Long-term decadal-scale shoreline change is an important parameter for quantifying the stability of coastal systems. The decadal-scale coastal change is controlled by processes that occur on short time scales (such as storms) and long-term processes (such as prevailing waves). The ability to predict decadal-scale shoreline change is not well established and the fundamental physical processes controlling this change are not well understood. Here we investigate the processes that create large-scale long-term shoreline change along the Outer Banks of North Carolina, an uninterrupted 60 km stretch of coastline, using both observations and a numerical modeling approach. Shoreline positions for a 24-yr period were derived from aerial photographs of the Outer Banks. Analysis of the shoreline position data showed that, although variable, the shoreline eroded an average of 1.5 m/yr throughout this period. The modeling approach uses a three-dimensional hydrodynamics-based numerical model coupled to a spectral wave model and simulates the full 24-yr time period on a spatial grid running on a short (second scale) time-step to compute the sediment transport patterns. The observations and the model results show similar magnitudes (O(105 m3/yr)) and patterns of alongshore sediment fluxes. Both the observed and the modeled alongshore sediment transport rates have more rapid changes at the north of our section due to continuously curving coastline, and possible effects of alongshore variations in shelf bathymetry. The southern section with a relatively uniform orientation, on the other hand, has less rapid transport rate changes. Alongshore gradients of the modeled sediment fluxes are translated into shoreline change rates that have agreement in some locations but vary in others. Differences between observations and model results are potentially influenced by geologic framework processes not included in the model. Both the observations and the model results show higher rates of erosion (∼−1 m/yr) averaged over the northern half of the section as compared to the southern half where the observed and modeled averaged net shoreline changes are smaller (<0.1 m/yr). The model indicates accretion in some shallow embayments, whereas observations indicate erosion in these locations. Further analysis identifies that the magnitude of net alongshore sediment transport is strongly dominated by events associated with high wave energy. However, both big- and small- wave events cause shoreline change of the same order of magnitude because it is the gradients in transport, not the magnitude, that are controlling shoreline change. Results also indicate that alongshore momentum is not a simple balance between wave breaking and bottom stress, but also includes processes of horizontal vortex force, horizontal advection and pressure gradient that contribute to long-term alongshore sediment transport. As a comparison to a more simple approach, an empirical formulation for alongshore sediment transport is used. The empirical estimates capture the effect of the breaking term in the hydrodynamics-based model, however, other processes that are accounted for in the hydrodynamics-based model improve the agreement with the observed alongshore sediment transport.
","language":"English","publisher":"Elsevier","publisherLocation":"New York, NY","doi":"10.1016/j.coastaleng.2016.11.014","usgsCitation":"Safak, I., List, J.H., Warner, J., and Kumar, N., 2017, Observations and 3D hydrodynamics-based modeling of decadal-scale shoreline change along the Outer Banks, North Carolina: Coastal Engineering, v. 120, p. 78-92, https://doi.org/10.1016/j.coastaleng.2016.11.014.","productDescription":"15 p.","startPage":"78","endPage":"92","ipdsId":"IP-071238","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469939,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/8747","text":"External Repository"},{"id":339382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Outer Banks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.3,\n 35.13\n ],\n [\n -75.07,\n 35.13\n ],\n [\n -75.07,\n 36.45\n ],\n [\n -76.3,\n 36.45\n ],\n [\n -76.3,\n 35.13\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"120","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58e8a540e4b09da6799d639d","contributors":{"authors":[{"text":"Safak, Ilgar 0000-0001-7675-0770 isafak@usgs.gov","orcid":"https://orcid.org/0000-0001-7675-0770","contributorId":5522,"corporation":false,"usgs":true,"family":"Safak","given":"Ilgar","email":"isafak@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":690245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"List, Jeffrey H. 0000-0001-8594-2491 jlist@usgs.gov","orcid":"https://orcid.org/0000-0001-8594-2491","contributorId":174581,"corporation":false,"usgs":true,"family":"List","given":"Jeffrey","email":"jlist@usgs.gov","middleInitial":"H.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":690247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":690246,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kumar, Nirnimesh","contributorId":190663,"corporation":false,"usgs":false,"family":"Kumar","given":"Nirnimesh","email":"","affiliations":[],"preferred":false,"id":690248,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184444,"text":"fs20173020 - 2017 - Summary of hydrologic conditions in Kansas, water year 2016","interactions":[],"lastModifiedDate":"2017-04-07T09:06:52","indexId":"fs20173020","displayToPublicDate":"2017-04-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3020","title":"Summary of hydrologic conditions in Kansas, water year 2016","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring sites in Kansas. Real-time data are collected at 216 streamgage sites and are verified throughout the year with regular measurements of streamflow made by USGS personnel. Annual assessments of hydrologic conditions are made by comparing statistical analyses of current and historical water year (WY) data for the period of record. A WY is the 12-month period from October 1 through September 30 and is designated by the calendar year in which the period ends. Long-term monitoring of hydrologic conditions in Kansas provides critical information for water-supply management, flood forecasting, reservoir operations, irrigation scheduling, bridge and culvert design, ecological monitoring, and many other uses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173020","usgsCitation":"Louen, J.M., 2017, Summary of hydrologic conditions in Kansas, water year 2016: U.S. Geological Survey Fact Sheet 2017–3020, 4 p., https://doi.org/10.3133/fs20173020.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-083593","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":339361,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3020/fs20173020.pdf","text":"Fact Sheet","size":"7.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3020"},{"id":339360,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3020/coverthb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.5977783203125,\n 39.10022600175347\n ],\n [\n -94.603271484375,\n 39.142842478062505\n ],\n [\n -94.658203125,\n 39.15988184949157\n ],\n [\n -94.7021484375,\n 39.18117526158749\n ],\n [\n -94.779052734375,\n 39.198205348894795\n ],\n [\n -94.82299804687499,\n 39.20671884491848\n ],\n [\n -94.833984375,\n 39.24501680713314\n ],\n [\n -94.8724365234375,\n 39.279041894366785\n ],\n [\n -94.910888671875,\n 39.3130504637139\n ],\n [\n -94.910888671875,\n 39.342794408952365\n ],\n [\n -94.8944091796875,\n 39.36827914916014\n ],\n [\n -94.8724365234375,\n 39.40648882684979\n ],\n [\n -94.921875,\n 39.38526381099774\n ],\n [\n -94.97131347656249,\n 39.42346418978382\n ],\n [\n -95.03173828125,\n 39.44891948347229\n ],\n [\n -95.0592041015625,\n 39.487084981687495\n ],\n [\n -95.108642578125,\n 39.54641191968671\n ],\n [\n -95.042724609375,\n 39.58452390500424\n ],\n [\n -95.0482177734375,\n 39.61838363831915\n ],\n [\n -95.03173828125,\n 39.66914219401813\n ],\n [\n -94.97131347656249,\n 39.67759833072648\n ],\n [\n -94.96856689453125,\n 39.74521015328692\n ],\n [\n -94.89990234375,\n 39.7240885773337\n ],\n [\n -94.85870361328125,\n 39.74521015328692\n ],\n [\n -94.8834228515625,\n 39.757879992021756\n ],\n [\n -94.888916015625,\n 39.79376521264885\n ],\n [\n -94.87518310546875,\n 39.82752244475985\n ],\n [\n -94.91638183593749,\n 39.84439484396462\n ],\n [\n -94.92462158203124,\n 39.884450178234395\n ],\n [\n -95.01800537109374,\n 39.89709437260048\n ],\n [\n -95.02349853515625,\n 39.87812720644829\n ],\n [\n -95.0701904296875,\n 39.86547951378614\n ],\n [\n -95.130615234375,\n 39.87601941962116\n ],\n [\n -95.14984130859374,\n 39.90130858574735\n ],\n [\n -95.18829345703125,\n 39.905522539728544\n ],\n [\n -95.2020263671875,\n 39.92448212528485\n ],\n [\n -95.24871826171875,\n 39.9434364619742\n ],\n [\n -95.31463623046875,\n 39.99605985169435\n ],\n [\n -102.041015625,\n 40.01078714046552\n ],\n [\n -102.052001953125,\n 37.00255267215955\n ],\n [\n -94.6142578125,\n 37.01132594307015\n ],\n [\n -94.5977783203125,\n 39.10022600175347\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
Hybridization between invasive and native species, a significant threat to worldwide biodiversity, is predicted to increase due to climate-induced expansions of invasive species. Long-term research and monitoring are crucial for understanding the ecological and evolutionary processes that modulate the effects of invasive species. Using a large, multi-decade genetics dataset (N = 582 sites, 12,878 individuals) with high-resolution climate predictions and extensive stocking records, we evaluate the spatiotemporal dynamics of hybridization between native cutthroat trout and invasive rainbow trout, the world’s most widely introduced invasive fish, across the northern Rocky Mountains of the United States. Historical effects of stocking and contemporary patterns of climatic variation were strongly related to the spread of hybridization across space and time. The probability of occurrence, extent of, and temporal changes in hybridization increased at sites in close proximity to historical stocking locations with greater rainbow trout propagule pressure, warmer water temperatures, and lower spring precipitation. Although locations with warmer water temperatures were more prone to hybridization, cold sites were not protected from invasion; 58% of hybridized sites had cold mean summer water temperatures (<11°C). Despite cessation of stocking over 40 years ago, hybridization increased over time at half (50%) of the locations with long-term data, the vast majority of which (74%) were initially non-hybridized, emphasizing the chronic, negative impacts of human-mediated hybridization. These results show that effects of climate change on biodiversity must be analyzed in the context of historical human impacts that set ecological and evolutionary trajectories.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13681","usgsCitation":"Muhlfeld, C.C., Kovach, R.P., Al-Chokhachy, R.K., Amish, S.J., Kershner, J.L., Leary, R., Lowe, W.H., Luikart, G., Matson, P., Schmetterling, D.A., Shepard, B.B., Westley, P.A., Whited, D., Whiteley, A.R., and Allendorf, F.W., 2017, Legacy introductions and climatic variation explain spatiotemporal patterns of invasive hybridization in a native trout: Global Change Biology, v. 23, no. 11, p. 4663-4674, https://doi.org/10.1111/gcb.13681.","productDescription":"12 p.","startPage":"4663","endPage":"4674","ipdsId":"IP-078684","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469946,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.13681","text":"Publisher Index 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rkovach@usgs.gov","contributorId":5772,"corporation":false,"usgs":true,"family":"Kovach","given":"Ryan","email":"rkovach@usgs.gov","middleInitial":"P.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":688709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Al-Chokhachy, Robert K. 0000-0002-2136-5098 ral-chokhachy@usgs.gov","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":1674,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","email":"ral-chokhachy@usgs.gov","middleInitial":"K.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":688710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amish, Stephen J.","contributorId":104799,"corporation":false,"usgs":false,"family":"Amish","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":5097,"text":"University of Montana, Division of Biological Sciences","active":true,"usgs":false}],"preferred":false,"id":688711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kershner, Jeffrey L. 0000-0002-7093-9860 jkershner@usgs.gov","orcid":"https://orcid.org/0000-0002-7093-9860","contributorId":310,"corporation":false,"usgs":true,"family":"Kershner","given":"Jeffrey","email":"jkershner@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":688712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leary, Robb F.","contributorId":126726,"corporation":false,"usgs":false,"family":"Leary","given":"Robb F.","affiliations":[{"id":6582,"text":"Montana Fish, Wildlife and Parks, Missoula, Montana 59801, USA","active":true,"usgs":false}],"preferred":false,"id":688713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lowe, Winsor 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A.","contributorId":20223,"corporation":false,"usgs":true,"family":"Schmetterling","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":689675,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shepard, Bradley B.","contributorId":145880,"corporation":false,"usgs":false,"family":"Shepard","given":"Bradley","email":"","middleInitial":"B.","affiliations":[{"id":6765,"text":"Montana State University, Department of Land Resources and Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":688716,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Westley, Peter A. H.","contributorId":190530,"corporation":false,"usgs":false,"family":"Westley","given":"Peter","email":"","middleInitial":"A. H.","affiliations":[],"preferred":false,"id":688717,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Whited, Diane","contributorId":126718,"corporation":false,"usgs":false,"family":"Whited","given":"Diane","affiliations":[{"id":6576,"text":"Flathead Lake Biological Station, University of Montana, Polson, MT 59860, USA","active":true,"usgs":false}],"preferred":false,"id":688718,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":688719,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Allendorf, Fred W.","contributorId":124525,"corporation":false,"usgs":false,"family":"Allendorf","given":"Fred","email":"","middleInitial":"W.","affiliations":[{"id":5084,"text":"Division of Biological Sciences, University of Montana, Missoula, MT","active":true,"usgs":false}],"preferred":false,"id":688720,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70186519,"text":"70186519 - 2017 - Acute sensitivity of the vernal pool fairy shrimp, Branchinecta lynchi (Anostraca; Branchinectidae), and surrogate species to 10 chemicals","interactions":[],"lastModifiedDate":"2017-04-05T08:54:31","indexId":"70186519","displayToPublicDate":"2017-04-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Acute sensitivity of the vernal pool fairy shrimp, Branchinecta lynchi (Anostraca; Branchinectidae), and surrogate species to 10 chemicals","docAbstract":"Vernal pool fairy shrimp, Branchinecta lynchi, (Branchiopoda; Anostraca) and other fairy shrimp species have been listed as threatened or endangered under the US Endangered Species Act. Because few data exist about the sensitivity of Branchinecta spp. to toxic effects of contaminants, it is difficult to determine whether they are adequately protected by water quality criteria. A series of acute (24-h) lethality/immobilization tests was conducted with 3 species of fairy shrimp (B. lynchi, Branchinecta lindahli, and Thamnocephalus platyurus) and 10 chemicals with varying modes of toxic action: ammonia, potassium, chloride, sulfate, chromium(VI), copper, nickel, zinc, alachlor, and metolachlor. The same chemicals were tested in 48-h tests with other branchiopods (the cladocerans Daphnia magna and Ceriodaphnia dubia) and an amphipod (Hyalella azteca), and in 96-h tests with snails (Physa gyrina and Lymnaea stagnalis). Median effect concentrations (EC50s) for B. lynchi were strongly correlated (r2 = 0.975) with EC50s for the commercially available fairy shrimp species T. platyurus for most chemicals tested. Comparison of EC50s for fairy shrimp and EC50s for invertebrate taxa tested concurrently and with other published toxicity data indicated that fairy shrimp were relatively sensitive to potassium and several trace metals compared with other invertebrate taxa, although cladocerans, amphipods, and mussels had similar broad toxicant sensitivity. Interspecies correlation estimation models for predicting toxicity to fairy shrimp from surrogate species indicated that models with cladocerans and freshwater mussels as surrogates produced the best predictions of the sensitivity of fairy shrimp to contaminants. The results of these studies indicate that fairy shrimp are relatively sensitive to a range of toxicants, but Endangered Species Act-listed fairy shrimp of the genus Branchinecta were not consistently more sensitive than other fairy shrimp taxa. Environ Toxicol Chem 2017;36:797–806. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.3723","usgsCitation":"Ivey, C.D., Besser, J.M., Ingersoll, C.G., Wang, N., Rogers, D.C., Raimondo, S., Bauer, C.R., and Hammer, E.J., 2017, Acute sensitivity of the vernal pool fairy shrimp, Branchinecta lynchi (Anostraca; Branchinectidae), and surrogate species to 10 chemicals: Environmental Toxicology and Chemistry, v. 36, no. 3, p. 797-806, https://doi.org/10.1002/etc.3723.","productDescription":"10 p.","startPage":"797","endPage":"806","ipdsId":"IP-079384","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":438382,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74J0C72","text":"USGS data release","linkHelpText":"Acute sensitivity of the vernal pool fairy shrimp, Branchinecta lynchi (Anostraca; Branchinectidae), and surrogate species to ten chemicals-Data"},{"id":339183,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-12-26","publicationStatus":"PW","scienceBaseUri":"58e6026ee4b09da6799ac679","contributors":{"authors":[{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":688563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":688564,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ingersoll, Christopher G. 0000-0003-4531-5949 cingersoll@usgs.gov","orcid":"https://orcid.org/0000-0003-4531-5949","contributorId":2071,"corporation":false,"usgs":true,"family":"Ingersoll","given":"Christopher","email":"cingersoll@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":688565,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":688566,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rogers, D. Christopher","contributorId":190496,"corporation":false,"usgs":false,"family":"Rogers","given":"D.","email":"","middleInitial":"Christopher","affiliations":[],"preferred":false,"id":688567,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Raimondo, Sandy","contributorId":150748,"corporation":false,"usgs":false,"family":"Raimondo","given":"Sandy","email":"","affiliations":[{"id":18090,"text":"U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL","active":true,"usgs":false}],"preferred":false,"id":688568,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bauer, Candice R.","contributorId":150724,"corporation":false,"usgs":false,"family":"Bauer","given":"Candice","email":"","middleInitial":"R.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":688569,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hammer, Edward J.","contributorId":150723,"corporation":false,"usgs":false,"family":"Hammer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":688570,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70120660,"text":"70120660 - 2017 - The logic of selecting an appropriate map projection in a Decision Support System (DSS)","interactions":[],"lastModifiedDate":"2017-06-07T15:48:46","indexId":"70120660","displayToPublicDate":"2017-04-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The logic of selecting an appropriate map projection in a Decision Support System (DSS)","docAbstract":"There are undeniable practical consequences to consider when choosing an appropriate map projection for a specific region. The surface of a globe covered by global, continental, and regional maps are so singular that each type distinctively affects the amount of distortion incurred during a projection transformation because of the an assortment of effects caused by distance, direction, scale , and area. A Decision Support System (DSS) for Map Projections of Small Scale Data was previously developed to help select an appropriate projection. This paper reports on a tutorial to accompany that DSS. The DSS poses questions interactively, allowing the user to decide on the parameters, which in turn determines the logic path to a solution. The objective of including a tutorial to accompany the DSS is achieved by visually representing the path of logic that is taken to a recommended map projection derived from the parameters the user selects. The tutorial informs the DSS user about the pedigree of the projection and provides a basic explanation of the specific projection design. This information is provided by informational pop-ups and other aids.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Choosing a Map Projection","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","publisherLocation":"Cham, Switzerland","doi":"10.1007/978-3-319-51835-0_10","usgsCitation":"Finn, M.P., Usery, E.L., Woodard, L.N., and Yamamoto, K.H., 2017, The logic of selecting an appropriate map projection in a Decision Support System (DSS), chap. of Choosing a Map Projection, p. 229-245, https://doi.org/10.1007/978-3-319-51835-0_10.","productDescription":"17 p.","startPage":"229","endPage":"245","ipdsId":"IP-053623","costCenters":[],"links":[{"id":342277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2017-04-05","publicationStatus":"PW","scienceBaseUri":"593910abe4b0764e6c5e884c","contributors":{"authors":[{"text":"Finn, Michael P. 0000-0003-0415-2194 mfinn@usgs.gov","orcid":"https://orcid.org/0000-0003-0415-2194","contributorId":2657,"corporation":false,"usgs":true,"family":"Finn","given":"Michael","email":"mfinn@usgs.gov","middleInitial":"P.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":519223,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Usery, E. Lynn 0000-0002-2766-2173 usery@usgs.gov","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":231,"corporation":false,"usgs":true,"family":"Usery","given":"E.","email":"usery@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":519222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodard, Laura N.","contributorId":9733,"corporation":false,"usgs":true,"family":"Woodard","given":"Laura","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":519225,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yamamoto, Kristina H. khyamamoto@usgs.gov","contributorId":4490,"corporation":false,"usgs":true,"family":"Yamamoto","given":"Kristina","email":"khyamamoto@usgs.gov","middleInitial":"H.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":519224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70185962,"text":"pp1833 - 2017 - Brackish groundwater in the United States","interactions":[],"lastModifiedDate":"2017-07-18T14:56:30","indexId":"pp1833","displayToPublicDate":"2017-04-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1833","title":"Brackish groundwater in the United States","docAbstract":"For some parts of the Nation, large-scale development of groundwater has caused decreases in the amount of groundwater that is present in aquifer storage and that discharges to surface-water bodies. Water supply in some areas, particularly in arid and semiarid regions, is not adequate to meet demand, and severe drought is affecting large parts of the United States. Future water demand is projected to heighten the current stress on groundwater resources. This combination of factors has led to concerns about the availability of freshwater to meet domestic, agricultural, industrial, mining, and environmental needs. To ensure the water security of the Nation, currently [2016] untapped water sources may need to be developed.
Brackish groundwater is an unconventional water source that may offer a partial solution to current and future water demands. In support of the national census of water resources, the U.S. Geological Survey completed the national brackish groundwater assessment to better understand the occurrence and characteristics of brackish groundwater in the United States as a potential water resource. Analyses completed as part of this assessment relied on previously collected data from multiple sources; no new data were collected. Compiled data included readily available information about groundwater chemistry, horizontal and vertical extents and hydrogeologic characteristics of principal aquifers (regionally extensive aquifers or aquifer systems that have the potential to be used as a source of potable water), and groundwater use. Although these data were obtained from a wide variety of sources, the compiled data are biased toward shallow and fresh groundwater resources; data representing groundwater that is at great depths and is saline were not as readily available.
One of the most important contributions of this assessment is the creation of a database containing chemical characteristics and aquifer information for the known areas with brackish groundwater in the United States. Previously published digital data relating to brackish groundwater resources were limited to a small number of State- and regional-level studies. Data sources for this assessment ranged from single publications to large datasets and from local studies to national assessments. Geochemical data included concentrations of dissolved solids, major ions, trace elements, nutrients, and radionuclides as well as physical properties of the water (pH, temperature, and specific conductance). Additionally, the database provides selected well information (location, yield, depth, and contributing aquifer) necessary for evaluating the water resource.
The assessment was divided into national-, regional-, and aquifer-scale analyses. National-scale analyses included evaluation of the three-dimensional distribution of observed dissolved-solids concentrations in groundwater, the three-dimensional probability of brackish groundwater occurrence, and the geochemical characteristics of saline (greater than or equal to 1,000 mg/L of dissolved solids) groundwater resources. Regional-scale analyses included a summary of the percentage of observed grid cell volume in the region that was occupied by brackish groundwater within the mixture of air, water, and rock for multiple depth intervals. Aquifer-scale analyses focused primarily on four regions that contained the largest amounts of observed brackish groundwater and included a generalized description of hydrogeologic characteristics from previously published work; the distribution of dissolved-solids concentrations; considerations for developing brackish groundwater resources, including a summary of other chemical characteristics that may limit the use of brackish groundwater and the ability of sampled wells producing brackish groundwater to yield useful amounts of water; and the amount of saline groundwater being used in 2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1833","collaboration":"Water Availability and Use Science Program","usgsCitation":"Stanton, J.S., Anning, D.W., Brown, C.J., Moore, R.B., McGuire, V.L., Qi, S.L., Harris, A.C., Dennehy, K.F., McMahon, P.B., Degnan, J.R., and Böhlke, J.K., 2017, Brackish groundwater in the United States: U.S. Geological Survey Professional Paper 1833, 185 p., https://doi.org/10.3133/pp1833.","productDescription":"Report: xii, 185 p.; Figures: 4 Oversize, 4 Layered; Appendixes: Table, 4, 3-D Figures; Fact Sheet; Read Me; Data Release; Project Site","numberOfPages":"202","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":338729,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/pp/1833/pp1833_fig27_interactive.pdf","text":"Figure 27 ","size":"922 kB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1833 Figure 27 Interactive","linkHelpText":"Principal aquifers within the Eastern Midcontinent region [layered pdf; 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
https://newengland.water.usgs.gov/
","tableOfContents":"Plumes of buoyant river water spread in the ocean from river mouths, and these plumes influence water quality, sediment dispersal, primary productivity, and circulation along the world’s coasts. Most investigations of river plumes have focused on large rivers in a coastal region, for which the physical spreading of the plume is assumed to be independent from the influence of other buoyant plumes. Here we provide new understanding of the spreading patterns of multiple plumes interacting along simplified coastal settings by investigating: (i) the relative likelihood of plume-to-plume interactions at different settings using geophysical scaling, (ii) the diversity of plume frontal collision types and the effects of these collisions on spreading patterns of plume waters using a two-dimensional hydrodynamic model, and (iii) the fundamental differences in plume spreading patterns between coasts with single and multiple rivers using a three-dimensional hydrodynamic model. Geophysical scaling suggests that coastal margins with numerous small rivers (watershed areas < 10,000 km2), such as found along most active geologic coastal margins, were much more likely to have river plumes that collide and interact than coastal settings with large rivers (watershed areas > 100,000 km2). When two plume fronts meet, several types of collision attributes were found, including refection, subduction and occlusion. We found that the relative differences in pre-collision plume densities and thicknesses strongly influenced the resulting collision types. The three-dimensional spreading of buoyant plumes was found to be influenced by the presence of additional rivers for all modeled scenarios, including those with and without Coriolis and wind. Combined, these results suggest that plume-to-plume interactions are common phenomena for coastal regions offshore of the world’s smaller rivers and for coastal settings with multiple river mouths in close proximity, and that the spreading and fate of river waters in these settings will be strongly influenced by these interactions. We conclude that new investigations are needed to characterize how plumes interact offshore of river mouths to better understand the transport and fate of terrestrial sources of pollution, nutrients and other materials in the ocean.
A comprehensive inventory of ecosystem services across the entire Great Lakes basin is currently lacking and is needed to make informed management decisions. A greater appreciation and understanding of ecosystem services, including both use and non-use services, may have avoided misguided resource management decisions in the past that resulted in negative legacies inherited by future generations. Given the interest in ecosystem services and lack of a coherent approach to addressing this topic in the Great Lakes, a summit was convened involving 28 experts working on various aspects of ecosystem services in the Great Lakes. The invited attendees spanned a variety of social and natural sciences. Given the unique status of the Great Lakes as the world's largest collective repository of surface freshwater, and the numerous stressors threatening this valuable resource, timing was propitious to examine ecosystem services. Several themes and recommendations emerged from the summit. There was general consensus that: 1) a comprehensive inventory of ecosystem services throughout the Great Lakes is a desirable goal but would require considerable resources; 2) more spatially and temporally intensive data are needed to overcome our data gaps, but the arrangement of data networks and observatories must be well-coordinated; 3) trade-offs must be considered as part of ecosystem services analyses; and 4) formation of a Great Lakes Institute for Ecosystem Services, to provide a hub for research, meetings, and training is desirable. Several challenges also emerged during the summit, which are discussed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2017.02.004","usgsCitation":"Steinman, A.D., Cardinale, B.J., Munns, W.R., Ogdahl, M.E., Allan, D.J., Angadi, T., Bartlett, S., Brauman, K.A., Byappanahalli, M., Doss, M., Dupont, D., Johns, A., Kashian, D., Lupi, F., McIntyre, P.B., Miller, T., Moore, M.P., Muenich, R.L., Poudel, R., Price, J., Provencher, B., Rea, A., Read, J., Renzetti, S., Sohngen, B., and Washburn, E., 2017, Ecosystem services in the Great Lakes: Journal of Great Lakes Research, v. 43, no. 3, p. 161-168, https://doi.org/10.1016/j.jglr.2017.02.004.","productDescription":"8 p.","startPage":"161","endPage":"168","ipdsId":"IP-082813","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469948,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2017.02.004","text":"Publisher Index Page"},{"id":339137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","volume":"43","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58e4b0b0e4b09da67999776a","contributors":{"authors":[{"text":"Steinman, Alan D.","contributorId":190417,"corporation":false,"usgs":false,"family":"Steinman","given":"Alan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":688394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cardinale, Bradley J.","contributorId":190418,"corporation":false,"usgs":false,"family":"Cardinale","given":"Bradley","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":688395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munns, Wayne R. 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BCP has contributed new insights across temporal and spatial scales into how ancient climate and environmental change have shaped faunas, emphasizing processes of assembly, persistence, and diversification across the vast Beringian region. BCP collections also represent baseline records of biotic diversity from across the northern high latitudes at a time of accelerated environmental change. These specimens and associated data form an unmatched resource for identifying hidden diversity, interpreting past responses to climate oscillations, documenting contemporary conditions, and anticipating outcomes for complex biological systems in a regime of ecological perturbation. Because of its dual focus on hosts and parasites, the BCP record also provides a foundation for comparative analyses that can document the effects of dynamic change on the geographic distribution, transmission dynamics, and emergence of pathogens. 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Specific objectives are to document (1) the data compilation and processing steps used to identify river and stream sites throughout the Nation suitable for water-quality, pesticide, and ecology trend analysis, (2) the statistical methods used to determine trends in target parameters, (3) considerations for water-quality, pesticide, and ecology data and streamflow data when modeling trends, (4) sensitivity analyses for selecting data and interpreting trend results with the Weighted Regressions on Time, Discharge, and Season method, and (5) the final trend results at each site. The scope of this study includes trends in water-quality concentrations and loads (nutrient, sediment, major ion, salinity, and carbon), pesticide concentrations and loads, and metrics for aquatic ecology (fish, invertebrates, and algae) for four time periods: (1) 1972–2012, (2) 1982–2012, (3) 1992–2012, and (4) 2002–12. In total, nearly 12,000 trends in concentration, load, and ecology metrics were evaluated in this study; there were 11,893 combinations of sites, parameters, and trend periods. The final trend results are presented with examples of how to interpret the results from each trend model. Interpretation of the trend results, such as causal analysis, is not included.
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U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
The U.S. Geological Survey (USGS), as part of the National Assessment of Storm-Induced Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms. On June 9, 2011, the USGS conducted an oblique aerial photographic survey from Dauphin Island, Alabama, to Breton Island, Louisiana, aboard a Beechcraft BE90 King Air (aircraft) at an altitude of 500 feet (ft) (152 meters (m)) and approximately 1,200 ft (366 m) offshore. This mission was conducted to collect baseline data for assessing incremental changes in the beach and nearshore area and can be used to assess future coastal change.
The photographs in this report are Joint Photographic Experts Group (JPEG) images. These photographs document the state of the barrier islands and other coastal features at the time of the survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1044","usgsCitation":"Morgan, K.L.M., 2017, Baseline coastal oblique aerial photographs collected from Dauphin Island, Alabama, to Breton Island, Louisiana, June 9, 2011: U.S. Geological Survey Data Series 1044, https://doi.org/10.3133/ds1044.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-077797","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":338169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1044/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"}},{"id":338168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1044/coverthb.jpg"}],"country":"United States","state":"Alabama, Louisiana","otherGeospatial":"Breton Island, Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.16778564453125,\n 29.602118211647333\n ],\n [\n -88.05816650390625,\n 29.602118211647333\n ],\n [\n -88.05816650390625,\n 30.28041626667403\n ],\n [\n -89.16778564453125,\n 30.28041626667403\n ],\n [\n -89.16778564453125,\n 29.602118211647333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
https://coastal.er.usgs.gov/
The U.S. Geological Survey (USGS), as part of the National Assessment of Storm-Induced Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms. On August 31, 2005, the USGS conducted an oblique aerial photographic survey from Panama City, Florida, to Lakeshore, Mississippi, and the Chandeleur Islands, Louisiana, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet and approximately 1,000 feet offshore. This mission was flown to collect post-Hurricane Katrina data, which can be used to assess incremental changes in the beach and nearshore area and can be used to assess future coastal change.
The photographs in this report are Joint Photographic Experts Group (JPEG) images. These photographs document the state of the barrier islands and other coastal features at the time of the survey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1033","usgsCitation":"Morgan, K.L.M., and DeWitt, N.T., 2017, Post-Hurricane Katrina coastal oblique aerial photographs collected from Panama City, Florida, to Lakeshore, Mississippi, and the Chandeleur Islands, Louisiana, August 31, 2005: U.S. Geological Survey Data Series 1033, https://doi.org/10.3133/ds1033.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079893","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":338244,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1033/coverthb.jpg"},{"id":338245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1033/index.html","text":"Report HTML"}],"country":"United States","state":"Florida","city":"Panama City","otherGeospatial":"Pensacola Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.20672607421875,\n 30.337324394179017\n ],\n [\n -87.19024658203124,\n 30.30176068632071\n ],\n [\n -86.87713623046875,\n 30.36102635890718\n ],\n [\n -86.59423828125,\n 30.372875188118016\n ],\n [\n -86.3140869140625,\n 30.351546261929034\n ],\n [\n -86.143798828125,\n 30.29701788337205\n ],\n [\n -85.94879150390625,\n 30.225848323247707\n ],\n [\n -85.7537841796875,\n 30.107117887092357\n ],\n [\n -85.6988525390625,\n 30.1380015549519\n ],\n [\n -85.98724365234375,\n 30.28990324883237\n ],\n [\n -86.2591552734375,\n 30.372875188118016\n ],\n [\n -86.46514892578124,\n 30.401306519203583\n ],\n [\n -86.77276611328125,\n 30.413150465068853\n ],\n [\n -86.96502685546875,\n 30.391830328088137\n ],\n [\n -87.20672607421875,\n 30.337324394179017\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
https://coastal.er.usgs.gov/
The importance of trophic linkages between aquatic and terrestrial ecosystems is predicted to vary as a function of subsidy quantity and quality relative to in situ resources. To test this prediction, I used multi-year diet data from Bonneville cutthroat trout Oncorhynchus clarki Utah in spring-fed and snowmelt-driven streams in the high desert of western North America. I documented that trout in spring-fed streams consumed more (number and weight) aquatic than terrestrial invertebrates, while trout in snowmelt-driven streams consumed a similar number of both prey types but consumed more terrestrial than aquatic invertebrates by weight. Trout in spring-fed streams consumed more aquatic invertebrates than trout in snowmelt streams and trout consumed more terrestrial invertebrates in snowmelt than in spring-fed streams. Up to 93% of trout production in spring-fed streams and 60% in snowmelt streams was fueled by aquatic invertebrates, while the remainder of trout production in each stream type was from terrestrial production. I found that the biomass and occurrence of consumed terrestrial invertebrates were not related to our measures of in situ resource quality or quantity in either stream type. These empirical data highlight the importance of autotrophic-derived production to trout in xeric regions.
","language":"English","publisher":"Oikos Publishers","publisherLocation":"La Crosse, WI","doi":"10.1080/02705060.2017.1284696","usgsCitation":"Sepulveda, A.J., 2017, Terrestrial–aquatic linkages in spring-fed and snowmelt-dominated streams: Journal of Freshwater Ecology, v. 32, no. 1, p. 288-299, https://doi.org/10.1080/02705060.2017.1284696.","productDescription":"12 p.","startPage":"288","endPage":"299","ipdsId":"IP-079530","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469951,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/02705060.2017.1284696","text":"Publisher Index Page"},{"id":339034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Bear River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.81816101074217,\n 42.30626081896345\n ],\n [\n -111.35879516601561,\n 42.30626081896345\n ],\n [\n -111.35879516601561,\n 42.66022161324799\n ],\n [\n -111.81816101074217,\n 42.66022161324799\n ],\n [\n -111.81816101074217,\n 42.30626081896345\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-15","publicationStatus":"PW","scienceBaseUri":"58e35f7ee4b09da67997eca5","contributors":{"authors":[{"text":"Sepulveda, Adam J. 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":150628,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":687968,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70199835,"text":"70199835 - 2017 - An integrated population model for bird monitoring in North America","interactions":[],"lastModifiedDate":"2018-10-01T14:34:33","indexId":"70199835","displayToPublicDate":"2017-04-01T14:34:23","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"An integrated population model for bird monitoring in North America","docAbstract":"Integrated population models (IPMs) provide a unified framework for simultaneously analyzing data sets of different types to estimate vital rates, population size, and dynamics; assess contributions of demographic parameters to population changes; and assess population viability. Strengths of an IPM include the ability to estimate latent parameters and improve the precision of parameter estimates. We present a hierarchical IPM that combines two broad‐scale avian monitoring data sets: count data from the North American Breeding Bird Survey (BBS) and capture–recapture data from the Monitoring Avian Productivity and Survivorship (MAPS) program. These data sets are characterized by large numbers of sample sites and observers, factors capable of inducing error in the sampling and observation processes. The IPM integrates the data sets by modeling the population abundance as a first‐order autoregressive function of the previous year's population abundance and vital rates. BBS counts were modeled as a log‐linear function of the annual index of population abundance, observation effects (observer identity and first survey year), and overdispersion. Vital rates modeled included adult apparent survival, estimated from a transient Cormack‐Jolly‐Seber model using MAPS data, and recruitment (surviving hatched birds from the previous season + dispersing adults) estimated as a latent parameter. An assessment of the IPM demonstrated it could recover true parameter values from 200 simulated data sets. The IPM was applied to data sets (1992–2008) of two bird species, Gray Catbird (Dumetella carolinensis) and Wood Thrush (Hylocichla mustelina) in the New England/Mid‐Atlantic coastal Bird Conservation Region of the United States. The Gray Catbird population was relatively stable (trend +0.4% per yr), while the Wood Thrush population nearly halved (trend −4.5% per yr) over the 17‐yr study period. IPM estimates of population growth rates, adult survival, and detection and residency probabilities were similar and as precise as estimates from the stand‐alone BBS and CJS models. A benefit of using the IPM was its ability to estimate the latent recruitment parameter. Annual growth rates for both species correlated more with recruitment than survival, and the relationship for Wood Thrush was stronger than for Gray Catbird. The IPM's unified modeling framework facilitates integration of these important data sets.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1493","usgsCitation":"Ahrestani, F.S., Saracco, J.F., Sauer, J.R., Pardieck, K.L., and Royle, J.A., 2017, An integrated population model for bird monitoring in North America: Ecological Applications, v. 27, no. 3, p. 916-924, https://doi.org/10.1002/eap.1493.","productDescription":"9 p.","startPage":"916","endPage":"924","ipdsId":"IP-080990","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":357969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-21","publicationStatus":"PW","scienceBaseUri":"5bc031aae4b0fc368eb53a40","contributors":{"authors":[{"text":"Ahrestani, Farshid S.","contributorId":208349,"corporation":false,"usgs":false,"family":"Ahrestani","given":"Farshid","email":"","middleInitial":"S.","affiliations":[{"id":37785,"text":"The Institute of Bird Populations","active":true,"usgs":false}],"preferred":false,"id":746840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saracco, James F.","contributorId":208350,"corporation":false,"usgs":false,"family":"Saracco","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":37785,"text":"The Institute of Bird Populations","active":true,"usgs":false}],"preferred":false,"id":746841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sauer, John R. 0000-0002-4557-3019 jrsauer@usgs.gov","orcid":"https://orcid.org/0000-0002-4557-3019","contributorId":146917,"corporation":false,"usgs":true,"family":"Sauer","given":"John","email":"jrsauer@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":746839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":746842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":746843,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70181997,"text":"70181997 - 2017 - Trends in snowmelt-related streamflow timing in the conterminous United States","interactions":[],"lastModifiedDate":"2018-08-07T14:33:22","indexId":"70181997","displayToPublicDate":"2017-04-01T14:33:14","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Trends in snowmelt-related streamflow timing in the conterminous United States","docAbstract":"Changes in snowmelt-related streamflow timing have implications for water availability and use as well as ecologically relevant shifts in streamflow. Historical trends in snowmelt-related streamflow timing (winter-spring center volume date, WSCVD) were computed for minimally disturbed river basins in the conterminous United States. WSCVD was computed by summing daily streamflow for a seasonal window then calculating the day that half of the seasonal volume had flowed past the gage. We used basins where at least 30 percent of annual precipitation was received as snow, and streamflow data were restricted to regionally based winter-spring periods to focus the analyses on snowmelt-related streamflow. Trends over time in WSCVD at gages in the eastern U.S. were relatively homogenous in magnitude and direction and statistically significant; median WSCVD was earlier by 8.2 days (1.1 days/decade) and 8.6 days (1.6 days/decade) for 1940–2014 and 1960–2014 periods respectively. Fewer trends in the West were significant though most trends indicated earlier WSCVD over time. Trends at low-to-mid elevation (<1600 m) basins in the West, predominantly located in the Northwest, had median earlier WSCVD by 6.8 days (1940–2014, 0.9 days/decade) and 3.4 days (1960–2014, 0.6 days/decade). Streamflow timing at high-elevation (⩾1600 m) basins in the West had median earlier WSCVD by 4.0 days (1940–2014, 0.5 days/decade) and 5.2 days (1960–2014, 0.9 days/decade). Trends toward earlier WSCVD in the Northwest were not statistically significant, differing from previous studies that observed many large and (or) significant trends in this region. Much of this difference is likely due to the sensitivity of trend tests to the time period being tested, as well as differences in the streamflow timing metrics used among the studies. Mean February–May air temperature was significantly correlated with WSCVD at 100 percent of the study gages (field significant, p < 0.0001), demonstrating the sensitivity of WSCVD to air temperature across snowmelt dominated basins in the U.S. WSCVD in high elevation basins in the West, however, was related to both air temperature and precipitation yielding earlier snowmelt-related streamflow timing under warmer and drier conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.051","usgsCitation":"Dudley, R.W., Hodgkins, G.A., McHale, M., Kolian, M., and Renard, B., 2017, Trends in snowmelt-related streamflow timing in the conterminous United States: Journal of Hydrology, v. 547, p. 208-221, https://doi.org/10.1016/j.jhydrol.2017.01.051.","productDescription":"14 p.","startPage":"208","endPage":"221","ipdsId":"IP-076605","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":469955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.01.051","text":"Publisher Index Page"},{"id":356297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"547","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b6fc6f5e4b0f5d57878ebad","contributors":{"authors":[{"text":"Dudley, Robert W. 0000-0002-0934-0568 rwdudley@usgs.gov","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":2223,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert","email":"rwdudley@usgs.gov","middleInitial":"W.","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":669220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":669221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McHale, Michael 0000-0003-3780-1816 mmchale@usgs.gov","orcid":"https://orcid.org/0000-0003-3780-1816","contributorId":177292,"corporation":false,"usgs":true,"family":"McHale","given":"Michael","email":"mmchale@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":669222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolian, Michael J.","contributorId":177290,"corporation":false,"usgs":false,"family":"Kolian","given":"Michael J.","affiliations":[],"preferred":false,"id":669223,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Renard, Benjamin","contributorId":177291,"corporation":false,"usgs":false,"family":"Renard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":669224,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261469,"text":"70261469 - 2017 - Global nonfuel mineral exploration trends 2001-2015","interactions":[],"lastModifiedDate":"2024-12-11T17:05:53.664076","indexId":"70261469","displayToPublicDate":"2017-04-01T11:03:06","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2755,"text":"Mining Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Global nonfuel mineral exploration trends 2001-2015","docAbstract":"This review summarizes significant exploration trends related to active sites and budgets, mineral commodities and regional factors for the years 2001-2015. Data were compiled by specialists in the USGS-NMIC, and reported annually in the USGS-NMIC Minerals Yearbook series and in the May issue of Mining Engineering magazine. External data for these analyses were derived from industry sources, published literature, and SNL Metals & Mining, an offering of S&P Global Market Intelligence (New York, NY).","language":"English","publisher":"Society for Mining, Metallurgy, & Exploration","usgsCitation":"Karl, N.A., and Wilburn, D.R., 2017, Global nonfuel mineral exploration trends 2001-2015: Mining Engineering, v. 69, no. 4, p. 30-37.","productDescription":"8 p.","startPage":"30","endPage":"37","ipdsId":"IP-081372","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":464991,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://me.smenet.org/abstract.cfm?preview=1&articleID=7496&page=30"},{"id":465022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"69","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karl, Nick A 0000-0003-2858-2498","orcid":"https://orcid.org/0000-0003-2858-2498","contributorId":246006,"corporation":false,"usgs":true,"family":"Karl","given":"Nick","email":"","middleInitial":"A","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":920665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilburn, David R. 0000-0002-5371-7617 wilburn@usgs.gov","orcid":"https://orcid.org/0000-0002-5371-7617","contributorId":1755,"corporation":false,"usgs":true,"family":"Wilburn","given":"David","email":"wilburn@usgs.gov","middleInitial":"R.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":920666,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193709,"text":"70193709 - 2017 - Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation","interactions":[],"lastModifiedDate":"2017-11-29T16:10:58","indexId":"70193709","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation","docAbstract":"Environmental DNA (eDNA) can be used as an assessment tool to detect populations of threatened species and provide fine-scale data required to make management decisions. The objectives of this project were to use quantitative PCR (qPCR) to: (i) detect spiked salamander DNA in soil, (ii) quantify eDNA degradation over time, (iii) determine detectability of salamander eDNA in a terrestrial environment using soil, faeces, and skin swabs, (iv) detect salamander eDNA in a mesocosm experiment. Salamander eDNA was positively detected in 100% of skin swabs and 66% of faecal samples and concentrations did not differ between the two sources. However, eDNA was not detected in soil samples collected from directly underneath wild-caught living salamanders. Salamander genomic DNA (gDNA) was detected in all qPCR reactions when spiked into soil at 10.0, 5.0, and 1.0 ng/g soil and spike concentration had a significant effect on detected concentrations. Only 33% of samples showed recoverable eDNA when spiked with 0.25 ng/g soil, which was the low end of eDNA detection. To determine the rate of eDNA degradation, gDNA (1 ng/g soil) was spiked into soil and quantified over seven days. Salamander eDNA concentrations decreased across days, but eDNA was still amplifiable at day 7. Salamander eDNA was detected in two of 182 mesocosm soil samples over 12 weeks (n = 52 control samples; n = 65 presence samples; n = 65 eviction samples). The discrepancy in detection success between experiments indicates the potential challenges for this method to be used as a monitoring technique for small-bodied wild terrestrial salamander populations.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.12667","usgsCitation":"Walker, D.M., Leys, J.E., Dunham, K.E., Oliver, J.C., Schiller, E.E., Stephenson, K.S., Kimrey, J.T., Wooten, J., and Rogers, M.W., 2017, Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation: Molecular Ecology Resources, v. 17, no. 6, p. 1223-1230, https://doi.org/10.1111/1755-0998.12667.","productDescription":"8 p.","startPage":"1223","endPage":"1230","ipdsId":"IP-080810","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-11","publicationStatus":"PW","scienceBaseUri":"5a003150e4b0531197b5a748","contributors":{"authors":[{"text":"Walker, Donald M.","contributorId":39132,"corporation":false,"usgs":false,"family":"Walker","given":"Donald","email":"","middleInitial":"M.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leys, Jacob E.","contributorId":199800,"corporation":false,"usgs":false,"family":"Leys","given":"Jacob","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunham, Kelly E.","contributorId":169093,"corporation":false,"usgs":false,"family":"Dunham","given":"Kelly","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Joshua C.","contributorId":199613,"corporation":false,"usgs":false,"family":"Oliver","given":"Joshua","email":"","middleInitial":"C.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schiller, Emily E.","contributorId":145533,"corporation":false,"usgs":false,"family":"Schiller","given":"Emily","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephenson, Kelsey S.","contributorId":100992,"corporation":false,"usgs":false,"family":"Stephenson","given":"Kelsey","email":"","middleInitial":"S.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720394,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kimrey, John T.","contributorId":199571,"corporation":false,"usgs":false,"family":"Kimrey","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720395,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wooten, Jessica","contributorId":190940,"corporation":false,"usgs":false,"family":"Wooten","given":"Jessica","email":"","affiliations":[{"id":35654,"text":"Centre College, Danville, KY, USA","active":true,"usgs":false}],"preferred":false,"id":720396,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720397,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70188346,"text":"70188346 - 2017 - Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures","interactions":[],"lastModifiedDate":"2017-06-06T16:08:28","indexId":"70188346","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures","docAbstract":"Stable carbon and hydrogen isotope signatures of methane, water, and inorganic carbon are widely utilized in natural gas systems for distinguishing microbial and thermogenic methane and for delineating methanogenic pathways (acetoclastic, hydrogenotrophic, and/or methylotrophic methanogenesis). Recent studies of coal and shale gas systems have characterized in situ microbial communities and provided stable isotope data (δD-CH4, δD-H2O, δ13C-CH4, and δ13C-CO2) from a wider range of environments than available previously. Here we review the principal biogenic methane-yielding pathways in coal beds and shales and the isotope effects imparted on methane, document the uncertainties and inconsistencies in established isotopic fingerprinting techniques, and identify the knowledge gaps in understanding the subsurface processes that govern H and C isotope signatures of biogenic methane. We also compare established isotopic interpretations with recent microbial community characterization techniques, which reveal additional inconsistencies in the interpretation of microbial metabolic pathways in coal beds and shales. Collectively, the re-assessed data show that widely-utilized isotopic fingerprinting techniques neglect important complications in coal beds and shales.
Isotopic fingerprinting techniques that combine δ13C-CH4 with δD-CH4 and/or δ13C-CO2have significant limitations: (1) The consistent ~ 160‰ offset between δD-H2O and δD-CH4 could imply that hydrogenotrophic methanogenesis is the dominant metabolic pathway in microbial gas systems. However, hydrogen isotopes can equilibrate between methane precursors and coexisting water, yielding a similar apparent H isotope signal as hydrogenotrophic methanogenesis, regardless of the actual methane formation pathway. (2) Non-methanogenic processes such as sulfate reduction, Fe oxide reduction, inputs of thermogenic methane, anaerobic methane oxidation, and/or formation water interaction can cause the apparent carbon isotope fractionation between δ13C-CH4 and δ13C-CO2(α13CCO2-CH4) to differ from the true methanogenic fractionation, complicating interpretation of methanogenic pathways. (3) Where little-fractionating non-methanogenic bacterial processes compete with highly-fractionating methanogenesis, the mass balance between CH4 and CO2 is affected. This has implications for δ13C values and provides an alternative interpretation for net C isotope signatures than solely the pathways used by active methanogens. (4) While most of the reviewed values of δD-H2O - δD-CH4 and α13CCO2-CH4 are apparently consistent with hydrogenotrophic methanogenesis as the dominant pathway in coal beds and shales, recent microbial community characterization techniques suggest a possible role for acetoclastic or methylotrophic methanogenesis in some basins.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2017.01.027","usgsCitation":"Vinson, D.S., Blair, N.E., Martini, A.M., Larter, S., Orem, W.H., and McIntosh, J.C., 2017, Microbial methane from in situ biodegradation of coal and shale: A review and reevaluation of hydrogen and carbon isotope signatures: Chemical Geology, v. 453, p. 128-145, https://doi.org/10.1016/j.chemgeo.2017.01.027.","productDescription":"18 p.","startPage":"128","endPage":"145","ipdsId":"IP-073590","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":469977,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2017.01.027","text":"Publisher Index Page"},{"id":342186,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"453","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5937bf2de4b0f6c2d0d9c756","contributors":{"authors":[{"text":"Vinson, David S.","contributorId":172390,"corporation":false,"usgs":false,"family":"Vinson","given":"David","email":"","middleInitial":"S.","affiliations":[{"id":25392,"text":"Department of Geography and Earth Science, University of North Carolina at Charlotte, North Carolina, USA","active":true,"usgs":false}],"preferred":false,"id":697341,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blair, Neal E.","contributorId":192674,"corporation":false,"usgs":false,"family":"Blair","given":"Neal","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":697342,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martini, Anna M.","contributorId":192675,"corporation":false,"usgs":false,"family":"Martini","given":"Anna","email":"","middleInitial":"M.","affiliations":[{"id":35249,"text":"Department of Geology, Amherst College","active":true,"usgs":false}],"preferred":false,"id":697343,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larter, Steve","contributorId":192676,"corporation":false,"usgs":false,"family":"Larter","given":"Steve","email":"","affiliations":[],"preferred":false,"id":697344,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":697340,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McIntosh, Jennifer C.","contributorId":139870,"corporation":false,"usgs":false,"family":"McIntosh","given":"Jennifer","email":"","middleInitial":"C.","affiliations":[{"id":13301,"text":"Department of Hydrology and Water Resources, University of Arizona, Tucson, Arizona","active":true,"usgs":false}],"preferred":false,"id":697345,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70194465,"text":"70194465 - 2017 - Grand challenges in understanding the interplay of climate and land changes","interactions":[],"lastModifiedDate":"2017-11-28T16:30:53","indexId":"70194465","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Grand challenges in understanding the interplay of climate and land changes","docAbstract":"Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-16-0012.1","usgsCitation":"Liu, S., Bond-Lamberty, B., Boysen, L.R., Ford, J.D., Fox, A., Gallo, K., Hatfield, J.L., Henebry, G.M., Huntington, T.G., Liu, Z., Loveland, T.R., Norby, R.J., Sohl, T.L., Steiner, A.L., Yuan, W., Zhang, Z., and Zhao, S., 2017, Grand challenges in understanding the interplay of climate and land changes: Earth Interactions, v. 21, p. 1-43, https://doi.org/10.1175/EI-D-16-0012.1.","productDescription":"Paper No. 2; 43 p.","startPage":"1","endPage":"43","ipdsId":"IP-073337","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":469960,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/11858/00-001M-0000-002D-26BD-F","text":"External 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MD","active":true,"usgs":false},{"id":13566,"text":"Joint Global Change Research Institute, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":723948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boysen, Lena R.","contributorId":200963,"corporation":false,"usgs":false,"family":"Boysen","given":"Lena","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":723949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, James D.","contributorId":200964,"corporation":false,"usgs":false,"family":"Ford","given":"James","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":723950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fox, Andrew","contributorId":190103,"corporation":false,"usgs":false,"family":"Fox","given":"Andrew","affiliations":[],"preferred":false,"id":723951,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gallo, Kevin 0000-0001-9162-5011 kgallo@usgs.gov","orcid":"https://orcid.org/0000-0001-9162-5011","contributorId":192334,"corporation":false,"usgs":true,"family":"Gallo","given":"Kevin","email":"kgallo@usgs.gov","affiliations":[],"preferred":true,"id":723952,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hatfield, Jerry L.","contributorId":71082,"corporation":false,"usgs":true,"family":"Hatfield","given":"Jerry","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723953,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Henebry, Geoffrey M.","contributorId":124528,"corporation":false,"usgs":false,"family":"Henebry","given":"Geoffrey","email":"","middleInitial":"M.","affiliations":[{"id":5087,"text":"Geographic Information Science Center of Excellence (GIScCE), South Dakota State University, Brookings, USA","active":true,"usgs":false}],"preferred":false,"id":723954,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Huntington, Thomas G. 0000-0002-9427-3530 thunting@usgs.gov","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":1884,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas","email":"thunting@usgs.gov","middleInitial":"G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":723944,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Liu, Zhihua","contributorId":105228,"corporation":false,"usgs":true,"family":"Liu","given":"Zhihua","email":"","affiliations":[],"preferred":false,"id":723955,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Loveland, Thomas R. 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140256,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","middleInitial":"R.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":723956,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Norby, Richard J. 0000-0002-0238-9828","orcid":"https://orcid.org/0000-0002-0238-9828","contributorId":167836,"corporation":false,"usgs":false,"family":"Norby","given":"Richard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":723957,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sohl, Terry L. 0000-0002-9771-4231 sohl@usgs.gov","orcid":"https://orcid.org/0000-0002-9771-4231","contributorId":648,"corporation":false,"usgs":true,"family":"Sohl","given":"Terry","email":"sohl@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":723958,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Steiner, Allison L.","contributorId":49261,"corporation":false,"usgs":true,"family":"Steiner","given":"Allison","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723959,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Yuan, Wenping","contributorId":83435,"corporation":false,"usgs":true,"family":"Yuan","given":"Wenping","email":"","affiliations":[],"preferred":false,"id":723960,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zhang, Zhao","contributorId":200965,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhao","email":"","affiliations":[],"preferred":false,"id":723961,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zhao, Shuqing","contributorId":9152,"corporation":false,"usgs":true,"family":"Zhao","given":"Shuqing","email":"","affiliations":[],"preferred":false,"id":723962,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70187329,"text":"70187329 - 2017 - A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations","interactions":[],"lastModifiedDate":"2017-04-28T15:43:43","indexId":"70187329","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations","docAbstract":"Researchers and practitioners alike often need to understand and characterize how water and solutes move through a stream in terms of the relative importance of in-stream and near-stream storage and transport processes. In-channel and subsurface storage processes are highly variable in space and time and difficult to measure. Storage estimates are commonly obtained using transient-storage models (TSMs) of the experimentally obtained solute-tracer test data. The TSM equations represent key transport and storage processes with a suite of numerical parameters. Parameter values are estimated via inverse modeling, in which parameter values are iteratively changed until model simulations closely match observed solute-tracer data. Several investigators have shown that TSM parameter estimates can be highly uncertain. When this is the case, parameter values cannot be used reliably to interpret stream-reach functioning. However, authors of most TSM studies do not evaluate or report parameter certainty. Here, we present a software tool linked to the One-dimensional Transport with Inflow and Storage (OTIS) model that enables researchers to conduct uncertainty analyses via Monte-Carlo parameter sampling and to visualize uncertainty and sensitivity results. We demonstrate application of our tool to 2 case studies and compare our results to output obtained from more traditional implementation of the OTIS model. We conclude by suggesting best practices for transient-storage modeling and recommend that future applications of TSMs include assessments of parameter certainty to support comparisons and more reliable interpretations of transport processes.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/690444","usgsCitation":"Ward, A.S., Kelleher, C.A., Mason, S.J., Wagener, T., McIntyre, N., McGlynn, B.L., Runkel, R.L., and Payn, R.A., 2017, A software tool to assess uncertainty in transient-storage model parameters using Monte Carlo simulations: Freshwater Science, v. 36, no. 1, p. 195-217, https://doi.org/10.1086/690444.","productDescription":"23 p.","startPage":"195","endPage":"217","ipdsId":"IP-074821","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":461661,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/2ec1a71e-046a-4faa-ad85-2f323af51119","text":"External Repository"},{"id":340632,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"590454a1e4b022cee40dc222","contributors":{"authors":[{"text":"Ward, Adam S.","contributorId":11508,"corporation":false,"usgs":true,"family":"Ward","given":"Adam","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":693393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelleher, Christa A.","contributorId":46417,"corporation":false,"usgs":true,"family":"Kelleher","given":"Christa","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":693394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mason, Seth J. K.","contributorId":191535,"corporation":false,"usgs":false,"family":"Mason","given":"Seth","email":"","middleInitial":"J. K.","affiliations":[],"preferred":false,"id":693395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagener, Thorsten","contributorId":176323,"corporation":false,"usgs":false,"family":"Wagener","given":"Thorsten","email":"","affiliations":[],"preferred":false,"id":693396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McIntyre, Neil","contributorId":191602,"corporation":false,"usgs":false,"family":"McIntyre","given":"Neil","email":"","affiliations":[],"preferred":false,"id":693397,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGlynn, Brian L.","contributorId":83012,"corporation":false,"usgs":true,"family":"McGlynn","given":"Brian","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":693398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":693392,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Payn, Robert A.","contributorId":36461,"corporation":false,"usgs":true,"family":"Payn","given":"Robert","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":693399,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70187205,"text":"70187205 - 2017 - Scale-specific habitat relationships influence patch occupancy: defining neighborhoods to optimize the effectiveness of landscape-scale grassland bird conservation","interactions":[],"lastModifiedDate":"2017-04-26T12:45:06","indexId":"70187205","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Scale-specific habitat relationships influence patch occupancy: defining neighborhoods to optimize the effectiveness of landscape-scale grassland bird conservation","docAbstract":"Context
Beyond the recognized importance of protecting large areas of contiguous habitat, conservation efforts for many species are complicated by the fact that patch suitability may also be affected by characteristics of the landscape within which the patch is located. Currently, little is known about the spatial scales at which species respond to different aspects of the landscape surrounding an occupied patch.
Objectives
Using grassland bird point count data, we describe an approach to evaluating scale-specific effects of landscape composition on patch occupancy.
Methods
We used data from 793 point count surveys conducted in idle and grazed grasslands across Wisconsin, USA from 2012 to 2014 to evaluate scale-dependencies in the response of grassland birds to landscape composition. Patch occupancy models were used to evaluate the relationship between occupancy and landscape composition at scales from 100 to 3000 m.
Results
Bobolink (Dolichonyx oryzivorus) exhibited a pattern indicating selection for grassland habitats in the surrounding landscape at all spatial scales while selecting against other habitats. Eastern Meadowlark (Sturnella magna) displayed evidence of scale sensitivity for all habitat types. Grasshopper Sparrow (Ammodramus savannarum) showed a strong positive response to pasture and idle grass at all scales and negatively to cropland at large scales. Unlike other species, patch occupancy by Henslow’s Sparrow (A. henslowii) was primarily influenced by patch area.
Conclusions
Our results suggest that both working grasslands (pasture) and idle conservation grasslands can play an important role in grassland bird conservation but also highlight the importance of considering species-specific patch and landscape characteristics for effective conservation.
Evidence of climate-change-driven shifts in plant and animal phenology have raised concerns that certain trophic interactions may be increasingly mismatched in time, resulting in declines in reproductive success. Given the constraints imposed by extreme seasonality at high latitudes and the rapid shifts in phenology seen in the Arctic, we would also expect Antarctic species to be highly vulnerable to climate-change-driven phenological mismatches with their environment. However, few studies have assessed the impacts of phenological change in Antarctica. Using the largest database of phytoplankton phenology, sea-ice phenology, and Adélie Penguin breeding phenology and breeding success assembled to date, we find that, while a temporal match between Penguin breeding phenology and optimal environmental conditions sets an upper limit on breeding success, only a weak relationship to the mean exists. Despite previous work suggesting that divergent trends in Adélie Penguin breeding phenology are apparent across the Antarctic continent, we find no such trends. Furthermore, we find no trend in the magnitude of phenological mismatch, suggesting that mismatch is driven by interannual variability in environmental conditions rather than climate-change-driven trends, as observed in other systems. We propose several criteria necessary for a species to experience a strong climate-change-driven phenological mismatch, of which several may be violated by this system.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.1749","usgsCitation":"Youngflesh, C., Jenouvrier, S., Li, Y., Ji, R., Ainley, D.G., Ballard, G., Barbraud, C., Delord, K., Dugger, K., Emmerson, L.M., Fraser, W.R., Hinke, J.T., Lyver, P.O., Olmastroni, S., Southwell, C.J., Trivelpiece, S.G., Trivelpiece, W.Z., and Lynch, H.J., 2017, Circumpolar analysis of the Adélie Penguin reveals the importance of environmental variability in phenological mismatch: Ecology, v. 98, no. 4, p. 940-951, https://doi.org/10.1002/ecy.1749.","productDescription":"12 p.","startPage":"940","endPage":"951","ipdsId":"IP-076568","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":469974,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/8890","text":"External 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Yun","contributorId":197732,"corporation":false,"usgs":false,"family":"Li","given":"Yun","affiliations":[],"preferred":false,"id":714113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ji, Rubao","contributorId":197699,"corporation":false,"usgs":false,"family":"Ji","given":"Rubao","affiliations":[],"preferred":false,"id":714114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ainley, David G.","contributorId":32039,"corporation":false,"usgs":false,"family":"Ainley","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":34154,"text":"Point Reyes Bird Observatory, Stinson Beach, CA","active":true,"usgs":false}],"preferred":false,"id":714115,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ballard, Grant","contributorId":197700,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","email":"","affiliations":[],"preferred":false,"id":714116,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barbraud, 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data","interactions":[],"lastModifiedDate":"2017-11-06T11:06:34","indexId":"70193667","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","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":"Estimating occupancy probability of moose using hunter survey data","docAbstract":"Monitoring rare species can be difficult, especially across large spatial extents, making conventional methods of population monitoring costly and logistically challenging. Citizen science has the potential to produce observational data across large areas that can be used to monitor wildlife distributions using occupancy models. We used citizen science (i.e., hunter surveys) to facilitate monitoring of moose (Alces alces) populations, an especially important endeavor because of their recent apparent declines in the northeastern and upper midwestern regions of the United States. To better understand patterns of occurrence of moose in New York, we used data collected through an annual survey of approximately 11,000 hunters between 2012 and 2014 that recorded detection–non-detection data of moose and other species. We estimated patterns of occurrence of moose in relation to land cover characteristics, climate effects, and interspecific interactions using occupancy models to analyze spatially referenced moose observations. Coniferous and deciduous forest with low prevalence of white-tailed deer (Odocoileus virginianus) had the highest probability of moose occurrence. This study highlights the potential of data collected using citizen science for understanding the spatial distribution of low-density species across large spatial extents and providing key information regarding where and when future research and management activities should be focused.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21207","usgsCitation":"Crum, N.J., Fuller, A.K., Sutherland, C.S., Cooch, E.G., and Hurst, J.E., 2017, Estimating occupancy probability of moose using hunter survey data: Journal of Wildlife Management, v. 81, no. 3, p. 521-534, https://doi.org/10.1002/jwmg.21207.","productDescription":"14 p.","startPage":"521","endPage":"534","ipdsId":"IP-074160","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":461649,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21207","text":"Publisher Index Page"},{"id":348253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.365966796875,\n 42.0125705565935\n ],\n [\n -73.267822265625,\n 42.0125705565935\n ],\n [\n -73.267822265625,\n 45.00753503123719\n ],\n [\n -76.365966796875,\n 45.00753503123719\n ],\n [\n -76.365966796875,\n 42.0125705565935\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-28","publicationStatus":"PW","scienceBaseUri":"5a07e90fe4b09af898c8cbe9","contributors":{"authors":[{"text":"Crum, Nathan J.","contributorId":200016,"corporation":false,"usgs":false,"family":"Crum","given":"Nathan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":720654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719841,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutherland, Christopher S.","contributorId":139375,"corporation":false,"usgs":false,"family":"Sutherland","given":"Christopher","email":"","middleInitial":"S.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":720655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cooch, Evan G.","contributorId":100673,"corporation":false,"usgs":true,"family":"Cooch","given":"Evan","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":720656,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hurst, Jeremy E.","contributorId":177504,"corporation":false,"usgs":false,"family":"Hurst","given":"Jeremy","email":"","middleInitial":"E.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":720657,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192500,"text":"70192500 - 2017 - Seasonal survival of adult female mottled ducks","interactions":[],"lastModifiedDate":"2017-10-26T14:34:00","indexId":"70192500","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","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":"Seasonal survival of adult female mottled ducks","docAbstract":"The mottled duck (Anas fulgivula) is a non-migratory duck dependent on coastal habitats to meet all of its life cycle requirements in the Western Gulf Coast (WGC) of Texas and Louisiana, USA. This population of mottled ducks has experienced a moderate decline during the past 2 decades. Adult survival has been identified as an important factor influencing population demography. Previous work based on band-recovery data has provided only annual estimates of survival. We assessed seasonal patterns of female mottled duck survival from 2009 to 2012 using individuals marked with satellite platform transmitter terminals (PTTs). We used temperature and movement sensors within each PTT to indicate potential mortality events. We estimated cumulative weekly survival and ranked factors influential in patterns of mortality using known-fate modeling in Program MARK. Models included 4 predictors: week; hunting and non-hunting periods; biological periods defined as breeding, brooding, molt, and pairing; and mass at time of capture. Models containing hunt periods, during and outside the mottled duck season, comprised essentially 100% of model weights where both legal and illegal harvest had a negative influence on mottled duck survival. Survival rates were low during 2009–2011 (12–38% annual rate of survival), when compared with the long-term banding average of 53% annual survival. During 2011, survival of female mottled ducks was the lowest annual rate (12%) ever documented and coincided with extreme drought. Management actions maximizing the availability of wetlands and associated upland habitats during hunting seasons and drought conditions may increase adult female mottled duck survival.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21221","usgsCitation":"Moon, J.A., Haukos, D.A., and Conway, W.C., 2017, Seasonal survival of adult female mottled ducks: Journal of Wildlife Management, v. 81, no. 3, p. 461-469, https://doi.org/10.1002/jwmg.21221.","productDescription":"9 p.","startPage":"461","endPage":"469","ipdsId":"IP-064529","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":461669,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21221","text":"Publisher Index Page"},{"id":347493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Chenier Plain Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.086669921875,\n 29.480252193344267\n ],\n [\n -93.74359130859375,\n 29.480252193344267\n ],\n [\n -93.74359130859375,\n 30.375244781665323\n ],\n [\n -95.086669921875,\n 30.375244781665323\n ],\n [\n -95.086669921875,\n 29.480252193344267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-09","publicationStatus":"PW","scienceBaseUri":"5a07e910e4b09af898c8cbf1","contributors":{"authors":[{"text":"Moon, Jena A.","contributorId":171483,"corporation":false,"usgs":false,"family":"Moon","given":"Jena","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":716433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":716081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Warren C.","contributorId":51550,"corporation":false,"usgs":true,"family":"Conway","given":"Warren","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":716434,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70189479,"text":"70189479 - 2017 - Community stability within the St. Marys River fish community: Evidence from trawl surveys","interactions":[],"lastModifiedDate":"2018-03-28T11:22:52","indexId":"70189479","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Community stability within the St. Marys River fish community: Evidence from trawl surveys","docAbstract":"A trawl survey was conducted in the Saint Marys River during 2010–2011 and we compared our results to a prior trawl survey conducted during 1979–1983 to look for long-term changes in the fish community, especially in terms of changes induced by invasive species. We found no substantive temporal differences in fish density, fish biomass, or fish diversity; lower trawl biomass during 2010–2011 was likely a result of day versus night trawling. The Saint Marys River remains a center of high fish diversity, invasive species remain rare, and the system continues to exhibit overall long-term stability. Trawling captured a wide range of fish species, but was likely not an effective stock assessment tool for managed game fish because catch rates were low or variable for all game species except yellow perch. Trawling appeared to be an effective tool for sampling connecting channel diversity, especially when large numbers of individuals are needed for directed studies, but annual sampling would be needed to use data to assess recruitment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2016.10.014","usgsCitation":"Schaeffer, J.S., Bowen, A.K., and Fielder, D.G., 2017, Community stability within the St. Marys River fish community: Evidence from trawl surveys: Journal of Great Lakes Research, v. 43, no. 2, p. 399-404, https://doi.org/10.1016/j.jglr.2016.10.014.","productDescription":"6 p.","startPage":"399","endPage":"404","ipdsId":"IP-074635","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469965,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2016.10.014","text":"Publisher Index Page"},{"id":343814,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"St. Marys River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.6441650390625,\n 45.920587344733654\n ],\n [\n -83.529052734375,\n 45.920587344733654\n ],\n [\n -83.529052734375,\n 46.558860303117164\n ],\n [\n -84.6441650390625,\n 46.558860303117164\n ],\n [\n -84.6441650390625,\n 45.920587344733654\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5968869ee4b0d1f9f05f597a","contributors":{"authors":[{"text":"Schaeffer, Jeffrey S.","contributorId":89083,"corporation":false,"usgs":true,"family":"Schaeffer","given":"Jeffrey","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":704875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bowen, Anjanette K.","contributorId":27398,"corporation":false,"usgs":true,"family":"Bowen","given":"Anjanette","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":704876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fielder, David G.","contributorId":127535,"corporation":false,"usgs":false,"family":"Fielder","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":7024,"text":"Michigan Department of Natural Resources, Fisheries Research Station","active":true,"usgs":false}],"preferred":false,"id":704877,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}