Population estimates derived from monitoring efforts can be sensitive to the survey method selected, potentially leading to biased estimates and low precision relative to true population size. While small unmanned aerial systems (UAS) present a unique opportunity to survey avian populations while limiting disturbance, relatively little is known about how this method compares with more traditional approaches. In this study we compared population estimates of Snowy (Egretta thula) and Cattle Egrets (Bubulcus ibis) in a mixed-species colony in the Chesapeake Bay (Maryland, USA) derived from UAS photo counts, flush counts, flight-line surveys, and in-colony nest counts along with the time required to derive an estimate via each approach. We found that UAS counts and flush counts produced lower pair estimates than nest counts and flight-line surveys (P < 0.05), and required dramatically less time (x̄ = 6, 8, 84 and 90 min, respectively). These results suggest that while UAS have the potential to collect valuable survey data from breeding colonies that are hard to reach or are especially sensitive to the disturbance inherent in other methods, inherent biases should be considered and caution should be used when comparing results between survey types.
Measuring river response to dam removal affords a rare, important opportunity to study fluvial response to sediment pulses on a large field scale. We present a before–after/control–impact study of the Carmel River, California, measuring fluvial geomorphic and grain-size evolution over 8 years, six of which postdated removal of a 32 m-high dam (one of the largest dams removed worldwide) and included 11 flow events exceeding the 2-year flood magnitude. We find that the reservoir-sediment pulse following dam removal was relatively small (97 000 ± 24 000 t over 4 years), owing to deliberate reservoir-sediment stabilization. Scaled to the size of the Carmel River watershed and compared against long-term bedrock denudation rates, the post-dam-removal sediment release was slightly less than the annualized long-term sediment export from this basin. New sediment transited >30 km to the river mouth in less than 2 years, assisted by floods 2 and 4 years after dam removal. The sediment pulse fined the downstream riverbed while causing mostly low-magnitude bed-elevation changes: commonly 0.5 to 1 m or smaller, occurring as discontinuous sediment patches or interstitial deposits, aside from the filling and subsequent partial scour of deep pools. There was no major geomorphic reset downstream from the dam site. Geomorphic changes were driven almost entirely by flow rather than by the modest increase in sediment supply, in contrast to recent examples from other large dam removals. The relatively minor disturbance caused by dam removal on the Carmel River is likely analogous to many future dam removals: a relatively small sediment pulse after deliberate limitation of reservoir-sediment erosion, and with an upstream dam remaining in place. Thus, a large dam removal need not lead to major downstream impacts.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5561","usgsCitation":"East, A.E., Harrison, L.R., Smith, D.P., Logan, J.B., and Bond, R., 2023, Six years of fluvial response to a large dam removal on the Carmel River, California, USA: Earth Surface Processes and Landforms, v. 48, no. 8, p. 1487-1501, https://doi.org/10.1002/esp.5561.","productDescription":"15 p.","startPage":"1487","endPage":"1501","ipdsId":"IP-146182","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":444561,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5561","text":"Publisher Index Page"},{"id":415160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Carmel River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62577522165773,\n 36.36105374788829\n ],\n [\n -121.58724434610056,\n 36.40905968854021\n ],\n [\n -121.61843600726598,\n 36.46293870830412\n ],\n [\n -121.750541866319,\n 36.53594773620604\n ],\n [\n -121.8532908678046,\n 36.61551540625254\n ],\n [\n -121.90741757394426,\n 36.588265345279865\n ],\n [\n -121.93261880515863,\n 36.54806169091083\n ],\n [\n -121.88413981443409,\n 36.520771377870034\n ],\n [\n -121.79850327393265,\n 36.50047233869989\n ],\n [\n -121.7839886060512,\n 36.469663631874326\n ],\n [\n -121.74712134963194,\n 36.40894399256348\n ],\n [\n -121.68209557572075,\n 36.36804816657221\n ],\n [\n -121.62606895769773,\n 36.3610353099688\n ],\n [\n -121.62577522165773,\n 36.36105374788829\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":868544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":868545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Douglas P.","contributorId":201716,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas","email":"","middleInitial":"P.","affiliations":[{"id":35924,"text":"California State University, Monterey Bay","active":true,"usgs":false}],"preferred":false,"id":868546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Logan, Joshua B. 0000-0002-6191-4119 jlogan@usgs.gov","orcid":"https://orcid.org/0000-0002-6191-4119","contributorId":2335,"corporation":false,"usgs":true,"family":"Logan","given":"Joshua","email":"jlogan@usgs.gov","middleInitial":"B.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":868547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bond, Rosealea","contributorId":201717,"corporation":false,"usgs":false,"family":"Bond","given":"Rosealea","affiliations":[{"id":12520,"text":"NOAA National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":868548,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240647,"text":"70240647 - 2023 - Hawai‘i residents’ perceptions of Kīlauea’s 2018 eruption information","interactions":[],"lastModifiedDate":"2023-02-10T13:19:04.275323","indexId":"70240647","displayToPublicDate":"2023-02-05T07:17:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7593,"text":"Volcanica","active":true,"publicationSubtype":{"id":10}},"title":"Hawai‘i residents’ perceptions of Kīlauea’s 2018 eruption information","docAbstract":"The 2018 eruption of Kīlauea Volcano was notable for its variety of large and spatially distinct hazards, simultaneously affecting three geographically disparate, culturally diverse regions in Hawaiʻi. We conducted a pilot study, consisting of 18 semi-structured interviews, two survey responses, and several informal conversations with Hawaiʻi residents to learn which sources/messengers of eruption information were deemed most trusted and credible. Participants' perceptions of the U.S. Geological Survey Hawaiian Volcano Observatory (HVO), community-based messengers, and traditional news media can be examined across four themes: relevance, expertise, sincerity, and pace. Among our interview participants, Lower East Rift Zone (LERZ) residents placed the highest trust in their community messengers, summit residents deemed HVO most trustworthy, and Kaʻū residents trusted information from both HVO and local news media. Our findings suggest that future official eruption communications would benefit from 1) designating communications personnel to act as community liaisons and 2) increasing pace and relevance of information delivery.
A pilot study among farming households in eastern Iowa was conducted to assess human exposure to neonicotinoids (NEOs). The study was in a region with intense crop and livestock production and where groundwater is vulnerable to surface-applied contaminants. In addition to paired outdoor (hydrant) water and indoor (tap) water samples from private wells, urine samples were collected from 47 adult male pesticide applicators along with the completions of dietary and occupational surveys. Estimated Daily Intake (EDI) were then calculated to examine exposures for different aged family members. NEOs were detected in 53% of outdoor and 55% of indoor samples, with two or more NEOs in 13% of samples. Clothianidin was the most frequently detected NEO in water samples. Human exposure was ubiquitous in urine samples. A median of 10 different NEOs and/or metabolites were detected in urine, with clothianidin, nitenpyram, thiamethoxam, 6-chloronicotinic acid, and thiacloprid amide detected in every urine samples analyzed. Dinotefuran, imidaclothiz, acetamiprid-N-desmethyl, and N-desmethyl thiamethoxam were found in ≥70% of urine samples. Observed water intake for study participants and EDIs were below the chronic reference doses (CRfD) and acceptable daily intake (ADI) standards for all NEOs indicating minimal risk from ingestion of tap water. The study results indicate that while the consumption of private well tap water provides a human exposure pathway, the companion urine results provide evidence that diet and/or other exposure pathways (e.g., occupational, house dust) may contribute to exposure more than water contamination. Further biomonitoring research is needed to better understand the scale of human exposure from different sources.
The 11 October 1918 devastating tsunami in northwest Puerto Rico had been used as an example for earthquake‐induced landslide tsunami hazard. Three pieces of evidence pointed to a landslide as the origin of the tsunami: the discovery of a large submarine landslide scar from bathymetry data collected by shipboard high‐resolution multibeam sonar, reported breaks of submarine cable within the scar, and the fit of tsunami models to flooding observations. Newly processed seafloor imagery collected by remotely operated vehicle (ROV) show, however, pervasive Fe–Mn crust (patina) on the landslide walls and floor, indicating that the landslide scar is at least several hundred years old.
Salt marshes with lush grass meadows teeming with shorebirds are iconic features of the Buzzards Bay coast and provide opportunities for recreation, aesthetic enjoyment, as well as important environmental benefits. These productive coastal wetlands are important because they protect properties from storm surges, remove nutrients from the water and carbon from the atmosphere, and provide critical habitats for fish, shellfish, and birds.
Found where the land meets the sea, salt marshes are naturally dynamic features that change with rising seas, waves, ice, and storms. In the past, humans purposely altered salt marshes by filling them to create buildable land or digging drainage ditches. These major alterations harmed marsh structure and health. In recent decades, however, marshes are degrading because of more diffuse and complex pressures such as nutrient pollution, sea level rise, major storms, and crab overgrazing. As a result, at many places along the East Coast, marshes have crumbling banks and large areas where the plants have died, leaving behind mudflats. The Buzzards Bay Coalition and the Buzzards Bay National Estuary Program began field monitoring of salt marshes around Buzzards Bay in 2019 to document changes (map below shows sites). We partnered with the U.S. Geological Survey and the Woodwell Climate Research Center to use aerial tools to investigate how different characteristics of the long-term marsh sites and their watersheds affect the marsh’s current health and likely future. This report brings together the results of on the ground monitoring with data from aerial imagery to look at marsh status at 12 long-term monitoring sites based on existing stressors, current marsh conditions, and potential for adaptation.
","language":"English","publisher":"Buzzards Bay Coalition","usgsCitation":"Jakuba, R.W., Besterman, A., Hoffart, L., Costa, J.E., Ganju, N., and Deegan, L., 2023, Buzzards Bay salt marshes: Vulnerability and adaptation potential, 32 p.","productDescription":"32 p.","ipdsId":"IP-136385","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416865,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.savebuzzardsbay.org/about-us/publications/special-reports/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Massachusetts","otherGeospatial":"Buzzards Bay salt marshes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.94176987700817,\n 41.41975469591827\n ],\n [\n -70.85599792880063,\n 41.42970016011691\n ],\n [\n -70.81532257191841,\n 41.450912058997574\n ],\n [\n -70.76934173370435,\n 41.472117024369965\n ],\n [\n -70.67738005727534,\n 41.509209028100685\n ],\n [\n -70.64554716928006,\n 41.53502841303356\n ],\n [\n -70.60045057795479,\n 41.73329455348431\n ],\n [\n -70.60045103343215,\n 41.77287411335024\n ],\n [\n -70.64466337786867,\n 41.76891720855332\n ],\n [\n -70.73043532607704,\n 41.76034307750683\n ],\n [\n -70.76757369540417,\n 41.73395394949611\n ],\n [\n -70.78525863317893,\n 41.66463088626202\n ],\n [\n -70.83742919961446,\n 41.66330972174504\n ],\n [\n -70.92231690093321,\n 41.6672731339705\n ],\n [\n -70.97448746736875,\n 41.6051516478891\n ],\n [\n -71.02931077447062,\n 41.53238027247775\n ],\n [\n -71.03638474958088,\n 41.50987071949933\n ],\n [\n -70.94176987700817,\n 41.41975469591827\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jakuba, R. W","contributorId":304709,"corporation":false,"usgs":false,"family":"Jakuba","given":"R.","email":"","middleInitial":"W","affiliations":[{"id":66149,"text":"Buzzards Bay Coalition","active":true,"usgs":false}],"preferred":false,"id":871466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Besterman, A.","contributorId":304711,"corporation":false,"usgs":false,"family":"Besterman","given":"A.","email":"","affiliations":[{"id":66149,"text":"Buzzards Bay Coalition","active":true,"usgs":false}],"preferred":false,"id":871468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoffart, L.","contributorId":304710,"corporation":false,"usgs":false,"family":"Hoffart","given":"L.","email":"","affiliations":[{"id":66149,"text":"Buzzards Bay Coalition","active":true,"usgs":false}],"preferred":false,"id":871467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Costa, J. E.","contributorId":304712,"corporation":false,"usgs":false,"family":"Costa","given":"J.","email":"","middleInitial":"E.","affiliations":[{"id":66149,"text":"Buzzards Bay Coalition","active":true,"usgs":false}],"preferred":false,"id":871469,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":871470,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deegan, L.","contributorId":304976,"corporation":false,"usgs":false,"family":"Deegan","given":"L.","email":"","affiliations":[],"preferred":false,"id":872135,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70245772,"text":"70245772 - 2023 - A recently discovered trachyte-hosted rare earth element-niobium-zirconium occurrence in northern Maine, USA","interactions":[],"lastModifiedDate":"2023-06-27T11:42:05.565969","indexId":"70245772","displayToPublicDate":"2023-02-01T06:40:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"A recently discovered trachyte-hosted rare earth element-niobium-zirconium occurrence in northern Maine, USA","docAbstract":"Reported here are geological, geophysical, mineralogical, and geochemical data on a previously unknown trachyte-hosted rare earth element (REE)-Nb-Zr occurrence at Pennington Mountain in northern Maine, USA. This occurrence was newly discovered by a regional multiparameter, airborne radiometric survey that revealed anomalously high equivalent Th (eTh) and U (eU), confirmed by a detailed ground radiometric survey and by portable X-Ray fluorescence (pXRF) and whole-rock analyses of representative rock samples. The mineralized area occurs within an elongate trachyte body (~1.2 km2) that intrudes Ordovician volcanic rocks. Geologic constraints suggest that the trachyte is also Ordovician in age. The eastern lobe (~900 × ~400 m) of the trachyte is pervasively brecciated with a matrix containing seams, lenses, and veinlets composed mainly of potassium feldspar, albite, and fine-grained zircon and monazite. Barite is locally abundant. Minor minerals within the matrix include columbite, bastnäsite, euxenite, chlorite, pyrite, sphalerite, and magnetite. The pXRF analyses of 22 samples (App. Table A1) collected from the eastern lobe demonstrate that this entire part of the trachyte is highly mineralized. Whole-rock geochemical analyses for samples from the eastern lobe document high average contents of Zr (1.17 wt %), Nb (1,656 ppm), Ba (3,132 ppm), Y (1,140 ppm), Hf (324 ppm), Ta (122 ppm), Th (124 ppm), U (36.5 ppm), Zn (689 ppm), and Sn (106 ppm). Among light REE, the highest average concentrations are shown by La (763 ppm) and Ce (1,479 ppm). For heavy REE (HREE), Dy and Er are the most abundant on average (167 and 114 ppm, respectively). No HREE-rich minerals such as xenotime have been identified; the HREE may reside chiefly in monazite and bastnäsite, and within the fine-grained zircon. Very strong positive correlations (R2) of 0.92 to 0.98 exist between Th and Zr, Nb, Y, Ce, Yb, and Sn, indicating that the radiometric data for eTh are valid proxies for concentrations of these metals in the mineralized rocks.
","language":"English","publisher":"Society for Economic Geologists","doi":"10.5382/econgeo.4993","usgsCitation":"Wang, C., Slack, J.F., Shah, A.K., Yates, M.G., Lentz, D.R., Whittaker, A.T., and Marvinney, R.G., 2023, A recently discovered trachyte-hosted rare earth element-niobium-zirconium occurrence in northern Maine, USA: Economic Geology, v. 118, no. 1, p. 1-13, https://doi.org/10.5382/econgeo.4993.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-143441","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":444655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5382/econgeo.4993","text":"Publisher Index Page"},{"id":418496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.73204287455003,\n 45.7741074745758\n ],\n [\n -67.73204287455003,\n 47.52268519237853\n ],\n [\n -70.36739756743843,\n 47.52268519237853\n ],\n [\n -70.36739756743843,\n 45.7741074745758\n ],\n [\n -67.73204287455003,\n 45.7741074745758\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"118","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Chunzeng 0000-0002-8362-5174","orcid":"https://orcid.org/0000-0002-8362-5174","contributorId":295415,"corporation":false,"usgs":false,"family":"Wang","given":"Chunzeng","email":"","affiliations":[{"id":63866,"text":"University of Maine at Presque-Isle","active":true,"usgs":false}],"preferred":false,"id":876284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":876285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":876286,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yates, Martin G.","contributorId":313571,"corporation":false,"usgs":false,"family":"Yates","given":"Martin","email":"","middleInitial":"G.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":876287,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lentz, David R.","contributorId":313573,"corporation":false,"usgs":false,"family":"Lentz","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":876288,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whittaker, Amber T.H.","contributorId":313574,"corporation":false,"usgs":false,"family":"Whittaker","given":"Amber","email":"","middleInitial":"T.H.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":876289,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marvinney, Robert G.","contributorId":131130,"corporation":false,"usgs":false,"family":"Marvinney","given":"Robert","email":"","middleInitial":"G.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":876290,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247111,"text":"70247111 - 2023 - Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States","interactions":[],"lastModifiedDate":"2023-07-25T15:10:34.1453","indexId":"70247111","displayToPublicDate":"2023-01-30T10:06:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States","docAbstract":"Phytoplankton is the essential primary producer in fresh surface water ecosystems. However, excessive phytoplankton growth due to eutrophication significantly threatens ecologic, economic, and public health. Therefore, phytoplankton identification and quantification are essential to understanding the productivity and health of freshwater ecosystems as well as the impacts of phytoplankton overgrowth (such as Cyanobacterial blooms) on public health. Microscopy is the gold standard for phytoplankton assessment but is time-consuming, has low throughput, and requires rich experience in phytoplankton morphology. Quantitative polymerase chain reaction (qPCR) is accurate and straightforward with high throughput. In addition, qPCR does not require expertise in phytoplankton morphology. Therefore, qPCR can be a useful alternative for molecular identification and enumeration of phytoplankton. Nonetheless, a comprehensive study is missing which evaluates and compares the feasibility of using qPCR and microscopy to assess phytoplankton in fresh water. This study 1) compared the performance of qPCR and microscopy in identifying and quantifying phytoplankton and 2) evaluated qPCR as a molecular tool to assess phytoplankton and indicate eutrophication. We assessed phytoplankton using both qPCR and microscopy in twelve large freshwater rivers across the United States from early summer to late fall in 2017, 2018, and 2019. qPCR- and microscope-based phytoplankton abundance had a significant positive linear correlation (adjusted R2 = 0.836, p-value < 0.001). Phytoplankton abundance had limited temporal variation within each sampling season and over the three years studied. The sampling sites in the midcontinent rivers had higher phytoplankton abundance than those in the eastern and western rivers. For instance, the concentration (geometric mean) of Bacillariophyta, Cyanobacteria, Chlorophyta, and Dinoflagellates at the sampling sites in the midcontinent rivers was approximately three times that at the sampling sites in the western rivers and approximately 18 times that at the sampling sites in the eastern rivers. Welch's analysis of variance indicates that phytoplankton abundance at the sampling sites in the midcontinent rivers was significantly higher than that at the sampling sites in the eastern rivers (p-value = 0.013) but was comparable to that at the sampling sites in the western rivers (p-value = 0.095). The higher phytoplankton abundance at the sampling sites in the midcontinent rivers was presumably because these rivers were more eutrophic. Indeed, low phytoplankton abundance occurred in oligotrophic or low trophic sites, whereas eutrophic sites had greater phytoplankton abundance. This study demonstrates that qPCR-based phytoplankton abundance can be a useful numerical indicator of the trophic conditions and water quality in freshwater rivers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2023.119679","usgsCitation":"Zhang, C., McIntosh, K.D., Sienkiewicz, N., Stelzer, E., Graham, J.L., and Lu, J., 2023, Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States: Water Research, v. 235, 119679, 16 p., https://doi.org/10.1016/j.watres.2023.119679.","productDescription":"119679, 16 p.","ipdsId":"IP-147638","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":444674,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10123349","text":"External Repository"},{"id":419308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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of East Greenland (EG) and Southern Beaufort Sea (SB) polar bears Ursus maritimus sampled in the spring of 2015-2019. Prey DNA was detected in half (49/92) of all samples, and when detected, ringed seal Pusa hispida was the predominant prey species, identified in 100% (22/22) of EG and 81% (22/27) of SB polar bear samples with prey DNA detected. Bearded seal Erignathus barbatus DNA was found in 19% (5/27) of SB polar bear samples for which prey DNA was detected. Prey DNA detection frequencies and relative abundances were compared to estimates from quantitative fatty acid signature analysis (QFASA) for a subset of SB polar bears. Ringed seal and bearded seal were the main prey identified by both methods, but QFASA also identified 2 cetacean prey species not found by prey DNA. Differences in DNA metabarcoding vs. QFASA results were likely related to the different dietary timescales captured by each approach, i.e. short-term vs. long-term diet, respectively. Prey DNA detection, sex/age class, and subpopulation significantly explained variation in polar bear gut bacterial composition. Polar bear samples with prey DNA detected were associated with higher abundances of the bacterial classes Clostridia and Bacilli and lower abundances of Negativicutes. Fecal DNA metabarcoding is thus useful for identifying recent prey of polar bears, complementing quantitative and likely longer-term QFASA estimates, and may help understand variation in the polar bear gut microbiome.
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critical element, and co-substituent trace elements in ZnS and mine wastes from four mineral districts where germanium is, or has been, produced within the United States. This contribution establishes a comprehensive workflow for characterizing Ge and other trace elements, which captures the full heterogeneity of samples through extensive pre-characterization. This process proceeded from optical microscopy, to scanning electron microscopy and cathodoluminescence (CL) imaging, to electron microprobe analysis, prior to synchrotron-based investigations. Utilizing non-destructive techniques enabled reanalysis, which proved essential for verifying observations and validating unexpected results. In cases where the Fe content was <0.3 wt% in ZnS, cathodoluminescence imaging proved to be an efficient means to qualitatively identify trace element zonation that could then be further explored by other micro-focused techniques. Micro-focused X-ray diffraction was used to map the distribution of the non-cubic ZnS polymorph, whereas micro-focused X-ray fluorescence (μ-XRF) phase mapping distinguished between Ge4+ hosted in primary ZnS and a weathering product, hemimorphite [Zn4Si2O7(OH)2·H2O]. Microprobe data and μ-XRF maps identified spatial relationships among trace elements in ZnS and implied substitutional mechanisms, which were further explored using Ge and copper (Cu) X-ray absorption near-edge spectroscopy (XANES). Both oxidation states of Ge (4+ and 2+) were identified in ZnS along with, almost exclusively, monovalent Cu. However, the relative abundance of Ge oxidation states varied among mineral districts and, sometimes, within samples. Further, bulk XANES measurements typically agreed with micro-focused XANES (μ-XANES) spectra, but unique micro-environments were detected, highlighting the importance of complementary bulk and micro-focused measurements. Some Ge μ-XANES utilized a high energy resolution fluorescence detector, which improved spectral resolution and spectral signal-to-noise ratio. This detector opens new opportunities for exploring byproduct critical elements in complex matrices. Overall, the non-destructive workflow employed here can be extended to other byproduct critical elements to more fully understand fundamental ore enrichment processes, which have practical implications for critical element exploration, resource quantification, and extraction.
The invasion of nonnative grasses threatens biodiversity and ecosystem function globally through competition with native plant species and increases to wildfire frequency and intensity. Management actions to reduce buffelgrass [Pennisetum ciliare (L.) Link], an invasive warm-season perennial bunchgrass, are widely implemented, with chemical and mechanical treatments extending over two decades within Saguaro National Park in the Sonoran Desert of North America. We assessed how the effectiveness of treatments to reduce P. ciliare cover spanning from 2011 to 2020 were influenced by stage of invasion, treatment type and intensity, and environmental conditions. An increase in treatment effectiveness was largely explained by high initial cover of P. ciliare, an indicator of a late invasion stage and associated with high treatment intensity. Treatments had potential to be effective in patches as small as 0.3-m2 P. ciliare canopy per 400-m−2 area (<0.001% canopy cover) across treatment types and environmental gradients. Chemical treatments had higher or equal effectiveness compared with mechanical treatments, and greater reductions in P. ciliare were associated with shorter average years of treatment interruptions, or gaps, and to a lesser degree, total years of treatment. In many cases, P. ciliare was reduced with as little as 2 yr of treatment, but more than 3 average years of treatment gap could result in reduced treatment effectiveness. There was generally higher treatment effectiveness on shallow slopes, north- and east-facing aspects, and on higher elevations within one district of the park. Our findings highlight that resource-intensive treatments in all but the smallest patches of P. ciliare have largely been effective. Further opportunities for improvement include more frequent surveillance, limiting treatment gaps to ≤3 yr in areas of low P. ciliare cover, and comparison of treated with untreated areas.
Adaptive efforts to achieve water quality objectives by modifying nutrient loading can have attendant impacts on fish habitats and fisheries. Thus, coordinating fishery and water quality management depends on knowledge of fish behavioral responses to habitat change. This study combined acoustic telemetry of fish with water quality modeling to understand how water quality management might impact fishery management. We examined habitat use of a native demersal fish, lake whitefish Coregonus clupeaformis, in Lake Erie. We focused on the summer stratified period when habitat was expected to be most limiting and used a forecast model to predict temperature and oxygen in the hypolimnion when fish were detected. As hypothesized, lake whitefish occupied a subset of available conditions with occupied habitats characterized by a cool, normoxic, hypolimnion. On some occasions fish were detected when the hypolimnion was predicted to be hypoxic, suggesting that fish were either displaced vertically or horizontally into marginal habitats or uncertainty in model predictions was high. Still, when hypolimnetic conditions were hypoxic, fish tended to move toward normoxia as expected, but when initial conditions were cold with high dissolved oxygen, fish movements were toward lower oxygen (but still normoxic) conditions. We also observed a high affinity for fish to remain near the southern shore in eastern Ohio, Pennsylvania, and New York. If current nutrient reduction objectives are achieved and the extent and severity of hypoxia is reduced, an expansion of lake whitefish habitat and distribution may have significance to the spatial regulation of fishing effort in Lake Erie.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.01.004","usgsCitation":"Kraus, R., Cook, H., Faust, M., Schmitt, J., Rowe, M., and Vandergoot, C., 2023, Habitat selection of a migratory freshwater fish in response to seasonal hypoxia as revealed by acoustic telemetry: Journal of Great Lakes Research, v. 49, no. 5, p. 1004-1014, https://doi.org/10.1016/j.jglr.2023.01.004.","productDescription":"11 p.","startPage":"1004","endPage":"1014","ipdsId":"IP-144704","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, New York, Ohio, Pennsylvania","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.45746247368997,\n 42.19332204270543\n ],\n [\n -83.58088952321158,\n 41.37751990998936\n ],\n [\n -81.36793483137687,\n 41.36610477953545\n ],\n [\n -79.12723694634781,\n 42.41574902379864\n ],\n [\n -78.7500162167698,\n 43.007471194229566\n ],\n [\n -81.13424345938826,\n 42.76252432461877\n ],\n [\n -82.2940150129951,\n 42.35073159163453\n ],\n [\n -83.45746247368997,\n 42.19332204270543\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, H. Andrew","contributorId":354648,"corporation":false,"usgs":false,"family":"Cook","given":"H. Andrew","affiliations":[{"id":65742,"text":"Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":936070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faust, Matthew D.","contributorId":354649,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew D.","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowe, Mark D.","contributorId":354650,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark D.","affiliations":[{"id":34438,"text":"NOAA-GLERL","active":true,"usgs":false}],"preferred":false,"id":936073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vandergoot, Christopher S.","contributorId":354651,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936074,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239747,"text":"sir20225107 - 2023 - Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021","interactions":[],"lastModifiedDate":"2026-02-23T19:32:52.536466","indexId":"sir20225107","displayToPublicDate":"2023-01-20T10:30:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5107","displayTitle":"Using Global Fiducials Library High-Resolution Imagery, Commercial Satellite Imagery, Landsat and Sentinel Satellite Imagery, and Aerial Photography to Monitor Change at East Timbalier Island, Louisiana, 1953–2021","title":"Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021","docAbstract":"This report documents morphological changes between 1953 and 2021 at East Timbalier Island, Louisiana, a Gulf of Mexico barrier island. East Timbalier Island, which was located west of the Mississippi River Delta at the front of Timbalier Bay, was one of the most rapidly changing barrier islands on Earth. Since aerial photographs were initially taken in 1953, the Island steadily lost length and area, finally eroding away by early summer 2021. After major storm events, sediment eroded from the Island and migrated hundreds of meters north. In August 1992, Hurricane Andrew breached the Island in several places, resulting in increased erosion and land loss. Until it completely eroded away, the Island underwent a cycle of washovers, vegetation removal, breaching, and erosion with sediment transport to the north. Satellite imagery shows that three such cycles occurred between 1992 and 2017, despite the partial restoration of the Island between 1998 and 2000. Each cycle increased the distance between the Island and the mainland to the east, reducing both the sediment supply from the east and the protection that Timbalier Bay and the adjacent coastal lands received from the barrier island.\n\nPreviously, the U.S. Geological Survey (USGS) National Civil Applications Center used 1-meter resolution imagery archived at the USGS Global Fiducials Library (GFL), collected between 2000 and 2010 by U.S. National Imaging Systems, to monitor the changes at the Island. New research expands this study retrospectively and prospectively using aerial photography collected from 1953 to 2012 and in 2020; declassified imagery collected in 1962, 1972, and 1975; DigitalGlobe satellite imagery collected since 2004; Landsat satellite imagery collected since 1972; Sentinel–2 satellite imagery collected since 2015; and GFL imagery collected from 1991 to 2020.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225107","isbn":"978-1-4113-4511-9","programNote":"Core Science Systems and the National Civil Applications Center","usgsCitation":"Fisher, G.B., Slonecker, E.T., Dilles, S.J., Molnia, B.F., and Angeli, K.M., 2023, Using Global Fiducials Library high-resolution imagery, commercial satellite imagery, Landsat and Sentinel satellite imagery, and aerial photography to monitor change at East Timbalier Island, Louisiana, 1953–2021 (ver. 1.1, May 2023): U.S. Geological Survey Scientific Investigations Report 2022–5107, 61 p., https://doi.org/10.3133/sir20225107.","productDescription":"Report: vii, 61 p.; Data Release","numberOfPages":"61","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123372","costCenters":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":411976,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O71HYS","text":"USGS data release","linkHelpText":"Six decades of change at East Timbalier Island, Louisiana"},{"id":411971,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5107/coverthb2.jpg"},{"id":416780,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2022/5107/versionHist.txt","size":"4.31 KB","linkFileType":{"id":2,"text":"txt"}},{"id":411972,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5107/sir20225107.pdf","text":"Report","size":"150 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5107"},{"id":411975,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5107/images/"},{"id":500455,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114279.htm","linkFileType":{"id":5,"text":"html"}},{"id":411974,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5107/sir20225107.XML"}],"country":"United States","state":"Louisiana","otherGeospatial":"East Timbalier Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.4,\n 29.125\n ],\n [\n -90.4,\n 29.033\n ],\n [\n -90.233,\n 29.033\n ],\n [\n -90.233,\n 29.125\n ],\n [\n -90.4,\n 29.125\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: January 2023; Version 1.1: May 2023","contact":"Director, National Civil Applications Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 562
Reston, VA 20192
Genetic diversity is critical to a population’s ability to overcome gradual environment change. Large-bodied wildlife existing in regions with relatively high human population density are vulnerable to isolation-induced genetic drift, population bottlenecks, and loss of genetic diversity. Moose (Alces americanus americanus) in eastern North America have a complex history of drastic population changes. Current and potential threats to moose populations in this region could be exacerbated by loss of genetic diversity and connectivity among subpopulations. Existing genetic diversity, gene flow, and population clustering and fragmentation of eastern North American moose are not well quantified, while physical and anthropogenic barriers to population connectivity already exist. Here, single nucleotide polymorphism (SNP) genotyping of 507 moose spanning five northeastern U.S. states and one southeastern Canadian province indicated low diversity, with a high proportion of the genomes sharing identity-by-state, with no consistent evidence of non-random mating. Gene flow estimates indicated bidirectionality between all pairs of sampled areas, with magnitudes reflecting clustering and differentiation patterns. A Discriminant Analysis of Principal Components analysis indicated that these genotypic data were best described with four clusters and indicated connectivity across the Saint Lawrence River and Seaway, a potential physical barrier to gene flow. Tests for genetic differentiation indicated restricted gene flow between populations across the Saint Lawrence River and Seaway, and between many sampled areas facing expanding human activity. These results document current genetic variation and connectivity of moose populations in eastern North America, highlight potential challenges to current population connectivity, and identify areas for future research and conservation.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10592-022-01496-w","usgsCitation":"Rosenblatt, E., Gieder, K., Donovan, T.M., Murdoch, J., Smith, T., Stephanie McKay, Heaton, M., Kalbfleisch, T., Murdoch, B., Bhattarai, S., Pacht, E., Verbist, E., Basnayake, V., and McKay, S., 2023, Genetic diversity and connectivity of moose (Alces americanus americanus) in eastern North America: Conservation Genetics, v. 24, p. 235-248, https://doi.org/10.1007/s10592-022-01496-w.","productDescription":"14 p.","startPage":"235","endPage":"248","ipdsId":"IP-139721","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481070,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-022-01496-w","text":"Publisher Index Page"},{"id":480758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"New York, Vermont, New Hampshire, Maine, and Massachusetts","otherGeospatial":"Quebec","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.93230224047862,\n 46.77041749427818\n ],\n [\n -73.10392768038764,\n 45.36416223111472\n ],\n [\n -76.0820355810502,\n 44.501512257211175\n ],\n [\n -78.14823440598961,\n 42.227731281773856\n ],\n [\n -70.01581937845573,\n 41.76312420903159\n ],\n [\n -66.879327626342,\n 44.56814221402969\n ],\n [\n -67.8315462407186,\n 47.380624153749466\n ],\n [\n -69.33446774900295,\n 47.532790444215905\n ],\n [\n -71.93230224047862,\n 46.77041749427818\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2023-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenblatt, Elias","contributorId":348978,"corporation":false,"usgs":false,"family":"Rosenblatt","given":"Elias","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923907,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gieder, Katherina","contributorId":348979,"corporation":false,"usgs":false,"family":"Gieder","given":"Katherina","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":923908,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923909,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murdoch, James","contributorId":348980,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923910,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Timothy P.L.","contributorId":349566,"corporation":false,"usgs":false,"family":"Smith","given":"Timothy P.L.","affiliations":[],"preferred":false,"id":924440,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephanie McKay","contributorId":348981,"corporation":false,"usgs":false,"family":"Stephanie McKay","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923911,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heaton, Michael P.","contributorId":348982,"corporation":false,"usgs":false,"family":"Heaton","given":"Michael P.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":923912,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kalbfleisch, Theodore S.","contributorId":348983,"corporation":false,"usgs":false,"family":"Kalbfleisch","given":"Theodore S.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":923913,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murdoch, Brenda M.","contributorId":348984,"corporation":false,"usgs":false,"family":"Murdoch","given":"Brenda M.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":923914,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bhattarai, Suraj","contributorId":348985,"corporation":false,"usgs":false,"family":"Bhattarai","given":"Suraj","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923915,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pacht, Emory","contributorId":348986,"corporation":false,"usgs":false,"family":"Pacht","given":"Emory","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923916,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Verbist, Emma","contributorId":348987,"corporation":false,"usgs":false,"family":"Verbist","given":"Emma","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923917,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Basnayake, Veronica","contributorId":348988,"corporation":false,"usgs":false,"family":"Basnayake","given":"Veronica","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":923918,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"McKay, Stephanie","contributorId":348990,"corporation":false,"usgs":false,"family":"McKay","given":"Stephanie","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":923919,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70239358,"text":"sir20225099 - 2023 - Recent history of glacial lake outburst floods, analysis of channel changes, and development of a two-dimensional flow and sediment transport model of the Snow River near Seward, Alaska","interactions":[],"lastModifiedDate":"2026-02-23T19:23:26.079763","indexId":"sir20225099","displayToPublicDate":"2023-01-12T09:48:28","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5099","displayTitle":"Recent History of Glacial Lake Outburst Floods, Analysis of Channel Changes, and Development of a Two-Dimensional Flow and Sediment Transport Model of the Snow River near Seward, Alaska","title":"Recent history of glacial lake outburst floods, analysis of channel changes, and development of a two-dimensional flow and sediment transport model of the Snow River near Seward, Alaska","docAbstract":"Snow Lake, a glacially dammed lake on the Snow Glacier near Seward, Alaska, drains rapidly every 14 months–3 years, causing flooding along the Snow River. Highway, railroad, and utility infrastructure on the lower Snow River floodplain is vulnerable to flood damage. Historical hydrology, geomorphology, and two-dimensional hydraulic and sediment transport modeling were used to assess the flood risks from Snow Lake outburst floods. Floods have become more frequent, peaked more rapidly, and have had generally higher peaks over the last 20 years as the Snow Glacier has thinned, translating to a greater potential for flood damage. Rapidly shifting channel locations and the occasional introduction of large volumes of debris to the river also threaten infrastructure on the floodplain and in the channel. An assessment of the historical channel planform between 1951 and 2019 showed that there have been more and less stable segments along the lower Snow River and that channel migration has generally been toward the east. An analysis of floodplain elevations using 2008 light detection and ranging (lidar) showed that the main channel is relatively high compared to floodplain channels that carry floodwaters along the railroad grade, so that once the main channel banks are overtopped water rapidly disperses throughout the floodplain. A two-dimensional flow and sediment transport model was developed, and its simulation results were compared to three past outburst floods from 2007, 2017, and 2019. Despite the complex floodplain and channel geometry, coarse resolution of the mesh, and sediment input data, the model successfully simulated areas of observed scour along the railroad grade and at the guidebank to the highway bridge. The modeled water-surface elevations generally replicated peak elevations recorded at a streamgage in the middle of the model domain and at pressure transducers installed on the floodplain and main channel, although there were discrepancies on the rising limb and some locations had a poorer fit than others. A model of a hypothetical check flood, approximately 150 percent of the largest recorded outburst flood, was developed to provide hydraulic variables to use when planning for infrastructure upgrades.
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Unconventional oil and gas development that uses horizontal drilling and hydraulic fracturing is rapidly changing the landscape and exponentially increasing oil production within the Williston Basin, especially in North Dakota and eastern Montana. The activities associated with unconventional oil and gas development are complex and wide reaching and include, in part, road and well-pad construction, leaks from pits or tanks, chemical spills, discharge of wastewater, drilling before casing installation, leaks during or after hydraulic fracturing, failed casing seals, pipeline breaks, abandoned wells, deep-well disposal of flowback or produced wastewater, and induced subsurface migration pathways that can potentially adversely affect the environmental resources within the Williston Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223088","usgsCitation":"Delzer, G.C., and Post van der Burg, M., 2023, Research needs identified for potential effects of energy development activities on environmental resources of the Williston Basin, United States: U.S. Geological Survey Fact Sheet 2022–3088, 6 p., https://doi.org/10.3133/fs20223088.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-142397","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":499561,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114229.htm","linkFileType":{"id":5,"text":"html"}},{"id":411643,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3088/images"},{"id":411641,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3088/fs20223088.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3088"},{"id":411642,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3088/fs20223088.XML"},{"id":411640,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3088/coverthb.jpg"},{"id":411720,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223088/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.85992433948707,\n 44.748962924301054\n ],\n [\n -100.27178237770465,\n 45.62378897009327\n ],\n [\n -99.72386467913583,\n 47.01666497234379\n ],\n [\n -98.92422494544115,\n 48.99070414841606\n ],\n [\n -106.85334014872393,\n 49.00199336720374\n ],\n [\n -106.20557284548465,\n 47.60278245148987\n ],\n [\n -105.42666681945474,\n 46.15028300648845\n ],\n [\n -104.32046634105996,\n 45.350667945249626\n ],\n [\n -103.35976723012234,\n 45.170260895064786\n ],\n [\n -102.7400085664068,\n 44.590251713951545\n ],\n [\n -101.96303401243244,\n 44.60899148746665\n ],\n [\n -101.85992433948707,\n 44.748962924301054\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Global demand for safe and sustainable water supplies necessitates a better understanding of contaminant exposures in potential reuse waters. In this study, we compared exposures and load contributions to surface water from the discharge of three reuse waters (wastewater effluent, urban stormwater, and agricultural runoff). Results document substantial and varying organic-chemical contribution to surface water from effluent discharges (e.g., disinfection byproducts [DBP], prescription pharmaceuticals, industrial/household chemicals), urban stormwater (e.g., polycyclic aromatic hydrocarbons, pesticides, nonprescription pharmaceuticals), and agricultural runoff (e.g., pesticides). Excluding DBPs, episodic storm-event organic concentrations and loads from urban stormwater were comparable to and often exceeded those of daily wastewater-effluent discharges. We also assessed if wastewater-effluent irrigation to corn resulted in measurable effects on organic-chemical concentrations in rain-induced agricultural runoff and harvested feedstock. Overall, the target-organic load of 491 g from wastewater-effluent irrigation to the study corn field during the 2019 growing season did not produce substantial dissolved organic-contaminant contributions in subsequent rain-induced runoff events. Out of the 140 detected organics in source wastewater-effluent irrigation, only imidacloprid and estrone had concentrations that resulted in observable differences between rain-induced agricultural runoff from the effluent-irrigated and nonirrigated corn fields. Analyses of pharmaceuticals and per-/polyfluoroalkyl substances in at-harvest corn-plant samples detected two prescription antibiotics, norfloxacin and ciprofloxacin, at concentrations of 36 and 70 ng/g, respectively, in effluent-irrigated corn-plant samples; no contaminants were detected in noneffluent irrigated corn-plant samples.
More than 2 million Californians rely on groundwater from privately owned domestic wells for drinking-water supply. This report summarizes a water-quality survey of domestic and small-system drinking-water supply wells in the Modesto, Turlock, and Merced subbasins of the San Joaquin Valley where more than 78,000 residents are estimated to use privately owned domestic wells. Results indicate that inorganic and organic constituents in groundwater were respectively present above regulatory (maximum contaminant level, MCL) benchmarks for public drinking-water quality in 37 percent and 9 percent of the aquifer area used for domestic drinking-water supplies (herein, “domestic groundwater resources”).
The most prevalent inorganic constituents exceeding regulatory benchmarks were nitrate, uranium, and arsenic. The only organic constituents exceeding regulatory benchmarks were the fumigant constituents 1,2,3-trichloropropane (1,2,3-TCP) and 1,2-dibromo-3-chloropropane (DBCP), but the herbicides atrazine and simazine were detected at low concentrations below one-tenth of regulatory benchmarks in 30 percent of domestic groundwater resources. Total dissolved solids (TDS) and manganese exceeded aesthetic-based (secondary maximum contaminant level [SMCL]) benchmarks for drinking water in 3 percent and 13 percent of domestic groundwater resources, respectively. Per- and polyfluoroalkyl substances (PFAS) were detected in 23 percent of domestic groundwater resources, with 4 percent exceeding California state notification or response levels for specific compounds. Total coliform bacteria were detected in 20 percent of domestic groundwater resources.
Elevated concentrations of nitrate, uranium, TDS, and pesticides (fumigant constituents and herbicides) are related to agricultural land use and were typically present at shallow depths up to 75 meters below land surface. Agriculturally derived constituents were detected in wells screened below the Corcoran Clay Member of the Tulare Formation (herein, “Corcoran Clay”) in the southeastern part of the study area, where the Corcoran Clay tends to be shallower and thinner than in areas to the northwest. Nitrate, uranium, and TDS were most prevalent in the northwest part of the study area proximal to the valley trough where soils are poorly drained and agricultural land uses are predominantly grain, alfalfa, and dairy farms. Pesticides tended to occur in groundwater below coarse-grained surficial deposits and within a northwest to southeast trending band along the eastern extent of the Corcoran Clay that typically demarcates the western extent of well-drained soils associated with perennial orchard crops. Elevated concentrations of arsenic tended to occur west of this band in reducing groundwater but also sometimes co-occurred with elevated nitrate in oxic groundwater, most likely because of geochemical conditions in agriculturally affected groundwater that can enhance the mobility of arsenic from aquifer sediments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221116","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"GAMA Program","usgsCitation":"Levy, Z.F., Balkan, M., and Shelton, J.L., 2023, Quality of groundwater used for domestic supply in the Modesto, Turlock, and Merced Subbasins of the San Joaquin Valley, California: U.S. Geological Survey Open-File Report 2022-1116, 13 p., https://doi.org/10.3133/ofr20221116.","productDescription":"Report: 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-139668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":411493,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R55KQ","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater-quality data in the Modesto-Turlock-Merced Domestic-Supply Aquifer Study Unit, 2020-2021: Results from the California GAMA Priority Basin Project"},{"id":411490,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1116/coverthb.jpg"},{"id":411494,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1116/images"},{"id":411491,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1116/ofr20221116.pdf","text":"Report","size":"6.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1116"},{"id":411492,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221116/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1116"},{"id":411495,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1116/ofr20221116.XML"},{"id":499725,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114178.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.79107113798727,\n 38.18457756338151\n ],\n [\n -121.79107113798727,\n 37.036293717738104\n ],\n [\n -119.63866490997714,\n 37.036293717738104\n ],\n [\n -119.63866490997714,\n 38.18457756338151\n ],\n [\n -121.79107113798727,\n 38.18457756338151\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"GAMA Project Chief
U.S. Geological Survey
California Water Science Center
6000 J Street
Placer Hall, Sacramento, CA 95819
Telephone number: (916) 278-3000
GAMA Program Unit Chief State Water Resources Control Board Division of Water Quality
PO Box 2231
Sacramento, CA 95812
Telephone number: (916) 341-5855
The Walker Top Granite (here formally named) is a peraluminous megacrystic granite that occurs in the Cat Square terrane, Inner Piedmont, part of the southern Appalachian Acadian-Neoacadian deformational and metamorphic core. The granite occurs as disconnected concordant to semi-concordant plutons in migmatitic, sillimanite zone rocks of the Brindle Creek thrust sheet. Locally garnet-bearing, the Walker Top Granite contains blocky alkali feldspar megacrysts 1–10 cm long in a groundmass of muscovite-biotite-quartz-plagioclase-alkali feldspar and accessory to trace zircon, titanite, epidote, sillimanite (xenocrysts), and apatite. It varies from granite to granodiorite and contains several xenoliths of biotite gneiss, amphibolite, quartzite, and in one location encloses charnockite (here formally named Vale Charnockite). New sensitive high-resolution ion microprobe U-Pb zircon magmatic crystallization ages obtained from the plutons of the Walker Top Granite are: 407 ± 1 Ma in the Brushy Mountains; 366 ± 2 Ma in the South Mountains; and 358 ± 5 Ma in the Vale–Cat Square area. An age of 366 ± 3 Ma was obtained from the Vale Charnockite at its type locality. Major-, trace-element, and isotopic chemistry indicates that Walker Top is a high-K, peraluminous granite, plotting as volcanic arc or syn-collisional on tectonic discrimination diagrams and suggests that it represents deep-seated anatectic magma with S- to I-type affinity. The alkali calcic, ferroan Vale Charnockite likely formed by deep crustal melting, and similar geochemical and trace-element compositions suggest a similar tectonic origin as Walker Top Granite. The discontinuous nature of the Walker Top Granite plutons precludes it intruded as a volcanic arc. Instead, the peraluminous nature, common xenoliths of surrounding country rock, and geochemical and isotopic signatures suggest it formed by partial melting of Cat Square and Tugaloo terrane rocks. Following emplacement and crystallization, Walker Top plutons were deformed into elliptical to linear shapes—SW-directed sheath folds—enveloped by partially melted, pelitic and quart-zofeldspathic rocks. Collectively, Walker Top and other plutons helped weaken the crust and facilitate lateral crustal flow in a SW-directed, tectonically driven orogenic channel during the Acadian-Neoacadian event. A comparison with the northern Appalachians recognizes a similar temporal magmatic and deformational history during the Acadian and Neoacadian orogenies, although while the Walker Top Granite intruded the lower plate during eastward subduction beneath the peri-Gondwanan Carolina superterrane, the northern Appalachian plutons intruded the upper plate during subduction of the Avalon superterrane westward beneath Laurentia. We hypothesize that a transform fault, located near the southern end of the New York promontory, accommodated oppositely directed lateral plate motion and different subduction polarity between the Carolina and Avalon superterranes during the Acadian and Neoacadian orogenies.
Belts of Cordilleran arc plutons in the eastern part of the Mojave crustal province, inboard from the southwestern North American plate boundary, record major magmatic pulses at ca. 180–160 and 75 Ma and smaller pulses at ca. 100 and 20 Ma. This cyclic magmatism likely reflects evolving plate-margin processes. Zircon Lu-Hf isotopic characteristics and inherited zircons for different-age plutons may relate magma sources to evolving tectonics. Sources similar in age to the bulk of the exposed Mojave crust (1.6–1.8 Ga) dominated the magmas. Rare zircons having εHf(t) values as low as −52 indicate that Cretaceous melt sources also included more ancient crustal components, such as Archean-derived detritus in supracrustal gneisses of the Vishnu basin. Some rocks signal contributions from mantle lithosphere (in the Miocene) or asthenosphere (middle Cretaceous).
Temporal shifts in isotopic pattern in this sample of the Cordillera relate to cyclic pulses of magmatic flux. Hf-isotopic pull-downs suggestive of dominantly crustal sources characterize the Jurassic and Late Cretaceous flare-ups. The Late Cretaceous flare-up, occurring near the onset of flat-slab subduction, produced abundant Proterozoic xenocrystic zircon and Hf isotopes implicating derivation largely from heterogeneous deep Mojave crust. Isotopic pull-ups characterize the lower-flux middle Cretaceous and Miocene magmatic episodes. The middle Cretaceous pulse ca. 105–95 Ma produced Mojave crust signals but also the isotopically most juvenile magmatic zircons, ranging upward to barely positive εHf values and suspected to signal an asthenosphere contribution. This may point toward transtension or slab retreat causing 105–95 Ma backarc extension in the Mojave hinterland of the Cordillera. That possibility of backarc extension raises questions about the tectonic environment of the contemporaneous main Sierra Nevada high-flux arc closer to the continental margin.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02438.1","usgsCitation":"Howard, K., Shaw, S., and Allen, C.M., 2023, Magmatic record of changing Cordilleran plate-boundary conditions—Insights from Lu-Hf isotopes in the Mojave Desert: Geosphere, v. 19, no. 1, p. 1-18, https://doi.org/10.1130/GES02438.1.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-122981","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":444958,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02438.1","text":"Publisher Index Page"},{"id":416749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.84712822361877,\n 35.76857570064546\n ],\n [\n -116.84712822361877,\n 34.10320204421376\n ],\n [\n -114.09074554989823,\n 34.10320204421376\n ],\n [\n -114.09074554989823,\n 35.76857570064546\n ],\n [\n -116.84712822361877,\n 35.76857570064546\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Howard, Keith A. 0000-0002-6462-2947","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":264832,"corporation":false,"usgs":true,"family":"Howard","given":"Keith A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":871675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaw, S.E.","contributorId":304803,"corporation":false,"usgs":false,"family":"Shaw","given":"S.E.","affiliations":[{"id":16788,"text":"Macquarie University","active":true,"usgs":false}],"preferred":false,"id":871676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Charlotte M. 0000-0002-7288-6758","orcid":"https://orcid.org/0000-0002-7288-6758","contributorId":292917,"corporation":false,"usgs":false,"family":"Allen","given":"Charlotte","email":"","middleInitial":"M.","affiliations":[{"id":63074,"text":"Research School of Earth Sciences, The Australian National University, Canberra, ACT 0200, Australia","active":true,"usgs":false}],"preferred":false,"id":871677,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239173,"text":"70239173 - 2023 - Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment","interactions":[],"lastModifiedDate":"2023-01-02T19:07:29.676936","indexId":"70239173","displayToPublicDate":"2023-01-02T13:01:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9346,"text":"Science of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment","docAbstract":"Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.srs.2022.100073","usgsCitation":"Gao, F., Jennewein, J., Hively, W.D., Soroka, A.M., Thieme, A., Bradley, D., Keppler, J., Mirsky, S., and Akumaga, U., 2023, Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment: Science of Remote Sensing, v. 7, 100073, 14 p., https://doi.org/10.1016/j.srs.2022.100073.","productDescription":"100073, 14 p.","ipdsId":"IP-144149","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":444975,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.srs.2022.100073","text":"Publisher Index Page"},{"id":411274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Chesapeake Bay, Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.069544467364,\n 37.94756618819288\n ],\n [\n -75.94812876408099,\n 37.94131461385136\n ],\n [\n -75.95701283993044,\n 37.899264516752396\n ],\n [\n -75.79709947463141,\n 37.901601263962206\n ],\n [\n -75.75564045399823,\n 37.94131461385136\n ],\n [\n -75.6993746402825,\n 37.950655814583385\n ],\n [\n -75.64014746794955,\n 37.94131461385136\n ],\n [\n -75.61053388178237,\n 37.99734400246169\n ],\n [\n -75.22555726161829,\n 38.02534265907727\n ],\n [\n -75.08341204801894,\n 38.27684897319932\n ],\n [\n -75.0389916687707,\n 38.447926991945224\n ],\n [\n -75.69049056443372,\n 38.45952234969701\n ],\n [\n -75.78229268154962,\n 39.723597608598226\n ],\n [\n -75.88594023313227,\n 39.71676420384449\n ],\n [\n -76.03993088119832,\n 39.44058615652014\n ],\n [\n -76.16430794309719,\n 39.36507397284154\n ],\n [\n -76.30053043946273,\n 39.1839704250678\n ],\n [\n -76.3390281014787,\n 39.046110820719235\n ],\n [\n -76.4219461427451,\n 38.850348316275074\n ],\n [\n -76.36568032902868,\n 38.47343431903974\n ],\n [\n -76.069544467364,\n 37.94756618819288\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Feng 0000-0002-1865-2846","orcid":"https://orcid.org/0000-0002-1865-2846","contributorId":70671,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","email":"","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":860675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":860676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hively, W. 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Martin et al. (2015) and Hostetler et al. (2018) applied statistical models that incorporated multiple data sources to estimate the statewide abundance of manatees from aerial surveys f lown in 2011–2012 and 2015–2016. We conducted additional aerial surveys in 2021–2022 and applied similar models to provide an updated abundance estimate. This report serves as an update to Hostetler et al. (2018), with most of the text and methodology adapted from the previous report, and provides updated population estimates based on the newly available data. We estimate that the number of manatees in Florida in 2021–2022 was 9,790 (95% Bayesian credible interval 8,350–11,730), of which 4,630 (3,960–5,420) were on the west coast of Florida and 5,160 (3,940–6,980) were on the east coast. These estimates and the associated uncertainty, in addition to being of immediate value to wildlife managers, are essential new data for incorporation into integrated population models and population viability analyses. We also provide context for interpreting the new estimates and perspectives for future modeling improvements.
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