Maintaining healthy, productive ecosystems in the face of pervasive and accelerating human impacts including climate change requires globally coordinated and sustained observations of marine biodiversity. Global coordination is predicated on an understanding of the scope and capacity of existing monitoring programs, and the extent to which they use standardized, interoperable practices for data management. Global coordination also requires identification of gaps in spatial and ecosystem coverage, and how these gaps correspond to management priorities and information needs. We undertook such an assessment by conducting an audit and gap analysis from global databases and structured surveys of experts. Of 371 survey respondents, 203 active, long-term (>5 years) observing programs systematically sampled marine life. These programs spanned about 7% of the ocean surface area, mostly concentrated in coastal regions of the United States, Canada, Europe, and Australia. Seagrasses, mangroves, hard corals, and macroalgae were sampled in 6% of the entire global coastal zone. Two-thirds of all observing programs offered accessible data, but methods and conditions for access were highly variable. Our assessment indicates that the global observing system is largely uncoordinated which results in a failure to deliver critical information required for informed decision-making such as, status and trends, for the conservation and sustainability of marine ecosystems and provision of ecosystem services. Based on our study, we suggest four key steps that can increase the sustainability, connectivity and spatial coverage of biological Essential Ocean Variables in the global ocean: (1) sustaining existing observing programs and encouraging coordination among these; (2) continuing to strive for data strategies that follow FAIR principles (findable, accessible, interoperable, and reusable); (3) utilizing existing ocean observing platforms and enhancing support to expand observing along coasts of developing countries, in deep ocean basins, and near the poles; and (4) targeting capacity building efforts. Following these suggestions could help create a coordinated marine biodiversity observing system enabling ecological forecasting and better planning for a sustainable use of ocean resources.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2021.737416","usgsCitation":"Satterthwaite, E.V., Bax, N.J., Miloslavich, P., Ratnarajah, L., Canonico, G., Dunn, D., Simmons, S.E., Carini, R., Evans, K., Allain, V., Appeltans, W., Batten, S., Benedetti-Cecchi, L., Bernard, A.T., Bristol, R., Benson, A., Buttigieg, P.L., Gerhardinger, L.C., Chiba, S., Davies, T.E., Duffy, J., Giron-Nava, A., Hsu, A.J., Kraberg, A.C., Kudela, R.M., Lear, D., Montes, E., Muller-Karger, F., O’Brien, T.D., Obura, D., Provoost, P., Pruckner, S., Rebelo, L., Selig, E.R., Kjesbu, O.S., Starger, C., Stuart-Smith, R.D., Vierros, M., Waller, J.S., Weatherdon, L.V., Wellman, T., and Zivian, A., 2021, Establishing the foundation for the global observing system for marine life: Frontiers in Marine Science, v. 8, 737416, 19 p., https://doi.org/10.3389/fmars.2021.737416.","productDescription":"737416, 19 p.","ipdsId":"IP-127529","costCenters":[{"id":191,"text":"Colorado Water Science 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monitoring","docAbstract":"The Long Term Resource Monitoring (LTRM) element of the Upper Mississippi River Restoration program employs a harvest method for sampling submersed aquatic vegetation (SAV) whereby a rake is dragged ~1.5 m over the substrate and plant materials are retrieved. “Plant density” (PD) scores indicate SAV abundance and are based on the amount of plant material collected on the teeth of the rake. Standard PD scores are ordered, whole numbers from 0 (no SAV on the rake) to 5 (80-100% of rake teeth full) and are assigned at each subsite for all species combined and for each individual species.
In LTRM monitoring between 1998 and 2018, ~73% of non-zero, all-species-combined PD scores were 1s, and ~89% of individual SAV species were 1s. The preponderance of PD = 1 scores along with the wide range of fresh mass represented by PD = 1 (quantified in Drake and Lund 2020) limits inference about SAV abundance from LTRM monitoring data.
Field personnel noted that small plant fragments comprised a substantial fraction of PD = 1 observations and proposed a modification of the existing LTRM methods where PD = 1 was subdivided to include “trace” scores to represent such small fragments. Trace was defined as PD = 0.08, indicating a maximum of 1 of 13 gaps in the sampling rake filled to the level of an original PD = 1. Amounts of plant material greater than PD = 0.08 and up to the original score of 1 were defined PD = +1. This study used field data collected in 2018 (scoring and fresh weights of scored plant materials) from 136 vegetated sites in Pools 4, 8 and 13 to evaluate the proposed subdivision and to examine among-pool differences in PD data. In the study data, 33% of all-species-combined observations and 69% of species (grouped by morphology) that would previously have received a score of 1 were classified as PD = 0.08. PD scores of 0.08, +1, and 2-3 represented statistically distinct amounts of fresh mass in rake samples. There were systematic differences in the mass of SAV reflected by PD score based on plant morphology and species composition. The mean fresh mass of plant materials assigned a given PD score varied among the three pools, suggesting bias attributable to personnel. To reduce this bias in future data collection efforts, the field crews incorporated a calibration of plant density scores in annual field training. The results presented here describe how including a trace PD score in LTRM data collection improves the description of SAV abundance and consequently estimates of biomass from those PD scores. LTRM vegetation crews have recorded trace scores in annual sampling since 2019 as extra information (i.e. which does not change the LTRM data stream as 0.08 and +1 scores can still be combined for PD=1). Trace data are not currently available to outside users through the LTRM data browser but are available from vegetation component personnel upon request.
","language":"English","publisher":"U.S. Army Corps of Engineers, Upper Mississippi River Restoration Program","usgsCitation":"Drake, D.C., Lund, E., and Bales, K., 2021, Evaluation of a “trace” plant density score in LTRM vegetation monitoring: Long Term Resource Monitoring Technical Report LTRM-2018BI03a, 32 p.","productDescription":"32 p.","ipdsId":"IP-106633","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391319,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/documents/publications/2021/drake_a_2021.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Lowenberg, Carol 0000-0002-2961-6808","orcid":"https://orcid.org/0000-0002-2961-6808","contributorId":221012,"corporation":false,"usgs":true,"family":"Lowenberg","given":"Carol","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":825693,"contributorType":{"id":2,"text":"Editors"},"rank":0}],"authors":[{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":825690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lund, Eric","contributorId":221777,"corporation":false,"usgs":false,"family":"Lund","given":"Eric","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":826584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bales, Kyle","contributorId":267952,"corporation":false,"usgs":false,"family":"Bales","given":"Kyle","affiliations":[{"id":24495,"text":"Iowa Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":825692,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227254,"text":"70227254 - 2021 - Antimicrobial resistance: Wildlife as indicators of anthropogenic environmental contamination across space and through time","interactions":[],"lastModifiedDate":"2022-01-06T12:14:26.52071","indexId":"70227254","displayToPublicDate":"2021-10-25T08:13:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1352,"text":"Current Biology","active":true,"publicationSubtype":{"id":10}},"title":"Antimicrobial resistance: Wildlife as indicators of anthropogenic environmental contamination across space and through time","docAbstract":"Prior assessments support wildlife as indicators of anthropogenically influenced antimicrobial resistance across the landscape. A ground-breaking new study suggests that wildlife may also provide information on antimicrobial resistance in the environment through time.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cub.2021.08.037","usgsCitation":"Ramey, A.M., 2021, Antimicrobial resistance: Wildlife as indicators of anthropogenic environmental contamination across space and through time: Current Biology, v. 31, no. 20, p. R1385-R1387, https://doi.org/10.1016/j.cub.2021.08.037.","productDescription":"3 p.","startPage":"R1385","endPage":"R1387","ipdsId":"IP-131759","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":450361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cub.2021.08.037","text":"Publisher Index Page"},{"id":393910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"20","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":830126,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70225695,"text":"70225695 - 2021 - Lagged wetland CH4 flux response in a historically wet year","interactions":[],"lastModifiedDate":"2021-11-03T12:52:20.561946","indexId":"70225695","displayToPublicDate":"2021-10-25T07:51:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Lagged wetland CH4 flux response in a historically wet year","docAbstract":"While a stimulating effect of plant primary productivity on soil carbon dioxide (CO2) emissions has been well documented, links between gross primary productivity (GPP) and wetland methane (CH4) emissions are less well investigated. Determination of the influence of primary productivity on wetland CH4 emissions (FCH4) is complicated by confounding influences of water table level and temperature on CH4 production, which also vary seasonally. Here, we evaluate the link between preceding GPP and subsequent FCH4 at two fens in Wisconsin using eddy covariance flux towers, Lost Creek (US-Los) and Allequash Creek (US-ALQ). Both wetlands are mosaics of forested and shrub wetlands, with US-Los being larger in scale and having a more open canopy. Co-located sites with multi-year observations of flux, hydrology, and meteorology provide an opportunity to measure and compare lag effects on FCH4 without interference due to differing climate. Daily average FCH4 from US-Los reached a maximum of 47.7 ηmol CH4 m−2 s−1 during the study period, while US-ALQ was more than double at 117.9 ηmol CH4 m−2 s−1. The lagged influence of GPP on temperature-normalized FCH4 (Tair-FCH4) was weaker and more delayed in a year with anomalously high precipitation than a following drier year at both sites. FCH4 at US-ALQ was lower coincident with higher stream discharge in the wet year (2019), potentially due to soil gas flushing during high precipitation events and lower water temperatures. Better understanding of the lagged influence of GPP on FCH4 due to this study has implications for climate modeling and more accurate carbon budgeting.
NASA's Voyager 1, Voyager 2, and Galileo spacecraft acquired hundreds of images of Jupiter's moon Europa. These images provide the only moderate- to high-resolution views of the moon's surface and are therefore a critical resource for scientific analysis and future mission planning. Unfortunately, uncertain knowledge of the spacecraft's position and pointing during image acquisition resulted in significant errors in the location of the images on the surface. The result is that adjacent images are poorly aligned, with some images displaced by more than 100 km from their correct location. These errors severely degrade the usability of the Voyager and Galileo imaging data sets. To improve the usability of these data sets, we used the U.S. Geological Survey Integrated Software for Imagers and Spectrometers to build a nearly global image tie-point network with more than 50,000 tie points and 135,000 image measurements on 481 Galileo and 221 Voyager images. A global least-squares bundle adjustment of our final Europa tie-point network calculated latitude, longitude, and radius values for each point by minimizing residuals globally, and resulted in root mean square (RMS) uncertainties of 246.6 m, 307.0 m, and 70.5 m in latitude, longitude, and radius, respectively. The total RMS uncertainty was 0.32 pixels. This work enables direct use of nearly the entire Galileo and Voyager image data sets for Europa. We are providing the community with updated NASA Navigation and Ancillary Information Facility Spacecraft, Planet, Instrument, C-matrix (pointing), and Events kernels, mosaics of Galileo images acquired during each observation sequence, and individual processed and projected level 2 images.
Baseflow is critical to sustaining streamflow in the Upper Colorado River Basin. Therefore, effective water resources management requires estimates of baseflow response to climatic changes. This study provides the first estimates of projected baseflow changes from historical (1984 – 2012) to thirty-year periods centered around 2030, 2050, and 2080 under warm/wet, median, and hot/dry climatic conditions using a hybrid statistical-deterministic baseflow model. Total baseflow supplied to the Lower Colorado River Basin may decline by up to 33%, although this value may increase in the near future by 6% under warm/wet conditions. The percentage of baseflow lost during in-stream transport is projected to increase by 1 - 5% relative to historical conditions. Results highlight that climate driven changes in high elevation hydrology have impacts on basin-wide water availability. Study results have implications for human and ecological water availability in one of the most heavily managed watersheds in the world.
Timber harvesting can influence headwater streams by altering stream productivity, with cascading effects on the food web and predators within, including stream salamanders. Although studies have examined shifts in salamander occupancy or abundance following timber harvest, few examine sublethal effects such as changes in growth and demography. To examine the effect of upland harvesting on growth of the stream-associated Ouachita dusky salamander (Desmognathus brimleyorum), we used capture–mark–recapture over three years at three headwater streams embedded in intensely managed pine forests in west-central Arkansas. The pine stands surrounding two of the streams were harvested, with retention of a 14- and 21-m-wide forested stream buffer on each side of the stream, whereas the third stream was an unharvested control. At the two treatment sites, measurements of newly metamorphosed salamanders were on average 4.0 and 5.7 mm larger post-harvest compared with pre-harvest. We next assessed the influence of timber harvest on growth of post-metamorphic salamanders with a hierarchical von Bertalanffy growth model that included an effect of harvest on growth rate. Using measurements from 839 individual D. brimleyorum recaptured between 1 and 6 times (total captures, n = 1229), we found growth rates to be 40% higher post-harvest. Our study is among the first to examine responses of individual stream salamanders to timber harvesting, and we discuss mechanisms that may be responsible for observed shifts in growth. Our results suggest timber harvest that includes retention of a riparian buffer (i.e., streamside management zone) may have short-term positive effects on juvenile stream salamander growth, potentially offsetting negative sublethal effects associated with harvest.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8238","usgsCitation":"Guzy, J.C., Halstead, B., Halloran, K.M., Homyack, J.A., and Willson, J.D., 2021, Increased growth rates of stream salamanders following forest harvesting: Ecology and Evolution, v. 11, no. 24, p. 17723-17733, https://doi.org/10.1002/ece3.8238.","productDescription":"11 p.","startPage":"17723","endPage":"17733","ipdsId":"IP-127689","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450369,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8238","text":"External Repository"},{"id":397302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","county":"Howard County","otherGeospatial":"Ouachita Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.306640625,\n 34.384246040152185\n ],\n [\n -93.416748046875,\n 34.384246040152185\n ],\n [\n -93.416748046875,\n 35.232159412017154\n ],\n [\n -94.306640625,\n 35.232159412017154\n ],\n [\n -94.306640625,\n 34.384246040152185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","noUsgsAuthors":false,"publicationDate":"2021-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Guzy, Jacquelyn C. 0000-0003-2648-398X","orcid":"https://orcid.org/0000-0003-2648-398X","contributorId":288520,"corporation":false,"usgs":true,"family":"Guzy","given":"Jacquelyn","email":"","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":838477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Halloran, Kelly M.","contributorId":288948,"corporation":false,"usgs":false,"family":"Halloran","given":"Kelly","email":"","middleInitial":"M.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":838479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Homyack, Jessica A.","contributorId":288949,"corporation":false,"usgs":false,"family":"Homyack","given":"Jessica","email":"","middleInitial":"A.","affiliations":[{"id":56610,"text":"Weyerhaeuser Company","active":true,"usgs":false}],"preferred":false,"id":838480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Willson, John D.","contributorId":288952,"corporation":false,"usgs":false,"family":"Willson","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":838481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229453,"text":"70229453 - 2021 - Surface-water/groundwater boundaries affect seasonal PFAS concentrations and PFAA precursor transformations","interactions":[],"lastModifiedDate":"2022-03-09T15:47:07.062788","indexId":"70229453","displayToPublicDate":"2021-10-23T09:32:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9161,"text":"Environmental Science: Processes & Impacts","active":true,"publicationSubtype":{"id":10}},"title":"Surface-water/groundwater boundaries affect seasonal PFAS concentrations and PFAA precursor transformations","docAbstract":"Elevated concentrations of per- and polyfluoroalkyl substances (PFAS) in drinking-water supplies are a major concern for human health. It is therefore essential to understand factors that affect PFAS concentrations in surface water and groundwater and the transformation of perfluoroalkyl acid (PFAA) precursors that degrade into terminal compounds. Surface-water/groundwater exchange can occur along the flow path downgradient from PFAS point sources and biogeochemical conditions can change rapidly at these exchange boundaries. Here, we investigate the influence of surface-water/groundwater boundaries on PFAS transport and transformation. To do this, we conducted an extensive field-based analysis of PFAS concentrations in water and sediment from a flow-through lake fed by contaminated groundwater and its downgradient surface-water/groundwater boundary (defined as ≤100 cm below the lake bottom). PFAA precursors comprised 45 ± 4.6% of PFAS (PFAA precursors + 18 targeted PFAA) in the predominantly oxic lake impacted by a former fire-training area and historical wastewater discharges. In shallow porewater downgradient from the lake, this percentage decreased significantly to 25 ± 11%. PFAA precursor concentrations decreased by 85% between the lake and 84–100 cm below the lake bottom. PFAA concentrations increased significantly within the surface-water/groundwater boundary and in downgradient groundwater during the winter months despite lower stable concentrations in the lake water source. These results suggest that natural biogeochemical fluctuations associated with surface-water/groundwater boundaries may lead to PFAA precursor loss and seasonal variations in PFAA concentrations. Results of this work highlight the importance of dynamic biogeochemical conditions along the hydrological flow path from PFAS point sources to potentially affected drinking water supplies.","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D1EM00329A","usgsCitation":"Tokranov, A.K., LeBlanc, D.R., Pickard, H.M., Ruyle, B.J., Barber, L., Hull, R.B., Sunderland, E.M., and Vecitis, C.D., 2021, Surface-water/groundwater boundaries affect seasonal PFAS concentrations and PFAA precursor transformations: Environmental Science: Processes & Impacts, v. 23, no. 12, p. 1893-1905, https://doi.org/10.1039/D1EM00329A.","productDescription":"13 p.","startPage":"1893","endPage":"1905","ipdsId":"IP-111866","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":450371,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1039/d1em00329a","text":"Publisher Index Page"},{"id":436134,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HPBFRT","text":"USGS data release","linkHelpText":"Concentrations of per- and polyfluoroalkyl substances (PFAS) and related chemical and physical data at and near surface-water/groundwater boundaries on Cape Cod, Massachusetts, 2016-19"},{"id":396919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Ashumet Pond, Cape Cod","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.55196762084961,\n 41.58412041539796\n ],\n [\n -70.48055648803711,\n 41.58412041539796\n ],\n [\n -70.48055648803711,\n 41.64867312729944\n ],\n [\n -70.55196762084961,\n 41.64867312729944\n ],\n [\n -70.55196762084961,\n 41.58412041539796\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tokranov, Andrea K. 0000-0003-4811-8641","orcid":"https://orcid.org/0000-0003-4811-8641","contributorId":255483,"corporation":false,"usgs":true,"family":"Tokranov","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":837521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LeBlanc, Denis R. 0000-0002-4646-2628","orcid":"https://orcid.org/0000-0002-4646-2628","contributorId":219907,"corporation":false,"usgs":true,"family":"LeBlanc","given":"Denis","email":"","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":837522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pickard, Heidi M. 0000-0001-8312-7522","orcid":"https://orcid.org/0000-0001-8312-7522","contributorId":261821,"corporation":false,"usgs":false,"family":"Pickard","given":"Heidi","email":"","middleInitial":"M.","affiliations":[{"id":53027,"text":"Harvard John A. Paulson School of Engineering and Applied Sciences","active":true,"usgs":false}],"preferred":false,"id":837523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruyle, Bridger J. 0000-0003-1941-4732","orcid":"https://orcid.org/0000-0003-1941-4732","contributorId":261820,"corporation":false,"usgs":false,"family":"Ruyle","given":"Bridger","email":"","middleInitial":"J.","affiliations":[{"id":53027,"text":"Harvard John A. Paulson School of Engineering and Applied Sciences","active":true,"usgs":false}],"preferred":false,"id":837524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barber, Larry B. 0000-0002-0561-0831","orcid":"https://orcid.org/0000-0002-0561-0831","contributorId":218953,"corporation":false,"usgs":true,"family":"Barber","given":"Larry B.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"preferred":true,"id":837525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hull, Robert B.","contributorId":193841,"corporation":false,"usgs":false,"family":"Hull","given":"Robert","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":837526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sunderland, Elsie M.","contributorId":151016,"corporation":false,"usgs":false,"family":"Sunderland","given":"Elsie","email":"","middleInitial":"M.","affiliations":[{"id":18166,"text":"Harvard University, Cambridge, M","active":true,"usgs":false}],"preferred":false,"id":837527,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vecitis, Chad D.","contributorId":193842,"corporation":false,"usgs":false,"family":"Vecitis","given":"Chad","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":837528,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225585,"text":"70225585 - 2021 - Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout","interactions":[],"lastModifiedDate":"2021-10-26T14:15:05.326145","indexId":"70225585","displayToPublicDate":"2021-10-23T09:13:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout","docAbstract":"Pacific walruses (Odobenus rosmarus divergens) are using coastal haulouts in the Chukchi Sea more often and in larger numbers to rest between foraging bouts in late summer and autumn in recent years, because climate warming has reduced availability of sea ice that historically had provided resting platforms near their preferred benthic feeding grounds. With greater numbers of walruses hauling out in large aggregations, new opportunities are presented for monitoring the population. Here we evaluate different types of satellite imagery for detecting and delineating the peripheries of walrus aggregations at a commonly used haulout near Point Lay, Alaska, in 2018–2020. We evaluated optical and radar imagery ranging in pixel resolutions from 40 m to ~1 m: specifically, optical imagery from Landsat, Sentinel-2, Planet Labs, and DigitalGlobe, and synthetic aperture radar (SAR) imagery from Sentinel-1 and TerraSAR-X. Three observers independently examined satellite images to detect walrus aggregations and digitized their peripheries using visual interpretation. We compared interpretations between observers and to high-resolution (~2 cm) ortho-corrected imagery collected by a small unoccupied aerial system (UAS). Roughly two-thirds of the time, clouds precluded clear optical views of the study area from satellite. SAR was unaffected by clouds (and darkness) and provided unambiguous signatures of walrus aggregations at the Point Lay haulout. Among imagery types with 4–10 m resolution, observers unanimously agreed on all detections of walruses, and attained an average 65% overlap (sd 12.0, n 100) in their delineations of aggregation boundaries. For imagery with ~1 m resolution, overlap agreement was higher (mean 85%, sd 3.0, n 11). We found that optical satellite sensors with moderate resolution and high revisitation rates, such as PlanetScope and Sentinel-2, demonstrated robust and repeatable qualities for monitoring walrus haulouts, but temporal gaps between observations due to clouds were common. SAR imagery also demonstrated robust capabilities for monitoring the Point Lay haulout, but more research is needed to evaluate SAR at haulouts with more complex local terrain and beach substrates.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13214266","usgsCitation":"Fischbach, A., and Douglas, D.C., 2021, Evaluation of satellite imagery for monitoring Pacific walruses at a large coastal haulout: Remote Sensing, v. 13, no. 21, 4266, 19 p., https://doi.org/10.3390/rs13214266.","productDescription":"4266, 19 p.","ipdsId":"IP-131033","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":450373,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13214266","text":"Publisher Index Page"},{"id":436135,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2UL7N","text":"USGS data release","linkHelpText":"Walrus Haulout Outlines Apparent from Satellite Imagery Near Point Lay Alaska, Autumn 2018-2020"},{"id":390960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Point Lay haulout area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.19366455078125,\n 69.33674271476097\n ],\n [\n -162.9986572265625,\n 69.6121624754292\n ],\n [\n -162.542724609375,\n 69.96796725849453\n ],\n [\n -162.48504638671875,\n 70.00368818988092\n ],\n [\n -162.73223876953122,\n 70.03372158435194\n ],\n [\n -163.289794921875,\n 69.70286804851057\n ],\n [\n -163.36669921875,\n 69.47778343567616\n ],\n [\n -163.3447265625,\n 69.337711892853\n ],\n [\n -163.19366455078125,\n 69.33674271476097\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"21","noUsgsAuthors":false,"publicationDate":"2021-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":200780,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony S.","email":"afischbach@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":825688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":825689,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70225561,"text":"ofr20211101 - 2021 - Detection and measurement of land-surface deformation, Pajaro Valley, Santa Cruz and Monterey counties, California, 2015–18","interactions":[],"lastModifiedDate":"2021-10-26T13:24:52.319349","indexId":"ofr20211101","displayToPublicDate":"2021-10-22T17:20:13","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1101","displayTitle":"Detection and Measurement of Land-Surface Deformation, Pajaro Valley, Santa Cruz and Monterey Counties, California, 2015–18","title":"Detection and measurement of land-surface deformation, Pajaro Valley, Santa Cruz and Monterey counties, California, 2015–18","docAbstract":"Land-surface deformation (subsidence) caused by groundwater withdrawal is identified as an undesirable result in the Pajaro Valley Water Management Agency’s Basin Management Plan and California’s Sustainable Groundwater Management Act. In Pajaro Valley, groundwater provides nearly 90 percent of the total water supply. To aid the development of sustainable groundwater management criteria, the U.S. Geological Survey, in cooperation with the Pajaro Valley Water Management Agency, performed an analysis of land-surface deformation (subsidence and uplift) in Pajaro Valley for 2015–18, using Interferometric Synthetic Aperture Radar and continuous Global Positioning System methods. Land-surface deformation results were then compared with subsurface geology and groundwater altitudes to better understand the hydromechanical response of the coastal aquifer system. The results indicate the land surface is generally stable with only small magnitudes (less than 1 inch) of seasonal land-surface deformation (subsidence in the summer and uplift in the winter) during 2015–18. During this time, the largest magnitude of land-surface deformation was less than 2 inches of subsidence and was localized in one area just north of the city limits of Watsonville, California. Groundwater altitudes during 2015–18 demonstrated seasonal variability and annual to multi-annual increases after reaching historical lows by the mid-1990s. The small magnitudes of land-surface deformation coupled with groundwater-altitude increases in most areas indicate that the subsidence likely is largely elastic and recoverable. The Corralitos-Pajaro Valley groundwater basin contains fine-grained (clay) sediments that have the potential for permanent aquifer-system compaction and resultant land subsidence. However, groundwater altitudes throughout the Pajaro Valley have increased above historical lows, and observed increases in groundwater altitudes coincided with changes in groundwater management activities. Observed relations between groundwater management activities and groundwater altitudes indicate that management of groundwater supplies could minimize the potential for permanent land-surface deformation in Pajaro Valley.
","language":"English","publisher":"U.S. Geological","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211101","collaboration":"Prepared in cooperation with the Pajaro Valley Water Management Agency","programNote":"Water Availability and Use Science Program","usgsCitation":"Brandt, J.T., Earll, M.M., Sneed, M., and Henson, W., 2021, Detection and measurement of land-surface deformation, Pajaro Valley, Santa Cruz and Monterey counties, California, 2015–18: U.S. Geological Survey Open-File Report 2021–1101, 16 p., https://doi.org/10.3133/ofr20211101.","productDescription":"Report: vi, 16 p.; Data Release","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-118756","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":390883,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FNARQO","linkHelpText":"Interferometric Synthetic Aperture Radar and Water Level Data, Pajaro Valley, Santa Cruz and Monterey Counties, California, 1970–2018"},{"id":390882,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1101/images"},{"id":390881,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1101/ofr20211101.xml"},{"id":390880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1101/ofr20211101.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1101/covrthb.jpg"}],"country":"United States","state":"California","county":"Monterey County, Santa Cruz County","otherGeospatial":"Pajaro Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.89468383789061,\n 36.71356812817935\n ],\n [\n -121.57058715820312,\n 36.71356812817935\n ],\n [\n -121.57058715820312,\n 36.97074107796435\n ],\n [\n -121.89468383789061,\n 36.97074107796435\n ],\n [\n -121.89468383789061,\n 36.71356812817935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The HayWired earthquake scenario, led by the U.S. Geological Survey (USGS), anticipates the impacts of a hypothetical moment magnitude 7.0 earthquake on the Hayward Fault. The fault runs along the east side of California’s San Francisco Bay and is among the most active and dangerous in the United States, passing through a densely urbanized and interconnected region. A scientifically realistic scenario is one way to learn from a large earthquake before one occurs in the bay region. The USGS and its partners in the HayWired Coalition are working to energize residents and businesses to engage in new and ongoing efforts to prepare the region for such a future earthquake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213054","usgsCitation":"Wein, A.M., Jones, J.L., Johnson, L.A., Kroll, C., Strauss, J., Witkowski, D., Cox, D.A., 2021, The HayWired Earthquake Scenario—Societal Consequences: U.S. Geological Survey Fact Sheet 2021–3054, 6 p., https://doi.org/10.3133/fs20213054.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-132490","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":553,"text":"Science Application for Risk Reduction (SAFRR)","active":false,"usgs":true}],"links":[{"id":390874,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20183016","text":"Fact Sheet 2018-3016","linkHelpText":"– The HayWired Earthquake Scenario—We Can Outsmart Disaster"},{"id":390873,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20175013V3","text":"Scientific Investigations Report 2017-5013 Volume 3","linkHelpText":"– The HayWired Earthquake Scenario—Societal Consequences"},{"id":390872,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20175013v2","text":"Scientific Investigations Report 2017-5013 Volume 2","linkHelpText":"– The HayWired Earthquake Scenario—Engineering Implications"},{"id":390871,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20175013v1","text":"Scientific Investigations Report 2017-5013 Volume 1","linkHelpText":"– The HayWired Earthquake Scenario—Earthquake Hazards"},{"id":390870,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3054/fs20213054.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390869,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3054/covrthb.jpg"},{"id":395080,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://geonarrative.usgs.gov/liquefactionandsealevelrise/","text":"Liquefaction and Sea-Level Rise","linkHelpText":"– A USGS storymap presenting the impacts of sea-level rise on liquefaction severity around the San Francisco Bay Area, California for the M7.0 ‘HayWired’ earthquake scenario along the Hayward Fault"},{"id":392899,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20175013","text":"Scientific Investigations Report 2017-5013","linkHelpText":"– The HayWired Earthquake Scenario"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.0908203125,\n 37.24782120155428\n ],\n [\n -121.26708984374999,\n 37.24782120155428\n ],\n [\n -121.26708984374999,\n 38.37611542403604\n ],\n [\n -123.0908203125,\n 38.37611542403604\n ],\n [\n -123.0908203125,\n 37.24782120155428\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Menlo Park, Calif.
Office—Earthquake Science Center
U.S. Geological Survey
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In the deserts of the Southwestern United States, increased off-highway vehicle use can lead to widespread vehicular damage to desert ecosystems. As the popularity and intensity of vehicle use on public lands continues, the Bureau of Land Management (BLM) is challenged to manage the routes used by recreationists while minimizing activity beyond designated routes and mitigating environmental impacts. Ecosystem function and habitat quality can be degraded by vehicle activities, especially when the activities are occurring outside authorized routes or authorized open areas. Restoration mitigates damage to soils and vegetation; however, methods vary across the desert, results appear to be inconsistent, and standardized monitoring plans do not exist. The Desert Renewable Energy Conservation Plan Land Use Plan Amendment to the California Desert Conservation Area Land Use Plan identified the need for, and directed implementation of, standardized monitoring of restoration, which includes minimizing surface disturbance to agency prescribed levels in areas of critical environmental concern and on California Desert National Conservation Lands. To assist the BLM in implementing the Desert Renewable Energy Conservation Plan Land Use Plan Amendment, we define ecological restoration as the process of halting or minimizing future degradation while simultaneously assisting the recovery of ecosystem function and community composition in relation to intact reference sites. The monitoring strategies provided in this protocol are used to restore degraded ecosystems after use of non-routes has ceased (non-designated routes or vehicle-caused linear disturbances) by applying techniques to improve edaphic properties, hydrologic function, and biotic community composition. This protocol also provides criteria that can be used to distinguish the status of non-routes and land parcels as “restored” or “disturbed.” This protocol was developed by the U.S. Geological Survey, in collaboration with BLM restoration practitioners, to identify standard restoration methods and establish criteria to determine when restoration is achieved. This protocol also develops new methods to increase restoration rates and successes on public lands in the southern California deserts. BLM’s long-term implementation plan for the evaluation of road restoration described in this report is to transition toward managing the work, including developing the workforce and long-term storage and management of the data during the next several years. This report is intended to be regularly updated as the program develops.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A17","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Esque, T.C., Jackson, K.R., Rice, A.M., Childers, J.K., Woods, C.S., Fesnock-Parker, A., Johnson, A.C., Price, L.J., Forgrave, K.E., Scoles-Sciulla, S.J., and DeFalco, L.A., 2021, Protocol for route restoration in California’s desert renewable energy conservation plan area: U.S. Geological Survey Techniques and Methods 2-A17, 60 p., https://doi.org/10.3133/tm2A17.","productDescription":"viii, 60 p.","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126835","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":390844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a17/covrthb.jpg"},{"id":390845,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a17/tm2a17.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390846,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/a17/tm2a17.xml"},{"id":390847,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/a17/images"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.09204101562501,\n 35.137879119634185\n ],\n [\n -116.03759765625,\n 35.137879119634185\n ],\n [\n -116.03759765625,\n 36.4566360115962\n ],\n [\n -118.09204101562501,\n 36.4566360115962\n ],\n [\n -118.09204101562501,\n 35.137879119634185\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
Translocations of North American prairie-grouse (genus Tympanuchus) present a conservation paradox wherein they are performed to augment, restore, or reintroduce populations, but translocated individuals exhibit a diminished ability to contribute to population restoration. For reintroduced populations without immigration, persistence can only be achieved through reproductive contributions by translocated individuals and their progeny. Due to the disruptive nature of translocation (e.g., physiological chronic stress), progeny produced at restoration sites may outperform founder populations in terms of demographics, but this hypothesis has yet to be tested. We reintroduced Columbian Sharp-tailed Grouse (T. phasianellus columbianus; CSTG) to north central Nevada from 2013 to 2017 and used integrated population models (IPMs) to evaluate the process of population establishment and estimate latent contributions of progeny hatched at the restoration site to population rate of change (
This paper investigates applicability of cassiterite to dating ore deposits in a wide age range. We report in situ LA-ICPMS U-Pb and Pb-Pb dating results (n = 15) of cassiterite from six ore deposits in Russia ranging in age from ~1.85 Ga to 93 Ma. The two oldest deposits dated at ~1.83–1.86 Ga are rare metal Vishnyakovskoe located in the East Sayan pegmatite belt and tin deposits within the Tuyukan ore region in the Baikal folded region. Rare metal skarn deposits of Pitkäranta ore field in the Ladoga region, Fennoscandian Shield are dated at ~1.54 Ga. Cassiterite from the Mokhovoe porphyry tin deposit located in western Transbaikalia is 810 ± 20 Ma. The youngest cassiterite was dated from the deposits Valkumei (Russian North East, 108 ± 2 Ma) and Merek (Russian Far East, 93 ± 2 Ma). Three methods of age calculations, including 208Pb/206Pb-207Pb/206Pb inverse isochron age, Tera-Wasserburg Concordia lower intercept age, and 207Pb-corrected 206Pb*/238U age were used and the comparison of the results is discussed. In all cases, the dated cassiterite from the ore deposits agreed, within error, with the established period of magmatism of the associated granitic rock
","language":"English","publisher":"MDPI","doi":"10.3390/min11111166","usgsCitation":"Neymark, L., Larin, A.M., and Moscati, R.J., 2021, Pb-Pb and U-Pb dating of cassiterite by in situ LA-ICPMS: Examples spanning ~1.85 Ga to ~100 Ma in Russia and implications for dating Proterozoic to Phanerozoic tin deposits.: Minerals, v. 11, 1166, 30 p., https://doi.org/10.3390/min11111166.","productDescription":"1166, 30 p.","ipdsId":"IP-132675","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":450377,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min11111166","text":"Publisher Index Page"},{"id":436137,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HK0RL5","text":"USGS data release","linkHelpText":"Pb-Pb and U-Pb data of Proterozoic to Phanerozoic cassiterite deposits in Russia"},{"id":391681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"Chuya-Kodar complex, East Sayan belt, Merek Greisen tin ore deposit, Mokhovoe Porphyry tin deposit, Pitkaranta Mining District, Valkumei silicate-sulfide vein tin deposit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 97.5,\n 52\n ],\n [\n 104,\n 52\n ],\n [\n 104,\n 55.37286814115173\n ],\n [\n 97.5,\n 55.37286814115173\n ],\n [\n 97.5,\n 52\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 112.5,\n 58\n ],\n [\n 115,\n 58\n ],\n [\n 115,\n 59\n ],\n [\n 112.5,\n 59\n ],\n [\n 112.5,\n 58\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 31,\n 61\n ],\n [\n 33,\n 61\n ],\n [\n 33,\n 62\n ],\n [\n 31,\n 62\n ],\n [\n 31,\n 61\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 114.5,\n 55.333\n ],\n [\n 115,\n 55.333\n ],\n [\n 115,\n 56\n ],\n [\n 114.5,\n 56\n ],\n [\n 114.5,\n 55.333\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 170,\n 68.75\n ],\n [\n 173,\n 68.75\n ],\n [\n 173,\n 70\n ],\n [\n 170,\n 70\n ],\n [\n 170,\n 68.75\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 130,\n 42\n ],\n [\n 142,\n 42\n ],\n [\n 142,\n 52\n ],\n [\n 130,\n 52\n ],\n [\n 130,\n 42\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2021-10-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Neymark, Leonid A. 0000-0003-4190-0278 lneymark@usgs.gov","orcid":"https://orcid.org/0000-0003-4190-0278","contributorId":140338,"corporation":false,"usgs":true,"family":"Neymark","given":"Leonid A.","email":"lneymark@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":826692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larin, Anatoly M. 0000-0001-5677-7415","orcid":"https://orcid.org/0000-0001-5677-7415","contributorId":268799,"corporation":false,"usgs":false,"family":"Larin","given":"Anatoly","email":"","middleInitial":"M.","affiliations":[{"id":55670,"text":"IPGG, RAS","active":true,"usgs":false}],"preferred":false,"id":826693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moscati, Richard J. 0000-0002-0818-4401 rmoscati@usgs.gov","orcid":"https://orcid.org/0000-0002-0818-4401","contributorId":2462,"corporation":false,"usgs":true,"family":"Moscati","given":"Richard","email":"rmoscati@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":826694,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238588,"text":"70238588 - 2021 - Novel insights into the genetic population connectivity of transient whale sharks (Rhincodon typus) in Pacific Panama provide crucial data for conservation efforts","interactions":[],"lastModifiedDate":"2022-12-01T13:17:51.909243","indexId":"70238588","displayToPublicDate":"2021-10-22T06:55:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Novel insights into the genetic population connectivity of transient whale sharks (Rhincodon typus) in Pacific Panama provide crucial data for conservation efforts","title":"Novel insights into the genetic population connectivity of transient whale sharks (Rhincodon typus) in Pacific Panama provide crucial data for conservation efforts","docAbstract":"The whale shark (Rhincodon typus) is an endangered and highly migratory species, of which solitary individuals or aggregations are observed in oceans worldwide and for which conservation efforts are hindered by a lack of comprehensive data on genetic population connectivity. Tissue samples were collected from wandering whale sharks in Pacific Panama to determine genetic diversity, phylogeographic origin, and possible global and local connectivity patterns using a 700–800 bp fragment of the mitochondrial control region gene. Genetic diversity among samples was high, with five new haplotypes and nine polymorphic sites identified among the 15 sequences. Haplotype diversity (Hd = 0.83) and nucleotide diversity (π = 0.00516) were similar to those reported in other studies. Our sequences, in particular haplotypes PTY1 and PTY2, were similar to those previously reported in the Arabian Gulf and the Western Indian Ocean populations (a novel occurrence in the latter case). Haplotypes PTY3, PTY4, and PTY5 were similar to populations in Mexico and the Gulf of California. In contrast, the only populations to which our Panamanian sequences were genetically dissimilar were those from the Atlantic Ocean. The absence of reference sequences in GenBank from southern sites in the Eastern Tropical Pacific, such as Galapagos (Ecuador), Gorgona and Malpelo Islands (Colombia), and Coco Island (Costa Rica), reduced our capacity to genetically define regional patterns. Genetic differentiation and connectivity were also assessed using an analysis of molecular variance (AMOVA), which showed a similar population structure (five groups) to the neighbor-joining tree. Other population features based on neutrality tests, such as Tajima’s D and Fu’s Fs statistics, showed positive values for Panama of 0.79 and 1.61, respectively. Positive values of these statistics indicate a lack of evidence for population expansion among the sampled individuals. Our results agree with previous reports suggesting that whale sharks can travel over long distances and that transboundary conservation measures may be effective for species protection.
Alluvial aquifers in Iowa have more wells with nitrate exceeding drinking-water standards than other aquifers; are susceptible to contamination by organic contaminants; and have high concentrations of naturally occurring iron and manganese in depositional areas that contain abundant organic matter. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, Iowa, studied the Cedar River alluvial aquifer in Linn County, Iowa, from 1990 to 2019 to understand the effect of municipal pumping on spatial and temporal hydrologic and water-quality variability. The Cedar River alluvial aquifer is the source of water for the city of Cedar Rapids, Iowa. Withdrawal of large quantities of water for municipal and industrial supply has altered the normal flow of water in the alluvial aquifer. Pumping induces flow from the Cedar River and the underlying bedrock aquifer into the alluvial aquifer.
Water quality in the alluvial aquifer varies along the Cedar River. Changes in nitrate, ammonia, manganese, and iron in the alluvial aquifer are seen as the upstream free-flowing reach of the Cedar River transitions to a partially regulated downstream reach, likely because of differences in reduction-oxidation conditions in the aquifer, which are controlled by infiltration from the Cedar River under normal conditions and when wells are being pumped. Nitrate, normally found in oxygenated environments, had the highest concentrations in the most upstream wells in the Seminole well field and the lowest concentrations in the most downstream wells in the East well field. In contrast, ammonia, manganese, and iron, normally found in greatest abundance in anoxic (reducing) conditions, had the greatest concentrations in the most downstream wells. Additionally, dissolved nitrate plus nitrite nitrogen concentrations in wells were substantially less and manganese concentrations were greater in production wells near backwater wetlands in contrast to wells near the Cedar River.
Temporal variability in water quality in the alluvial aquifer was driven by pumping that increased flow from the Cedar River into the alluvial aquifer and ultimately led to changes in reduction-oxidation conditions of the aquifer. Increasing dissolved nitrate plus nitrite nitrogen concentrations in the Cedar River from 1990 to 2019 were mirrored in the alluvial aquifer. Anoxic conditions are prevalent in the alluvial aquifer next to the Cedar River when the aquifer is not under pumping stress. However, production well pumping caused induced infiltration of oxygenated river water into the aquifer resulting in increased dissolved nitrate plus nitrite nitrogen concentrations and pesticides and decreased naturally occurring dissolved iron and manganese.
Hydrologic and water-quality conditions in the Cedar River alluvial aquifer from 1990 to 2019 provide baseline conditions needed to evaluate the effects of current and future nutrient reduction efforts and land-use changes in the Cedar River Basin on water quality of the Cedar River alluvial aquifer and its source water, the Cedar River. This summary and analysis provide information that can assist the City of Cedar Rapids Utilities Water Department in managing groundwater resources, and provides information that could be used develop a groundwater-quality model to characterize variability over larger areas of the alluvial aquifer, allowing water providers to plan for future water needs of their users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215110","usgsCitation":"Kalkhoff, S.J., 2021, Hydrologic and water-quality conditions in the Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019: U.S. Geological Survey Scientific Investigations Report 2021–5110, 61 p., https://doi.org/10.3133/sir20215110.","productDescription":"Report: ix, 61 p.; Data Release; Dataset","numberOfPages":"76","onlineOnly":"Y","ipdsId":"IP-121189","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390747,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5110/coverthb.jpg"},{"id":390748,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5110/sir20215110.pdf","text":"Report","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5110"},{"id":390749,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z7VKOU","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Hydrologic and water quality data from the Cedar River and Cedar River alluvial aquifer, Linn County, Iowa, 1990–2019"},{"id":390750,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Iowa","county":"Linn County","otherGeospatial":"Cedar River Alluvial Aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.3649,42.2964],[-91.3651,42.2082],[-91.3653,42.1215],[-91.3661,42.0343],[-91.3669,41.948],[-91.3677,41.8603],[-91.4836,41.8608],[-91.5989,41.8612],[-91.716,41.862],[-91.8318,41.8617],[-91.8329,41.9485],[-91.8338,42.0366],[-91.8342,42.1242],[-91.8328,42.2087],[-91.8319,42.2987],[-91.7153,42.2971],[-91.5969,42.2959],[-91.4809,42.296],[-91.3649,42.2964]]]},\"properties\":{\"name\":\"Linn\",\"state\":\"IA\"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Arizona pronghorn (Antilocapra americana) population numbers have declined over the last century due to unregulated-harvest, population fragmentation, urban expansion, and habitat loss. Captive breeding, reintroductions, and translocations have helped to curb decline and boost population numbers of the endangered Sonoran subspecies (A. a. sonoriensis). To assess the effect of on-going management actions on the Sonoran subspecies, we collected multi-locus genotype data and performed tests of genetic differentiation and population structure in comparison to the non-endangered American subspecies (A. a. americana). We provide updated estimates of genetic diversity and relatedness to serve as a benchmark for future management toward further recovery of Sonoran pronghorn. Management actions have upheld distinction between the two subspecies in Arizona and stemmed further genetic diversity loss while avoiding an increase in inbreeding within the captive-bred Sonoran population.
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These buildings include the tallest San Francisco building, the 61-story Salesforce Tower, and the tallest California building, the 73-story Wilshire Grand Tower, as well as a 51-story residential building in Los Angeles and a 24-story government building in San Diego. Various system identification methods are used to analyze the largest earthquake response records retrieved from seismic arrays installed in each of the four buildings. Significant structural dynamics characteristics (fundamental periods and critical damping percentages) are extracted. 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Since 2006 the USGS has pursued a strategy of incrementally developing and rolling out EEW for increasingly larger areas and uses. As funding from federal and state budgets grew the system became more capable, detection methods were developed and improved, core network sensor stations were built or upgraded, and partners were enlisted to deliver alerts and implement protective actions. In the fall of 2018, the system became sufficiently functional to publicly declare it “open for business” in all three states for use by licensed partners to alert personnel in limited settings and take automated machine-to-machine actions. State-wide public alerting began in California in October of 2019, expanded to Oregon in March of 2021, and to Washington in May of 2021. Today millions of people can receive ShakeAlert-powered EEW through a variety of delivery methods and dozens of machine-to-machine protective systems are in place in transportation systems, utilities, fire stations, schools, hospitals, and public and private buildings. The ShakeAlert System implementation plan calls for a supporting network of 1,675 seismic stations. 1,129 (73%) have been completed and the rest should be done by 2025.
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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Given, Douglas D. 0000-0002-3277-5121 doug@usgs.gov","orcid":"https://orcid.org/0000-0002-3277-5121","contributorId":201870,"corporation":false,"usgs":true,"family":"Given","given":"Douglas","email":"doug@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":924621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"West Coast ShakeAlert Project Team","contributorId":349872,"corporation":false,"usgs":false,"family":"West Coast ShakeAlert Project Team","affiliations":[],"preferred":false,"id":925004,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224935,"text":"sir20205100 - 2021 - Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies","interactions":[],"lastModifiedDate":"2023-03-03T15:45:09.446861","indexId":"sir20205100","displayToPublicDate":"2021-10-21T08:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5100","displayTitle":"Hydrology and Water Quality of the Great Dismal Swamp, Virginia and North Carolina, and Implications for Hydrologic-Management Goals and Strategies","title":"Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies","docAbstract":"The Great Dismal Swamp is a peat wetland in the Coastal Plain of southeastern Virginia and northeastern North Carolina. Timber harvesting and the construction of ditches to drain the swamp and facilitate the harvesting are collectively implicated in changes that altered the wetland forests, caused subsidence and decomposition of the peat, and increased the risk of fire. In response to these changes, managers have implemented strategies to control water levels and rewet the swamp using a network of 64 adjustable-height, water-control structures on the ditches. Rewetting the swamp is intended to re-establish the original wetland-forest types, reduce the risk of fire, reduce subsidence and decomposition of the peat, enhance peat accretion, and reduce the risk of fire. Knowledge of responses of the swamp to hydrologic controls, however, is critical to developing and implementing effective management goals and strategies. Because the 2008 South One fire reemphasized the need for this knowledge, the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service began studies in 2009 to identify critical hydrologic controls and responses to these controls.
These studies identified water sources, topography, the two-layered hydraulic characteristics of the peat, the absence of peat in some areas, the ditch and road network, water-control structures on the ditches, the Dismal Swamp Canal and associated infrastructure, and wetland forests as the primary hydrologic controls. Precipitation is the only water source across much of the swamp. The eastward flow of streams and groundwater from the Isle of Wight Plain, across the Suffolk scarp, and into the swamp are additional water sources to the western part of the swamp. Vertical differences in the hydraulic characteristics of the peat reflect an upper peat having a high hydraulic conductivity and specific yield overlying a lower peat and sand having lower hydraulic conductivity and specific yield. The upper peat forms the main aquifer for the storage, flow, and release of water from the swamp. Maintaining water in the upper peat is critical to water availability to the wetland forests because of these properties.
Groundwater flows from the swamp into the ditches and the Dismal Swamp Canal where it discharges into nearby streams. Discharge typically is to the closest ditch except where a spoil-pile road that impedes flow intervenes between the swamp and the ditch. When groundwater levels in a ditch are about 2 feet lower than levels in the other three ditches surrounding a part of the swamp, however, most groundwater typically discharges to the ditch having the lower level. This occurs even if a spoil-pile road intervenes between the swamp and the ditch having the lower level. Flow to a single ditch shifts watershed boundaries and groundwater divides toward the ditches having higher water levels and demonstrates how flow and discharge are controlled by ditch water levels. Consequently, managing water levels based on these and other hydrologic controls and responses is critical to achieving management objectives.
The chemistry of water across the swamp shows the effects of the peat. Dissolved organic carbon concentrations in the groundwater are among the highest reported globally, ranging from 55 to 195 milligrams per liter. The pH of groundwater and ditch water is commonly less than 4.0 standard units because of organic acids. A relation between the pH and specific conductance of groundwater and ditch water reflects water sources, flow paths, and the chemical evolution, as waters from the different sources mix and flow along the paths.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205100","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Speiran, G.K., and Wurster, F.C., 2021, Hydrology and water quality of the Great Dismal Swamp, Virginia and North Carolina, and implications for hydrologic-management goals and strategies: U.S. Geological Survey Scientific Investigations Report 2020-5100, 104 p., https://doi.org/10.3133/sir20205100.","productDescription":"xii, 104 p.","numberOfPages":"104","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108950","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":436139,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZVW9C8","text":"USGS data release","linkHelpText":"Hydrologic, water-quality, fire, forest-cover, and other data, the Great Dismal Swamp, Virginia and North Carolina"},{"id":390256,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20205100/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2020-5100"},{"id":390255,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5100/images"},{"id":390252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5100/coverthb.jpg"},{"id":390253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5100/sir20205100.pdf","text":"Report","size":"20.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5100"},{"id":390254,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5100/sir20205100.XML"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Great Dismal Swamp","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.651611328125,\n 36.575835338491736\n ],\n [\n -76.65710449218749,\n 36.41244153535644\n ],\n [\n -76.5142822265625,\n 36.32397712011261\n ],\n [\n -76.3714599609375,\n 36.36822190085109\n ],\n [\n -76.25061035156251,\n 36.4345419190089\n ],\n [\n -76.2835693359375,\n 36.85325222344016\n ],\n [\n -76.4483642578125,\n 36.87522650673951\n ],\n [\n -76.61865234374999,\n 36.84006462037767\n ],\n [\n -76.651611328125,\n 36.575835338491736\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Virginia and West Virginia Water Science Center
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This report addresses system characterization of the Indian Space Research Organisation Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2 is a medium-resolution satellite launched in 2011 on the Polar Satellite Launch Vehicle-C16 launch vehicle. Resourcesat-2 carries the same sensing elements as Resourcesat-1 (launched in October 2003) and provides continuity for the mission. The objectives of the Resourcesat mission are to provide remote sensing data services to global users, focusing on data for integrated land and water resources management.
Resourcesat-2A is identical to Resourcesat-2 and was launched in 2016 on the Polar Satellite Launch Vehicle-C36 launch vehicle for continuity of data and improved temporal resolution. The two satellites operating in tandem improved the revisit capability from 5 days to 2–3 days. The Resourcesat-2 platform is of Indian Remote Sensing Satellites-1C/1D–P3 heritage and was built by the Indian Space Research Organisation. Resourcesat-2 and Resourcesat-2A carry the Advanced Wide Field Sensor and LISS–3, as well as the Linear Imaging Self Scanning-4 for medium-resolution imaging. More information on Indian Space Research Organisation satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that LISS–3 has an interior geometric performance in the range of −4.620 (−0.154 pixel) to 13.230 meters (m; 0.441 pixel) in easting and −12.360 (−0.412 pixel) to 1.500 m (0.050 pixel) in northing in band-to-band registration, an exterior geometric error of −27.805 (−0.927 pixel) to 26.578 m (0.886 pixel) in easting and −35.341 (−1.178 pixel) to −6.286 m (−0.210 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.096 to 0.036 in offset and 0.585–0.946 in slope, and a spatial performance in the range of 1.87–1.95 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.045–0.070.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030H","usgsCitation":"Ramaseri Chandra, S.N., Christopherson, J., Anderson, C., Stensaas, G.L., and Kim, M., 2021, System characterization report on Resourcesat-2 Linear Imaging Self Scanning-3 (LISS–3) sensor (ver. 1.2, December 2024), chap. H of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 20 p., https://doi.org/10.3133/ofr20211030H.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126659","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":433262,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1030/h/versionHist.txt","text":"Version History","size":"2.07 KB","linkFileType":{"id":2,"text":"txt"}},{"id":390427,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/h/images"},{"id":390426,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.xml","size":"75.7 kB","linkFileType":{"id":8,"text":"xml"}},{"id":390425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/h/ofr20211030h.pdf","text":"Report","size":"3.06 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–H"},{"id":390424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/h/coverthb4.jpg"},{"id":464526,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030H/full"}],"edition":"Version 1.0: October 21, 2021; Version 1.1: August 29, 2024; Version 1.2: December 2, 2024","contact":"Director, Earth Resources Observation and Science Center
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47914 252nd Street
Sioux Falls, SD 57198
Many applications in wildlife management require knowledge of the sex of individual animals. The Yuma Ridgway's rail Rallus obsoletus yumanensis is an endangered marsh bird with monomorphic plumage and secretive behaviors, thereby complicating sex determination in field studies. We collected morphometric measurements from 270 adult Yuma Ridgway's rails and quantified the plumage and mandible color of 91 of those individuals throughout their geographic range to evaluate intersexual differences in morphology and coloration. We genetically sexed a subset of adult Yuma Ridgway's rails (n = 101) and used these individuals to determine the optimal combination of measurements (based on discriminant function analyses) to distinguish between sexes. Males averaged significantly larger than females in all measurements, and the optimal discriminant function contained whole leg, culmen, and tail measurements and classified correctly 97.8% (95% CI: 92.5–100.0%) of genetically sexed individuals. We used two additional functions that classified correctly ≥ 95.6% of genetically sexed Yuma Ridgway's rails to assign sex to individuals with missing measurements. These simple models provide managers and researchers with a practical tool to determine the sex of Yuma Ridgway's rails based on morphometric measurements. Although color measurements were not in the most accurate discriminant functions, we quantified subtle intersexual differences in the color of mandibles and greater coverts of Yuma Ridgway's rails. These results document sex-specific patterns in coloration that allow future researchers to test hypotheses to determine the mechanisms underlying sex-based differences in plumage coloration.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-20-095","usgsCitation":"Conway, C.J., Harrity, E.J., and Michael, L.E., 2021, Sexual dimorphism in morphology and plumage of endangered Yuma Ridgway’s Rails: A model for documenting sex: Journal of Fish and Wildlife Management, v. 12, no. 2, p. 464-474, https://doi.org/10.3996/JFWM-20-095.","productDescription":"11 p.","startPage":"464","endPage":"474","ipdsId":"IP-126314","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450384,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-20-095","text":"Publisher Index Page"},{"id":397003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.8505859375,\n 32.38923910985902\n ],\n [\n -111.29150390625,\n 32.38923910985902\n ],\n [\n -111.29150390625,\n 36.62434536776987\n ],\n [\n -116.8505859375,\n 36.62434536776987\n ],\n [\n -116.8505859375,\n 32.38923910985902\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrity, E. J.","contributorId":288332,"corporation":false,"usgs":false,"family":"Harrity","given":"E.","email":"","middleInitial":"J.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":837765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michael, L. E.","contributorId":288333,"corporation":false,"usgs":false,"family":"Michael","given":"L.","email":"","middleInitial":"E.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":837766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229718,"text":"70229718 - 2021 - Diets and stable isotope signatures of native and nonnative Leucisid fishes advances our understanding of the Yellowstone Lake food web","interactions":[],"lastModifiedDate":"2022-03-16T16:44:09.597649","indexId":"70229718","displayToPublicDate":"2021-10-20T11:39:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6476,"text":"Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Diets and stable isotope signatures of native and nonnative Leucisid fishes advances our understanding of the Yellowstone Lake food web","docAbstract":"(1) Many forage fishes, such as Leucisids (minnows) have depauperate studies on diet composition or stable isotope signatures, as these fishes are often only viewed as food for higher trophic levels. The need exists to understand and document the diet and stable isotope signatures of Leucisids (redside shiner, longnose dace, lake chub) in relation to the community ecology and food-web dynamics in Yellowstone Lake, especially given an invasive piscivore introduction and potential future effects of climate change on the Yellowstone Lake ecosystem. (2) Diet data collected during summer of 2020 were analyzed by species using proportion by number, frequency of occurrence, and mean proportion by weight, and diet overlap was compared using Schoener’s index (D). Stable isotope (δ15N and δ13C) values were estimated by collecting tissue during the summer of 2020 by species, and isotopic overlap was compared using 40% Bayesian ellipses. (3) Nonnative redside shiners and lake chub had similar diets, and native longnose dace diet differed from the nonnative Leucisids. Diet overlap was also higher between the nonnative Leucisids, and insignificant when comparing native and nonnative Leucisids. No evidence existed to suggest a difference in δ15N signatures among the species. Longnose dace had a mean δ13C signature of −15.65, indicating an decreased reliance on pelagic prey compared to nonnative Leucisids. Nonnative redside shiners and lake chub shared 95% of isotopic niche space, but stable isotope overlap was <25% for comparisons between native longnose dace and the nonnative Leucisids. (4) This study established the diet composition and stable isotope signatures of Leusicids residing in Yellowstone Lake, thus expanding our knowledge of Leucisid feeding patterns and ecology in relation to the native and nonnative species in the ecosystem. We also expand upon our knowledge of Leucisids in North America. Additionally, quantifying minnow diets can provide a baseline for understanding food web response to invasive suppression management actions.
","language":"English","publisher":"MDPI","doi":"10.3390/fishes6040051","usgsCitation":"Glassic, H., Guy, C.S., and Koel, T., 2021, Diets and stable isotope signatures of native and nonnative Leucisid fishes advances our understanding of the Yellowstone Lake food web: Fishes, v. 6, no. 4, 51, 10 p., https://doi.org/10.3390/fishes6040051.","productDescription":"51, 10 p.","ipdsId":"IP-130868","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450387,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fishes6040051","text":"Publisher Index Page"},{"id":397183,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Lake Yellowstone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58837890625,\n 44.23929609118664\n ],\n [\n -110.15,\n 44.23929609118664\n ],\n [\n -110.15,\n 44.60806814444478\n ],\n [\n -110.58837890625,\n 44.60806814444478\n ],\n [\n -110.58837890625,\n 44.23929609118664\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Glassic, Hayley C.","contributorId":288563,"corporation":false,"usgs":false,"family":"Glassic","given":"Hayley C.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":838086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koel, Todd M.","contributorId":288564,"corporation":false,"usgs":false,"family":"Koel","given":"Todd M.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":838087,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225534,"text":"pp1867G - 2021 - A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","interactions":[{"subject":{"id":70225534,"text":"pp1867G - 2021 - A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","indexId":"pp1867G","publicationYear":"2021","noYear":false,"chapter":"G","displayTitle":"A Decade of Geodetic Change at Kīlauea’s Summit— Observations, Interpretations, and Unanswered Questions from Studies of the 2008–2018 Halema‘uma‘u Eruption","title":"A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":1}],"isPartOf":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"lastModifiedDate":"2024-06-26T15:54:23.569219","indexId":"pp1867G","displayToPublicDate":"2021-10-20T10:42:24","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1867","chapter":"G","displayTitle":"A Decade of Geodetic Change at Kīlauea’s Summit— Observations, Interpretations, and Unanswered Questions from Studies of the 2008–2018 Halema‘uma‘u Eruption","title":"A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption","docAbstract":"On March 19, 2008, a small explosion heralded the onset of an extraordinary eruption at the summit of Kīlauea Volcano. The following 10 years provided unprecedented access to an actively circulating lava lake located within a region monitored by numerous geodetic tools, including Global Navigation Satellite System (GNSS), interferometric synthetic aperture radar (InSAR), tilt, and gravity. These datasets revealed a range of processes occurring on widely different timescales. Over years, pressure change within the summit magmatic system, determined from ground deformation and lava-lake surface height, seems to have been governed by broad variations in the rate of magma supply from the mantle to the volcano’s shallow magmatic system, as well as changes in the efficiency of East Rift Zone (ERZ) magma transport and eruption. Over weeks to months, intrusions at the summit and along the ERZ, where new eruptive vents commonly formed and intrusions were primed by extension from south-flank motion, were a result of short-term increases in magma supply or waning lava effusion from the ERZ. Waning lava effusion caused magma to back up behind the ERZ eruptive vent all the way to the summit. ERZ intrusions and eruptions caused rapid depressurization of the summit magmatic system, whereas summit intrusions resulted in complex deformation patterns as magma moved to and from two main sub-caldera storage areas. Over hours to days, pressure changes were caused by episodic deflation-inflation (DI) events and possibly small summit intrusions, and deformation of the rim of the summit eruptive vent revealed instabilities that indicated an increased potential for collapse and minor explosive activity. Finally, over timescales of minutes to hours, gas pistoning, summit explosions, very-long-period seismic events, and even the airborne eruptive plume had clear manifestations in geodetic datasets, providing insights into the causes and consequences of those processes. The diversity and quantity of geodetic observations shed important light on this exceptional and well-documented decade-long summit eruption and its accompanying phenomena, yet numerous questions remain about the causal mechanisms, physical processes, and magmatic conditions associated with eruptive and intrusive activity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1867G","usgsCitation":"Poland, M.P., Miklius, A., Johanson, I.A., and Anderson, K.R., 2021, A decade of geodetic change at Kīlauea’s summit—Observations, interpretations, and unanswered questions from studies of the 2008–2018 Halemaʻumaʻu eruption, chap. G of Patrick, M., Orr, T., Swanson, D., and Houghton, B., eds., The 2008–2018 summit lava lake at Kīlauea Volcano, Hawaiʻi: U.S. Geological Survey Professional Paper 1867, 29 p., https://doi.org/10.3133/pp1867G.","productDescription":"vi, 29 p.","numberOfPages":"29","onlineOnly":"N","ipdsId":"IP-123914","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":390677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1867/g/pp1867g.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":390676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1867/g/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.32539367675778,\n 19.37334071336406\n ],\n [\n -155.20797729492188,\n 19.37334071336406\n ],\n [\n -155.20797729492188,\n 19.454938719968595\n ],\n [\n -155.32539367675778,\n 19.454938719968595\n ],\n [\n -155.32539367675778,\n 19.37334071336406\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact HVO
Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue
Suite A-8
Hilo, HI 96720
Increases in nitrogen applications to the land surface since the 1950s have led to a cascade of negative environmental impacts, including degradation of drinking water supplies, nutrient enrichment of aquatic ecosystems and contributions to global climate change. In this study, groundwater, streambed porewater, and stream sampling were used to establish trends in nitrate concentrations and how redox gradients influence nitrate transport across diverse glacial terranes. Decadal sampling has found that elevated nitrate concentrations in shallow groundwater beneath cropland have been sustained for decades. Redox gradients established in the saturated zone using dissolved O2, iron, nitrate and excess N2 from denitrification suggest that nitrate-bearing zones are thin in glacial terranes dominated by fine materials. These thin nitrate-bearing zones lead to suboxic, low nitrate streambed porewater and limit the contributions of nitrate to streams from slow-flow groundwater. In contrast, thick oxic zones in more coarse-grained glacial terranes allow nitrate to reach deeper groundwater, resulting in streambed porewater with elevated nitrate concentrations and causing a large portion of stream nitrate to be derived from slow-flow groundwater. Groundwater age tracer data indicate that denitrification occurs more quickly in the terrane dominated by fine material than in the more coarse-grained terrane. The quicker depletion of nitrate in the more fine-grained terrane suggests that the thinner oxic zone in this terrane is due, in part, to the greater availability and reactivity of electron donors in this terrane than in the more coarse-grained terrane. Groundwater age tracer data and hydrograph separation analysis suggest that saturated zone lag times between when changes in land use practices occur and when changes in stream water are fully observed may vary widely across hydrogeologic settings.
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