Understanding recruitment dynamics is an essential part of effective fisheries management, whether the focus is on conservation, harvest policy development, or invasive species control. We developed a model that estimates lake-wide Ricker stock-recruitment relations for invasive sea lampreys (Petromyzon marinus) in each of the five Laurentian Great Lakes to inform future control efforts. We fit adult-to-adult models, taking advantage of a long time series of lake-wide, adult, sea lamprey abundance estimates. We incorporated proportional contributions at age for the stock as well as additional explanatory variables sea lamprey weight, as a surrogate for fecundity, and lampricide quantity applied, as a surrogate for anthropogenic mortality, to explain residual recruitment variability. The best model incorporated equal cohort contributions from the adult stock (that matured 5, 6, and 7 years prior to recruitment), a single productivity parameter (
Standard approaches to probabilistic seismic‐hazard assessment (PSHA) assume that earthquakes are random, independent events that follow a Poisson distribution of occurrences in a given time period (Cornell, 1968). To overcome the limitations of the Poisson assumption, such as ignoring earthquake clustering, we introduce an analytic method for PSHA that uses order statistics to allow for arbitrary distributions of earthquake occurrence. Cornell (1968) used the Poisson assumption to achieve a computationally efficient method that enables users to explore the impact of parameters used in earthquake occurrence and ground‐motion models. We apply our order statistics method to the highly clustered seismicity associated with caldera collapses at Kīlauea and explore the general implications of non‐Poisson behavior for PSHA. We find that non‐Poisson behavior has the greatest impact for high probabilities of exceedance, low‐mean rates of occurrence, and multiple exceedances. Those conditions can be important for applications such as operating standards for buildings and infrastructure engineering, standards for temporary structures and during construction, the insurance industry, the design of earthquake early warning, and to assess hazards due to clustered processes such as aftershock sequences and earthquake swarms. The commonly used rate of exceedance hides the difference between the hazard due to a non‐Poisson distribution and a Poisson distribution with the same mean rate of earthquakes. Thus, including non‐Poisson behavior in PSHA means that we must plot and discuss PSHA results as the probability and not the rate of exceedance.
In arid and semiarid environments, evapotranspiration (ET) is the primary discharge component in the water balance, with potential ET exceeding precipitation. For this reason, reliable estimates of ET are needed to construct accurate water budgets in these environments. Remote sensing affords the ability to provide fast, accurate, field-scale ET estimates, but these methods have largely been restricted to deep rooted (phreatophytic) plant communities underlain by shallow groundwater. We used 13 years of data from a 3-ha drainage lysimeter in a semiarid sagebrush steppe and Landsat normalized difference vegetation index (NDVI) data to calibrate a generalized least squares model capable of predicting vadose zone ET in a high elevation upland ecosystem. Annual precipitation was the best predictor of annual ET, as they were nearly balanced every year analysed (mean difference = 3 mm). We incorporated reference crop ET and a linear combination of NDVI and precipitation to capably predict ET on a subannual, lag-determined interval of 48 days, with a mean error of only 9.92% across all observations. To our knowledge, this is the first vegetation index-ET algorithm calibrated in a semiarid upland plant community using field-scale lysimetry. Vadose zone ET is particularly important at waste disposal sites in the Desert Southwest, where accurate and spatially explicit ET estimates are needed for monitoring potential mobilization and transport of contaminants past the root zone into local aquifers and for monitoring and modelling effects of recharge on flow and transport of contaminants in underlying aquifers.
U.S. coastal economies and communities are facing an unprecedented and growing number of impacts to coastal ecosystems including beach and fishery closures, harmful algal blooms, loss of critical habitat, as well as shoreline damage. This paper synthesizes our present understanding of the dynamics of human and ecosystem health in coastal systems with a focus on the need to better understand nearshore physical process interactions with coastal pollutants and ecosystems (e.g. fate and transport, circulation, depositional environment, climate change). It is organized around two major topical areas and six subtopic areas: 1) Identifying and mitigating coastal pollutants, including fecal pollution, nutrients and harmful algal blooms, and microplastics; and 2) Resilient coastal ecosystems, which focuses on coastal fisheries, shellfish and natural and nature-based features (NNBF). Societal needs and the tools and technologies needed to address them are discussed for each subtopic. Recommendations for scientific research, observations, community engagement, and policies aim to help prioritize future research and investments. A better understanding of coastal physical processes and interactions with coastal pollutants and resilient ecosystems (e.g. fate and transport, circulation, depositional environment, climate change) is a critical need. Other research recommendations include the need to quantify potential threats to human and ecosystem health through accurate risk assessments and to quantify the resulting hazard risk reduction of natural and nature-based features; improve pollutant and ecosystem impacts forecasting by integrating frequent and new data points into existing and novel models; collect environmental data to calibrate and validate models to predict future impacts on coastal ecosystems and their evolution due to anthropogenic stressors (land-based pollution, overfishing, coastal development), climate change, and sea level rise; and develop lower cost and rapid response tools to help coastal managers better respond to pollutant and ecosystem threats.
","language":"English","publisher":"American Shore and Beach Preservation Association (ASBPA)","doi":"10.34237/1009018","usgsCitation":"Elko, N., Foster, D., Kleinheinz, G., Raubenheimer, B., Brander, S., Kinzelman, J., Kritzer, J.P., Munroe, D., Storlazzi, C.D., Sutula, M., Mercer, A., Coffin, S., Fraioli, C., Ginger, L., Morrison, E., Parent-Doliner, G., Akan, C., Canestrelli, A., DiBenedetto, M., Lang, J., and Simm, J., 2022, Human and ecosystem health in coastal systems: Shore and Beach, v. 90, no. 1, p. 64-91, https://doi.org/10.34237/1009018.","productDescription":"28 p.","startPage":"64","endPage":"91","ipdsId":"IP-137023","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":396764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"90","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Elko, Nicole","contributorId":287920,"corporation":false,"usgs":false,"family":"Elko","given":"Nicole","affiliations":[{"id":61663,"text":"American Shore and Beach Preservation Association","active":true,"usgs":false}],"preferred":false,"id":837196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Diane","contributorId":194421,"corporation":false,"usgs":false,"family":"Foster","given":"Diane","affiliations":[],"preferred":false,"id":837197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kleinheinz, Gregory","contributorId":287921,"corporation":false,"usgs":false,"family":"Kleinheinz","given":"Gregory","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":837198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Raubenheimer, Britt","contributorId":194340,"corporation":false,"usgs":false,"family":"Raubenheimer","given":"Britt","email":"","affiliations":[],"preferred":false,"id":837199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brander, Suzanne","contributorId":287922,"corporation":false,"usgs":false,"family":"Brander","given":"Suzanne","email":"","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":837200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kinzelman, Julie","contributorId":207713,"corporation":false,"usgs":false,"family":"Kinzelman","given":"Julie","affiliations":[{"id":37612,"text":"City of Racine Health Department","active":true,"usgs":false}],"preferred":false,"id":837201,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kritzer, Jacob P.","contributorId":287923,"corporation":false,"usgs":false,"family":"Kritzer","given":"Jacob","email":"","middleInitial":"P.","affiliations":[{"id":61666,"text":"Northeastern Regional Association of Coastal Ocean Observing Systems","active":true,"usgs":false}],"preferred":false,"id":837202,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Munroe, Daphne","contributorId":268069,"corporation":false,"usgs":false,"family":"Munroe","given":"Daphne","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":837203,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science 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Board","active":true,"usgs":false}],"preferred":false,"id":837207,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Fraioli, Carolyn","contributorId":287927,"corporation":false,"usgs":false,"family":"Fraioli","given":"Carolyn","email":"","affiliations":[{"id":61667,"text":"New York State Department of State","active":true,"usgs":false}],"preferred":false,"id":837208,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ginger, Luke","contributorId":287928,"corporation":false,"usgs":false,"family":"Ginger","given":"Luke","email":"","affiliations":[{"id":61668,"text":"Heal the Bay","active":true,"usgs":false}],"preferred":false,"id":837209,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Morrison, Elise","contributorId":287929,"corporation":false,"usgs":false,"family":"Morrison","given":"Elise","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":837210,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Parent-Doliner, Gabrielle","contributorId":287930,"corporation":false,"usgs":false,"family":"Parent-Doliner","given":"Gabrielle","email":"","affiliations":[{"id":61669,"text":"Water Rangers","active":true,"usgs":false}],"preferred":false,"id":837211,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Akan, Cigdem","contributorId":287931,"corporation":false,"usgs":false,"family":"Akan","given":"Cigdem","email":"","affiliations":[{"id":61670,"text":"University of Northern Florida","active":true,"usgs":false}],"preferred":false,"id":837212,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Canestrelli, Alberto","contributorId":287932,"corporation":false,"usgs":false,"family":"Canestrelli","given":"Alberto","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":837213,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"DiBenedetto, Michelle","contributorId":287933,"corporation":false,"usgs":false,"family":"DiBenedetto","given":"Michelle","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":837214,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Lang, Jackelyn","contributorId":287934,"corporation":false,"usgs":false,"family":"Lang","given":"Jackelyn","email":"","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":837215,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Simm, Jonathan","contributorId":287935,"corporation":false,"usgs":false,"family":"Simm","given":"Jonathan","email":"","affiliations":[{"id":61671,"text":"H.R. Wallingford","active":true,"usgs":false}],"preferred":false,"id":837216,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70262533,"text":"70262533 - 2022 - Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains","interactions":[],"lastModifiedDate":"2025-01-23T17:03:28.007798","indexId":"70262533","displayToPublicDate":"2022-03-01T10:58:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains","docAbstract":"In the central Appalachians of Virginia and West Virginia, the Virginia northern flying squirrel (Glaucomys sabrinus fuscus; VNFS) is a subspecies of northern flying squirrel generally associated with red spruce (Picea rubens)-dominated forests at high elevations. Listed as endangered by the
U.S. Fish and Wildlife Service from 1985 to 2013, the VNFS currently is the subject of a 10-year post-delisting assessment. Still considered a state-listed species in Virginia and a species of greatest conservation need in West Virginia, the VNFS serves as a focal target for red spruce restoration activities in the High Allegheny Region (HAR) of the two states. Owing to the cryptic nature of VNFS and its low detection probability in live-capture surveys, managers in the region rely on habitat models to assess probable presence. Using long-term nest-box, live-trapping, and radio-telemetry data matched with updated high elevation forest-type coverage data for the region, we created a new VNFS resource selection function and spatial coverage map. Inputting red spruce cover, increasing elevation, and decreasing landform index (increasing site shelteredness) composed the best model explaining VNFS occurrence. The calculated amount of low-quality habitat was congruent with previous modeling efforts; however, inclusion of more VNFS occurrence records in the current effort indicated that previous efforts substantially underestimated the amount (>400%) of extant high quality VNFS habitat. We estimate the HAR to contain approximately 197,952 ha with ≥0.50 predicted probability of occurrence of VNFS. In addition to potentially improving current and future VNFS live-capture surveys, with this model managers may better target forests for red spruce restoration to increase high elevation forest ecological integrity and to improve habitat patch connectedness for VNFS.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Ford, W., Diggins, C., De La Cruz, J., and Silvis, A., 2022, Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 9, p. 168-175.","productDescription":"8 p.","startPage":"168","endPage":"175","ipdsId":"IP-129565","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia, West Virginia","otherGeospatial":"High Allegheny Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.54094480023322,\n 36.94509861616578\n ],\n [\n -79.74649202772338,\n 36.97475579082641\n ],\n [\n -77.97606039422519,\n 38.33504579423493\n ],\n [\n -77.55782664323384,\n 39.373334368570056\n ],\n [\n -78.88106642468261,\n 39.62337985209078\n ],\n [\n -80.73280550358555,\n 38.17478880030487\n ],\n [\n -80.94924239314835,\n 37.25410993243544\n ],\n [\n -80.54094480023322,\n 36.94509861616578\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. 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While expert decisions can add confidence to aspects of the modeling process by ensuring only reasonable models are employed, expert decisions also introduce human bias into assessments. This bias presents a source of error that may affect the performance of the models and resulting resource estimates. Our study aims to reduce expert input through robust data-driven analyses and better-suited data science techniques, with the goals of saving time, reducing bias, and improving predictive ability. We present six favorability maps for geothermal resources in the western United States created using two strategies applied to three modern machine learning algorithms (logistic regression, support-vector machines, and XGBoost). To provide a direct comparison to previous assessments, we use the same input data as the 2008 U.S. Geological Survey (USGS) conventional moderate- to high-temperature geothermal resource assessment. The six new favorability maps required far less expert decision-making, but broadly agree with the previous assessment. Despite the fact that the 2008 assessment results employed linear methods, the non-linear machine learning algorithms (i.e., support-vector machines and XGBoost) produced greater agreement with the previous assessment than the linear machine learning algorithm (i.e., logistic regression). It is not surprising that geothermal systems depend on non-linear combinations of features, and we postulate that the expert decisions during the 2008 assessment accounted for system non-linearities. Substantial challenges to applying machine learning algorithms to predict geothermal resource favorability include severe class imbalance (i.e., there are very few known geothermal systems compared to the large area considered), and while there are known geothermal systems (i.e., positive labels), all other sites have an unknown status (i.e., they are unlabeled), instead of receiving a negative label (i.e., the known/proven absence of a geothermal resource). We address both challenges through a custom undersampling strategy that can be used with any algorithm and then evaluated using F1 scores.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 47th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"47th Stanford Geothermal Workshop","conferenceDate":"Feb 7-9, 2022","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Mordensky, S.P., Lipor, J., DeAngelo, J., Burns, E., and Lindsey, C.R., 2022, Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions, in Proceedings, 47th workshop on geothermal reservoir engineering, Stanford, CA, Feb 7-9, 2022, 18 p.","productDescription":"18 p.","ipdsId":"IP-135047","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":401053,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401039,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=35430"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.25976562499999,\n 31.728167146023935\n ],\n [\n -103.0078125,\n 31.952162238024975\n ],\n [\n -102.74414062499999,\n 36.80928470205937\n ],\n [\n -103.623046875,\n 41.902277040963696\n ],\n [\n -104.150390625,\n 49.15296965617042\n ],\n [\n -123.134765625,\n 48.980216985374994\n ],\n [\n -123.662109375,\n 48.22467264956519\n ],\n [\n -125.24414062499999,\n 48.22467264956519\n ],\n [\n -124.892578125,\n 42.68243539838623\n ],\n [\n -124.71679687499999,\n 39.774769485295465\n ],\n [\n -120.76171875,\n 34.45221847282654\n ],\n [\n -117.50976562499999,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.76880048488168\n ],\n [\n -110.830078125,\n 31.42866311735861\n ],\n [\n -107.9296875,\n 31.57853542647338\n ],\n [\n -106.25976562499999,\n 31.728167146023935\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843643,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":843644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Cary Ruth 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":292016,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843647,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236380,"text":"70236380 - 2022 - The Coles Hill uranium deposit, Virginia, USA: Geology, geochemistry, geochronology, and genetic model","interactions":[],"lastModifiedDate":"2022-09-22T18:58:36.948329","indexId":"70236380","displayToPublicDate":"2022-03-01T09:48:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"The Coles Hill uranium deposit, Virginia, USA: Geology, geochemistry, geochronology, and genetic model","docAbstract":"The Coles Hill uranium deposit with an indicated resource of about 130 million lbs. of U3O8 is the largest unmined uranium deposit in the United States. The deposit is hosted in the Taconian (approximately 480 – 450 Ma) Martinsville igneous complex, which consists of the Ordovician Leatherwood Granite (granodiorite) and Silurian Rich Acres Formation (diorite). The host rock was metamorphosed to orthogneiss during the Alleghanian orogeny (approximately 325 – 260 Ma) when it also underwent dextral strike-slip movement along the Brookneal shear zone. During the Triassic, extensional tectonics led to the development of the Dan River Basin that lies east of Coles Hill. The mineralized zone is hosted in brittle structures in the footwall of the Triassic Chatham fault that forms the western edge of the basin. Within brittle fracture zones, uranium silicate and uranium-bearing fluorapatite with traces of brannerite form veins and breccia fill with chlorite, quartz, calcite, titanium oxide, pyrite, and calcite. Uranium silicates also coat and replace primary titanite, zircon, ilmenite, and sulfides. Sodium metasomatism preceded and accompanied uranium mineralization, pervasively altering host rock, and forming albite from primary feldspar, depositing limpid albite rims on feldspar, altering titanite to titanium oxide and calcite and forming riebeckite. Various geothermometers suggest temperatures of less than approximately ~200°C during mineralization. In situ U-Pb analyses of titanite, Ti-oxide, and apatite, and Rb/Sr and U/Pb isotope-systematics of whole rock samples resolve the timing of geologic processes affecting Coles Hill. The host Leatherwood granite containing primary euhedral titanite is dated at 450 – 445 Ma, in agreement with previously obtained ages from zircon in the Martinsville igneous complex. A regional metamorphic event at 330 – 310 Ma formed anhedral titanite and some apatite, re-equilibrated whole rock Rb/Sr and U-Pb isotopes and is interpreted to have coincided with movement along the Brookneal shear zone. During shearing and metamorphism primary refractory uranium-bearing minerals including titanite, zircon, and uranothorite were recrystallized and uranium was liberated and incorporated locally into hematite, clay, and other fine-grained minerals. Uranium mineralization was accompanied by a metasomatic episode between 250 and 200 Ma that reset the Rb-Sr and U-Pb isotope systems, forming titanite and apatite that are associated and in places intimately intergrown with uranium silicate dating mineralization. This event coincides with rifting that formed the Dan River Basin and was a precursor to the breakup of Pangea. Based on the close spatial and temporal association of uranium with apatite, we conclude that uranium was carried as a uranyl-phosphate complex. The release of calcium during sodium metasomatic alteration of primary calcic feldspar and titanite in the host rock initiated successive reactions in which uranium and phosphate in mineralizing fluids combined with calcium to form U-enriched fluorapatite. Excess uranium was locally reduced by coupled redox reactions involving ferrous iron and sulfide minerals in the host rock, forming uranium silicates. Based on the deposit mineralogy, oxygen isotope geochemistry and trace element characteristics of uranium silicate and gangue minerals the primary mineralizing fluids likely included connate and/or meteoric water sourced from the local Dan River Basin. High heat flow related to Mesozoic rifting may have driven these (P-Na-F-rich) brines through local aquifers and into basin margin faults, transporting uranium from the basin or mobilizing uranium from previously formed U-minerals in the Brookneal shear zone, or from U-enriched older basement rock.
","language":"English","publisher":"Geoscience World","doi":"10.5382/econgeo.4874","usgsCitation":"Hall, S., Beard, J., Potter, C.J., Bodnar, R., Neymark, L.A., Paces, J.B., Johnson, C.A., Breit, G., Zielinski, R.A., and Aylor, G.J., 2022, The Coles Hill uranium deposit, Virginia, USA: Geology, geochemistry, geochronology, and genetic model: Economic Geology, v. 117, no. 2, p. 273-304, https://doi.org/10.5382/econgeo.4874.","productDescription":"32 p.","startPage":"273","endPage":"304","ipdsId":"IP-114752","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":467196,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5382/econgeo.4874","text":"External 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Jr.","contributorId":296174,"corporation":false,"usgs":false,"family":"Aylor","given":"G.","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":27992,"text":"Virginia Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":850835,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70237201,"text":"70237201 - 2022 - USGS invasive carp database management and integration support","interactions":[],"lastModifiedDate":"2022-10-04T14:55:20.0136","indexId":"70237201","displayToPublicDate":"2022-03-01T09:47:50","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"USGS invasive carp database management and integration support","docAbstract":"Bigheaded carps (Bighead Carp and Silver Carp) tracking, monitoring, and contracted removal will continue throughout the Illinois River and Upper Mississippi River as part of an adaptive management effort to mitigate, control, and contain bigheaded carps. Other fish will also be tracked to maintain a holistic view of the transmitter distribution in the Upper Illinois River Waterway. To facilitate these actions, a need to compile and analyze invasive carp-related data from all agencies exists. Invasive carp-related data include all data sources that could inform the MRWG objectives or projects. These data, often in disparate formats, must be integrated into a common format that allows all agencies the opportunity to assess invasive carp monitoring, control, and removal efforts. Ensuring the interoperability of these datasets allows for their use in various analyzes and modeling efforts. Implementing an interoperable data management framework provides the mechanisms for end users to find and use integrated data. Integrating data for use in modeling and analysis furthers the partnership’s collective understanding of bigheaded carp life history, distribution, and movement and can be used to facilitate adaptive management actions (e.g., directing monitoring, sampling, and removal efforts, assessing invasive carp abundance to support modeling efforts, informing deployment of control actions, etc.). An effective data management strategy will streamline the data update process, providing all agencies with timely data and analyses in support of informed decision-making processes.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2021 Invasive carp interim summary report","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Invasive Carp Regional Coordinating Committee","usgsCitation":"Harrison, T.J., Brey, M.K., and Stone, J., 2022, USGS invasive carp database management and integration support, 4 p.","productDescription":"4 p.","startPage":"109","endPage":"112","ipdsId":"IP-139244","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":407860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407845,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Illinois River, upper Mississippi River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.14306640625,\n 36.421282443649496\n ],\n [\n -88.11035156249999,\n 40.6306300839918\n ],\n [\n -87.73681640625,\n 41.95131994679697\n ],\n [\n -88.681640625,\n 43.644025847699496\n ],\n [\n -89.12109375,\n 46.118941506107056\n ],\n [\n -91.0986328125,\n 46.51351558059737\n ],\n [\n -93.1640625,\n 46.483264729155586\n ],\n [\n -93.80126953124999,\n 47.754097979680026\n ],\n [\n -94.833984375,\n 48.06339653776211\n ],\n [\n -96.3720703125,\n 46.73986059969267\n ],\n [\n -96.26220703125,\n 44.809121700077355\n ],\n [\n -94.8779296875,\n 42.21224516288584\n ],\n [\n -92.548828125,\n 39.57182223734374\n ],\n [\n -91.1865234375,\n 37.37015718405753\n ],\n [\n -90.10986328125,\n 36.98500309285596\n ],\n [\n -90.28564453124999,\n 36.63316209558658\n ],\n [\n -89.80224609374999,\n 36.12012758978146\n ],\n [\n -89.14306640625,\n 36.421282443649496\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":853659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stone, Jayme 0000-0002-0512-3072","orcid":"https://orcid.org/0000-0002-0512-3072","contributorId":251712,"corporation":false,"usgs":false,"family":"Stone","given":"Jayme","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":853618,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70264283,"text":"70264283 - 2022 - Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020","interactions":[],"lastModifiedDate":"2025-03-10T15:02:41.714948","indexId":"70264283","displayToPublicDate":"2022-03-01T09:41:36","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020","docAbstract":"We sampled juvenile wild Steelhead Trout Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize population attributes and investigate life-history metrics, particularly migratory patterns, and early life-stage survival. We used passive integrated transponder (PIT) tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles and adults. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead Trout have been released in the Wind River subbasin since 1997, and hatchery adults are estimated at less than one percent of spawners in most years. Over twenty years of Steelhead Trout status and trend monitoring and research in the subbasin is contributing to understanding of population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, which had an outdated adult ladder and contributed to increased water temperatures reducing performance of juvenile Steelhead Trout.
Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research, Monitoring, and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (https://www.cbfish.org/ProgramStrategy.mvc/Index). Specifically, this work addresses the substrategies of 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and Uncertainties Research regarding differing life histories of a wild Steelhead Trout population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives.
During summer and fall 2020, we PIT-tagged 1,415 Steelhead parr (age-0 and age-1) in the Trout Creek and upper Wind River watersheds. Recaptures and detections of PIT-tagged Steelhead Trout parr happened through repeat headwater sampling, smolt trap operations, and instream PTISs and Columbia River PIT-tag detection infrastructure. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead Trout parr, smolts, and adults, providing data to population assessments, and life-cycle research and modeling.
Detection data from PIT-tagged adult Steelhead Trout at PTISs allow assessment of adult escapement to tributary watersheds within the Wind River subbasin. Adult Steelhead Trout detection efficiency estimates at our primary PTIS in Trout Creek have been greater than 92 percent during eight of the past nine years and have exceeded 90% at our primary PTIS in the Wind River the past three years. Adult escapement estimates to tributary watersheds are helping evaluate the efficacy of the 2009 removal of Hemlock Dam from rkm 2.0 of Trout Creek. The dam had potential negative effects on Steelhead Trout populations in Trout Creek due to hydrologic impairment, increased temperatures, and adult passage issues. Hemlock Dam was laddered for adult passage, but not to modern standards, which likely resulted in avoidance by some adult Steelhead Trout.
We continue to improve our PTISs in the Wind River subbasin. The improvements in siting and addition of grid power to the upper Wind River PTIS (site code WRU, rkm 27.6) during 2016 and 2017, and the addition of the Mine Reach site (site code MIN, rkm 36.0) have much improved PIT-tagged fish monitoring in the upper Wind River watershed. The paired PTIS design in the upper Wind River watershed (sites WRU and MIN), matches that in the Trout Creek watershed (sites TRC and TC4) and will allow comparisons of Steelhead Trout population metrics between the two watersheds as response to Hemlock Dam removal continues and future restoration efforts occur in Trout Creek. We installed two new PTISs during 2020. Both were installed downstream of our primary interrogation sites on Trout Creek and in the mainstem Wind River. We hope the two new sites will provide interrogations information that will allow us to better estimate detection efficiencies of downstream moving juvenile Steelhead Trout at the primary interrogation sites. The additional interrogations will be particularly important for those fish tagged with 9-mm PIT tags as less information from downstream locations is available from them. These sites and other status and trend data will allow evaluation of further planned restoration within the watershed, particularly that proposed for the headwaters of Trout Creek.
Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. We have estimated that from 15 to 51% of parr tagged as age-0 fish in headwater areas make downstream migrations at age 1 for additional rearing during both spring and fall. We have estimated that up to 27% of Steelhead Trout parr, tagged as age-1 fish, make downstream migrations during fall. These findings raise questions about where parr most successfully rear and whether migrations are density or habitat quality driven. Broader monitoring programs would give a more comprehensive understanding of juvenile Steelhead Trout production and rearing and productivity contribution.
Repeat sampling at consistent locations in the subbasin has enabled assessment of juvenile Steelhead Trout growth patterns. Growth rates (relative change in weight) of age-0 PITtagged parr during summer were similar across the subbasin but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly relative growth for parr tagged at age-0 is similar across the subbasin.
Non-native Brook Trout Salvelinus confluentus are present in the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Mean percent-of-catch that is Brook Trout, at four sample sites in Trout Creek, has declined from the period 1998 – 2003 to the period 2011 – 2020. Percent-of-catch and number of Brook Trout at the Trout Creek sites from 2011 through 2020 declined, though both metrics increased in 2018.
Evaluation and planning of restoration efforts are critical to ensure efficient use of resources. Assessing Steelhead Trout life history variation in the Wind River subbasin will inform research and tracking of many populations and help inform habitat restoration and water allocation planning. Movement of Steelhead Trout parr from natal areas to other rearing areas raises questions regarding juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead Trout parr allow investigation of such questions. Increasingly detailed viable salmonid population information, such as that provided by PIT-tagging and instream PTIS networks like those in the Wind River can provide data to inform fisheries policy and management and understand life-history strategies and limiting factors. Such efforts also provide assessment of long-term effects of habitat restoration actions such as the removal of Hemlock Dam on Trout Creek, and the proposed Stage-0 restoration effort for upper Trout Creek, which would be a large-scale effort to reset sections of stream within their floodplain, restoring connectivity and interaction with surrounding landscape.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I., 2022, Wind River subbasin restoration: Annual Report of U.S. Geological Survey activities January 2020 through December 2020, 71 p.","productDescription":"71 p.","ipdsId":"IP-137356","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":483143,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483119,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P190880","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Wind River subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.1275,\n 46\n ],\n [\n -122.1275,\n 45.75\n ],\n [\n -121.8,\n 45.75\n ],\n [\n -121.8,\n 46\n ],\n [\n -122.1275,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian 0000-0002-3842-3485","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":217811,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930257,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237373,"text":"70237373 - 2022 - Modeling functional flows in California rivers","interactions":[],"lastModifiedDate":"2022-10-12T14:30:29.820543","indexId":"70237373","displayToPublicDate":"2022-03-01T09:17:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Modeling functional flows in California rivers","docAbstract":"Environmental flows are critical to the recovery and conservation of freshwater ecosystems worldwide. However, estimating\ndesired ranges of environmental flows across large, diverse landscapes is challenging. To advance protections of environmental flows for streams in California, USA, we developed a statewide modeling approach focused on functional components of the natural flow regime. Functional flow components in California streams—fall pulse flows, wet season peak flows and base flows, the spring flow recession, and dry season baseflows—support essential physical and ecological processes in riverine ecosystems. These functional flow components can be represented by functional flow metrics (FFMs) and quantified by their magnitude, timing, frequency, duration, and rate-of-change from daily streamflow records. After quantifying FFMs at reference-quality streamflow gages in California, we used machine-learning methods to estimate their natural range of values for all stream reaches in the state based on physical watershed characteristics and climatic factors. We found that the models performed well in predicting FFMs in streams across a diversity of landscape and climate contexts, according to several model performance criteria. Using the predicted FFM values, we established initial estimates of ecological flows that are expected to support critical functions and are broadly protective of ecosystem health. Modeling functional flows statewide offers a pathway for increasing the pace and scale of environmental flow protections in California and beyond.","language":"English","publisher":"Frontiers in Environmental Science","doi":"10.3389/fenvs.2022.787473","usgsCitation":"Grantham, T.E., Carlisle, D.M., Howard, J., Lane, B., Lusardi, R., Obester, A., Sandoval-Solis, S., Stanford, B., Stein, E.D., Taniguchi-Quan, K.T., Yarnell, S.M., and Zimmerman, J.K., 2022, Modeling functional flows in California rivers: Frontiers in Environmental Science, v. 10, 787473, 11 p., https://doi.org/10.3389/fenvs.2022.787473.","productDescription":"787473, 11 p.","ipdsId":"IP-132706","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448653,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2022.787473","text":"Publisher Index Page"},{"id":435941,"rank":0,"type":{"id":30,"text":"Data 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Here, we used a data-driven approach to identify regional patterns and exogenous controls on P–Q interactions. We applied an information flow analysis, which quantifies uncertainty reduction, to a daily time series of P and Q from 671 watersheds across the conterminous United States. We first demonstrated that information transfer from P to Q primarily reflects the quickflow component of water-budgets, based on a watershed model. Readily quantifiable information flows show a functional relationship with model parameters, suggesting utility for model calibration. Second, applied to real watersheds, P–Q information flows exhibit seasonally varying behavior within regions in a manner consistent with dominant runoff generation mechanisms. However, the timing and the magnitude of information flows also reflect considerable subregional heterogeneity, likely attributable to differences in watershed size, baseflow contributions, and variation in aerial coverage of preferential flow paths. A regression analysis showed that a combination of climate and watershed characteristics are predictive of P–Q information flows. Though information flows cannot, in most cases, uniquely determine dominant runoff mechanisms, they provide a means to quantify the heterogeneous outcomes of those mechanisms within regions, thereby serving as a benchmarking tool for models developed at the regional scale. Last, information flows characterize regionally specific ways in which catchment connectivity changes from the wet to dry season.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR030186","usgsCitation":"Moges, E., Ruddell, B., Zhang, L., Driscoll, J.M., and Larsen, L., 2022, Strength and memory of precipitation's control over streamflow across the conterminous United States: Water Resources Research, v. 58, no. 3, e2021WR030186, 20 p., https://doi.org/10.1029/2021WR030186.","productDescription":"e2021WR030186, 20 p.","ipdsId":"IP-128702","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr030186","text":"Publisher Index Page"},{"id":397106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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FY 2022 funding will produce final transition probability estimates from the newly-developed Bayesian multi-state model, continue the maintenance of real-time telemetry to inform contingency actions, and produce a study plan to refine fishing mortality estimates using telemetry data.
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In large numbers, these microorganisms and their toxins present a risk to human health. Cyanobacterial abundance in stream biofilms is typically related to single or a limited set of environmental factors, mainly light availability, water temperature, and nutrient concentrations. However, these factors may act synergistically with watershed characteristics and other stressors, such as anthropogenic pollutants, to affect cyanobacteria. We investigated the influence of multiple regional and local variables on the abundance of benthic cyanobacterial genera in streams using all subsets generalized additive modeling. We examined watershed factors (topography, geology, and climate) alongside in-stream factors (geomorphology, hydrology, pH, specific conductance, nutrients, organic contaminants, and dissolved metals) from 76 sites along an urban gradient in the northeast United States. Each genus responded to a distinct combination of environmental variables, demonstrating strong intergeneric variation in environmental selection of realized niches. Four of the 7 potentially toxigenic genera that we modeled were positively influenced by water temperature or nutrients. Nonetheless, watershed characteristics, streamflow, and/or other water quality pollutants were equally or more influential for the potentially toxigenic genera. Additionally, the relationships between cyanobacterial abundance and environmental factors varied in shape and direction across many genera. In particular, with increasing concentrations of herbicides, polychlorinated biphenyls, or metals, the abundance of roughly half of the affected genera decreased, while the others increased. These results likely demonstrate novel toxic effects of the pollutants on cyanobacterial genera in the environment, while indicating that unmeasured biotic interactions may lead to positive responses for other genera. Our results emphasize the need to consider variables beyond those that are most frequently measured or implicated (e.g., water temperature and nutrients) to more fully understand the environmental conditions that influence the distributions and abundance of potentially harmful cyanobacteria.
As human populations grow, decisions regarding use of the world's finite land base become increasingly complex. We adopted a land use–conflict scenario involving renewable energy to illustrate one potential cause of these conflicts and resulting tradeoff decisions. Renewable energy industries wishing to expand operations in the United States are limited by multijurisdictional regulations in finding developable land. Interest groups entreat industries to avoid land for various reasons, including avoidance of prime wildlife habitat in accordance with an “avoidance-first” mitigation strategy. By applying a uniform set of rules for renewable energy facilities to the Prairie Pothole Region and portions of the Northern Great Plains, we evaluated the effects of regulations and avoidance of prime wildlife habitat on the amount of land available for development. In our scenario, existing regulations excluded 39% of the project area from potential development, with human infrastructure accounting for 30% (10–66% among states), whereas federally protected species accounted for < 1% at project area and state levels. Unregulated lands accounted for 61% of the project area, with conservation areas predicted as high-quality sites for breeding grassland birds and waterfowl and for migrating whooping cranes Grus americana accounting for 19% within the project area (6–27% among states). This model demonstrated a limited land base available for new development when accounting for regulations and concerns of a subset of societal interest groups. Additional interest groups likely will have different and competing concerns, further emphasizing the complexity of future land-use decisions as the available land base for development diminishes.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-21-036","usgsCitation":"Shaffer, J.A., Niemuth, N.D., Loesch, C.R., Derby, C.E., Pearse, A.T., Barnes, K.W., Shaffer, T.L., and Ryba, A.J., 2022, Limited land base and competing land uses force societal tradeoffs when siting energy development: Journal of Fish and Wildlife Management, v. 13, no. 1, p. 106-123, https://doi.org/10.3996/JFWM-21-036.","productDescription":"18 p.","startPage":"106","endPage":"123","ipdsId":"IP-122448","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":448664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-036","text":"Publisher Index Page"},{"id":405160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Jill A. 0000-0003-3172-0708 jshaffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":3184,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill","email":"jshaffer@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niemuth, Neal D. 0009-0006-9637-5588","orcid":"https://orcid.org/0009-0006-9637-5588","contributorId":204334,"corporation":false,"usgs":false,"family":"Niemuth","given":"Neal","email":"","middleInitial":"D.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":848974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loesch, Charles R. 0000-0003-3090-1566","orcid":"https://orcid.org/0000-0003-3090-1566","contributorId":213437,"corporation":false,"usgs":false,"family":"Loesch","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":848975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Derby, Clayton E.","contributorId":295253,"corporation":false,"usgs":false,"family":"Derby","given":"Clayton","email":"","middleInitial":"E.","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":848976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnes, Kevin W.","contributorId":295254,"corporation":false,"usgs":false,"family":"Barnes","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":848978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaffer, Terry L. 0000-0001-6950-8951 tshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-6950-8951","contributorId":3192,"corporation":false,"usgs":true,"family":"Shaffer","given":"Terry","email":"tshaffer@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848979,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ryba, Adam J.","contributorId":204335,"corporation":false,"usgs":false,"family":"Ryba","given":"Adam","email":"","middleInitial":"J.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":848980,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70262183,"text":"70262183 - 2022 - Comparison of methods for estimating density and population trends for low-density Asian bears","interactions":[],"lastModifiedDate":"2025-01-15T17:38:27.828111","indexId":"70262183","displayToPublicDate":"2022-02-28T11:28:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of methods for estimating density and population trends for low-density Asian bears","docAbstract":"Populations of bears in Asia are vulnerable to extinction and effective monitoring is critical to measure and direct conservation efforts. Population abundance (local density) or growth (λ) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of camera traps and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a systematic review of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local telemetry data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02058","usgsCitation":"Morin, D., Boulanger, J., Bischof, R., Lee, D., Ngoprasert, D., Fuller, A.K., McLellan, B., Steinmetz, R., Sharma, S., Garshelis, D., Gopalaswamy, A.M., Nawaz, M.A., and Karanth, U., 2022, Comparison of methods for estimating density and population trends for low-density Asian bears: Global Ecology and Conservation, e02058, 21 p., https://doi.org/10.1016/j.gecco.2022.e02058.","productDescription":"e02058, 21 p.","ipdsId":"IP-135458","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467198,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02058","text":"Publisher Index Page"},{"id":466442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Morin, Dana 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To help understand changes in the steam reservoir, repeat magnetotelluric (MT) measurements are being collected once a year from 2021-2023. These data will be compared and modeled to provide 4-D images of changes within the reservoir. Joint inversion with passive seismic data will be done to further constrain changes observed in the geophysical models. This study describes the first repeat survey and provides comparisons with MT data collected in 2017. In April 2021, 41 of the 42 MT stations collected in 2017 were repeated in addition to 14 new stations in the southern part of the steam field. Calculating residual phase tensors from MT responses between the two surveys shows compartmentalized changes within the steam field. Changes are observed at periods longer than 1 second with the largest changes of up to 30 percent observed at periods of 30 seconds. The residual phase tensors also show good repeatability between the surveys for periods less than 1 second, with changes on the order of 1 percent. To model the data, the preferred 3D resistivity volume that resulted from the inversion of the 2017 data is employed as the starting model for inversion of the new data. The two resulting inversion models are then subtracted to identify areas of change within the reservoir.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 47th Workshop on Geothermal Reservoir Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 7-9, 2022","conferenceLocation":"Stanford University, Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Peacock, J., Alumbaugh, D., Mitchell, M.A., and Hartline, C., 2022, Repeat magnetotelluric measurements to monitor The Geysers steam field in northern California, in Proceedings, 47th Workshop on Geothermal Reservoir Engineering, Stanford University, Stanford, CA, February 7-9, 2022, 5 p.","productDescription":"5 p.","ipdsId":"IP-137117","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":409386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":409385,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2022/Peacock.pdf#:~:text=The%20Geysers%20in%20northern%20California%20is%20the%20world%E2%80%99s,provide%204D%20images%20of%20changes%20within%20the%20reservoir."}],"country":"United States","state":"California","otherGeospatial":"The Geysers geothermal field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.20281290983681,\n 39.084693014863575\n ],\n [\n -123.20281290983681,\n 38.506970947896036\n ],\n [\n -122.27760768172527,\n 38.506970947896036\n ],\n [\n -122.27760768172527,\n 39.084693014863575\n ],\n [\n -123.20281290983681,\n 39.084693014863575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":857141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alumbaugh, David 0000-0002-6975-7197","orcid":"https://orcid.org/0000-0002-6975-7197","contributorId":299109,"corporation":false,"usgs":false,"family":"Alumbaugh","given":"David","email":"","affiliations":[{"id":64775,"text":"Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":857142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Michael Albert 0000-0001-5070-8793","orcid":"https://orcid.org/0000-0001-5070-8793","contributorId":299110,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"","middleInitial":"Albert","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":857144,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartline, Craig","contributorId":213429,"corporation":false,"usgs":false,"family":"Hartline","given":"Craig","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":857143,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241859,"text":"70241859 - 2022 - Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions","interactions":[],"lastModifiedDate":"2023-03-29T12:11:48.331001","indexId":"70241859","displayToPublicDate":"2022-02-28T07:08:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2344,"text":"Journal of Hydrometeorology","active":true,"publicationSubtype":{"id":10}},"title":"Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions","docAbstract":"High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-21-0045.1","usgsCitation":"Stern, M.A., Flint, L.E., Flint, A.L., Boynton, R.M., Stewart, J.A., Wright, J.W., and Thorne, J.H., 2022, Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions: Journal of Hydrometeorology, v. 23, no. 3, p. 293-308, https://doi.org/10.1175/JHM-D-21-0045.1.","productDescription":"16 p.","startPage":"293","endPage":"308","ipdsId":"IP-127192","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":448670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jhm-d-21-0045.1","text":"Publisher Index Page"},{"id":414886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":867968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boynton, Ryan M 0000-0002-3952-2573","orcid":"https://orcid.org/0000-0002-3952-2573","contributorId":303743,"corporation":false,"usgs":false,"family":"Boynton","given":"Ryan","email":"","middleInitial":"M","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":867969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stewart, Joseph A E","contributorId":247751,"corporation":false,"usgs":false,"family":"Stewart","given":"Joseph","email":"","middleInitial":"A E","affiliations":[{"id":49638,"text":"USGS WERC & UC Davis","active":true,"usgs":false}],"preferred":false,"id":867970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wright, Jessica W","contributorId":303744,"corporation":false,"usgs":false,"family":"Wright","given":"Jessica","email":"","middleInitial":"W","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":867971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thorne, James H.","contributorId":139144,"corporation":false,"usgs":false,"family":"Thorne","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":12659,"text":"U C Davis","active":true,"usgs":false}],"preferred":false,"id":867972,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70231893,"text":"70231893 - 2022 - Simple relationships between residence time and annual nutrient retention, export, and loading for estuaries","interactions":[],"lastModifiedDate":"2022-06-01T11:46:06.234257","indexId":"70231893","displayToPublicDate":"2022-02-27T06:42:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Simple relationships between residence time and annual nutrient retention, export, and loading for estuaries","docAbstract":"Simple mathematical models are derived from mass balances for water and transported substance to provide insight into the relationships between import, export, transport, and internal removal for nonconservative substances in an estuary. Extending previous work, our models explicitly include water and substance inputs from the ocean and are expressed in terms of timescales (i.e., mean residence time and the timescale for net removal). Steady-state, timescale-based expressions for ratios of export to import, retention to import, and net export to loading, as well as for loading and annually averaged concentration, are provided. The net export:loading model explains the underlying mechanisms for a well-known empirical relationship between fractional net export and residence time derived by other authors. Although our simplified models are first-order approximations, the relative importance of physical and biochemical processes influencing export or retention of a substance can be assessed using mean residence time and the timescale for net removal. Assumptions employed in deriving the simplified models (e.g., well-mixed, dynamic steady state) may not be met for real estuaries. However, model application to Chesapeake Bay for 1985–2012 demonstrates that interannual variations in total nitrogen (TN) net export:loading can be evaluated, and annual nutrient loadings can be well estimated using numerically modeled time-varying mean residence time, observation-based mean concentration, freshwater inflow, and an appropriately estimated removal timescale. Our model shows that net fractional export of TN loading ranges from 0.3 to 0.5 over the 28-yr period. The models can be employed for other substances and water bodies if the underlying assumptions are applicable.
Deltaic floodplains are highly vulnerable to relative sea level rise (RSLR) depending on the sediment supply from river channels that provides elevation capital as adaptation mechanism. In river channels where levees have restricted sediment supply to coastal deltaic floodplains, river sediment diversions have been proposed as a restoration strategy to increase elevation allowing for marshes to establish and cope with RSLR. The response of coastal wetlands to surface elevation has been well-defined for estuarine marshes, but models for coastal deltaic floodplain marshes have not been resolved. Here we coupled field observations from biomass plots and a mesocosm experiment (‘marsh organ’) with remote sensing techniques to assess biomass allocation of tidal freshwater marsh species in response to gradients in hydroperiod in Wax Lake Delta (WLD), coastal Louisiana, U.S.A.. We found that, contrary to salt-tolerant species, Colocasia esculenta aboveground biomass (AGB) is strongly positively correlated with percent inundated time (R2 = 0.79, P < 0.001), increasing from (mean ± 1SE) 186 ± 69 g/m2 in the supratidal zone to 1422 ± 148 g/m2 beyond its natural occurrence range in the lower intertidal zone. Belowground biomass consistently exceeded AGB at 2363 ± 294 g/m2 on average across elevation treatments. We also found that C. esculenta expanded its surface coverage area by 31% in five years consistent with the growth and emergence of WLD's subaqueous platforms, reflecting this species ability to cope with higher inundation time. In contrast to earlier studies conducted in brackish and saline settings, where longer hydroperiods had negative effects on biomass accumulation, our data suggest that tidal freshwater marshes can cope with longer hydroperiods caused by river sediment diversions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2022.107784","usgsCitation":"Rovai, A.S., Twilley, R.R., Christiensen, A., McCall, A., Jensen, D.J., Snedden, G., Morris, J.T., and Cavell, J.A., 2022, Biomass allocation of tidal freshwater marsh species in response to natural and manipulated hydroperiod in coastal deltaic floodplains: Estuarine, Coastal and Shelf Science, v. 268, 107784, 12 p., https://doi.org/10.1016/j.ecss.2022.107784.","productDescription":"107784, 12 p.","ipdsId":"IP-125365","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":448675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.lsu.edu/oceanography_coastal_pubs/1218","text":"Publisher Index Page"},{"id":396773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"268","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rovai, Andre S.","contributorId":167671,"corporation":false,"usgs":false,"family":"Rovai","given":"Andre","email":"","middleInitial":"S.","affiliations":[{"id":24801,"text":"Federal University of Santa Catarina, Dept. Ecology and Zoology, Brazil","active":true,"usgs":false}],"preferred":false,"id":837318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Twilley, Robert R.","contributorId":34585,"corporation":false,"usgs":false,"family":"Twilley","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christiensen, Alexandra","contributorId":288065,"corporation":false,"usgs":false,"family":"Christiensen","given":"Alexandra","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCall, Annabeth","contributorId":288067,"corporation":false,"usgs":false,"family":"McCall","given":"Annabeth","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837321,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jensen, Daniel J.","contributorId":288071,"corporation":false,"usgs":false,"family":"Jensen","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":837322,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Snedden, Gregg 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":213411,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":837323,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morris, James T.","contributorId":288074,"corporation":false,"usgs":false,"family":"Morris","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":61699,"text":"Belle W. Baruch Institute for Marine and Coastal Sciences, University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":837324,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cavell, John A.","contributorId":288075,"corporation":false,"usgs":false,"family":"Cavell","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837325,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70262057,"text":"70262057 - 2022 - Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a","interactions":[],"lastModifiedDate":"2025-01-10T16:15:07.781209","indexId":"70262057","displayToPublicDate":"2022-02-25T10:05:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a","title":"Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a","docAbstract":"We conducted a macroscale study of 2210 shallow lakes (mean depth ≤ 3 m or a maximum depth ≤ 5 m) in the Upper Midwestern and Northeastern USA. We asked the following: What are the patterns and drivers of shallow lake total phosphorus (TP), chlorophyll a (CHLa), and TP–CHLa relationships at the macroscale, how do these differ from those for 4360 non-shallow lakes, and do results differ by hydrologic connectivity class? Spatial patterns and Bayesian hierarchical models indicated that shallow lakes had higher TP and CHLa than non-shallow lakes, connected shallow lakes were more productive than unconnected shallow lakes, and there was regional variation in these patterns. Important predictors of TP and CHLa included lake-specific watershed:lake area ratio, forested land use/cover, and baseflow; unconnected lakes were more difficult to predict than connected lakes; and region-specific predictors were mostly unimportant. Shallow lake TP–CHLa relationships were less steep than for non-shallow lakes and these relationships varied regionally. Our results, combined with the facts that only 23% of lakes in the study extent have depth data and that shallow and unconnected lakes are undersampled, have important implications for estimates of lake contributions to global cycles that are based mainly on large (and deeper) lakes.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-022-04811-1","usgsCitation":"Spence Cheruvelil, K., Webster, K., King, K., Poisson, A., and Wagner, T., 2022, Taking a macroscale perspective to improve understanding of shallow lake total phosphorus and chlorophyll a: Hydrobiologia, v. 849, p. 3663-3677, https://doi.org/10.1007/s10750-022-04811-1.","productDescription":"15 p.","startPage":"3663","endPage":"3677","ipdsId":"IP-130204","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":465993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Illinois, Indiana, Iowa, Maine, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, 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\"}}]}","volume":"849","noUsgsAuthors":false,"publicationDate":"2022-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Spence Cheruvelil, Kendra","contributorId":348079,"corporation":false,"usgs":false,"family":"Spence Cheruvelil","given":"Kendra","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webster, Katherine","contributorId":348080,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Katelyn","contributorId":348081,"corporation":false,"usgs":false,"family":"King","given":"Katelyn","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922925,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poisson, Autumn C.","contributorId":348082,"corporation":false,"usgs":false,"family":"Poisson","given":"Autumn C.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922926,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922922,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243724,"text":"70243724 - 2022 - Active forest management accelerates carbon storage in plantation forests in Lishui, southern China","interactions":[],"lastModifiedDate":"2023-05-18T13:48:54.445288","indexId":"70243724","displayToPublicDate":"2022-02-25T08:39:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5054,"text":"Forest Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Active forest management accelerates carbon storage in plantation forests in Lishui, southern China","docAbstract":"China has committed to achieving peak CO2 emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics.
We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes.
1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 Mg·ha‒1) and 64 years (232.04 Mg·ha‒1), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 Tg C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest.
This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO2 emissions and reach carbon neutrality.
Recharge from preferential flow through mega-thick (100–1,000 m) unsaturated zones is a pervasive phenomenon, as demonstrated with a case study of volcanic highland recharge areas in the Great Basin province in southern Nevada, USA. Statistically significant rising water-level trends occur for most study-area wells and resulted from a relatively wet period (1969–2005) in south-central Nevada. Wet and dry winters control water-level trends, with water levels rising within a few months to a year following a wet-winter recharge event and declining during sustained dry periods. Even though a megadrought has persisted since 2000, this drought condition did not preclude major recharge events. Modern groundwater reaching the water table is consistent with previous geochemical studies of the study area that indicate mixing of modern and late Pleistocene recharge water. First-order approximations and simple mixing models of modern and late Pleistocene water indicate that 10 to 40 percent of recharge is preferential flow and that modern recharge may play a larger role in the water budget than previously thought.
The Republican River is the primary inflow to Milford Lake and drains areas of Kansas, Nebraska, and Colorado. Milford Lake has been listed as impaired and designated hypereutrophic by the Kansas Department of Health and Environment because of excessive nutrient loading. Milford Lake had confirmed harmful algal blooms every summer from 2011 through 2017 and in 2020 and 2021.
In the lower Republican River drainage basin, the Regional Conservation Partnership Program, administered by the Natural Resources Conservation Service, provides reimbursement to agricultural producers that implement best management practices intended to decrease sediment and nutrient runoff and loading into Milford Lake. Sediment and nutrient loads could potentially be driving factors in the development of harmful algal blooms in the reservoir.
Since July 2018, the U.S. Geological Survey, in cooperation with the Kansas Water Office, has collected continuous and discrete water-quality data at the Republican River at Clay Center, Kansas, streamgage (U.S. Geological Survey station 06856600), which is about 15 river miles upstream from Milford Lake. This report documents site-specific regression models for the computation of continuous concentrations of suspended sediment, total nitrogen, total phosphorus, and total carbon developed using continuous and discrete data collected from July 24, 2018, the date of continuous water-quality monitor installation, through March 31, 2021. The objective of this study is to characterize sediment and nutrient transport in the Milford Lake drainage basin before, during, and after best management practice implementation using the models described in this report.
The explanatory variable turbidity explained a high amount (72–96 percent) of the variance in suspended-sediment, total nitrogen, total phosphorus, and total carbon concentrations. Statistical plots for the four selected models showed the desired normality and homoscedasticity in residuals, and model standard error ratios indicated that recomputing each selected model after removing a randomly selected 10 percent of the data did not substantially change model coefficients.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225016","collaboration":"Prepared in cooperation with the Kansas Water Office","usgsCitation":"Leiker, B.M., 2022, Linear regression model documentation for computing water-quality constituent concentrations using continuous real-time water-quality data for the Republican River, Clay Center, Kansas, July 2018 through March 2021: U.S. Geological Survey Scientific Investigations Report 2022–5016, 13 p., https://doi.org/10.3133/sir20225016.","productDescription":"Report: vi, 13 p.; 4 Appendixes; Dataset","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-133566","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":396373,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016_appendix1.pdf","text":"Appendix 1","size":"846 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016 Appendix 1","linkHelpText":"—Model Archive Summary for Suspended Sediment at U.S. Geological Survey Station 06856600, Republican River at Clay Center, Kansas, during July 2018 through March 2021"},{"id":396372,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016"},{"id":396371,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5016/coverthb.jpg"},{"id":396377,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5016/images"},{"id":396374,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016_appendix2.pdf","text":"Appendix 2","size":"788 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016 Appendix 2","linkHelpText":"—Model Archive Summary for Total Nitrogen at U.S. Geological Survey Station 06856600, Republican River at Clay Center, Kansas, during July 2018 through March 2021"},{"id":396375,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016_appendix3.pdf","text":"Appendix 3","size":"681 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016 Appendix 3","linkHelpText":"—Model Archive Summary for Total Phosphorus at U.S. Geological Survey Station 06856600, Republican River at Clay Center, Kansas, during July 2018 through March 2021"},{"id":396376,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016_appendix4.pdf","text":"Appendix 4","size":"757 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5016 Appendix 4","linkHelpText":"—Model Archive Summary for Total Carbon at U.S. Geological Survey Station 06856600, Republican River at Clay Center, Kansas, during July 2018 through March 2021"},{"id":396378,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5016/sir20225016.XML","linkFileType":{"id":8,"text":"xml"}},{"id":396379,"rank":9,"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"},{"id":502367,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112525.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Kansas","city":"Clay Center","otherGeospatial":"Republican River drainage basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.8170166015625,\n 39.03838632847035\n ],\n [\n -96.767578125,\n 39.172658670429946\n ],\n [\n -96.866455078125,\n 39.35129035526705\n ],\n [\n -97.01202392578125,\n 39.5146359327835\n ],\n [\n -97.0147705078125,\n 39.65857056750545\n ],\n [\n -96.99829101562499,\n 39.76632525654491\n ],\n [\n -97.14385986328125,\n 39.87601941962116\n ],\n [\n -97.4102783203125,\n 39.8992015115692\n ],\n [\n -97.74810791015625,\n 39.905522539728544\n ],\n [\n -97.92938232421875,\n 39.90762941952987\n ],\n [\n -98.22601318359375,\n 39.78954439311165\n ],\n [\n -98.228759765625,\n 39.65222681530652\n ],\n [\n -97.83050537109375,\n 39.50615988027491\n ],\n [\n -97.51190185546874,\n 39.42346418978382\n ],\n [\n -97.2344970703125,\n 39.3130504637139\n ],\n [\n -96.98181152343749,\n 39.034119526392836\n ],\n [\n -96.84997558593749,\n 39.00211029922515\n ],\n [\n -96.8170166015625,\n 39.03838632847035\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
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Ecologically relevant references are useful for evaluating ecosystem recovery, but references that are temporally static may be less useful when environmental conditions and disturbances are spatially and temporally heterogeneous. This challenge is particularly acute for ecosystems dominated by sagebrush (Artemisia spp.), where communities may require decades to recover from disturbance. We demonstrated application of a dynamic reference approach to studying sagebrush recovery using three decades of sagebrush cover estimates from remote sensing (1985–2018). We modelled recovery on former oil and gas well pads (n = 1200) across southwestern Wyoming, USA, relative to paired references identified by the Disturbance Automated Reference Toolset. We also used quantile regression to account for unmodelled heterogeneity in recovery, and projected recovery from similar disturbance across the landscape. Responses to weather and site-level factors often differed among quantiles, and sagebrush recovery on former well pads increased more when paired reference sites had greater sagebrush cover. Little (<5%) of the landscape was projected to recover within 100 years for low to mid quantiles, and recovery often occurred at higher elevations with cool and moist annual conditions. Conversely, 48%–78% of the landscape recovered quickly (within 25 years) for high quantiles of sagebrush cover. Our study demonstrates advantages of using dynamic reference sites when studying vegetation recovery, as well as how additional inferences obtained from quantile regression can inform management.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8508","usgsCitation":"Monroe, A., Nauman, T.W., Aldridge, C.L., O’Donnell, M.S., Duniway, M.C., Cade, B.S., Manier, D., and Anderson, P.J., 2022, Assessing vegetation recovery from energy development using a dynamic reference approach: Ecology and Evolution, v. 12, no. 2, p. 1-22, https://doi.org/10.1002/ece3.8508.","productDescription":"e8508, 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-129277","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":448700,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.8508","text":"Publisher Index Page"},{"id":435946,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OP5D76","text":"USGS data release","linkHelpText":"Sagebrush recovery analyzed with a dynamic 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