No abstract available.
","language":"English","publisher":"BioOne","doi":"10.1656/045.028.s1111","usgsCitation":"Nelson, S., McDonough MacKenzie, C., Morelli, T.L., Wason, J., Wentzell, B., Hovel, R.A., Hodgkins, G.A., Miller-Rushing, A.J., Miller, D., Tatko, S., Cross, A., and Pounch, M., 2022, Introduction: Climate change in the mountains of Maine and the Northeast: Northeastern Naturalist, v. 28, no. 11, p. ii-ix, https://doi.org/10.1656/045.028.s1111.","productDescription":"10 p.","startPage":"ii","endPage":"ix","ipdsId":"IP-130303","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":410695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.4848699643923,\n 47.607085121892226\n ],\n [\n -78.4848699643923,\n 41.01705361593531\n ],\n [\n -66.53681906604862,\n 41.01705361593531\n ],\n [\n -66.53681906604862,\n 47.607085121892226\n ],\n [\n -78.4848699643923,\n 47.607085121892226\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"28","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nelson, Sarah","contributorId":167199,"corporation":false,"usgs":false,"family":"Nelson","given":"Sarah","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":859426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDonough MacKenzie, Caitlin","contributorId":300107,"corporation":false,"usgs":false,"family":"McDonough MacKenzie","given":"Caitlin","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":859427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":859428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wason, Jay","contributorId":300108,"corporation":false,"usgs":false,"family":"Wason","given":"Jay","email":"","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":859429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wentzell, Bryan","contributorId":300109,"corporation":false,"usgs":false,"family":"Wentzell","given":"Bryan","email":"","affiliations":[{"id":65019,"text":"Maine Mountain Collaborative","active":true,"usgs":false}],"preferred":false,"id":859430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hovel, Rachel A.","contributorId":171740,"corporation":false,"usgs":false,"family":"Hovel","given":"Rachel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":859431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859432,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miller-Rushing, Abe J.","contributorId":189062,"corporation":false,"usgs":false,"family":"Miller-Rushing","given":"Abe","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":859433,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Miller, David","contributorId":300112,"corporation":false,"usgs":false,"family":"Miller","given":"David","affiliations":[{"id":65021,"text":"Rangeley Lakes Heritage Trust","active":true,"usgs":false}],"preferred":false,"id":859434,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tatko, Steve","contributorId":300113,"corporation":false,"usgs":false,"family":"Tatko","given":"Steve","email":"","affiliations":[{"id":65018,"text":"Appalachian Mountain Club","active":true,"usgs":false}],"preferred":false,"id":859435,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cross, Amanda","contributorId":300114,"corporation":false,"usgs":false,"family":"Cross","given":"Amanda","affiliations":[{"id":39965,"text":"Maine Department of Inland Fisheries and Wildlife","active":true,"usgs":false}],"preferred":false,"id":859436,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pounch, Mike","contributorId":300115,"corporation":false,"usgs":false,"family":"Pounch","given":"Mike","email":"","affiliations":[{"id":65022,"text":"Baxter State Park","active":true,"usgs":false}],"preferred":false,"id":859437,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70227289,"text":"70227289 - 2022 - Mesilla / Conejos-Médanos Basin: U.S.-Mexico transboundary water resources and research needs","interactions":[],"lastModifiedDate":"2022-01-25T17:42:14.142072","indexId":"70227289","displayToPublicDate":"2022-01-06T07:16:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Mesilla / Conejos-Médanos Basin: U.S.-Mexico transboundary water resources and research needs","docAbstract":"Although the Greater Caucasus Mountains have played a central role in absorbing late Cenozoic convergence between the Arabian and Eurasian plates, the orogenic architecture and the ways in which it accommodates modern shortening remain debated. Here, we addressed this problem using geologic mapping along two transects across the southern half of the western Greater Caucasus to reveal a suite of regionally coherent stratigraphic packages that are juxtaposed across a series of thrust faults, which we call the North Georgia fault system. From south to north within this system, stratigraphically repeated ~5–10-km-thick thrust sheets show systematically increasing bedding dip angles (<30° in the south to subvertical in the core of the range). Likewise, exhumation depth increases toward the core of the range, based on low-temperature thermochronologic data and metamorphic grade of exposed rocks. In contrast, active shortening in the modern system is accommodated, at least in part, by thrust faults along the southern margin of the orogen. Facilitated by the North Georgia fault system, the western Greater Caucasus Mountains broadly behave as an in-sequence, southward-propagating imbricate thrust fan, with older faults within the range progressively abandoned and new structures forming to accommodate shortening as the thrust propagates southward. We suggest that the single-fault-centric “Main Caucasus thrust” paradigm is no longer appropriate, as it is a system of faults, the North Georgia fault system, that dominates the architecture of the western Greater Caucasus Mountains.
Predictive modeling of submerged archaeological sites requires accurate sea-level predictions in order to reconstruct coastal paleogeography and associated geographic features that may have influenced the locations of occupation sites such as rivers and embayments. Earlier reconstructions of the paleogeography of parts of the western U.S. coast used an assumption of eustatic sea level, but this neglects the large spatial variations in relative sea level (RSL) associated with glacial isostatic adjustment (GIA) and tectonics. Subsequent work using a one-dimensional (1-D) solid Earth model showed that reconstructions that accounted for GIA result in significant differences from those based on eustatic sea level. However, these analyses neglected the complex three-dimensional (3-D) solid Earth structure associated with the Cascadia subduction zone that has also strongly influenced RSL along the Oregon-Washington (OR-WA) coast, requiring that the paleogeographic reconstructions must also account for this effect. Here we use RSL predictions from a 3-D solid Earth model that have been validated by RSL data to update previous paleogeographic reconstructions of the OR-WA coast for the last 12 kyr based on a 1-D solid Earth model. The large differences in the spatial variations in RSL on the OR-WA continental shelves predicted by the 3-D model relative to eustatic and 1-D models demonstrate that accurate reconstructions of coastal paleogeography for predictive modeling of submerged archaeological sites need to account for 3-D viscoelastic Earth structure in areas of complex tectonics.
The U.S. Geological Survey, in cooperation with DeKalb County Department of Watershed Management, established a long-term water-quantity and water-quality monitoring program in 2012 to monitor and analyze the hydrologic and water-quality conditions of 15 watersheds in DeKalb County, Georgia—an urban and suburban area located in north-central Georgia that includes the easternmost part of the City of Atlanta. This report synthesizes the watershed characteristics and monitoring data collected for the first 5 years of the program, 2012 through 2016. The study area was predominantly medium-density residential (43.9 percent), commercial/industrial/institutional (21.4 percent), forest/park/agriculture (13.6 percent), and high-density residential (11.5 percent) land uses. Land-surface slope averaged 8.7 percent, imperviousness averaged 25.3 percent, and population density averaged 2,936 people per square mile. Watershed imperviousness ranged from 8.7 to 36.6 percent.
In the study area for 2014 to 2016 (when streamflow data were available for all watersheds), runoff represented 40.9 percent of precipitation. Hydrograph separations indicated that 43 percent of runoff occurred as base flow, whereas the remainder occurred as stormflow. Higher watershed imperviousness was significantly related to higher amounts of runoff (Pearson product-moment correlation coefficient [r] = 0.517), higher runoff ratios (r = 0.646), and lower amounts (r = −0.637) and proportions (r = −0.898) of base-flow runoff. Stormwater best management practices have been implemented in the study watersheds; however, these practices do not appear to fully mitigate the effects of urban development and land use on stream hydrology.
Total copper, lead, and zinc concentrations in base-flow and stormflow samples exceeded the national recommended aquatic life criteria for chronic and acute conditions, respectively, to varying degrees. Escherichia coli density predictive regression models indicated that the U.S. Environmental Protection Agency’s Beach Action Value was exceeded at individual watersheds between 44.6 and 100 percent of the time. Exceedance of the Beach Action Value indicates possible unsafe conditions for primary contact recreation and could be used for timely notification of the potential health risks. Annual loads and yields were estimated for 15 constituents. Loads were typically higher for years with higher runoff while variations among watershed yields appear associated with watershed and land use characteristics. The lowest yields for almost all constituents occurred in the Stone Mountain Creek watershed—likely the result of the retention of sediment and reduction of nutrients in Stone Mountain Lake and two smaller downstream reservoirs within the watershed. The Little Stone Mountain Creek watershed also had some of the lowest yields for most constituents, likely due to the lack of many pollutant sources associated with its predominantly medium-density residential land use (95.5 percent), but had the highest total nitrate plus nitrite yields. The Intrenchment Creek watershed consistently had some of the highest yields across all constituents except for total nitrate plus nitrite. The high yields may be related to its high percentage of impervious area (36.0 percent) and high amount of heavily developed land use (high-density residential, 29.9 percent and commercial/industrial/institutional, 26.0 percent). Mean watershed constituent yields in this study were significantly higher than those from a similar analysis of 13 suburban to urban watersheds in adjacent Gwinnett County for 6 of the 10 constituents compared.
This study provides a thorough assessment of watershed characteristics, hydrology, and water-quality conditions of the 15 study watersheds and can be used to identify possible factors that affect runoff and water quality. Watershed managers can use these data and analyses to inform management decisions regarding the designated uses of streams, minimization of flooding, protection of aquatic habitats, and optimization of the effectiveness of best management practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215126","collaboration":"Prepared in cooperation with DeKalb County Department of Watershed Management","usgsCitation":"Aulenbach, B.T., Kolb, K., Joiner, J.K., and Knaak, A.E., 2022, Hydrology and water quality in 15 watersheds in DeKalb County, Georgia, 2012–16: U.S. Geological Survey Scientific Investigations Report 2021–5126, 105 p., https://doi.org/10.3133/sir20215126.","productDescription":"Report: xii, 105 p.; Data Release; Database","numberOfPages":"105","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-117184","costCenters":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":393756,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M6WCRH","text":"USGS data release","linkHelpText":"Streamwater constituent load data, models, and estimates for 15 watersheds in DeKalb County, Georgia, 2012–2016"},{"id":393757,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5126/sir20215126.pdf","text":"Report","size":"7.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5126"},{"id":393754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5126/coverthb.jpg"},{"id":502127,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112069.htm","linkFileType":{"id":5,"text":"html"}},{"id":393759,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5126/sir20215126.XML"},{"id":393758,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5126/images/"}],"country":"United States","state":"Georgia","county":"DeKalb County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-84.2707,33.955],[-84.2656,33.9482],[-84.2672,33.9473],[-84.2711,33.9459],[-84.2727,33.945],[-84.2716,33.9423],[-84.2699,33.9396],[-84.2676,33.936],[-84.2653,33.931],[-84.2601,33.9192],[-84.2596,33.9192],[-84.2562,33.9143],[-84.2523,33.9116],[-84.2511,33.9111],[-84.25,33.9107],[-84.2495,33.9107],[-84.2484,33.9103],[-84.2478,33.9103],[-84.2467,33.9098],[-84.2444,33.9085],[-84.2439,33.9076],[-84.2428,33.9076],[-84.2417,33.9071],[-84.2406,33.9072],[-84.2394,33.9063],[-84.2378,33.9049],[-84.2366,33.9045],[-84.2339,33.9031],[-84.2333,33.9031],[-84.2327,33.9027],[-84.2316,33.9018],[-84.2311,33.9013],[-84.2299,33.9005],[-84.2288,33.8986],[-84.2282,33.8977],[-84.2271,33.8959],[-84.2265,33.8955],[-84.226,33.8946],[-84.2254,33.8937],[-84.2248,33.8932],[-84.2243,33.8928],[-84.2226,33.8923],[-84.2215,33.891],[-84.2198,33.8892],[-84.2192,33.8883],[-84.217,33.886],[-84.2164,33.8851],[-84.2164,33.8842],[-84.2153,33.8842],[-84.2147,33.8838],[-84.2142,33.8833],[-84.2069,33.8761],[-84.2057,33.8748],[-84.2046,33.8734],[-84.2035,33.8725],[-84.2029,33.8721],[-84.2024,33.8721],[-84.2018,33.8721],[-84.2013,33.8721],[-84.2002,33.8717],[-84.1996,33.8717],[-84.1991,33.8717],[-84.1979,33.8712],[-84.1968,33.8708],[-84.1963,33.8703],[-84.1957,33.8699],[-84.1946,33.8694],[-84.1907,33.8672],[-84.1901,33.8668],[-84.1802,33.8646],[-84.1785,33.8633],[-84.1762,33.861],[-84.1751,33.8601],[-84.1745,33.8592],[-84.174,33.8583],[-84.1734,33.8578],[-84.1729,33.8578],[-84.1717,33.8574],[-84.1712,33.8574],[-84.1706,33.857],[-84.169,33.857],[-84.1684,33.857],[-84.1673,33.8565],[-84.1667,33.8565],[-84.1656,33.8557],[-84.164,33.8552],[-84.1612,33.8539],[-84.1606,33.8539],[-84.1595,33.8534],[-84.1584,33.8534],[-84.1573,33.853],[-84.1567,33.8526],[-84.1539,33.8485],[-84.1534,33.8485],[-84.1522,33.8476],[-84.1517,33.8467],[-84.1511,33.8462],[-84.15,33.8453],[-84.1466,33.8417],[-84.1461,33.8413],[-84.1416,33.8391],[-84.1383,33.8373],[-84.1371,33.8364],[-84.1366,33.8364],[-84.136,33.8359],[-84.1355,33.8355],[-84.1349,33.835],[-84.1343,33.8346],[-84.1338,33.8332],[-84.1338,33.8319],[-84.1338,33.831],[-84.1332,33.8305],[-84.1326,33.8296],[-84.1315,33.8287],[-84.1304,33.8274],[-84.1287,33.8256],[-84.1281,33.8256],[-84.127,33.8237],[-84.1264,33.8224],[-84.1259,33.8215],[-84.1248,33.8206],[-84.1231,33.8188],[-84.1225,33.8188],[-84.1214,33.8174],[-84.1208,33.8165],[-84.1208,33.8156],[-84.1202,33.8152],[-84.1197,33.8143],[-84.1191,33.8138],[-84.1186,33.8129],[-84.118,33.8125],[-84.1169,33.8111],[-84.1158,33.8098],[-84.1146,33.8084],[-84.1141,33.8075],[-84.1135,33.8075],[-84.113,33.8075],[-84.1124,33.8071],[-84.1118,33.8066],[-84.1113,33.8057],[-84.1107,33.8057],[-84.1102,33.8057],[-84.1096,33.8053],[-84.1091,33.8053],[-84.1085,33.8053],[-84.1079,33.8048],[-84.1068,33.8039],[-84.1057,33.8035],[-84.1052,33.8026],[-84.104,33.8017],[-84.1035,33.8012],[-84.1029,33.8013],[-84.1024,33.8013],[-84.1013,33.8008],[-84.1007,33.8008],[-84.099,33.7999],[-84.0985,33.7995],[-84.0979,33.799],[-84.0968,33.7972],[-84.0957,33.7963],[-84.094,33.7959],[-84.0934,33.795],[-84.0929,33.7941],[-84.0923,33.7941],[-84.0918,33.7927],[-84.0912,33.7923],[-84.0906,33.7914],[-84.0895,33.7909],[-84.0884,33.79],[-84.0873,33.7896],[-84.0867,33.7896],[-84.0862,33.7882],[-84.0845,33.7869],[-84.0822,33.7842],[-84.0806,33.7824],[-84.08,33.7815],[-84.0772,33.7788],[-84.075,33.777],[-84.0738,33.7761],[-84.0722,33.7756],[-84.0716,33.7752],[-84.0711,33.7752],[-84.0683,33.7747],[-84.0655,33.7743],[-84.0644,33.7739],[-84.0633,33.7734],[-84.0617,33.773],[-84.06,33.773],[-84.0583,33.7726],[-84.0578,33.7721],[-84.0572,33.7721],[-84.0567,33.7717],[-84.0561,33.7708],[-84.0555,33.7703],[-84.055,33.7699],[-84.0538,33.7685],[-84.0533,33.7681],[-84.0527,33.7672],[-84.0522,33.7667],[-84.0516,33.7667],[-84.0511,33.7667],[-84.0505,33.7663],[-84.0499,33.7654],[-84.0494,33.7654],[-84.0488,33.7658],[-84.0483,33.7658],[-84.0472,33.7654],[-84.0466,33.7645],[-84.0455,33.7636],[-84.0444,33.7627],[-84.0438,33.7627],[-84.0433,33.7622],[-84.0438,33.7618],[-84.0433,33.7609],[-84.0427,33.7604],[-84.0421,33.7595],[-84.0416,33.7586],[-84.041,33.7582],[-84.0399,33.7564],[-84.0393,33.7532],[-84.0393,33.7523],[-84.0382,33.7518],[-84.0365,33.7514],[-84.0365,33.75],[-84.0359,33.75],[-84.0359,33.7491],[-84.0359,33.7482],[-84.0353,33.7482],[-84.0326,33.7473],[-84.0318,33.7471],[-84.0386,33.7414],[-84.0572,33.7266],[-84.0641,33.7129],[-84.0711,33.6997],[-84.0744,33.6933],[-84.0867,33.6695],[-84.098,33.648],[-84.1023,33.6398],[-84.1157,33.6147],[-84.1169,33.6178],[-84.1186,33.6215],[-84.1192,33.6224],[-84.1197,33.6224],[-84.1208,33.6228],[-84.1214,33.6228],[-84.1225,33.6232],[-84.1236,33.6237],[-84.1236,33.6255],[-84.1242,33.626],[-84.1253,33.6264],[-84.1275,33.6268],[-84.128,33.6268],[-84.1286,33.6277],[-84.1286,33.6291],[-84.1286,33.6305],[-84.1292,33.6314],[-84.1298,33.6314],[-84.1303,33.6314],[-84.1309,33.6314],[-84.1314,33.6314],[-84.1342,33.6313],[-84.1347,33.6309],[-84.1353,33.6309],[-84.1358,33.6309],[-84.1364,33.6308],[-84.1369,33.6304],[-84.1375,33.6304],[-84.138,33.6304],[-84.1386,33.6304],[-84.1391,33.6308],[-84.1397,33.6308],[-84.1402,33.6313],[-84.1408,33.6313],[-84.1408,33.6322],[-84.1414,33.6326],[-84.1408,33.6331],[-84.1408,33.634],[-84.1408,33.6349],[-84.1425,33.6367],[-84.1442,33.6367],[-84.1447,33.6371],[-84.1453,33.6371],[-84.1469,33.638],[-84.1481,33.6385],[-84.1514,33.6398],[-84.1519,33.6407],[-84.1542,33.6452],[-84.1548,33.6461],[-84.1559,33.6457],[-84.1586,33.642],[-84.1613,33.6383],[-84.1618,33.6379],[-84.1635,33.6383],[-84.1646,33.6388],[-84.1651,33.6383],[-84.1657,33.6383],[-84.1668,33.6387],[-84.1685,33.6387],[-84.169,33.6392],[-84.1696,33.6396],[-84.1701,33.6396],[-84.1707,33.64],[-84.1713,33.6405],[-84.1724,33.6405],[-84.1729,33.6409],[-84.1729,33.6418],[-84.1702,33.646],[-84.1758,33.6464],[-84.1873,33.6463],[-84.1995,33.6466],[-84.2254,33.6468],[-84.2252,33.6308],[-84.2456,33.6306],[-84.2453,33.647],[-84.2497,33.6469],[-84.253,33.6469],[-84.2541,33.6469],[-84.2673,33.6472],[-84.2817,33.6475],[-84.3004,33.6478],[-84.3126,33.6481],[-84.3501,33.6481],[-84.3495,33.6799],[-84.3497,33.6886],[-84.3493,33.7],[-84.3494,33.7377],[-84.3491,33.75],[-84.349,33.7754],[-84.3488,33.8009],[-84.3488,33.8291],[-84.3482,33.8609],[-84.3483,33.8964],[-84.3484,33.9019],[-84.3478,33.9314],[-84.3476,33.951],[-84.3479,33.9682],[-84.3379,33.9706],[-84.3374,33.9711],[-84.3368,33.9706],[-84.3251,33.9671],[-84.324,33.9672],[-84.3235,33.9676],[-84.3224,33.9676],[-84.3213,33.9676],[-84.3202,33.9677],[-84.3196,33.9672],[-84.3179,33.9663],[-84.3168,33.9663],[-84.3085,33.9632],[-84.3068,33.9623],[-84.3057,33.9614],[-84.3046,33.961],[-84.3029,33.9606],[-84.3007,33.9597],[-84.3001,33.9592],[-84.2996,33.9597],[-84.2984,33.9597],[-84.2973,33.9602],[-84.2962,33.9602],[-84.2951,33.9598],[-84.294,33.9598],[-84.2907,33.9584],[-84.289,33.958],[-84.2873,33.9576],[-84.2857,33.9571],[-84.2845,33.9567],[-84.2806,33.9563],[-84.279,33.9576],[-84.2768,33.9581],[-84.2762,33.9545],[-84.2707,33.955]]]},\"properties\":{\"name\":\"DeKalb\",\"state\":\"GA\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive, Suite 500
Norcross, GA 30093
The Des Moines River alluvial aquifer is an important source of water for Des Moines Water Works, the municipal water utility that provides residential and commercial water resources to the residents of Des Moines, Iowa, and surrounding municipalities. As an initial step in developing a better understanding of the groundwater resources of the Des Moines River alluvial aquifer, the U.S. Geological Survey constructed a steady-state numerical groundwater flow model in cooperation with Des Moines Water Works to simulate water-table elevations in the Des Moines River alluvial aquifer near Prospect Park in Des Moines under winter low-flow conditions.
A simple conceptual model consisting of a hydrogeologic framework, water budget, and inferred water-table elevation map was developed for the model area. The inferred water-table elevation map was constructed based on general knowledge of hydrogeology within the model area and was used to set calibration targets for numerical model calibration. A steady-state numerical model was constructed based on the conceptual model using MODFLOW-NWT to simulate an area of about 15 square kilometers near Prospect Park in Des Moines. Parameter ESTimation software was used for model calibration to assess and optimize performance of the horizontal hydraulic conductivity and recharge parameters. The numerical groundwater flow model and supporting data are available in the USGS data release associated with this report, which contains the model archive.
Performance of the calibrated steady-state model was assessed by comparing observed and simulated water-table elevations, as well as estimated and simulated contributions to streamflow within the model area. The difference between observed water-table elevations and simulated water-table elevations was −0.1 meter at the majority of calibration targets, with the negative value indicating an overestimation of the simulated water-table elevation value compared to the observed water-table elevation value, and the root mean square error was 0.13 meter, which represents about 20 percent of the difference in observed water-table elevations. The simulated value of contributions to streamflow within the model area was considered similar to the estimated value, increasing confidence in the ability of the model to accurately represent the groundwater flow system in the Des Moines River alluvial aquifer in the model area during winter low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211110","collaboration":"Prepared in cooperation with Des Moines Water Works","usgsCitation":"FitzGerald, K.M., Ha, W.S., Haj, A.E., Gruhn, L.R., Bristow, E.L., and Weber, J.R., 2022, A steady-state groundwater flow model for the Des Moines River alluvial aquifer near Prospect Park, Des Moines, Iowa: U.S. Geological Survey Open-File Report 2021–1110, 20 p., https://doi.org/10.3133/ofr20211110.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130288","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501532,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112070.htm","linkFileType":{"id":5,"text":"html"}},{"id":393916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1110/ofr20211110.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1110"},{"id":393915,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1110/coverthb.jpg"},{"id":393917,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":393918,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Prospect Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.65175247192383,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.611463744813506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The Niobrara River of northern Nebraska is a valuable water resource that sustains irrigated agriculture and recreation, as well as a diverse ecosystem. Large-quantity withdrawals from the source aquifer system have the potential to reduce the flow into the river and to adversely affect the free-flowing condition of the Niobrara National Scenic River (NSR). Therefore, to understand the magnitude and characteristics of those flows, the U.S. Geological Survey (USGS), in cooperation with the National Park Service, began a study to quantify seepage gains/losses along the eastern half of the Niobrara NSR and to create a map characterizing the base-flow recession time constant (tau) in the Niobrara NSR study area.
In 2016, a seepage study was completed to quantify seepage gains/losses along the eastern half of the Niobrara NSR. The seepage study results indicated that the main-stem streamflow on the Niobrara River increases 375 cubic feet per second (ft3/s) in the 39.9-mile study reach (river mile 119.3 to river mile 79.4). Although most of the streamflow increases are attributed to tributary inflows (297 ft3/s, 79 percent), 78 ft3/s are attributed to seepage gains within the reach. Seepage rates in the study reach ranged from 1.41 cubic feet per second per mile ([ft3/s]/mi) to 2.56 (ft3/s)/mi, with a mean seepage rate of 2 (ft3/s)/mi.
Tau values were calculated at 10 sites in the Niobrara NSR study area, and kriging geostatistical techniques were used to develop a contour map to estimate tau values at locations where streamflow was not measured. The minimum tau value was 12.1 days at Willow Creek at Atwood Road near Carns, Nebraska (USGS station 06463670), and the maximum value was 45.5 days at Tyler Falls at Fort Niobrara National Wildlife Refuge near Valentine, Nebr. (USGS station 06461150).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215102","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Strauch, K.R., and Soenksen, P.J., 2022, Main-stem seepage and base-flow recession time constants in the Niobrara National Scenic River Basin, Nebraska, 2016–18: U.S. Geological Survey Scientific Investigations Report 2021–5102, 17 p., https://doi.org/10.3133/sir20215102.","productDescription":"Report: vi, 17 p.; Data Release; Dataset","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125025","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":393921,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PDP1BI","text":"USGS data release","linkHelpText":"Datasets used to map the base-flow recession time constants in the Niobrara National Scenic River in Nebraska, 2016–18"},{"id":502117,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112068.htm","linkFileType":{"id":5,"text":"html"}},{"id":393922,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5102/sir20215102.pdf","text":"Report","size":"2.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5102"},{"id":393919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5102/coverthb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Niobrara National Scenic River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.974609375,\n 41.64007838467894\n ],\n [\n -100.08544921874999,\n 41.64007838467894\n ],\n [\n -100.08544921874999,\n 42.956422511073335\n ],\n [\n -103.974609375,\n 42.956422511073335\n ],\n [\n -103.974609375,\n 41.64007838467894\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The lesser prairie-chicken (Tympanuchus pallidicinctus) is a species of conservation concern on the Southern High Plains of Texas and New Mexico, USA. Because fragmentation and isolation have increased since pre-settlement, dispersal through this heterogeneous landscape may be constrained, with serious implications for conservation and management of this species. Our objectives were to quantify landscape connectivity for lesser prairie-chickens within a patch network of potentially isolated leks (breeding display grounds), and examine effects of land use change on modeled lesser prairie-chicken movements through the landscape. We used graph theory to quantify structural landscape connectivity and circuit theory to quantify functional landscape connectivity for lesser prairie-chickens in the Sand Shinnery Oak Prairie Ecoregion of the Southern High Plains. There was a high degree of clustering among leks (n = 1,023 leks), with a 41.9-km coalescence distance of the network. We identified 3 leks as cutpoints within the network, meaning if the habitat patches containing these leks were fragmented, the remaining leks would become isolated from each other. We also identified several leks that were important for maintaining overall population connectivity for lesser prairie-chickens on the Southern High Plains. Conservation Reserve Program land was important for maintaining connectivity among leks to the north and west of the main lek core area located in New Mexico, but wind energy development constrained pathways to the north and south of this main lek core area. Our results suggest that landscape connectivity was reduced by row-crop agriculture and energy production and facilitated by the Conservation Reserve Program within the Sand Shinnery Oak Prairie Ecoregion.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22146","usgsCitation":"Schilder, L., Heintzman, L., Mcintyre, N., Harryman, S., Hagen, C., Martin, R.E., Boal, C.W., and Grisham, B., 2022, Structural and functional landscape connectivity for lesser prairie-chickens in the Sand Shinnery Oak Prairie Ecoregion: Journal of Wildlife Management, v. 86, no. 1, e22146, 17 p., https://doi.org/10.1002/jwmg.22146.","productDescription":"e22146, 17 p.","ipdsId":"IP-123187","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433460,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Texas","otherGeospatial":"Sand Shinnery Oak Prairie ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.08205634648147,\n 35.20735590338778\n ],\n [\n -105.08205634648147,\n 32.04246611821024\n ],\n [\n -101.86163388662169,\n 32.04246611821024\n ],\n [\n -101.86163388662169,\n 35.20735590338778\n ],\n [\n -105.08205634648147,\n 35.20735590338778\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Schilder, L.J.","contributorId":341785,"corporation":false,"usgs":false,"family":"Schilder","given":"L.J.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heintzman, L.J.","contributorId":341786,"corporation":false,"usgs":false,"family":"Heintzman","given":"L.J.","email":"","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mcintyre, N.E.","contributorId":215186,"corporation":false,"usgs":false,"family":"Mcintyre","given":"N.E.","email":"","affiliations":[{"id":39194,"text":"Department of Biological Sciences, Texas Tech University, Lubbock, TX 79409-3131 USA","active":true,"usgs":false}],"preferred":false,"id":908895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harryman, S.","contributorId":341788,"corporation":false,"usgs":false,"family":"Harryman","given":"S.","email":"","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":908896,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hagen, C.A.","contributorId":276129,"corporation":false,"usgs":false,"family":"Hagen","given":"C.A.","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":908897,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martin, R. E.","contributorId":138911,"corporation":false,"usgs":false,"family":"Martin","given":"R.","email":"","middleInitial":"E.","affiliations":[{"id":12575,"text":"Ecological Associates, Inc, Jensen Beach, Florida","active":true,"usgs":false}],"preferred":false,"id":908898,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908899,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Grisham, Blake A.","contributorId":341793,"corporation":false,"usgs":false,"family":"Grisham","given":"Blake A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":908900,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227175,"text":"sir20215125 - 2022 - Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20","interactions":[],"lastModifiedDate":"2026-04-02T20:01:25.048208","indexId":"sir20215125","displayToPublicDate":"2022-01-05T10:55:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5125","displayTitle":"Continuous Monitoring of Nutrient and Sediment Loads from the Des Plaines River at Route 53 at Joliet, Illinois, Water Years 2018–20","title":"Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20","docAbstract":"The Des Plaines River in southern Wisconsin and northern Illinois is the principal conduit for the discharge of wastewater effluent and stormwater runoff from the greater Chicago metropolitan area. In November 2017, the U.S. Geological Survey, in cooperation with the Metropolitan Water Reclamation District of Greater Chicago, installed a continuous monitoring station to measure water quality and streamflow in the Des Plaines River at Joliet, Illinois. Surrogate models encompassing continuous data and discrete water-quality samples were used to estimate loads of nitrate, total phosphorus, and suspended sediment. Comparisons to other major rivers in Illinois show that the Des Plaines River is a substantial contributor to statewide loading estimates for nitrate and total phosphorus but only a minor contributor to suspended sediment. Future loading estimates of total phosphorus could include more research into the effects of combined sewage overflows because these effects likely increased model uncertainty. The results in this report document current loadings and provide a baseline from which to assess future water-quality management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215125","collaboration":"Prepared in cooperation with Metropolitan Water Reclamation District of Greater Chicago","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"Peake, C.S., and Hodson, T.O., 2022, Continuous monitoring of nutrient and sediment loads from the Des Plaines River at Route 53 at Joliet, Illinois, water years 2018–20 (ver. 1.1, February 2022): U.S. Geological Survey Scientific Investigations Report 2021–5125, 15 p., https://doi.org/10.3133/sir20215125.","productDescription":"Report: vii, 15 p.; Data Release; Database","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-129874","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":393780,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M4BH1C","text":"USGS data release","linkHelpText":"Modeled nutrient and sediment concentrations from the Des Plaines River at Route 53 at Joliet, Illinois, based on continuous monitoring from October 1, 2017, through September 30, 2020"},{"id":396575,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5125/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":393783,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5125/images/"},{"id":393782,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5125/sir20215125.XML"},{"id":393781,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5125/sir20215125.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5125"},{"id":393778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5125/coverthb2.jpg"},{"id":502126,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112067.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois","city":"Joliet","otherGeospatial":"Des Plaines River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.25,\n 41.5\n ],\n [\n -87.5,\n 41.5\n ],\n [\n -87.5,\n 42.25\n ],\n [\n -88.25,\n 42.25\n ],\n [\n -88.25,\n 41.5\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 5, 2022; Version 1.1: February 28, 2022","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
We use particle tracking to determine contributing areas (CAs) to wells for transient flow models that simulate cyclic domestic pumping and extreme recharge events in a small synthetic watershed underlain by dipping sedimentary rocks. The CAs consist of strike-oriented bands at locations where the water table intersects high-hydraulic conductivity beds, and from which groundwater flows to the pumping well. Factors that affect the size and location of the CAs include topographic flow directions, rock dip direction, cross-bed fracture density, and position of the well relative to streams. For an effective fracture porosity (ne) of 10-4, the fastest advective travel times from CAs to wells are only a few hours. These results indicate that wells in this type of geologic setting can be highly vulnerable to contaminants or pathogens flushed into the subsurface during extreme recharge events. Increasing ne to 10-3 results in modestly smaller CAs and delayed well vulnerability due to slower travel times. CAs determined for steady-state models of the same setting, but with long-term average recharge and pumping rates, are smaller than CAs in the models with extreme recharge. Also, the earliest-arriving particles arrive at the wells later in the steady-state models than in the extreme-recharge models. The results highlight the importance of characterizing geologic structure, simulating plausible effective porosities, and simulating pumping and recharge transience when determining CAs in fractured rock aquifers to assess well vulnerability under extreme precipitation events.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.13169","usgsCitation":"Tiedeman, C.R., and Shapiro, A.M., 2022, Contributing areas to domestic wells in dipping sedimentary rocks under extreme recharge events: Groundwater, v. 60, no. 4, p. 460-476, https://doi.org/10.1111/gwat.13169.","productDescription":"17 p.","startPage":"460","endPage":"476","ipdsId":"IP-128281","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":449254,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gwat.13169","text":"Publisher Index Page"},{"id":436015,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93DZ84P","text":"USGS data release","linkHelpText":"MODFLOW-NWT and MODPATH6 models used to simulate contributing areas in hypothetical sedimentary rock aquifers under extreme recharge events"},{"id":394014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Tiedeman, Claire R. 0000-0002-0128-3685 tiedeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0128-3685","contributorId":196777,"corporation":false,"usgs":true,"family":"Tiedeman","given":"Claire","email":"tiedeman@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":830286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shapiro, Allen M. 0000-0002-6425-9607 ashapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-6425-9607","contributorId":2164,"corporation":false,"usgs":true,"family":"Shapiro","given":"Allen","email":"ashapiro@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":830287,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227317,"text":"70227317 - 2022 - Disease and secondary sexual traits: Effects of pneumonia on horn size of bighorn sheep","interactions":[],"lastModifiedDate":"2022-02-15T16:23:37.652149","indexId":"70227317","displayToPublicDate":"2022-01-05T07:39:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Disease and secondary sexual traits: Effects of pneumonia on horn size of bighorn sheep","docAbstract":"Secondary sexual traits (e.g., horns and antlers) have ecological and evolutionary importance and are of management interest for game species. Yet, how these traits respond to emerging threats like infectious disease remains underexplored. Infectious pneumonia threatens bighorn sheep (Ovis canadensis) populations across North America and we hypothesized it may also reduce horn growth in male sheep. We assess the effect of pneumonia on horn size in male bighorn sheep using 12 herd datasets from across the western United States that had horn growth and disease data. Disease resulted in 12–35% reduction in increment (yearly) length and 3–13% reduction in total horn length in exposed individuals. The disease effect was prolonged when pathogens continued to circulate in sheep populations. Further, disease likely delays the age at which horns reach ¾-curl and prevents achievement of full-curl. This is further evidenced with 6 of the 12 herds experiencing an increase in average age at harvest following die-off events.
Calcareous nannofossil assemblages from middle to upper Eocene sediments of the western Tarim Basin indicate two important episodes of marine incursion into the basin. The first episode represents a period of shallowing upward in the Wulagen Formation, which is dated as Zone CNE13 (Lutetian) by the co-occurrence of Discoaster bifax, Chiasmolithus solitus, and common Reticulofenestra umbilicus. The presence of a diverse assemblage of discoasters in the basal Wulagen suggests deposition occurred in oligotrophic, warm water with a connection to the open ocean. Progressive shallowing over time led to the formation of a restricted basin in which only Coccolithus pelagicus could survive. The second major episode of marine incursion is preserved in the middle part of the Bashibulake Formation, which is dated as Zone CNE17 (Bartonian/Priabonian) based on the presence of common Cribrocentrum erbae. The interval between the two marine incursions was dominated by subaerial exposure and evaporation, resulting in the deposition of gypsum at the top of the Wulagen Formation. Although maximum regression at the end of the Lutetian is followed by sea-level rise at the beginning of the Bartonian globally, it is clear that local tectonics played a crucial role in regional marine incursions into the Tarim Basin during deposition of the Wulagen Formation.
Widespread amphibian declines were well documented at the end of the 20th century, raising concerns about the need to identify individual and interactive contributors to this global trend. At the same time, there was growing interest in the use of amphibians as ecological indicators. In the United States, wetland and amphibian monitoring programs were launched in some national parks as a necessary first step to evaluating the status and trends of amphibian populations within some of North America’s most protected areas. In Grand Teton and Yellowstone national parks, a multi-species amphibian monitoring program was launched by many of the authors in 2006 and continues to this day. This Viewpoint Article serves as a self-evaluation of our journey from conception through implementation of an ongoing, long-term monitoring program. This self-evaluation should provide a framework and guidance for other monitoring programs. We address whether we are fulfilling the program’s main objective of describing status and trends of the four amphibian species, discuss how a one-size-fits-all monitoring approach does not serve all species equally, and describe opportunities to bolster our core work using emerging statistical approaches and thoughtful integration of remote sensing and molecular tools. We also describe how the data generated over the program’s first 15 years have been useful beyond our initial goal of characterizing status and trend. Notably, our integration of climate datasets has allowed us to describe wetland and species-specific amphibian responses to variations in climate drivers. Documenting climate links to amphibian occurrence and their primary habitats has allowed us to identify which species, habitat types, and subregions within this large, protected landscape are most vulnerable to anticipated climate change. Recognizing that tools and threats change over time, it will be important to adapt our original monitoring design to maximize opportunities and use of resulting information. Maintaining engagement by multiple stakeholders and expanding our funding portfolio will also be necessary to sustain our program into the future. Finally, collaboration has become standard for long-term, cross-jurisdictional, landscape-scale monitoring. We argue that collaborative monitoring facilitates resource sharing, leveraging of limited funds, completion of work, and mutual learning. Such collaboration also increases the efficacy of conservation.
In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.
Yasur volcano, Vanuatu is a continuously active open-vent basaltic-andesite stratocone with persistent and long-lived eruptive activity. We present results from a seismo-acoustic field experiment at Yasur, providing locally dense broad-band seismic and infrasonic network coverage from 2016 July 27 to August 3. We corroborate our seismo-acoustic observations with coincident video data from cameras deployed at the crater and on an unoccupied aircraft system (UAS). The waveforms contain a profusion of signals reflecting Yasur’s rapidly occurring and persistent explosive activity. The typical infrasonic signature of Yasur explosions is a classic short-duration and often asymmetric explosion waveform characterized by a sharp compressive onset and wideband frequency content. The dominant seismic signals are numerous repetitive very-long-period (VLP) signals with periods of ∼2–10 s. The VLP seismic events are ‘high-rate’, reoccurring near-continuously throughout the data set with short interevent times (∼20–60 s). We observe variability in the synchronization of seismic VLP and acoustic sources. Explosion events clearly delineated by infrasonic waveforms are underlain by seismic VLPs. However, strong seismic VLPs also occur with only a weak infrasonic expression. Multiplet analysis of the seismic VLPs reveals a systematic progression in the seismo-acoustic source decoupling. The same dominant seismic VLP multiplet occurs with and without surficial explosions and infrasound, and these transitions occur over a timescale of a few days during our field campaign. We subsequently employ template matching, stacking, and full-waveform inversion to image the source mechanism of the dominant VLP multiplet. Inversion of the dominant VLP multiplet stack points to a composite source consisting of either a dual-crack (plus forces) or pipe-crack (plus forces) mechanism. The derived mechanisms correspond to a point-source directly beneath the summit vents with centroid depths in the range ∼900–1000 m below topography. All mechanisms suggest a northeast trending crack dipping relatively shallowly to the northwest and indicate a VLP source centroid and mechanism controlled by a stable structural geologic feature beneath Yasur. We interpret the results in the framework of gas slug ascent through the conduit responsible for Yasur explosions. The VLP mechanism and timing with infrasound (when present) are explained by a shallow-buffered top-down model in which slug ascent is relatively aseismic until reaching the base of a shallow section. Slug disruption in this shallow zone triggers a pressure disturbance that propagates downward and couples at the conduit base (VLP centroid). If the shallow section is open, an explosion propagates to the surface, producing infrasound. In the case of (the same multiplet) VLPs occurring without surficial explosions and weak or no infrasound, the decoupling of the dominant VLPs at ∼900–1000 m depth from surficial explosions and infrasound strongly indicates buffering of the terminal slug ascent. This buffering could be achieved by a variety of conditions at or directly beneath the vents, such as a high-viscosity layer of crystal-rich magma, a debris cap from backfill, a foam layer, or a combination of these. The dominant VLP at Yasur captured by our experiment has a source depth and mechanism separated from surface processes and is stable over time.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab533","usgsCitation":"Matoza, R.S., Chouet, B.A., Jolly, A., Dawson, P.B., Fitzgerald, R.H., Kennedy, B.M., Fee, D., Iezzi, A., Kilgour, G.N., Garaebiti, E., and Cevuard, S., 2022, High-rate very-long-period seismicity at Yasur volcano, Vanuatu: source mechanism and decoupling from surficial explosions and infrasound: Geophysical Journal International, v. 230, no. 1, p. 392-426, https://doi.org/10.1093/gji/ggab533.","productDescription":"35 p.","startPage":"392","endPage":"426","ipdsId":"IP-135455","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449266,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggab533","text":"Publisher Index Page"},{"id":413341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Yasur volcano, Vanuatu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 169.33766678462047,\n -19.539368583023972\n ],\n [\n 169.33766678462047,\n -19.662219083395883\n ],\n [\n 169.49415550272943,\n -19.662219083395883\n ],\n [\n 169.49415550272943,\n -19.539368583023972\n ],\n [\n 169.33766678462047,\n -19.539368583023972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"230","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Matoza, Robin S","contributorId":215528,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":864922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chouet, Bernard A","contributorId":302631,"corporation":false,"usgs":false,"family":"Chouet","given":"Bernard","email":"","middleInitial":"A","affiliations":[{"id":65522,"text":"Arzier-Le Muids","active":true,"usgs":false}],"preferred":false,"id":864923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jolly, A.D. 0000-0003-1020-9062","orcid":"https://orcid.org/0000-0003-1020-9062","contributorId":296487,"corporation":false,"usgs":true,"family":"Jolly","given":"A.D.","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":864924,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dawson, Phillip B. 0000-0003-4065-0588 dawson@usgs.gov","orcid":"https://orcid.org/0000-0003-4065-0588","contributorId":206751,"corporation":false,"usgs":true,"family":"Dawson","given":"Phillip","email":"dawson@usgs.gov","middleInitial":"B.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":864925,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzgerald, Rebecca H","contributorId":302632,"corporation":false,"usgs":false,"family":"Fitzgerald","given":"Rebecca","email":"","middleInitial":"H","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":864926,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kennedy, Ben M. 0000-0001-7235-6493","orcid":"https://orcid.org/0000-0001-7235-6493","contributorId":270276,"corporation":false,"usgs":false,"family":"Kennedy","given":"Ben","email":"","middleInitial":"M.","affiliations":[{"id":37172,"text":"University of Canterbury","active":true,"usgs":false}],"preferred":false,"id":864927,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fee, David 0000-0002-0936-9977","orcid":"https://orcid.org/0000-0002-0936-9977","contributorId":267231,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":13097,"text":"Geophysical Institute, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":864928,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":864929,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kilgour, Geoff N","contributorId":302633,"corporation":false,"usgs":false,"family":"Kilgour","given":"Geoff","email":"","middleInitial":"N","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":864930,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Garaebiti, E.","contributorId":302634,"corporation":false,"usgs":false,"family":"Garaebiti","given":"E.","email":"","affiliations":[{"id":65523,"text":"Vanuatu Meteorology and Geohazards Department","active":true,"usgs":false}],"preferred":false,"id":864931,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cevuard, Sandrine","contributorId":296494,"corporation":false,"usgs":false,"family":"Cevuard","given":"Sandrine","email":"","affiliations":[{"id":64079,"text":"Vanuatu Meteorology and Geohazards Department, Port Vila, Vanuatu","active":true,"usgs":false}],"preferred":false,"id":864932,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70254806,"text":"70254806 - 2022 - Climatic drivers and ecological impacts of a rapid range expansion by non-native smallmouth bass","interactions":[],"lastModifiedDate":"2024-06-10T16:36:37.181839","indexId":"70254806","displayToPublicDate":"2022-01-04T11:28:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Climatic drivers and ecological impacts of a rapid range expansion by non-native smallmouth bass","docAbstract":"Smallmouth bass (Micropterus dolomieu) are a globally introduced fish species that have experienced widespread range expansions in recent decades and which can have deleterious effects on native fish communities. Rapidly assessing their expansions will aid conservation and management actions geared towards controlling their spread and mitigating their impacts. Smallmouth bass have recently experienced a rapid upstream expansion in a Great Plains river (Laramie River, Wyoming, USA), which provided an opportunity to evaluate the drivers and impacts of this expansion by using a modified before-after, control-impact (BACI) design. Our objectives were to test whether climatic drivers (temperature, precipitation, flow) were related to this range expansion and subsequent effects of the expansion on native fish communities. Smallmouth bass population size in Grayrocks Reservoir increased following a climatically extreme wet year, with statistically extreme amounts of spring-time and June precipitation creating high discharge events that coincided with the upstream expansion. Unlike previous studies highlighting the invasive nature of smallmouth bass, the modified BACI analysis revealed no declines in species richness induced by the expansion. However, there was evidence that native small-bodied minnow species (family Leuciscidae) declined in relative abundance and that community-level and species-level trophic niches were compressed for invaded sites. Our findings provide important insight into how climatic extremes can prompt biological invasions that can alter community composition and food web structure even if local extirpations do not occur.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10530-021-02724-z","usgsCitation":"Kirk, M.A., Maitland, B., Hickerson, B.T., Walters, A.W., and Rahel, F., 2022, Climatic drivers and ecological impacts of a rapid range expansion by non-native smallmouth bass: Biological Invasions, v. 24, p. 1311-1326, https://doi.org/10.1007/s10530-021-02724-z.","productDescription":"16 p.","startPage":"1311","endPage":"1326","ipdsId":"IP-130981","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467207,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":429777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Laramie River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.4233646467736,\n 42.345457333880034\n ],\n [\n -105.45001084859337,\n 42.345457333880034\n ],\n [\n -105.45001084859337,\n 41.907720974986404\n ],\n [\n -104.4233646467736,\n 41.907720974986404\n ],\n [\n -104.4233646467736,\n 42.345457333880034\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2022-01-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirk, Mark A.","contributorId":337682,"corporation":false,"usgs":false,"family":"Kirk","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":902615,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maitland, Bryan M.","contributorId":337683,"corporation":false,"usgs":false,"family":"Maitland","given":"Bryan M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":902616,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hickerson, Brian T.","contributorId":337684,"corporation":false,"usgs":false,"family":"Hickerson","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":902617,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rahel, Frank J.","contributorId":337685,"corporation":false,"usgs":false,"family":"Rahel","given":"Frank J.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":902618,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227180,"text":"70227180 - 2022 - Landscape and stocking effects on population genetics of Tennessee Brook Trout","interactions":[],"lastModifiedDate":"2022-03-28T16:36:37.268059","indexId":"70227180","displayToPublicDate":"2022-01-04T10:26:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Landscape and stocking effects on population genetics of Tennessee Brook Trout","docAbstract":"Throughout their range, Brook Trout (Salvelinus fontinalis) occupy thousands of disjunct drainages with varying levels of disturbance, which presents substantial challenges for conservation. Within the southern Appalachian Mountains, fragmentation and genetic drift have been identified as key threats to the genetic diversity of the Brook Trout populations. In addition, extensive historic stocking of domestic lineages of Brook Trout to augment fisheries may have eroded endemic diversity and impacted locally adapted populations. We used 12 microsatellite loci to describe patterns of genetic diversity within 108 populations of wild Brook Trout from Tennessee and used linear models to explore the impacts of land use, drainage area, and hatchery stockings on metrics of genetic diversity, effective population size, and hatchery introgression. We found levels of within-population diversity varied widely, although many populations showed very limited diversity. The extent of hatchery introgression also varied across the landscape, with some populations showing high affinity to hatchery lineages and others appearing to retain their endemic character. However, we found relatively weak relationships between genetic metrics and landscape characteristics, suggesting that contemporary landscape variables are not strongly related to observed patterns of genetic diversity. We consider this result to reflect both the complex history of these populations and the challenges associated with accurately defining drainages for each population. Our study highlights the importance of genetic data to guide management decisions, as complex processes interact to shape the genetic structure of populations and make it difficult to infer the status of unsampled populations.
The Klamath Mountains gold province is the second most important historical producer in California, having produced more than 7 Moz of gold from both lode and placer sources. Hydrothermal muscovite grains from gold-bearing veins provide the first 40Ar/39Ar age constraints indicative of a protracted period of mineralization in the Klamath Mountains. The data indicate that the window for orogenic gold mineralization in the Klamath Mountains was from ∼160–140 Ma, coinciding in age with the oldest orogenic gold deposits in the Sierra Nevada foothills province to the south, although mineralization continued in the latter along the Mother Lode belt for another 20–30 million years. Despite the broad age overlap between hydrothermal activity and magmatism, the former relates to a regional thermal event, and there is no genetic link between the two. Instead, a correlation exists between the timing of lode gold formation and discrete tectonic events, changes in stress regime, and fault movement. The maximum age corresponds to a major plate reorganization in the Pacific basin and initial gold mineralization continuing into the Sierra Nevada foothills gold province to the south. The minimum age corresponds to the westerly lateral offset of the Klamath Mountains from the Sierra Nevada and the then-active arc, marking the termination of both magmatism and gold-producing hydrothermal activity in the Klamath Mountains. Lode gold mineralization in the Klamath Mountains is compatible with a crustal source of metals and fluids that were released during metamorphic devolatilization and focused via hydrothermal fluid flow along regional faults.
The seismic potential of the Lesser Antilles megathrust remains poorly known, despite the potential hazard it poses to numerous island populations and its proximity to the Americas. As it has not produced any large earthquakes in the instrumental era, the megathrust is often assumed to be aseismic. However, historical records of great earthquakes in the 19th century and earlier, which were most likely megathrust ruptures, demonstrate that the subduction is not entirely aseismic. Recent occurrences of giant earthquakes in areas where such events were previously thought to be improbable have illustrated the importance of critically evaluating the seismic potential of other “low-hazard” subduction zones, such as the Lesser Antilles.
Using the method of coral microatoll paleogeodesy developed in Sumatra, we examine 20th-century vertical deformation on the forearc islands of the Lesser Antilles and model the underlying strain accumulation on the megathrust. Our data indicate that the eastern coasts of the forearc islands have been subsiding by up to ∼8 mm/yr relative to sites closer to the arc, suggesting that on the time scale of the 20th century, a portion of the megathrust just east of the forearc islands has been locked. Our findings are in contrast to recent models based on satellite geodesy that suggest little or no strain accumulation anywhere along the Lesser Antilles megathrust. This discrepancy is potentially explained by the different time scales of measurement, as recent studies elsewhere have indicated that interseismic coupling patterns may vary on decadal time scales and that century-scale or longer records are required to fully assess seismic potential. The accumulated strain we have detected will likely be released in future megathrust earthquakes, uplifting previously subsiding areas and potentially causing widespread damage from strong ground motion and tsunami waves.
Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2–7 metrics were compared for three measures of performance. After ranking the best-performing MMIs, we selected the final MMIs by evaluating stress-response relations in independent regional datasets of diatom samples paired with measures of several water-quality stressors, including herbicides and streamflow flashiness. Each regional MMI performed well at calibration sites and represented diverse aspects of the structure and function of diatom communities. Most metrics included in the best MMIs were modeled to account for natural variation including climate, topography, soil characteristics, lithology, and groundwater influence on streamflow. MMI performance improved with higher numbers of component metrics, but this effect diminished beyond six metrics. Component metrics of MMIs were associated with a broad suite of measured stressors in every region, including salinity, nutrients, herbicides, and streamflow flashiness. We provide a web-based software application that allows users in the conterminous United States to apply our MMIs to their own datasets and compare MMI scores from their sites to a broader regional context.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108513","usgsCitation":"Carlisle, D.M., Spaulding, S., Tyree, M., Schulte, N.O., Lee, S.S., Mitchell, R., and Pollard, A.A., 2022, A web-based tool for assessing the condition of benthic diatom assemblages in streams and rivers of the conterminous United States: Ecological Indicators, v. 135, 108512, 13 p., https://doi.org/10.1016/j.ecolind.2021.108513.","productDescription":"108512, 13 p.","ipdsId":"IP-123713","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":449280,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108513","text":"Publisher Index Page"},{"id":436017,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XD9398","text":"USGS data release","linkHelpText":"Data Release for: A Web-Based Tool for Assessing the Condition of Benthic Diatom Assemblages in Streams and Rivers of the Conterminous United States"},{"id":408213,"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":"135","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carlisle, Daren M. 0000-0002-7367-348X dcarlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-7367-348X","contributorId":513,"corporation":false,"usgs":true,"family":"Carlisle","given":"Daren","email":"dcarlisle@usgs.gov","middleInitial":"M.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":854301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":223186,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":854302,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tyree, Meredith","contributorId":207506,"corporation":false,"usgs":false,"family":"Tyree","given":"Meredith","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":854303,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulte, Nicholas O. 0000-0001-6284-4987","orcid":"https://orcid.org/0000-0001-6284-4987","contributorId":290510,"corporation":false,"usgs":false,"family":"Schulte","given":"Nicholas","email":"","middleInitial":"O.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":854304,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lee, Sylvia S","contributorId":245621,"corporation":false,"usgs":false,"family":"Lee","given":"Sylvia","email":"","middleInitial":"S","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":854305,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mitchell, Richard M.","contributorId":215406,"corporation":false,"usgs":false,"family":"Mitchell","given":"Richard M.","affiliations":[{"id":39239,"text":"USEPA, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":854306,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pollard, Amina A.","contributorId":297613,"corporation":false,"usgs":false,"family":"Pollard","given":"Amina","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":854307,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249307,"text":"70249307 - 2022 - Active‐source interferometry in marine and terrestrial environments: Importance of directionality and stationary phase","interactions":[],"lastModifiedDate":"2023-10-04T12:28:40.614882","indexId":"70249307","displayToPublicDate":"2022-01-04T07:27:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Active‐source interferometry in marine and terrestrial environments: Importance of directionality and stationary phase","docAbstract":"We utilize active‐source seismic interferometry with dense seismic arrays both offshore and onland to explore the utility of this method to create virtual sources and reveal body‐wave reflections in these two different environments. We first utilize data from an ocean‐bottom cable (OBC) array in the Gulf of Mexico with equal numbers of sources (160 airgun shots) and receivers (160 ocean‐bottom four‐component sensors). We next use data from a geophone array across the Bighorn Mountains of Wyoming with many receivers (1300 vertical‐component geophones) but a small number of sources (14 borehole active‐source shots). We find that the OBC virtual source results, which produce strong reflections from sub‐seafloor structures, are far superior to the onland results which lack usable reflections, and we explore reasons for these differences through a set of selective stacking approaches. We present techniques to account for the direction the seismic waves travel (directionality) and stationary phase and show that improvements can be made when incorporating these corrections. Although interferometric methods are based on assumptions of large numbers of widely distributed actual sources, we find that selective exclusion of potentially problematic source–receiver pairs can yield improved results. These geometric adjustments to active‐source interferometry methods have utility for dense‐nodal‐array surveys that are now common in academic studies, but that often suffer from sparse source geometry.
Harpacticoid copepods can be a substantial component of the meiobenthic community in lakes and serve an ecological role as detritivores. Here we present the first species-level lake-wide quantitative assessment of the harpacticoid assemblage of Lake Ontario with emphasis on the status of nonindigenous species. Additionally, we provide COI-5P sequences of harpacticoid taxa through Barcode of Life Data System (BOLD). Harpacticoids were collected at depths from 0.1 to 184 m and from a range of substrates from August to September 2018 as part of the Cooperative Science and Monitoring Initiative (CSMI) offshore benthic survey. Twenty-six meiobenthic samples were analyzed using microscopy for community composition analysis of harpacticoids. We found thirteen indigenous and three nonindigenous species of harpacticoid, with the introduced species dominating at shallow depths. The community transitioned from nonindigenous to indigenous species dominance as depth increased. Nonindigenous species accounted for 79% of the community (by abundance) at depths <20 m, 55% from 20 to 40 m, and only 24% at depths >40 m. The nonindigenous species encountered included the first detections of Schizopera borutzkyi (Monchenko, 1967) and Heteropsyllus nunni (Coull, 1975) from Lake Ontario. S. borutzkyi was the most abundant harpacticoid species in the lake, approaching a maximum density of 50,000/m2 and a lake-wide average density of 7,900/m2. Numerically important indigenous species included Bryocamptus nivalis (Willey, 1925), Canthocamptus robertcokeri (Wilson, 1958), Canthocamptus staphylinoides (Pearse, 1905), and Moraria cristata (Chappuis, 1929). The prevalence of nonindigenous harpacticoids in the meiobenthos of Lake Ontario suggests further investigations of Great Lakes meiofauna communities are warranted.
Globally, sea turtle research and conservation efforts are underway to identify important high-use areas where these imperiled individuals may be resident for weeks to months to years. In the southeastern Gulf of Mexico, recent telemetry studies highlighted post-nesting foraging sites for federally endangered green turtles (Chelonia mydas) around the Florida Keys. In order to delineate additional areas that may serve as inter-nesting, migratory, and foraging hotspots for reproductively active females nesting in peninsular southwest Florida, we satellite-tagged 14 green turtles that nested at two sites along the southeast Gulf of Mexico coastline between 2017 and 2019: Sanibel and Keewaydin Islands. Prior to this study, green turtles nesting in southwest Florida had not previously been tracked and their movements were unknown. We used switching state space modeling to show that an area off Cape Sable (Everglades), Florida Bay, and the Marquesas Keys are important foraging areas that support individuals that nest on southwest Florida mainland beaches. Turtles were tracked for 39–383 days, migrated for a mean of 4 days, and arrived at their respective foraging grounds in the months of July through September. Turtles remained resident in their respective foraging sites until tags failed, typically after several months, where they established mean home ranges (50% kernel density estimate) of 296 km2. Centroid locations for turtles at common foraging sites were 1.2–36.5 km apart. The area off southwest Florida Everglades appears to be a hotspot for these turtles during both inter-nesting and foraging; this location was also used by turtles that were previously satellite tagged in the Dry Tortugas after nesting. Further evaluation of this important habitat is warranted. Understanding where and when imperiled yet recovering green turtles forage and remain resident is key information for designing surveys of foraging resources and developing additional protection strategies intended to enhance population recovery trajectories.
The Jerusalem cricket subfamily Stenopelmatinae is distributed from south-western Canada through the western half of the United States to as far south as Ecuador. Recently, the generic classification of this subfamily was updated to contain two genera, the western North American Ammopelmatus, and the Mexican, and central and northern South American Stenopelmatus. The taxonomy of the latter genus was also revised, with 5, 13 and 14 species being respectively validated, declared as nomen dubium and described as new. Despite this effort, the systematics of Stenopelmatus is still far from complete. Here, we generated sequences of the mitochondrial DNA barcoding locus and performed two distinct DNA sequence-based approaches to assess the species’ limits among several populations of Stenopelmatus, with emphasis on populations from central and south-east Mexico. We reconstructed the phylogenetic relationships among representative species of the main clades within the genus using nuclear 3RAD data and carried out a molecular clock analysis to investigate its biogeographic history. The two DNA sequence-based approaches consistently recovered 34 putative species, several of which are apparently undescribed. Our estimates of phylogeny confirmed the recent generic update of Stenopelmatinae and revealed a marked phylogeographic structure within Stenopelmatus. Based on our results, we propose the existence of four species-groups within the genus (the faulkneri, talpa, Central America and piceiventris species-groups). The geographic distribution of these species-groups and our molecular clock estimates are congruent with the geological processes that took place in mountain ranges along central and southern Mexico, particularly since the Neogene. Our study emphasises the necessity to continue performing more taxonomic and phylogenetic studies on Stenopelmatus to clarify its actual species richness and evolutionary history in Mesoamerica.