The Upper Floridan aquifer is the uppermost, reliable aquifer in southwest Georgia. The aquifer lies on top of the Claiborne, Clayton, and Cretaceous aquifers, all of which exhibited water level declines in the 1960s and 1970s. The U.S. Geological Survey has been working cooperatively with Albany Utilities to monitor groundwater quality and availability in these aquifers since 1977.
During January 2020, nine wells were sampled—six for anions, metals, and nitrate plus nitrite as nitrogen, and three for anions, metals, and pesticides. Nitrate plus nitrite as nitrogen concentrations ranged from 2.4 milligrams per liter (mg/L) to 10.4 mg/L, and no pesticides were detected. Nitrate plus nitrite as nitrogen concentrations in well 12L277, open to the Upper Floridan aquifer, have been above the U.S. Environmental Protection Agency Maximum Contaminant Level of 10 mg/L for nitrates in drinking water since 2014.
Flow direction in the Upper Floridan aquifer is to the south and toward the Flint River. Water levels varied during the past year above and below period of record median values. Water levels in the Upper Floridan aquifer were primarily above median levels. Water levels in the Claiborne aquifer were above median levels, whereas water levels in the Clayton and Cretaceous aquifers were below median levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201120","collaboration":"Prepared in cooperation with Albany Utilities","usgsCitation":"Gordon, D.W., 2020, Groundwater quality and groundwater levels in Dougherty County, Georgia, April 2019 through March 2020: U.S. Geological Survey Open-File Report 2020–1120, 12 p., https://doi.org/10.3133/ofr20201120.","productDescription":"vi, 12 p.","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118275","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":380954,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1120/coverthb.jpg"},{"id":380955,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1120/ofr20201120.pdf","text":"Report","size":"1.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1120"}],"country":"United 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South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
Density-dependent growth has been well documented among stream-dwelling Brown Trout Salmo trutta populations. In Spearfish Creek, South Dakota, biomass of adult Brown Trout (>200 mm) is about three times greater than that reported for similar Black Hills streams, whereas the mean length of adult fish is about 30% less. Here, we evaluate density reduction as a management tool for improving the growth rate of stream-dwelling Brown Trout. We compared age-specific growth of wild Brown Trout in stream sections receiving 50% reductions in fish abundance (removal sections) to that of fish in sections containing natural densities (control sections). Annual growth in length and weight of older Brown Trout (age > 2) was greater in removal sections compared to control sections, particularly among fish between 230 and 280 mm TL (ages 3–4). We attribute the growth response to constraints imposed by food availability and the strong feeding hierarchies, characteristic of larger (older) Brown Trout. Data collected from PIT-tagged fish that were recaptured 2 years after fish removal efforts revealed that growth responses owing to density reduction likely did not extend beyond 1 year. Nonetheless, among cohorts that showed improved growth after the first year, we found that their growth advantage was sustained into the second year. At removal sections, age-3 and older Brown Trout that experienced greater growth after 1 year were larger than fish from control sections 2 years later at age 5 and older. Identification of cohort(s) for which density-dependent growth is most pronounced could help to focus efforts on targeted reduction of specific fish sizes/ages that minimize the cost–benefit ratio of fish removal efforts.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10535","usgsCitation":"Rehm, T.R., Chipps, S.R., and Davis, J., 2020, Effects of density reduction on age-specific growth of stream-dwelling Brown Trout: North American Journal of Fisheries Management, v. 40, no. 6, p. 1355-1366, https://doi.org/10.1002/nafm.10535.","productDescription":"12 p.","startPage":"1355","endPage":"1366","ipdsId":"IP-115916","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Spearfish Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.9306640625,\n 44.46662112356575\n ],\n [\n -103.79196166992188,\n 44.46662112356575\n ],\n [\n -103.79196166992188,\n 44.538121733294545\n ],\n [\n -103.9306640625,\n 44.538121733294545\n ],\n [\n -103.9306640625,\n 44.46662112356575\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Rehm, Travis R.","contributorId":275829,"corporation":false,"usgs":false,"family":"Rehm","given":"Travis","email":"","middleInitial":"R.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":834345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834344,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Jacob L.","contributorId":275831,"corporation":false,"usgs":false,"family":"Davis","given":"Jacob L.","affiliations":[{"id":56698,"text":"South Dakota Department of Game, Fish, and Parks","active":true,"usgs":false}],"preferred":false,"id":834346,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216787,"text":"70216787 - 2020 - Creating annotations for web ontology language ontology generated from relational databases","interactions":[],"lastModifiedDate":"2021-10-01T14:22:39.432326","indexId":"70216787","displayToPublicDate":"2020-12-10T09:20:34","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Creating annotations for web ontology language ontology generated from relational databases","docAbstract":"Many approaches that have been proposed that allow users to create a Web Ontology Language (OWL) ontology from a relational database fail to include metadata that are inherent to the database tables. Without metadata, the resulting ontology lacks annotation properties. These properties are key when performing ontology alignment. This paper proposes a method to include relevant metadata through annotation properties to OWL ontologies, which furthers the ability to integrate and use data from multiple unique ontologies. The described method is applied to geospatial data collected from The National Map, a data source hosted by the U. S. Geological Survey. Following that method, an ontology was manually created that used the metadata from The National Map. Because a manual approach is prone to human error, an automated approach to storing and converting metadata into annotation properties is discussed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Knowledge graphs and semantic web. KGSWC 2020","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Second Iberoamerican Conference and First Indo-American Conference, KGSWC 2020","conferenceDate":"Nov 26–27, 2020","conferenceLocation":"Mérida, Mexico","language":"English","publisher":"Springer","doi":"10.1007/978-3-030-65384-2_4","usgsCitation":"Wagner, M.E., Fry, T.E., Bourquin, J.J., and Varanka, D.E., 2020, Creating annotations for web ontology language ontology generated from relational databases, in Knowledge graphs and semantic web. KGSWC 2020, Mérida, Mexico, Nov 26–27, 2020, p. 45-60, https://doi.org/10.1007/978-3-030-65384-2_4.","productDescription":"16 p.","startPage":"45","endPage":"60","ipdsId":"IP-120265","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":390118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Matthew Edward 0000-0002-3987-072X","orcid":"https://orcid.org/0000-0002-3987-072X","contributorId":245472,"corporation":false,"usgs":true,"family":"Wagner","given":"Matthew","email":"","middleInitial":"Edward","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":806256,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fry, Tanner Edward 0000-0001-9828-0394","orcid":"https://orcid.org/0000-0001-9828-0394","contributorId":245473,"corporation":false,"usgs":true,"family":"Fry","given":"Tanner","email":"","middleInitial":"Edward","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":806257,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bourquin, Jacques Jules 0000-0001-8376-138X","orcid":"https://orcid.org/0000-0001-8376-138X","contributorId":245474,"corporation":false,"usgs":true,"family":"Bourquin","given":"Jacques","email":"","middleInitial":"Jules","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":806258,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varanka, Dalia E. 0000-0003-2857-9600 dvaranka@usgs.gov","orcid":"https://orcid.org/0000-0003-2857-9600","contributorId":1296,"corporation":false,"usgs":true,"family":"Varanka","given":"Dalia","email":"dvaranka@usgs.gov","middleInitial":"E.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":806259,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216894,"text":"70216894 - 2020 - Cretaceous to Oligocene magmatic and tectonic evolution of the western Alaska Range: Insights from U-Pb and 40Ar/39Ar geochronology","interactions":[],"lastModifiedDate":"2024-01-04T01:20:22.926672","indexId":"70216894","displayToPublicDate":"2020-12-10T08:30:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Cretaceous to Oligocene magmatic and tectonic evolution of the western Alaska Range: Insights from U-Pb and 40Ar/39Ar geochronology","title":"Cretaceous to Oligocene magmatic and tectonic evolution of the western Alaska Range: Insights from U-Pb and 40Ar/39Ar geochronology","docAbstract":"New U-Pb and 40Ar/39Ar ages integrated with geologic mapping and observations across the western Alaska Range constrain the distribution and tectonic setting of Cretaceous to Oligocene magmatism along an evolving accretionary plate margin in south-central Alaska. These rocks were emplaced across basement domains that include Neoproterozoic to Jurassic carbonate and siliciclastic strata of the Farewell terrane, Triassic and Jurassic plutonic and volcanic rocks of the Peninsular terrane, and Jurassic and Cretaceous siliciclastic strata of the Kahiltna assemblage. Plutonic rocks of different ages also host economic mineralization including intrusion-related Au, porphyry Cu-Mo-Au, polymetallic veins and skarns, and peralkaline intrusion-related rare-earth elements. The oldest intrusive suites were emplaced ca. 104–80 Ma into the Peninsular terrane only prior to final accretion. Deformation of the northern Kahiltna succession and underlying Farewell terrane occurred at ca. 97 Ma, and more widespread deformation ca. 80 Ma involved south-vergent folding and thrusting of the Kahiltna assemblage that records collisional accretion of the Peninsular-Wrangellia terrane and juxtaposition of sediment wedges formed on the inboard and outboard terranes. More widespread magmatism ca. 75–55 Ma occurred in two general pulses, each having distinct styles of localized deformation. Circa 75–65 Ma plutons were emplaced in a transpressional setting and stitch the accreted Peninsular and Wrangellia terranes to the Farewell terrane. Circa 65–55 Ma magmatism occurred across the entire range and extends for more than 200 km inboard from the inferred position of the continental margin. The Paleocene plutonic suite generally reflects shallower emplacement depths relative to older suites and is associated with more abundant andesitic to rhyolitic volcanic rocks. Deformation ca. 58–56 Ma was concentrated along two high-strain zones, the most prominent of which is 1 km wide, strikes east-northeast, and accommodated dextral oblique motion. Emplacement of widespread intermediate to mafic dikes ca. 59–51 Ma occurred before a notable magmatic lull from ca. 51–44 Ma reflecting a late Paleocene to early Eocene slab window. Magmatism resumed ca. 44 Ma, recording the transition from slab window to renewed subduction that formed the Aleutian-Meshik arc to the southwest. In the western Alaska Range, Eocene magmatism included emplacement of the elongate north-south Merrill Pass pluton and large volumes of ca. 44–37 Ma andesitic flows, tuffs, and lahar deposits. Finally, a latest Eocene to Oligocene magmatic pulse involved emplacement of a compositionally variable but spatially concentrated suite of magmas ranging from gabbro to peralkaline granite ca. 35–26 Ma, followed by waning magmatism that coincided with initiation of Yakutat shallow-slab subduction. Cretaceous to Oligocene magmatism throughout the western Alaska Range collectively records terrane accretion, translation, and integration together with evolving subduction dynamics that have shaped the southern Alaska margin since the middle Mesozoic.
Nitrate, age tracer, and continuous groundwater-level data were interpreted in conjunction with airborne electromagnetic (AEM) survey data to understand the movement of nitrate within the Bazile Groundwater Management Area (BGMA) in northeastern Nebraska. Previously published age tracer data and nitrate data indicated vertical stratification of groundwater quality. Younger groundwater sampled within shallow parts of the aquifer had higher concentrations of nitrate, with 70 percent exceeding the U.S. Environmental Protection Agency maximum contaminant level of 10 milligrams per liter. In contrast, groundwater sampled from deeper parts of the aquifer indicated that nitrate concentrations were less than 2 milligrams per liter and that groundwater likely recharged prior to widespread use of commercial fertilizer.
The hydrostratigraphic interpretation of AEM profiles indicated that shallow and deep monitoring wells were often screened within the same homogenous zone of aquifer material. In contrast, test-hole logs indicated that there often are fine-grained layers within these homogenous zones that separate the shallow and deep monitoring well screens, but these fine-grained layers are not detected by the AEM technique because of decreased resolution of the AEM technique with depth.
The stratification of groundwater ages and nitrate concentrations likely was caused by groundwater-flow paths of different length, location and time of recharge, and denitrification. Within paleochannels interpreted from AEM and test-hole data, pesticides detected in groundwater generally coincide with elevated nitrate concentrations. Continuous groundwater-level data from four monitoring well nests indicated that groundwater pumping can impose or increase downward hydraulic gradients and facilitate the downward movement of nitrate into deeper parts of the High Plains aquifer. Given the density of irrigation wells within the BGMA, this effect on the hydraulic gradient is likely prevalent in other areas of the BGMA. Understanding seasonal water-level changes can allow water managers to better predict and assess the hydraulic gradient and the vulnerability of groundwater in deeper parts of the High Plains aquifer.
Nitrate, age tracer, and continuous groundwater-level data within the BGMA were interpreted in conjunction with AEM data as a case demonstration of the Nebraska Geocloud. The Nebraska Geocloud was initiated to protect taxpayer investments in AEM data collection and realize maximum benefit of these data by creating a publicly available, online digital database for long-term data storage. The Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts; the University of Nebraska-Lincoln Conservation and Survey Division, Nebraska Natural Resources Commission, Nebraska Department of Natural Resources; and the U.S. Geological Survey entered a cooperative agreement to begin a program of data management and research aimed at understanding the best use of AEM for groundwater sustainability and management. Resulting case-study interpretations are provided to guide use of the Nebraska Geocloud to assess water-quality conditions and can be used by water managers and staff to address applicable water resource problems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205113","collaboration":"Prepared in cooperation with the Nebraska Natural Resources Commission; Nebraska Department of Natural Resources; and Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts","usgsCitation":"Hobza, C.M., and Steele, G.V., 2020, Interpretation of hydrogeologic data to support groundwater management, Bazile Groundwater Management Area, northeast Nebraska, 2019—A case demonstration of the Nebraska Geocloud (ver. 1.1, December 15, 2020): U.S. Geological Survey Scientific Investigations Report 2020–5113, 46 p., https://doi.org/10.3133/sir20205113.","productDescription":"Report: viii, 45 p.; Tables: 4, 5, and 6 (.xlsx and .csv); Data Release; Version 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Groundwater Management Area, 2000–17"},{"id":380914,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table4.csv","text":"Table 4","size":"6.47 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5113 Table 4","linkHelpText":"— Monitoring wells completed in the High Plains aquifer where continuous water-level data were recorded within the Bazile Groundwater Management Area, 2013–18"},{"id":380913,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table4.xlsx","text":"Table 4","size":"17.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5113 Table 4","linkHelpText":"— Monitoring wells completed in the High Plains aquifer where continuous water-level data were recorded within the Bazile Groundwater Management Area, 2013–18"},{"id":380916,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5113/sir20205113_table5.csv","text":"Table 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5231 South 19th Street
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Across the globe, conflicting priorities exist in how land and resources are managed. In the American West, conflicts are common on public lands with historical mandates for multiple uses. We explored the impacts of multiple uses of land in a case study of Agassiz's Desert Tortoises (Gopherus agassizii), a federally threatened species, in the western Sonoran Desert. The tortoise has declined for many reasons, most of which relate to management of land and habitat. Frequently cited causes are livestock grazing, roads, vehicle-oriented recreation, predators, and disease. In spring of 2009, we conducted a survey to evaluate relationships between desert tortoises, vegetation associations, topography, predators, and anthropogenic uses. We sampled a 93-km2 area with 200 independent 1-ha plots. Density (± SE) of adult tortoises was low, 2.0 ± 1.0/km2, and the annualized death rate for adults during the 4 yr preceding the survey was high, 13.1%/yr. We observed tortoise sign, most of which was recent, on 22% of the 200 plots, primarily in the southwestern part of the study area. More tortoise sign occurred on plots with Brittlebush (Encelia spp.) vegetation at higher elevations. Most plots (91.0%) had ≥1 human-related impacts: feral burro scat (Equus asinus; 84.0%), recent vehicle tracks and trails (34.0%), trash (28.0%), burro trails and wallows (26.5%), and old vehicle tracks (24.0%). We used a multimodel approach to model presence of tortoise sign on the basis of 12 predictor variables, and calculated model-averaged predictions for the probability of tortoise presence. Importance values revealed two apparent top drivers: feral burros and vegetation association. This is the first study to identify a negative association between presence of desert tortoises and feral burros.
The Sacramento splittail (Pogonichthys macrolepidotus) is composed of two genetically distinct populations endemic to the San Francisco Estuary (SFE). The allopatric upstream spawning habitat of the Central Valley (CV) population connects with the sympatric rearing grounds via relatively low salinity waters, whereas the San Pablo (SP) population must pass through the relatively high-salinity Upper SFE to reach its allopatric downstream spawning habitat. We hypothesize that if migration through SFE salinities to SP spawning grounds is more challenging for adult CV than SP splittail, then salinity tolerance, osmoregulatory capacity, and metabolic responses to salinity will differ between populations. Osmoregulatory disturbances, assessed by measuring plasma osmolality and ions, muscle moisture and Na+-K+-ATPase activity after 168 to 336 h at 11‰ salinity, showed evidence for a more robust osmoregulatory capacity in adult SP relative to CV splittail. While both resting and maximum metabolic rates were elevated in SP splittail in response to increased salinity, CV splittail metabolic rates were unaffected by salinity. Further, the calculated difference between resting and maximum metabolic values, aerobic scope, did not differ significantly between populations. Therefore, improved osmoregulation came at a metabolic cost for SP splittail but was not associated with negative impacts on scope for aerobic metabolism. These results suggest that SP splittail may be physiologically adjusted to allow for migration through higher-salinity waters. The trends in interpopulation variation in osmoregulatory and metabolic responses to salinity exposures support our hypothesis of greater salinity-related challenges to adult CV than SP splittail migration and are consistent with our previous findings for juvenile splittail populations, further supporting our recommendation of population-specific management.
","language":"English","publisher":"Society for Experimental Biology","doi":"10.1093/conphys/coaa098","usgsCitation":"Verhille, C.E., Dabruzzi, T.F., Cocherell, D.E., Mahardja, B., Feyrer, F.V., Foin, T.C., Baerwald, M.R., and Fangue, N.A., 2020, Inter-population differences in salinity tolerance of adult wild Sacramento splittail: osmoregulatory and metabolic responses to salinity: Conservation Physiology, v. 8, no. 1, coaa098, 19 p., https://doi.org/10.1093/conphys/coaa098.","productDescription":"coaa098, 19 p.","ipdsId":"IP-125708","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":454684,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coaa098","text":"Publisher Index Page"},{"id":382871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.3489990234375,\n 37.112145754751516\n ],\n [\n -120.860595703125,\n 37.112145754751516\n ],\n [\n -120.860595703125,\n 39.07464374293251\n ],\n [\n -123.3489990234375,\n 39.07464374293251\n ],\n [\n -123.3489990234375,\n 37.112145754751516\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Verhille, Christine E.","contributorId":174642,"corporation":false,"usgs":false,"family":"Verhille","given":"Christine","email":"","middleInitial":"E.","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":809577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dabruzzi, Theresa F.","contributorId":174643,"corporation":false,"usgs":false,"family":"Dabruzzi","given":"Theresa","email":"","middleInitial":"F.","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":809578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cocherell, Dennis E.","contributorId":174644,"corporation":false,"usgs":false,"family":"Cocherell","given":"Dennis","email":"","middleInitial":"E.","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":809579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahardja, Brian","contributorId":174645,"corporation":false,"usgs":false,"family":"Mahardja","given":"Brian","email":"","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":809580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809581,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Foin, Theodore C.","contributorId":174646,"corporation":false,"usgs":false,"family":"Foin","given":"Theodore","email":"","middleInitial":"C.","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":809582,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Baerwald, Melinda R.","contributorId":171890,"corporation":false,"usgs":false,"family":"Baerwald","given":"Melinda","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":809583,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fangue, Nann A.","contributorId":152479,"corporation":false,"usgs":false,"family":"Fangue","given":"Nann","email":"","middleInitial":"A.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":809584,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70217335,"text":"70217335 - 2020 - Tidal wetland resilience to increased rates of sea level rise in the Chesapeake Bay: Introduction to the special feature","interactions":[],"lastModifiedDate":"2021-01-18T17:16:31.373834","indexId":"70217335","displayToPublicDate":"2020-12-09T11:13:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Tidal wetland resilience to increased rates of sea level rise in the Chesapeake Bay: Introduction to the special feature","docAbstract":"The papers in this Special Feature are the result of the first Marsh Resilience Summit in the Chesapeake Bay region, which occurred in February 2019. The Chesapeake Bay region has one of the highest rates of relative sea level rise in the U.S., jeopardizing over 1000 km2 of tidal wetlands along with other coastal lands. The goal of the Summit and this collection of articles is to analyze tidal wetland response to accelerating sea level rise and the effect their response will have on adaptation planning for surrounding communities. Ten Summit presenters share their research in this Special Feature. In this Introduction, we summarize their findings on evaluating restoration potential at the site-specific level, measuring and projecting marsh migration and erosions rates, describing impacts of wetland migration on a marsh dependent animal, effects on human communities, and finally the roles of property owners and government on future tidal wetland extent. These contributions demonstrate that tidal marsh distribution is dynamic in response to sea level rise, and that social, legal, and policy tools can be used and further developed to enable opportunities for restoring or conserving wetlands when stakeholders are engaged effectively. The papers here and feedback from Summit participants illuminate diverse priorities, research unknowns, and next steps for land use planning toward resilience of the Chesapeake Bay region that also can inform global communities.
An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2020.2219","usgsCitation":"Lasky, J.R., Hooten, M., and Adler, P., 2020, What processes must we understand to forecast regional-scale population dynamics?: Proceedings of the Royal Society B: Biological Sciences, v. 287, no. 1940, 20202219, 12 p., https://doi.org/10.1098/rspb.2020.2219.","productDescription":"20202219, 12 p.","ipdsId":"IP-122452","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":454688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2020.2219","text":"Publisher Index Page"},{"id":429878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"287","issue":"1940","noUsgsAuthors":false,"publicationDate":"2020-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Lasky, Jesse R.","contributorId":338090,"corporation":false,"usgs":false,"family":"Lasky","given":"Jesse","email":"","middleInitial":"R.","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":902949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":902948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adler, Peter B.","contributorId":338091,"corporation":false,"usgs":false,"family":"Adler","given":"Peter B.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":902950,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70224299,"text":"70224299 - 2020 - Analyzing vegetation change in a sagebrush ecosystem using long-term field observations and Landsat imagery in Wyoming","interactions":[],"lastModifiedDate":"2021-09-21T13:32:48.181241","indexId":"70224299","displayToPublicDate":"2020-12-09T08:29:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Analyzing vegetation change in a sagebrush ecosystem using long-term field observations and Landsat imagery in Wyoming","docAbstract":"The importance of monitoring shrublands to detect and understand changes through time is increasingly recognized as critical to management. This research focuses on ecological change observed over 10 yr of field observation at 126 plots and over 35 yr of the Landsat archive in a shrubland ecosystem. Field data consisting of the fractional cover of shrubs, sagebrush, herbs, litter, and bare ground components were collected to be directly comparable to Landsat time-series predictions at an ecoregion level. We used these data to test three hypotheses. First, that precipitation and temperature govern changes in the proportions of shrubland components on an interannual time scale. Second, that longer-term component change is related to climate change. Finally, that change intensity varies by shrubland communities clustered by biophysical conditions. We found that the field observations and Landsat times-series predictions generally responded similarly to interannual variation in weather, chiefly driven by precipitation. Landsat times-series data provided a reasonable means of scaling up the findings of the field observations to a larger temporal and spatial window. The results of the analysis indicate that shrubland component change intensity significantly varies by biophysical clusters, and indicate a significant increase in the cover of shrubs and sagebrush in long-term monitoring plots between 2008 and 2017 and in the Landsat time-series data across the Wyoming Basin study area from 1985 to 2017 and from 2008 to 2017. Our results indicate that the Landsat time series can be used to answer critical questions regarding the influence of climate change and the suitability of management practices in shrubland ecosystems.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3311","usgsCitation":"Shi, H., Homer, C., Rigge, M.B., Postma, K., and Xian, G.Z., 2020, Analyzing vegetation change in a sagebrush ecosystem using long-term field observations and Landsat imagery in Wyoming: Ecosphere, v. 11, no. 12, e03311, 20 p., https://doi.org/10.1002/ecs2.3311.","productDescription":"e03311, 20 p.","ipdsId":"IP-113232","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":454690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3311","text":"Publisher Index Page"},{"id":389545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.972900390625,\n 40.95501133048621\n ],\n [\n -108.48999023437499,\n 40.95501133048621\n ],\n [\n -108.48999023437499,\n 42.601619944327965\n ],\n [\n -110.972900390625,\n 42.601619944327965\n ],\n [\n -110.972900390625,\n 40.95501133048621\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Homer, Collin 0000-0003-4755-8135","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":238918,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823503,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":265826,"corporation":false,"usgs":true,"family":"Postma","given":"Kory","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":823504,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":823505,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216166,"text":"ds1130 - 2020 - Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019","interactions":[],"lastModifiedDate":"2020-12-10T13:21:13.083251","indexId":"ds1130","displayToPublicDate":"2020-12-09T07:22:02","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1130","displayTitle":"Population Estimates for Selected Breeding Seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019","title":"Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019","docAbstract":"Kīlauea Point National Wildlife Refuge (KPNWR) is an important seabird breeding site located at the northeastern tip of Kauaʻi in the main Hawaiian Islands. Despite the regional significance of KPNWR as one of the most important breeding sites for red-tailed tropicbirds (Phaethon rubricauda), red-footed boobies (Sula sula), and wedge-tailed shearwaters (Ardenna pacifica) in the main Hawaiian Islands, robust and accurate population surveys have not been consistently conducted and recent information is lacking. In this study, we completed comprehensive population surveys for these three species during the 2019 breeding season. Using direct censusing methods (ground-searching, visual and photographic counts), we determined that 387 red-tailed tropicbird and 5,049 red-footed booby breeding pairs nested at KPNWR in 2019. Additionally, we performed surveys of aerially displaying tropicbirds to estimate a potential population of 30 white-tailed tropicbird (Phaethon lepturus) breeding pairs at KPNWR. Using a stratified-random plot-sampling method, we estimated that 20,998 wedge-tailed shearwater pairs nested at KPNWR in 2019. The breeding population size results in this study are greater than those reported in the past for KPNWR. We suggest that the red-tailed tropicbird breeding population has increased since the mid-2000s (when population estimates were last made), whereas red-footed booby numbers likely have remained similar and 2019 results show an increase from past estimates because of the more comprehensive methods used in this study. The results of these surveys provide current and accurate population sizes for these species that can serve as (1) benchmarks for future management and monitoring at KPNWR and (2) important components of population-level assessments of seabird vulnerability to potential offshore wind energy development in the main Hawaiian Islands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1130","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management and the U.S. Fish and Wildlife Service Kauaʻi National Wildlife Refuge Complex","usgsCitation":"Felis, J.J., Kelsey, E.C., Adams, J., Stenske, J.G., and White, L.M., 2020, Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019: U.S. Geological Survey Data Series 1130, 32 p., https://doi.org/10.3133/ds1130.","productDescription":"Report: viii, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119737","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":380277,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1130/ds1130.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1130"},{"id":380278,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93MPDR1","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019"},{"id":380276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1130/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Point 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 -159.40844535827637,\n 22.218873658623284\n ],\n [\n -159.37668800354004,\n 22.218873658623284\n ],\n [\n -159.37668800354004,\n 22.238816053514743\n ],\n [\n -159.40844535827637,\n 22.238816053514743\n ],\n [\n -159.40844535827637,\n 22.218873658623284\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Air temperatures (Ta) are rising in a changing climate, increasing extreme temperature events. Examining how Ta increases are influencing extreme temperatures at the soil surface and belowground in the soil profile can refine our understanding of the ecological consequences of rising temperatures. In this paper, we validate surface and soil temperature (Ts: 0–100-cm depth) simulations in the SOILWAT2 model for 29 locations comprising 5 ecosystem types in the central and western USA. We determine the temperature characteristics of these locations from 1980 to 2015, and explore simulations of Ta and Ts change over 2030–2065 and 2065–2100 time periods using General Circulation Model (GCM) projections and the RCP 8.5 emissions scenario. We define temperature extremes using a nonstationary peak over threshold method, quantified from standard deviations above the mean (0-σ: an event
Information concerning the availability, use, and quality of water in Pointe Coupee Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, 364 million gallons per day (Mgal/d) of water were withdrawn in Pointe Coupee Parish, including about 39.87 Mgal/d from groundwater sources and 323.72 Mgal/d from surface-water sources. Withdrawals for power generation accounted for 89 percent (323.98 Mgal/d) of the total water withdrawn. Withdrawals for agricultural use, composed of aquaculture, general irrigation, livestock, and rice irrigation, accounted for 8 percent (29.29 Mgal/d) of the total water withdrawn. Other categories of use included public supply, industrial, and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2014. The large increase in surface-water withdrawals from 1980 to 1985 is attributable to an increase of 262 Mgal/d for power-generation use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203054","usgsCitation":"White, V.E., 2020, Water resources of Pointe Coupee Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3054, 6 p., https://doi.org/10.3133/fs20203054.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-102167","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":381092,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3054/coverthb.jpg"},{"id":381093,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3054/fs20203054.pdf","text":"Report","size":"2.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3054"},{"id":381094,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"}],"country":"United States","state":"Louisiana","county":"Pointe Coupee Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.7504,31.0193],[-91.736,31.0153],[-91.7248,31.0084],[-91.7141,31.0066],[-91.7093,31.0098],[-91.7061,31.0167],[-91.7045,31.0176],[-91.6992,31.0199],[-91.6938,31.0203],[-91.6869,31.0176],[-91.6709,31.0108],[-91.6639,31.0035],[-91.6612,30.9967],[-91.6586,30.9944],[-91.6586,30.9916],[-91.6618,30.987],[-91.6634,30.9848],[-91.6639,30.977],[-91.6622,30.9706],[-91.6574,30.9514],[-91.6451,30.9291],[-91.6525,30.9185],[-91.661,30.8952],[-91.662,30.86],[-91.6594,30.8541],[-91.6529,30.8482],[-91.6375,30.8418],[-91.6252,30.8404],[-91.5997,30.8469],[-91.588,30.8478],[-91.5768,30.846],[-91.5709,30.8423],[-91.5667,30.8364],[-91.5624,30.8373],[-91.5592,30.84],[-91.5566,30.8487],[-91.5592,30.872],[-91.5624,30.8816],[-91.5662,30.8866],[-91.5742,30.893],[-91.5859,30.8962],[-91.6046,30.8971],[-91.6131,30.9003],[-91.6163,30.9122],[-91.6099,30.9168],[-91.5859,30.9168],[-91.5689,30.9191],[-91.5497,30.9168],[-91.5321,30.904],[-91.5214,30.8903],[-91.5156,30.8752],[-91.5156,30.8615],[-91.5219,30.8483],[-91.5273,30.8103],[-91.54,30.7912],[-91.5512,30.7806],[-91.564,30.7765],[-91.5805,30.7747],[-91.5906,30.7692],[-91.5948,30.7637],[-91.5964,30.7578],[-91.5937,30.7491],[-91.5719,30.7354],[-91.5533,30.7308],[-91.5384,30.7331],[-91.523,30.7381],[-91.5001,30.7413],[-91.4804,30.7409],[-91.4602,30.7354],[-91.4507,30.7363],[-91.4288,30.7432],[-91.398,30.7578],[-91.381,30.7591],[-91.3756,30.7559],[-91.3624,30.7394],[-91.3528,30.7225],[-91.3418,30.6727],[-91.3354,30.6603],[-91.3312,30.6585],[-91.3338,30.6539],[-91.312,30.6484],[-91.3174,30.637],[-91.3201,30.6329],[-91.3355,30.616],[-91.3498,30.6041],[-91.3631,30.59],[-91.3653,30.5877],[-91.3653,30.5845],[-91.3653,30.579],[-91.3648,30.5689],[-91.3945,30.569],[-91.3977,30.569],[-91.3993,30.569],[-91.4009,30.5621],[-91.4056,30.5557],[-91.4078,30.5406],[-91.4147,30.5406],[-91.4147,30.5255],[-91.4152,30.5191],[-91.4147,30.5118],[-91.4821,30.5114],[-91.4815,30.4972],[-91.4853,30.4972],[-91.5261,30.4972],[-91.5584,30.4885],[-91.5568,30.483],[-91.5701,30.4826],[-91.5818,30.4825],[-91.5839,30.4967],[-91.6253,30.4972],[-91.7011,30.4975],[-91.7568,30.4978],[-91.7525,30.5079],[-91.7472,30.5093],[-91.7361,30.5084],[-91.7324,30.5102],[-91.7319,30.5125],[-91.733,30.5203],[-91.7425,30.5317],[-91.7559,30.5596],[-91.7575,30.5628],[-91.757,30.5687],[-91.7549,30.5742],[-91.7544,30.5861],[-91.7512,30.5994],[-91.755,30.6126],[-91.7539,30.6176],[-91.7508,30.6231],[-91.7449,30.6254],[-91.7412,30.6327],[-91.7444,30.6401],[-91.7466,30.6588],[-91.7445,30.6625],[-91.7328,30.668],[-91.7323,30.6725],[-91.7366,30.6794],[-91.7335,30.7018],[-91.7372,30.7118],[-91.7468,30.7237],[-91.7485,30.7301],[-91.7581,30.7415],[-91.7576,30.7493],[-91.7554,30.7534],[-91.7565,30.7607],[-91.7613,30.7675],[-91.7688,30.7858],[-91.7779,30.794],[-91.787,30.7976],[-91.7987,30.8104],[-91.8067,30.8104],[-91.8089,30.8145],[-91.8078,30.8204],[-91.8041,30.8291],[-91.7977,30.8337],[-91.7978,30.8442],[-91.8154,30.8483],[-91.8202,30.8533],[-91.8202,30.8583],[-91.816,30.8634],[-91.8101,30.862],[-91.8048,30.8639],[-91.8021,30.8721],[-91.8033,30.8835],[-91.8012,30.8968],[-91.8023,30.9132],[-91.8002,30.926],[-91.8035,30.9402],[-91.8025,30.9543],[-91.8063,30.9699],[-91.8057,30.9744],[-91.8041,30.9781],[-91.7978,30.9827],[-91.7807,30.9868],[-91.777,30.9909],[-91.7754,31.0005],[-91.7728,31.0051],[-91.768,31.0092],[-91.7563,31.0143],[-91.7504,31.0193]]]},\"properties\":{\"name\":\"Pointe Coupee\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
The northern long‐eared bat (Myotis septentrionalis) is one of the bat species most affected by white‐nose syndrome. Population declines attributed to white‐nose syndrome contributed to the species’ listing as federally threatened under the 1973 Endangered Species Act. Although one of the most abundant Myotine bats in eastern North America prior to white‐nose syndrome, little is known about northern long‐eared bats in the upper Midwest, USA. We assessed the habitat associations of the northern long‐eared bats on a regional scale using occupancy models that accounted for uncertainty in nightly detection to provide needed information on the distribution as white‐nose syndrome has recently arrived in this area. We monitored bat activity using zero‐crossing frequency‐division bat detectors for 10–15 nights at 20 detector sites at each of 3 sampling areas in Michigan, USA, and 6 sampling areas in Wisconsin, USA, stratified by mesic and xeric habitat types. We constructed northern long‐eared bat nightly detection histories for our occupancy analysis using maximum likelihood estimates from 2 commercially‐available automated identification programs: Kaleidoscope and Echoclass. We sampled for a total of 2,174 detector‐nights. Both Kaleidoscope and Echoclass identified northern long‐eared bat passes on 110 detector‐nights, whereas on 1,968 detector‐nights neither program identified a northern long‐eared bat call. Only one program or the other identified northern long‐eared bat calls on 206 detector‐nights, indicating an overall agreement rate of 35% on nights when calls were detected. We analyzed these data using an occupancy analysis accounting for the potential for false positives to assess the relationship between northern long‐eared bat presence and habitat characteristics. Our analyses indicated that the probability of a false positive at a site was low (0.015; 95% CI 0.009–0.021), and detection probability, but not occupancy, declined from 2015 to 2016 for sites in Wisconsin sampled in both years. Occupancy was positively associated with distance into the forest interior (distance from nearest road).
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1138","usgsCitation":"Hyzy, B.A., Russell, R., Silvis, A., Ford, W., Riddle, J.D., and Russell, K.R., 2020, Occupancy and detectability of northern long-eared bats in the Lake States Region: Wildlife Society Bulletin, v. 44, no. 4, p. 732-740, https://doi.org/10.1002/wsb.1138.","productDescription":"9 p.","startPage":"732","endPage":"740","ipdsId":"IP-095702","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":381445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.52734374999999,\n 42.53689200787315\n ],\n [\n -87.802734375,\n 42.601619944327965\n ],\n [\n -87.6708984375,\n 44.574817404670306\n ],\n [\n -87.802734375,\n 45.042478050891546\n ],\n [\n -87.03369140625,\n 45.73685954736049\n ],\n [\n -85.4736328125,\n 46.07323062540835\n ],\n [\n -85.869140625,\n 46.649436163350245\n ],\n [\n -86.7041015625,\n 46.45299704748289\n ],\n [\n -88.00048828124999,\n 46.9502622421856\n ],\n [\n -88.9453125,\n 46.965259400349275\n ],\n [\n -90.37353515625,\n 46.63435070293566\n ],\n [\n -90.98876953125,\n 46.63435070293566\n ],\n [\n -90.76904296874999,\n 46.9052455464292\n ],\n [\n -91.97753906249999,\n 46.7248003746672\n ],\n [\n -92.28515625,\n 45.321254361171476\n ],\n [\n -91.0546875,\n 44.071800467511565\n ],\n [\n -90.52734374999999,\n 42.53689200787315\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Hyzy, Brenna A.","contributorId":171457,"corporation":false,"usgs":false,"family":"Hyzy","given":"Brenna","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":806604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":806605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":806606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riddle, Jason D.","contributorId":146462,"corporation":false,"usgs":false,"family":"Riddle","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":806607,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Russell, Kevin R.","contributorId":150351,"corporation":false,"usgs":false,"family":"Russell","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":806609,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216763,"text":"ofr20201127 - 2020 - Investigation of U.S. Foreign Reliance on Critical Minerals—U.S. Geological Survey technical input document in response to Executive Order No. 13953 Signed September 30, 2020","interactions":[],"lastModifiedDate":"2020-12-08T13:26:09.759958","indexId":"ofr20201127","displayToPublicDate":"2020-12-07T14:58:00","publicationYear":"2020","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":"2020-1127","displayTitle":"Investigation of U.S. Foreign Reliance on Critical Minerals—U.S. Geological Survey Technical Input Document in Response to Executive Order No. 13953 Signed September 30, 2020","title":"Investigation of U.S. Foreign Reliance on Critical Minerals—U.S. Geological Survey technical input document in response to Executive Order No. 13953 Signed September 30, 2020","docAbstract":"Over the past few decades (1990–2019), the United States has become reliant on foreign sources to meet domestic demand for a large and growing number of mineral commodities. In combination with recent trends towards progressively concentrated supply of mineral commodities from a limited number of countries, this heightened import reliance may increase the risk to the United States economy and national security. Several factors obscure the true net import reliance of mineral commodities essential to the United States, including indirect trade reliance, embedded trade reliance, and foreign ownership. This report provides a detailed overview of contributions to and trends of these mineral commodity supply risks and provides an outline of the salient factors pertaining to each mineral commodity’s supply chain. It also describes some additional considerations and provides a general framework for evaluating different strategies aimed at reducing net import reliance and supply risk.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201127","usgsCitation":"Nassar, N.T., Alonso, E., and Brainard, J.L., 2020, Investigation of U.S. Foreign Reliance on Critical Minerals—U.S. Geological Survey Technical Input Document in Response to Executive Order No. 13953 Signed September 30, 2020 (Ver. 1.1, December 7, 2020): U.S. Geological Survey Open-File Report 2020–1127, 37 p., https://doi.org/10.3133/ofr20201127.","productDescription":"vii, 37 p.","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123693","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":381043,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1127/ofr20201127.pdf","text":"Report","size":"4.71 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1127"},{"id":381064,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1127/versionHist.txt","size":"1.4 KB","linkFileType":{"id":2,"text":"txt"}},{"id":381023,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1127/coverthb3.jpg"}],"edition":"Version 1.0: December 4, 2020; Version 1.1: December 7, 2020","contact":"National Minerals Information Center
U.S. Geological Survey
988 National Center
12201 Sunrise Valley Drive
Reston, VA 20191
In 2011, an experiment was undertaken to examine spring synchrony between the endangered Karner blue butterfly (Lycaeides melissa samuelis) (Kbb) and its obligate host plant, wild blue lupine (Lupinus perennis) at Indiana Dunes National Lakeshore (INDU), where the southernmost population of Kbb occurred at the time of this study. From 2012 to 2014, field‐placed Kbb eggs were observed for larvae hatching in conjunction with observations of lupine emergence in oak savanna habitat. In 2012, 61% of Kbb hatched when <5% of lupine had emerged due to an extreme early spring event as compared to subsequent years where temporal overlap was >15% between Kbb and lupine. Laboratory experiments testing the sensitivity of Kbb hatching to warm temperatures during the winter of 2011–2012 confirmed that Kbb eggs were susceptible to temperature‐induced hatching. In the summer of 2012, second generation Kbb larvae feeding on sun‐exposed lupine had higher mortality due to the heat and drought conditions that resulted in earlier plant senescence. Following 2012, Kbb were no longer observed at INDU. This observation illustrates the pressing need for adaptive management strategies that account for extreme weather events brought on by climate change.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.147","usgsCitation":"Patterson, T., Grundel, R., Dzurisin, J., Knutson, R.L., and Hellmann, J., 2020, Evidence of an extreme weather‐induced phenological mismatch and a local extirpation of the endangered Karner blue butterfly: Conservation Science and Practice, v. 2, e147, 13 p., https://doi.org/10.1111/csp2.147.","productDescription":"e147, 13 p.","ipdsId":"IP-105784","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.147","text":"Publisher Index Page"},{"id":380790,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","otherGeospatial":"Indiana Dunes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.99249267578125,\n 41.65136676586814\n ],\n [\n -86.90597534179688,\n 41.69547509615208\n ],\n [\n -86.91970825195312,\n 41.71700538790365\n ],\n [\n -87.12432861328125,\n 41.65239288426814\n ],\n [\n -87.12432861328125,\n 41.61030862727467\n ],\n [\n -87.01309204101562,\n 41.638025739250786\n ],\n [\n -86.99249267578125,\n 41.65136676586814\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2019-12-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Patterson, Tamatha 0000-0002-1648-8114 tpatterson@usgs.gov","orcid":"https://orcid.org/0000-0002-1648-8114","contributorId":201149,"corporation":false,"usgs":true,"family":"Patterson","given":"Tamatha","email":"tpatterson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":805552,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grundel, Ralph 0000-0002-2949-7087 rgrundel@usgs.gov","orcid":"https://orcid.org/0000-0002-2949-7087","contributorId":2444,"corporation":false,"usgs":true,"family":"Grundel","given":"Ralph","email":"rgrundel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":805670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzurisin, Jason D. K.","contributorId":151006,"corporation":false,"usgs":false,"family":"Dzurisin","given":"Jason D. K.","affiliations":[{"id":16905,"text":"University of Notre Dame, Dept. of Biological Sciences, Notre Dame, IN, 46556, USA","active":true,"usgs":false}],"preferred":false,"id":805671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knutson, Randy L.","contributorId":72752,"corporation":false,"usgs":true,"family":"Knutson","given":"Randy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":805672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hellmann, Jessica","contributorId":197645,"corporation":false,"usgs":false,"family":"Hellmann","given":"Jessica","affiliations":[],"preferred":false,"id":805673,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219105,"text":"70219105 - 2020 - The elephant in the lab (and field): Contamination in aquatic environmental DNA studies","interactions":[],"lastModifiedDate":"2021-03-24T12:18:35.139333","indexId":"70219105","displayToPublicDate":"2020-12-07T07:15:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"The elephant in the lab (and field): Contamination in aquatic environmental DNA studies","docAbstract":"The rapid evolution of environmental (e)DNA methods has resulted in knowledge gaps in smaller, yet critical details like proper use of negative controls to detect contamination. Detecting contamination is vital for confident use of eDNA results in decision-making. We conducted two literature reviews to summarize (a) the types of quality assurance measures taken to detect contamination of eDNA samples from aquatic environments, (b) the occurrence, frequency and attribution (i.e., putative sources) of unexpected amplification in these quality assurance samples, and (c) how results were interpreted when contamination occurred. In the first literature review, we reviewed 156 papers and found that 91% of targeted and 73% of metabarcoding eDNA studies reported inclusion of negative controls within their workflows. However, a large percentage of targeted (49%) and metabarcoding (80%) studies only reported negative controls for laboratory procedures, so results were potentially blind to field contamination. Many of the 156 studies did not provide critical methodological information and amplification results of negative controls. In our second literature review, we reviewed 695 papers and found that 30 targeted and 32 metabarcoding eDNA studies reported amplification of negative controls. This amplification occurred at similar proportions for field and lab workflow steps in targeted and metabarcoding studies. These studies most frequently used amplified negative controls to delimit a detection threshold above which is considered significant or provided rationale for why the unexpected amplifications did not affect results. In summary, we found that there has been minimal convergence over time on negative control implementation, methods, and interpretation, which suggests that increased rigor in these smaller, yet critical details remains an outstanding need. We conclude our review by highlighting several studies that have developed especially effective quality assurance, control and mitigation methods.
","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2020.609973","usgsCitation":"Sepulveda, A., Hutchins, P.R., Forstchen, M., Mckeefry, M., and Swigris, A.M., 2020, The elephant in the lab (and field): Contamination in aquatic environmental DNA studies: Frontiers in Ecology and Evolution, v. 8, 12 p., https://doi.org/10.3389/fevo.2020.609973.","productDescription":"12 p.","ipdsId":"IP-122976","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":454698,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2020.609973","text":"Publisher Index Page"},{"id":384627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2020-12-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hutchins, Patrick R. 0000-0001-5232-0821 phutchins@usgs.gov","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":198337,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick","email":"phutchins@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forstchen, Meghan","contributorId":255678,"corporation":false,"usgs":false,"family":"Forstchen","given":"Meghan","email":"","affiliations":[],"preferred":false,"id":812800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mckeefry, Madeline","contributorId":255679,"corporation":false,"usgs":false,"family":"Mckeefry","given":"Madeline","email":"","affiliations":[],"preferred":false,"id":812801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Swigris, Anna M","contributorId":255682,"corporation":false,"usgs":false,"family":"Swigris","given":"Anna","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":812802,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216770,"text":"sir20205112 - 2020 - Source-tracking approach for detecting and identifying sources of wastewater in waters of Hawaiʻi","interactions":[],"lastModifiedDate":"2020-12-07T12:53:35.806349","indexId":"sir20205112","displayToPublicDate":"2020-12-04T13:51:45","publicationYear":"2020","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":"2020-5112","displayTitle":"Source-Tracking Approach for Detecting and Identifying Sources of Wastewater in Waters of Hawaiʻi","title":"Source-tracking approach for detecting and identifying sources of wastewater in waters of Hawaiʻi","docAbstract":"Elevated concentrations of nutrients and the fecal-indicator bacteria enterococci are occasionally detected in Hawai‘i’s surface waters by the State of Hawai‘i Department of Health Clean Water Branch. Management efforts to improve the water quality of surface waters are complicated by the fact that nutrients and enterococci can originate from several sources, including wastewater, animal waste, and soils. Wastewater often is the suspected source of nutrients and bacteria, but the source may not always be unequivocally identifiable from the Clean Water Branch’s routine monitoring efforts. This report—prepared in cooperation with the State of Hawai‘i Department of Health Clean Water Branch—describes a source-tracking approach for Hawai‘i that is meant to help investigators determine whether wastewater is present in the environment and where wastewater might be originating, if it is present. Wastewater sources include domestic wastewater entering the environment through on-site disposal systems and municipal wastewater entering the environment through leaky sewer systems or injection-well disposal systems. The source-tracking approach relies on the use of field-measured water properties and multiple chemical tracers of wastewater, including optical brighteners, nutrients, hydrogen and oxygen isotopes in water, nitrogen and oxygen isotopes in nitrate, organic waste compounds, and human-use pharmaceutical compounds. The source-tracking approach proposes the following sequence of steps for investigators to execute: (1) gather background information on the study area, (2) conduct trolling-instrument surveys of physical properties of surface water and identify groundwater-discharge locations, (3) collect water samples at reconnaissance sites and analyze the samples for detergent optical brighteners and specific conductance, (4) collect water samples at targeted sites and have appropriate laboratories analyze the samples for chemical tracers of wastewater, and (5) evaluate analytical results for chemical tracers of wastewater and conclude whether wastewater is present in sampled waters. The conclusions can guide management and stakeholder efforts to protect and improve the quality of Hawai‘i’s water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205112","collaboration":"Prepared in cooperation with the State of Hawaiʻi Department of Health Clean Water Branch","usgsCitation":"Johnson, A.G., 2020, Source-tracking approach for detecting and identifying sources of wastewater in waters of Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2020–5112, 53 p., https://doi.org/10.3133/sir20205112.","productDescription":"viii, 53 p.","onlineOnly":"Y","ipdsId":"IP-109904","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":381006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5112/coverthb.jpg"},{"id":381007,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5112/sir20205112.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Director, Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
The western subspecies of the purple martin (Progne subis arboricola) is currently listed as a “critically” sensitive species in four ecoregions of western Oregon: Coast Range, Klamath Mountains, West Cascades, and Willamette Valley (Oregon Department of Fish and Wildlife, 2019). Importantly distinct from the abundant and widespread eastern subspecies (Progne subis subis), the western subspecies is of particular concern to Federal forest managers. Whereas the eastern subspecies is almost entirely dependent on artificial human-provided housing, the western subspecies continues to rely on natural cavities for nesting habitat (Bettinger, 2003). Accurate estimates of the regional abundance of the western purple martin are difficult to obtain; the most recent statewide census for Oregon, conducted in 2005, estimated the population at 1,100 pairs (Western Purple Martin Working Group, 2010). Several factors, including a small population size, loss of breeding habitat, and reductions in the number of suitable nesting sites have put populations of the western purple martin at risk throughout much of the Pacific Northwest region (Rockwell, 2019).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201130","usgsCitation":"Hagar, J.C., and Branch, E.C., 2020, Western purple martin (Progne subis arboricola) occurrence on the Siuslaw National Forest, summer 2019: U.S. Geological Survey Open-File Report 2020-1130, 25 p., https://doi.org/10.3133/ofr20201130.","productDescription":"iv, 25 p.","onlineOnly":"Y","ipdsId":"IP-117452","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":380937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1130/coverthb.jpg"},{"id":380938,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1130/ofr20201130.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1130"}],"country":"United States","state":"Oregon","otherGeospatial":"Siuslaw National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.14001464843749,\n 43.691707903073805\n ],\n [\n -123.42041015624999,\n 43.691707903073805\n ],\n [\n -123.42041015624999,\n 44.66865287227321\n ],\n [\n -124.14001464843749,\n 44.66865287227321\n ],\n [\n -124.14001464843749,\n 43.691707903073805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Mercury is an environmentally ubiquitous neurotoxin, and its methylated form presents health risks to humans and other biota, primarily through dietary intake. Because methylmercury bioaccumulates and biomagnifies in living tissue, concentrations progressively increase at higher trophic positions in ecosystem food webs. Therefore, the greatest health risks are for organisms at the highest trophic positions and for humans who consume organisms such as fish from these high trophic positions. Data on environmental mercury concentrations in various media and biota provide a basis for comparison among sites and regions and for evaluating ecosystem health risks. The U.S. Geological Survey, in cooperation with the Natural Resources Department, Bad River Band of Lake Superior Chippewa, have compiled a dataset from analyses of mercury concentrations in surface water, bed sediment, fish tissue, Rana clamitans (green frog) tissue, Haliaeetus leucocephalus (bald eagle) feathers, Lontra canadensis (North American river otter) hair, Zizania palustris (northern wild rice), and litterfall from samples collected in the Bad River watershed, Wisconsin during 2004–18. These data originated from either the Natural Resources Department or another agency based on samples collected within or near to Bad River Tribal lands before transfer to the U.S. Geological Survey for compilation and analysis at the onset of the project. This report describes the compiled mercury dataset, provides comparisons to similar measurements in the region and elsewhere, and evaluates health risks to humans and to the sampled biota. Except for litterfall, data were not collected on a consistent, regular basis over a sufficient period to evaluate temporal patterns. The reported mercury concentrations are generally similar to those reported elsewhere in the upper Great Lakes region. Reported values are consistent with atmospheric deposition as the principal source and reflect a favorable environment for mercury methylation. Fish mercury concentrations increased at higher food web positions and generally increased with length in most species measured. Sander vitreus (walleye) present the greatest risk to humans among fishes considered here because of their high trophic position and associated elevated mercury concentrations in combination with relatively high walleye consumption rates by the Native American community. Methylmercury concentrations in wild rice are generally low and likely pose little health risk. Despite reports of declining atmospheric mercury deposition across eastern North America during the past decade, a downward trend in litterfall mercury deposition was not evident in samples collected during 2012–18. Limitations in this data compilation and analysis were noted due to missing information such as collection dates and site locations for some samples. Regular monitoring of mercury in litterfall and surface waters along with periodic collection of fish would enable evaluation of temporal change in the mercury cycle that might affect future risk to humans and aquatic ecosystem inhabitants.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201095","collaboration":"Prepared in cooperation with the Natural Resources Department, Bad River Band of Lake Superior Chippewa","usgsCitation":"Burns, D.A., 2020, Compilation of mercury data and associated risk to human and ecosystem health, Bad River Band of Lake Superior Chippewa, Wisconsin (ver 1.1, December 2020): U.S. Geological Survey Open-File Report 2020–1095, 19 p., https://doi.org/10.3133/ofr20201095.","productDescription":"Report: vii, 19 p.; Database; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-110861","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":377882,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- USGS water data for the Nation"},{"id":377880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1095/ofr20201095.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1095"},{"id":377879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1095/coverthb2.jpg"},{"id":377881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HRS2C3","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Mercury data from the Bad River Watershed, Wisconsin, 2004–2018"},{"id":380931,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1095/versionHist.txt","size":"448 B","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bad River Tribal Lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.48751831054686,\n 46.54658317951774\n ],\n [\n -90.4779052734375,\n 46.57868671298067\n ],\n [\n -90.51223754882812,\n 46.599449464868584\n ],\n [\n -90.59600830078125,\n 46.63057868059483\n ],\n [\n -90.69488525390625,\n 46.69184147024343\n ],\n [\n -90.78140258789062,\n 46.71632714994794\n ],\n [\n -90.7855224609375,\n 46.66734468444288\n ],\n [\n -90.83221435546875,\n 46.62020426357956\n ],\n [\n -90.8294677734375,\n 46.57774276255591\n ],\n [\n -90.83770751953125,\n 46.39619977845332\n ],\n [\n -90.55343627929688,\n 46.409457767475764\n ],\n [\n -90.54931640625,\n 46.54280504427768\n ],\n [\n -90.48751831054686,\n 46.54658317951774\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 2020; Version 1.1: December 2020","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The cratered highlands located northwest of Isidis Planitia have been recognized as one of the best preserved Noachian landscapes currently exposed on Mars; the area hosts a record of diverse surface processes, diagenesis, and aqueous alteration. This region has consistently been considered a high priority for landed-mission exploration and includes the anticipated landing site of the Mars 2020 Perseverance rover within Jezero crater. Past mapping, focused on Jezero crater and the surrounding area, Nili Planum, has varied in spatial extent, map scale, and purpose, though no previous maps have provided a continuous, high-resolution geologic map at uniform scale connecting the two locations. This map represents the first, large-scale, continuous geologic map spanning both Jezero crater and Nili Planum that is based on high-resolution images.
The map area contains the majority of both Jezero crater and Nili Planum at a publication map scale of 1:75,000, which was chosen to encompass the Jezero and southern Nili Planum landing sites under consideration for the Mars 2020 mission at the time of project initiation. This map covers an area that is exactly 1° by 1° (~60 by 60 km), spanning lat 76.8° N. to long 77.8° E. and lat 17.7° to long 18.7° N. The primary base map used for this geologic map is composed of Mars Reconnaissance Orbiter’s Context Camera (CTX) images, compiled into a 6 meter per pixel (m/pixel) mosaic. A nighttime Thermal Emission Imaging System 100 m/pixel image mosaic, digital terrain models constructed from CTX images, High-Resolution Stereo Camera (HRSC) topographic data, and High Resolution Imaging Science Experiment (HiRise) images also aided in unit identification and the assessment of stratigraphic relations. We defined map units on the basis of various characteristics visible in the CTX data at map scale, such as their texture, tone, morphology, marginal characteristics, geographic location, and stratigraphic relations to other units. Some units occur solely within Jezero crater, while Nili Planum contains a sequence of units that are present across the broader northwest Isidis Planitia region. Other units occur in both Jezero crater and Nili Planum, including bedrock, aeolian, and crater units. This map publication provides a regional geologic framework that connects the geologic units across Jezero crater and Nili Planum and the history they imply, facilitates future local-scale observations by landed missions of the Jezero crater and Nili Planum region, and enables the extrapolation of units that have been defined primarily by mineralogic composition to areas where there is no existing orbital spectroscopic data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3464","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Sun, V.Z., and Stack, K.M., 2020, Geologic map of Jezero crater and the Nili Planum region, Mars: U.S. Geological Survey Scientific Investigations Map 3464, pamphlet 14 p., 1 sheet, scale 1:75,000, https://doi.org/10.3133/sim3464.","productDescription":"Pamphlet: iv, 14 p.; 1 Map: 56.60 x 45.62 inches; Metadata; Database; Read Me","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-118085","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436704,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3464 Geologic Map of Jezero Crater and the Nili Planum Region"},{"id":380236,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_pamphlet.pdf","text":"Pamphlet","size":"728 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464 Pamphlet"},{"id":380235,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3464/sim3464.pdf","text":"Map","size":"36.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464"},{"id":380240,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_database.zip","size":"349.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3464 Database"},{"id":380239,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_readme.txt","size":"4 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Readme txt"},{"id":380238,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.xml","size":"21 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3464 Metadata xml"},{"id":380237,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.txt","size":"21 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Metadata txt"},{"id":380234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3464/coverthb.jpg"},{"id":400813,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"Interactive map","linkHelpText":"- Geologic Map of Jezero Crater and the Nili Planum Region, Mars, 1:75,000. Sun and Stack (2020)"}],"contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The purpose of this study is to characterize streamflow availability under natural (unregulated) low-flow conditions for streams in southeast Kaua‘i, Hawai‘i. The nine main study-area basins, from north to south, include Wailua River, Hanamā‘ulu, Nāwiliwili, Pūʻali, Hulēʻia, Waikomo, Lāwaʻi, and Wahiawa Streams, and Hanapēpē River. The results of this study can be used by water managers to develop technically sound instream-flow standards for the study-area streams.
Low-flow characteristics for natural streamflow conditions were represented by flow-duration discharges that are equaled or exceeded between 95 and 50 percent of the time. Short-term continuous-record stream-gaging stations that monitored low flows on Waiahi and right branch Lāwaʻi Streams were established to serve as potential index stations for partial-record sites in the study area. Continuous-record stream-gaging station on Hanapēpē River monitored natural flow during calendar year 2017 and the streamflow record during that period was used to estimate low-flow characteristics at the station. Partial-record sites were established on 3 main streams and 15 tributary streams, upstream from existing surface-water diversions. Low-flow characteristics were determined using historical and current streamflow data from continuous-record stream-gaging stations and miscellaneous sites, as well as additional data collected as part of this study. Low-flow-duration discharges for the following streams were estimated for the 59-year base period (water years 1961–2019) using two record-augmentation techniques: right branch ʻŌpaekaʻa Stream, North Fork Wailua River, north and south fork Waikoko Streams, ‘Ili‘ili‘ula Stream, north and south fork Hanamāʻulu Streams, Kamo‘oloa Stream, Pāohia Stream, Ku‘ia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River. The 95-percent flow-duration discharges (Q95) ranged from 0.018 to 42 cubic feet per second (ft3/s). The 50-percent flow-duration discharges (Q50) ranged from 1.1 to 69 ft3/s. Upper-bound estimates of low-flow duration discharges at partial-record sites on south fork Hanamāʻulu, Hanamāʻulu tributary, ʻŌmaʻo, and Pōʻeleʻele Streams were estimated based on the highest discharges measured as part of this study during Q95 to Q50 flow conditions, which were 0.44, 0.40, 0.19, and 0.22 ft3/s, respectively. Measured discharges on Nāwiliwili, Pū‘ali, and left branch Wahiawa Streams do not correlate with data at any active long-term continuous-record stream-gaging stations (10 or more complete water years of natural-flow record) and therefore low-flow duration discharges could not be estimated.
This study also estimated streamflow gains and losses using seepage-run discharge measurements in eight of the nine study basins (Pūʻali Stream basin was excluded). A majority of the streams gained flow downstream from the uppermost diversions. Measured seepage-gain rates ranged between 0.03 and 24.3 ft3/s per mile of stream reach. Seepage gains are presumed to originate mainly from groundwater discharge in the Wailua River, Hanamā‘ulu Stream, Nāwiliwili Stream, Hulēʻia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River basins. Under natural-flow conditions and flow conditions of the seepage runs, a majority of the study-area streams flow continuously from the mountains to the ocean. Where a stream discharges into a reservoir––Hanamā‘ulu and Wahiawa Streams––a dry reach may occur immediately downstream from the reservoir to the point of seepage gain in the stream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205128","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Cheng, C.L., 2020, Low-flow characteristics of streams from Wailua to Hanapēpē, Kauaʻi, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2020–5128, 57 p., https://doi.org/10.3133/sir20205128.","productDescription":"viii, 57 p.","onlineOnly":"Y","ipdsId":"IP-119175","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":380936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5128/sir20205128.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5128"},{"id":380935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5128/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.35806274414062,\n 22.02072633149476\n ],\n [\n -159.43496704101562,\n 22.051277605102463\n ],\n [\n -159.554443359375,\n 22.071641456092383\n ],\n [\n -159.6258544921875,\n 22.03345683012737\n ],\n [\n -159.6533203125,\n 21.963424936844223\n ],\n [\n -159.65744018554688,\n 21.923937190109623\n ],\n [\n -159.59838867187497,\n 21.872969071537096\n ],\n [\n -159.43222045898438,\n 21.857675083878423\n ],\n [\n -159.33334350585935,\n 21.930306923001126\n ],\n [\n -159.31823730468747,\n 21.97106645968614\n ],\n [\n -159.33059692382812,\n 22.01945321869661\n ],\n [\n -159.35806274414062,\n 22.02072633149476\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pacific Islands Water Science Center
Inouye Regional Center
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Water samples from 122 sites in the U.S. Geological Survey National Water Quality Network were collected in 2013–17 to document ambient water-quality conditions in surface water of the United States and to determine status and trends of loads and concentrations for nutrients, contaminants, and sediment to estuaries and streams. Quality-control (QC) samples collected in the field with environmental samples were combined with QC samples from laboratory processing to provide information and documentation about the quality of the environmental data.
Quality assurance for inorganic and organic compounds assessed in the National Water Quality Network includes collection of field blanks to determine contamination bias and field replicates to determine variability bias. No contamination bias was found for 6 of the 13 nutrient compounds analyzed, and some potential contamination bias for some years was found for the other 7 nutrient compounds. Contamination bias was not found for carbon compounds or ultraviolet-absorbance measurements and was not assessed for sediment. All major ions and trace elements except potassium and lithium showed moderate contamination bias for at least 1 water year; generally, this bias was not at environmentally relevant concentrations. All compounds in the nutrient, carbon, and sediment group and in the major ions and trace elements group had low variability both in detection frequency and in concentration. Exceptions to this low variability were total particulate inorganic carbon and sediment for 2015, both of which are particulate substances with intrinsically high sampling variability.
The risk of contamination bias for pesticides in National Water Quality Network samples was low, as indicated by very few detections in field blanks. Sixteen pesticide compounds showed potential contamination bias based on unexpected detections in third-party blind spikes (false-positive results for compounds that are not included in the spike mixture of a sample, where the identity as a QC sample is unknown to the analyst), and 47 different compounds (out of 225 pesticide compounds) showed potential contamination bias from laboratory blanks. However, when timing and relative magnitudes of detections in blank samples, environmental samples, and benchmark concentrations are considered, most of this potential contamination is not relevant to interpretation of published pesticide results. Overall variability in detection frequency for pesticides from field replicates was low or moderate. Also based on field replicates, 55 pesticides had overall high variability in concentrations for at least 1 water year, although these assessments likely overestimate high variability.
At least 1 QC issue was found for 87 pesticides; however, most of the QC issues had no or little effect on the interpretation of environmental results because the U.S. Geological Survey National Water Quality Laboratory addressed the QC issue before publishing the environmental results, environmental results were almost entirely nondetections, concentrations of environmental results were higher than potential contamination bias, or benchmark concentrations were orders of magnitude higher than all environmental results. Eight compounds affected by two QC issues had a benchmark less than 100 nanograms per liter and warranted careful consideration of timing and magnitude of QC results in relation to surface-water results before interpretive use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205116","usgsCitation":"Medalie, L., and Bexfield, L.M., 2020, Quality of data from the U.S. Geological Survey National Water Quality Network for water years 2013–17: U.S. Geological Survey Scientific Investigations Report 2020–5116, 21 p., https://doi.org/10.3133/sir20205116.","productDescription":"Report: v, 21 p.; Data Releases; 9 Tables","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-115536","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":436706,"rank":17,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94F31R8","text":"USGS data release","linkHelpText":"Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
This report describes considerations for incorporating routine quality-assessment and quality-control evaluations into U.S. Geological Survey discrete water-sampling programs and projects. U.S. Geological Survey water-data science in 2020 is characterized by robustness, external reproducibility, collaborative large-volume data analysis, and efficient delivery of water-quality data. Confidence in data, or robustness, can be increased by supplementing traditional field-based quality-control data with laboratory quality control (QC) data, such as third-party blind spikes and blind blanks, laboratory blanks, and laboratory-reagent spikes. Laboratory quality-control data can provide additional information about bias and variability, method performance, and false-positive and false-negative rates that are not available from field QC data alone. Reproducibility is supported by means of standardizing metadata and documentation. Collaborative analysis brings together disparate elements of various types of quality-control review and communicates persistent data quality issues for compounds to data users internal and external to the U.S. Geological Survey. Efficient delivery of water-quality data is achieved when quality-control review is accomplished in the same expedited (near real-time) time frame as distribution of environmental results to the public and might be improved with consideration given to data versioning or to a system of alerting data users to data interpretation that might differ from originally published data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201109","usgsCitation":"Medalie, L., 2020, Considerations for incorporating quality control into water quality sampling strategies for the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2020–1109, 5 p., https://doi.org/10.3133/ofr20201109.","productDescription":"iii, 5 p.","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120022","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":380650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1109/coverthb.jpg"},{"id":380651,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1109/ofr20201109.pdf","text":"Report","size":"935 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1109"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.