{"pageNumber":"306","pageRowStart":"7625","pageSize":"25","recordCount":184769,"records":[{"id":70247111,"text":"70247111 - 2023 - Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States","interactions":[],"lastModifiedDate":"2023-07-25T15:10:34.1453","indexId":"70247111","displayToPublicDate":"2023-01-30T10:06:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States","docAbstract":"

Phytoplankton is the essential primary producer in fresh surface water ecosystems. However, excessive phytoplankton growth due to eutrophication significantly threatens ecologic, economic, and public health. Therefore, phytoplankton identification and quantification are essential to understanding the productivity and health of freshwater ecosystems as well as the impacts of phytoplankton overgrowth (such as Cyanobacterial blooms) on public health. Microscopy is the gold standard for phytoplankton assessment but is time-consuming, has low throughput, and requires rich experience in phytoplankton morphology. Quantitative polymerase chain reaction (qPCR) is accurate and straightforward with high throughput. In addition, qPCR does not require expertise in phytoplankton morphology. Therefore, qPCR can be a useful alternative for molecular identification and enumeration of phytoplankton. Nonetheless, a comprehensive study is missing which evaluates and compares the feasibility of using qPCR and microscopy to assess phytoplankton in fresh water. This study 1) compared the performance of qPCR and microscopy in identifying and quantifying phytoplankton and 2) evaluated qPCR as a molecular tool to assess phytoplankton and indicate eutrophication. We assessed phytoplankton using both qPCR and microscopy in twelve large freshwater rivers across the United States from early summer to late fall in 2017, 2018, and 2019. qPCR- and microscope-based phytoplankton abundance had a significant positive linear correlation (adjusted R2 = 0.836, p-value < 0.001). Phytoplankton abundance had limited temporal variation within each sampling season and over the three years studied. The sampling sites in the midcontinent rivers had higher phytoplankton abundance than those in the eastern and western rivers. For instance, the concentration (geometric mean) of Bacillariophyta, Cyanobacteria, Chlorophyta, and Dinoflagellates at the sampling sites in the midcontinent rivers was approximately three times that at the sampling sites in the western rivers and approximately 18 times that at the sampling sites in the eastern rivers. Welch's analysis of variance indicates that phytoplankton abundance at the sampling sites in the midcontinent rivers was significantly higher than that at the sampling sites in the eastern rivers (p-value = 0.013) but was comparable to that at the sampling sites in the western rivers (p-value = 0.095). The higher phytoplankton abundance at the sampling sites in the midcontinent rivers was presumably because these rivers were more eutrophic. Indeed, low phytoplankton abundance occurred in oligotrophic or low trophic sites, whereas eutrophic sites had greater phytoplankton abundance. This study demonstrates that qPCR-based phytoplankton abundance can be a useful numerical indicator of the trophic conditions and water quality in freshwater rivers.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2023.119679","usgsCitation":"Zhang, C., McIntosh, K.D., Sienkiewicz, N., Stelzer, E., Graham, J.L., and Lu, J., 2023, Using cyanobacteria and other phytoplankton to assess trophic conditions: A qPCR-based, multi-year study in twelve large rivers across the United States: Water Research, v. 235, 119679, 16 p., https://doi.org/10.1016/j.watres.2023.119679.","productDescription":"119679, 16 p.","ipdsId":"IP-147638","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":444674,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10123349","text":"External Repository"},{"id":419308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"235","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Chiqian","contributorId":317281,"corporation":false,"usgs":false,"family":"Zhang","given":"Chiqian","email":"","affiliations":[{"id":68989,"text":"Department of Civil and Environmental Engineering, College of Sciences and Engineering, Southern University and A&M College, Baton Rouge, Louisiana, United States","active":true,"usgs":false}],"preferred":false,"id":878916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McIntosh, Kyle D.","contributorId":317282,"corporation":false,"usgs":false,"family":"McIntosh","given":"Kyle","email":"","middleInitial":"D.","affiliations":[{"id":68990,"text":"Oak Ridge Institute for Science and Education at the United States Environmental Protection Agency’s Office of Research and Development, Oakridge, TN","active":true,"usgs":false}],"preferred":false,"id":878917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sienkiewicz, Nathan","contributorId":317283,"corporation":false,"usgs":false,"family":"Sienkiewicz","given":"Nathan","email":"","affiliations":[{"id":68991,"text":"Office of Research and Development, United States Environmental Protection Agency, Cincinnati, Ohio, United States","active":true,"usgs":false}],"preferred":false,"id":878918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":878919,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":878920,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lu, Jingrang","contributorId":288917,"corporation":false,"usgs":false,"family":"Lu","given":"Jingrang","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":878921,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240042,"text":"sir20225124 - 2023 - Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota","interactions":[],"lastModifiedDate":"2026-02-23T20:52:37.679793","indexId":"sir20225124","displayToPublicDate":"2023-01-30T09:30:21","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5124","displayTitle":"Hydrologic Change in the St. Louis River Basin from Iron Mining on the Mesabi Iron Range, Northeastern Minnesota","title":"Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota","docAbstract":"

This study compares the results of two regional steady-state U.S. Geological Survey Modular Three-Dimensional Finite-Difference Ground-Water Flow (MODFLOW) models constructed to quantify the hydrologic changes in the St. Louis River Basin from iron mining on the Mesabi Iron Range in northeastern Minnesota. The U.S. Geological Survey collaborated in this study with bands of the Minnesota Chippewa Tribe, and the Minnesota Pollution Control Agency to inform management decisions about aquatic resources in the St. Louis River Basin. A model constructed and calibrated to represent average 1995–2015 mining conditions produced regional groundwater heads and flows. A pre-mining scenario model was constructed from this mining model but had the land and bedrock surfaces restored to pre-mining topographies and had modeled mining features (mine pits, tailings basins, waste-rock piles, and mining-disturbed areas) eliminated to represent general pre-mining stratigraphy and hydrogeology. Many of the features important to the hydrology of this mining area (like individual mine pits) are difficult to represent in groundwater models and required the use of modeling tools to indirectly account for their effects. The difference between the results of these two models represents mining’s effects on the hydrology in the Mesabi Iron Range area of the St Louis River Basin. The mining and pre-mining regional models also can provide boundary conditions and initial properties for future local or site-specific groundwater-flow models in the area.

Total groundwater flow through the mining model is 171 million cubic feet per day. Areal recharge is the largest source of groundwater (78 and 81 percent of total groundwater flow in the mining and pre-mining scenario models, respectively). Seepage from streams and lakes provides another 17 percent of the total groundwater flow through both models. Water leaves aquifers through seepage to streams (discharge as base flow, 43 percent in both models) and areal seepage to the land surface (surface seepage), for example to wetlands (45 and 49 percent, mining and pre-mining scenario models respectively).

Comparison of the results from the mining and pre-mining scenario models shows that iron mining has produced measurable hydrologic changes in the St. Louis River Basin, but that most of those changes and the highest magnitude changes occur near the mining features. Flow changes to and from surface-water bodies like streams and wetlands were analyzed in detail because of their importance in sustaining surface waters and aquatic life. Overall, groundwater flow in the mining model was 3.62 million cubic feet per day (2.2 percent) greater than total pre-mining model groundwater flow. This was caused by an increase in recharge from tailings basins and a decrease in discharge from surface seepage. Groundwater discharge to mine pits was the largest change in groundwater flows between the models (a change representing 2.8 percent of total pre-mining model groundwater flow). Net recharge to groundwater from tailings basins (2.4 percent), net decrease in surface seepage from groundwater (2.7 percent), and net increase in seepage to streams (1.0 percent) were all in this same range of total pre-mining model groundwater flow. Groundwater lost through mine-pit withdrawals was nearly offset by groundwater gained through recharge from tailings basins. However, because losses and gains occurred in different areas, the effect of mining can have more substantial effects on local areas than the model-wide averages represent.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225124","collaboration":"Prepared in cooperation with bands of the Minnesota Chippewa Tribe, the Great Lakes Indian Fish & Wildlife Commission, and the Minnesota Pollution Control Agency","usgsCitation":"Cowdery, T.K., Baker, A.C., Haserodt, M.J., Feinstein, D.T., and Hunt, R.J., 2023, Hydrologic change in the St. Louis River Basin from iron mining on the Mesabi Iron Range, northeastern Minnesota: U.S. Geological Survey Scientific Investigations Report 2022–5124, 59 p., https://doi.org/10.3133/sir20225124.","productDescription":"Report: viii, 59 p.; 2 Data Releases","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-122102","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":412380,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5124/sir20225124.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":412376,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5124/images"},{"id":412373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5124/coverthb.jpg"},{"id":412374,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5124/sir20225124.pdf","text":"Report","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5124"},{"id":500466,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114303.htm","linkFileType":{"id":5,"text":"html"}},{"id":412504,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225124/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412378,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z60MJ0","text":"USGS data release","linkHelpText":"Soil-water-balance model data sets for the St. Louis River drainage basin, northeast Minnesota, 1995–2010"},{"id":412377,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6KSBJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT simulations of regional groundwater flow under mining and pre-mining scenarios near the Mesabi Iron Range within the St. Louis River Basin, northeastern Minnesota"}],"country":"United States","state":"Minnesota","otherGeospatial":"Mesabi Iron Range, St Louis River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.333,\n 48\n ],\n [\n -93.3333,\n 47\n ],\n [\n -91.666,\n 47\n ],\n [\n -91.666,\n 48\n ],\n [\n -93.333,\n 48\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-01-31","noUsgsAuthors":false,"publicationDate":"2023-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Cowdery, Timothy K. 0000-0001-9402-6575 cowdery@usgs.gov","orcid":"https://orcid.org/0000-0001-9402-6575","contributorId":456,"corporation":false,"usgs":true,"family":"Cowdery","given":"Timothy","email":"cowdery@usgs.gov","middleInitial":"K.","affiliations":[],"preferred":true,"id":862567,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baker, Anna C. 0000-0001-8194-7535 abaker@usgs.gov","orcid":"https://orcid.org/0000-0001-8194-7535","contributorId":4689,"corporation":false,"usgs":true,"family":"Baker","given":"Anna","email":"abaker@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862568,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haserodt, Megan J. 0000-0002-8304-090X mhaserodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8304-090X","contributorId":174791,"corporation":false,"usgs":true,"family":"Haserodt","given":"Megan","email":"mhaserodt@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862569,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":214256,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel","email":"","middleInitial":"T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":862570,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunt, Randall J. 0000-0001-6465-9304 rjhunt@usgs.gov","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":1129,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall","email":"rjhunt@usgs.gov","middleInitial":"J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862571,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241044,"text":"70241044 - 2023 - National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product","interactions":[],"lastModifiedDate":"2023-03-08T14:51:21.272891","indexId":"70241044","displayToPublicDate":"2023-01-30T08:40:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5571,"text":"Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product","docAbstract":"

The National Land Cover Database (NLCD) 2016 products show that, between 2001 and 2016, nearly half of the land cover change in the conterminous United States (CONUS) involved forested areas. To ensure the quality of NLCD land cover and land cover change products, it is important to accurately detect the location and time of forest disturbance. We designed a comprehensive strategy to integrate a continuous time series forest change detection method and a discrete 2-date forest change detection method to produce the NLCD 1986–2019 forest disturbance product, which shows the most recent forest disturbance date between the years 1986 and 2019 for every 2- to 3-year interval. This method, the Time-Series method Using Normalized Spectral Distance (NSD) index (TSUN), uses NSD to detect multi-date forest land cover changes and was shown to be easily extended to a new date even when new images were processed in a different way than previous date images. The discrete 2-date method uses the Multi-Index Integrated Change Analysis (MIICA) method to detect changes between 2-date images. A method based on confidence and object grouping was designed to combine the multiple MIICA outputs to improve change detection accuracy. Finally, an aggregation scheme was implemented to combine the TSUN output, the integrated MIICA results, and ancillary data to produce the NLCD 2019 forest disturbance 1986–2019 product. The initial accuracy assessments from 1,600 samples over 4 Landsat path/rows show that the producer’s and user’s accuracies of the 2001–2019 forest disturbance map are 76% and 74%, respectively. The final CONUS-wide forest disturbance product is provided at https://www.mrlc.gov/nlcd-2019-science-research-products.

","language":"English","publisher":"AAAS","doi":"10.34133/remotesensing.0021","usgsCitation":"Jin, S., Dewitz, J., Li, C., Sorenson, D.G., Zhu, Z., Shogib, R., Danielson, P., Granneman, B., Costello, C., Case, A., and Gass, L., 2023, National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product: Journal of Remote Sensing, v. 3, 0021, 14 p., https://doi.org/10.34133/remotesensing.0021.","productDescription":"0021, 14 p.","ipdsId":"IP-147293","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":444676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.34133/remotesensing.0021","text":"Publisher Index Page"},{"id":413853,"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 \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2023-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jin, Suming 0000-0001-9919-8077","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":302919,"corporation":false,"usgs":false,"family":"Jin","given":"Suming","affiliations":[{"id":65581,"text":"AFDS, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865829,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dewitz, Jon 0000-0002-0458-212X","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":215192,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":865830,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":865831,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sorenson, Daniel G. 0000-0003-0365-9444 dsorenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0365-9444","contributorId":2898,"corporation":false,"usgs":true,"family":"Sorenson","given":"Daniel","email":"dsorenson@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865832,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhu, Zhe","contributorId":260473,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":865833,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shogib, Rakibul 0000-0001-6524-7838","orcid":"https://orcid.org/0000-0001-6524-7838","contributorId":302920,"corporation":false,"usgs":false,"family":"Shogib","given":"Rakibul","email":"","affiliations":[{"id":65582,"text":"KBR, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865834,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Danielson, Patrick 0000-0002-2990-2783","orcid":"https://orcid.org/0000-0002-2990-2783","contributorId":302921,"corporation":false,"usgs":false,"family":"Danielson","given":"Patrick","affiliations":[{"id":65582,"text":"KBR, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865835,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Granneman, Brian 0000-0002-1910-0955","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":302922,"corporation":false,"usgs":false,"family":"Granneman","given":"Brian","affiliations":[{"id":65582,"text":"KBR, under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865836,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Costello, Catherine 0000-0001-7158-2675","orcid":"https://orcid.org/0000-0001-7158-2675","contributorId":223238,"corporation":false,"usgs":true,"family":"Costello","given":"Catherine","email":"","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865837,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Case, Adam 0000-0002-6342-5853","orcid":"https://orcid.org/0000-0002-6342-5853","contributorId":302923,"corporation":false,"usgs":false,"family":"Case","given":"Adam","affiliations":[{"id":65583,"text":"Innovate! Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":865838,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865839,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70240258,"text":"70240258 - 2023 - Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau","interactions":[],"lastModifiedDate":"2023-03-01T17:23:31.199955","indexId":"70240258","displayToPublicDate":"2023-01-30T07:12:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau","docAbstract":"

Livestock removal is increasingly used as a management option to mitigate the negative impacts of grazing-related disturbances on rangelands. Removal generally increases plant cover, but it is unclear when, where, and by how much plant and soil cover changes can be expected. On the Colorado Plateau, complex geology, topography, soils, and climate all interact to mediate the relationship between land cover, climate, and disturbance. In this study we used new developments in land cover mapping and analysis to assess landscape plant and bare soil cover up to 30 years after livestock removal from two grazing allotments in Capitol Reef National Park, Utah, USA. Results indicate that livestock removal increases plant cover 0.17-0.32% per year and reduces bare soil cover 0.34-0.41% per year, although these rates may be suppressed by warming temperatures. Soils, assessed through Soil Geomorphic Units, played a strong but complex role in mediating land cover changes through time. These results suggest that livestock removal is an effective strategy for increasing plant cover and reducing bare soil on the Colorado Plateau, but including soil information in decision making will enhance efficiency by improving manager's ability to prioritize management actions effectively across space and through time.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/acb728","usgsCitation":"McNellis, B.E., Knight, A.C., Nauman, T.W., Chambers, S., Brungard, C., Fick, S., Livensperger, C., Borthwick, C., and Duniway, M.C., 2023, Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau: Environmental Research Letters, v. 18, 034034, 18 p., https://doi.org/10.1088/1748-9326/acb728.","productDescription":"034034, 18 p.","ipdsId":"IP-145563","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444677,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1088/1748-9326/acb728","text":"Publisher Index Page"},{"id":435478,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EK2PHY","text":"USGS data release","linkHelpText":"Plant cover, climate, grazing disturbance, and soil class data from 1991-2020 compiled from remotely sensed data on two retired grazing allotments in Capitol Reef National Park, Utah, USA"},{"id":412611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","noUsgsAuthors":false,"publicationDate":"2023-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McNellis, Brandon E 0000-0001-9604-8727","orcid":"https://orcid.org/0000-0001-9604-8727","contributorId":271065,"corporation":false,"usgs":true,"family":"McNellis","given":"Brandon","email":"","middleInitial":"E","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chambers, Samuel Norton 0000-0002-4734-2855","orcid":"https://orcid.org/0000-0002-4734-2855","contributorId":297994,"corporation":false,"usgs":true,"family":"Chambers","given":"Samuel Norton","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":863116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brungard, C.W.","contributorId":301936,"corporation":false,"usgs":false,"family":"Brungard","given":"C.W.","email":"","affiliations":[{"id":65369,"text":"Department of Plant and Environmental Sciences. New Mexico State University. Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":863117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fick, S.E.","contributorId":301937,"corporation":false,"usgs":false,"family":"Fick","given":"S.E.","email":"","affiliations":[{"id":65370,"text":"formerly: US Geological Survey, Southwest Biological Science Center, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":863118,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Livensperger, C.G.","contributorId":301938,"corporation":false,"usgs":false,"family":"Livensperger","given":"C.G.","affiliations":[{"id":65371,"text":"National Park Service, Capitol Reef National Park, Fruita, UT; National Park Service, Northern Colorado Plateau Network, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":863119,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Borthwick, C.G.","contributorId":301939,"corporation":false,"usgs":false,"family":"Borthwick","given":"C.G.","email":"","affiliations":[{"id":65372,"text":"National Park Service, Capitol Reef National Park, Fruita, UT","active":true,"usgs":false}],"preferred":false,"id":863120,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863121,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241176,"text":"70241176 - 2023 - Changes in suspended-sediment yields under divergent land-cover disturbance histories: A comparison of two large watersheds, Olympic Mountains, USA","interactions":[],"lastModifiedDate":"2023-06-27T16:41:52.717587","indexId":"70241176","displayToPublicDate":"2023-01-30T07:02:15","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Changes in suspended-sediment yields under divergent land-cover disturbance histories: A comparison of two large watersheds, Olympic Mountains, USA","docAbstract":"

Improvements in timber harvest practices and reductions in harvest volumes over the past half century are commonly presumed to have reduced sediment loads in many western US rivers. However, direct assessments in larger watersheds are relatively sparse. Here, we compare 2019–21 sediment concentrations against those of the late 1970s in the Bogachiel and Calawah  River watersheds, adjacent and similarly sized (~300 km2) basins in the western Olympic Mountains of Washington State. The Calawah River watershed has experienced significant land-cover disturbance, including a large 1951 fire, extensive post-fire salvage logging, and relatively high rates of timber harvest through the 1990s. In contrast, the Bogachiel River watershed did not burn, and experienced only modest timber harvest that largely post-dated 1970s sediment monitoring. Channel-width trends suggest the Calawah River was still recovering from 1950s disturbances in the late 1970s. We found that 2019–21 suspended-sediment loads in the Calawah River were 2.3–2.6 times lower than would have been expected based on 1970s sediment rating curves, while recent loads in the Bogachiel River were a factor of 1.4 ± 1.0 lower. We consider the plausibility and possible explanations of declining concentrations in the less-disturbed Bogachiel River. Suspended-sediment yields in the Bogachiel River were two times higher than yields in the Calawah River, which is attributed to a combination of modestly higher precipitation, more efficient runoff generation, and more extensive and erodible Quaternary valley fills in the Bogachiel River. Regional shifts in flood hydrology have also influenced suspended-sediment loads in both watersheds. Our results then document a significant decline in suspended-sediment concentrations in the Calawah River over the past half century. Reduced land-cover disturbance provides the simplest and most likely explanation for this decline, though the wide range of possible concentration changes in the Bogachiel River leaves open possibilities that other processes (human, natural, or methodologic) could be a factor.

","language":"English","publisher":"Wiley","doi":"10.1002/esp.5556","usgsCitation":"Jaeger, K.L., Anderson, S.W., and Dunn, S., 2023, Changes in suspended-sediment yields under divergent land-cover disturbance histories: A comparison of two large watersheds, Olympic Mountains, USA: Earth Surface Processes and Landforms, v. 48, no. 7, p. 1398-1413, https://doi.org/10.1002/esp.5556.","productDescription":"16 p.","startPage":"1398","endPage":"1413","ipdsId":"IP-144931","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":444679,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5556","text":"Publisher Index Page"},{"id":435479,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95L5ADD","text":"USGS data release","linkHelpText":"Supporting Spatial Data for Sediment Studies in the Bogachiel and Calawah River Watersheds, Washington"},{"id":414086,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.35640085378517,\n 48.1377522152041\n ],\n [\n -124.35640085378517,\n 47.1761943193718\n ],\n [\n -122.80798616934896,\n 47.1761943193718\n ],\n [\n -122.80798616934896,\n 48.1377522152041\n ],\n [\n -124.35640085378517,\n 48.1377522152041\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866349,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Sarah B. 0000-0003-4463-0074","orcid":"https://orcid.org/0000-0003-4463-0074","contributorId":291768,"corporation":false,"usgs":false,"family":"Dunn","given":"Sarah B.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":866350,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70242920,"text":"70242920 - 2023 - Peat decomposition and erosion contribute to pond deepening in a temperate salt marsh","interactions":[],"lastModifiedDate":"2023-04-24T11:45:32.606708","indexId":"70242920","displayToPublicDate":"2023-01-30T06:41:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Peat decomposition and erosion contribute to pond deepening in a temperate salt marsh","docAbstract":"

Salt marsh ponds expand and deepen over time, potentially reducing ecosystem carbon storage and resilience. The water filled volumes of ponds represent missing carbon due to prevented soil accumulation and removal by erosion and decomposition. Removal mechanisms have different implications as eroded carbon can be redistributed while decomposition results in loss. We constrained ponding effects on carbon dynamics in a New England marsh and determined whether expansion and deepening impact nearby soils by conducting geochemical characterizations of cores from three ponds and surrounding high marshes and models of wind-driven erosion. Radioisotope profiles demonstrate that ponds are not depositional environments and that contemporaneous marsh accretion represents prevented accumulation accounting for 32%–42% of the missing carbon. Erosion accounted for 0%–38% and was bracketed using radioisotope inventories and wind-driven resuspension models. Decomposition, calculated by difference, removes 22%–68%, and when normalized over pond lifespans, produces rates that agree with previous metabolism measurements. Pond surface soils contain new contributions from submerged primary producers and evidence of microbial alteration of underlying peat, as higher levels of detrital biomarkers and thermal stability indices, compared to the marsh. Below pond surface horizons, soil properties and organic matter composition were similar to the marsh, indicating that ponding effects are shallow. Soil bulk density, elemental content, and accretion rates were similar between marsh sites but different from ponds, suggesting that lateral effects are spatially confined. Consequently, ponds negatively impact ecosystem carbon storage but at current densities are not causing pervasive degradation of marshes in this system.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JG007063","usgsCitation":"Luk, S., Eagle, M.J., Mariotti, G., Gosselin, K., Sanderman, J., and Spivak, A.C., 2023, Peat decomposition and erosion contribute to pond deepening in a temperate salt marsh: Biogeosciences, v. 128, no. 2, e2022JG007063, 19 p., https://doi.org/10.1029/2022JG007063.","productDescription":"e2022JG007063, 19 p.","ipdsId":"IP-144333","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":444682,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jg007063","text":"Publisher Index Page"},{"id":416169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.89116181462337,\n 42.8226718850903\n ],\n [\n -70.89116181462337,\n 42.6915958038642\n ],\n [\n -70.70631330997368,\n 42.6915958038642\n ],\n [\n -70.70631330997368,\n 42.8226718850903\n ],\n [\n -70.89116181462337,\n 42.8226718850903\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Luk, Sheron","contributorId":247610,"corporation":false,"usgs":false,"family":"Luk","given":"Sheron","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":870201,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":870202,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mariotti, Giulio","contributorId":207541,"corporation":false,"usgs":false,"family":"Mariotti","given":"Giulio","email":"","affiliations":[{"id":37557,"text":"Louisiana State University, Baton Rouge LA","active":true,"usgs":false}],"preferred":false,"id":870203,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gosselin, Kelsey","contributorId":247614,"corporation":false,"usgs":false,"family":"Gosselin","given":"Kelsey","email":"","affiliations":[{"id":49592,"text":"Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":870204,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sanderman, Jonathan","contributorId":187477,"corporation":false,"usgs":false,"family":"Sanderman","given":"Jonathan","email":"","affiliations":[],"preferred":false,"id":870205,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spivak, Amanda C.","contributorId":191376,"corporation":false,"usgs":false,"family":"Spivak","given":"Amanda","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":870206,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240150,"text":"70240150 - 2023 - iBluff: An open-source R package for geomorphic analysis of coastal bluffs/cliffs","interactions":[],"lastModifiedDate":"2023-01-31T12:35:05.584499","indexId":"70240150","displayToPublicDate":"2023-01-30T06:32:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5923,"text":"SoftwareX","active":true,"publicationSubtype":{"id":10}},"title":"iBluff: An open-source R package for geomorphic analysis of coastal bluffs/cliffs","docAbstract":"

The R package iBluff is designed for coastal bluffs/bluffs morphological analysis and offers an automatic and reproducible alternative to identify bluff edges using a bare earth digital elevation model (DEM) instead of hand digitizing. This package extracts elevation profiles along automatically identified transects on the bluff-face, bluff top, toe, secondary inflections, relative concavity/convexity of bluff-face, and beach dunes (crests and troughs). The package requires at a minimum a bare earth DEM as a raster and a generalized line shapefile (shoreline) approximately parallel with the bluff-face. Both files should be in the same projected coordinate system. The iBluff package was developed to expand and generalize studies of high-relief coastal areas, investigate erosion and seasonality, and could be extended to use three-dimensional (3D) point-cloud data instead of a DEM.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.softx.2023.101325","usgsCitation":"Palaseanu-Lovejoy, M., 2023, iBluff: An open-source R package for geomorphic analysis of coastal bluffs/cliffs: SoftwareX, v. 21, 101325, 8 p., https://doi.org/10.1016/j.softx.2023.101325.","productDescription":"101325, 8 p.","ipdsId":"IP-147257","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":444684,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.softx.2023.101325","text":"Publisher Index Page"},{"id":412490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"21","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":862773,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70264031,"text":"70264031 - 2023 - Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses","interactions":[],"lastModifiedDate":"2025-03-05T15:18:51.590688","indexId":"70264031","displayToPublicDate":"2023-01-30T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses","docAbstract":"

1. Integrating ecological theory with empirical methods is ubiquitous in ecology using hierarchical Bayesian models. However, there has been little development focused on integration of ecological theory into models for survival analysis. Survival is a fundamental process, linking individual fitness with population dynamics, but incorporating life history strategies to inform survival estimation can be challenging because mortality processes occur at multiple scales.

2. We develop an approach to survival analysis, incorporating model constraints based on a species' life history strategy using functional analytical tools. Specifically, we structurally separate intrinsic patterns of mortality that arise from age-specific processes (e.g. increasing survival during early life stages due to growth or maturation, versus senescence) from extrinsic mortality patterns that arise over different periods of time (e.g. seasonal temporal shifts). We use shape constrained generalized additive models (CGAMs) to obtain age-specific hazard functions that incorporate theoretical information based on classical survivorship curves into the age component of the model and capture extrinsic factors in the time component.

3. We compare the performance of our modelling approach to standard survival modelling tools that do not explicitly incorporate species life history strategy in the model structure, using metrics of predictive power, accuracy, efficiency and computation time. We applied these models to two case studies that reflect different functional shapes for the underlying survivorship curves, examining age-period survival for white-tailed deer Odocoileus virginianus in Wisconsin, USA and Columbian sharp-tailed grouse Tympanuchus phasianellus columbianus in Colorado, USA.

","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210x.14057","usgsCitation":"Ketz, A., Storm, D., Barker, R., Apa, A.D., Oliva-Aviles, C., and Walsh, D.P., 2023, Assimilating ecological theory with empiricism: Using constrained generalized additive models to enhance survival analyses: Methods in Ecology and Evolution, v. 14, no. 3, p. 952-967, https://doi.org/10.1111/2041-210x.14057.","productDescription":"16 p.","startPage":"952","endPage":"967","ipdsId":"IP-141205","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487398,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.14057","text":"Publisher Index Page"},{"id":482898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wisconsin","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-106.190554,40.997607],[-102.051718,41.002377],[-102.04224,36.993083],[-102.814616,37.000783],[-105.029228,36.992729],[-109.045223,36.999084],[-109.041762,38.16469],[-109.060062,38.275489],[-109.050076,41.000659],[-106.190554,40.997607]]],[[[-90.403306,47.026693],[-90.413133,47.013533],[-90.464079,46.994636],[-90.455502,47.051331],[-90.437271,47.073483],[-90.393848,47.075956],[-90.403306,47.026693]]],[[[-90.730883,46.873096],[-90.677989,46.897527],[-90.667776,46.890037],[-90.718547,46.864531],[-90.756052,46.830595],[-90.760991,46.838277],[-90.749816,46.861806],[-90.730883,46.873096]]],[[[-90.764857,46.946524],[-90.741417,46.9636],[-90.717456,46.957966],[-90.694487,46.93671],[-90.689302,46.918563],[-90.737107,46.914712],[-90.764857,46.946524]]],[[[-90.568938,46.847391],[-90.683356,46.813275],[-90.685753,46.805003],[-90.65531,46.799173],[-90.656358,46.789745],[-90.716456,46.785418],[-90.763647,46.754927],[-90.787751,46.753301],[-90.783086,46.772939],[-90.791517,46.784713],[-90.736609,46.799654],[-90.712381,46.820743],[-90.656946,46.843476],[-90.622048,46.872872],[-90.602619,46.872715],[-90.568938,46.847391]]],[[[-90.572383,46.958835],[-90.530597,46.968099],[-90.50988,46.959108],[-90.545105,46.917287],[-90.569169,46.920309],[-90.637124,46.906724],[-90.654796,46.919249],[-90.634507,46.942944],[-90.572383,46.958835]]],[[[-87.335299,45.211327],[-87.33622,45.173174],[-87.327284,45.157363],[-87.376777,45.177298],[-87.375569,45.196633],[-87.335299,45.211327]]],[[[-90.962901,46.962028],[-90.980316,46.971578],[-90.98222,46.985417],[-90.93104,47.000857],[-90.92474,46.990532],[-90.931127,46.965334],[-90.962901,46.962028]]],[[[-90.757147,47.03372],[-90.643623,47.041177],[-90.608824,47.007558],[-90.560936,47.037013],[-90.544875,47.017383],[-90.552867,46.999686],[-90.609715,46.991208],[-90.667685,46.951261],[-90.712032,46.98526],[-90.767985,47.002327],[-90.776921,47.024324],[-90.757147,47.03372]]],[[[-87.405658,44.860098],[-87.384821,44.865532],[-87.385396,44.889964],[-87.405361,44.909626],[-87.393752,44.933751],[-87.322117,45.034201],[-87.264877,45.081361],[-87.257449,45.121644],[-87.240813,45.137559],[-87.238426,45.166492],[-87.224065,45.174551],[-87.200385,45.163819],[-87.147709,45.190711],[-87.119887,45.193242],[-87.122708,45.221786],[-87.109541,45.255397],[-87.078316,45.265723],[-87.057627,45.292838],[-87.043895,45.284767],[-87.015797,45.29919],[-86.97778,45.290684],[-86.970355,45.278455],[-86.983597,45.264971],[-86.976711,45.246146],[-86.985973,45.215872],[-87.002806,45.211773],[-87.00754,45.222127],[-87.032521,45.222274],[-87.040909,45.211535],[-87.045242,45.158798],[-87.030225,45.147382],[-87.054282,45.120074],[-87.05078,45.088663],[-87.079552,45.070783],[-87.081866,45.059103],[-87.121156,45.058311],[-87.138024,45.015327],[-87.163477,45.004913],[-87.187585,44.971606],[-87.188582,44.952193],[-87.1717,44.931476],[-87.215808,44.906744],[-87.204815,44.877199],[-87.267061,44.847025],[-87.282561,44.814729],[-87.313363,44.794237],[-87.353789,44.701915],[-87.401629,44.631191],[-87.435766,44.606472],[-87.467089,44.553557],[-87.517597,44.375696],[-87.545382,44.321385],[-87.541382,44.294018],[-87.508457,44.229755],[-87.512903,44.192808],[-87.563181,44.144195],[-87.6458,44.105222],[-87.656062,44.051919],[-87.683361,44.020139],[-87.736178,43.880421],[-87.726772,43.812885],[-87.700251,43.76735],[-87.709885,43.735795],[-87.702685,43.687596],[-87.789105,43.564844],[-87.797608,43.52731],[-87.793239,43.492783],[-87.807799,43.461136],[-87.872504,43.380178],[-87.911787,43.250406],[-87.881085,43.170609],[-87.900496,43.126],[-87.866487,43.074419],[-87.894813,43.042497],[-87.896836,43.02053],[-87.889342,43.003647],[-87.845181,42.962015],[-87.847745,42.889595],[-87.824,42.836649],[-87.766675,42.784896],[-87.780782,42.752973],[-87.782023,42.710008],[-87.802377,42.676651],[-87.819407,42.617327],[-87.810873,42.58732],[-87.812273,42.52982],[-87.800477,42.49192],[-88.786681,42.491983],[-90.640927,42.508302],[-90.643927,42.540401],[-90.685487,42.589614],[-90.709204,42.636078],[-90.921155,42.685406],[-90.949213,42.685573],[-91.026786,42.724228],[-91.035418,42.73734],[-91.054801,42.740529],[-91.06468,42.750914],[-91.060129,42.759986],[-91.069549,42.769628],[-91.078665,42.827678],[-91.095114,42.834966],[-91.100565,42.883078],[-91.144706,42.905964],[-91.14655,42.963345],[-91.179457,43.067427],[-91.177003,43.131846],[-91.1462,43.152405],[-91.119115,43.200366],[-91.05791,43.253968],[-91.107237,43.313645],[-91.201847,43.349103],[-91.21477,43.365874],[-91.19767,43.395334],[-91.205551,43.422949],[-91.232276,43.450952],[-91.216035,43.481142],[-91.218292,43.514434],[-91.243183,43.540309],[-91.231865,43.581822],[-91.268748,43.615348],[-91.263856,43.647662],[-91.273252,43.666623],[-91.268455,43.709824],[-91.243955,43.773046],[-91.262436,43.792166],[-91.277695,43.837741],[-91.310991,43.867381],[-91.320605,43.888491],[-91.357426,43.917231],[-91.364736,43.934884],[-91.406011,43.963929],[-91.43738,43.999962],[-91.59207,44.031372],[-91.610487,44.04931],[-91.657,44.071409],[-91.68153,44.0974],[-91.707491,44.103906],[-91.719097,44.128853],[-91.808064,44.159262],[-91.872369,44.199167],[-91.892698,44.231105],[-91.887824,44.254171],[-91.895652,44.273008],[-91.924613,44.291815],[-91.916191,44.318094],[-91.970266,44.365842],[-92.061637,44.404124],[-92.232472,44.445434],[-92.291005,44.485464],[-92.314071,44.538014],[-92.336114,44.554004],[-92.455105,44.561886],[-92.518358,44.575183],[-92.54806,44.567792],[-92.567226,44.60177],[-92.584711,44.599861],[-92.621456,44.615017],[-92.621733,44.638983],[-92.632105,44.649027],[-92.660988,44.660884],[-92.737259,44.717155],[-92.807317,44.750364],[-92.805287,44.768361],[-92.766102,44.834966],[-92.763706,44.872129],[-92.774571,44.898084],[-92.757557,44.911214],[-92.750645,44.937299],[-92.770304,44.978967],[-92.764604,45.028767],[-92.793282,45.047178],[-92.802911,45.065403],[-92.740509,45.113396],[-92.756807,45.151866],[-92.752542,45.171772],[-92.767408,45.190166],[-92.751708,45.218666],[-92.760249,45.2496],[-92.752666,45.269565],[-92.761868,45.284938],[-92.704794,45.326526],[-92.69892,45.339364],[-92.704054,45.35366],[-92.650422,45.398507],[-92.646768,45.437929],[-92.652698,45.454527],[-92.677219,45.462864],[-92.724337,45.512223],[-92.72465,45.536744],[-92.745591,45.553016],[-92.775988,45.568478],[-92.823309,45.560934],[-92.881136,45.573409],[-92.887067,45.644148],[-92.869193,45.717568],[-92.784621,45.764196],[-92.776496,45.790014],[-92.757815,45.806574],[-92.761712,45.833861],[-92.739991,45.846283],[-92.734039,45.868108],[-92.712503,45.891705],[-92.676607,45.90637],[-92.659549,45.922937],[-92.639116,45.924555],[-92.640115,45.932478],[-92.551933,45.951651],[-92.549806,45.967986],[-92.530516,45.981918],[-92.464481,45.976267],[-92.442259,46.016177],[-92.428555,46.024241],[-92.35176,46.015685],[-92.335335,46.059422],[-92.294069,46.078346],[-92.287392,46.667342],[-92.265993,46.651041],[-92.259692,46.657141],[-92.242493,46.649241],[-92.207092,46.651941],[-92.205492,46.664741],[-92.176091,46.686341],[-92.204691,46.704041],[-92.191764,46.715483],[-92.148691,46.71514],[-92.13789,46.73954],[-92.108777,46.749105],[-92.08949,46.74924],[-92.03399,46.708939],[-91.961889,46.682539],[-91.790473,46.694624],[-91.593442,46.753345],[-91.511077,46.757453],[-91.411799,46.78964],[-91.369387,46.793745],[-91.315061,46.826729],[-91.256873,46.836833],[-91.226796,46.86361],[-91.207524,46.865835],[-91.178292,46.844259],[-91.134668,46.87249],[-91.136512,46.860975],[-91.107323,46.857469],[-91.090916,46.88267],[-91.050153,46.883037],[-91.034518,46.903053],[-90.998848,46.915975],[-90.968419,46.94391],[-90.92204,46.931372],[-90.880358,46.957661],[-90.855874,46.962232],[-90.786595,46.927019],[-90.751031,46.887963],[-90.77017,46.876296],[-90.798545,46.823922],[-90.835008,46.790366],[-90.854916,46.788952],[-90.862333,46.768135],[-90.885021,46.756341],[-90.853644,46.694464],[-90.885869,46.670374],[-90.911281,46.663083],[-90.924487,46.625417],[-90.949532,46.603019],[-90.942101,46.588573],[-90.909815,46.582703],[-90.772455,46.635097],[-90.755381,46.646225],[-90.756312,46.66182],[-90.739565,46.689943],[-90.558141,46.586384],[-90.538346,46.581182],[-90.505909,46.589614],[-90.440085,46.562365],[-90.418136,46.566094],[-90.387228,46.533663],[-90.350121,46.537337],[-90.336921,46.554076],[-90.310859,46.539365],[-90.316983,46.517319],[-90.272599,46.521127],[-90.263018,46.502777],[-90.230363,46.509705],[-90.188996,46.469015],[-90.193294,46.463143],[-90.180336,46.456746],[-90.17786,46.440548],[-90.163422,46.434605],[-90.152936,46.401293],[-90.118827,46.359241],[-90.119468,46.3397],[-89.09163,46.138505],[-88.837991,46.030176],[-88.815427,46.022954],[-88.784411,46.032709],[-88.776187,46.015931],[-88.730675,46.026535],[-88.718397,46.013284],[-88.698716,46.017903],[-88.674606,46.010567],[-88.664802,45.989835],[-88.616405,45.9877],[-88.598093,46.017623],[-88.589755,46.005602],[-88.514601,46.019926],[-88.492495,45.992157],[-88.458658,45.999391],[-88.416914,45.975323],[-88.384318,45.988113],[-88.334628,45.968808],[-88.330296,45.956625],[-88.30952,45.959369],[-88.295264,45.951253],[-88.249117,45.963663],[-88.23314,45.947405],[-88.201852,45.945173],[-88.191991,45.95274],[-88.121864,45.92075],[-88.096496,45.917273],[-88.106136,45.900811],[-88.101814,45.883504],[-88.073944,45.875593],[-88.075146,45.864832],[-88.13364,45.823128],[-88.131834,45.811312],[-88.109506,45.803584],[-88.103247,45.791361],[-88.072091,45.780261],[-87.989831,45.794827],[-87.98087,45.776977],[-87.989829,45.772945],[-87.963452,45.75822],[-87.875813,45.753888],[-87.85548,45.726943],[-87.805867,45.706841],[-87.809181,45.700337],[-87.780737,45.675458],[-87.823164,45.662732],[-87.824102,45.647138],[-87.780845,45.614599],[-87.777199,45.588499],[-87.792372,45.563055],[-87.829346,45.568776],[-87.833591,45.562529],[-87.80339,45.538272],[-87.793447,45.498372],[-87.805773,45.473139],[-87.861697,45.434473],[-87.849322,45.403872],[-87.887828,45.358122],[-87.88517,45.351736],[-87.865675,45.358213],[-87.849899,45.344651],[-87.800464,45.353608],[-87.754104,45.349442],[-87.693956,45.389893],[-87.657349,45.368752],[-87.648126,45.339396],[-87.687498,45.298055],[-87.712184,45.239014],[-87.724156,45.233236],[-87.726175,45.21264],[-87.741732,45.198201],[-87.735135,45.171538],[-87.683902,45.144135],[-87.657135,45.107568],[-87.587147,45.089495],[-87.610395,45.075617],[-87.625748,45.045157],[-87.630298,44.976865],[-87.76262,44.965796],[-87.817713,44.951914],[-87.837647,44.933091],[-87.844299,44.918524],[-87.827751,44.891229],[-87.832764,44.880939],[-87.865898,44.840988],[-87.902166,44.824708],[-87.941453,44.75608],[-87.964714,44.74357],[-87.983065,44.72073],[-87.990081,44.669791],[-88.00334,44.65963],[-88.009766,44.637081],[-88.001943,44.603909],[-88.012395,44.602438],[-88.042261,44.567344],[-88.005518,44.539216],[-87.970702,44.530292],[-87.929001,44.535993],[-87.901206,44.568887],[-87.903689,44.581317],[-87.867941,44.607606],[-87.809076,44.636189],[-87.756048,44.649117],[-87.71978,44.693246],[-87.721252,44.722361],[-87.611852,44.836743],[-87.581635,44.851638],[-87.534723,44.85625],[-87.515142,44.869596],[-87.478489,44.863572],[-87.433128,44.892741],[-87.421007,44.887869],[-87.405658,44.860098]]],[[[-86.880572,45.331467],[-86.899488,45.322588],[-86.896922,45.298521],[-86.904362,45.296662],[-86.925681,45.3242],[-86.956054,45.342202],[-86.946791,45.361845],[-86.95497,45.383194],[-86.943041,45.41525],[-86.937393,45.420966],[-86.917686,45.40789],[-86.877502,45.413981],[-86.855993,45.407777],[-86.830353,45.410852],[-86.828731,45.428461],[-86.810055,45.422619],[-86.805415,45.407324],[-86.822083,45.406868],[-86.841432,45.389601],[-86.863367,45.365],[-86.869031,45.333244],[-86.880572,45.331467]]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"14","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Ketz, Alison","contributorId":347969,"corporation":false,"usgs":false,"family":"Ketz","given":"Alison","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":929532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storm, Daniel J.","contributorId":341059,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":929533,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barker, Rachel","contributorId":301098,"corporation":false,"usgs":false,"family":"Barker","given":"Rachel","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":929534,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Apa, Anthony D.","contributorId":272966,"corporation":false,"usgs":false,"family":"Apa","given":"Anthony","email":"","middleInitial":"D.","affiliations":[{"id":40103,"text":"cdpw","active":true,"usgs":false}],"preferred":false,"id":929535,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oliva-Aviles, Cristian","contributorId":301099,"corporation":false,"usgs":false,"family":"Oliva-Aviles","given":"Cristian","affiliations":[{"id":65304,"text":"Genentech","active":true,"usgs":false}],"preferred":false,"id":929536,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":929537,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256548,"text":"70256548 - 2023 - The first documented interaction between a long-tailed weasel (Mustela frenata) and a plains spotted skunk (Spilogale interrupta) carcass","interactions":[],"lastModifiedDate":"2024-08-23T13:48:44.826956","indexId":"70256548","displayToPublicDate":"2023-01-29T10:12:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The first documented interaction between a long-tailed weasel (Mustela frenata) and a plains spotted skunk (Spilogale interrupta) carcass","title":"The first documented interaction between a long-tailed weasel (Mustela frenata) and a plains spotted skunk (Spilogale interrupta) carcass","docAbstract":"

A novel interaction between a long-tailed weasel (Mustela frenata) and a plains spotted skunk (Spilogale interrupta) carcass is detailed. In November 2020, a farmer in Edmunds County in north-central South Dakota sent in a video recording of a long-tailed weasel with a spotted skunk carcass. Location of the event, carcass condition, and recorded behavior of the long-tailed weasel offer probable, but unconfirmed, evidence that the spotted skunk was killed by the long-tailed weasel.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9758","usgsCitation":"White, K., Stafford, J.D., and Lonsinger, R.C., 2023, The first documented interaction between a long-tailed weasel (Mustela frenata) and a plains spotted skunk (Spilogale interrupta) carcass: Ecology and Evolution, v. 13, no. 1, e9758, 5 p., https://doi.org/10.1002/ece3.9758.","productDescription":"e9758, 5 p.","ipdsId":"IP-135025","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":444688,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9758","text":"Publisher Index Page"},{"id":433064,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","county":"Edmunds County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-99.7096,45.5953],[-99.3464,45.5941],[-98.73,45.5911],[-98.7242,45.5905],[-98.7254,45.4963],[-98.7246,45.33],[-98.7249,45.2459],[-99.2054,45.2454],[-99.2177,45.2465],[-99.3414,45.2462],[-99.4515,45.2453],[-99.4735,45.2464],[-99.5751,45.2458],[-99.6962,45.2465],[-99.7111,45.2462],[-99.7096,45.5953]]]},\"properties\":{\"name\":\"Edmunds\",\"state\":\"SD\"}}]}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"White, K.M.","contributorId":341090,"corporation":false,"usgs":false,"family":"White","given":"K.M.","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stafford, Joshua D. 0000-0001-7590-8708 jstafford@usgs.gov","orcid":"https://orcid.org/0000-0001-7590-8708","contributorId":267260,"corporation":false,"usgs":true,"family":"Stafford","given":"Joshua","email":"jstafford@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907930,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250078,"text":"70250078 - 2023 - Lessons learned in knowledge co-production for climate-smart decision-making","interactions":[],"lastModifiedDate":"2023-11-16T12:39:27.946106","indexId":"70250078","displayToPublicDate":"2023-01-28T06:37:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1563,"text":"Environmental Science and Policy","active":true,"publicationSubtype":{"id":10}},"title":"Lessons learned in knowledge co-production for climate-smart decision-making","docAbstract":"

Knowledge co-production, a process that involves both creators and users of information in knowledge generation, is growing in popularity in the conservation and ecology fields. While examples of successful co-production are becoming more common, many barriers and challenges remain in this work. Here, we reflect on our experiences in knowledge co-production from three recent case studies, using a prominent framework to understand and improve our efforts at each phase of the co-production process. Our reflections yield insights that may help other scientists seeking to support decision-making. We found that paying particular attention to the composition of the team and connecting with agency representatives early and often are key to success. Long-term commitment to the project and the people involved are also key. We conclude with suggestions for refining the framework to incorporate our primary lessons learned and include the valuation of a plurality of knowledge systems and empowerment as an ultimate impact of knowledge co-production.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsci.2023.01.010","usgsCitation":"Rosemartin, A., Crimmins, T., Gerst, K.L., Posthumus, E.E., Ramirez, A.R., Wallace, C.S., and Morelli, T.L., 2023, Lessons learned in knowledge co-production for climate-smart decision-making: Environmental Science and Policy, v. 141, p. 178-187, https://doi.org/10.1016/j.envsci.2023.01.010.","productDescription":"10 p.","startPage":"178","endPage":"187","ipdsId":"IP-133517","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":444690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsci.2023.01.010","text":"Publisher Index Page"},{"id":422651,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rosemartin, Alyssa","contributorId":175226,"corporation":false,"usgs":false,"family":"Rosemartin","given":"Alyssa","affiliations":[],"preferred":false,"id":888242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Crimmins, Theresa 0000-0001-9592-625X","orcid":"https://orcid.org/0000-0001-9592-625X","contributorId":222414,"corporation":false,"usgs":false,"family":"Crimmins","given":"Theresa","email":"","affiliations":[{"id":40537,"text":"USA National Phenology Network, National Coordinating Office; University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":888243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerst, Katherine L.","contributorId":196324,"corporation":false,"usgs":false,"family":"Gerst","given":"Katherine","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":888244,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Posthumus, Erin E. 0000-0003-3855-2380","orcid":"https://orcid.org/0000-0003-3855-2380","contributorId":204418,"corporation":false,"usgs":false,"family":"Posthumus","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":40537,"text":"USA National Phenology Network, National Coordinating Office; University of Arizona, School of Natural Resources and the Environment","active":true,"usgs":false}],"preferred":false,"id":888245,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramirez, Aaron R.","contributorId":149780,"corporation":false,"usgs":false,"family":"Ramirez","given":"Aaron","email":"","middleInitial":"R.","affiliations":[{"id":17824,"text":"UC Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":888246,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wallace, Cynthia S. 0000-0003-0001-8828 cwallace@usgs.gov","orcid":"https://orcid.org/0000-0003-0001-8828","contributorId":331632,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia","email":"cwallace@usgs.gov","middleInitial":"S.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":888247,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":888248,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247102,"text":"70247102 - 2023 - Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs","interactions":[],"lastModifiedDate":"2023-07-25T15:05:03.23572","indexId":"70247102","displayToPublicDate":"2023-01-27T09:54:26","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2025,"text":"International Journal for Parasitology: Parasites and Wildlife","active":true,"publicationSubtype":{"id":10}},"title":"Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs","docAbstract":"

Parasite infrapopulation size - the population of parasites affecting a single host - is a central metric in parasitology. However, parasites are small and elusive such that imperfect detection is expected. Repeated sampling of parasites during primary sampling occasions (e.g., each host capture) informs the detection process. Here, we estimate flea (Siphonaptera) infrapopulation size on black-tailed prairie dogs (Cynomys ludovicianus, BTPDs) as a proof-of-concept for estimating parasite infrapopulations given imperfect detection. From Jun–Aug 2011, we live-trapped 299 BTPDs for a total of 573 captures on 20 plots distributed among 13 colonies at the Vermejo Park Ranch, New Mexico, USA. During each capture, an anesthetized BTPD was combed 3 times consecutively, 15 s each, to remove and count fleas. Each flea (n = 4846) was linked to the BTPD from which it was collected and assigned an encounter history (’100’, ‘010’, ‘001’). We analyzed the encounter histories using Huggins closed captures models, setting recapture probabilities to 0, thereby accounting for flea removal from hosts. The probability of detecting an individual flea (p) increased with Julian date; field personnel may have become more efficient at combing fleas as the field season progressed. Combined p across 3 combings equaled 0.99. Estimates of flea infrapopulation size were reasonable and followed the negative binomial distribution. Our general approach may be broadly applicable to estimating infrapopulation sizes for parasites. The utility of this approach increases as p declines but, if p is very low, inference is likely limited.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijppaw.2023.01.002","usgsCitation":"Eads, D.A., Huyvaert, K.P., and Biggins, D.E., 2023, Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs: International Journal for Parasitology: Parasites and Wildlife, v. 20, p. 117-121, https://doi.org/10.1016/j.ijppaw.2023.01.002.","productDescription":"5 p.","startPage":"117","endPage":"121","ipdsId":"IP-146545","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444692,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijppaw.2023.01.002","text":"Publisher Index Page"},{"id":419306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Vermejo Park Ranch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.8,\n 36.6\n ],\n [\n -104.8,\n 36.5\n ],\n [\n -104.7,\n 36.5\n ],\n [\n -104.7,\n 36.6\n ],\n [\n -104.8,\n 36.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":878902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Huyvaert, Kathryn P.","contributorId":202514,"corporation":false,"usgs":false,"family":"Huyvaert","given":"Kathryn","email":"","middleInitial":"P.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":878903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":878904,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256545,"text":"70256545 - 2023 - Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs","interactions":[],"lastModifiedDate":"2024-08-22T14:59:53.775057","indexId":"70256545","displayToPublicDate":"2023-01-27T09:42:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs","docAbstract":"

Recreational-grade side-scan sonar (SSS) has become an invaluable tool for inland fisheries, particularly when characterizing underwater habitat, but it is being increasingly used for enumerating large-bodied (> 1 m total length [TL]) aquatic fauna. We used SSS in river and reservoir environments to evaluate methods for identifying and counting Paddlefish Polyodon spathula, a large pelagic planktivore of recreational and economic importance that can exceed 2 m in length and weigh over 70 kg. We assessed accuracy and precision among readers to identify Paddlefish by assigning confidence scores (1–3; with 3 being more confident) to sonar images of a ballistics-gel filled fiberglass replica Paddlefish. Readers varied in their confidence scores for the replica Paddlefish and no reader could identify the target beyond 25 m from the transducer. Afterwards, we used SSS to survey several kilometers of a reservoir during summer residency and a large river during springtime spawning migrations. Two readers counted Paddlefish images in the SSS recordings and we estimated population size in the surveyed area with distance sampling. In the reservoir, the number of Paddlefish counted ranged from 172 to 184. In the river, the number of Paddlefish counted ranged from 165 to 617. The exponential model of distance was most-supported for detection in both environments, except there was support for a half-norm distribution for one reader in the river. In the reservoir, abundance estimates were statistically similar between readers at approximately 1500 (7/ha) in the total scanned area. In the river, similar abundance estimates were obtained with the half-norm model from one reader compared to the exponential model of the other reader, resulting in approximately 1500 individuals (30/ha) in the surveyed area. The application of SSS to count Paddlefish has some clear advantages to traditional methods, such as gill netting, and can be done at multiple times of the year. Distance sampling methods compensated for differences in counts among readers, indicating distance sampling can produce similar abundance estimates even when variation in counts exists among readers.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2023.106639","usgsCitation":"Wolfenkoehler, W., Long, J.M., Gary, R., Snow, R., Schooley, J.D., Bruckerhoff, L.A., and Lonsinger, R.C., 2023, Viability of side-scan sonar to enumerate Paddlefish, a large pelagic freshwater fish, in rivers and reservoirs: Fisheries Research, v. 261, 106639, 9 p., https://doi.org/10.1016/j.fishres.2023.106639.","productDescription":"106639, 9 p.","ipdsId":"IP-142414","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Keystone Lake, Lake Carl Blackwell, Verdigris River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.32079029272471,\n 36.18635094806682\n ],\n [\n -97.32079029272471,\n 36.089827328506544\n ],\n [\n -97.1696333849386,\n 36.089827328506544\n ],\n [\n -97.1696333849386,\n 36.18635094806682\n ],\n [\n -97.32079029272471,\n 36.18635094806682\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.2644501788143,\n 36.125328208556084\n ],\n [\n -96.2240244941739,\n 36.16506963468771\n ],\n [\n -96.21875157878593,\n 36.22747975513728\n ],\n [\n -96.45427513277778,\n 36.332329984806975\n ],\n [\n -96.47536679432932,\n 36.306838838970805\n ],\n [\n -96.420880001988,\n 36.254414197425305\n ],\n [\n -96.3945154250485,\n 36.210463747118126\n ],\n [\n -96.29081475575383,\n 36.213300005475446\n ],\n [\n -96.29081475575383,\n 36.13952388632518\n ],\n [\n -96.2644501788143,\n 36.125328208556084\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.52086370103724,\n 36.84221290357827\n ],\n [\n -95.63789552781877,\n 36.84221290357827\n ],\n [\n -95.63789552781877,\n 36.6881372389295\n ],\n [\n -95.52086370103724,\n 36.6881372389295\n ],\n [\n -95.52086370103724,\n 36.84221290357827\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"261","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfenkoehler, Wyatt","contributorId":341077,"corporation":false,"usgs":false,"family":"Wolfenkoehler","given":"Wyatt","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gary, Ryan","contributorId":341078,"corporation":false,"usgs":false,"family":"Gary","given":"Ryan","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":907910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snow, Richard A.","contributorId":341079,"corporation":false,"usgs":false,"family":"Snow","given":"Richard A.","affiliations":[{"id":81697,"text":"Oklahoma Fishery Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":907911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schooley, Jason D.","contributorId":341080,"corporation":false,"usgs":false,"family":"Schooley","given":"Jason","email":"","middleInitial":"D.","affiliations":[{"id":81698,"text":"Paddlefish Research Center","active":true,"usgs":false}],"preferred":false,"id":907912,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bruckerhoff, Lindsey Ann 0000-0002-9523-4808","orcid":"https://orcid.org/0000-0002-9523-4808","contributorId":292594,"corporation":false,"usgs":true,"family":"Bruckerhoff","given":"Lindsey","email":"","middleInitial":"Ann","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907913,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907914,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247865,"text":"70247865 - 2023 - Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001","interactions":[],"lastModifiedDate":"2023-08-22T13:39:04.295519","indexId":"70247865","displayToPublicDate":"2023-01-27T08:32:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001","docAbstract":"

In 1999–2001, Guagua Pichincha volcano, Ecuador, produced a series of cyclic explosive and effusive eruptions. Rock samples, including dense blocks and pumiceous clasts collected during the eruption sequence, and ballistic bombs later collected from the crater floor, provide information about magma storage, ascent, decompression, degassing, repressurization, and regassing prior to eruption. Pairs of Fe-Ti oxides indicate equilibrium within 1.2–1.5 log units above the NNO oxidation buffer and equilibrium temperatures from 805 to 905 °C. Melt inclusions record H2O contents of 2.7–4.6 wt% and CO2 contents (uncorrected for CO2 segregation into bubbles) from 19 to 310 ppm. Minimum melt inclusion saturation pressures fall between 69 and 168 MPa, or equilibration depths of 2.8 and 6.8 km, the lower end of which is coincident with the maximum inferred equilibration depths for the most vesicular breadcrust bombs sampled. Amphibole phenocrysts lack breakdown rims (except for one sample) and plagioclase phenocrysts have abundant oscillatory compositional zones. Plagioclase areal microlite number densities (Na) range over less than one order of magnitude (8.9×103–8.7×104 mm-2) among all samples, with the exception of a dense, low crystallinity sample (Na = 3.0×103 mm−2) and a pumiceous sample erupted on 17 December 1999 (Na = 1.7×103 mm−2). Plagioclase microlite shapes include tabular, hopper, and swallowtail forms. Taken together, the relatively high plagioclase microlite number densities, the high number of oscillatory zones in plagioclase phenocrysts, the presence of CO2 in groundmass glass, seismicity, and time-varying tilt cycles provide a picture of sudden evacuation of magma residing at different levels in the shallow conduit. Explosive eruptions punctuate inter-eruptive repose periods marked by time-varying rates of degassing (volatile fluxing) and re-pressurization. Shallow residence time in the conduit was sufficient to allow precipitation of silica-phase in the groundmass, but insufficient to allow breakdown of hornblende phenocrysts, with the one exception of the final dome sample from 2000, which has the longest preceding repose time. These results support a model of cyclic pressure cycling, volatile exsolution and regassing, and magma decompression decoupled from ascent.

","language":"English","publisher":"Springer","doi":"10.1007/s00445-023-01626-3","usgsCitation":"Wright, H.M., Cioni, R., Cashman, K.V., Mothes, P., and Rosi, M., 2023, Decompression and degassing, repressurization, and regassing during cyclic eruptions at Guagua Pichincha volcano, Ecuador, 1999–2001: Bulletin of Volcanology, v. 85, 12, 24 p., https://doi.org/10.1007/s00445-023-01626-3.","productDescription":"12, 24 p.","ipdsId":"IP-143035","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":444695,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-023-01626-3","text":"Publisher Index Page"},{"id":420012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Guagua Pichincha Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.64295621873246,\n -0.14110488224878281\n ],\n [\n -78.64295621873246,\n -0.201181738857926\n ],\n [\n -78.58224335456136,\n -0.201181738857926\n ],\n [\n -78.58224335456136,\n -0.14110488224878281\n ],\n [\n -78.64295621873246,\n -0.14110488224878281\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"85","noUsgsAuthors":false,"publicationDate":"2023-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Heather M. 0000-0001-9013-507X hwright@usgs.gov","orcid":"https://orcid.org/0000-0001-9013-507X","contributorId":3949,"corporation":false,"usgs":true,"family":"Wright","given":"Heather","email":"hwright@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":880787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cioni, Raffaello 0000-0002-2526-9095","orcid":"https://orcid.org/0000-0002-2526-9095","contributorId":328622,"corporation":false,"usgs":false,"family":"Cioni","given":"Raffaello","email":"","affiliations":[{"id":78424,"text":"Universita degli Studi di Firenzi","active":true,"usgs":false}],"preferred":false,"id":880788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cashman, Katharine V.","contributorId":199542,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine","email":"","middleInitial":"V.","affiliations":[{"id":13025,"text":"Department of Geological Sciences, University of Oregon","active":true,"usgs":false}],"preferred":false,"id":880789,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mothes, Patricia","contributorId":178532,"corporation":false,"usgs":false,"family":"Mothes","given":"Patricia","affiliations":[],"preferred":false,"id":880790,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosi, Mauro","contributorId":206499,"corporation":false,"usgs":false,"family":"Rosi","given":"Mauro","email":"","affiliations":[],"preferred":false,"id":880791,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240770,"text":"70240770 - 2023 - Landscape and connectivity metrics as a spatial tool to support invasive annual grass management decisions","interactions":[],"lastModifiedDate":"2023-03-01T17:27:19.786874","indexId":"70240770","displayToPublicDate":"2023-01-27T07:27:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Landscape and connectivity metrics as a spatial tool to support invasive annual grass management decisions","docAbstract":"

The spatial patterns and context of invasions are increasingly recognized as important for successful and efficient management actions. Beyond mapping occurrence or percent cover in pixels, spatial summary information that describes the size and arrangement of patches in the context of a larger landscape (e.g., infested regions, connected patch networks) can add a depth of information for managing invasive grasses that threaten native ecosystems. Few invasive annual grass analyses have explored the use of landscape and circuit-based connectivity metrics to characterize and compare spatial patterns of invasion. To assess the transferability and applicability of these landscape ecology analyses, we calculated landscape metrics (4 area-based, 3 configuration) and a connectivity metric (circuit-based centrality), using a weighted-average map of invasive annual grass cover in the Great Basin, USA. We calculated metrics at local and regional scales, allowing invasion statistics to be compared across the landscape and illustrating varying patterns of invasion extent and connectedness. We found the metrics provided additional, complementary information at the sampled local and regional scales beyond abundance measures alone. We also illustrated how key metrics could be used to categorize and map areas needing different management strategies, for example, where strategies could proactively protect uninvaded cores, disconnect fine fuel patches, or contain established invasions. The landscape and connectivity metric approach can be applied across scales to spatially target patches locally, provide broader context within a single region, as well as to compare metrics and spatial variation in patterns among different regions.

","language":"English","publisher":"Springer","doi":"10.1007/s10530-022-02945-w","usgsCitation":"Buchholtz, E.K., Heinrichs, J., and Crist, M., 2023, Landscape and connectivity metrics as a spatial tool to support invasive annual grass management decisions: Biological Invasions, v. 25, p. 637-644, https://doi.org/10.1007/s10530-022-02945-w.","productDescription":"8 p.","startPage":"637","endPage":"644","ipdsId":"IP-139058","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444697,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10530-022-02945-w","text":"Publisher Index Page"},{"id":435480,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B4H00Q","text":"USGS data release","linkHelpText":"Landscape and connectivity metrics based on invasive annual grass cover from 2016-2018 summarized at 15 kilometer grid cells in the Great Basin, USA"},{"id":413280,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.10368059965884,\n 44.852271924466294\n ],\n [\n -119.58419968653757,\n 44.131579771142185\n ],\n [\n -120.55058615625657,\n 40.160272981776586\n ],\n [\n -119.93561294825372,\n 37.908635991022905\n ],\n [\n -116.28970035795007,\n 35.442907909031135\n ],\n [\n -112.38022782135972,\n 38.11628851194823\n ],\n [\n -110.71101482820848,\n 42.953528205336\n ],\n [\n -111.63347464021301,\n 44.50870422504519\n ],\n [\n -113.03912768707703,\n 45.00776554269126\n ],\n [\n -115.10368059965884,\n 44.852271924466294\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2023-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Buchholtz, Erin K. 0000-0002-1985-9531","orcid":"https://orcid.org/0000-0002-1985-9531","contributorId":300162,"corporation":false,"usgs":true,"family":"Buchholtz","given":"Erin","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":864777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":864778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crist, Michele R.","contributorId":178453,"corporation":false,"usgs":false,"family":"Crist","given":"Michele R.","affiliations":[],"preferred":false,"id":864779,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240144,"text":"70240144 - 2023 - New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences","interactions":[],"lastModifiedDate":"2023-01-30T12:51:57.454932","indexId":"70240144","displayToPublicDate":"2023-01-27T06:50:11","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences","docAbstract":"Geothermal well data from Southern Methodist University and the U.S. Geological Survey (USGS) were used to create maps of estimated background conductive heat flow across the Great Basin region of the western United States. These heat flow maps were generated as part of the USGS hydrothermal and Enhanced Geothermal Systems resource assessment process, and the creation process seeks to remove the influence of hydrothermal convection from the predictions of the background conductive heat flow. The heat flow maps were constructed using a custom-developed iterative process using weighted regression, in which convectively influenced outliers were de-emphasized by assigning lower weights to measurements with heat flow values further from the estimated local trend (e.g., local convective influence). The local linear weighted regression algorithm is two-dimensional locally estimated scatterplot smoothing where smoothness was controlled by varying the number of nearby wells used for each local interpolation.\nThree maps resulting from conductive heat flow models are detailed in this paper, highlighting the influence of measurement confidence. The three maps use either: measurements from all wells with equal weight (no confidence weights), or one of two different published categorization methods to de-emphasize low-quality measurements; one categorization method graded thermal gradient quality, the other categorization method graded thermal conductivity quality. Each map is an estimate of background conductive heat flow as a function of reported data quality, and a point coverage is also provided for all wells in the compiled dataset. The point coverage includes an important new attribute for geothermal wells: the residual, which can be interpreted as the departure of a well from the estimated background heat flow conditions, and the value of the residual may be useful in identifying the influence of fluids (hydrothermal or groundwater) on conductive heat flow. Of the three maps presented, the map that de-emphasized the impact of wells with low-quality thermal gradient measurements appears to perform best because it did not incorporate many of the wells in the Snake River Plain that do not penetrate the aquifer and are therefore very unlikely to reflect true conductive conditions.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 48th Workshop on Geothermal Reservoir Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"48th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 6-8, 2023","conferenceLocation":"Stanford, California","language":"English","publisher":"Stanford Geothermal Workshop","usgsCitation":"DeAngelo, J., Burns, E., Gentry, E., Batir, J.F., Lindsey, C.R., and Mordensky, S.P., 2023, New maps of conductive heat flow in the Great Basin, USA: Separating conductive and convective influences, in Proceedings, 48th Workshop on Geothermal Reservoir Engineering, Stanford, California, February 6-8, 2023, 13 p.","productDescription":"13 p.","ipdsId":"IP-149016","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":412439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":412435,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/GeoConf/papers/SGW/2023/Deangelo.pdf?t=1674862190"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.94931681460088,\n 43.31269307515126\n ],\n [\n -121.94931681460088,\n 34.37043992080774\n ],\n [\n -110.40572044760874,\n 34.37043992080774\n ],\n [\n -110.40572044760874,\n 43.31269307515126\n ],\n [\n -121.94931681460088,\n 43.31269307515126\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":862754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":862755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gentry, Emilie","contributorId":293494,"corporation":false,"usgs":false,"family":"Gentry","given":"Emilie","email":"","affiliations":[{"id":63314,"text":"Petrolern","active":true,"usgs":false}],"preferred":false,"id":862756,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Batir, Joseph F.","contributorId":293495,"corporation":false,"usgs":false,"family":"Batir","given":"Joseph","email":"","middleInitial":"F.","affiliations":[{"id":63314,"text":"Petrolern","active":true,"usgs":false}],"preferred":false,"id":862757,"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":862758,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":862759,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240125,"text":"70240125 - 2023 - Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska","interactions":[],"lastModifiedDate":"2023-01-30T12:36:58.473885","indexId":"70240125","displayToPublicDate":"2023-01-27T06:33:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska","docAbstract":"

The frequency of large supraglacial landslides (rock avalanches) occurring in glacial environments is thought to be increasing due to feedbacks with climate warming and permafrost degradation. However, it is difficult to (i) test this; (ii) establish cause–effect relationships; and (iii) determine associated lag-times, due to both temporal and spatial biases in detection rates. Here we applied the Google Earth Engine supraglacial debris input detector (GERALDINE) to Glacier Bay National Park & Preserve (GLBA), Alaska. We find that the number of rock avalanches (RAs) has previously been underestimated by 53 %, with a bias in past detections towards large area RAs. In total, GLBA experienced 69 RAs during 1984–2020, with the highest frequency in the last three years. Of these, 58 % were deposited into the accumulation zone and then sequestered into the ice within two years. RA sources clustered spatially at high elevations and around certain peaks and ridges, predominantly at the boundary of modelled permafrost likelihood. They also clustered temporally, occurring mainly between May and September when air temperatures were high enough to initiate rock-permafrost degradation mechanisms. There was a chronic background debris supply from RAs, with at least one RA occurring in all but nine years; however, a debris rich period during 2012–2016 was driven by three large RAs delivering 44 % of all (1984–2020) debris (by area). Comparable investigation of slope-failures in other remote currently glaciated regions is lacking. If RA rates are similar elsewhere, especially the bias towards emplacement onto/into accumulation zones, their contribution to glacial sediment budgets has been globally underestimated.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2023.108591","usgsCitation":"Smith, W., Dunning, S.A., Ross, N., Telling, J., Bessette-Kirton, E., Shugar, D., Coe, J.A., and Geertsema, M., 2023, Revising supraglacial rock avalanche magnitudes and frequencies in Glacier Bay National Park, Alaska: Geomorphology, v. 425, 108591, 15 p., https://doi.org/10.1016/j.geomorph.2023.108591.","productDescription":"108591, 15 p.","ipdsId":"IP-135862","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":444700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.geomorph.2023.108591","text":"Publisher Index Page"},{"id":412436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -138.03863703041065,\n 59.29498993664896\n ],\n [\n -138.03863703041065,\n 58.044630678420305\n ],\n [\n -134.97475265482453,\n 58.044630678420305\n ],\n [\n -134.97475265482453,\n 59.29498993664896\n ],\n [\n -138.03863703041065,\n 59.29498993664896\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"425","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, William 0000-0002-7134-7592","orcid":"https://orcid.org/0000-0002-7134-7592","contributorId":301834,"corporation":false,"usgs":false,"family":"Smith","given":"William","affiliations":[{"id":33636,"text":"Newcastle University","active":true,"usgs":false}],"preferred":false,"id":862693,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunning, Stuart A. 0000-0002-2310-7367","orcid":"https://orcid.org/0000-0002-2310-7367","contributorId":301835,"corporation":false,"usgs":false,"family":"Dunning","given":"Stuart","email":"","middleInitial":"A.","affiliations":[{"id":33636,"text":"Newcastle University","active":true,"usgs":false}],"preferred":false,"id":862694,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Neil 0000-0002-8338-4905","orcid":"https://orcid.org/0000-0002-8338-4905","contributorId":301836,"corporation":false,"usgs":false,"family":"Ross","given":"Neil","email":"","affiliations":[{"id":33636,"text":"Newcastle University","active":true,"usgs":false}],"preferred":false,"id":862695,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Telling, Jon 0000-0002-8180-0979","orcid":"https://orcid.org/0000-0002-8180-0979","contributorId":301837,"corporation":false,"usgs":false,"family":"Telling","given":"Jon","email":"","affiliations":[{"id":33636,"text":"Newcastle University","active":true,"usgs":false}],"preferred":false,"id":862696,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bessette-Kirton, Erin K. 0000-0002-2797-0694","orcid":"https://orcid.org/0000-0002-2797-0694","contributorId":225097,"corporation":false,"usgs":false,"family":"Bessette-Kirton","given":"Erin K.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":862697,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shugar, Dan H. 0000-0002-6279-8420","orcid":"https://orcid.org/0000-0002-6279-8420","contributorId":224588,"corporation":false,"usgs":false,"family":"Shugar","given":"Dan H.","affiliations":[{"id":40894,"text":"University of Calgary, Calgary, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":862698,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":862699,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Geertsema, M. 0000-0002-4650-8251","orcid":"https://orcid.org/0000-0002-4650-8251","contributorId":167412,"corporation":false,"usgs":false,"family":"Geertsema","given":"M.","affiliations":[],"preferred":false,"id":862700,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70239897,"text":"ofr20221111 - 2023 - Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2021","interactions":[],"lastModifiedDate":"2023-03-01T13:59:05.52129","indexId":"ofr20221111","displayToPublicDate":"2023-01-26T14:05:46","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1111","displayTitle":"Continuous Stream Discharge, Salinity, and Associated Data Collected in the Lower St. Johns River and Its Tributaries, Florida, 2021","title":"Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2021","docAbstract":"

The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, by 7 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.

This is the sixth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. Prior reports in this series documented data collected from October 2015 to September 2020. This report contains information pertinent to data collection during the 2021 water year, from October 2020 to September 2021. There were no modifications this year to the previously installed monitoring network. Data at each station were compared for the length of the project and on a yearly basis to show the annual variability of discharge and salinity in the project area.

Discharge and salinity varied widely during the 2021 water year data collection period, which included above-average rainfall for four of the five counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 6 years considered for this study. Annual mean discharge at Durbin Creek was highest among the tributaries, followed by Trout River, Clapboard Creek, Ortega River, Pottsburg Creek at U.S. 90, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, Cedar River, and Broward River, whose annual mean discharge was lowest. Annual mean discharge at 7 of the 10 tributary monitoring sites was higher for the 2021 water year than for the 2020 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 6 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2021 water year than for the 2020 water year and ranked second among the annual totals computed for the 6 years considered for this study.

Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2020 water year, annual mean salinity at all monitoring locations was lower for the 2021 water year except at the tributary site of Durbin Creek, which remained the same. The 2021 annual mean salinity at all sites ranked second lowest since the beginning of the study in 2016 except at Julington Creek and Racy Point, which tied for lowest, and Durbin Creek, which had the same value for each year.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221111","issn":"ISSN 2331-1258","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2023, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2021: U.S. Geological Survey Open-File Report 2022–1111, 48 p., https://doi.org/10.3133/ofr20221111.","productDescription":"Report: x, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-139675","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":413532,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221111/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412288,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1111/ofr20221111.XML","linkFileType":{"id":8,"text":"xml"}},{"id":412285,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1111/coverthb.jpg"},{"id":412286,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1111/ofr20221111.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":412287,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1111/images"},{"id":412289,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—U.S. Geological Survey National Water Information System database"}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.31115628870195,\n 30.583300030597925\n ],\n [\n -82.31115628870195,\n 29.490035998849976\n ],\n [\n -81.03179238276725,\n 29.490035998849976\n ],\n [\n -81.03179238276725,\n 30.583300030597925\n ],\n [\n -82.31115628870195,\n 30.583300030597925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Caribbean-Florida Water Science Center
U.S. Geological Survey 
4446 Pet Lane, Suite 108
Lutz, FL 33559 

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2023-01-25","noUsgsAuthors":false,"publicationDate":"2023-01-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ryan, Patrick J. 0000-0002-1490-4938 pryan@usgs.gov","orcid":"https://orcid.org/0000-0002-1490-4938","contributorId":203974,"corporation":false,"usgs":true,"family":"Ryan","given":"Patrick","email":"pryan@usgs.gov","middleInitial":"J.","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862297,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239930,"text":"sir20225089 - 2023 - Interaction of a legacy groundwater contaminant plume with the Little Wind River from 2015 through 2017, Riverton Processing site, Wyoming","interactions":[],"lastModifiedDate":"2026-02-23T19:20:42.551781","indexId":"sir20225089","displayToPublicDate":"2023-01-26T12:30:05","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5089","displayTitle":"Interaction of a Legacy Groundwater Contaminant Plume with the Little Wind River from 2015 Through 2017, Riverton Processing Site, Wyoming","title":"Interaction of a legacy groundwater contaminant plume with the Little Wind River from 2015 through 2017, Riverton Processing site, Wyoming","docAbstract":"

The Riverton Processing site was a uranium mill 4 kilometers southwest of Riverton, Wyoming, that prepared uranium ore for nuclear reactors and weapons from 1958 to 1963. The U.S. Department of Energy completed surface remediation of the uranium tailings in 1989; however, groundwater below and downgradient from the tailings site and nearby Little Wind River was not remediated. Beginning in 2010, a series of floods along the Little Wind River began to mobilize contaminants in the unsaturated zone, resulting in substantial increases of uranium and other contaminants of concern in monitoring wells completed inside the contaminant plume. In 2011, the U.S. Department of Energy started a series of university and Government agency retrospective and field investigations to understand the processes controlling contaminant increases in the groundwater plume. The goals of the field investigations described in this report were to (1) identify and quantify the contaminant flux and potential associated biological effects from groundwater associated with the legacy plume as it enters a perennial stream reach, and (2) assess chemical exposure and potential effects to biological receptors from the interaction of the contaminant plume and the river.

Field investigations along the Little Wind River were completed by the U.S. Geological Survey during 2015–17 in cooperation with the U.S. Department of Energy Office of Legacy Management to characterize: (1) seepage areas and seepage rates; (2) pore-water and bed sediment chemistry and hyporheic exchange and reactive loss; and (3) exposure pathways and biological receptors. All data collected during the study are contained in two U.S. Geological Survey data releases, available at https://doi.org/10.5066/F7BR8QX4 and https://doi.org/10.5066/P9J9VJBR. A variety of tools and methods were used during the field characterizations. Streambed temperature mapping, electrical resistivity tomography, electromagnetic induction, fiber-optic distributed temperature sensing, tube seepage meters, vertical thermal sensor arrays, and an environmental tracer (radon) were used to identify areas of groundwater seepage and associated seepage rates along specific sections of the study reach of the river. Drive points, minipiezometers, diffusive equilibrium in thin-film/diffusive gradients in thin-film probes, bed-sediment samples, and equal discharge increment sampling methods were used to characterize pore-water chemistry, estimate hyporheic exchange and reactive loss of selected chemical constituents, and quantify contaminant loadings entering the study reach. Sampling and analysis of surface sediments, filamentous algae, periphytic algae, and macroinvertebrates were used to characterize biological exposure pathways, metal uptake, and receptors.

Areas of focused groundwater discharge identified by the fiber-optic distributed temperature sensing surveys corresponded closely with areas of elevated electrical conductivity identified by the electromagnetic induction survey results in the top 5 meters of sediment. During three monitoring periods in 2016, the mean vertical seepage rate measured with tube seepage meters was 0.45 meter per day, ranging from −0.02 to 1.55 meters per day. Five of the 11 locations where vertical thermal profile data were collected along the study reach during August 2017 indicated mean upwelling values ranging from 0.11 to 0.23 meter per day. Radon data collected from the Little Wind River during June, July, and August 2016 indicated a consistent inflow of groundwater to the central part of the study reach, in the area congruous with the center of the previously mapped groundwater plume discharge zone. During August 2017, the greatest attenuation of uranium from reactive loss in pore-water samples was observed at three locations along the study reach, at depths between 6 and 15 centimeters, and similar trends in molybdenum attenuation were also observed. Bed-sediment concentration profiles collected during 2017 also indicated attenuation of uranium and molybdenum from groundwater during hyporheic mixing of surface water with the legacy plume during groundwater upwelling into the river. Streamflow measurements combined with equal discharge increment water sampling along the study reach indicated an increase in dissolved uranium concentrations in the downstream direction during 2016 and 2017. Net uranium load entering the Little Wind River study reach was about 290 and 435 grams per day during 2016 and 2017, respectively. Biological samples indicated that low levels of uranium and molybdenum exposure were confined to the benthos in the Little Wind River within and immediately downstream from the perimeter of the groundwater plume. Concentrations of molybdenum and uranium in filamentous algae were consistently low at all sites in the study reach with no indication of increased exposure of dissolved bioavailable molybdenum or uranium at sites next to or downstream from the groundwater plume.

Comparison of the August 2017 results from electromagnetic induction, tube seepage meters, vertical thermal profiling, and pore-water chemistry surveys were in general agreement in identifying areas with upwelling groundwater conditions along the study reach. However, the electroconductivity values measured with electromagnetic induction in the top 100 centimeters of sediment did not agree with sodium concentrations measured in pore-water samples collected at similar streambed depths. Differences and similarities between multiple methods can result in additional insights into hydrologic and biogeochemical processes that may be occurring along a reach of a river system interacting with shallow groundwater inputs. It may be advantageous to apply a variety of geophysical, geochemical, hydrologic, and biological tools at other Uranium Mill Tailings Remedial Action (https://www.energy.gov/sites/prod/files/2014/10/f19/UMTRCA.pdf) sites during the investigation of legacy contaminant plume interactions with surface-water systems.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225089","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Naftz, D.L., Fuller, C.C., Runkel, R.L., Solder, J., Gardner, W.P., Terry, N., Briggs, M.A., Short, T.M., Cain, D.J., Dam, W.L., Byrne, P.A., and Campbell, J.R., 2023, Interaction of a legacy groundwater contaminant plume with the Little Wind River from 2015 through 2017, Riverton Processing site, Wyoming: U.S. Geological Survey Scientific Investigations Report 2022–5089, 66 p., https://doi.org/10.3133/sir20225089.","productDescription":"Report: xi, 66 p.; 3 Datasets; 2 Data Releases","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-123760","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":412328,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8QX4","text":"USGS data release","linkHelpText":"Hydrologic, biogeochemical, and radon data collected within and adjacent to the Little Wind River near Riverton, Wyoming (ver. 1.1, January 2019)"},{"id":412329,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J9VJBR","text":"USGS data release","linkHelpText":"Geophysical data collected within and adjacent to the Little Wind River near Riverton, Wyoming"},{"id":412324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5089/coverthb.jpg"},{"id":412325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5089/sir20225089.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5089"},{"id":412330,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://gems.lm.doe.gov/","text":"U.S. Department of Energy Office of Legacy Management Geospatial Environmental Mapping System database","linkHelpText":"—Riverton, WY, Processing site"},{"id":412331,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412332,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":500452,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114285.htm","linkFileType":{"id":5,"text":"html"}},{"id":412327,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5089/images"},{"id":412326,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5089/sir20225089.XML"}],"country":"United States","state":"Wyoming","city":"Riverton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109,\n 43.5\n ],\n [\n -109,\n 42.5\n ],\n [\n -107.5,\n 42.5\n ],\n [\n -107.5,\n 43.5\n ],\n [\n -109,\n 43.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-01-26","noUsgsAuthors":false,"publicationDate":"2023-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Solder, John 0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":222003,"corporation":false,"usgs":true,"family":"Solder","given":"John","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862407,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gardner, W. Payton 0000-0003-0664-001X","orcid":"https://orcid.org/0000-0003-0664-001X","contributorId":206198,"corporation":false,"usgs":false,"family":"Gardner","given":"W.","email":"","middleInitial":"Payton","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":862408,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Terry, Neil C. 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","middleInitial":"C.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":862413,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862414,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Short, Terry M. 0000-0001-9941-4593 tmshort@usgs.gov","orcid":"https://orcid.org/0000-0001-9941-4593","contributorId":1718,"corporation":false,"usgs":true,"family":"Short","given":"Terry","email":"tmshort@usgs.gov","middleInitial":"M.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":862415,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cain, Daniel J. 0000-0002-3443-0493 djcain@usgs.gov","orcid":"https://orcid.org/0000-0002-3443-0493","contributorId":1784,"corporation":false,"usgs":true,"family":"Cain","given":"Daniel","email":"djcain@usgs.gov","middleInitial":"J.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":862416,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dam, William L","contributorId":248589,"corporation":false,"usgs":false,"family":"Dam","given":"William L","affiliations":[{"id":49955,"text":"Conserve-Prosper LLC","active":true,"usgs":false}],"preferred":false,"id":862417,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Byrne, Patrick A.","contributorId":247578,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","email":"","middleInitial":"A.","affiliations":[{"id":49583,"text":"Liverpool John Moores University","active":true,"usgs":false}],"preferred":false,"id":862418,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Campbell, James R. 0000-0002-2760-3149","orcid":"https://orcid.org/0000-0002-2760-3149","contributorId":50156,"corporation":false,"usgs":true,"family":"Campbell","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":862419,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70239871,"text":"ofr20231001 - 2023 - Assessment of habitat use by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Willamette River Basin, 2020–21","interactions":[],"lastModifiedDate":"2023-01-27T11:53:34.04232","indexId":"ofr20231001","displayToPublicDate":"2023-01-26T12:01:59","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1001","displayTitle":"Assessment of Habitat Use by Juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Willamette River Basin, Oregon, 2020–21","title":"Assessment of habitat use by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Willamette River Basin, 2020–21","docAbstract":"

We conducted a field study during 2020–21 to describe habitat use patterns of juvenile Chinook salmon (Oncorhynchus tshawytscha) in the mainstem Willamette, McKenzie, and Santiam Rivers and to evaluate how habitat suitability criteria affected the predictive accuracy of a hydraulic habitat model. Two approaches were used to collect habitat use data: a stratified sampling design was used to ensure that a representative sample of available habitats was included in our sampling; and a targeted sampling design was used to collect additional data in habitat cells where juvenile Chinook salmon were observed. Habitat attributes and fish presence data were collected in habitat cells that were approximately 2 square meters during April, June, and July. A total of 632 cells were sampled during the study and included habitat located in the main channel (373 cells), side channels (228 cells), and in alcoves (31 cells). Juvenile Chinook salmon were observed in 42 percent of the cells located in the main channel, 38 percent of the cells located in side channels, and 7 percent of the cells located in alcoves. We used logistic regression to develop resource selection functions for April, June, and July, which produced probability-based predictions of habitat use for juvenile Chinook salmon based on water velocity and water depth. The resource selection functions revealed a habitat shift by juvenile Chinook salmon to locations with higher water velocities and greater water depths from April to July as juvenile Chinook salmon size increased. The resource selection functions that we developed are an important addition to habitat modeling in the Willamette River basin because they were developed from in-basin data, capture seasonal differences in habitat use, and facilitate probability-based estimates of habitat use for juvenile Chinook salmon. These advancements will improve habitat modeling efforts for juvenile Chinook salmon during spring and summer months within the Willamette River.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231001","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Hansen, G.S., Perry, R.W., Kock, T.J., White, J.S., Haner, P.V., Plumb, J.M., and Wallick, J.R., 2023, Assessment of habitat use by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Willamette River Basin, 2020–21: U.S. Geological Survey Open-File Report 2023–1001, 20 p., https://doi.org/10.3133/ofr20231001.","productDescription":"vii, 20 p.","onlineOnly":"Y","ipdsId":"IP-141847","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":412251,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1001/coverthb.jpg"},{"id":412252,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1001/ofr20231001.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1001"},{"id":412254,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1001/images"},{"id":412255,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1001/ofr20231001.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.70681047535611,\n 46.26773381073258\n ],\n [\n -124.70681047535611,\n 42.583539358952294\n ],\n [\n -121.08286121390995,\n 42.583539358952294\n ],\n [\n -121.08286121390995,\n 46.26773381073258\n ],\n [\n -124.70681047535611,\n 46.26773381073258\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016

","tableOfContents":"","publishedDate":"2023-01-26","noUsgsAuthors":false,"publicationDate":"2023-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hansen, Gabriel S. 0000-0001-6272-3632 ghansen@usgs.gov","orcid":"https://orcid.org/0000-0001-6272-3632","contributorId":3422,"corporation":false,"usgs":true,"family":"Hansen","given":"Gabriel","email":"ghansen@usgs.gov","middleInitial":"S.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kock, Tobias J. 0000-0001-8976-0230 tkock@usgs.gov","orcid":"https://orcid.org/0000-0001-8976-0230","contributorId":3038,"corporation":false,"usgs":true,"family":"Kock","given":"Tobias","email":"tkock@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862214,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, James S. 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":290253,"corporation":false,"usgs":false,"family":"White","given":"James","email":"jameswhite@usgs.gov","middleInitial":"S.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":862215,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haner, Philip V. 0000-0001-6940-487X phaner@usgs.gov","orcid":"https://orcid.org/0000-0001-6940-487X","contributorId":2364,"corporation":false,"usgs":true,"family":"Haner","given":"Philip","email":"phaner@usgs.gov","middleInitial":"V.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862216,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862217,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wallick, J. Rose 0000-0002-9392-272X rosewall@usgs.gov","orcid":"https://orcid.org/0000-0002-9392-272X","contributorId":3583,"corporation":false,"usgs":true,"family":"Wallick","given":"J. Rose","email":"rosewall@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862218,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239819,"text":"ofr20221112 - 2023 - Simulation of regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018","interactions":[],"lastModifiedDate":"2026-02-10T21:14:02.219453","indexId":"ofr20221112","displayToPublicDate":"2023-01-26T10:05:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1112","displayTitle":"Simulation of Regional Groundwater Flow and Advective Transport of Per- and Polyfluoroalkyl Substances, Joint Base McGuire-Dix-Lakehurst and Vicinity, New Jersey, 2018","title":"Simulation of regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018","docAbstract":"

A three-dimensional numerical model of groundwater flow was developed and calibrated for the unconsolidated New Jersey Coastal Plain aquifers underlying Joint Base McGuire-Dix-Lakehurst (JBMDL) and vicinity, New Jersey, to evaluate groundwater flow pathways of per- and polyfluoroalkyl substances (PFAS) contamination associated with use of aqueous film forming foam (AFFF) at the base. The regional subsurface flow model spans an area of approximately 518 square miles around JBMDL and is based on a previously developed hydrogeologic framework of the area. Steady-state flow in the unconsolidated aquifers was simulated using the MODFLOW 6 groundwater flow model, which is able to account for hydrostratigraphic pinchouts and discontinuities in the Coastal Plain aquifers underlying JBMDL. To account for local patterns of fluid flow driving advective subsurface migration of PFAS, the grid was refined using quadtree meshes spanning 21 areas where historical AFFF use was identified, five off-site reconnaissance areas identified by AFCEC as areas in which the occurrence of PFAS is most likely to pose a potential danger to local drinking water supplies, and along streams that behave as drains in the base-flow-dominated Coastal Plain.

Following grid refinement, four physical processes known to govern subsurface flow were introduced to the model. These included effective precipitation recharge, discharge to streams and stream-connected wetlands, regional inflows and outflows along the model bottom, and withdrawals from wells, each of which were incorporated into the model as either external or internal boundary conditions. To account for effective precipitation recharge, a specified-flow boundary was assigned along the top of the model. Similarly, regional flows predicted using the modified U.S Geological Survey’s New Jersey Coastal Plain Regional Aquifer System Analysis model were treated as specified-flow boundary conditions along the bottom of the model. Base-flow losses were treated as drains along streams delineated using a 10-foot LiDAR dataset. Drains were also assigned to cells falling within stream-connected National Hydrologic Database wetlands. Finally, well-pumpage data mined from the New Jersey Water Transfer database were added to the model to account for extraction of groundwater through pumping from industrial-supply and drinking-water-supply wells. Along model edges established at groundwater divides, where the net flux of water across the boundary is equal to zero, natural no-flow boundary conditions were imposed.

The refined flow model was calibrated using the parameter-estimation (PEST) program, which adjusts model parameters by performing a gradient search over the sum-of-squared-error objective function until the parameter set that produces simulated water levels and base flows most closely matches 544 water levels and 20 estimated base flows and closely adheres to initial parameter estimates. Based on the analysis of calibration residuals, the model did not appear to be affected by significant model structural error.

The MODPATH particle-tracking algorithm was used to estimate advective transport paths of PFAS in the vicinity of JBMDL. Forward tracking was used to determine paths of PFAS away from AFFF source areas to streams, wetlands, pumping wells, and geographic areas that PFAS may contaminate. Additionally, reverse tracking was used to determine particle pathlines away from off-site PFAS reconnaissance areas, or areas within which all sources of PFAS might be advectively transported into subsurface drinking-water supplies, to locations at land surface that may indicate a source of PFAS.

The coupled and calibrated groundwater flow and particle-tracking transport model provide valuable tools for predicting the relative extent of PFAS contamination from onsite legacy source areas. The calibrated model also provides measures of water-level and base-flow observation influence that can help guide future data-collection efforts related to groundwater and surface water sampling for PFAS.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221112","collaboration":"Prepared in cooperation with the U.S. Air Force","usgsCitation":"Fiore, A.R., and Colarullo, S.J., 2023, Simulation of regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018: U.S. Geological Survey Open-File Report 2022–1112, 41 p., 2 pls., https://doi.org/10.3133/ofr20221112.","productDescription":"Report: ix, 41 p.; 2 Plates: 35.00 x 45.00 inches and 45.00 x 30.00 inches; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-129806","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":412124,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EK4CZS","text":"USGS data release","linkHelpText":"MODFLOW6 and MODPATH7 used to simulate regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":412125,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112.XML"},{"id":412123,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1112"},{"id":412121,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1112/coverthb.jpg"},{"id":412126,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1112/images/"},{"id":412129,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112_plate1.pdf","text":"Plate 1","size":"212 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Forward particle tracks from aqueous film-forming foam source areas 1 to 15 and reverse particle tracks from per- and polyfluoroalkyl substances reconnaissance areas 4 and 14, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":412122,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112.pdf","text":"Report","size":"7.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1112"},{"id":412130,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112_plate2.pdf","text":"Plate 2","size":"200 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Forward particle tracks from aqueous film-forming foam source areas 16 to 21 and reverse particle tracks from per- and polyfluoroalkyl substances reconnaissance areas 16 to 19, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":499723,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114286.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.77016941849112,\n 40.156458843115274\n ],\n [\n -74.77016941849112,\n 39.93505011875061\n ],\n [\n -74.17559168378837,\n 39.93505011875061\n ],\n [\n -74.17559168378837,\n 40.156458843115274\n ],\n [\n -74.77016941849112,\n 40.156458843115274\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2023-01-26","noUsgsAuthors":false,"publicationDate":"2023-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Fiore, Alex R. 0000-0002-0986-5225 afiore@usgs.gov","orcid":"https://orcid.org/0000-0002-0986-5225","contributorId":4977,"corporation":false,"usgs":true,"family":"Fiore","given":"Alex","email":"afiore@usgs.gov","middleInitial":"R.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862034,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colarullo, Susan J. 0000-0003-4504-0068","orcid":"https://orcid.org/0000-0003-4504-0068","contributorId":205315,"corporation":false,"usgs":true,"family":"Colarullo","given":"Susan","email":"","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862035,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70239931,"text":"fs20233004 - 2023 - Rangeland Condition Monitoring Assessment and Projection, 1985–2021","interactions":[],"lastModifiedDate":"2026-02-04T20:33:36.72143","indexId":"fs20233004","displayToPublicDate":"2023-01-26T09:48:28","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3004","displayTitle":"Rangeland Condition Monitoring Assessment and Projection, 1985–2021","title":"Rangeland Condition Monitoring Assessment and Projection, 1985–2021","docAbstract":"

The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project quantifies the percentage cover of rangeland components across the western United States using Landsat imagery from 1985 to 2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, nonsagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013 to 2018 and additional field data; for example, Bureau of Land Management Assessment, Inventory, and Monitoring instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. Neural network models have replaced Cubist models as our classifier. Continuous Change Detection and Classification synthetic Landsat images were obtained for six monthly periods for each region and were added as predictors. These data enhance the phenologic detail of imagery, improving discrimination among components. Postprocessing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience classes. Additionally, postprocessing has been enhanced through a revised noise detection model, based on third order polynomial models for each component and each pixel. These data can be used to answer critical questions regarding the effect of climate change and the suitability of management practices. Component products can be downloaded from the Multi-Resolution Land Characteristics Consortium website at https://www.mrlc.gov/data.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/fs20233004","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Rigge, M.B., 2023, Rangeland Condition Monitoring Assessment and Projection, 1985–2021: U.S. Geological Survey Fact Sheet 2023–3004, 6 p., https://doi.org/10.3133/fs20233004.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-148071","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499564,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114284.htm","linkFileType":{"id":5,"text":"html"}},{"id":412363,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233004/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412361,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3004/images"},{"id":412360,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3004/fs20233004.XML"},{"id":412356,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3004/coverthb.jpg"},{"id":412359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3004/fs20233004.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023~3004"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.89909633393279,\n 29.0682826215172\n ],\n [\n -102.29956963626972,\n 29.8090966774663\n ],\n [\n -100.87258967244983,\n 29.458733387985006\n ],\n [\n -100.13050111858448,\n 28.240769760178893\n ],\n [\n -100.11959574261397,\n 30.292475426842415\n ],\n [\n -101.92842251021852,\n 31.470932982781036\n ],\n [\n -103.36510762980765,\n 32.48047296916185\n ],\n [\n -100.01803379637153,\n 36.1490156792113\n ],\n [\n -99.27913819165613,\n 37.572777501564005\n ],\n [\n -103.20469410438153,\n 39.32750900585228\n ],\n [\n -104.0623305315396,\n 39.89802382204118\n ],\n [\n -101.43686566661609,\n 42.267519647308234\n ],\n [\n -100.61853110579801,\n 49.04539216233715\n ],\n [\n -123.36052981830295,\n 48.977345645871026\n ],\n [\n -123.7205891173356,\n 48.25142184919315\n ],\n [\n -124.9072340092169,\n 48.68713021246529\n ],\n [\n -124.91684175179148,\n 42.98584722268464\n ],\n [\n -124.79221124944036,\n 40.3836585578014\n ],\n [\n -122.5469831787114,\n 35.7301435211089\n ],\n [\n -120.64609875158641,\n 34.26400897631564\n ],\n [\n -117.38696400421276,\n 32.43463720185768\n ],\n [\n -114.95825417619707,\n 32.70325877979326\n ],\n [\n -110.57738226870978,\n 31.261610018918134\n ],\n [\n -108.12199560515279,\n 31.139851729510895\n ],\n [\n -108.13958017851652,\n 31.827219447629645\n ],\n [\n -106.284256297648,\n 31.694405166866275\n ],\n [\n -104.3199988686059,\n 29.50364197108891\n ],\n [\n -103.16152984843166,\n 28.840396342272896\n ],\n [\n -102.89909633393279,\n 29.0682826215172\n ],\n [\n -102.89909633393279,\n 29.0682826215172\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-01-26","noUsgsAuthors":false,"publicationDate":"2023-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":862550,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70244190,"text":"70244190 - 2023 - Fecal DNA metabarcoding shows credible short-term prey detections and explains variation in the gut microbiome of two polar bear subpopulations","interactions":[],"lastModifiedDate":"2023-06-07T14:27:21.126999","indexId":"70244190","displayToPublicDate":"2023-01-26T09:26:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10098,"text":"Marine Ecology Progress Series (MEPS)","active":true,"publicationSubtype":{"id":10}},"title":"Fecal DNA metabarcoding shows credible short-term prey detections and explains variation in the gut microbiome of two polar bear subpopulations","docAbstract":"

This study developed and evaluated DNA metabarcoding to identify the presence of pinniped and cetacean prey DNA in fecal samples of East Greenland (EG) and Southern Beaufort Sea (SB) polar bears Ursus maritimus sampled in the spring of 2015-2019. Prey DNA was detected in half (49/92) of all samples, and when detected, ringed seal Pusa hispida was the predominant prey species, identified in 100% (22/22) of EG and 81% (22/27) of SB polar bear samples with prey DNA detected. Bearded seal Erignathus barbatus DNA was found in 19% (5/27) of SB polar bear samples for which prey DNA was detected. Prey DNA detection frequencies and relative abundances were compared to estimates from quantitative fatty acid signature analysis (QFASA) for a subset of SB polar bears. Ringed seal and bearded seal were the main prey identified by both methods, but QFASA also identified 2 cetacean prey species not found by prey DNA. Differences in DNA metabarcoding vs. QFASA results were likely related to the different dietary timescales captured by each approach, i.e. short-term vs. long-term diet, respectively. Prey DNA detection, sex/age class, and subpopulation significantly explained variation in polar bear gut bacterial composition. Polar bear samples with prey DNA detected were associated with higher abundances of the bacterial classes Clostridia and Bacilli and lower abundances of Negativicutes. Fecal DNA metabarcoding is thus useful for identifying recent prey of polar bears, complementing quantitative and likely longer-term QFASA estimates, and may help understand variation in the polar bear gut microbiome.

","language":"English","doi":"10.3354/meps14228","usgsCitation":"Franz, M., Whyte, L., Atwood, T.C., Menning, D.M., Sonsthagen, S.A., Talbot, S., Laidre, K.L., Gonzalez, E., and McKinney, M., 2023, Fecal DNA metabarcoding shows credible short-term prey detections and explains variation in the gut microbiome of two polar bear subpopulations: Marine Ecology Progress Series (MEPS), v. 704, p. 131-147, https://doi.org/10.3354/meps14228.","productDescription":"17 p.","startPage":"131","endPage":"147","ipdsId":"IP-140646","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":417914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Greenland, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -146.46935331781273,\n 69.92266883825783\n ],\n [\n -119.6584750085187,\n 69.92266883825783\n ],\n [\n -119.6584750085187,\n 77.71810907511048\n ],\n [\n -146.46935331781273,\n 77.71810907511048\n ],\n [\n -146.46935331781273,\n 69.92266883825783\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -42.47730166184854,\n 59.32520773360736\n ],\n [\n -18.715750027562365,\n 69.74254926662084\n ],\n [\n -6.968284040350653,\n 81.65832235660355\n ],\n [\n -38.97902383446578,\n 82.40445519221694\n ],\n [\n -45.90932351629701,\n 59.89142210804414\n ],\n [\n -42.47730166184854,\n 59.32520773360736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"704","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Franz, Megan","contributorId":289877,"corporation":false,"usgs":false,"family":"Franz","given":"Megan","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":874824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whyte, L","contributorId":306134,"corporation":false,"usgs":false,"family":"Whyte","given":"L","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":874825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":874826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Menning, Damian M. 0000-0003-3547-3062 dmenning@usgs.gov","orcid":"https://orcid.org/0000-0003-3547-3062","contributorId":205131,"corporation":false,"usgs":true,"family":"Menning","given":"Damian","email":"dmenning@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":874827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":874828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbot, Sandra","contributorId":291357,"corporation":false,"usgs":false,"family":"Talbot","given":"Sandra","affiliations":[{"id":40349,"text":"USGS Alaska Science Center (former employee)","active":true,"usgs":false}],"preferred":false,"id":874829,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Laidre, Kristin L.","contributorId":191798,"corporation":false,"usgs":false,"family":"Laidre","given":"Kristin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":874830,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonzalez, Emmanuel","contributorId":306136,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Emmanuel","email":"","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":874831,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McKinney, Melissa","contributorId":222146,"corporation":false,"usgs":false,"family":"McKinney","given":"Melissa","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":874832,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241463,"text":"70241463 - 2023 - Using corrected and imputed polarity measurements to improve focal mechanisms in a regional earthquake catalog near the Mt. Lewis Fault Zone, California","interactions":[],"lastModifiedDate":"2024-06-27T16:56:34.358189","indexId":"70241463","displayToPublicDate":"2023-01-26T07:05:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6453,"text":"Journal of Geophysical Research Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Using corrected and imputed polarity measurements to improve focal mechanisms in a regional earthquake catalog near the Mt. Lewis Fault Zone, California","docAbstract":"

We utilized relative polarity measurements and machine learning techniques to better resolve focal mechanisms and stress orientations considering a catalog of ∼29,000 relocated earthquakes that occurred during 1984–2021 in the southeastern San Francisco Bay Area. Earthquake focal mechanisms are commonly produced using P wave first motion polarities, which traditionally requires events to be well-recorded across a seismic network with good focal sphere coverage. We adapted recently developed approaches that are less dependent on high signal-to-noise records and exploit similar waveforms to produce relative polarity and amplitude measurements between earthquake pairs. These techniques were previously only applied on localized earthquake sequences, and we further developed these approaches so that they can be utilized for regional catalogs. We validated or corrected manually identified polarities by performing polarity consensuses using earthquake pairs. Missing and unreliable polarity measurements were imputed using iterative random forests, an unsupervised ensemble machine learning method. Relative P and S wave amplitude measurements were made between earthquakes, constraining S/P ratios for low signal-to-noise waveforms. Using these techniques, we were able to reduce focal mechanism uncertainties by an average of ∼13° and produced well-constrained focal mechanisms for ∼6 times as many earthquakes than those produced using only the traditionally derived polarities. We performed stress inversions using the focal mechanisms by grouping the focal mechanism results into a quadtree structure. Our stress results are consistent with previous work, albeit at a higher spatial resolution, and demonstrate these techniques can aid our understanding of fault structures and kinematics in more detail than was previously possible.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB025660","usgsCitation":"Skoumal, R., Hardebeck, J.L., and Shelly, D.R., 2023, Using corrected and imputed polarity measurements to improve focal mechanisms in a regional earthquake catalog near the Mt. Lewis Fault Zone, California: Journal of Geophysical Research Solid Earth, v. 128, no. 2, e2022JB025660, 18 p., https://doi.org/10.1029/2022JB025660.","productDescription":"e2022JB025660, 18 p.","ipdsId":"IP-145501","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":498008,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jb025660","text":"Publisher Index Page"},{"id":414426,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mt. Lewis Fault Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.05461864915148,\n 37.836734388728814\n ],\n [\n -122.05461864915148,\n 36.69851901351632\n ],\n [\n -120.79193137635762,\n 36.69851901351632\n ],\n [\n -120.79193137635762,\n 37.836734388728814\n ],\n [\n -122.05461864915148,\n 37.836734388728814\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Skoumal, Robert","contributorId":217693,"corporation":false,"usgs":true,"family":"Skoumal","given":"Robert","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":866927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":866928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":866929,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239862,"text":"fs20223085 - 2023 - National Civil Applications Center","interactions":[],"lastModifiedDate":"2023-03-14T11:03:01.512362","indexId":"fs20223085","displayToPublicDate":"2023-01-26T06:15:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3085","displayTitle":"National Civil Applications Center","title":"National Civil Applications Center","docAbstract":"

Introduction 

The U.S. Geological Survey (USGS) National Civil Applications Center (NCAC) analyzes remote-sensing data from the Intelligence Community (IC) and the U.S. Department of Defense (DOD) to support public safety missions and to study land-surface and environmental changes. The NCAC provides remotely sensed images to USGS scientists and other civilian Federal agencies; the images come from intelligence and military sensors, referred to as U.S. National Imagery Systems (USNIS), and unclassified commercial satellite data purchased by the DOD. Often these data are referred to as Geospatial Intelligence (GEOINT), which is defined in U.S. Code, title 10, section 467 as “the exploitation and analysis of imagery and geospatial information to describe, assess, and visually depict physical features and geographically referenced activities on or about the earth. Geospatial intelligence consists of imagery, imagery intelligence, and geospatial information.” The NCAC also provides the secretariat with staff and manages the U.S. interagency Civil Applications Committee (CAC), which oversees and facilitates the appropriate civilian uses of overhead remote-sensing technology and data collected by military and intelligence systems and commercial sources. Funded by the USGS National Land Imaging Program, the NCAC operates facilities in Reston, Virginia, and Lakewood, Colorado.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223085","programNote":"National Land Imaging Program","usgsCitation":"Young, P.M., 2023, National Civil Applications Center (ver. 1.1, March 2023): U.S. Geological Survey Fact Sheet 2022–3085, 4 p., https://doi.org/10.3133/fs20223085.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-129954","costCenters":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"links":[{"id":412228,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3085/fs20223085.pdf","text":"Report","size":"1.92 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3085"},{"id":412227,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3085/coverthb3.jpg"},{"id":414012,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3085/versionHist.txt","size":"653 B"}],"edition":"Version 1.0: January 2023; Version 1.1: March 2023","contact":"

Director, National Civil Applications Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 562
Reston, VA 20192
Email: cac@usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-01-26","revisedDate":"2023-03-13","noUsgsAuthors":false,"publicationDate":"2023-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Paul M. 0000-0002-6733-6452","orcid":"https://orcid.org/0000-0002-6733-6452","contributorId":301138,"corporation":false,"usgs":true,"family":"Young","given":"Paul","email":"","middleInitial":"M.","affiliations":[{"id":36171,"text":"National Civil Applications Center","active":true,"usgs":true}],"preferred":true,"id":862193,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239929,"text":"ofr20221113 - 2023 - Sampling and analysis plan for the Koocanusa Reservoir and upper Kootenai River, Montana, water-quality monitoring program, 2021","interactions":[],"lastModifiedDate":"2023-01-26T11:47:58.268612","indexId":"ofr20221113","displayToPublicDate":"2023-01-25T13:29:52","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1113","displayTitle":"Sampling and Analysis Plan for the Koocanusa Reservoir and Upper Kootenai River, Montana, Water-Quality Monitoring Program, 2021","title":"Sampling and analysis plan for the Koocanusa Reservoir and upper Kootenai River, Montana, water-quality monitoring program, 2021","docAbstract":"

In 2021, the U.S. Geological Survey will collect water-quality samples and environmental data from 3 sites in Koocanusa Reservoir and from 1 site in the Kootenai River. The transboundary Koocanusa Reservoir is in southeastern British Columbia, Canada, and northwestern Montana, United States, and was formed with the construction of Libby Dam on the Kootenai River 26 kilometers upstream from Libby, Montana. Two of the reservoir sites and the Kootenai River site, in the Libby Dam tailwater (the outflow of the reservoir flow into the Kootenai River), are equipped with automated, high-frequency ServoSipper water samplers. At the two reservoir sites, these samplers are mounted to pontoon platforms and automatically collect samples from multiple depths; a ServoSipper sampler was deployed at one site in 2019, and another ServoSipper sampler will be deployed at a second site in 2021. Discrete water-quality samples will be collected monthly at two depths at the river site and at two of the reservoir sites. The goal of this project is to collect multidepth, high-frequency vertical and temporal water-quality samples and data to understand the limnological and biological processes that control variations and trends in selenium concentrations and loads throughout Koocanusa Reservoir and in the Libby Dam tailwater at the southern end of the reservoir. This sampling and analysis plan documents the organization, sampling and data-collection scheme and design, pre- and post-collection processes, and quality-assurance and quality-control procedures.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221113","usgsCitation":"Caldwell Eldridge, S.L., Schaar, M.A., Reese, C.B., Bussell, A.M., and Chapin, T., 2023, Sampling and analysis plan for the Koocanusa Reservoir and upper Kootenai River, Montana, water-quality monitoring program, 2021: U.S. Geological Survey Open-File Report 2022–1113, 32 p., https://doi.org/10.3133/ofr20221113.","productDescription":"ix, 32 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-137190","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":412312,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1113/ofr20221113.XML"},{"id":412310,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1113/coverthb.jpg"},{"id":412311,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1113/ofr20221113.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1113"},{"id":412313,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1113/images"},{"id":412323,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221113/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Koocanusa Reservoir, Upper Kootenai River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.10472590374278,\n 49.02558777092872\n ],\n [\n -116.10472590374278,\n 47.62376452411149\n ],\n [\n -113.60090641401644,\n 47.62376452411149\n ],\n [\n -113.60090641401644,\n 49.02558777092872\n ],\n [\n -116.10472590374278,\n 49.02558777092872\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-01-25","noUsgsAuthors":false,"publicationDate":"2023-01-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Caldwell Eldridge, Sara L. 0000-0001-8838-8940 seldridge@usgs.gov","orcid":"https://orcid.org/0000-0001-8838-8940","contributorId":4981,"corporation":false,"usgs":true,"family":"Caldwell Eldridge","given":"Sara","email":"seldridge@usgs.gov","middleInitial":"L.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":862393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaar, Melissa A. 0000-0002-7278-6116 mschaar@usgs.gov","orcid":"https://orcid.org/0000-0002-7278-6116","contributorId":301215,"corporation":false,"usgs":true,"family":"Schaar","given":"Melissa","email":"mschaar@usgs.gov","middleInitial":"A.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":862394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reese, Chad B. 0000-0003-1193-5760 creese@usgs.gov","orcid":"https://orcid.org/0000-0003-1193-5760","contributorId":301216,"corporation":false,"usgs":true,"family":"Reese","given":"Chad","email":"creese@usgs.gov","middleInitial":"B.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":862395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bussell, Ashley M. 0000-0003-4586-7305","orcid":"https://orcid.org/0000-0003-4586-7305","contributorId":301217,"corporation":false,"usgs":false,"family":"Bussell","given":"Ashley","middleInitial":"M.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":862396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chapin, Thomas 0000-0001-6587-0734 tchapin@usgs.gov","orcid":"https://orcid.org/0000-0001-6587-0734","contributorId":758,"corporation":false,"usgs":true,"family":"Chapin","given":"Thomas","email":"tchapin@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":862397,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}