Stable isotope analyses of hair have been used to estimate the consumption of human foods by American black bears (Ursus americanus). Consumption of human foods influences body mass and reproductive success of bears. However, the underlying factors that cause some bears to become conflict bears and resort to consuming human foods as a portion of their diet are not fully understood. We collected hair samples for stable isotope analysis from 51 black bears in Great Smoky Mountains National Park, Tennessee, USA in 2006. We used δ13C values of hairs to determine if the bears were consuming C3-based (natural foods) or C4-based (human foods) diets, and δ15N values, which increase with more meat in the diet, as a further indication of the consumption of human foods. Male bears with the heaviest tooth wear had a combination of higher δ15N and δ13C values, suggesting that they consume human foods to a greater extent than do other black bears. Based on our results, we hypothesize that tooth wear, and thus dental health, may play a role in the consumption of human foods by larger, male bears.
","language":"English","publisher":"Springer","doi":"10.1007/s42991-022-00310-x","usgsCitation":"Hatch, K.A., Kester, K.A., Loveless, A., Roeder, B.L., and van Manen, F.T., 2022, Tooth wear and the apparent consumption of human foods among American black bears (Ursus americanus) in Great Smoky Mountains National Park, USA: Mammalian Biology, v. 2022, p. 1-9, https://doi.org/10.1007/s42991-022-00310-x.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-135072","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":410467,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Great Smoky Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.16066837920717,\n 35.58386066242302\n ],\n [\n -84.13594350533924,\n 35.53751345134553\n ],\n [\n -84.118090722136,\n 35.54757027719998\n ],\n [\n -84.08513173776126,\n 35.53416089582571\n ],\n [\n -84.04118642526144,\n 35.535278429900345\n ],\n [\n -84.00685414987083,\n 35.53974841051803\n ],\n [\n -84.01784047799529,\n 35.5084533158893\n ],\n [\n -83.99312123971458,\n 35.48385575321835\n ],\n [\n -83.9629088373703,\n 35.48385575321835\n ],\n [\n -83.97526845651134,\n 35.47043572684541\n ],\n [\n -83.94780263619884,\n 35.459250660398254\n ],\n [\n -83.90523061471424,\n 35.471554147937766\n ],\n [\n -83.8722716303395,\n 35.515160434894085\n ],\n [\n -83.84892568307336,\n 35.511806945426926\n ],\n [\n -83.82145986276088,\n 35.51627823358889\n ],\n [\n -83.76240834908957,\n 35.55427412712673\n ],\n [\n -83.72120961862085,\n 35.55650861920057\n ],\n [\n -83.69786367135471,\n 35.559860240523165\n ],\n [\n -83.63194570260458,\n 35.559860240523165\n ],\n [\n -83.6072264643239,\n 35.57326532424598\n ],\n [\n -83.58662709908953,\n 35.55650861920057\n ],\n [\n -83.48912343697995,\n 35.55874304898751\n ],\n [\n -83.4355650873702,\n 35.60341856192727\n ],\n [\n -83.40260610299549,\n 35.61011773909294\n ],\n [\n -83.3490477533864,\n 35.64025709406553\n ],\n [\n -83.34630117135463,\n 35.6547645829193\n ],\n [\n -83.33256826119838,\n 35.66034368480365\n ],\n [\n -83.31471547799579,\n 35.64695318551118\n ],\n [\n -83.29136953072965,\n 35.65364871577222\n ],\n [\n -83.24467763619867,\n 35.691579451620726\n ],\n [\n -83.23918447213646,\n 35.71945819648104\n ],\n [\n -83.18439655404555,\n 35.72312228727148\n ],\n [\n -83.15281086068674,\n 35.757676798373936\n ],\n [\n -83.11435871224911,\n 35.763248701987635\n ],\n [\n -83.07865314584261,\n 35.782190255096964\n ],\n [\n -83.05118732553015,\n 35.77996207126307\n ],\n [\n -82.99762897592107,\n 35.76547735416608\n ],\n [\n -82.95368366342055,\n 35.786646435405245\n ],\n [\n -82.94269733529609,\n 35.81449190173652\n ],\n [\n -82.90973835092072,\n 35.836761246374294\n ],\n [\n -82.90836505990549,\n 35.861250307545816\n ],\n [\n -82.88776569467113,\n 35.87237919871434\n ],\n [\n -82.90424518685847,\n 35.91354250461066\n ],\n [\n -82.89325885873335,\n 35.92355196418599\n ],\n [\n -82.88227253060822,\n 35.94356708298763\n ],\n [\n -82.85343341928052,\n 35.94023158177386\n ],\n [\n -82.82322101693623,\n 35.91131800824972\n ],\n [\n -82.79575519662376,\n 35.92355196418599\n ],\n [\n -82.7682893763113,\n 35.96580015976322\n ],\n [\n -82.7779024134205,\n 35.98913815464185\n ],\n [\n -82.82047443490512,\n 35.98691578579175\n ],\n [\n -82.83832721810839,\n 36.004692983661016\n ],\n [\n -82.87128623589025,\n 36.02246615304918\n ],\n [\n -82.99900230034343,\n 35.9924715692585\n ],\n [\n -83.10048771514481,\n 35.978247722622726\n ],\n [\n -83.33669376983195,\n 35.98269288600295\n ],\n [\n -83.54268742217556,\n 35.90486638837581\n ],\n [\n -83.60585880889455,\n 35.93378232027078\n ],\n [\n -83.74593449248812,\n 35.87593988698475\n ],\n [\n -83.74593449248812,\n 35.83141694658565\n ],\n [\n -83.82833195342556,\n 35.7868690107902\n ],\n [\n -84.16066837920717,\n 35.58386066242302\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2022","noUsgsAuthors":false,"publicationDate":"2022-11-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Hatch, Kent A.","contributorId":299909,"corporation":false,"usgs":false,"family":"Hatch","given":"Kent","email":"","middleInitial":"A.","affiliations":[{"id":64976,"text":"Long Island University Post","active":true,"usgs":false}],"preferred":false,"id":859002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kester, Kimberly A.","contributorId":299910,"corporation":false,"usgs":false,"family":"Kester","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[{"id":64976,"text":"Long Island University Post","active":true,"usgs":false}],"preferred":false,"id":859003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loveless, Amanda","contributorId":299911,"corporation":false,"usgs":false,"family":"Loveless","given":"Amanda","email":"","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":859004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roeder, Beverly L.","contributorId":299912,"corporation":false,"usgs":false,"family":"Roeder","given":"Beverly","email":"","middleInitial":"L.","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":859005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":859006,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262061,"text":"70262061 - 2022 - Freshwater corridors in the conterminous US: A coarse-filter approach based on lake-stream networks","interactions":[],"lastModifiedDate":"2025-01-10T16:29:15.253176","indexId":"70262061","displayToPublicDate":"2022-12-14T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Freshwater corridors in the conterminous US: A coarse-filter approach based on lake-stream networks","docAbstract":"Maintaining regional-scale freshwater connectivity is challenging owing to the dendritic, easily fragmented structure of freshwater networks, but is essential for promoting ecological resilience under climate change. Although the importance of stream network connectivity has been recognized, lake-stream network connectivity has largely been ignored. Furthermore, protected areas are generally not designed to maintain or encompass entire freshwater networks. We applied a coarse-filter approach to identify potential freshwater corridors for diverse taxa by calculating connectivity scores for 385 lake-stream networks across the conterminous US based on network size, structure, resistance to fragmentation, and dam prevalence. We also identified 2080 disproportionately important lakes for maintaining intact networks (i.e., “hubs”; 2% of all network lakes) and analyzed the protection status of hubs and potential freshwater corridors. Just 3% of networks received high connectivity scores based on their large size and structure (medians of 1303 lakes, 498.6 km north-south stream distance), but these also contained a median of 454 dams. In contrast, undammed networks (17% of networks) were considerably smaller (medians of 6 lakes, 7.2 km north-south stream distance), indicating that the functional connectivity of the largest potential freshwater corridors in the conterminous US currently may be diminished compared to smaller, undammed networks. Network lakes and hubs were protected at similar rates nationally across different levels of protection (8-18% and 6-20%, respectively), but were generally more protected in the western US. Our results indicate that conterminous US protection of major freshwater corridors and the hubs that maintain them generally fell short of the international conservation goal of protecting an ecologically representative, well-connected set of fresh waters (≥ 17%) by 2020 (Aichi Target 11). Conservation planning efforts might consider focusing on restoring natural hydrologic connectivity at or near hubs, particularly in larger networks, less protected, or biodiverse regions, to support freshwater biodiversity conservation under climate change.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4326","usgsCitation":"McCullough, I., Hanly, P., King, K., and Wagner, T., 2022, Freshwater corridors in the conterminous US: A coarse-filter approach based on lake-stream networks: Ecosphere, v. 13, no. 12, e4326, 18 p., https://doi.org/10.1002/ecs2.4326.","productDescription":"e4326, 18 p.","ipdsId":"IP-130894","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4326","text":"Publisher Index Page"},{"id":465995,"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":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"McCullough, Ian M.","contributorId":348093,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanly, Patrick J.","contributorId":348094,"corporation":false,"usgs":false,"family":"Hanly","given":"Patrick J.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"King, Katelyn B.S.","contributorId":348095,"corporation":false,"usgs":false,"family":"King","given":"Katelyn B.S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":922934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922931,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238833,"text":"ofr20221107 - 2022 - Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report","interactions":[],"lastModifiedDate":"2023-09-18T20:00:12.711841","indexId":"ofr20221107","displayToPublicDate":"2022-12-13T13:23:14","publicationYear":"2022","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":"2022-1107","displayTitle":"Black Abalone Surveys at Naval Base Ventura County, San Nicolas Island, California: 2021, Annual Report","title":"Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report","docAbstract":"The U.S. Geological Survey monitors a suite of intertidal black abalone sites at San Nicolas Island, California, in cooperation with the U.S. Navy, which owns the island. The nine rocky intertidal sites were established in 1980 to study the potential effect of translocated sea otters on the intertidal black abalone population at the island. The sites were monitored from 1981 to 1997, usually annually or biennially. Monitoring resumed in 2001 and has been completed annually since then. Since 2018, the work has been carried out by the U.S. Geological Survey Western Ecological Research Center. The study sites became particularly important, from a management perspective, after a virulent disease decimated black abalone populations throughout southern California beginning in the mid-1980s. The disease, withering syndrome, was first observed on San Nicolas Island in 1992 and during the next few years, it reduced the population there by more than 99 percent. The species was subsequently listed as endangered under the Endangered Species Act in 2009.
The subject of this report is the 2021 survey of the sites and how the measured population status compares to the long-term data (collected over several decades) at San Nicolas Island. During the last two decades, the total monitored black abalone population at the island has grown approximately ten-fold after the disease related decline, from about 200 to more than 2,000 abalone. Since it was first consistently measured in 2005, the mean distance between adjacent black abalone has decreased substantially from approximately 50 centimeters to less than 15 centimeters, indicating that abalone are close enough together at several of the sites to reproduce successfully. The 2021 counts were the first since 2016 to indicate a possible decline in the monitored population at San Nicolas Island. Although still more than ten times the population counted in 2001, counts on the transects dropped by 13.6 percent from the survey count in 2020. The most significant decline was the loss of 341 abalone, from the previous count of 547, on a transect that since 2002 had the highest count of all 44 transects. Between 2020 and 2021, there were increases and decreases at the sites and the transects at each site. Although the 2021 count was lower than the 2020 count, it was the second highest since 1997. Based on the number of small abalone counted, recruitment rates were similar to most years since 2008 and higher than the rates observed before the population declines resulting from withering syndrome.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221107","collaboration":"Prepared in cooperation with the U.S. Navy","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Kenner, M.C., and Yee, J.L., 2022, Black Abalone surveys at Naval Base Ventura County, San Nicolas Island, California—2021, annual report: U.S. Geological Survey Open-File Report 2022–1107, 34 p., https://doi.org/10.3133/ofr20221107.","productDescription":"vii, 34 p.","onlineOnly":"Y","ipdsId":"IP-144353","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":410409,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1107/ofr20221107.XML"},{"id":410407,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221107/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1107"},{"id":410408,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1107/images"},{"id":410406,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1107/ofr20221107.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1107"},{"id":410405,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1107/coverthb.jpg"}],"country":"United States","state":"California","county":"Ventura County","otherGeospatial":"San Nicolas Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.59425743862556,\n 33.2937160305206\n ],\n [\n -119.59425743862556,\n 33.20294676390226\n ],\n [\n -119.4170269925379,\n 33.20294676390226\n ],\n [\n -119.4170269925379,\n 33.2937160305206\n ],\n [\n -119.59425743862556,\n 33.2937160305206\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
This study evaluated carbon dioxide (CO2) injected into water as a possible behavioral stimulant to enhance capture and removal of invasive red swamp crayfish (RSC, Procambarus clarkii [Girard, 1852]) from a retention pond in southeastern Michigan. Objectives of this study were (1) to determine if target CO2 concentrations were attainable within the infested pond and (2) to determine if CO2 treatment was effective to push RSC either towards shorelines or onto dry land, where they could be collected and removed. Carbon dioxide was applied directly into one treatment pond (about [~]2,500 cubic meters) in Novi, Michigan. Two nearby ponds in Livonia, Mich., were used as untreated control ponds. Crayfish removal efficiency was evaluated in all ponds using baited traps and shoreline surveys. Results showed that the CO2 treatment pond reached its target concentration of greater than (>) 200 milligrams per liter (mg/L) of CO2, a benchmark determined from previous laboratory studies, approximately 11 hours after injection started, and maintained concentrations between 200 and 351 mg/L of CO2 for about 2.5 days. During treatment, some emergent crayfish were observed near influent culverts around the pond, which possibly brought about a behavioral response. However, the number of individuals and crayfish observations were minimal and infrequent. Crayfish continued to be removed throughout CO2 treatment with baited traps and perimeter surveys, but differences in catch rates between the treatment and control ponds were not apparent and confounded by a temporal decline in catch rates across all ponds. Overall, this study demonstrated that open-water treatment applications with CO2 are possible, but its effectiveness to enhance RSC removal was unclear because of the limited crayfish observations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221105","collaboration":"Prepared in cooperation with Michigan Department of Natural Resources, Michigan State University, and Auburn University","programNote":"Biological Threats and Invasive Species Research Program","usgsCitation":"Smerud, J., Rivera, J., Johnson, T., Tix, J., Fredricks, K., Barbour, M., Herbst, S., Thomas, S., Nathan, L., Roth, B., Smith, K., Allert, A., Stoeckel, J., and Cupp, A., 2022, Field application of carbon dioxide as a behavioral control method for invasive red swamp crayfish (Procambarus clarkii) in southeastern Michigan water retention ponds: U.S. Geological Survey Open-File Report 2022–1105, 12 p., https://doi.org/10.3133/ofr20221105.","productDescription":"Report: vii, 12 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-138063","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":501841,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113941.htm","linkFileType":{"id":5,"text":"html"}},{"id":410370,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221105/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":410273,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99OUHMV","text":"USGS data release","linkHelpText":"Water quality and atmospheric carbon dioxide data for field application of carbon dioxide during summer 2018 as a behavioral control method for invasive red swamp crayfish (Procambarus clarkii) in southeastern Michigan water retention ponds"},{"id":410272,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1105/images"},{"id":410271,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1105/ofr20221105.XML"},{"id":410270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1105/ofr20221105.pdf","text":"Report","size":"1.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1105"},{"id":410269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1105/coverthb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.44162797914937,\n 42.44723847720684\n ],\n [\n -83.44162797914937,\n 42.42580674596837\n ],\n [\n -83.3843952063379,\n 42.42580674596837\n ],\n [\n -83.3843952063379,\n 42.44723847720684\n ],\n [\n -83.44162797914937,\n 42.44723847720684\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
No abstract available.
","language":"English","publisher":"San Diego Association of Governments","usgsCitation":"Preston, K.L., Perkins, E., Brown, C., McCutcheon, S.J., Bernabe, A.E., Luciani, E., Kus, B., and Wynn, S., 2022, State of the Regional Preserve System in western San Diego County, 425 p.","productDescription":"425 p.","ipdsId":"IP-147124","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":414619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":414597,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sdmmp.com/view_article.php?cid=SDMMP_CID_71_63979fd98137e#"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.12389525448718,\n 32.55187919418343\n ],\n [\n -116.55244310819583,\n 32.5815668626354\n ],\n [\n -116.71291939585285,\n 33.12737230744057\n ],\n [\n -117.19043420302773,\n 33.33363414773244\n ],\n [\n -117.38222293705692,\n 33.206005600396665\n ],\n [\n -117.26088720736499,\n 32.96989434633531\n ],\n [\n -117.28437154214421,\n 32.835159168666536\n ],\n [\n -117.2726293747546,\n 32.69033753274033\n ],\n [\n -117.12389525448718,\n 32.5452806004228\n ],\n [\n -117.12389525448718,\n 32.55187919418343\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Preston, Kristine L. 0000-0002-6958-1128 kpreston@usgs.gov","orcid":"https://orcid.org/0000-0002-6958-1128","contributorId":207765,"corporation":false,"usgs":true,"family":"Preston","given":"Kristine","email":"kpreston@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, Emily E. 0000-0002-6286-3480","orcid":"https://orcid.org/0000-0002-6286-3480","contributorId":225022,"corporation":false,"usgs":true,"family":"Perkins","given":"Emily E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Christopher W. 0000-0002-2545-9171","orcid":"https://orcid.org/0000-0002-2545-9171","contributorId":240860,"corporation":false,"usgs":true,"family":"Brown","given":"Christopher W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCutcheon, Sarah Joelle 0000-0002-9377-0527","orcid":"https://orcid.org/0000-0002-9377-0527","contributorId":303334,"corporation":false,"usgs":true,"family":"McCutcheon","given":"Sarah","email":"","middleInitial":"Joelle","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bernabe, Annabelle E 0000-0001-7453-8805","orcid":"https://orcid.org/0000-0001-7453-8805","contributorId":303335,"corporation":false,"usgs":true,"family":"Bernabe","given":"Annabelle","email":"","middleInitial":"E","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luciani, Emilie","contributorId":303336,"corporation":false,"usgs":false,"family":"Luciani","given":"Emilie","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":867275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kus, Barbara E. 0000-0002-3679-3044 barbara_kus@usgs.gov","orcid":"https://orcid.org/0000-0002-3679-3044","contributorId":3026,"corporation":false,"usgs":true,"family":"Kus","given":"Barbara E.","email":"barbara_kus@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867276,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wynn, Susan","contributorId":303337,"corporation":false,"usgs":false,"family":"Wynn","given":"Susan","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":867277,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70238829,"text":"ofr20221095 - 2022 - Assessment of significant sand resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California","interactions":[],"lastModifiedDate":"2026-03-30T20:46:36.091721","indexId":"ofr20221095","displayToPublicDate":"2022-12-13T09:16:00","publicationYear":"2022","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":"2022-1095","displayTitle":"Assessment of Significant Sand Resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand Littoral Cell Study Areas along the Continental Shelf of California","title":"Assessment of significant sand resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California","docAbstract":"The Sand Resources Project was established through collaborative agreements between the U.S. Geological Survey (USGS), the Bureau of Ocean Energy Management (BOEM), and the California Ocean Protection Council (OPC) with the purpose of evaluating sand and gravel resources in Federal and California State Waters for potential use in future beach-nourishment projects. Project partners worked in collaboration with California Coastal Sediment Management Workgroup (CSMW) members to define priority study areas for this work based on the potential for finding sand within the broader region and the needs for this sand as shown by beach erosion areas of concern in the adjacent littoral cells. The final study areas were defined to be (1) the San Francisco Littoral Cell, (2) the Oceanside Littoral Cell, and (3) the Silver Strand Littoral Cell.
A two-stage approach was used to assess the study areas. The initial stage was a synthesis of the existing geophysical and sediment-sampling data in each area. This allowed for evaluations of the data availability, data gaps, and general patterns of sediment thickness and grain size. This synthesis was published in a separate USGS open-file report (Warrick and others, 2022). The findings from this assessment were used to refine study area boundaries and develop sampling plans for stage two of the project.
Stage two of the project is the collection, processing, and synthesis of new data, including high-resolution geophysical surveys and sediment cores—this report addresses the second stage. The work focuses on two of the study areas—the San Francisco and the Oceanside Littoral Cells, where several research cruises have been conducted. A more limited, exploratory approach was used for the Silver Strand Littoral Cell, owing to the lack of existing high-resolution bathymetric data for this study area. The data collected provide new information about the three study areas, including sediment thickness, grain-size distributions, and total organic carbon.
Sediment in all three study areas of the Sand Resources Study was suitable for beach nourishment, as reflected by their grain-size distributions and sediment thicknesses. For example, sandy sediment in the San Francisco Littoral Cell study area was on and immediately outside of the ebb-tidal bar of the San Francisco Bay, a landform that has a strong influence on grain-size patterns of the region. The presence of thick sediment deposits in this area was interpreted to be a function of tectonics, which has caused physical features that include a graben north of the Golden Gate whose deposits were thicker and siltier than the remaining area. Sandy sediment on the inner and outer parts of the continental shelf in the Oceanside Littoral Cell may be useful for nourishment, whereas the midshelf between these areas was dominated by silty sediment. Sediment in the Silver Strand Littoral Cell, which was only sampled selectively, had the greatest potential for beach nourishment because of the greater prevalence of beach-comparable grain sizes, especially in the more distal and deeper areas where medium sands were found.
The Sand Resources Project did identify several sandy regions of the continental shelf that are deeper than dredging technologies currently (2022) available in the United States, which are generally limited to 30 meters (m) water depth or less. Although sandy sediment exists in all three study areas at water depths of 30 m or less, additional sediment supplies—most of which are in Federal Waters—are present in deeper settings, especially for the Oceanside and Silver Strand Littoral Cell study areas. Although the Silver Strand Littoral Cell study area was found to be considerably replete in sand resources, these conclusions are based on a limited sampling exercise across that study area. Thus, it may be beneficial to complete a more thorough characterization of the sediment resources in the Silver Strand Littoral Cell study area if it is determined that a need for sandy coastal sediment exists in this region.
As a result of the Sand Resources Project, several areas of sand resources in Federal and California State Waters were found where they were previously unknown. As such, this project may provide important data for future coastal-management decisions in California, and it should provide a model for future investigations of sediment resources in other regions of the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221095","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management and the State of California Ocean Protection Council","usgsCitation":"Warrick, J.A., Conrad, J.E., Papesh, A., Lorenson, T., and Sliter, R.W., 2022, Assessment of significant sand resources in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California: U.S. Geological Survey Open-File Report 2022–1095, 104 p., https://doi.org/10.3133/ofr20221095.","productDescription":"Report: viii, 104 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-130530","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501837,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113943.htm","linkFileType":{"id":5,"text":"html"}},{"id":410366,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UELSBU","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and sampling data collected offshore Oceanside, southern California during field activity 2017-686-FA from 2017-10-23 to 2017-10-31"},{"id":410365,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9690BEK","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and core sample data collected offshore Oceanside to San Diego, southern California, during field activity 2018-638-FA from 2018-05-21 to 2018-05-26"},{"id":410364,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LBG9H5","text":"USGS data release","description":"USGS data release","linkHelpText":"Geophysical and core sample data collected offshore San Francisco, California, during field activity 2019-649-FA from 2019-10-11 to 2019-10-18"},{"id":410367,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221094","text":"OFR 2022-1094 —","linkHelpText":"Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment"},{"id":410363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1095/ofr20221095.pdf","text":"Report","size":"12.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1095"},{"id":410362,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1095/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco, Oceanside, and Silver Strand littoral cell study areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.32845828309286,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 37.818674364541195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.13365815618306,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.493660702226535\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.26754558635474,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.52924818029179\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St
Santa Cruz, CA 95060
In North America, native unionid mussels are imperiled due to factors such as habitat degradation, pollution, and invasive species. One of the most substantial threats is that posed by dreissenid mussels, which are invasive mussels that attach to hard substrates including unionid shells and can restrict movement and feeding of unionids. This dreissenid mussel biofouling of unionids varies spatially in large ecosystems, such as the Great Lakes, with some areas having low enough biofouling to form effective refugia where unionid mussels might persist. Here, we measured biofouling on mussels suspended in cages over the growing season (generally first week in June to last week of August) over 3 years in nearshore areas in Lake Erie (2014–2016), Lake Michigan (Grand Traverse Bay, 2015 and Green Bay, 2016), and Lake Huron (2015). Biofouling varied substantially by years within Lake Erie, with increasingly higher biofouling rates each year. Although dreissenid mussels are present throughout these lakes, we observed very low biofouling in Grand Traverse Bay (Lake Michigan) and Saginaw Bay (Lake Huron), with no dreissenid mussels in 8 of 9 sites across these two bays. Sampling in the rivermouth of the Fox River (Wisconsin) and the Maumee River (Ohio) both showed very high biofouling in areas adjacent to the outlet of these tributaries into Green Bay and Maumee Bay (Lake Erie), respectively. These watersheds are dominated by agriculture, and we would expect high growth of primary producers (i.e., mussel food) and primary consumers (unionids and zebra mussels) in these areas compared to the other sampled bays or the open waters of the Great Lakes.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9557","usgsCitation":"Larson, J.H., Bailey, S., and Evans, M.A., 2022, Biofouling of a unionid mussel by dreissenid mussels in nearshore zones of the Great Lakes: Ecology and Evolution, v. 12, no. 12, e9557, 11 p., https://doi.org/10.1002/ece3.9557.","productDescription":"e9557, 11 p.","ipdsId":"IP-119013","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":445674,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9557","text":"Publisher Index Page"},{"id":435596,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RL5BU4","text":"USGS data release","linkHelpText":"Biofouling and mussel growth from mussels deployed in Great Lakes embayments (2013-2016)"},{"id":424273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","otherGeospatial":"Grand Traverse Bay, Green Bay, Saginaw Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.61003738442467,\n 44.756931452092545\n ],\n [\n -85.52803993650261,\n 44.86389372309026\n ],\n [\n -85.48307359409397,\n 44.976271285175784\n ],\n [\n -85.60210214752885,\n 44.98188439355215\n ],\n [\n -85.66029388476409,\n 44.84139183490089\n ],\n [\n -85.61003738442467,\n 44.756931452092545\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.62607211384494,\n 44.13681310024381\n ],\n [\n -83.62607211384494,\n 43.89732876661685\n ],\n [\n -83.25259294281665,\n 43.89732876661685\n ],\n [\n -83.25259294281665,\n 44.13681310024381\n ],\n [\n -83.62607211384494,\n 44.13681310024381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.15455872752736,\n 44.31587096210228\n ],\n [\n -87.73632981104318,\n 44.66243758388063\n ],\n [\n -87.97406621030899,\n 44.83198507088326\n ],\n [\n -88.27876424634985,\n 44.388255504467054\n ],\n [\n -88.15455872752736,\n 44.31587096210228\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":891820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Sean 0000-0003-0361-7914 sbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-0361-7914","contributorId":198515,"corporation":false,"usgs":true,"family":"Bailey","given":"Sean","email":"sbailey@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":891821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":891822,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238830,"text":"ofr20221094 - 2022 - Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment","interactions":[],"lastModifiedDate":"2026-03-30T20:45:03.699515","indexId":"ofr20221094","displayToPublicDate":"2022-12-13T08:57:20","publicationYear":"2022","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":"2022-1094","displayTitle":"Compilation of Existing Data for Sand Resource Studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand Littoral Cell Study Areas along the Continental Shelf of California—Strategy for Field Studies and Sand Resource Assessment","title":"Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment","docAbstract":"The Sand Resources Project was established through collaborative agreements between the U.S. Geological Survey (USGS), the Bureau of Ocean Energy Management (BOEM), and the California Ocean Protection Council (OPC) with the purpose of evaluating sand and gravel resources in Federal and California State Waters for potential use in future beach-nourishment projects. Project partners worked in collaboration with California Coastal Sediment Management Workgroup (CSMW) members to define priority study areas for this work based on the potential for finding sand within the broader region and the needs for this sand as shown by beach erosion areas of concern in the adjacent littoral cells. The final study areas were defined to be (1) the San Francisco Littoral Cell, (2) the Oceanside Littoral Cell, and (3) the Silver Strand Littoral Cell.
A two-stage approach was used to assess the study areas. This report addresses the initial stage, which is a synthesis of the existing geophysical and sediment-sampling data in each area. This allowed for evaluations of the data availability, data gaps, and general patterns of sediment thickness and grain size. This report provides a description of the methods and results of this synthesis. The findings from this work were used to refine study area boundaries and develop sampling plans for stage two of the project.
Stage two of the project, the results of which will be published separately, will be the collection, processing, and synthesis of new data, including high-resolution geophysical surveys and sediment cores within the three study areas. The data collected will provide new information about the three study areas including sediment thickness, grain-size distributions, and total organic carbon. The description and results of stage two of the work is included in another USGS report (Warrick and others, 2022).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221094","collaboration":"Prepared in cooperation with the Bureau of Ocean Energy Management and the State of California Ocean Protection Council","usgsCitation":"Warrick, J.A., Conrad, J.E., Papesh, A., Lorenson, T., and Sliter, R.W., 2022, Compilation of existing data for sand resource studies in Federal and California State Waters of the San Francisco, Oceanside, and Silver Strand littoral cell study areas along the continental shelf of California—Strategy for field studies and sand resource assessment: U.S. Geological Survey Open-File Report 2022–1094, 21 p., https://doi.org/10.3133/ofr20221094.","productDescription":"vii, 21 p.","onlineOnly":"Y","ipdsId":"IP-142746","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":410369,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1094/ofr20221094.pdf","text":"Report","size":"6.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1094"},{"id":410368,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1094/coverthb.jpg"},{"id":501836,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113942.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"San Francisco, Oceanside, and Silver Strand littoral cell study areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.32845828309286,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 37.818674364541195\n ],\n [\n -123.23587958971336,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 36.79251013661299\n ],\n [\n -122.32845828309286,\n 37.818674364541195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.13365815618306,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.93175821350262\n ],\n [\n -117.80008333663935,\n 32.493660702226535\n ],\n [\n -117.13365815618306,\n 32.493660702226535\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.26754558635474,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.1318551432673\n ],\n [\n -117.66427888472828,\n 33.52924818029179\n ],\n [\n -118.26754558635474,\n 33.52924818029179\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St
Santa Cruz, CA 95060
Accurate and complete data on fire locations and burned areas are needed to quantify trends and patterns of fire occurrence, characterize drivers of fire, project future fire pattern behavior, and help with assessments of fire effects on natural and social systems. The Landsat Collection 2 Level-3 Burned Area science product is designed to identify burned areas across all ecosystems (for example, forests, shrublands, and grasslands) for Landsat 4–9 data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223083","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Burned Area science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3083, 2 p., https://doi.org/10.3133/fs20223083.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139624","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":418240,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3083/coverthb2.jpg"},{"id":410296,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418243,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3083/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418241,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3083/fs20223083.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3038"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 20, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product provides raster data that represent surface water inundation per pixel in Landsat 4–9 imagery. The Collection 2 Dynamic Surface Water Extent science product contains six acquisition-based raster products relating to surface water. Surface water extent is modulated by weather and climate, stream network hydrology, and geological processes such as isostatic rebound. Land use, ecosystem and service management, and overall water management also are affected by changes in surface water extent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223084","usgsCitation":"U.S. Geological Survey, 2022, Landsat Collection 2 Level-3 Dynamic Surface Water Extent science product (ver. 1.1, June 2023): U.S. Geological Survey Fact Sheet 2022–3084, 2 p., https://doi.org/10.3133/fs20223084.","productDescription":"Report: 2 p.; Dataset","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-139625","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":410308,"rank":1,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":418247,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3084/coverthb2.jpg"},{"id":418249,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2022/3084/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":418248,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3084/fs20223084.pdf","text":"Report","size":"1.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3084"}],"edition":"Version 1.0: December 13, 2022; Version 1.1: June 21, 2023","contact":"Customer Services, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Many organizations in the Delaware River Basin (DRB) monitor surface-water quality for regulatory, scientific, and decision-making purposes. In support of these purposes, over 260,000 water-quality records provided by 8 different organizations were compiled, screened, and used to generate water-quality trends in the DRB. These trends, for periods of record that end in 2018, were generated for 124 sites and up to 16 constituents using 2 trend methods: the Seasonal Kendall Test and the Weighted Regressions on Time, Discharge, and Season model. Seasonal Kendall Tests were performed on all water-quality records to detect monotonic trends in concentration over the period of record and for as many as four additional trend periods (1978–2018, 1998–2018, 2003–18, and 2008–18). The Weighted Regressions on Time, Discharge, and Season model was applied to water-quality records that passed more stringent screening criteria and was used to detect monontonic and nonmonotonic trends, account for variations in streamflow, and estimate annual concentrations. These two trend methods produced different trend directions less than 1 percent of the time, illustrating general agreement between the methods despite the different approaches and data input requirements. Overall, the changes in concentration for salinity constituents (specific conductance and total dissolved solids), chloride, and sodium were increases; those increases were some of the largest changes observed in the basin, and they occurred at faster rates over time. Total dissolved solids concentration trends at 4 of the 60 sites increased from below to above the level of concern threshold (a secondary drinking water threshold) over the period of record, indicating potentially meaningful degradation in water quality. Nutrient constituent (ammonia, nitrate, orthophosphate, total nitrogen, and total phosphorus) concentrations tended to decrease over the period of record, although fewer sites had significant trends and the changes in concentration were smaller compared to the salinity constituents. Total nitrogen and total phosphorus were the only nutrient constituents to have decreasing concentration trends that crossed from above to below the level of concern threshold, U.S. Environmental Protection Agency (EPA) ecoregional nutrient criteria, (EPA, undated c). This finding indicates water-quality improvement at sites with these trends (nine sites with total nitrogen trends and one site with a total phosphorus trend), although many sites were still in exceedance of the level of concern. Trends for total suspended solids and some major ions (calcium, magnesium, potassium) were largely nonsignificant or variable between sites, with no prevalent patterns across the DRB; however, sulfate concentrations decreased at most sites. Cumulative land-surface change within each watershed had a strong positive relation with changes in water-quality concentrations for the salinity constituents and most major ions, but not for the other constituents, indicating that land-surface changes are related to the sources and transport of these constituents. Investigating long-term trends (a decade or longer) in water quality can help the DRB water management community quantify the success of management practices and identify potential threats to water availability.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225097","programNote":"Water Availability and Use Science Program, National Water Quality Program","usgsCitation":"Shoda, M.E., and Murphy, J.C., 2022, Water-quality trends in the Delaware River Basin calculated using multisource data and two methods for trend periods ending in 2018: U.S. Geological Survey Scientific Investigations Report 2022–5097, 60 p., https://doi.org/10.3133/sir20225097.","productDescription":"Report v, 60 p.; 2 Data Releases","numberOfPages":"60","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122487","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":410210,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KMWNJ5","text":"USGS data release","linkHelpText":"Water-quality trends for rivers and streams in the Delaware River Basin using Weighted Regressions on Time, Discharge, and Season (WRTDS) models, Seasonal Kendall Trend (SKT) tests, and multisource data, water years 1978–2018"},{"id":410211,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PX8LZO","text":"USGS data release","linkHelpText":"Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin"},{"id":410212,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5097/sir20225097.XML"},{"id":410208,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5097/sir20225097.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5097"},{"id":410207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5097/coverthb.jpg"},{"id":433058,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233014","text":"USGS Fact Sheet 2023-3014","linkFileType":{"id":5,"text":"html"}},{"id":410209,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225097/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5097"},{"id":410213,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5097/images/"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.68904449140135,\n 38.5435194562952\n ],\n [\n -75.2278145853991,\n 38.47477347574127\n ],\n [\n -75.03014462568353,\n 38.95461543352394\n ],\n [\n -74.87640132368297,\n 39.566811726970116\n ],\n [\n -74.28339144453736,\n 40.70854805354401\n ],\n [\n -74.1735748002507,\n 41.56866114554592\n ],\n [\n -73.77823488082065,\n 42.41747384003048\n ],\n [\n -74.21750145796516,\n 42.64406475670245\n ],\n [\n -74.96425463911187,\n 42.44989429480822\n ],\n [\n -75.44744787397136,\n 42.043433704426945\n ],\n [\n -75.88671445111639,\n 41.486448364247764\n ],\n [\n -76.19420105511806,\n 40.79174109891525\n ],\n [\n -76.23812771283252,\n 40.37473571760768\n ],\n [\n -76.01849442425974,\n 39.702128532888\n ],\n [\n -75.75493447797302,\n 39.14224249538836\n ],\n [\n -75.68904449140135,\n 38.5435194562952\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Coordinator
Water Availability and Use Science Program
National Water Quality Program
U.S. Geological Survey
Geologic maps are the fundamental building blocks of surface and subsurface three-dimensional geologic framework models of the Earth’s crust. However, as the production and availability of geologic map databases continues to increase, inconsistent data models and the lack of synthesized, national geologic map data at scales appropriate for informed decision making negatively affect the functional integration of geologic map data with other national datasets. The Geologic Map Schema (GeMS) is the publication and archive database standard for geologic map data funded by the U.S. Geological Survey National Cooperative Geologic Mapping Program, and standardizes the organization and content of a single map database. However, synthesizing multiple databases into a seamless geologic map database creates a different set of challenges and database needs than GeMS was designed to accommodate. The Seamless Integrated Geologic Mapping (SIGMa) extension is designed to expand the capabilities of GeMS by enabling integration of map-based geoscience data. In particular, the SIGMa extension enables capturing a diverse and ever-changing set of map units, produced by many contributors operating independently, and by incremental and noncontiguous assembly and publication. Feature-level metadata fields allow data sources and digital compilation methods to be attributed separately and a relational structure is designed to support the link between data sources and features attributed with multiple data sources. Instead of paragraph-style map-unit descriptions that can be highly inconsistent, SIGMa parses fundamental map-unit attributes, including material, genetic process, and age, into thematically specific fields. The SIGMa extension uses a hierarchical map-unit organization to facilitate a dynamic and evolving, formation-level stratigraphic framework. The hierarchy is developed around geologic provinces that represent temporally restricted geologic events, processes, and settings. Geologic provinces can include magmatic events, depositional settings associated with tectonic processes or stable continental margins, and processes that are actively shaping the modern landscape. A geologic province hierarchy places map units into a geologic context at subregional to continental scales and provides the flexibility to support incremental assembly of the stratigraphy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225115","usgsCitation":"Turner, K.J., Workman, J.B., Colgan, J.P., Gilmer, A.K., Berry, M.E., Johnstone, S.A., Warrell, K.F., Dechesne, M., VanSistine, D.P., Thompson, R.A., Hudson, A.M., Zellman, K.L., Sweetkind, D., and Ruleman, C.A., 2022, The Seamless Integrated Geologic Mapping (SIGMa) extension to the Geologic Map Schema (GeMS): U.S. Geological Survey Scientific Investigations Report 2022–5115, 33 p., https://doi.org/10.3133/sir20225115.","productDescription":"vii, 33 p.","onlineOnly":"Y","ipdsId":"IP-125234","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":410653,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225115/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5115"},{"id":410248,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5115/sir20225115.xml"},{"id":410247,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5115/images"},{"id":410246,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5115/sir20225115.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5115"},{"id":410245,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5115/coverthb.jpg"}],"contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Digital flood-inundation maps for a 2.4-mile reach of the Schoharie Creek in North Blenheim, New York, were created by the U.S. Geological Survey (USGS) in cooperation with the New York Power Authority. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://fim.wim.usgs.gov/fim/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Schoharie Creek near North Blenheim, N.Y. (station number 01350212). Near-real-time stage at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/. Flood profiles were computed for the stream reach by means of a two-dimensional implicit finite-volume hydraulic model. The model was calibrated by using the active (as of April 2021) stage-discharge ratings at the USGS streamgages on the Schoharie Creek near North Blenheim (station number 01350212) and at North Blenheim (station number 01350180) and documented high-water marks in the study reach from the floods of August 28, 2011; January 19, 1996; and April 4, 1987.
The hydraulic model was used to compute 13 water-surface profiles for flood stages at 1-foot intervals referenced to the datum at the streamgage on the Schoharie Creek near North Blenheim, N.Y. (01350212). These profiles range from 14 feet, or near bankfull, to 26 feet, which is the highest whole-foot increment on the stage-discharge rating for the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.52-foot vertical accuracy and 3.3-foot [1-meter] horizontal resolution) to delineate the area flooded at each stage. Flood inundation maps, along with near-real-time stage data from USGS streamgages, can provide emergency management personnel and residents with information critical for flood-response activities, such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225119","collaboration":"Prepared in cooperation with the New York Power Authority","usgsCitation":"Nystrom, E.A., 2022, Flood-inundation maps for Schoharie Creek in North Blenheim, New York: U.S. Geological Survey Scientific Investigations Report 2022–5119, 14 p., https://doi.org/10.3133/sir20225119.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122520","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":410180,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92YVB9V","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Geospatial dataset for flood inundation maps of Schoharie Creek in North Blenheim, New York"},{"id":410178,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5119/sir20225119.pdf","text":"Report","size":"49.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5119"},{"id":410181,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5119/sir20225119.XML"},{"id":410182,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5119/images/"},{"id":410179,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225119/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5119"},{"id":410177,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5119/coverthb.jpg"}],"country":"United States","state":"New York","city":"North Blenheim","otherGeospatial":"Schoharie Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.43435753120507,\n 42.481471549691946\n ],\n [\n -74.46929205403138,\n 42.481471549691946\n ],\n [\n -74.46929205403138,\n 42.457988603472074\n ],\n [\n -74.43435753120507,\n 42.457988603472074\n ],\n [\n -74.43435753120507,\n 42.481471549691946\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Islands support unique plants, animals, and human societies found nowhere else on the Earth. Local and global stressors threaten the persistence of island ecosystems, with invasive species being among the most damaging, yet solvable, stressors. While the threat of invasive terrestrial mammals on island flora and fauna is well recognized, recent studies have begun to illustrate their extended and destructive impacts on adjacent marine environments. Eradication of invasive mammals and restoration of native biota are promising tools to address both island and ocean management goals. The magnitude of the marine benefits of island restoration, however, is unlikely to be consistent across the globe. We propose a list of six environmental characteristics most likely to affect the strength of land–sea linkages: precipitation, elevation, vegetation cover, soil hydrology, oceanographic productivity, and wave energy. Global databases allow for the calculation of comparable metrics describing each environmental character across islands. Such metrics can be used today to evaluate relative potential for coupled land–sea conservation efforts and, with sustained investment in monitoring on land and sea, can be used in the future to refine science-based planning tools for integrated land–sea management. As conservation practitioners work to address the effects of climate change, ocean stressors, and biodiversity crises, it is essential that we maximize returns from our management investments. Linking efforts on land, including eradication of island invasive mammals, with marine restoration and protection should offer multiplied benefits to achieve concurrent global conservation goals.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2122354119","usgsCitation":"Sandin, S.A., Becker, P.A., Becker, C., Brown, K., Erazo, N.G., Figuerola, C., Fisher, R., Friedlander, A., Fukami, T., Graham, N.A., Gruner, D.S., Holmes, N.D., Holthuijzen, W.A., Jones, H.P., Rios, M., Samaniego, A., Sechrest, W., Semmens, B.X., Thornton, H.E., Thurber, R.V., Wails, C., Wolf, C.A., and Zgliczynski, B.J., 2022, Harnessing island–ocean connections to maximize marine benefits of island conservation: PNAS, v. 119, no. 51, e2122354119, 9 p., https://doi.org/10.1073/pnas.2122354119.","productDescription":"e2122354119, 9 p.","ipdsId":"IP-144780","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":445677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9907155","text":"Publisher Index Page"},{"id":412678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"51","noUsgsAuthors":false,"publicationDate":"2022-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Sandin, Stuart A.","contributorId":301995,"corporation":false,"usgs":false,"family":"Sandin","given":"Stuart","email":"","middleInitial":"A.","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becker, Penny A.","contributorId":173445,"corporation":false,"usgs":false,"family":"Becker","given":"Penny","email":"","middleInitial":"A.","affiliations":[{"id":27230,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":863264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Becker, Ceiba","contributorId":301997,"corporation":false,"usgs":false,"family":"Becker","given":"Ceiba","email":"","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Kate","contributorId":301999,"corporation":false,"usgs":false,"family":"Brown","given":"Kate","email":"","affiliations":[{"id":65382,"text":"Global Island Partnership, New Zealand","active":true,"usgs":false}],"preferred":false,"id":863266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Erazo, Natalia G.","contributorId":302000,"corporation":false,"usgs":false,"family":"Erazo","given":"Natalia","email":"","middleInitial":"G.","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863267,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Figuerola, Cielo","contributorId":302001,"corporation":false,"usgs":false,"family":"Figuerola","given":"Cielo","email":"","affiliations":[{"id":26976,"text":"Island Conservation, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":863268,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863269,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Friedlander, Alan M.","contributorId":302003,"corporation":false,"usgs":false,"family":"Friedlander","given":"Alan M.","affiliations":[{"id":65384,"text":"National Geographic Society, Washington DC","active":true,"usgs":false}],"preferred":false,"id":863270,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fukami, Tadashi","contributorId":195506,"corporation":false,"usgs":false,"family":"Fukami","given":"Tadashi","email":"","affiliations":[],"preferred":false,"id":863271,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Graham, Nicholas A. J.","contributorId":302005,"corporation":false,"usgs":false,"family":"Graham","given":"Nicholas","email":"","middleInitial":"A. J.","affiliations":[{"id":65385,"text":"Lancaster Environment Centre, Lancaster University, UK","active":true,"usgs":false}],"preferred":false,"id":863272,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Gruner, Daniel S.","contributorId":195507,"corporation":false,"usgs":false,"family":"Gruner","given":"Daniel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":863273,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Holmes, Nick D.","contributorId":172091,"corporation":false,"usgs":false,"family":"Holmes","given":"Nick","email":"","middleInitial":"D.","affiliations":[{"id":26976,"text":"Island Conservation, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":863274,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Holthuijzen, Wieteke A.","contributorId":302006,"corporation":false,"usgs":false,"family":"Holthuijzen","given":"Wieteke","email":"","middleInitial":"A.","affiliations":[{"id":65387,"text":"University of Tennessee - Knoxville, TN","active":true,"usgs":false}],"preferred":false,"id":863275,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Jones, Holly P.","contributorId":270944,"corporation":false,"usgs":false,"family":"Jones","given":"Holly","email":"","middleInitial":"P.","affiliations":[{"id":56228,"text":"Northern Illinois University, Department of Biological Sciences; Northern Illinois University, Institute for the Study of the Environment, Sustainability, and Energy","active":true,"usgs":false}],"preferred":false,"id":863276,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rios, Mariela","contributorId":302007,"corporation":false,"usgs":false,"family":"Rios","given":"Mariela","email":"","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863277,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Samaniego, Araceli","contributorId":302008,"corporation":false,"usgs":false,"family":"Samaniego","given":"Araceli","email":"","affiliations":[{"id":65388,"text":"Manaaki Whenua – Landcare Research, New Zealand","active":true,"usgs":false}],"preferred":false,"id":863278,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sechrest, Wes","contributorId":302009,"corporation":false,"usgs":false,"family":"Sechrest","given":"Wes","email":"","affiliations":[{"id":65389,"text":"Re:wild, Austin, TX","active":true,"usgs":false}],"preferred":false,"id":863279,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Semmens, Brice X.","contributorId":149775,"corporation":false,"usgs":false,"family":"Semmens","given":"Brice","email":"","middleInitial":"X.","affiliations":[{"id":17820,"text":"Scripps Institution of Oceanography, University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":863280,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Thornton, Hazel E.","contributorId":302010,"corporation":false,"usgs":false,"family":"Thornton","given":"Hazel","email":"","middleInitial":"E.","affiliations":[{"id":65390,"text":"United Nations Environment Programme, World Conservation Monitoring Centre, UK","active":true,"usgs":false}],"preferred":false,"id":863281,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Thurber, Rebecca Vega","contributorId":302011,"corporation":false,"usgs":false,"family":"Thurber","given":"Rebecca","email":"","middleInitial":"Vega","affiliations":[{"id":65391,"text":"Oregon State University, Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":863282,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Wails, Christy","contributorId":302012,"corporation":false,"usgs":false,"family":"Wails","given":"Christy","affiliations":[{"id":65392,"text":"Virginia Tech, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":863283,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Wolf, Coral A.","contributorId":302013,"corporation":false,"usgs":false,"family":"Wolf","given":"Coral","email":"","middleInitial":"A.","affiliations":[{"id":26976,"text":"Island Conservation, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":863284,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Zgliczynski, Brian J.","contributorId":302014,"corporation":false,"usgs":false,"family":"Zgliczynski","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":35051,"text":"Scripps Institution of Oceanography, UC San Diego","active":true,"usgs":false}],"preferred":false,"id":863285,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}} ,{"id":70238780,"text":"70238780 - 2022 - A channel sampling strategy for measurement of mineral modal and chemical composition of drill cores: Application to lower oceanic crustal rocks from IODP Expedition 345 to the Hess Deep rift","interactions":[],"lastModifiedDate":"2022-12-12T15:00:33.468536","indexId":"70238780","displayToPublicDate":"2022-12-12T08:43:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3356,"text":"Scientific Drilling","active":true,"publicationSubtype":{"id":10}},"title":"A channel sampling strategy for measurement of mineral modal and chemical composition of drill cores: Application to lower oceanic crustal rocks from IODP Expedition 345 to the Hess Deep rift","docAbstract":"We report a new sampling strategy for collecting representative samples of drill core. By splitting the core with a diamond saw into working and archive halves, the saw cuttings constitute a “channel” sample, the best subsample from which to obtain an average mineralogical and geochemical composition of a core. We apply this procedure to sampling core of the lower oceanic crust in the Hess Deep obtained during Expedition 345 of the Integrated Ocean Drilling Program (now International Ocean Discovery Program).
Our results show that particles produced by sawing range from sand to clay sizes. Sand- and silt-sized cuttings can be sampled with a spatula, whereas clay-sized particles remained in suspension after 12 h and could be collected only by settling, aided by centrifuge. X-ray diffraction (XRD) analysis and Rietveld refinement show that phyllosilicates were fractionated into the clay-sized fraction. Thus, collection of both the sedimented fraction and the clay-sized suspended fraction (commonly > 15 wt % of the total) is necessary to capture the whole sample. The strong positive correlation between the recovered sample mass (in grams) and length of core cut demonstrates that this sampling protocol was uniform and systematic, with almost 1.4 g sediment produced per centimeter of core cut. We show that major-element concentrations of our channel samples compare favorably with the compositions of billet-sized samples analyzed aboard the JOIDES Resolution, but the results show that individual billet analyses are rarely representative of the whole core recovered. A final test of the validity of our methods comes from the strong positive correlation between the loss on ignition (LOI) values of our channel samples and the H2O contents calculated from the modal mineralogy obtained by X-ray diffraction and Rietveld refinement. This sampling procedure shows that grain-sized fractionation modifies both mineralogical and chemical compositions; nevertheless, this channel sampling method is a reliable method of obtaining representative samples of bulk cores. With the ever-increasing precision offered by modern analytical instrumentation, this sampling protocol allows the accuracy of the analytical results to keep pace.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/sd-31-71-2022","usgsCitation":"Wintsch, R.P., Meyer, R., Bish, D., Deasy, R.T., Nozaka, T., and Johnson, C., 2022, A channel sampling strategy for measurement of mineral modal and chemical composition of drill cores: Application to lower oceanic crustal rocks from IODP Expedition 345 to the Hess Deep rift: Scientific Drilling, v. 31, p. 71-84, https://doi.org/10.5194/sd-31-71-2022.","productDescription":"14 p.","startPage":"71","endPage":"84","ipdsId":"IP-133634","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":445680,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/sd-31-71-2022","text":"Publisher Index Page"},{"id":410280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Hess Deep Rift, Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.57372796106893,\n 3.330136493398328\n ],\n [\n -111.57372796106893,\n -1.0981582918846584\n ],\n [\n -101.19437373696735,\n -1.0981582918846584\n ],\n [\n -101.19437373696735,\n 3.330136493398328\n ],\n [\n -111.57372796106893,\n 3.330136493398328\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationDate":"2022-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wintsch, Robert P.","contributorId":192913,"corporation":false,"usgs":false,"family":"Wintsch","given":"Robert","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":858574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Romain","contributorId":148991,"corporation":false,"usgs":false,"family":"Meyer","given":"Romain","email":"","affiliations":[{"id":17609,"text":"Deutsche GeoForchungsZentrum Potsdam","active":true,"usgs":false}],"preferred":false,"id":858575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bish, David","contributorId":291943,"corporation":false,"usgs":false,"family":"Bish","given":"David","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":858576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Deasy, Ryan T. 0000-0002-7530-803X","orcid":"https://orcid.org/0000-0002-7530-803X","contributorId":299762,"corporation":false,"usgs":true,"family":"Deasy","given":"Ryan","middleInitial":"T.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":858577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nozaka, Toshio","contributorId":299763,"corporation":false,"usgs":false,"family":"Nozaka","given":"Toshio","email":"","affiliations":[{"id":64944,"text":"Okayama University","active":true,"usgs":false}],"preferred":false,"id":858578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Carley","contributorId":299764,"corporation":false,"usgs":false,"family":"Johnson","given":"Carley","email":"","affiliations":[{"id":64945,"text":"Marathon","active":true,"usgs":false}],"preferred":false,"id":858579,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239015,"text":"70239015 - 2022 - Evaluating the sensitivity of multi-dimensional model predictions of salmon habitat to the source of remotely sensed river bathymetry","interactions":[],"lastModifiedDate":"2022-12-21T12:40:22.808277","indexId":"70239015","displayToPublicDate":"2022-12-12T06:37:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the sensitivity of multi-dimensional model predictions of salmon habitat to the source of remotely sensed river bathymetry","docAbstract":"Multi-dimensional numerical models are fundamental tools for investigating biophysical processes in aquatic ecosystems. Remote sensing techniques increase the feasibility of applying such models at riverscape scales, but tests of model performance on large rivers have been limited. We evaluated the potential to develop two-dimensional (2D) and three-dimensional (3D) hydrodynamic models for a 1.6-km reach of a large gravel-bed river using three sources of remotely sensed river bathymetry. We estimated depth from hyperspectral image data acquired from conventional and uncrewed aircraft and multispectral satellite imagery. Our results indicated that modeled water depth errors were similar between 2D and 3D models, with depth residuals that were comparable to the uncertainty associated with the bathymetry used as input. We found good agreement between measured and modeled depth-averaged velocities generated by 2D and 3D models, while 3D models provided superior predictions of near-bed velocities. We found that optimal model performance occurred for lower flow resistance values than previously reported in the literature, possibly as a consequence of the high-resolution bathymetry used as model input. Model predictions of winter-run Chinook salmon (Oncorhynchus tshawytscha) spawning and rearing habitat were not sensitive to the source of bathymetric information, but bioenergetic predictions related to adult holding costs were influenced by the input bathymetry. Our results suggest that hyperspectral imagery acquired from piloted and/or uncrewed aircraft can be used to map the bathymetry of clear-flowing, relatively shallow large rivers with sufficient accuracy to support multi-dimensional flow model development; models developed from multispectral satellite imagery had more limited predictive capability.
Interspecific interactions among walleye Sander vitreus, lake whitefish Coregonus clupeaformis, and yellow perch Perca flavescens in Green Bay could influence the population status of each species, but potential trophic interactions are poorly understood. Our objectives were to determine if diet assemblages for each species and diet overlap among species varied spatially and temporally within Green Bay. Adult walleye (≥ 381 mm total length (TL); N = 981), lake whitefish (≥ 432 mm TL; N = 1507), and yellow perch (≥ 150 mm TL; N = 1174) were collected during May-October of 2018 and 2019 from multiple locations in southern and northern Green Bay. Diet assemblages of all three species varied between zones but walleye diets were more temporally variable (among months within zones and between years) than diets of lake whitefish or yellow perch. Lake whitefish represented a seasonally important prey item for walleye in southern Green Bay, composing 10% and 41% of walleye diets by weight in May and June, respectively. Yellow perch generally composed < 15% of walleye diets by weight but were consumed at a broader spatiotemporal scale than lake whitefish. Diet overlap between walleye and both lake whitefish and yellow perch was generally weak or moderate, whereas diet overlap between whitefish and perch was generally strong. Our assessment of adult trophic interactions suggests that changes in the population status of one species could influence fisheries for all three, and we identify additional research questions to address potential population-level effects of these trophic interactions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.09.005","usgsCitation":"Koeniga, L., Dembkowski, D., Hansen, S., Tsehaye, I., Tammie J. Paoli, Zorn, T., and Isermann, D.A., 2022, Diet composition and overlap for adult walleye, lake whitefish, and yellow perch in Green Bay, Lake Michigan: Journal of Great Lakes Research, v. 48, no. 6, p. 1681-1695, https://doi.org/10.1016/j.jglr.2022.09.005.","productDescription":"15 p.","startPage":"1681","endPage":"1695","ipdsId":"IP-140407","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay, Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.25433685919877,\n 45.59617874752695\n ],\n [\n -87.9984005897609,\n 44.918805118493594\n ],\n [\n -87.98617785518738,\n 44.57675244172867\n ],\n [\n -87.3651194846989,\n 44.78875465437872\n ],\n [\n -86.56980682225971,\n 45.71326930572798\n ],\n [\n -87.03874882070055,\n 45.85231391585981\n ],\n [\n -87.25433685919877,\n 45.59617874752695\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Koeniga, Lucas D.","contributorId":348678,"corporation":false,"usgs":false,"family":"Koeniga","given":"Lucas D.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923697,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel J.","contributorId":348681,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923698,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Scott P.","contributorId":348684,"corporation":false,"usgs":false,"family":"Hansen","given":"Scott P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923699,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tsehaye, Iyob","contributorId":348687,"corporation":false,"usgs":false,"family":"Tsehaye","given":"Iyob","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tammie J. Paoli","contributorId":348689,"corporation":false,"usgs":false,"family":"Tammie J. Paoli","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923701,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zorn, Troy G.","contributorId":348692,"corporation":false,"usgs":false,"family":"Zorn","given":"Troy G.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923702,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923703,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70239422,"text":"70239422 - 2022 - Lithologic and geochemical observations of the lower Eagle Ford from the USGS GC-2 core in the East Texas Basin","interactions":[],"lastModifiedDate":"2026-03-18T15:49:31.332606","indexId":"70239422","displayToPublicDate":"2022-12-10T10:45:14","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Lithologic and geochemical observations of the lower Eagle Ford from the USGS GC-2 core in the East Texas Basin","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"38th Annual GCSSEPM Foundation Perkins-Rosen Research Conference and Core Workshop 2022: The Cenomanian-Turonian stratigraphic interval across the Americas","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Curran Associates","usgsCitation":"Flaum, J.A., Paxton, S.T., Birdwell, J.E., and French, K.L., 2022, Lithologic and geochemical observations of the lower Eagle Ford from the USGS GC-2 core in the East Texas Basin, in 38th Annual GCSSEPM Foundation Perkins-Rosen Research Conference and Core Workshop 2022: The Cenomanian-Turonian stratigraphic interval across the Americas, v. 38, p. 93-99.","productDescription":"7 p.","startPage":"93","endPage":"99","ipdsId":"IP-146329","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":501256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Flaum, Jason A. 0000-0003-1251-1142","orcid":"https://orcid.org/0000-0003-1251-1142","contributorId":300809,"corporation":false,"usgs":true,"family":"Flaum","given":"Jason","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":861535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Stanley T. 0000-0002-9098-1740 spaxton@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-1740","contributorId":739,"corporation":false,"usgs":true,"family":"Paxton","given":"Stanley","email":"spaxton@usgs.gov","middleInitial":"T.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":861536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":861537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"French, Katherine L. 0000-0002-0153-8035","orcid":"https://orcid.org/0000-0002-0153-8035","contributorId":205462,"corporation":false,"usgs":true,"family":"French","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":861538,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274238,"text":"70274238 - 2022 - Facies identification through lithologic and geochemical observation of the Oceanic Anoxic Event 2 interval in the Cretaceous Western Interior Seaway—Examples from the Rebecca K. Bounds core","interactions":[],"lastModifiedDate":"2026-03-18T15:43:28.371447","indexId":"70274238","displayToPublicDate":"2022-12-10T10:39:16","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Facies identification through lithologic and geochemical observation of the Oceanic Anoxic Event 2 interval in the Cretaceous Western Interior Seaway—Examples from the Rebecca K. Bounds core","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"38th Annual GCSSEPM Foundation Perkins-Rosen Research Conference and Core Workshop 2022: The Cenomanian-Turonian stratigraphic interval across the Americas","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Curran Associates","usgsCitation":"Flaum, J.A., and Kalbas, J., 2022, Facies identification through lithologic and geochemical observation of the Oceanic Anoxic Event 2 interval in the Cretaceous Western Interior Seaway—Examples from the Rebecca K. Bounds core, in 38th Annual GCSSEPM Foundation Perkins-Rosen Research Conference and Core Workshop 2022: The Cenomanian-Turonian stratigraphic interval across the Americas, v. 38, p. 66-73.","productDescription":"8 p.","startPage":"66","endPage":"73","ipdsId":"IP-146914","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":501255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Flaum, Jason A. 0000-0003-1251-1142","orcid":"https://orcid.org/0000-0003-1251-1142","contributorId":300809,"corporation":false,"usgs":true,"family":"Flaum","given":"Jason","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":957142,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalbas, Jay","contributorId":367236,"corporation":false,"usgs":false,"family":"Kalbas","given":"Jay","affiliations":[],"preferred":false,"id":957143,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70242930,"text":"70242930 - 2022 - Quantifying aspects of rangeland health at watershed scales in Colorado using remotely sensed data products","interactions":[],"lastModifiedDate":"2023-04-24T12:03:26.112971","indexId":"70242930","displayToPublicDate":"2022-12-09T07:00:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying aspects of rangeland health at watershed scales in Colorado using remotely sensed data products","docAbstract":"Allegheny woodrats Neotoma magister are an imperiled small mammal species most associated with emergent rock habitats in the central Appalachian Mountains and the Ohio River Valley. The monitoring of populations and their spatiotemporal distributions typically has relied on labor-intensive livetrapping. The use of remote-detecting cameras holds promise for being an equally or more effective method to determine species presence, although trap-based captures permit the estimation of other parameters (e.g., survival, population size, site fidelity). In 2017, 2018, and 2020 we compared standard livetrapping with paired cameras for determining site occupancy of Allegheny woodrats in the central Appalachian Mountains of western Virginia. We further examined the influence of baited vs. unbaited cameras at several sites of confirmed occupancy in 2019. We observed that the detection probability using cameras was approximately 1.7 times that of live traps. Also, detection probability at baited camera traps was 1.3–2.0 times that of unbaited camera traps. Estimates of occupancy ranged from 0.44 to 0.49. Our findings suggest that the use of baited remote-detecting cameras provides a more effective method than livetrapping for detecting Allegheny woodrats. Our study provides a framework for the development of a large-scale, long-term monitoring protocol of Allegheny woodrats with the goals of identifying changes in the distribution of the species and quantifying local extinction and colonization rates at emergent rock outcrops and caves throughout the species' known distribution.
","language":"English","publisher":"Allen Press","doi":"10.3996/jfwm-21-037","usgsCitation":"Thorne, E., Powers, K., Reynolds, R., Beckner, M., Ellis, K., and Ford, W., 2022, Comparison of two detection methods of a declining rodent, the Allegheny woodrat, in Virginia: Journal of Fish and Wildlife Management, v. 13, no. 2, p. 396-406, https://doi.org/10.3996/jfwm-21-037.","productDescription":"11 p.","startPage":"396","endPage":"406","ipdsId":"IP-128487","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467138,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-037","text":"Publisher Index Page"},{"id":466627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"western Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.57409261674525,\n 36.66461181034798\n ],\n [\n -80.81091916110393,\n 36.533855981387575\n ],\n [\n -78.85974224585911,\n 36.5841620972601\n ],\n [\n -78.65794697364551,\n 36.66461181034798\n ],\n [\n -78.65794697364551,\n 38.46405975952615\n ],\n [\n -79.66268668892582,\n 38.48697812141013\n ],\n [\n -80.35661920699134,\n 37.64631193624106\n ],\n [\n -82.02034087287542,\n 37.53617858823333\n ],\n [\n -83.57409261674525,\n 36.66461181034798\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Thorne, Emily D.","contributorId":348807,"corporation":false,"usgs":false,"family":"Thorne","given":"Emily D.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":923790,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Powers, Karen E.","contributorId":348808,"corporation":false,"usgs":false,"family":"Powers","given":"Karen E.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":923791,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reynolds, Richard J.","contributorId":348811,"corporation":false,"usgs":false,"family":"Reynolds","given":"Richard J.","affiliations":[{"id":56188,"text":"Virginia Department of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":923794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beckner, Makayla E.","contributorId":348809,"corporation":false,"usgs":false,"family":"Beckner","given":"Makayla E.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":923792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellis, Karissa A.","contributorId":348810,"corporation":false,"usgs":false,"family":"Ellis","given":"Karissa A.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":923793,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":923789,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238927,"text":"70238927 - 2022 - Decades of global sturgeon conservation efforts are threatened by an expanding captive culture industry","interactions":[],"lastModifiedDate":"2023-02-14T14:48:20.225927","indexId":"70238927","displayToPublicDate":"2022-12-08T09:50:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Decades of global sturgeon conservation efforts are threatened by an expanding captive culture industry","docAbstract":"After centuries of overexploitation and habitat loss, many of the world's sturgeon (Acipenseridae) populations are at the brink of extinction. Although significant resources are invested into the conservation and restoration of imperiled sturgeons, the burgeoning commercial culture industry poses an imminent threat to the persistence of many populations. In the past decade, the number and distribution of captive sturgeon facilities has grown exponentially and now encompasses diverse interest groups ranging from hobby aquarists to industrial-scale commercial facilities. Expansion of sturgeon captive culture has largely fallen outside the purview of existing regulatory frameworks, raising concerns that continued growth of this industry has real potential to jeopardize conservation of global sturgeon populations. Here, we highlight some of the most significant threats commercial culture poses to wild populations, with particular emphasis on how releases can accelerate wild population declines through mechanisms such as hybridization, introgression, competition, and disease transmission. We also note that in some circumstances, commercial captive culture has continued to motivate harvest of wild populations, potentially accelerating species' declines. Given the prevalence and trajectory of sturgeon captive culture programs, we comment on modifications to regulatory frameworks that could improve the ability of captive culture to support wild sturgeon conservation.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10865","usgsCitation":"White, S.L., Fox, D.A., Beridze, T., Bolden, S.K., Johnson, R.L., Savoy, T.F., Scheele, F., Schreier, A., and Kazyak, D., 2022, Decades of global sturgeon conservation efforts are threatened by an expanding captive culture industry: Fisheries Magazine, v. 48, no. 2, p. 54-61, https://doi.org/10.1002/fsh.10865.","productDescription":"8 p.","startPage":"54","endPage":"61","ipdsId":"IP-139797","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":467139,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/63487","text":"External Repository"},{"id":410632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, Dewayne A.","contributorId":117052,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","email":"","middleInitial":"A.","affiliations":[{"id":12970,"text":"Department of Agriculture and Natural Resources, Delaware State University","active":true,"usgs":false}],"preferred":false,"id":859203,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beridze, Tamar","contributorId":299977,"corporation":false,"usgs":false,"family":"Beridze","given":"Tamar","email":"","affiliations":[{"id":63351,"text":"Ilia State University","active":true,"usgs":false}],"preferred":false,"id":859204,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bolden, Stephania K","contributorId":299978,"corporation":false,"usgs":false,"family":"Bolden","given":"Stephania","email":"","middleInitial":"K","affiliations":[{"id":64993,"text":"NMFS (retired)","active":true,"usgs":false}],"preferred":false,"id":859205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859206,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Savoy, Thomas F","contributorId":299979,"corporation":false,"usgs":false,"family":"Savoy","given":"Thomas","email":"","middleInitial":"F","affiliations":[{"id":62986,"text":"Connecticut Department of Energy and Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":859207,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scheele, Fleur","contributorId":299983,"corporation":false,"usgs":false,"family":"Scheele","given":"Fleur","email":"","affiliations":[],"preferred":false,"id":859219,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schreier, Andrea D","contributorId":299980,"corporation":false,"usgs":false,"family":"Schreier","given":"Andrea D","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":859208,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":859209,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238783,"text":"70238783 - 2022 - The economic costs of chronic wasting disease in the United States","interactions":[],"lastModifiedDate":"2022-12-12T14:33:23.384233","indexId":"70238783","displayToPublicDate":"2022-12-08T08:29:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"The economic costs of chronic wasting disease in the United States","docAbstract":"Cervids are economically important to a wide range of stakeholders and rights holders in the United States. The continued expansion of chronic wasting disease (CWD), a fatal neurodegenerative disease affecting wild and farmed cervids, poses a direct and indirect threat to state and federal government agency operations and cervid related economic activity. However, the scale of this disease’s direct economic costs is largely unknown. I synthesized existing publicly available data and stakeholder-provided data to estimate CWD’s costs within the continental United States. Federal government agencies collectively spent over $284.1 million on CWD-related efforts between 2000 and 2021, with $203.6 million of this total being spent by the U.S. Department of Agriculture’s Animal and Plant Health Inspection Service. In fiscal year 2020, state natural resources agencies and state agriculture/animal health agencies spent over $25.5 million and $2.9 million, respectively, on CWD-related work. Natural resources agencies in states with known CWD cases spent over 8 times as much on CWD as agencies from states with no known cases. The farmed cervid industry spent at least $307,950 on CWD sampling in 2020, though a lack of available data prevented a complete assessment of costs to this industry. Based on limited data, CWD’s economic effects on the hunting industry (i.e., outfitters and guides, companies leasing land to cervid hunters), may be negligible at this time. Overall, however, the realized economic costs of CWD appear considerable, and it is likely that the number of stakeholders financially affected by this disease and regulations meant to stem its spread will continue to grow. By understanding the current economic impacts of CWD, we are better positioned to assess the costs and benefits of investments in management and research and to understand the magnitude of this disease’s broader societal impacts.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0278366","usgsCitation":"Chiavacci, S.J., 2022, The economic costs of chronic wasting disease in the United States: PLoS ONE, v. 17, no. 12, e0278366, 18 p., https://doi.org/10.1371/journal.pone.0278366.","productDescription":"e0278366, 18 p.","ipdsId":"IP-139191","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":445688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0278366","text":"Publisher Index Page"},{"id":410277,"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 \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.67138671875,\n 54.686534234529695\n ],\n [\n -129.9462890625,\n 55.36662484928637\n ],\n [\n -130.1220703125,\n 56.145549500679074\n ],\n [\n -131.9677734375,\n 56.9449741808516\n ],\n [\n -135.3076171875,\n 59.833775202184206\n ],\n [\n -136.38427734375,\n 59.65664225341022\n ],\n [\n -136.6259765625,\n 59.23217626921806\n ],\n [\n -137.52685546875,\n 58.938673187948304\n ],\n [\n -137.65869140625,\n 59.33318942659219\n ],\n [\n -138.8232421875,\n 60.009970961180386\n ],\n [\n -139.21874999999997,\n 60.108670463036\n ],\n [\n -139.04296875,\n 60.403001945865476\n ],\n [\n -139.85595703125,\n 60.337823495982015\n ],\n [\n -140.99853515625,\n 60.337823495982015\n ],\n [\n -141.15234374999997,\n 69.71810669906763\n ],\n [\n -143.4375,\n 70.17020068549206\n ],\n [\n -145.1953125,\n 70.08056215839737\n ],\n [\n -149.765625,\n 70.58341752317065\n ],\n [\n -152.40234375,\n 70.61261423801925\n ],\n [\n -152.314453125,\n 70.95969716686398\n ],\n [\n -157.1484375,\n 71.35706654962706\n ],\n [\n -159.9609375,\n 70.8734913192635\n ],\n [\n -162.0703125,\n 70.31873847853124\n ],\n [\n -163.916015625,\n 69.06856318696033\n ],\n [\n -166.376953125,\n 68.942606818121\n ],\n [\n -166.376953125,\n 68.26938680456564\n ],\n [\n -163.30078125,\n 66.86108230224609\n ],\n [\n -161.982421875,\n 66.47820814385636\n ],\n [\n -163.564453125,\n 66.08936427047088\n ],\n [\n -163.564453125,\n 66.6181218846659\n ],\n [\n -165.76171875,\n 66.40795547978848\n ],\n [\n -168.0908203125,\n 65.69447579373418\n ],\n [\n -166.55273437499997,\n 65.14611484756372\n ],\n [\n -166.904296875,\n 65.05360170595502\n ],\n [\n -166.3330078125,\n 64.41592147626879\n ],\n [\n -162.861328125,\n 64.39693778132846\n ],\n [\n -160.927734375,\n 64.90491004905083\n ],\n [\n -161.0595703125,\n 64.47279382008166\n ],\n [\n -161.4990234375,\n 64.49172504435471\n ],\n [\n -160.8837890625,\n 63.87939001720202\n ],\n [\n -161.1474609375,\n 63.470144746565424\n ],\n [\n -162.6416015625,\n 63.64625919492172\n ],\n [\n -163.212890625,\n 63.05495931065107\n ],\n [\n -164.2236328125,\n 63.37183226679281\n ],\n [\n -166.1572265625,\n 61.75233128411639\n ],\n [\n -165.3662109375,\n 60.54377524118842\n ],\n [\n -167.431640625,\n 60.326947742998414\n ],\n [\n -167.255859375,\n 59.866883195210214\n ],\n [\n -165.8935546875,\n 59.7563950493563\n ],\n [\n -162.68554687499997,\n 59.734253447591364\n ],\n [\n -162.3779296875,\n 60.174306261926034\n ],\n [\n -161.806640625,\n 59.46740794183739\n ],\n [\n -162.0263671875,\n 59.108308258604964\n ],\n [\n -161.806640625,\n 58.768200159239576\n ],\n [\n -162.20214843749997,\n 58.65408464530598\n ],\n [\n -160.83984375,\n 58.44773280389084\n ],\n [\n -159.9609375,\n 58.6769376725869\n ],\n [\n -159.08203125,\n 58.309488840677645\n ],\n [\n -156.88476562499997,\n 58.92733441827545\n ],\n [\n -157.5,\n 58.516651799363785\n ],\n [\n -157.8076171875,\n 57.61010702068388\n ],\n [\n -161.54296875,\n 56.022948079627454\n ],\n [\n -168.6181640625,\n 53.4357192066942\n ],\n [\n -174.9462890625,\n 52.26815737376817\n ],\n [\n -178.2421875,\n 51.83577752045248\n ],\n [\n -173.1884765625,\n 51.590722643120145\n ],\n [\n -162.5537109375,\n 54.23955053156177\n ],\n [\n -155.302734375,\n 55.52863052257191\n ],\n [\n -151.4794921875,\n 57.51582286553883\n ],\n [\n -146.9970703125,\n 60.08676274626006\n ],\n [\n -145.546875,\n 60.21799073323445\n ],\n [\n -144.228515625,\n 59.689926220143356\n ],\n [\n -142.3828125,\n 59.93300042374631\n ],\n [\n -138.3837890625,\n 58.83649009392136\n ],\n [\n -135.6591796875,\n 56.31653672211301\n ],\n [\n -133.2421875,\n 54.521081495443596\n ],\n [\n -130.67138671875,\n 54.686534234529695\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.796875,\n 44.902577996288876\n ],\n [\n -67.67578124999999,\n 45.583289756006316\n ],\n [\n -67.939453125,\n 47.57652571374621\n ],\n [\n -69.2578125,\n 47.338822694822\n ],\n [\n -71.19140625,\n 45.27488643704891\n ],\n [\n -75.146484375,\n 44.96479793033101\n ],\n [\n -78.046875,\n 43.644025847699496\n ],\n [\n -79.1015625,\n 43.51668853502906\n ],\n [\n -79.1015625,\n 42.87596410238256\n ],\n [\n -82.68310546875,\n 41.65649719441145\n ],\n [\n -83.14453125,\n 42.049292638686836\n ],\n [\n -83.07861328125,\n 42.374778361114195\n ],\n [\n -82.529296875,\n 42.601619944327965\n ],\n [\n -82.24365234375,\n 43.6599240747891\n ],\n [\n -82.41943359375,\n 45.058001435398275\n ],\n [\n -83.60595703125,\n 45.85941212790755\n ],\n [\n -83.49609375,\n 46.027481852486645\n ],\n [\n -83.7158203125,\n 46.164614496897094\n ],\n [\n -83.95751953125,\n 46.07323062540835\n ],\n [\n -84.24316406249999,\n 46.558860303117164\n ],\n [\n -84.72656249999999,\n 46.558860303117164\n ],\n [\n -84.90234375,\n 46.92025531537451\n ],\n [\n -88.41796875,\n 48.3416461723746\n ],\n [\n -89.3408203125,\n 47.96050238891509\n ],\n [\n -90.76904296874999,\n 48.122101028190805\n ],\n [\n -90.87890625,\n 48.22467264956519\n ],\n [\n -91.51611328125,\n 48.10743118848039\n ],\n [\n -92.2412109375,\n 48.37084770238366\n ],\n [\n -92.39501953125,\n 48.23930899024907\n ],\n [\n -92.94433593749999,\n 48.61838518688487\n ],\n [\n -93.44970703125,\n 48.63290858589535\n ],\n [\n -94.7021484375,\n 48.748945343432936\n ],\n [\n -94.833984375,\n 49.23912083246698\n ],\n [\n -95.1416015625,\n 49.396675075193976\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -123.22265625000001,\n 48.99463598353405\n ],\n [\n -123.0908203125,\n 48.80686346108517\n ],\n [\n -123.24462890625,\n 48.66194284607006\n ],\n [\n -123.1787109375,\n 48.32703913063476\n ],\n [\n -124.78271484375,\n 48.472921272487824\n ],\n [\n -124.93652343749999,\n 48.16608541901253\n ],\n [\n -124.365234375,\n 46.58906908309182\n ],\n [\n -124.541015625,\n 44.15068115978094\n ],\n [\n -124.93652343749999,\n 42.69858589169842\n ],\n [\n -124.541015625,\n 41.22824901518529\n ],\n [\n -124.73876953125,\n 40.43022363450862\n ],\n [\n -124.03564453125,\n 39.35129035526705\n ],\n [\n -124.01367187499999,\n 38.8225909761771\n ],\n [\n -122.05810546875,\n 36.12012758978146\n ],\n [\n -120.95947265624999,\n 34.88593094075317\n ],\n [\n -120.80566406250001,\n 34.08906131584994\n ],\n [\n -118.21289062499999,\n 32.2313896627376\n ],\n [\n -117.22412109375,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.713355353177555\n ],\n [\n -114.78515624999999,\n 32.491230287947594\n ],\n [\n -110.98388671874999,\n 31.3348710339506\n ],\n [\n -108.21533203125,\n 31.297327991404266\n ],\n [\n -108.2373046875,\n 31.765537409484374\n ],\n [\n -106.435546875,\n 31.765537409484374\n ],\n [\n -104.9853515625,\n 30.600093873550072\n ],\n [\n -104.47998046875,\n 29.592565403314087\n ],\n [\n -103.20556640625,\n 28.94086176940557\n ],\n [\n -102.65625,\n 29.76437737516313\n ],\n [\n -102.3486328125,\n 29.84064389983441\n ],\n [\n -101.49169921875,\n 29.7453016622136\n ],\n [\n -100.83251953125,\n 29.267232865200878\n ],\n [\n -100.30517578125,\n 28.246327971048842\n ],\n [\n -99.60205078124999,\n 27.586197857692664\n ],\n [\n -99.47021484375,\n 27.31321389856826\n ],\n [\n -99.228515625,\n 26.52956523826758\n ],\n [\n -98.2177734375,\n 26.05678288577881\n ],\n [\n -97.75634765625,\n 26.03704188651584\n ],\n [\n -97.44873046875,\n 25.839449402063185\n ],\n [\n -97.20703125,\n 25.93828707492375\n ],\n [\n -96.8994140625,\n 26.194876675795218\n ],\n [\n -96.78955078125,\n 27.858503954841247\n ],\n [\n -93.75732421875,\n 29.420460341013133\n ],\n [\n -90.2197265625,\n 28.998531814051795\n ],\n [\n -88.22021484375,\n 29.05616970274342\n ],\n [\n -87.91259765625,\n 30.14512718337613\n ],\n [\n -86.5283203125,\n 30.183121842195515\n ],\n [\n -85.2978515625,\n 29.49698759653577\n ],\n [\n -84.13330078125,\n 29.80251790576445\n ],\n [\n -82.81494140625,\n 28.555576049185973\n ],\n [\n -83.21044921875,\n 27.800209937418252\n ],\n [\n -82.77099609375,\n 26.941659545381516\n ],\n [\n -82.08984375,\n 25.878994400196202\n ],\n [\n -81.5625,\n 25.264568475331583\n ],\n [\n -82.28759765625,\n 24.467150664739002\n ],\n [\n -82.0458984375,\n 24.046463999666567\n ],\n [\n -80.6396484375,\n 24.56710835257599\n ],\n [\n -79.78271484375,\n 25.34402602913433\n ],\n [\n -79.60693359375,\n 27.27416111737468\n ],\n [\n -80.68359375,\n 30.713503990354965\n ],\n [\n -80.66162109375,\n 31.50362930577303\n ],\n [\n -76.81640625,\n 34.07086232376631\n ],\n [\n -75.16845703124999,\n 35.263561862152095\n ],\n [\n -75.498046875,\n 37.055177106660814\n ],\n [\n -73.58642578125,\n 39.90973623453719\n ],\n [\n -71.3671875,\n 40.84706035607122\n ],\n [\n -69.63134765625,\n 40.9964840143779\n ],\n [\n -70.0048828125,\n 42.342305278572816\n ],\n [\n -70.3564453125,\n 42.89206418807337\n ],\n [\n -67.2802734375,\n 44.37098696297173\n ],\n [\n -67.0166015625,\n 44.69989765840318\n ],\n [\n -66.796875,\n 44.902577996288876\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Chiavacci, Scott J. 0000-0003-3579-8377","orcid":"https://orcid.org/0000-0003-3579-8377","contributorId":206161,"corporation":false,"usgs":true,"family":"Chiavacci","given":"Scott","email":"","middleInitial":"J.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":858695,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70238763,"text":"ofr20221088 - 2022 - Assessment of vulnerabilities and opportunities to restore marsh sediment supply at Nisqually River Delta, west-central Washington","interactions":[],"lastModifiedDate":"2022-12-09T20:48:54.83197","indexId":"ofr20221088","displayToPublicDate":"2022-12-08T08:00:44","publicationYear":"2022","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":"2022-1088","displayTitle":"Assessment of Vulnerabilities and Opportunities to Restore Marsh Sediment Supply at Nisqually River Delta, West-Central Washington","title":"Assessment of vulnerabilities and opportunities to restore marsh sediment supply at Nisqually River Delta, west-central Washington","docAbstract":"A cascading set of hazards to coastal environments is intimately tied to sediment transport and includes the flooding and erosion of shorelines and habitats that support communities, industry, infrastructure, and ecosystem functions (for example, habitats critical to fisheries). This report summarizes modeling and measurement data used to evaluate the sediment budget of the Nisqually River Delta, the vulnerability of the largest estuary restoration project in Puget Sound at the Billy Frank Jr. Nisqually National Wildlife Refuge, and the role of coastal hydrodynamics and potential restoration alternatives for recovering sediment delivery to its marshes. The 2009 Brown’s Farm Restoration achieved many goals toward recovering salmon habitat, but the understanding of the delta and restoration area sediment budgets remain poorly quantified. Specifically, quantitative estimates of the amount of sediment delivered to the delta and restored marsh areas, which had subsided in response to historical diking and draining for grazing, were identified as important information needs. Forecasts of potential outcomes of proposed adaptive distributary channel restoration actions were also prioritized to inform potential solutions. These estimates can be used to evaluate whether sufficient sediment is available for marsh recovery downstream from Alder Lake, which traps about 90 percent the Nisqually River sediment load that could reach the delta. Additionally, quantitative sediment information was identified to help prioritize opportunities to recover and maintain the area marshes and guide ecosystem restoration investments across the delta to reduce the vulnerability of the system to drowning under projected sea level rise.
A coupled, numerical hydrodynamic-sediment transport model and measurements of the sediment load just upstream from the delta were used to evaluate the (1) availability of sediment for marsh recovery, (2) sediment transport dynamics across the estuary, and (3) potential outcomes of distributary reconnection alternatives under existing and projected conditions of streamflow and sea level. Modeling and measurements indicated that the volume of fluvial sediment load reaching and accumulating in the restoration area ranges from 7 to 32 percent and identified that restoration alternatives could recover about an additional 10–12 percent under current and projected sea-level rise by the year 2100. At these rates of sediment delivery, 85–200+ years may be necessary to fill for marsh vegetation development and maintenance. The model also reveals the sensitivity of sediment transport and accumulation to sediment properties, hydrodynamics, and wave conditions. The low sediment accumulation results in large part because of the role of waves in directing sediment transport offshore and challenges of restoring geomorphic processes suited to maintaining habitat structure where opportunity exists or least conflicts with land use. The findings therefore have implications for siting, phasing, and implementing strategies to route and retain sediment. This study shows that opportunities to recover sediment higher in the tidal prism, where a greater hydraulic gradient and gravity could promote progradation and greater sediment retention, may be more effective than alternatives lower in the tidal prism implemented to date and assessed in this study. Furthermore, the modeling indicates that distributary channel restoration also may provide additional benefits to society by reducing flood stage, and therefore, flood hazards surrounding the delta.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221088","collaboration":"Prepared in cooperation with Nisqually Indian Tribe, U.S. Fish and Wildlife Service, Billy Frank Jr. Nisqually National Wildlife Refuge, and Washington Department of Fish and Wildlife Estuary and Salmon Restoration Program","usgsCitation":"Grossman, E.E., Crosby, S.C., Stevens, A.W., Nowacki, D.J., vanAredonk, N.R., and Curran, C.A., 2022, Assessment of vulnerabilities and opportunities to restore marsh sediment supply at Nisqually River Delta, west-central Washington: U.S. Geological Survey Open-File Report 2022–1088, 50 p., https://doi.org/10.3133/ofr20221088.","productDescription":"Report: ix, 50 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-121432","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":410185,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GF0SG7","text":"USGS data release","description":"USGS data release","linkHelpText":"Stage, water velocity and water quality data collected in the Lower Nisqually River, McAllister Creek and tidal channels of the Nisqually River Delta, Thurston County, Washington, February 11, 2016 to September 18, 2017 (ver. 1.1, December, 2019)"},{"id":410186,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95N6CIT","text":"USGS data release","description":"USGS data release","linkHelpText":"Topobathymetric Model of Puget Sound, Washington, 1887 to 2017"},{"id":410184,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1088/ofr20221088.pdf","text":"Report","size":"32.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1088"},{"id":410183,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1088/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.12450821055306,\n 47.12862354087443\n ],\n [\n -123.12450821055306,\n 45.666890715537136\n ],\n [\n -121.49348505325844,\n 45.666890715537136\n ],\n [\n -121.49348505325844,\n 47.12862354087443\n ],\n [\n -123.12450821055306,\n 47.12862354087443\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Dynamic natural processes govern snow distribution in mountainous environments throughout the world. Interactions between these different processes create spatially variable patterns of snow depth across a landscape. Variations in accumulation and redistribution occur at a variety of spatial scales, which are well established for moderate mountain terrain. However, spatial patterns of snow depth variability in steep, complex mountain terrain have not been fully explored due to insufficient spatial resolutions of snow depth measurement. Recent advances in uncrewed aerial systems (UASs) and structure from motion (SfM) photogrammetry provide an opportunity to map spatially continuous snow depths at high resolutions in these environments. Using UASs and SfM photogrammetry, we produced 11 snow depth maps at a steep couloir site in the Bridger Range of Montana, USA, during the 2019–2020 winter. We quantified the spatial scales of snow depth variability in this complex mountain terrain at a variety of resolutions over 2 orders of magnitude (0.02 to 20 m) and time steps (4 to 58 d) using variogram analysis in a high-performance computing environment. We found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability within complex mountain terrain and that snow depths are autocorrelated within horizontal distances of 15 m at our study site. The results of this research have the potential to reduce uncertainty currently associated with snowpack and snow water resource analysis by documenting and quantifying snow depth variability and snowpack evolution on relatively inaccessible slopes in complex terrain at high spatial and temporal resolutions.
","language":"English","publisher":"Copernicus Journals","doi":"10.5194/tc-16-4907-2022","usgsCitation":"Miller, Z., Peitzsch, E.H., Sproles, E.A., Birkeland, K.W., and Palomaki, R.T., 2022, Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain: The Cryosphere, v. 16, no. 12, p. 4907-4930, https://doi.org/10.5194/tc-16-4907-2022.","productDescription":"24 p.","startPage":"4907","endPage":"4930","ipdsId":"IP-139965","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":445693,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-16-4907-2022","text":"Publisher Index Page"},{"id":435598,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCIA1R","text":"USGS data release","linkHelpText":"2020 winter timeseries of UAS derived digital surface models (DSMs) from the Hourglass study site, Bridger Mountains, Montana, USA"},{"id":410274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bridger Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94,\n 45.84\n ],\n [\n -110.94,\n 45.830\n ],\n [\n -110.93,\n 45.83\n ],\n [\n -110.93,\n 45.84\n ],\n [\n -110.94,\n 45.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858562,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sproles, Eric A. 0000-0003-1245-1653","orcid":"https://orcid.org/0000-0003-1245-1653","contributorId":299760,"corporation":false,"usgs":false,"family":"Sproles","given":"Eric","email":"","middleInitial":"A.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":858563,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birkeland, Karl W.","contributorId":209943,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","email":"","middleInitial":"W.","affiliations":[{"id":38033,"text":"U.S.D.A. Forest Service National Avalanche Center, Bozeman, Montana, USA","active":true,"usgs":false}],"preferred":false,"id":858564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palomaki, Ross T. 0000-0002-3304-9914","orcid":"https://orcid.org/0000-0002-3304-9914","contributorId":299761,"corporation":false,"usgs":false,"family":"Palomaki","given":"Ross","email":"","middleInitial":"T.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":858565,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}