6388],[-89.7866,43.6411],[-89.779,43.6411],[-89.7195,43.643],[-89.6,43.6427],[-89.4837,43.6423],[-89.3648,43.6427],[-89.2453,43.643]]]},\"properties\":{\"name\":\"Columbia\",\"state\":\"WI\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gotkowitz, Madeline","contributorId":268135,"corporation":false,"usgs":false,"family":"Gotkowitz","given":"Madeline","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":825890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sellwood, Steven M.","contributorId":268136,"corporation":false,"usgs":false,"family":"Sellwood","given":"Steven","email":"","middleInitial":"M.","affiliations":[{"id":55571,"text":"TRC Companies, Inc.","active":true,"usgs":false}],"preferred":false,"id":825892,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267787,"text":"70267787 - 2021 - Fire refugia in old-growth forests: Predicting habitat persistence to support land management in an era of rapid global change","interactions":[],"lastModifiedDate":"2025-06-03T14:12:39.411691","indexId":"70267787","displayToPublicDate":"2021-09-30T10:11:42","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":9141,"text":"Final Report","active":true,"publicationSubtype":{"id":2}},"title":"Fire refugia in old-growth forests: Predicting habitat persistence to support land management in an era of rapid global change","docAbstract":"Recent stand-replacing wildfires in late-successional and old-growth (LSOG) forests have increased land manager interest in fire refugia, which could provide vital habitat for threatened and endangered species during a time of rapid change. The overall goal of this project was to model, map, and share information essential for the conservation of LSOG forest ecosystems in the U.S. Pacific Northwest, within a diverse co-production team of state and federal land managers. We developed statistical models of contemporary (2002-2017) fire refugia, non-stand-replacing fire (NSR), and high-severity fire based on topography, fuels, fire weather, fire behavior and climate. Independent models were built for two ecoregions (Figure 1), one encompassing the Douglas-fir/western hemlock forests of the northwestern portion of our study area and the other encompassing dry-mixed conifer forests of the eastern Cascades and Klamath-Siskiyou region. We used these models to produce probability surface maps for fire refugia, NSR, and high-severity fire under low, moderate, and extreme fire weather and fire growth scenarios. These maps and associated products provide timely information about the likely persistence, change, and loss of LSOG forests under current and future climate conditions.","language":"English","publisher":"Oregon State University","usgsCitation":"Naficy, C.E., Meigs, G., Gregory, M., Davis, R., Bell, D.M., Dugger, K., Wiens, J.D., and Krawchuk, M.A., 2021, Fire refugia in old-growth forests: Predicting habitat persistence to support land management in an era of rapid global change: Final Report, 39 p.","productDescription":"39 p.","ipdsId":"IP-135786","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489323,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://firerefugia.forestry.oregonstate.edu/findings"},{"id":489389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.03230454175225,\n 48.98129296044047\n ],\n [\n -123.23629505743158,\n 49.021189699759475\n ],\n [\n -122.36411639022937,\n 47.53290523042941\n ],\n [\n -122.735187412137,\n 48.16641453033125\n ],\n [\n -124.8905678391362,\n 48.456759846434124\n ],\n [\n -124.28837165472417,\n 44.57003722150395\n ],\n [\n -124.68283057104817,\n 42.67310902261141\n ],\n [\n -124.20999230992997,\n 41.18665658280105\n ],\n [\n -124.55296007164978,\n 40.23706044199622\n ],\n [\n -123.88224360324604,\n 39.64402305418474\n ],\n [\n -123.79771977835343,\n 38.75178009790977\n ],\n [\n -122.3664015891323,\n 37.62043867256661\n ],\n [\n -120.98881658816403,\n 38.95590221404629\n ],\n [\n -119.90347242862897,\n 40.51176766538154\n ],\n [\n -119.03230454175225,\n 48.98129296044047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Naficy, Cameron E.","contributorId":298154,"corporation":false,"usgs":false,"family":"Naficy","given":"Cameron","email":"","middleInitial":"E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":938881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meigs, Garrett W.","contributorId":356212,"corporation":false,"usgs":false,"family":"Meigs","given":"Garrett W.","affiliations":[{"id":37093,"text":"Washington State Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":938882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gregory, Matt J.","contributorId":356213,"corporation":false,"usgs":false,"family":"Gregory","given":"Matt J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":938883,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Ray","contributorId":356214,"corporation":false,"usgs":false,"family":"Davis","given":"Ray","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":938884,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bell, David M.","contributorId":34423,"corporation":false,"usgs":true,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":938885,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938886,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wiens, J. David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":468,"corporation":false,"usgs":true,"family":"Wiens","given":"J.","email":"jwiens@usgs.gov","middleInitial":"David","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":938887,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Krawchuk, Meg A.","contributorId":187425,"corporation":false,"usgs":false,"family":"Krawchuk","given":"Meg","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":938888,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70230282,"text":"70230282 - 2021 - Genetic analysis of red lionfish Pterois volitans from Florida, USA, leads to alternative North Atlantic introduction scenarios","interactions":[],"lastModifiedDate":"2023-06-09T13:58:52.645892","indexId":"70230282","displayToPublicDate":"2021-09-30T08:45:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Genetic analysis of red lionfish Pterois volitans from Florida, USA, leads to alternative North Atlantic introduction scenarios","title":"Genetic analysis of red lionfish Pterois volitans from Florida, USA, leads to alternative North Atlantic introduction scenarios","docAbstract":"

The red lionfish Pterois volitans is a successful invasive predator across the western North Atlantic, Caribbean, and Gulf of Mexico. The southeast coast of Florida (USA) has been identified as the original introduction location, but genetic analyses including Florida lionfish have yet to investigate introduction scenarios. Here, we assessed the potential lionfish invasion pathways using 1795 sequences from previously published mitochondrial D-loop sequences (n = 1558) and new samples (n = 237) from 6 locations: The Bahamas, Florida Keys, northwest Florida, North Carolina, Panamá, and southeast Florida. None of the assessed Florida lionfish (n = 394) contained the H05-H09 D-loop haplotypes found in The Bahamas, North Carolina, and Bermuda (the Northern Region), indicating that Florida was not the source for these haplotypes. Assessing the mitochondrial population structure, the Florida east coast lionfish grouped with the Caribbean/Gulf of Mexico, as opposed to the Northern Region. To further explore connectivity and invasion pathways, 14 nuclear microsatellite loci were multiplexed on lionfish collected from 15 locations (n = 394). As found in other nuclear lionfish studies, the analyses identified a lack of population structure likely due to founding effects and/or inbreeding in aquaculture brood stocks. Together, the significant haplotype differences and H01-H04 haplotypes refute Florida as the sole source of red lionfish introduction. The results of this study support alternative invasion scenarios, in which Florida was colonized as a secondary introduction site or by individuals from the Northern Region. Understanding invasive species’ population boundaries and dispersal patterns informs local control efforts and management planning for future invasive species introductions.

","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13841","usgsCitation":"Hunter, M., Beaver, C., Johnson, N., Bors, E.K., Mignucci-Giannoni, A.A., Silliman, B.R., Buddo, D., Searle, L., and Diaz-Ferguson, E., 2021, Genetic analysis of red lionfish Pterois volitans from Florida, USA, leads to alternative North Atlantic introduction scenarios: Marine Ecology Progress Series, v. 675, p. 133-151, https://doi.org/10.3354/meps13841.","productDescription":"19 p.; Data Release","startPage":"133","endPage":"151","ipdsId":"IP-124454","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450610,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps13841","text":"Publisher Index Page"},{"id":398208,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417866,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YTIXSE"}],"country":"Bahamas, Panama, United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.14453125,\n 23.745125865762923\n ],\n [\n -75.73974609375,\n 23.745125865762923\n ],\n [\n -75.73974609375,\n 27.391278222579277\n ],\n [\n -83.14453125,\n 27.391278222579277\n ],\n [\n -83.14453125,\n 23.745125865762923\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.1875,\n 28.9600886880068\n ],\n [\n -83.671875,\n 28.9600886880068\n ],\n [\n -83.671875,\n 30.65681556429287\n ],\n [\n -87.1875,\n 30.65681556429287\n ],\n [\n -87.1875,\n 28.9600886880068\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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Center","active":true,"usgs":true}],"preferred":true,"id":839857,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839858,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bors, Eleanor K. 0000-0003-1751-1467","orcid":"https://orcid.org/0000-0003-1751-1467","contributorId":289825,"corporation":false,"usgs":false,"family":"Bors","given":"Eleanor","email":"","middleInitial":"K.","affiliations":[{"id":62258,"text":"Cooperative Institute for Climate, Ocean, and Ecosystem Studies, University of Washington","active":true,"usgs":false}],"preferred":false,"id":839859,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mignucci-Giannoni, Antonio A. 0000-0003-1443-4873","orcid":"https://orcid.org/0000-0003-1443-4873","contributorId":289826,"corporation":false,"usgs":false,"family":"Mignucci-Giannoni","given":"Antonio","email":"","middleInitial":"A.","affiliations":[{"id":62259,"text":"Centro de Conservación de Manatíes del Caribe, Universidad Interamericana de Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":839860,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Silliman, Brian R. 0000-0001-6360-650X","orcid":"https://orcid.org/0000-0001-6360-650X","contributorId":289827,"corporation":false,"usgs":false,"family":"Silliman","given":"Brian","email":"","middleInitial":"R.","affiliations":[{"id":62261,"text":"Division of Marine Science and Conservation, Nicholas School of the Environment, Duke University","active":true,"usgs":false}],"preferred":false,"id":839861,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buddo, Dayne","contributorId":139669,"corporation":false,"usgs":false,"family":"Buddo","given":"Dayne","email":"","affiliations":[{"id":12874,"text":"Centre for Marine Sciences, University of the West Indies, Queen’s Highway, P.O Box 35, Discovery Bay, St. Ann, Jamaica","active":true,"usgs":false}],"preferred":false,"id":839862,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Searle, Linda","contributorId":139670,"corporation":false,"usgs":false,"family":"Searle","given":"Linda","email":"","affiliations":[{"id":12875,"text":"ECOMAR, PO Box 1234 Belize City, Belize","active":true,"usgs":false}],"preferred":false,"id":839863,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Diaz-Ferguson, Edgardo","contributorId":139668,"corporation":false,"usgs":false,"family":"Diaz-Ferguson","given":"Edgardo","email":"","affiliations":[{"id":12873,"text":"U.S. Fish and Wildlife Service, Conservation Genetics Laboratory, Warm Springs, Georgia","active":true,"usgs":false}],"preferred":false,"id":839864,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70230391,"text":"70230391 - 2021 - Invasion frustration: Can biotic resistance explain the small geographic range of non-native croaking gourami Trichopsis vittata (Cuvier, 1831) in Florida, USA?","interactions":[],"lastModifiedDate":"2023-06-09T13:59:56.966874","indexId":"70230391","displayToPublicDate":"2021-09-30T06:40:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":868,"text":"Aquatic Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Invasion frustration: Can biotic resistance explain the small geographic range of non-native croaking gourami Trichopsis vittata (Cuvier, 1831) in Florida, USA?","docAbstract":"

Croaking gourami Trichopsis vittata is a non-native fish species that has maintained a reproducing population in Florida, USA, since at least the 1970s. However, unlike most other non-native fishes in Florida, T. vittata has not spread beyond its very small (ca. 5 km²) range. We suspected the inability of T. vittata to colonize new habitats may be due to biotic resistance by the native eastern mosquitofish Gambusia holbrooki. In laboratory experiments, we show that G. holbrooki causes physical damage to T. vittata and that T. vittata’s growth is reduced in the presence of G. holbrooki. While the effects of G. holbrooki on T. vittata were sub-lethal, they were severe enough to hamper its growth and could affect recruitment in the wild. These results support the hypothesis that small non-native fishes may be excluded from establishment or may only establish small ranges due to pressure from G. holbrooki. Biotic resistance may reduce invasion success and thus consideration of species interactions is useful for natural resource managers trying to evaluate the potential risk of new invaders.

","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre (REABIC)","doi":"10.3391/ai.2021.16.3.08","usgsCitation":"Schofield, P., Tuckett, Q.M., Slone, D., Reaver, K., and Hill, J.H., 2021, Invasion frustration: Can biotic resistance explain the small geographic range of non-native croaking gourami Trichopsis vittata (Cuvier, 1831) in Florida, USA?: Aquatic Invasions, v. 16, no. 3, p. 512-526, https://doi.org/10.3391/ai.2021.16.3.08.","productDescription":"15 p.; Data Release","startPage":"512","endPage":"526","ipdsId":"IP-097602","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450624,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/ai.2021.16.3.08","text":"Publisher Index 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Florida","active":true,"usgs":false}],"preferred":false,"id":840163,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70268411,"text":"70268411 - 2021 - Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations","interactions":[],"lastModifiedDate":"2025-06-25T14:13:55.200306","indexId":"70268411","displayToPublicDate":"2021-09-29T09:12:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations","docAbstract":"Traditionally, fisheries management has focused on the abundance, distribution, and size structure of populations. Although these factors remain key aspects of management, a large and growing body of evidence highlights the importance of genetics in conserving wild populations, especially when populations are small and isolated (Frankham et al. 2017). Local adaptations are very common among fishes and help populations cope with specific conditions in their local environment (Fraser et al. 2011). The field of conservation genetics and genomics is highly technical and has advanced rapidly in recent years, offering a wealth of information to support brook trout conservation and restoration. A major impediment to successfully incorporating these advances into conservation outcomes is that most fisheries managers have only a basic understanding of fish genetics and its relevance to their management decisions.","language":"English","publisher":"STAC","usgsCitation":"Kazyak, D.C., Hallerman, E.M., Maloney, L., Faulkner, S., Welsh, A., Coombs, J., Whiteley, A., Rash, J., White, S.L., Bartron, M.L., Kulp, M.A., and Meek, M., 2021, Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-173712","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":491275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":491274,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.chesapeake.org/stac/events/understanding-genetics-for-successful-conservation-and-restoration-of-resilient-chesapeake-bay-brook-trout-populations/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.71040420312731,\n 39.694677079680844\n ],\n [\n -76.93796273435163,\n 39.694677079680844\n ],\n [\n -76.93796273435163,\n 37.158701995783204\n ],\n [\n -75.71040420312731,\n 37.158701995783204\n ],\n [\n -75.71040420312731,\n 39.694677079680844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":941248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hallerman, E. 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Climate change threatens global biodiversity. Many species vulnerable to climate change are important to humans for nutritional, cultural, and economic reasons. Polar bears Ursus maritimus are threatened by sea-ice loss and represent a subsistence resource for Indigenous people. We applied a novel population modeling-management framework that is based on species life history and accounts for habitat loss to evaluate subsistence harvest for the Chukchi Sea (CS) polar bear subpopulation. Harvest strategies followed a state-dependent approach under which new data were used to update the harvest on a predetermined management interval. We found that a harvest strategy with a starting total harvest rate of 2.7% (˜85 bears/yr at current abundance), a 2:1 male-to-female ratio, and a 10-yr management interval would likely maintain subpopulation abundance above maximum net productivity level for the next 35 yr (approximately three polar bear generations), our primary criterion for sustainability. Plausible bounds on starting total harvest rate were 1.7–3.9%, where the range reflects uncertainty due to sampling variation, environmental variation, model selection, and differing levels of risk tolerance. The risk of undesired demographic outcomes (e.g., overharvest) was positively related to harvest rate, management interval, and projected declines in environmental carrying capacity; and negatively related to precision in population data. Results reflect several lines of evidence that the CS subpopulation has been productive in recent years, although it is uncertain how long this will last as sea-ice loss continues. Our methods provide a template for balancing trade-offs among protection, use, research investment, and other factors. Demographic risk assessment and state-dependent management will become increasingly important for harvested species, like polar bears, that exhibit spatiotemporal variation in their response to climate change.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2461","usgsCitation":"Regehr, E.V., Runge, M.C., Von Duyke, A.L., Wilson, R., Polasek, L., Rode, K.D., Hostetter, N.J., and Converse, S.J., 2021, Demographic risk assessment for a harvested species threatened by climate change: Polar bears in the Chukchi Sea: Ecological Applications, v. 31, no. 8, e02461, 13 p., https://doi.org/10.1002/eap.2461.","productDescription":"e02461, 13 p.","ipdsId":"IP-119837","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450636,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2461","text":"External Repository"},{"id":429876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.44241992486556,\n 73.07923094153199\n ],\n [\n -179.9,\n 73.07923094153199\n ],\n [\n -179.9,\n 66.33440002284189\n ],\n [\n -157.44241992486556,\n 66.33440002284189\n ],\n [\n -157.44241992486556,\n 73.07923094153199\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":902941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Von Duyke, Andrew L.","contributorId":214208,"corporation":false,"usgs":false,"family":"Von Duyke","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":38995,"text":"North Slope Borough Department of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":903129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Ryan R. ","contributorId":222456,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan R. ","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":903130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Polasek, Lori","contributorId":338318,"corporation":false,"usgs":false,"family":"Polasek","given":"Lori","email":"","affiliations":[],"preferred":false,"id":903131,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":902942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":903132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902943,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224534,"text":"ofr20211080 - 2021 - Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making","interactions":[],"lastModifiedDate":"2021-09-29T11:36:22.700641","indexId":"ofr20211080","displayToPublicDate":"2021-09-28T09:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1080","displayTitle":"Optimization of Salt Marsh Management at the Rachel Carson National Wildlife Refuge, Maine, Through Use of Structured Decision Making","title":"Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making","docAbstract":"

Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop an example of a prototype tool for optimizing tidal marsh management decisions for selected marsh management units at the Rachel Carson National Wildlife Refuge in Maine. The goal was to create a prototype that could be available for future implementation. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of seven marsh management units within the refuge and estimated the outcomes of each action in terms of regional performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale.

Management costs were estimated using limited available information, and estimated costs of individual management actions reflected relative differences among actions rather than actual expected expenditures. Results from this prototype showed how, for the objectives, actions, and estimated outcomes used for this example, total management benefits may increase consistently up to a certain estimated cost, and may continue to increase, at a lower rate, with further expenditures. Potential management actions in optimal portfolios at moderate total estimated costs included breaching or removing dikes, roads, or embankments; planting Spartina alterniflora (smooth cordgrass); and digging runnels, or shallow creeks, on the marsh platform to improve surface-water drainage. Potential management actions in optimal portfolios at high estimated costs (for example, up to $550,000) included breaching embankments to restore tidal exchange followed by planting salt marsh vegetation. The potential management benefits were derived from predicted increases in the numbers of tidal marsh obligate birds and spiders (as an indicator of trophic health), and expected improvement in the capacity of marsh elevation to keep pace with sea-level rise and reduced duration of marsh-surface inundation. The prototype presented here does not resolve current management decisions; rather, it provides a framework for decision making at the Rachel Carson National Wildlife Refuge that can be updated for implementation as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., O’Brien, K.M., Benvenuti, B., and Kleinert, R., 2021, Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1080, 35 p., https://doi.org/10.3133/ofr20211080.","productDescription":"vi, 35 p.","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126540","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":389743,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1080/coverthb.jpg"},{"id":389744,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.pdf","text":"Report","size":"4.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1080"},{"id":389737,"rank":1,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":389746,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1080/images/"},{"id":389747,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.XML"}],"country":"United States","state":"Maine","otherGeospatial":"Rachel Carson National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.63796997070312,\n 43.20417480788432\n ],\n [\n -70.61325073242188,\n 43.153101551466385\n ],\n [\n -70.477294921875,\n 43.257205668363206\n ],\n [\n -70.43472290039062,\n 43.38508989465156\n ],\n [\n -70.53634643554688,\n 43.393073720674415\n ],\n [\n -70.63796997070312,\n 43.31418735795809\n ],\n [\n -70.63796997070312,\n 43.20417480788432\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-09-28","noUsgsAuthors":false,"publicationDate":"2021-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Neckles, Hilary A. 0000-0002-5662-2314 hneckles@usgs.gov","orcid":"https://orcid.org/0000-0002-5662-2314","contributorId":3821,"corporation":false,"usgs":true,"family":"Neckles","given":"Hilary","email":"hneckles@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823954,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823955,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nagel, Jessica L. 0000-0002-4437-0324 jnagel@usgs.gov","orcid":"https://orcid.org/0000-0002-4437-0324","contributorId":3976,"corporation":false,"usgs":true,"family":"Nagel","given":"Jessica","email":"jnagel@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":823956,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adamowicz, Susan C.","contributorId":174712,"corporation":false,"usgs":false,"family":"Adamowicz","given":"Susan","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":823957,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mikula, Toni","contributorId":208473,"corporation":false,"usgs":false,"family":"Mikula","given":"Toni","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":823958,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Brien, Kathleen M.","contributorId":265993,"corporation":false,"usgs":false,"family":"O’Brien","given":"Kathleen","email":"","middleInitial":"M.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":823959,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Benvenuti, Bri","contributorId":265994,"corporation":false,"usgs":false,"family":"Benvenuti","given":"Bri","email":"","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":823960,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kleinert, Ryan","contributorId":265995,"corporation":false,"usgs":false,"family":"Kleinert","given":"Ryan","email":"","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":823961,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223859,"text":"sir20215085 - 2021 - National assessment of helium resources within known natural gas reservoirs","interactions":[],"lastModifiedDate":"2022-04-14T16:05:09.4376","indexId":"sir20215085","displayToPublicDate":"2021-09-28T08:50:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5085","displayTitle":"National Assessment of Helium Resources Within Known Natural Gas Reservoirs","title":"National assessment of helium resources within known natural gas reservoirs","docAbstract":"

Using available data, the U.S. Geological Survey estimated that 306 billion cubic feet of recoverable helium is presently within the known geologic natural gas reservoirs of the United States.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215085","usgsCitation":"Brennan, S.T., Rivera, J.L., Varela, B.A., and Park, A.J., 2021, National assessment of helium resources within known natural gas reservoirs: U.S. Geological Survey Scientific Investigations Report 2021–5085, 5 p., https://doi.org/10.3133/sir20215085.","productDescription":"Report: vi, 5 p.; Data Release","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112618","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":389388,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215085/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5085"},{"id":389060,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92QL79J","text":"USGS data release","linkHelpText":"Dataset of helium concentrations in United States wells"},{"id":389058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5085/coverthb.jpg"},{"id":389059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5085/sir20215085.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5085"},{"id":389386,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5085/images/"},{"id":389385,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5085/sir20215085.XML"}],"contact":"

Director, Energy Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2021-09-28","noUsgsAuthors":false,"publicationDate":"2021-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Brennan, Sean T. 0000-0002-7102-9359 sbrennan@usgs.gov","orcid":"https://orcid.org/0000-0002-7102-9359","contributorId":559,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean","email":"sbrennan@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":823010,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivera, Jennifer L. 0000-0001-5838-3110","orcid":"https://orcid.org/0000-0001-5838-3110","contributorId":265581,"corporation":false,"usgs":true,"family":"Rivera","given":"Jennifer L.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":823011,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Varela, Brian A. 0000-0001-9849-6742 bvarela@usgs.gov","orcid":"https://orcid.org/0000-0001-9849-6742","contributorId":178091,"corporation":false,"usgs":true,"family":"Varela","given":"Brian","email":"bvarela@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":823012,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Park, Andy J. 0000-0003-1454-1150 apark@usgs.gov","orcid":"https://orcid.org/0000-0003-1454-1150","contributorId":2384,"corporation":false,"usgs":true,"family":"Park","given":"Andy","email":"apark@usgs.gov","middleInitial":"J.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":823013,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227486,"text":"70227486 - 2021 - Late Cenozoic paleogeographic reconstruction of the San Francisco Bay Area from analysis of stratigraphy, tectonics, and tephrochronology","interactions":[],"lastModifiedDate":"2022-01-19T14:41:31.246868","indexId":"70227486","displayToPublicDate":"2021-09-27T08:40:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1726,"text":"GSA Memoirs","active":true,"publicationSubtype":{"id":10}},"title":"Late Cenozoic paleogeographic reconstruction of the San Francisco Bay Area from analysis of stratigraphy, tectonics, and tephrochronology","docAbstract":"The Neogene stratigraphic and tectonic history of the Mount Diablo area is a consequence of the passage of the Mendocino Triple Junction (MTJ) by the San Francisco Bay area between 12 and 6 Ma, volcanism above a slab-window trailing the MTJ, and crustal transpression beginning ~8-6 Ma, when the Pacific Plate and Sierra Nevada microplate began to converge obliquely. Between ~12-6 Ma, parts of the Sierra Nevada microplate were displaced by faults splaying from the main trace of the San Andreas Fault and incorporated into the Pacific Plate. The Mount Diablo anticlinorium was formed by crustal compression within a left-stepping, restraining bend of the eastern San Andreas Fault system (SAF), with southwest-verging thrusting beneath, and with possible clockwise rotation between faults on its southeast and northwest. At ~10,5 Ma, a drainage divide formed between the northern Great Central Valley (GCV) and the ocean. Regional uplift accelerated at ~6 Ma with onset of transpression between the Pacific and North American plates. Marine deposition ceased in the eastern Coast Range basins as a consequence of the regional uplift accompanying passage of the MTJ, and trailing slab-window volcanism. From ~11 to ~5 Ma, andesitic volcanic intrusive rocks and lavas were erupted along the northwest crest of the central to northern Sierra Nevada and were deposited on its western slope, providing abundant sediment to northern Great Central Valley (GCV) and the northeastern Coast Ranges. Sediment filled the GCV, overtopped the Stockton fault and arch forming one large, south-draining system that flowed into a marine embayment at its southwestern end, the ancestral San Joaquin Sea. This marine embayment shrunk with time and by ~2.3 Ma was eventually cut off from the ocean. Fluvial drainage continued southwest in GCV until it was cut off in turn, probably by some combination of sea level fluctuations and transpression along the SAF that uplifted, lengthened and narrowed the outlet channel. As a consequence, a great lake, Lake Clyde, formed in the GCV at ~1.4 Ma, occupying all of the ancestral San Joaquin Valley and part of ancestral Sacramento Valley. The lake rose and fell with global glacial and interglacial cycles. After a long, extreme glacial period, Marine Oxygen Isotope Stage (MOIS) 16, it overtopped Carquinez sill at 0.63 Ma and drained via San Francisco valley (now Bay) and the Colma gap, into the Merced marine embayment of the Pacific Ocean. Later, a new outlet for GCV drainage formed between ~75 and ~130 ka ago., when the Colma gap closed due to transpression and right-slip on the SAF, and Duxbury Point at the south end of Pt. Reyes Peninsula moved sufficiently northwest along the SAF to unblock a bedrock notch, the feature we now call the Golden Gate.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Regional geology of Mount Diablo, California: Its tectonic evolution on the North America plate boundary: Geological Society of America memoir 217","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.1217(17)","usgsCitation":"Sarna-Wojcicki, A., 2021, Late Cenozoic paleogeographic reconstruction of the San Francisco Bay Area from analysis of stratigraphy, tectonics, and tephrochronology: GSA Memoirs, v. 217, p. 443-472, https://doi.org/10.1130/2021.1217(17).","productDescription":"30 p.","startPage":"443","endPage":"472","ipdsId":"IP-129812","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":394516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mount Diablo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.94103240966797,\n 37.84164803953047\n ],\n [\n -121.87957763671874,\n 37.84164803953047\n ],\n [\n -121.87957763671874,\n 37.90289686954944\n ],\n [\n -121.94103240966797,\n 37.90289686954944\n ],\n [\n -121.94103240966797,\n 37.84164803953047\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"217","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sarna-Wojcicki, Andrei 0000-0002-0244-9149","orcid":"https://orcid.org/0000-0002-0244-9149","contributorId":267781,"corporation":false,"usgs":true,"family":"Sarna-Wojcicki","given":"Andrei","affiliations":[{"id":55498,"text":"U.S. Geological Survey, Emeritus","active":true,"usgs":false}],"preferred":false,"id":831153,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70225171,"text":"70225171 - 2021 - Late Cenozoic tephrochronology of the Mount Diablo area within the evolving plate-tectonic boundary zone of northern California","interactions":[],"lastModifiedDate":"2021-10-15T12:48:38.049629","indexId":"70225171","displayToPublicDate":"2021-09-27T07:44:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1726,"text":"GSA Memoirs","active":true,"publicationSubtype":{"id":10}},"title":"Late Cenozoic tephrochronology of the Mount Diablo area within the evolving plate-tectonic boundary zone of northern California","docAbstract":"

We present a tephrochronologic/chronostratigraphic database for the Mount Diablo area and greater San Francisco Bay region that provides a spatial and temporal framework for geologic studies in the region, including stratigraphy, paleogeography, tectonics, quantification of earth surface processes, recurrence of natural hazards, and climate change. We identified and correlated 34 tephra layers within this region using the chemical composition of their volcanic glasses, stratigraphic sequence, and isotopic and other dating techniques. Tephra layers range in age from ca. 65 ka to ca. 29 Ma, as determined by direct radiometric techniques or by correlation to sites where they have been dated. The tephra layers are of Quaternary or Neogene age except for two that are of Oligocene age. We correlated the tephra layers among numerous sites throughout northern California. Source areas of the tephra layers are the Snake River–Yellowstone hotspot trend of northern Nevada, southern Idaho, and western Wyoming; the Nevadaplano caldera complex of central Nevada; the Jemez Mountains–Valles Caldera in northwestern New Mexico; the Southern Nevada volcanic field and related source areas in eastern California and west-central Nevada; the Quien Sabe–Sonoma volcanic centers of the California Coast Ranges; and the young Cascade Range volcanic centers of northeastern California and Oregon.

","language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.1217(16)","usgsCitation":"Sarna-Wojcicki, A., Sullivan, R., Deino, A.L., Walkup, L., Wagner, J.R., and Wan, E., 2021, Late Cenozoic tephrochronology of the Mount Diablo area within the evolving plate-tectonic boundary zone of northern California: GSA Memoirs, v. 217, p. 393-441, https://doi.org/10.1130/2021.1217(16).","productDescription":"48 p.","startPage":"393","endPage":"441","ipdsId":"IP-128373","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":450642,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/mwr.s.15149043","text":"External Repository"},{"id":390560,"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 -125.595703125,\n 31.653381399664\n ],\n [\n -108.544921875,\n 31.653381399664\n ],\n [\n -108.544921875,\n 49.26780455063753\n ],\n [\n -125.595703125,\n 49.26780455063753\n ],\n [\n -125.595703125,\n 31.653381399664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"217","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sarna-Wojcicki, Andrei 0000-0002-0244-9149","orcid":"https://orcid.org/0000-0002-0244-9149","contributorId":267781,"corporation":false,"usgs":true,"family":"Sarna-Wojcicki","given":"Andrei","affiliations":[{"id":55498,"text":"U.S. Geological Survey, Emeritus","active":true,"usgs":false}],"preferred":false,"id":825249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Raymond 0000-0001-6616-6132","orcid":"https://orcid.org/0000-0001-6616-6132","contributorId":267782,"corporation":false,"usgs":false,"family":"Sullivan","given":"Raymond","email":"","affiliations":[{"id":55500,"text":"San Francisco State Univ. Emeritus","active":true,"usgs":false}],"preferred":false,"id":825250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deino, Alan L. 0000-0002-0099-9382","orcid":"https://orcid.org/0000-0002-0099-9382","contributorId":218428,"corporation":false,"usgs":false,"family":"Deino","given":"Alan","email":"","middleInitial":"L.","affiliations":[{"id":38176,"text":"Berkeley Geochronology Center","active":true,"usgs":false}],"preferred":false,"id":825251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walkup, Laura 0000-0002-1962-5364","orcid":"https://orcid.org/0000-0002-1962-5364","contributorId":205009,"corporation":false,"usgs":true,"family":"Walkup","given":"Laura","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":825254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, J. Ross 0000-0002-8909-145X","orcid":"https://orcid.org/0000-0002-8909-145X","contributorId":267783,"corporation":false,"usgs":false,"family":"Wagner","given":"J.","email":"","middleInitial":"Ross","affiliations":[{"id":55501,"text":"Geologist, Albany, Calif.","active":true,"usgs":false}],"preferred":false,"id":825252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wan, Elmira 0000-0002-9255-112X ewan@usgs.gov","orcid":"https://orcid.org/0000-0002-9255-112X","contributorId":3434,"corporation":false,"usgs":true,"family":"Wan","given":"Elmira","email":"ewan@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":825253,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224591,"text":"70224591 - 2021 - Culverts delay upstream and downstream migrations of river herring (Alosa spp.)","interactions":[],"lastModifiedDate":"2021-12-10T16:56:10.521733","indexId":"70224591","displayToPublicDate":"2021-09-26T07:10:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Culverts delay upstream and downstream migrations of river herring (Alosa spp.)","title":"Culverts delay upstream and downstream migrations of river herring (Alosa spp.)","docAbstract":"

Alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis) are iteroparous anadromous fish found throughout the East Coast of North America. The phenology of anadromous fish migrations is important for fitness, and the duration of spawning migrations has been compressed in recent years in response to climate change. Anthropogenic barriers to movement, such as dams and culverts at road-stream crossings, can further disrupt migration phenology by delaying movement and increasing predation risk. We used passive integrated transponder (PIT) telemetry to quantify upstream and downstream migratory delay at five road-stream-crossing culverts on the Herring River (MA, USA). Groundspeeds were reduced at all culverts in both directions, confirming that the culverts impede movement despite high passage proportions. The cumulative delay of the culverts on the upstream migration was sufficient to more than double the amount of time required to traverse the river if the culverts had been absent. Furthermore, the presence of snapping turtles (Chelydra serpentina) ambushing river herring within one of the culverts resulted in reduced passage rates beyond the reduction in movement caused by the physical structure itself. This highlights that physical barriers can create cascading ecological consequences and the importance of taking a holistic approach to understanding barrier effects.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.3859","usgsCitation":"Alcott, D., Goerig, E., and Castro-Santos, T.R., 2021, Culverts delay upstream and downstream migrations of river herring (Alosa spp.): River Research and Applications, v. 37, no. 10, p. 1400-1412, https://doi.org/10.1002/rra.3859.","productDescription":"13 p.","startPage":"1400","endPage":"1412","ipdsId":"IP-130879","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":389939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","city":"Wellfleet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.10787963867188,\n 41.881831370505594\n ],\n [\n -69.92111206054686,\n 41.881831370505594\n ],\n [\n -69.92111206054686,\n 41.97786911170172\n ],\n [\n -70.10787963867188,\n 41.97786911170172\n ],\n [\n -70.10787963867188,\n 41.881831370505594\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Alcott, Derrick 0000-0001-7765-1889","orcid":"https://orcid.org/0000-0001-7765-1889","contributorId":257975,"corporation":false,"usgs":false,"family":"Alcott","given":"Derrick","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":824227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goerig, Elsa","contributorId":261644,"corporation":false,"usgs":false,"family":"Goerig","given":"Elsa","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":824228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":824229,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224531,"text":"70224531 - 2021 - Evaluating the state-of-the-art in remote volcanic eruption characterization Part I: Raikoke volcano, Kuril Islands","interactions":[],"lastModifiedDate":"2021-09-24T15:47:08.603752","indexId":"70224531","displayToPublicDate":"2021-09-24T10:28:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the state-of-the-art in remote volcanic eruption characterization Part I: Raikoke volcano, Kuril Islands","docAbstract":"

Raikoke, a small, unmonitored volcano in the Kuril Islands, erupted in June 2019. We integrate data from satellites (including Sentinel-2, TROPOMI, MODIS, Himawari-8), the International Monitoring System (IMS) infrasound network, and global lightning detection network (GLD360) with information from local authorities and social media to retrospectively characterize the eruptive sequence and improve understanding of the pre-, syn- and post- eruptive behavior. We observe six infrasound pulses beginning on 21 June at 17:49:55 UTC as well as the main Plinian phase on 21 June at 22:29 UTC. Each pulse is tracked in space and time using lightning and satellite imagery as the plumes drift eastward. Post-eruption visible satellite imagery shows expansion of the island's surface area, an increase in crater size, and a possibly-linked algal bloom south of the island. We use thermal satellite imagery and plume modeling to estimate plume height at 10–12 km asl and 1.5–2 × 106 kg/s mass eruption rate. Remote infrasound data provide insight into syn-eruptive changes in eruption intensity. Our analysis illustrates the value of interdisciplinary analyses of remote data to illuminate eruptive processes. However, our inability to identify deformation, pre-eruptive outgassing, and thermal signals, which may reflect the relatively short duration (~12 h) of the eruption and minimal land area around the volcano and/or the character of closed-system eruptions, highlights current limitations in the application of remote sensing for eruption detection and characterization.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2021.107354","usgsCitation":"McKee, K., Smith, C.M., Reath, K., Snee, E., Maher, S., Matoza, R.S., Carn, S.A., Mastin, L.G., Anderson, K.R., Damby, D., Roman, D., Degterev, A., Rybin, A., Chibisova, M., Assink, J.D., de Negri Levia, R., and Perttu, A., 2021, Evaluating the state-of-the-art in remote volcanic eruption characterization Part I: Raikoke volcano, Kuril Islands: Journal of Volcanology and Geothermal Research, v. 419, p. 1-14, https://doi.org/10.1016/j.jvolgeores.2021.107354.","productDescription":"107354, 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-131053","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":450671,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2021.107354","text":"Publisher Index 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]\n }\n }\n ]\n}","volume":"419","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Kathleen 0000-0003-3189-9189","orcid":"https://orcid.org/0000-0003-3189-9189","contributorId":265977,"corporation":false,"usgs":false,"family":"McKee","given":"Kathleen","email":"","affiliations":[{"id":54848,"text":"Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":823913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra Marie 0000-0003-2653-4249 cassandrasmith@usgs.gov","orcid":"https://orcid.org/0000-0003-2653-4249","contributorId":257000,"corporation":false,"usgs":true,"family":"Smith","given":"Cassandra","email":"cassandrasmith@usgs.gov","middleInitial":"Marie","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":823914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reath, 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USA","active":true,"usgs":false}],"preferred":false,"id":823917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Matoza, Robin S.","contributorId":257265,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":823918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carn, Simon A","contributorId":191165,"corporation":false,"usgs":false,"family":"Carn","given":"Simon","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":823919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mastin, Larry G. 0000-0002-4795-1992 lgmastin@usgs.gov","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":555,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"lgmastin@usgs.gov","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science 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Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":823923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Degterev, Artem 0000-0001-6284-8830","orcid":"https://orcid.org/0000-0001-6284-8830","contributorId":265980,"corporation":false,"usgs":false,"family":"Degterev","given":"Artem","email":"","affiliations":[{"id":54851,"text":"Sakhalin Volcanic Eruptions Response Team (SVERT), Institute of Marine Geology and Geophysics, Yuzhno-Sakhalinsk, Russia","active":true,"usgs":false}],"preferred":false,"id":823924,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rybin, Alexander 0000-0002-7734-0172","orcid":"https://orcid.org/0000-0002-7734-0172","contributorId":265981,"corporation":false,"usgs":false,"family":"Rybin","given":"Alexander","email":"","affiliations":[{"id":54851,"text":"Sakhalin Volcanic Eruptions Response Team (SVERT), Institute of Marine Geology and Geophysics, Yuzhno-Sakhalinsk, Russia","active":true,"usgs":false}],"preferred":false,"id":823925,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Chibisova, Marina 0000-0003-0677-6945","orcid":"https://orcid.org/0000-0003-0677-6945","contributorId":265982,"corporation":false,"usgs":false,"family":"Chibisova","given":"Marina","email":"","affiliations":[{"id":54851,"text":"Sakhalin Volcanic Eruptions Response Team (SVERT), Institute of Marine Geology and Geophysics, Yuzhno-Sakhalinsk, Russia","active":true,"usgs":false}],"preferred":false,"id":823926,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Assink, Jelle D.","contributorId":236650,"corporation":false,"usgs":false,"family":"Assink","given":"Jelle","email":"","middleInitial":"D.","affiliations":[{"id":47493,"text":"R and D Seismology and Acoustics, Royal Netherlands Meteorological Institute (KNMI), Utrechtseweg 297, 3731 GA De Bilt, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":823927,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"de Negri Levia, Rodrigo 0000-0003-1283-2579","orcid":"https://orcid.org/0000-0003-1283-2579","contributorId":265983,"corporation":false,"usgs":false,"family":"de Negri Levia","given":"Rodrigo","email":"","affiliations":[{"id":54852,"text":"Department of Earth Science and Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA; NDC-CTBT of the Chilean Nuclear Energy Commission, Chile","active":true,"usgs":false}],"preferred":false,"id":823928,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Perttu, Anna 0000-0003-3590-1549","orcid":"https://orcid.org/0000-0003-3590-1549","contributorId":265984,"corporation":false,"usgs":false,"family":"Perttu","given":"Anna","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":823929,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70223871,"text":"sir20215015 - 2021 - Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013","interactions":[],"lastModifiedDate":"2021-09-27T12:03:37.39516","indexId":"sir20215015","displayToPublicDate":"2021-09-24T09:50:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5015","displayTitle":"Methods for Estimating Regional Skewness of Annual Peak Flows in Parts of Eastern New York and Pennsylvania, Based on Data Through Water Year 2013","title":"Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013","docAbstract":"

Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP–III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the skew of the annual peak flows for a given streamgage and a regional skew. This report presents an estimate of regional skew for a study area encompassing parts of eastern New York and Pennsylvania. A total of 232 candidate U.S. Geological Survey streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length (PRL) of at least 36 years, 183 streamgages were selected for use in the study.

Flood frequencies for candidate streamgages were analyzed by employing the expected moments algorithm, which extends the method of moments so that it can accommodate interval, censored, and historical/paleo flow data, as well as the multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Ten basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 1 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model that had a regional skew coefficient of 0.32 and an average variance of prediction at a new streamgage (AVPnew, which corresponds to the mean square error [MSE] of 0.11) was selected. The AVPnew corresponds to an effective record length of 68 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215015","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2021, Methods for estimating regional skewness of annual peak flows in parts of eastern New York and Pennsylvania, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2021–5015, 38 p., https://doi.org/10.3133/sir20215015.","productDescription":"Report: vi, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114558","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5015/coverthb.jpg"},{"id":389080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5015/sir20215015.pdf","text":"Report","size":"6.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5015"},{"id":389081,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PGAL0D","text":"USGS data release","linkHelpText":"Regional flood skew for parts of the mid-Atlantic region (hydrologic unit 02) in eastern New York and Pennsylvania"}],"country":"United States","state":"New York, Pennsylvania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.56396484375,\n 39.740986355883564\n ],\n [\n -75.03662109375,\n 40.04443758460856\n ],\n [\n -74.86083984375,\n 40.34654412118006\n ],\n [\n -75.16845703124999,\n 40.713955826286046\n ],\n [\n -74.970703125,\n 41.062786068733026\n ],\n [\n -74.619140625,\n 41.393294288784865\n ],\n [\n -74.1796875,\n 41.65649719441145\n ],\n [\n -73.54248046875,\n 42.17968819665961\n ],\n [\n -73.23486328124999,\n 43.02071359427862\n ],\n [\n -73.32275390625,\n 43.628123412124616\n ],\n [\n -73.45458984375,\n 43.77109381775651\n ],\n [\n -73.80615234375,\n 43.35713822211053\n ],\n [\n -74.28955078125,\n 43.14909399920127\n ],\n [\n -74.77294921875,\n 42.79540065303723\n ],\n [\n -75.34423828125,\n 42.73087427928485\n ],\n [\n -75.82763671875,\n 42.68243539838623\n ],\n [\n -76.35498046875,\n 42.68243539838623\n ],\n [\n -76.88232421875,\n 42.68243539838623\n ],\n [\n -77.23388671874999,\n 42.45588764197166\n ],\n [\n -77.607421875,\n 42.19596877629178\n ],\n [\n -77.607421875,\n 42.01665183556825\n ],\n [\n -77.6513671875,\n 41.409775832009565\n ],\n [\n -77.80517578125,\n 41.1290213474951\n ],\n [\n -77.9150390625,\n 40.53050177574321\n ],\n [\n -78.11279296875,\n 40.16208338164617\n ],\n [\n -78.46435546875,\n 39.67337039176558\n ],\n [\n -75.56396484375,\n 39.740986355883564\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2021-09-24","noUsgsAuthors":false,"publicationDate":"2021-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Veilleux, Andrea G. 0000-0002-8742-4660 aveilleux@usgs.gov","orcid":"https://orcid.org/0000-0002-8742-4660","contributorId":203278,"corporation":false,"usgs":true,"family":"Veilleux","given":"Andrea","email":"aveilleux@usgs.gov","middleInitial":"G.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":823495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Daniel M. 0000-0002-0432-450X dwagner@usgs.gov","orcid":"https://orcid.org/0000-0002-0432-450X","contributorId":4531,"corporation":false,"usgs":true,"family":"Wagner","given":"Daniel","email":"dwagner@usgs.gov","middleInitial":"M.","affiliations":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":823048,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70226764,"text":"70226764 - 2021 - Hypogenic karst of the Great Basin","interactions":[],"lastModifiedDate":"2021-12-10T12:56:19.56044","indexId":"70226764","displayToPublicDate":"2021-09-24T06:39:42","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Hypogenic karst of the Great Basin","docAbstract":"

Discoveries in the 1980s greatly expanded speleologists’ understanding of the role that hypogenic groundwater flow can play in developing caves at depth. Ascending groundwater charged with carbon dioxide and, especially, hydrogen sulfide can readily dissolve carbonate bedrock just below and above the water table. Sulfuric acid speleogenesis, in which anoxic, rising, sulfidic groundwater mixes with oxygenated cave atmosphere to form aggressive sulfuric acid (H2SO4) formed spectacular caves in Carlsbad Caverns National Park, USA. Cueva de Villa Luz in Mexico provides an aggressively active example of sulfuric acid speleogenesis processes, and the Frasassi Caves in Italy preserve the results of sulfuric acid speleogenesis in its upper levels while sulfidic groundwater currently enlarges cave passages in the lower levels.

Many caves in east-central Nevada and western Utah (USA) are products of hypogenic speleogenesis and formed before the current topography fully developed. Wet climate during the late Neogene and Pleistocene brought extensive meteoric infiltration into the caves, and calcite speleothems (e.g., stalactites, stalagmites, shields) coat the walls and floors of the caves, concealing evidence of the earlier hypogenic stage. However, by studying the speleogenetic features in well-established sulfuric acid speleogenesis caves, evidence of hypogenic, probably sulfidic, speleogenesis in many Great Basin caves can be teased out. Compelling evidence of hypogenic speleogenesis in these caves include folia, mammillaries, bubble trails, cupolas, and metatyuyamunite. Sulfuric acid speleogenesis signs include hollow coralloid stalagmites, trays, gypsum crust, pseudoscallops, rills, and acid pool notches. Lehman Caves in Great Basin National Park is particularly informative because a low-permeability capstone protected about half of the cave from significant meteoric infiltration, preserving early speleogenetic features.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Field Excursions from the 2021 GSA Section Meetings","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of America","doi":"10.1130/2020.0061(05)","usgsCitation":"Hose, L.D., DuChene, H.R., Jones, D., Baker, G.M., Havlena, Z., Sweetkind, D.S., and Powell, D., 2021, Hypogenic karst of the Great Basin, chap. of Field Excursions from the 2021 GSA Section Meetings, v. 61, p. 77-114, https://doi.org/10.1130/2020.0061(05).","productDescription":"38 p.","startPage":"77","endPage":"114","ipdsId":"IP-124815","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":450693,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/fld.s.16620391.v1","text":"External Repository"},{"id":392721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"New Mexico, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.172119140625,\n 31.952162238024975\n ],\n [\n -104.161376953125,\n 31.952162238024975\n ],\n [\n -104.161376953125,\n 32.59310597426537\n ],\n [\n -105.172119140625,\n 32.59310597426537\n ],\n [\n -105.172119140625,\n 31.952162238024975\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.79589843749999,\n 39.027718840211605\n ],\n [\n -114.169921875,\n 39.027718840211605\n ],\n [\n -114.169921875,\n 40.88029480552824\n ],\n [\n -115.79589843749999,\n 40.88029480552824\n ],\n [\n -115.79589843749999,\n 39.027718840211605\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.59277343749999,\n 17.97873309555617\n ],\n [\n -90.7470703125,\n 17.97873309555617\n ],\n [\n -90.7470703125,\n 18.979025953255267\n ],\n [\n -92.59277343749999,\n 18.979025953255267\n ],\n [\n -92.59277343749999,\n 17.97873309555617\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"61","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hose, Louise D.","contributorId":269963,"corporation":false,"usgs":false,"family":"Hose","given":"Louise","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":828185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DuChene, Harvey R.","contributorId":269964,"corporation":false,"usgs":false,"family":"DuChene","given":"Harvey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":828186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Daniel","contributorId":269965,"corporation":false,"usgs":false,"family":"Jones","given":"Daniel","affiliations":[],"preferred":false,"id":828187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baker, Gretchen M.","contributorId":54894,"corporation":false,"usgs":true,"family":"Baker","given":"Gretchen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":828188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Havlena, Zoe","contributorId":269966,"corporation":false,"usgs":false,"family":"Havlena","given":"Zoe","email":"","affiliations":[],"preferred":false,"id":828189,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sweetkind, Donald S. 0000-0003-0892-4796 dsweetkind@usgs.gov","orcid":"https://orcid.org/0000-0003-0892-4796","contributorId":139913,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald","email":"dsweetkind@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":828190,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Powell, Doug","contributorId":269967,"corporation":false,"usgs":false,"family":"Powell","given":"Doug","email":"","affiliations":[],"preferred":false,"id":828191,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230404,"text":"70230404 - 2021 - Informing future condition scenario planning for habitat specialists of the imperiled pine rockland ecosystem of South Florida","interactions":[],"lastModifiedDate":"2022-04-12T13:20:09.477273","indexId":"70230404","displayToPublicDate":"2021-09-23T08:12:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"title":"Informing future condition scenario planning for habitat specialists of the imperiled pine rockland ecosystem of South Florida","docAbstract":"

This project evaluated habitat conditions for two species found in the imperiled pine rockland ecosystem—the Rim Rock Crowned Snake (Tantilla oolitica) and the Key Ring-Necked Snake (Diadophis punctatus acricus). The Rim Rock Crowned Snake historically occurred in eastern Miami-Dade County (hereafter, mainland) as well as throughout the Florida Keys, whereas the Key Ring-Necked Snake occurs only in lower Florida Keys (Enge et al. 2004; Mays and Enge 2016). Both species are very elusive, small (< 20 cm in length) and primarily fossorial. Pine rockland habitat is rapidly disappearing in South Florida, with < 3 percent of its original extent remaining. Saltwater intrusion from hurricanes and sea-level rise (SLR), and human development pose the greatest threats to the longevity of this ecosystem which, in turn, places species that are endemic to this unique habitat at risk of extinction.

The Rim Rock Crowned Snake and the Key Ringed-Necked Snake are being considered for listing by the U.S. Fish and Wildlife Service (USFWS). To aid the agency’s decision, it must be able to forecast species’ responses to potential future environmental conditions, as well as to different conservation and management actions. Yet, the information needed to complete these forecasts—such as population trends, life history traits, habitat use, and future land use and climate conditions—is often lacking for most rare species. This is especially problematic for assessments of species resiliency to changes in climate and land use.

When these types of data are lacking, information on habitat quality can be used to help determine how a species will respond to change. First, this project gathered current and historical records for both species from various sources such as museum specimens, inventories, and other personal account. Then, we identified potential future changes in habitat that could result from different management actions, such as habitat acquisition or restoration, and environmental conditions, such as changes in the frequency and intensity of tropical storms and rates of SLR. Researchers then explored the potential impacts of these habitat condition changes on the Rim Rock Crowned Snake and Key Ring-Necked Snake.

This information can be used by the USFWS to help make decisions about the need to protect these species under the Endangered Species Act and could inform the conservation, management, and recovery of other at-risk species found in the pine rockland ecosystem. This work supports the Secretary of Interior’s priority to create a conservation stewardship legacy by using science to identify best practices to manage land and water resource and adapt to changes in the environment.

","language":"English","publisher":"Southeast Climate Adaptation Science Center","usgsCitation":"Walls, S.C., 2021, Informing future condition scenario planning for habitat specialists of the imperiled pine rockland ecosystem of South Florida: Final Report, 18 p.","productDescription":"18 p.","ipdsId":"IP-129367","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":398537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398518,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/science/pine-rocklands/"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.0458984375,\n 24.287026865376436\n ],\n [\n -79.9365234375,\n 24.287026865376436\n ],\n [\n -79.9365234375,\n 26.244156283890756\n ],\n [\n -82.0458984375,\n 26.244156283890756\n ],\n [\n -82.0458984375,\n 24.287026865376436\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walls, Susan C. 0000-0001-7391-9155 swalls@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":138952,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","email":"swalls@usgs.gov","middleInitial":"C.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":840331,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230220,"text":"70230220 - 2021 - Red knot stopover population size and migration ecology at Delaware Bay, USA, 2021","interactions":[],"lastModifiedDate":"2024-03-27T15:49:12.524999","indexId":"70230220","displayToPublicDate":"2021-09-22T10:42:04","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Red knot stopover population size and migration ecology at Delaware Bay, USA, 2021","docAbstract":"

Red Knots (Calidris canutus rufa) stop at Delaware Bay during northward migration to feed on eggs of horseshoe crabs (Limulus polyphemus). The northward migration of C. c. rufa coincides with the spawning of horseshoe crabs whose eggs are the perfect food for a migrating Red Knot (Karpanty et al. 2006, Haramis et al. 2007). Horseshoe crabs are therefore an important food resource for Red Knots as well as other shorebirds at Delaware Bay.

Horseshoe crabs have been harvested since at least 1990 for use as bait in American eel (Anguilla rostrata) and whelk (Busycon) fisheries (Kreamer and Michels 2009). In the late 1990s and early 2000s the number of Red Knots found at Delaware Bay declined dramatically from ~50,000 to ~13,000 (Niles et al. 2008). At the same time the number of horseshoe crabs harvested also declined and avian conservation biologists hypothesized that unregulated harvest of horseshoe crabs from Delaware Bay in the 1990s prevented sufficient refueling during stopover for successful migration to the breeding grounds, nesting, and survival for the remainder of the annual cycle (McGowan et al. 2011).

The harvest of horseshoe crabs in the Delaware Bay region has been managed by the Atlantic States Marine Fisheries Commission (ASMFC) since 2012 using an Adaptive Resource Management (ARM) framework (McGowan et al. 2015b). The ARM framework was designed to constrain the harvest so that number of spawning crabs would not limit the number of Red Knots stopping at Delaware Bay during migration. This management framework to achieve multiple objectives requires an estimate each year of both the crab population and the Red Knot stopover population size to inform harvest recommendations (McGowan et al. 2015a). We have estimated the stopover population size using mark-resight data on individually-marked birds and a Jolly-Seber model for open populations since 2011.

","language":"English","publisher":"Atlantic States Marine Fisheries Commission","usgsCitation":"Lyons, J.E., 2021, Red knot stopover population size and migration ecology at Delaware Bay, USA, 2021, 21 p.","productDescription":"21 p.","ipdsId":"IP-135416","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":427147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398095,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://dnrec.delaware.gov/fish-wildlife/conservation/shorebirds/research/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Delaware, New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.09403509407363,\n 38.74026331013363\n ],\n [\n -74.92922524720622,\n 38.95518554423097\n ],\n [\n -74.86023507875021,\n 39.17242937484494\n ],\n [\n -75.47348102058082,\n 39.53695640590777\n ],\n [\n -75.45818237996806,\n 39.725833415896574\n ],\n [\n -75.62300710583959,\n 39.7375634879601\n ],\n [\n -75.68813576821344,\n 39.5812414619472\n ],\n [\n -75.61529784001694,\n 39.40677166373757\n ],\n [\n -75.46198775359684,\n 39.16648631014266\n ],\n [\n -75.34700185696957,\n 38.90151720709986\n ],\n [\n -75.09403509407363,\n 38.74026331013363\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":222844,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":839581,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70226600,"text":"70226600 - 2021 - Shallow marine ecosystem collapse and recovery during the Paleocene-Eocene Thermal Maximum","interactions":[],"lastModifiedDate":"2021-12-02T14:30:55.839361","indexId":"70226600","displayToPublicDate":"2021-09-21T07:15:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Shallow marine ecosystem collapse and recovery during the Paleocene-Eocene Thermal Maximum","docAbstract":"

The Paleocene-Eocene Thermal Maximum (PETM), the most well-studied transient hyperthermal event in Earth history, is characterized by prominent and dynamic changes in global marine ecosystems. Understanding such biotic responses provides valuable insights into future scenarios in the face of anthropogenic warming. However, evidence of the PETM biotic responses is largely biased towards deep-sea records, whereas shallow-marine evidence remains scarce and elusive. Here we investigate a shallow-marine microfaunal record from Maryland, eastern United States, to comprehensively document the shallow-marine biotic response to the PETM. We applied birth-death modeling to estimate the local diversity dynamics, combined with evaluation of time-variable preservation artifacts. We discovered strong increase of species disappearance and appearance predating the onset and at the final recovery phase of the PETM, respectively. Our paleoecological analyses indicate that bathymetric habitat compression due to extreme warmth and oxygen minimum zone expansion caused shallow-marine benthic species extirpation and ecosystem perturbation during the PETM; and that rapid recovery and diversification followed the PETM disaster, thus contributing new understanding to the shallow-marine biotic changes in a broad context of global warming.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloplacha.2021.103649","usgsCitation":"Tian, S., Yasuhara, M., Huang, H., Condamine, F.L., and Robinson, M.M., 2021, Shallow marine ecosystem collapse and recovery during the Paleocene-Eocene Thermal Maximum: Global and Planetary Change, v. 207, 103649, 12 p., https://doi.org/10.1016/j.gloplacha.2021.103649.","productDescription":"103649, 12 p.","ipdsId":"IP-117443","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":450742,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gloplacha.2021.103649","text":"Publisher Index Page"},{"id":392298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.18743896484374,\n 38.66406704456946\n ],\n [\n -75.73974609375,\n 38.66406704456946\n ],\n [\n -75.73974609375,\n 39.10022600175347\n ],\n [\n -76.18743896484374,\n 39.10022600175347\n ],\n [\n -76.18743896484374,\n 38.66406704456946\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"207","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tian, Skye Y","contributorId":269550,"corporation":false,"usgs":false,"family":"Tian","given":"Skye Y","affiliations":[{"id":55550,"text":"University of Hong Kong","active":true,"usgs":false}],"preferred":false,"id":827434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yasuhara, Moriaki","contributorId":178705,"corporation":false,"usgs":false,"family":"Yasuhara","given":"Moriaki","email":"","affiliations":[],"preferred":false,"id":827435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huang, Huai-Hsuan M","contributorId":269552,"corporation":false,"usgs":false,"family":"Huang","given":"Huai-Hsuan M","affiliations":[{"id":55550,"text":"University of Hong Kong","active":true,"usgs":false}],"preferred":false,"id":827436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Condamine, Fabien L.","contributorId":269623,"corporation":false,"usgs":false,"family":"Condamine","given":"Fabien","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":827573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Marci M. 0000-0002-9200-4097 mmrobinson@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-4097","contributorId":2082,"corporation":false,"usgs":true,"family":"Robinson","given":"Marci","email":"mmrobinson@usgs.gov","middleInitial":"M.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":827437,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224266,"text":"sir20215062 - 2021 - Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017","interactions":[],"lastModifiedDate":"2021-09-21T11:32:14.387182","indexId":"sir20215062","displayToPublicDate":"2021-09-20T09:49:44","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5062","displayTitle":"Development of Regression Equations for the Estimation of the Magnitude and Frequency of Floods at Rural, Unregulated Gaged and Ungaged Streams in Puerto Rico Through Water Year 2017","title":"Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017","docAbstract":"

The methods of computation and estimates of the magnitude of flood flows were updated for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent chance exceedance levels for 91 streamgages on the main island of Puerto Rico by using annual peak-flow data through 2017. Since the previous flood frequency study in 1994, the U.S. Geological Survey has collected additional peak flows at additional streamgages, and Puerto Rico has experienced numerous flood events. This updated study was performed using longer annual peak-flow datasets from more stations to provide more representative equations to predict flood flows. Screening criteria for these streamgages included 10 or more years of annual peak-flow data, unregulated flow, and less than 10 percent impervious drainage area.

The magnitude and frequency of floods at selected streamgages in Puerto Rico were estimated using updated methods outlined in Bulletin 17C. The new procedures include a regional skew analysis that incorporates Bayesian regression techniques, the Expected Moments Algorithm to better represent missing record and estimate parameters of the log-Pearson Type III distribution, and the Multiple Grubbs-Beck test for low outlier detection.

Regional regression equations were developed to estimate peak-flow statistics at ungaged locations by using selected basin and climatic characteristics as explanatory variables. These variables were determined from digital spatial datasets and geographic information systems by using the most recent data available. Ordinary least-squares regression techniques were used to filter the basin characteristics and determine two separate regions, region 1 (west) and region 2 (east), based on residuals. A generalized least-squares procedure was used to account for cross-correlation of sites and develop the final set of equations that have drainage area as the only explanatory variable. The average standard errors of prediction ranged from 18.7 to 46.7 percent in region 1 and 33.4 to 57.6 percent in region 2 for all annual exceedance probabilities (AEPs) examined. The updated statistics showed a greater accuracy of prediction when compared to those from the previous study using drainage area as the only explanatory variable for all AEPs examined in region 1 and the 0.01 and 0.002 AEP flows for region 2. When compared to equations developed in the previous study that have drainage area, mean annual rainfall, and (or) depth-to-rock as explanatory variables, the updated statistics show a greater accuracy of prediction in region 1 at AEP flows of 0.02 and lower (that is, higher flows). Those developed for region 2 do not show a greater accuracy of prediction for any AEP flows when compared to the equations having multiple explanatory variables in the previous study.

The calculated regression equations, basin characteristics, and at-site statistics will be incorporated into the U.S. Geological Survey web application, StreamStats (https://streamstats.usgs.gov/ss/). This application allows users to select a location on a stream, whether gaged or ungaged, to obtain estimates of basin characteristics and flow statistics.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215062","usgsCitation":"Ryan, P.J., Gotvald, A.J., Hazelbaker, C.L., Veilleux, A.G., and Wagner, D.M., 2021, Development of regression equations for the estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017: U.S. Geological Survey Scientific Investigations Report 2021–5062, 37 p., https://doi.org/10.3133/sir20215062.","productDescription":"Report: v, 37 p.; Appendix Tables: 3; Data Release","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123614","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":389343,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91XT14B","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data files for the development of regression equations for estimation of the magnitude and frequency of floods at rural, unregulated gaged and ungaged streams in Puerto Rico through water year 2017"},{"id":389335,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5062/coverthb.jpg"},{"id":389336,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062.pdf","text":"Report","size":"4.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5062"},{"id":389337,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_2.1.csv","text":"Appendix Table 2.1 (.csv format)","size":"5.89 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— Streamgages operated by the U.S. Geological Survey (USGS) in Puerto Rico that were used in the regional skew analysis"},{"id":389340,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_1.xlsx","text":"Appendix 1 (.xlsx format)","size":"30.9 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— Streamgages considered for development of regional regression equations in Puerto Rico and details of at-site statistic inputs"},{"id":389338,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_2.1.xlsx","text":"Appendix Table 2.1 (.xlsx format)","size":"19.6 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— Streamgages operated by the U.S. Geological Survey (USGS) in Puerto Rico that were used in the regional skew analysis"},{"id":389339,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_1.csv","text":"Appendix 1 (.csv format)","size":"20.7 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— Streamgages considered for development of regional regression equations in Puerto Rico and details of at-site statistic inputs"},{"id":389341,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_3.csv","text":"Appendix 3 (.csv format)","size":"80.4 kB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"— At-site, regression equation, and weighted magnitude, variance, and prediction intervals of annual exceedance probability floods for select unregulated streamgages in Puerto Rico"},{"id":389342,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5062/sir20215062_appendix_3.xlsx","text":"Appendix 3 (.xlsx format)","size":"134 kB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"— At-site, regression equation, and weighted magnitude, variance, and prediction intervals of annual exceedance probability floods for select unregulated streamgages in Puerto Rico"}],"country":"United States","otherGeospatial":"Puerto 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Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-09-20","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryan, Patrick J. 0000-0002-1490-4938 pryan@usgs.gov","orcid":"https://orcid.org/0000-0002-1490-4938","contributorId":203974,"corporation":false,"usgs":true,"family":"Ryan","given":"Patrick","email":"pryan@usgs.gov","middleInitial":"J.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true},{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":823409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gotvald, Anthony J. 0000-0002-9019-750X agotvald@usgs.gov","orcid":"https://orcid.org/0000-0002-9019-750X","contributorId":1970,"corporation":false,"usgs":true,"family":"Gotvald","given":"Anthony","email":"agotvald@usgs.gov","middleInitial":"J.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazelbaker, Cody L. 0000-0001-5170-9149","orcid":"https://orcid.org/0000-0001-5170-9149","contributorId":265802,"corporation":false,"usgs":true,"family":"Hazelbaker","given":"Cody","email":"","middleInitial":"L.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823411,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Veilleux, Andrea G. 0000-0002-8742-4660 aveilleux@usgs.gov","orcid":"https://orcid.org/0000-0002-8742-4660","contributorId":203278,"corporation":false,"usgs":true,"family":"Veilleux","given":"Andrea","email":"aveilleux@usgs.gov","middleInitial":"G.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":823412,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Daniel M. 0000-0002-0432-450X dwagner@usgs.gov","orcid":"https://orcid.org/0000-0002-0432-450X","contributorId":4531,"corporation":false,"usgs":true,"family":"Wagner","given":"Daniel","email":"dwagner@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823413,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224270,"text":"sir20215075 - 2021 - Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States","interactions":[],"lastModifiedDate":"2021-09-20T14:34:44.807541","indexId":"sir20215075","displayToPublicDate":"2021-09-20T06:57:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5075","displayTitle":"Development of a Screening Tool To Examine Lake and Reservoir Susceptibility to Eutrophication in Selected Watersheds of the Eastern and Southeastern United States","title":"Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States","docAbstract":"

This report describes a new screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States using estimated nutrient loading and flushing rates with measures of waterbody morphometry. To that end, the report documents the compiled data and methods (R-script) used to categorize waterbodies by Carlson’s Trophic State Index. Assessments were completed for 232 lakes and reservoirs having a surface area greater than or equal to 0.1 square kilometer in watersheds that drain to the Atlantic and eastern Gulf of Mexico coasts of the United States and in watersheds within the Tennessee River Basin. Waterbodies were categorized by type—natural lakes, headwater reservoirs, and downstream reservoirs—and were assessed independently. Recursive partitioning and the model-based boosting routine were used to create four-node regression trees to group waterbodies into five endpoints from low-to-high measures of Secchi depth, and concentrations of chlorophyll a and microcystin according to shared nutrient loading, flushing rate, and morphometric characteristics. Trophic state designations were assigned based on the average value within each of the five endpoints. An application (procedure) is provided using the tool to examine the susceptibility of a given waterbody of interest to eutrophication. Results of this study can aid water-resource managers in prioritizing lake and reservoir protection and restoration efforts based on the susceptibility of these waterbodies to eutrophication relative to nutrient loading, flushing rate, and morphometric characteristics.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215075","usgsCitation":"Green, W.R., Hoos, A.B., Wilson, A.E., and Heal, E.N., 2021, Development of a screening tool to examine lake and reservoir susceptibility to eutrophication in selected watersheds of the eastern and southeastern United States: U.S. Geological Survey Scientific Investigations Report 2021–5075, 59 p., https://doi.org/10.3133/sir20215075.","productDescription":"Report: vi, 59 p.; Data Release","numberOfPages":"70","onlineOnly":"Y","ipdsId":"IP-097274","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":389348,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5075/coverthb.jpg"},{"id":389349,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5075/sir20215075.pdf","text":"Report","size":"4.58 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5075"},{"id":389350,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K7EOH0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Nutrient loading, flushing rate, and lake morphometry data used to identify trophic states in selected watersheds of the eastern and southeastern United States"}],"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 -73.916015625,\n 40.51379915504413\n ],\n [\n -73.71826171874999,\n 40.896905775860006\n ],\n [\n -73.05908203125,\n 41.19518982948959\n ],\n [\n -71.60888671875,\n 41.343824581185686\n ],\n [\n -70.48828125,\n 41.21172151054787\n ],\n [\n -69.7412109375,\n 41.83682786072714\n ],\n [\n -70.48828125,\n 42.69858589169842\n ],\n [\n -70.09277343749999,\n 43.54854811091286\n ],\n [\n -67.1044921875,\n 44.574817404670306\n ],\n [\n -66.884765625,\n 44.824708282300236\n ],\n [\n -67.236328125,\n 45.182036837015886\n ],\n [\n -67.43408203124999,\n 45.259422036351694\n ],\n [\n -67.39013671875,\n 45.583289756006316\n ],\n [\n -67.8515625,\n 45.706179285330855\n ],\n [\n -67.87353515625,\n 47.15984001304432\n ],\n [\n -68.22509765625,\n 47.338822694822\n ],\n [\n -68.90625,\n 47.234489635299184\n ],\n [\n -69.08203125,\n 47.45780853075031\n ],\n [\n -69.2138671875,\n 47.487513008956554\n ],\n [\n -70.048828125,\n 46.694667307773116\n ],\n [\n -70.224609375,\n 46.17983040759436\n ],\n [\n -70.55419921875,\n 45.66012730272194\n ],\n [\n -70.90576171875,\n 45.27488643704891\n ],\n [\n -71.279296875,\n 45.321254361171476\n ],\n [\n -71.47705078125,\n 44.99588261816546\n ],\n [\n -74.86083984375,\n 45.01141864227728\n ],\n [\n -75.73974609375,\n 44.5435052132082\n ],\n [\n -74.8828125,\n 44.18220395771566\n ],\n [\n -74.33349609375,\n 43.91372326852401\n ],\n [\n -75.12451171875,\n 43.46886761482925\n ],\n [\n -75.9375,\n 43.29320031385282\n ],\n [\n -76.88232421875,\n 42.4234565179383\n ],\n [\n -77.431640625,\n 42.65012181368022\n ],\n [\n -77.9150390625,\n 42.22851735620852\n ],\n [\n -77.76123046875,\n 41.902277040963696\n ],\n [\n -78.57421875,\n 40.64730356252251\n ],\n [\n -79.4970703125,\n 39.791654835253425\n ],\n [\n -79.95849609375,\n 38.013476231041935\n ],\n [\n -80.52978515625,\n 37.24782120155428\n ],\n [\n -81.40869140625,\n 35.37113502280101\n ],\n [\n -83.43017578125,\n 35.35321610123823\n ],\n [\n -85.10009765625,\n 35.38904996691167\n ],\n [\n -86.90185546874999,\n 35.38904996691167\n ],\n [\n -87.802734375,\n 35.17380831799959\n ],\n [\n -89.07714843749999,\n 34.63320791137959\n ],\n [\n -89.05517578125,\n 33.37641235124676\n ],\n [\n -88.87939453125,\n 32.80574473290688\n ],\n [\n -89.8681640625,\n 31.85889704445453\n ],\n [\n -89.93408203124999,\n 31.316101383495624\n ],\n [\n -89.736328125,\n 30.694611546632277\n ],\n [\n -89.45068359374999,\n 30.14512718337613\n ],\n [\n -86.8359375,\n 30.088107753367257\n ],\n [\n -85.62744140625,\n 29.935895213372444\n ],\n [\n -84.9462890625,\n 29.401319510041485\n ],\n [\n -84.287109375,\n 29.878755346037977\n ],\n [\n -82.85888671875,\n 29.036960648558267\n ],\n [\n -83.0126953125,\n 27.97499795326776\n ],\n [\n -81.5625,\n 25.403584973186703\n ],\n [\n -80.88134765625,\n 24.607069137709683\n ],\n [\n -79.89257812499999,\n 25.005972656239187\n ],\n [\n -79.5849609375,\n 26.92206991673282\n ],\n [\n -81.2109375,\n 30.939924331023445\n ],\n [\n -77.431640625,\n 34.21634468843463\n ],\n [\n -75.65185546874999,\n 34.97600151317588\n ],\n [\n -75.60791015625,\n 37.142803443716836\n ],\n [\n -75.12451171875,\n 38.35888785866677\n ],\n [\n -73.80615234375,\n 39.99395569397331\n ],\n [\n -73.916015625,\n 40.51379915504413\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-09-20","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Green, W. Reed 0000-0002-5778-0955","orcid":"https://orcid.org/0000-0002-5778-0955","contributorId":29856,"corporation":false,"usgs":true,"family":"Green","given":"W.","email":"","middleInitial":"Reed","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoos, Anne B. 0000-0001-9845-7831","orcid":"https://orcid.org/0000-0001-9845-7831","contributorId":217256,"corporation":false,"usgs":true,"family":"Hoos","given":"Anne B.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Alan E.","contributorId":71492,"corporation":false,"usgs":false,"family":"Wilson","given":"Alan","email":"","middleInitial":"E.","affiliations":[],"preferred":true,"id":823419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heal, Elizabeth N. 0000-0002-1196-4708","orcid":"https://orcid.org/0000-0002-1196-4708","contributorId":265803,"corporation":false,"usgs":true,"family":"Heal","given":"Elizabeth N.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823420,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225531,"text":"70225531 - 2021 - Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA","interactions":[],"lastModifiedDate":"2021-12-10T17:01:34.289166","indexId":"70225531","displayToPublicDate":"2021-09-18T08:12:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA","docAbstract":"

Groundwater total dissolved solids (TDS) distribution was mapped with a three-dimensional (3D) model, and it was found that TDS variability is largely controlled by stratigraphy and geologic structure. General TDS patterns in the San Joaquin Valley of California (USA) are attributed to predominantly connate water composition and large-scale recharge from the adjacent Sierra Nevada. However, in smaller areas, stratigraphy and faulting play an important role in controlling TDS. Here, the relationship of stratigraphy and structure to TDS concentration was examined at Poso Creek Oil Field, Kern County, California. The TDS model was constructed using produced water TDS samples and borehole geophysics. The model was used to predict TDS concentration at discrete locations in 3D space and used a Gaussian process to interpolate TDS over a volume. In the overlying aquifer, TDS is typically <1,000 mg/L and increases with depth to ~1,200–3,500 mg/L in the hydrocarbon zone below the Macoma claystone—a regionally extensive, fine-grained unit—and reaches ~7,000 mg/L in isolated places. The Macoma claystone creates a vertical TDS gradient in the west where it is thickest, but control decreases to the east where it pinches out and allows freshwater recharge. Previously mapped normal faults were found to exhibit inconsistent control on TDS. In one case, high-density faulting appears to prevent recharge from flushing higher-TDS connate water. Elsewhere, the high-throw segments of a normal fault exhibit variable behavior, in places blocking lower-TDS recharge and in other cases allowing flushing. Importantly, faults apparently have differential control on oil and groundwater.

","language":"English","publisher":"Springer","doi":"10.1007/s10040-021-02381-5","usgsCitation":"Stephens, M.J., Shimabukuro, D.H., Chang, W., Gillespie, J.M., and Levinson, Z., 2021, Stratigraphic and structural controls on groundwater salinity variations in the Poso Creek Oil Field, Kern County, California, USA: Hydrogeology Journal, v. 29, p. 2803-2820, https://doi.org/10.1007/s10040-021-02381-5.","productDescription":"18 p.","startPage":"2803","endPage":"2820","ipdsId":"IP-113290","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":450766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-021-02381-5","text":"Publisher Index Page"},{"id":390661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Poso Creek Oil 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Michael J. 0000-0001-8995-9928","orcid":"https://orcid.org/0000-0001-8995-9928","contributorId":205895,"corporation":false,"usgs":true,"family":"Stephens","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shimabukuro, David H. 0000-0002-6106-5284","orcid":"https://orcid.org/0000-0002-6106-5284","contributorId":208209,"corporation":false,"usgs":false,"family":"Shimabukuro","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":825461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chang, Will","contributorId":267870,"corporation":false,"usgs":false,"family":"Chang","given":"Will","affiliations":[],"preferred":false,"id":825462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gillespie, Janice M. 0000-0003-1667-3472","orcid":"https://orcid.org/0000-0003-1667-3472","contributorId":219675,"corporation":false,"usgs":true,"family":"Gillespie","given":"Janice","email":"","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":825463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Levinson, Zack","contributorId":267875,"corporation":false,"usgs":false,"family":"Levinson","given":"Zack","email":"","affiliations":[],"preferred":false,"id":825464,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241450,"text":"70241450 - 2021 - Distribution, abundance and spatial variability of microplastic pollution on the surface of Lake Superior","interactions":[],"lastModifiedDate":"2023-03-21T11:44:20.818634","indexId":"70241450","displayToPublicDate":"2021-09-18T06:42:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Distribution, abundance and spatial variability of microplastic pollution on the surface of Lake Superior","docAbstract":"

In 2014, 94 paired neuston net samples (0.5 mm mesh) were collected from the surface waters of Lake Superior. These samples comprise the most comprehensive surface water survey for microplastics of any of the Great Lakes to date, and the first to employ double net trawls. Microplastic abundance estimates showed wide variability, ranging between 4000 to more than 100,000 particles/km2 with most locations having abundances between 20,000 to 50,000 particles/km2. The average abundance in Lake Superior was ~30,000 particles/km2 which was similar to previous estimates within this Laurentian Great Lake and suggests a total count of more than 2.4 billion (1.7 to 3.3 billion, 95% confidence interval) particles across the lake’s surface. Distributions of plastic particles, characterized by size fraction and type, differed between nearshore and offshore samples, and between samples collected in the eastern versus western portion of the lake. Most of the particles found were fibers (67%), and most (62%) were contained in the smallest classified size fraction (0.50–1 mm). The most common type of polymer found was polyethylene (51%), followed by polypropylene (19%). This is consistent with global plastics production and results obtained from other studies. No statistically significant difference was detected between the paired net samples, indicating that single net sampling should produce a representative estimate of microplastic particle abundance and distribution within a body of water.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.08.005","usgsCitation":"Cox, K., Brocious, E., Courtenay, S., Vinson, M., and Mason, S.J., 2021, Distribution, abundance and spatial variability of microplastic pollution on the surface of Lake Superior: Journal of Great Lakes Research, v. 47, no. 5, p. 1358-1364, https://doi.org/10.1016/j.jglr.2021.08.005.","productDescription":"6 p.","startPage":"1358","endPage":"1364","ipdsId":"IP-130920","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450773,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://scholarsphere.psu.edu/resources/d0390b54-5948-4007-83f2-4498b00919e9","text":"External Repository"},{"id":414422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.36493471418689,\n 32.14383279973586\n ],\n [\n -111.36493471418689,\n 30.417046183219966\n ],\n [\n -104.51237611072469,\n 30.417046183219966\n ],\n [\n -104.51237611072469,\n 32.14383279973586\n ],\n [\n -111.36493471418689,\n 32.14383279973586\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.87929721575556,\n 46.241105586985555\n ],\n [\n -83.610772437996,\n 46.241105586985555\n ],\n [\n -83.610772437996,\n 49.22397055892117\n ],\n [\n -92.87929721575556,\n 49.22397055892117\n ],\n [\n -92.87929721575556,\n 46.241105586985555\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cox, K","contributorId":303233,"corporation":false,"usgs":false,"family":"Cox","given":"K","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":866875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brocious, E.","contributorId":303234,"corporation":false,"usgs":false,"family":"Brocious","given":"E.","email":"","affiliations":[{"id":65723,"text":"Penn State Erie","active":true,"usgs":false}],"preferred":false,"id":866876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Courtenay, S","contributorId":303235,"corporation":false,"usgs":false,"family":"Courtenay","given":"S","email":"","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":866877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vinson, Mark R. 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":866878,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mason, Seth J. K.","contributorId":191535,"corporation":false,"usgs":false,"family":"Mason","given":"Seth","email":"","middleInitial":"J. K.","affiliations":[],"preferred":false,"id":866879,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226855,"text":"70226855 - 2021 - A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping","interactions":[],"lastModifiedDate":"2023-11-08T16:32:06.862608","indexId":"70226855","displayToPublicDate":"2021-09-15T06:59:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping","docAbstract":"

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112670","usgsCitation":"Li, C., Xian, G.Z., Zhou, Q., and Pengra, B., 2021, A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping: Remote Sensing of Environment, v. 266, 112670, 19 p., https://doi.org/10.1016/j.rse.2021.112670.","productDescription":"112670, 19 p.","ipdsId":"IP-123712","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":450816,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112670","text":"Publisher Index Page"},{"id":393007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"266","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":828505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":828506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":828507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pengra, Bruce 0000-0003-2497-8284","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":264539,"corporation":false,"usgs":false,"family":"Pengra","given":"Bruce","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":828508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223887,"text":"70223887 - 2021 - Phenotypic variation in Brook Trout Salvelinus fontinalis (Mitchill) at broad spatial scales makes morphology an insufficient basis for taxonomic reclassification of the species","interactions":[],"lastModifiedDate":"2021-09-13T14:09:17.279058","indexId":"70223887","displayToPublicDate":"2021-09-09T09:01:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9341,"text":"Ichthyology & Herpetology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Phenotypic variation in Brook Trout Salvelinus fontinalis (Mitchill) at broad spatial scales makes morphology an insufficient basis for taxonomic reclassification of the species","title":"Phenotypic variation in Brook Trout Salvelinus fontinalis (Mitchill) at broad spatial scales makes morphology an insufficient basis for taxonomic reclassification of the species","docAbstract":"

It was recently proposed that there are three new species of Salvelinus with microendemic distributions in the Great Smoky Mountains National Park, Tennessee, USA. The three species of Salvelinus were hypothesized to be distinct from their congener Brook Trout S. fontinalis based on three meristic traits—pored lateral-line scales, vertebral counts, and number of basihyal teeth. After analyses that included specimens sampled from a larger portion of the geographic range of S. fontinalis, we conclude that the three populations of Salvelinus recently described as new species are not morphometrically distinct from Brook Trout and consider all three to be synonyms of S. fontinalis. Moreover, the low number of specimens originally examined conflates morphological differences among populations with sexual dimorphism and/or phenotypic plasticity, both of which are documented extensively in Brook Trout but were not controlled for in the species descriptions. While there is currently insufficient phenotypic or genotypic evidence to support the hypothesis of three new species that are distinct from S. fontinalis, we acknowledge the need to understand the unique selection pressures that shape evolutionary trajectories in small, isolated populations of Brook Trout and to conserve evolutionarily significant sources of genotypic and phenotypic diversity. To that end, we provide comments on research opportunities to support Brook Trout conservation, including the importance of collaborative, range-wide phylogenetic studies to identify the most appropriate scales of management efforts.

","language":"English","publisher":"American Society of Ichthyologists and Herpetologists","doi":"10.1643/i2020154","usgsCitation":"White, S.L., Kazyak, D., Harrington, R.C., Kulp, M.A., Rash, J.M., Weathers, T.C., and Near, T.J., 2021, Phenotypic variation in Brook Trout Salvelinus fontinalis (Mitchill) at broad spatial scales makes morphology an insufficient basis for taxonomic reclassification of the species: Ichthyology & Herpetology, v. 109, no. 3, p. 743-751, https://doi.org/10.1643/i2020154.","productDescription":"9 p.","startPage":"743","endPage":"751","ipdsId":"IP-124765","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450855,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1643/i2020154","text":"Publisher Index Page"},{"id":389145,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Tennessee","otherGeospatial":"Great Smoky Mountains Park, Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.38317871093749,\n 40.72644570551446\n ],\n [\n -72.88330078125,\n 40.72644570551446\n ],\n [\n -72.88330078125,\n 40.925964939514294\n ],\n [\n -73.38317871093749,\n 40.925964939514294\n ],\n [\n -73.38317871093749,\n 40.72644570551446\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.111328125,\n 35.36217605914681\n ],\n [\n -82.93853759765625,\n 35.36217605914681\n ],\n [\n -82.93853759765625,\n 35.871246850027966\n ],\n [\n -84.111328125,\n 35.871246850027966\n ],\n [\n -84.111328125,\n 35.36217605914681\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"109","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":823091,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":823092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrington, Richard C","contributorId":265606,"corporation":false,"usgs":false,"family":"Harrington","given":"Richard","email":"","middleInitial":"C","affiliations":[{"id":37550,"text":"Yale University","active":true,"usgs":false}],"preferred":false,"id":823093,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":823094,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rash, Jacob M","contributorId":218128,"corporation":false,"usgs":false,"family":"Rash","given":"Jacob","email":"","middleInitial":"M","affiliations":[{"id":39760,"text":"Division of Inland Fisheries, North Carolina Wildlife Resources Commission","active":true,"usgs":false}],"preferred":false,"id":823095,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weathers, T. Casey","contributorId":218129,"corporation":false,"usgs":false,"family":"Weathers","given":"T.","email":"","middleInitial":"Casey","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":823155,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Near, Thomas J","contributorId":265607,"corporation":false,"usgs":false,"family":"Near","given":"Thomas","email":"","middleInitial":"J","affiliations":[{"id":37550,"text":"Yale University","active":true,"usgs":false}],"preferred":false,"id":823096,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224627,"text":"70224627 - 2021 - Hotspot dune erosion on an intermediate beach","interactions":[],"lastModifiedDate":"2021-10-01T13:25:35.688431","indexId":"70224627","displayToPublicDate":"2021-09-08T08:21:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Hotspot dune erosion on an intermediate beach","docAbstract":"

A large, low pressure Nor’easter storm and Hurricane Joaquin contributed to multiple weeks of sustained, elevated wave and water level conditions along the southeastern Atlantic coast of the United States in Fall 2015. Sea level anomalies in excess of 1 m and offshore wave heights of up to 4 m were recorded during these storms, as observed at the U.S. Army Corps of Engineers’ Field Research Facility in Duck, NC, USA. In response to these energetic oceanographic conditions, there were highly variable morphologic changes to the dune over short spatial scales (<km) which included a range of responses from vertical dune scarping to no measureable response. The portion of the study area with the largest dune erosion occurred at a location fronted by an abnormally deep nearshore bathymetric feature, which altered surf-zone waves and hydrodynamics. The pre-storm beach and dune topography also varied throughout the study area, additionally influencing the frequency of dune collision and contributing to the spatially variable erosion patterns. This work uses field datasets and numerical modeling tools to investigate the causation of hotspot dune erosion at the Field Research Facility. Three different numerical models were tested against the available data in order to assess model skill at resolving complex spatial dune erosion patterns. The three models successfully reproduce the general spatial trends in alongshore variable responses, although not necessarily the details of profile response or net erosion magnitude. Analysis of the model outputs, in conjunction with the available field data, suggests that the observed hotspot dune erosion is related to a complex combination of both topographic and bathymetric controls on the processes driving dune erosion. Therefore, the most simplistic model tested, which only accounts for alongshore variations in topographic profile details, can only predict hotspot dune erosion in locations where steep beach and/or dune topography is the primary control on collisional dune impacts. The higher fidelity models, which account for feedback effects from subaqueous morphology, are similarly able to predict the locations of maximum hotspot erosion, but are sensitive to beach over-steepening and/or errors in wave runup calculations that can lead to over-prediction of simulated dune erosion. This work highlights that numerous existing tools are capable of identifying the foredune regions at most risk from hotspot erosion, as well as the need for continued research to improve representation of all relevant intra-storm morphodynamic processes.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2021.103998","usgsCitation":"Cohn, N., Brodie, K., Johnson, B., and Palmsten, M.L., 2021, Hotspot dune erosion on an intermediate beach: Coastal Engineering, v. 170, 103998, 21 p., https://doi.org/10.1016/j.coastaleng.2021.103998.","productDescription":"103998, 21 p.","ipdsId":"IP-124727","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2021.103998","text":"Publisher Index Page"},{"id":390112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"170","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cohn, Nicholas","contributorId":266145,"corporation":false,"usgs":false,"family":"Cohn","given":"Nicholas","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":824404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brodie, Katherine","contributorId":266146,"corporation":false,"usgs":false,"family":"Brodie","given":"Katherine","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":824405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Bradley","contributorId":266147,"corporation":false,"usgs":false,"family":"Johnson","given":"Bradley","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":824406,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":824407,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229777,"text":"70229777 - 2021 - Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","interactions":[],"lastModifiedDate":"2022-03-17T15:32:45.548565","indexId":"70229777","displayToPublicDate":"2021-09-07T10:17:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","docAbstract":"
  1. Translocations are essential for re-establishing wildlife populations. As they sometimes fail, it is critical to assess factors that influence their success pre-translocation.
  2. Socioecological suitability models (SESMs) integrate social acceptance and ecological suitability to enable identification of areas where wildlife populations will expand, which makes it likely that SESMs will also be useful for predicting translocation success.
  3. To inform site selection for potential elk Cervus canadensis reintroduction to north-eastern Minnesota, United States, we developed broadscale maps of social acceptance from surveys of local residents and landowners, animal use equivalence (AUE) from forage measured in the field and empirical conflict risk from geospatial data. Resulting SESMs integrated social acceptance favourability scores, AUE and conflict risk, and weighted SESMs showed the relative influences of acceptance and conflict.
  4. Social acceptance was positive for local residents and landowners (mean ≥ 5.4; scale of 1–7). AUE (scaled to an elk home range) ranged between 1 and 9 elk/16 km2 during winter, and from 14 to 83 elk/16 km2 during summer. Human–elk conflict risk was low (mean ≤ 0.10; scaled 0–1), increasing from north to south. Geographical distributions differed for social acceptance, AUE and conflict risk, and weighted SESMs revealed unsuitable areas that were otherwise obscured.
  5. Synthesis and applications. Integrating human–wildlife conflict risk into SESMs shows where social acceptance of translocated species is likely to erode, even where viewed favourably pre-translocation, to inform translocation planning by highlighting interactions between key factors. Such integrated models supplement existing reintroduction biology frameworks by supporting decision-making and knowledge development. In north-eastern Minnesota, natural resource managers who are considering elk reintroductions are using SESMs reported here to identify where human–elk conflict is unlikely to result in an isolated elk population and where addressing concerns for area residents about conflict risk is essential.
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