{"pageNumber":"916","pageRowStart":"22875","pageSize":"25","recordCount":184925,"records":[{"id":70193311,"text":"70193311 - 2018 - Timelines and mechanisms of wildlife population recovery following the Exxon Valdez oil spill","interactions":[],"lastModifiedDate":"2018-02-28T09:39:33","indexId":"70193311","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5536,"text":"Deep Sea Research Part II: Topical Studies in Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Timelines and mechanisms of wildlife population recovery following the Exxon Valdez oil spill","docAbstract":"<p><span>Research and monitoring activities over the 28 years since the T/V&nbsp;</span><i data-reactid=\"172\">Exxon Valdez</i><span><span>&nbsp;</span>ran aground and spilled oil into Prince William Sound, Alaska have led to an improved understanding of how wildlife populations were damaged, as well as the mechanisms and timelines of recovery. A key finding was that for some species, such as harlequin ducks and sea otters, chronic oil spill effects persisted for at least two decades and were a larger influence on population dynamics over the long term than acute effects of the spill. These data also offer insights into population variation resulting from factors other than the oil spill. For example, while many seabirds experienced direct and indirect effects of the spill, population trajectories of some piscivorous birds, including pigeon guillemots and marbled murrelets, were linked to long-term environmental changes independent of spill effects. Another species, killer whales, suffered population declines due to acute spill effects that have not been resolved despite lack of chronic direct effects, representing a novel pathway of long-term injury. The observed variation in mechanisms and timelines of recovery is linked to species specific life history and natural history traits, and thus may be useful for predicting population recovery for other species following other spills.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr2.2017.04.007","usgsCitation":"Esler, D., Ballachey, B.E., Matkin, C.O., Cushing, D., Kaler, R., Bodkin, J.L., Monson, D., Esslinger, G.G., and Kloecker, K.A., 2018, Timelines and mechanisms of wildlife population recovery following the Exxon Valdez oil spill: Deep Sea Research Part II: Topical Studies in Oceanography, v. 147, p. 36-42, https://doi.org/10.1016/j.dsr2.2017.04.007.","productDescription":"7 p.","startPage":"36","endPage":"42","ipdsId":"IP-080594","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":461135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr2.2017.04.007","text":"Publisher Index Page"},{"id":348051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -151.875,\n              58.6769376725869\n            ],\n            [\n              -143.08593749999997,\n              58.6769376725869\n            ],\n            [\n              -143.08593749999997,\n              62.30879369102805\n            ],\n            [\n              -151.875,\n              62.30879369102805\n            ],\n            [\n              -151.875,\n              58.6769376725869\n            ]\n          ]\n   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bballachey@usgs.gov","orcid":"https://orcid.org/0000-0003-1855-9171","contributorId":2966,"corporation":false,"usgs":true,"family":"Ballachey","given":"Brenda","email":"bballachey@usgs.gov","middleInitial":"E.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":718635,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matkin, Craig O.","contributorId":192145,"corporation":false,"usgs":false,"family":"Matkin","given":"Craig","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":718636,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cushing, Daniel","contributorId":199323,"corporation":false,"usgs":false,"family":"Cushing","given":"Daniel","affiliations":[],"preferred":false,"id":718637,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaler, Robert","contributorId":199324,"corporation":false,"usgs":false,"family":"Kaler","given":"Robert","email":"","affiliations":[],"preferred":false,"id":718638,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bodkin, James L. 0000-0003-1641-4438 jbodkin@usgs.gov","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":748,"corporation":false,"usgs":true,"family":"Bodkin","given":"James","email":"jbodkin@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":718639,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":718640,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Esslinger, George G. 0000-0002-3459-0083 gesslinger@usgs.gov","orcid":"https://orcid.org/0000-0002-3459-0083","contributorId":131009,"corporation":false,"usgs":true,"family":"Esslinger","given":"George","email":"gesslinger@usgs.gov","middleInitial":"G.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":718641,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kloecker, Kimberly A. 0000-0002-2461-968X kkloecker@usgs.gov","orcid":"https://orcid.org/0000-0002-2461-968X","contributorId":3442,"corporation":false,"usgs":true,"family":"Kloecker","given":"Kimberly","email":"kkloecker@usgs.gov","middleInitial":"A.","affiliations":[{"id":116,"text":"Alaska Science Center Biology 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,{"id":70192606,"text":"70192606 - 2018 - Small-scale genetic structure in an endangered wetland specialist: possible effects of landscape change and population recovery","interactions":[],"lastModifiedDate":"2018-01-24T15:50:38","indexId":"70192606","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Small-scale genetic structure in an endangered wetland specialist: possible effects of landscape change and population recovery","docAbstract":"<p><span>The effects of anthropogenic landscape change on genetic population structure are well studied, but the temporal and spatial scales at which genetic structure can develop, especially in taxa with high dispersal capabilities like birds, are less well understood. We investigated population structure in the Hawaiian gallinule (</span><i class=\"EmphasisTypeItalic \">Gallinula galeata sandvicensis</i><span>), an endangered wetland specialist bird on the island of O`ahu (Hawai`i, USA). Hawaiian gallinules have experienced a gradual population recovery from near extinction in the 1950s, and have recolonized wetlands on O`ahu in the context of a rapidly urbanizing landscape. We genotyped 152 Hawaiian gallinules at 12 microsatellite loci and sequenced a 520 base-pair fragment of the ND2 region of mitochondrial DNA (mtDNA) from individuals captured at 13 wetland locations on O`ahu in 2014–2016. We observed moderate to high genetic structuring (overall microsatellite F</span><sub>ST</sub><span> = 0.098, mtDNA F</span><sub>ST</sub><span> = 0.248) among populations of Hawaiian gallinules occupying wetlands at very close geographic proximity (e.g., 1.5–55&nbsp;km). Asymmetry in gene flow estimates suggests that Hawaiian gallinules may have persisted in 2–3 strongholds which served as source populations that recolonized more recently restored habitats currently supporting large numbers of birds. Our results highlight that genetic structure can develop in taxa that are expanding their range after severe population decline, and that biologically significant structuring can occur over small geographic distances, even in avian taxa.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10592-017-1020-0","usgsCitation":"van Rees, C.B., Reed, J.M., Wilson, R.E., Underwood, J., and Sonsthagen, S.A., 2018, Small-scale genetic structure in an endangered wetland specialist: possible effects of landscape change and population recovery: Conservation Genetics, v. 19, no. 1, p. 129-142, https://doi.org/10.1007/s10592-017-1020-0.","productDescription":"14 p.","startPage":"129","endPage":"142","ipdsId":"IP-086333","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":348064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"O'ahu","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -158.35968017578125,\n              21.21642046916312\n            ],\n            [\n              -157.60574340820312,\n              21.21642046916312\n            ],\n            [\n              -157.60574340820312,\n              21.749295836732088\n            ],\n            [\n              -158.35968017578125,\n              21.749295836732088\n            ],\n            [\n              -158.35968017578125,\n              21.21642046916312\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"19","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59fadd1fe4b0531197b13c72","contributors":{"authors":[{"text":"van Rees, Charles B.","contributorId":198604,"corporation":false,"usgs":false,"family":"van Rees","given":"Charles","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":716535,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, J. Michael","contributorId":198605,"corporation":false,"usgs":false,"family":"Reed","given":"J.","email":"","middleInitial":"Michael","affiliations":[],"preferred":false,"id":716536,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":716537,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Underwood, Jared G.","contributorId":139332,"corporation":false,"usgs":false,"family":"Underwood","given":"Jared G.","affiliations":[],"preferred":false,"id":716538,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":716534,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70194480,"text":"70194480 - 2018 - Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: Effects of scale and climate change","interactions":[],"lastModifiedDate":"2017-11-29T12:39:47","indexId":"70194480","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: Effects of scale and climate change","docAbstract":"<p><span>Climate-change driven increases in water temperature pose challenges for aquatic organisms. Predictions of impacts typically do not account for fine-grained spatiotemporal thermal patterns in rivers. Patches of cooler water could serve as refuges for anadromous species like salmon that migrate during summer. We used high-resolution remotely sensed water temperature data to characterize summer thermal heterogeneity patterns for 11,308&nbsp;km of second–seventh-order rivers throughout the Pacific Northwest and northern California (USA). We evaluated (1) water temperature patterns at different spatial resolutions, (2) the frequency, size, and spacing of cool thermal patches suitable for Pacific salmon (i.e., contiguous stretches ≥ 0.25&nbsp;km, ≤ 15&nbsp;°C&nbsp;and ≥ 2&nbsp;°C, aooler than adjacent water), and (3) potential influences of climate change on availability of cool patches. Thermal heterogeneity was nonlinearly related to the spatial resolution of water temperature data, and heterogeneity at fine resolution (&lt; 1&nbsp;km) would have been difficult to quantify without spatially continuous data. Cool patches were generally &gt; 2.7 and &lt; 13.0&nbsp;km long, and spacing among patches was generally &gt; 5.7 and &lt; 49.4&nbsp;km. Thermal heterogeneity varied among rivers, some of which had long uninterrupted stretches of warm water ≥ 20&nbsp;°C, and others had many smaller cool patches. Our models predicted little change in future thermal heterogeneity among rivers, but within-river patterns sometimes changed markedly compared to contemporary patterns. These results can inform long-term monitoring programs as well as near-term climate-adaptation strategies.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s00027-017-0557-9","usgsCitation":"Fullerton, A., Torgersen, C.E., Lawer, J., Steel, E.A., Ebersole, J.L., and Lee, S., 2018, Longitudinal thermal heterogeneity in rivers and refugia for coldwater species: Effects of scale and climate change: Aquatic Sciences, v. 80, https://doi.org/10.1007/s00027-017-0557-9.","productDescription":"Article 3; 15p.","startPage":"15","ipdsId":"IP-090182","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"links":[{"id":469171,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/5854952","text":"External Repository"},{"id":349527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -124.71679687499999,\n              39\n            ],\n            [\n              -112.763671875,\n              39\n            ],\n            [\n              -112.763671875,\n              49.081062364320736\n            ],\n            [\n              -124.71679687499999,\n              49.081062364320736\n            ],\n            [\n              -124.71679687499999,\n              39\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"80","edition":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-21","publicationStatus":"PW","scienceBaseUri":"5a60fad8e4b06e28e9c227d5","contributors":{"authors":[{"text":"Fullerton, A.H.","contributorId":200991,"corporation":false,"usgs":false,"family":"Fullerton","given":"A.H.","email":"","affiliations":[],"preferred":false,"id":724027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":724026,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawer, J.J.","contributorId":200992,"corporation":false,"usgs":false,"family":"Lawer","given":"J.J.","email":"","affiliations":[],"preferred":false,"id":724028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steel, E. A.","contributorId":200993,"corporation":false,"usgs":false,"family":"Steel","given":"E.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724029,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebersole, J. L.","contributorId":74221,"corporation":false,"usgs":false,"family":"Ebersole","given":"J.","email":"","middleInitial":"L.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":724030,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, S.Y.","contributorId":200994,"corporation":false,"usgs":false,"family":"Lee","given":"S.Y.","email":"","affiliations":[],"preferred":false,"id":724031,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70192806,"text":"70192806 - 2018 - Tracing biogeochemical subsidies from glacier runoff into Alaska's coastal marine food webs","interactions":[],"lastModifiedDate":"2018-01-05T14:17:02","indexId":"70192806","displayToPublicDate":"2017-11-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Tracing biogeochemical subsidies from glacier runoff into Alaska's coastal marine food webs","docAbstract":"<p><span>Nearly half of the freshwater discharge into the Gulf of Alaska originates from landscapes draining glacier runoff, but the influence of the influx of riverine organic matter on the trophodynamics of coastal marine food webs is not well understood. We quantified the ecological impact of riverine organic matter subsidies to glacier-marine habitats by developing a multi-trophic level Bayesian three-isotope mixing model. We utilized large gradients in stable (δ</span><sup>13</sup><span>C, δ</span><sup>15</sup><span>N, δ</span><sup>2</sup><span>H) and radiogenic (Δ</span><sup>14</sup><span>C) isotopes that trace riverine and marine organic matter sources as they are passed from lower to higher trophic levels in glacial-marine habitats. We also compared isotope ratios between glacial-marine and more oceanic habitats. Based on isotopic measurements of potential baseline sources, ambient water and tissues of marine consumers, estimates of the riverine organic matter source contribution to upper trophic-level species including fish and seabirds ranged from 12% to 44%. Variability in resource use among similar taxa corresponded to variation in species distribution and life histories. For example, riverine organic matter assimilation by the glacier-nesting seabirds Kittlitz's murrelet (</span><i>Brachyramphus brevirostris</i><span>) was greater than that of the forest-nesting marbled murrelet (</span><i>B.&nbsp;marmoratus</i><span>). The particulate and dissolved organic carbon in glacial runoff and near surface coastal waters was aged (12100–1500&nbsp;years BP<span>&nbsp;</span></span><sup>14</sup><span>C-age) but dissolved inorganic carbon and biota in coastal waters were young (530&nbsp;years BP<span>&nbsp;</span></span><sup>14</sup><span>C-age to modern). Thus terrestrial-derived subsidies in marine food webs were primarily composed of young organic matter sources released from glacier ecosystems and their surrounding watersheds. Stable isotope compositions also revealed a divergence in food web structure between glacial-marine and oceanic sites. This work demonstrates linkages between terrestrial and marine ecosystems, and facilitates a greater understanding of how climate-driven changes in freshwater runoff have the potential to alter food web dynamics within coastal marine ecosystems in Alaska.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13875","usgsCitation":"Arimitsu, M.L., Hobson, K.A., Webber, D.N., Piatt, J.F., Hood, E.W., and Fellman, J.B., 2018, Tracing biogeochemical subsidies from glacier runoff into Alaska's coastal marine food webs: Global Change Biology, v. 24, no. 1, p. 387-398, https://doi.org/10.1111/gcb.13875.","productDescription":"12 p.","startPage":"387","endPage":"398","ipdsId":"IP-085458","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":469172,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/59558","text":"External Repository"},{"id":438079,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z036D9","text":"USGS data release","linkHelpText":"Biogeochemical Subsidies from Glacier Runoff into Alaska Coastal Marine Food Webs, Gulf of Alaska, 2012-2013"},{"id":348063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -148.95263671875,\n              59.6954703349364\n            ],\n            [\n              -145.283203125,\n              59.6954703349364\n            ],\n            [\n              -145.283203125,\n              61.62206526043813\n            ],\n            [\n              -148.95263671875,\n              61.62206526043813\n            ],\n            [\n              -148.95263671875,\n              59.6954703349364\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"24","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-23","publicationStatus":"PW","scienceBaseUri":"59fadd1fe4b0531197b13c6f","contributors":{"authors":[{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":717011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hobson, Keith A.","contributorId":190909,"corporation":false,"usgs":false,"family":"Hobson","given":"Keith","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":717012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Webber, D’Arcy N.","contributorId":198740,"corporation":false,"usgs":false,"family":"Webber","given":"D’Arcy","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":717013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":717014,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hood, Eran W.","contributorId":198165,"corporation":false,"usgs":false,"family":"Hood","given":"Eran","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":717015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fellman, Jason B.","contributorId":198741,"corporation":false,"usgs":false,"family":"Fellman","given":"Jason","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":717016,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70193072,"text":"70193072 - 2018 - Greater sage-grouse population trends across Wyoming","interactions":[],"lastModifiedDate":"2018-01-24T15:51:33","indexId":"70193072","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2018","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":"Greater sage-grouse population trends across Wyoming","docAbstract":"<p><span>The scale at which analyses are performed can have an effect on model results and often one scale does not accurately describe the ecological phenomena of interest (e.g., population trends) for wide-ranging species: yet, most ecological studies are performed at a single, arbitrary scale. To best determine local and regional trends for greater sage-grouse (</span><i>Centrocercus urophasianus</i><span>) in Wyoming, USA, we modeled density-independent and -dependent population growth across multiple spatial scales relevant to management and conservation (Core Areas [habitat encompassing approximately 83% of the sage-grouse population on ∼24% of surface area in Wyoming], local Working Groups [7 regional areas for which groups of local experts are tasked with implementing Wyoming's statewide sage-grouse conservation plan at the local level], Core Area status (Core Area vs. Non-Core Area) by Working Groups, and Core Areas by Working Groups). Our goal was to determine the influence of fine-scale population trends (Core Areas) on larger-scale populations (Working Group Areas). We modeled the natural log of change in population size (</span><span class=\"math-equation-construct\" data-equation-construct=\"true\"><span class=\"math-equation-image\" data-equation-image=\"true\"><img class=\"inlineGraphic\" src=\"http://onlinelibrary.wiley.com/store/10.1002/jwmg.21386/asset/equation/jwmg21386-math-0001.png?v=1&amp;s=2556af55897eeca9ae5982921cf5b63fdff4c53d\" alt=\"math formula\" data-mce-src=\"http://onlinelibrary.wiley.com/store/10.1002/jwmg.21386/asset/equation/jwmg21386-math-0001.png?v=1&amp;s=2556af55897eeca9ae5982921cf5b63fdff4c53d\"></span></span><span><span>&nbsp;</span>peak M lek counts) by time to calculate the finite rate of population growth (</span><i>λ</i><span>) for each population of interest from 1993 to 2015. We found that in general when Core Area status (Core Area vs. Non-Core Area) was investigated by Working Group Area, the 2 populations trended similarly and agreed with the overall trend of the Working Group Area. However, at the finer scale where Core Areas were analyzed separately, Core Areas within the same Working Group Area often trended differently and a few large Core Areas could influence the overall Working Group Area trend and mask trends occurring in smaller Core Areas. Relatively close fine-scale populations of sage-grouse can trend differently, indicating that large-scale trends may not accurately depict what is occurring across the landscape (e.g., local effects of gas and oil fields may be masked by increasing larger populations).<span>&nbsp;</span></span></p>","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.21386","usgsCitation":"Edmunds, D.R., Aldridge, C.L., O’Donnell, M.S., and Monroe, A., 2018, Greater sage-grouse population trends across Wyoming: Journal of Wildlife Management, v. 82, p. 397-412, https://doi.org/10.1002/jwmg.21386.","productDescription":"16 p.","startPage":"397","endPage":"412","ipdsId":"IP-087426","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21386","text":"Publisher Index Page"},{"id":347946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-110.048476,40.997555],[-110.121639,40.997101],[-110.125709,40.99655],[-110.237848,40.995427],[-110.250709,40.996089],[-110.375714,40.994947],[-110.500718,40.994746],[-110.539819,40.996346],[-110.715026,40.996347],[-110.750727,40.996847],[-111.046723,40.997959],[-111.046551,41.251716],[-111.0466,41.360692],[-111.046264,41.377731],[-111.045789,41.565571],[-111.045818,41.579845],[-111.046689,42.001567],[-111.047109,42.142497],[-111.047107,42.148971],[-111.047058,42.182672],[-111.047097,42.194773],[-111.047074,42.280787],[-111.04708,42.34942],[-111.046801,42.504946],[-111.046719,42.513118],[-111.046017,42.582723],[-111.043564,42.722624],[-111.044135,42.874924],[-111.043959,42.96445],[-111.043957,42.969482],[-111.043924,42.975063],[-111.044129,43.018702],[-111.044156,43.020052],[-111.044206,43.022614],[-111.044034,43.024581],[-111.044034,43.024844],[-111.044033,43.026411],[-111.044094,43.02927],[-111.043997,43.041415],[-111.044058,43.04464],[-111.044063,43.046302],[-111.044086,43.054819],[-111.044117,43.060309],[-111.04415,43.066172],[-111.044162,43.068222],[-111.044143,43.072364],[-111.044235,43.177121],[-111.044266,43.177236],[-111.044232,43.18444],[-111.044168,43.189244],[-111.044229,43.195579],[-111.044617,43.31572],[-111.045205,43.501136],[-111.045706,43.659112],[-111.04588,43.681033],[-111.046118,43.684902],[-111.046051,43.685812],[-111.04611,43.687848],[-111.046421,43.722059],[-111.046435,43.726545],[-111.04634,43.726957],[-111.046715,43.815832],[-111.046515,43.908376],[-111.046917,43.974978],[-111.047064,43.983467],[-111.047349,43.999921],[-111.049077,44.020072],[-111.048751,44.060403],[-111.048751,44.060838],[-111.048633,44.062903],[-111.048452,44.114831],[-111.049119,44.124923],[-111.049695,44.353626],[-111.049148,44.374925],[-111.049216,44.435811],[-111.049194,44.438058],[-111.048974,44.474072],[-111.055208,44.624927],[-111.055333,44.666263],[-111.055511,44.725343],[-111.056416,44.749928],[-111.056888,44.866658],[-111.055629,44.933578],[-111.056207,44.935901],[-111.055199,45.001321],[-111.044275,45.001345],[-110.785008,45.002952],[-110.761554,44.999934],[-110.750767,44.997948],[-110.705272,44.992324],[-110.552433,44.992237],[-110.547165,44.992459],[-110.48807,44.992361],[-110.402927,44.99381],[-110.362698,45.000593],[-110.342131,44.999053],[-110.324441,44.999156],[-110.28677,44.99685],[-110.199503,44.996188],[-110.110103,45.003905],[-110.026347,45.003665],[-110.025544,45.003602],[-109.99505,45.003174],[-109.875735,45.003275],[-109.798687,45.002188],[-109.75073,45.001605],[-109.663673,45.002536],[-109.574321,45.002631],[-109.386432,45.004887],[-109.375713,45.00461],[-109.269294,45.005283],[-109.263431,45.005345],[-109.103445,45.005904],[-109.08301,44.99961],[-109.062262,44.999623],[-108.621313,45.000408],[-108.578484,45.000484],[-108.565921,45.000578],[-108.500679,44.999691],[-108.271201,45.000251],[-108.249345,44.999458],[-108.238139,45.000206],[-108.218479,45.000541],[-108.14939,45.001062],[-108.000663,45.001223],[-107.997353,45.001565],[-107.911743,45.001292],[-107.750654,45.000778],[-107.608854,45.00086],[-107.607824,45.000929],[-107.49205,45.00148],[-107.351441,45.001407],[-107.13418,45.000109],[-107.125633,44.999388],[-107.105685,44.998734],[-107.084939,44.996599],[-107.074996,44.997004],[-107.050801,44.996424],[-106.892875,44.995947],[-106.888773,44.995885],[-106.263586,44.993788],[-106.024814,44.993688],[-105.928184,44.993647],[-105.914258,44.999986],[-105.913382,45.000941],[-105.848065,45.000396],[-105.076607,45.000347],[-105.038405,45.000345],[-105.025266,45.00029],[-105.019284,45.000329],[-105.01824,45.000437],[-104.765063,44.999183],[-104.759855,44.999066],[-104.72637,44.999518],[-104.665171,44.998618],[-104.663882,44.998869],[-104.470422,44.998453],[-104.470117,44.998453],[-104.250145,44.99822],[-104.057698,44.997431],[-104.055914,44.874986],[-104.056496,44.867034],[-104.055963,44.768236],[-104.055963,44.767962],[-104.055934,44.72372],[-104.05587,44.723422],[-104.055777,44.700466],[-104.055938,44.693881],[-104.05581,44.691343],[-104.055877,44.571016],[-104.055892,44.543341],[-104.055927,44.51773],[-104.055389,44.249983],[-104.054487,44.180381],[-104.054562,44.141081],[-104.05495,43.93809],[-104.055077,43.936535],[-104.055488,43.853477],[-104.055488,43.853476],[-104.055138,43.750421],[-104.055133,43.747105],[-104.054902,43.583852],[-104.054885,43.583512],[-104.05484,43.579368],[-104.055032,43.558603],[-104.054787,43.503328],[-104.054786,43.503072],[-104.054779,43.477815],[-104.054766,43.428914],[-104.054614,43.390949],[-104.054403,43.325914],[-104.054218,43.30437],[-104.053884,43.297047],[-104.053876,43.289801],[-104.053127,43.000585],[-104.052863,42.754569],[-104.052809,42.749966],[-104.052583,42.650062],[-104.052741,42.633982],[-104.052586,42.630917],[-104.052773,42.611766],[-104.052775,42.61159],[-104.052775,42.610813],[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 \"}}]}","volume":"82","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-27","publicationStatus":"PW","scienceBaseUri":"59f98bb0e4b0531197af9fcf","contributors":{"authors":[{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":717821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":717822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":140876,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":717823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":717824,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70193229,"text":"70193229 - 2018 - Quantitative tools for implementing the new definition of significant portion of the range in the U.S. Endangered Species Act","interactions":[],"lastModifiedDate":"2018-01-05T14:21:05","indexId":"70193229","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative tools for implementing the new definition of significant portion of the range in the U.S. Endangered Species Act","docAbstract":"In 2014, the Fish and Wildlife Service (FWS) and National Marine Fisheries Service announced a new policy interpretation for the U.S. Endangered Species Act (ESA). According to the act, a species must be listed as threatened or endangered if it is determined to be threatened or endangered in a significant portion of its range (SPR). The 2014 policy seeks to provide consistency by establishing that a portion of the range should be considered significant if the associated individuals’ “removal would cause the entire species to become endangered or threatened.” We reviewed 20 quantitative techniques used to assess whether a portion of a species’ range is significant according to the new guidance. Our assessments are based on the 3R criteria—redundancy (i.e., buffering from catastrophe), resiliency (i.e., ability to withstand stochasticity), and representation (i.e., ability to evolve)—that the FWS uses to determine if a species merits listing. We identified data needs for each quantitative technique and considered which methods could be implemented given the data limitations typical of rare species. We also identified proxies for the 3Rs that may be used with limited data. To assess potential data availability, we evaluated 7 example species by accessing data in their species status assessments, which document all the information used during a listing decision. In all species, an SPR could be evaluated with at least one metric for each of the 3Rs robustly or with substantial assumptions. Resiliency assessments appeared most constrained by limited data, and many species lacked information on connectivity between subpopulations, genetic variation, and spatial variability in vital rates. These data gaps will likely make SPR assessments for species with complex life histories or that cross national boundaries difficult. Although we reviewed techniques for the ESA, other countries require identification of significant areas and could benefit from this research.","language":"English","publisher":"Wiley","doi":"10.1111/cobi.12963","usgsCitation":"Earl, J.E., Nicol, S., Wiederholt, R., Diffendorfer, J.E., Semmens, D.J., Flockhart, D.T., Mattsson, B., McCracken, G., Norris, D.R., Thogmartin, W.E., and Lopez-Hoffman, L., 2018, Quantitative tools for implementing the new definition of significant portion of the range in the U.S. Endangered Species Act: Conservation Biology, v. 32, no. 1, p. 35-49, https://doi.org/10.1111/cobi.12963.","productDescription":"15 p.","startPage":"35","endPage":"49","ipdsId":"IP-084220","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":503757,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://zotero.org/groups/5435545/items/TRDY2P4X","text":"External 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,{"id":70193306,"text":"70193306 - 2018 - Geochemical and Pb isotopic characterization of soil, groundwater, human hair, and corn samples from the Domizio Flegreo and Agro Aversano area (Campania region, Italy)","interactions":[],"lastModifiedDate":"2017-12-11T13:20:42","indexId":"70193306","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical and Pb isotopic characterization of soil, groundwater, human hair, and corn samples from the Domizio Flegreo and Agro Aversano area (Campania region, Italy)","docAbstract":"<p id=\"sp0080\">A geochemical survey was carried out to investigate metal contamination in the Domizio Littoral and Agro Aversano area (Southern Italy) by means of soil, groundwater, human hair and corn samples. Pb isotope ratios were also determined to identify the sources of metals. Specifically, the investigation focused on topsoils (<i>n</i>&nbsp;=&nbsp;1064), groundwater (<i>n</i>&nbsp;=&nbsp;26), 25 human hair (<i>n</i>&nbsp;=&nbsp;24) and corn samples (<i>n</i>&nbsp;=&nbsp;13). Topsoils have been sampled and analysed in a previous study for 53 elements (including potentially harmful ones), and determined by ICP-MS after dissolving with aqua regia. Groundwater was analysed for 72 elements by ICP-MS and by ICP-ES. Samples of human hair were prepared and analysed for 16 elements by ICP-MS. Dried corn collected at several farms were also analysed for 53 elements by ICP-MS. The isotopic ratios of <sup>206</sup>Pb/<sup>207</sup>Pb and <sup>208</sup>Pb/<sup>207</sup>Pb in selected topsoil (<i>n</i>&nbsp;=&nbsp;24), groundwater (<i>n</i>&nbsp;=&nbsp;9), human hair (<i>n</i>&nbsp;=&nbsp;9) and corn (<i>n</i>&nbsp;=&nbsp;4) samples were analysed from both eluates and residues to investigate possible anthropogenic contamination and geogenic contributions. All data were processed and mapped by ArcGis software to produce interpolated maps and contamination factor maps of potentially harmful elements, in accordance with Italian Environmental Law (Legislative Decree 152/06). Results show that soil sampling sites are characterized by As, Cd, Co, Cr, Cu, Hg, Pb, Se, and Zn contents exceeding the action limits established for residential land use (RAL) and, in some cases, also the action limits for industrial land use (IAL) as established by Legislative Decree 152/06. A map of contamination factors and a map showing the degrees of contamination indicate that the areas in the municipalities of Acerra, Casoria and Giugliano have been affected by considerable anthropogenic-related pollution. To interpret the isotopic data and roughly estimate proportion of Pb from an anthropogenic source we broadly defined possible natural and anthropogenic Pb end-member fields based on literature data. For example, we summarized data for Vesuvius and Campi Flegrei volcanic rocks, gasoline, and aerosol deposits.</p><p id=\"sp0085\">Lead isotope data show mixing between geogenic and anthropogenic sources. Topsoil, groundwater, human hair and corn samples show a greater contribution from geogenic sources like the Yellow Tuff (from Campi Flegrei) and volcanic rocks from Mt. Vesuvius. Aerosols, fly ash and gasoline (anthropogenic sources) have also been contributors. In detail, 46% of the topsoil residues, 96% of topsoil leachates, 88% of groundwater, 90% of human hair, and 25% of corn samples indicate that &gt;&nbsp;50% percent of the lead in this area can be ascribed to anthropogenic activity.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2017.01.007","usgsCitation":"Rezza, C., Albanese, S., Ayuso, R.A., Lima, A., Sorvari, J., and De Vivo, B., 2018, Geochemical and Pb isotopic characterization of soil, groundwater, human hair, and corn samples from the Domizio Flegreo and Agro Aversano area (Campania region, Italy): Journal of Geochemical Exploration, v. 184, no. B, p. 318-332, https://doi.org/10.1016/j.gexplo.2017.01.007.","productDescription":"15 p.","startPage":"318","endPage":"332","ipdsId":"IP-078832","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":347919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","state":"Campania region","otherGeospatial":"Agro Aversano, Domizio Flegreo","volume":"184","issue":"B","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f98ba7e4b0531197af9f9c","contributors":{"authors":[{"text":"Rezza, Carmela","contributorId":199318,"corporation":false,"usgs":false,"family":"Rezza","given":"Carmela","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":718740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albanese, Stefano","contributorId":199319,"corporation":false,"usgs":false,"family":"Albanese","given":"Stefano","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":718741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":718742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lima, Annamaria","contributorId":176910,"corporation":false,"usgs":false,"family":"Lima","given":"Annamaria","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":718743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sorvari, Jaana","contributorId":199320,"corporation":false,"usgs":false,"family":"Sorvari","given":"Jaana","email":"","affiliations":[{"id":6718,"text":"Aalto University, Finland","active":true,"usgs":false}],"preferred":false,"id":718744,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"De Vivo, Benedetto","contributorId":199321,"corporation":false,"usgs":false,"family":"De Vivo","given":"Benedetto","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":718745,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70195456,"text":"70195456 - 2018 - The Pleurobemini (Bivalvia: Unionida) revisited: Molecular species delineation using a mitochondrial DNA gene reveals multiple conspecifics and undescribed species","interactions":[],"lastModifiedDate":"2018-06-04T16:15:23","indexId":"70195456","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5630,"text":"Invertebrate Systematics","active":true,"publicationSubtype":{"id":10}},"title":"The Pleurobemini (Bivalvia: Unionida) revisited: Molecular species delineation using a mitochondrial DNA gene reveals multiple conspecifics and undescribed species","docAbstract":"<p><span>The Pleurobemini (Bivalvia: Unionida) represent approximately one-third of freshwater mussel diversity in North America. Species identification within this group is challenging due to morphological convergence and phenotypic plasticity. Accurate species identification, including characterisation of currently unrecognised taxa, is required to develop effective conservation strategies because many species in the group are imperiled. We examined 575&nbsp;</span><i>cox1</i><span><span>&nbsp;</span>sequences from 110 currently recognised species (including 13<span>&nbsp;</span></span><i>Fusconaia</i><span><span>&nbsp;</span>and 21<span>&nbsp;</span></span><i>Pleurobema</i><span><span>&nbsp;</span>species) to understand phylogenetic relationships among pleurobemine species (mainly<span>&nbsp;</span></span><i>Fusconaia</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>Pleurobema</i><span>) and to delineate species boundaries. The results of phylogenetic analyses showed no geographic structure within widespread species and illustrated a close relationship between<span>&nbsp;</span></span><i>Elliptio lanceolata</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>Parvaspina collina</i><span>. Constraint tests supported monophyly of the genera<span>&nbsp;</span></span><i>Fusconaia</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>Pleurobema</i><span>, including the subgenus<span>&nbsp;</span></span><i>P</i><span>. (</span><i>Sintoxia</i><span>). Furthermore, results revealed multiple conspecifics, including<span>&nbsp;</span></span><i>P. hanleyianum</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>P. troschelianum</i><span>,<span>&nbsp;</span></span><i>P. chattanoogaense</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>P. decisum</i><span>,<span>&nbsp;</span></span><i>P. clava</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>P. oviforme</i><span>,<span>&nbsp;</span></span><i>P. rubrum</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>P. sintoxia</i><span>,<span>&nbsp;</span></span><i>F. askewi</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>F. lananensis</i><span>, and<span>&nbsp;</span></span><i>F. cerina</i><span><span>&nbsp;</span>and<span>&nbsp;</span></span><i>F. flava</i><span>. Species delimitation analyses identified three currently unrecognised taxa (two in<span>&nbsp;</span></span><i>Fusconaia</i><span><span>&nbsp;</span>and one in<span>&nbsp;</span></span><i>Pleurobema</i><span>). Further investigation using additional genetic markers and other lines of evidence (e.g. morphology, life history, ecology) are necessary before any taxonomic changes are formalised.</span></p>","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/IS17059","usgsCitation":"Inoue, K., Hayes, D.M., Harris, J.L., Johnson, N.A., Morrison, C.L., Eackles, M.S., King, T., Jones, J.W., Hallerman, E.M., Christian, A.D., and Randklev, C.R., 2018, The Pleurobemini (Bivalvia: Unionida) revisited: Molecular species delineation using a mitochondrial DNA gene reveals multiple conspecifics and undescribed species: Invertebrate Systematics, v. 32, no. 3, p. 689-702, https://doi.org/10.1071/IS17059.","productDescription":"14 p.","startPage":"689","endPage":"702","ipdsId":"IP-087919","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":351687,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee76fe4b0da30c1bfc29a","contributors":{"authors":[{"text":"Inoue, Kentaro","contributorId":202526,"corporation":false,"usgs":false,"family":"Inoue","given":"Kentaro","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":728689,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, David M.","contributorId":202525,"corporation":false,"usgs":false,"family":"Hayes","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":36472,"text":"Eastern Kentucky University","active":true,"usgs":false}],"preferred":false,"id":728688,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harris, John L.","contributorId":201225,"corporation":false,"usgs":false,"family":"Harris","given":"John","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":728690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Nathan A. 0000-0001-5167-1988 najohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":4175,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","email":"najohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":728687,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morrison, Cheryl L. 0000-0001-9425-691X cmorrison@usgs.gov","orcid":"https://orcid.org/0000-0001-9425-691X","contributorId":146488,"corporation":false,"usgs":true,"family":"Morrison","given":"Cheryl","email":"cmorrison@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":728691,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eackles, Michael S. meackles@usgs.gov","contributorId":4371,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":728692,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"King, Tim","contributorId":83179,"corporation":false,"usgs":true,"family":"King","given":"Tim","affiliations":[],"preferred":false,"id":728693,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jones, Jess W.","contributorId":84279,"corporation":false,"usgs":true,"family":"Jones","given":"Jess","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":728694,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hallerman, Eric M.","contributorId":202528,"corporation":false,"usgs":false,"family":"Hallerman","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":728695,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Christian, Alan D.","contributorId":202529,"corporation":false,"usgs":false,"family":"Christian","given":"Alan","email":"","middleInitial":"D.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":728696,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Randklev, Charles R.","contributorId":202530,"corporation":false,"usgs":false,"family":"Randklev","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":728697,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70192464,"text":"70192464 - 2018 - Post-wildfire landscape change and erosional processes from repeat terrestrial lidar in a steep headwater catchment, Chiricahua Mountains, Arizona, USA","interactions":[],"lastModifiedDate":"2017-10-31T14:33:38","indexId":"70192464","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Post-wildfire landscape change and erosional processes from repeat terrestrial lidar in a steep headwater catchment, Chiricahua Mountains, Arizona, USA","docAbstract":"<p><span>Flooding and erosion after wildfires present increasing hazard as climate warms, semi-arid lands become drier, population increases, and the urban interface encroaches farther into wildlands. We quantify post-wildfire erosion in a steep, initially unchannelized, 7.5</span><span>&nbsp;</span><span>ha headwater catchment following the 2011 Horseshoe 2 Fire in the Chiricahua Mountains of southeastern Arizona. Using time-lapse cameras, rain gauges, and repeat surveys by terrestrial laser scanner, we quantify the response of a burned landscape to subsequent precipitation events. Repeat surveys provide detailed pre-and post-rainfall measurements of landscape form associated with a range of weather events. The first post-fire precipitation led to sediment delivery equivalent to 0.017</span><span>&nbsp;</span><span>m of erosion from hillslopes and 0.12</span><span>&nbsp;</span><span>m of erosion from colluvial hollows. Volumetrically, 69% of sediment yield was generated from hillslope erosion and 31% was generated from gully channel establishment in colluvial hollows. Processes on hillslopes included erosion by extensive shallow overland flow, formation of rills and gullies, and generation of sediment-laden flows and possibly debris flows. Subsequent smaller rain events caused ongoing hillslope erosion and local deposition and erosion in gullies. Winter freeze-thaw led to soil expansion, likely related to frost-heaving, causing a net centimeter-scale elevation increase across soil-mantled slopes. By characterizing landscape form, the properties of near-surface materials, and measuring both precipitation and landscape change, we can improve our empirical understanding of landscape response to environmental forcing. This detailed approach to studying landscape response to wildfires may be useful in the improvement of predictive models of flood, debris flow and sedimentation hazards used in post-wildfire response assessments and land management, and may help improve process-based models of landscape evolution.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2017.09.028","usgsCitation":"DeLong, S.B., Youberg, A.M., DeLong, W.M., and Murphy, B.P., 2018, Post-wildfire landscape change and erosional processes from repeat terrestrial lidar in a steep headwater catchment, Chiricahua Mountains, Arizona, USA: Geomorphology, v. 300, p. 13-30, https://doi.org/10.1016/j.geomorph.2017.09.028.","productDescription":"18 p.","startPage":"13","endPage":"30","ipdsId":"IP-070321","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2017.09.028","text":"Publisher Index Page"},{"id":347894,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Chiricahua Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.2833,\n              31.9\n            ],\n            [\n              -109.2667,\n              31.9\n            ],\n            [\n              -109.2667,\n              31.9167\n            ],\n            [\n              -109.2833,\n              31.9167\n            ],\n            [\n              -109.2833,\n              31.9\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"300","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f98ba2e4b0531197af9f84","contributors":{"authors":[{"text":"DeLong, Stephen B. 0000-0002-0945-2172 sdelong@usgs.gov","orcid":"https://orcid.org/0000-0002-0945-2172","contributorId":5240,"corporation":false,"usgs":true,"family":"DeLong","given":"Stephen","email":"sdelong@usgs.gov","middleInitial":"B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":715980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Youberg, Ann M. 0000-0002-2005-3674","orcid":"https://orcid.org/0000-0002-2005-3674","contributorId":172609,"corporation":false,"usgs":false,"family":"Youberg","given":"Ann","email":"","middleInitial":"M.","affiliations":[{"id":6672,"text":"former: USGS Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, AZ. Current address:  TN-SCORE, Univ of Tennessee, Knoxville, TN, e-mail: jennen@gmail.com","active":true,"usgs":false}],"preferred":true,"id":715981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeLong, Whitney M.","contributorId":198416,"corporation":false,"usgs":false,"family":"DeLong","given":"Whitney","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":715982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murphy, Brendan P.","contributorId":198417,"corporation":false,"usgs":false,"family":"Murphy","given":"Brendan","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":715983,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70193214,"text":"70193214 - 2018 - Position-specific 13C distributions within propane from experiments and natural gas samples","interactions":[],"lastModifiedDate":"2017-10-31T11:08:39","indexId":"70193214","displayToPublicDate":"2017-10-31T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Position-specific 13C distributions within propane from experiments and natural gas samples","docAbstract":"<p>Site-specific carbon isotope measurements of organic compounds potentially recover information that is lost in a conventional, ‘bulk’ isotopic analysis. Such measurements are useful because isotopically fractionating processes may have distinct effects at different molecular sites, and thermodynamically equilibrated populations of molecules tend to concentrate heavy isotopes in one molecular site versus another. Most recent studies of site-specific <sup>13</sup>C in organics use specialized Nuclear Magnetic Resonance (NMR) techniques or complex chemical degradations prior to mass spectrometric measurements. Herein we present the first application of a new mass spectrometric technique that reconstructs the site-specific carbon isotope composition of propane based on measurements of the<sup> 13</sup>C/<sup>12</sup>C ratios of two or more fragment ions that sample different proportions of the terminal and central carbon sites. We apply this method to propane from laboratory experiments and natural gas samples to explore the relationships between site-specific carbon isotope composition, full-molecular δ<sup>13</sup>C, thermal maturity, and variation in organic matter precursors. Our goal is to advance the understanding of the sources and histories of short-chain alkanes within geologic systems. Our findings suggest that propane varies in its site-specific carbon isotope structure, which is correlated with increasing thermal maturity, first increasing in terminal position δ<sup>13</sup>C and then increasing in both center and terminal position δ<sup>13</sup>C. This pattern is observed in both experimental and natural samples, and is plausibly explained by a combination of site-specific, temperature-dependent isotope effects associated with conversion of different precursor molecules (kerogen, bitumen, and/or oil) to propane, differences in site-specific isotopic contents of those precursors, and possibly distillation of reactive components of those precursors with increasing maturity. We hypothesize that the largest changes in site-specific isotopic content of propane occur when bitumen and/or oil replace kerogen as the dominant precursors. If correct, this phenomenon could have significant utility for understanding gas generation in thermogenic petroleum systems.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2017.09.042","usgsCitation":"Piasecki, A., Sessions, A.L., Lawson, M., Ferreira, A., Santos Neto, E.V., Ellis, G.S., Lewan, M., and Eilers, J., 2018, Position-specific 13C distributions within propane from experiments and natural gas samples: Geochimica et Cosmochimica Acta, v. 220, p. 110-124, https://doi.org/10.1016/j.gca.2017.09.042.","productDescription":"15 p.","startPage":"110","endPage":"124","ipdsId":"IP-083819","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":347826,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"220","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f98b9fe4b0531197af9f7b","contributors":{"authors":[{"text":"Piasecki, Alison","contributorId":199113,"corporation":false,"usgs":false,"family":"Piasecki","given":"Alison","email":"","affiliations":[],"preferred":false,"id":718218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sessions, Alex L.","contributorId":172980,"corporation":false,"usgs":false,"family":"Sessions","given":"Alex","email":"","middleInitial":"L.","affiliations":[{"id":27133,"text":"Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena CA","active":true,"usgs":false}],"preferred":false,"id":718219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawson, Michael","contributorId":199115,"corporation":false,"usgs":false,"family":"Lawson","given":"Michael","email":"","affiliations":[],"preferred":false,"id":718220,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferreira, A.A.","contributorId":18273,"corporation":false,"usgs":true,"family":"Ferreira","given":"A.A.","email":"","affiliations":[],"preferred":false,"id":718221,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Santos Neto, E. V.","contributorId":58191,"corporation":false,"usgs":true,"family":"Santos Neto","given":"E.","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":718222,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ellis, Geoffrey S. 0000-0003-4519-3320 gsellis@usgs.gov","orcid":"https://orcid.org/0000-0003-4519-3320","contributorId":1058,"corporation":false,"usgs":true,"family":"Ellis","given":"Geoffrey","email":"gsellis@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":718217,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lewan, Michael 0000-0001-6347-1553 mlewan@usgs.gov","orcid":"https://orcid.org/0000-0001-6347-1553","contributorId":173938,"corporation":false,"usgs":true,"family":"Lewan","given":"Michael","email":"mlewan@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":718223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Eilers, J.M.","contributorId":29103,"corporation":false,"usgs":true,"family":"Eilers","given":"J.M.","email":"","affiliations":[],"preferred":false,"id":718224,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70192433,"text":"70192433 - 2018 - Passive restoration following ungulate removal in a highly disturbed tropical wet forest devoid of native seed dispersers","interactions":[],"lastModifiedDate":"2019-12-21T08:41:20","indexId":"70192433","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Passive restoration following ungulate removal in a highly disturbed tropical wet forest devoid of native seed dispersers","docAbstract":"<p><span>Overabundant ungulate populations can alter forests. Concurrently, global declines of seed dispersers may threaten native forest structure and function. On an island largely devoid of native vertebrate seed dispersers, we monitored forest succession for 7 years following ungulate exclusion from a 5-ha area and adjacent plots with ungulates still present. We observed succession from open scrub to forest and understory cover by non-native plants declined. Two trees, native&nbsp;</span><i>Hibiscus tiliaceus</i><span><span>&nbsp;</span>and non-native<span>&nbsp;</span></span><i>Leucaena leucocephala</i><span>, accounted for most forest regeneration, with the latter dominant. Neither species is dependent on animal dispersers nor was there strong evidence that plants dependent on dispersers migrated into the 5-ha study area. Passive restoration following ungulate removal may facilitate restoration, but did not show promise for fully restoring native forest on Guam. Restoration of native forest plants in bird depopulated areas will likely require active outplanting of native seedlings, control of factors resulting in bird loss, and reintroduction of seed dispersers.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/rec.12559","usgsCitation":"Nafus, M.G., Savidge, J.A., Yackel Adams, A.A., Christy, M.T., and Reed, R., 2018, Passive restoration following ungulate removal in a highly disturbed tropical wet forest devoid of native seed dispersers: Restoration Ecology, v. 26, no. 2, p. 331-337, https://doi.org/10.1111/rec.12559.","productDescription":"7 p.","startPage":"331","endPage":"337","ipdsId":"IP-085577","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":438080,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SN07DK","text":"USGS data release","linkHelpText":"Dataset: Passive restoration following ungulate removal in a highly disturbed tropical wet forest devoid of native seed dispersers"},{"id":347714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Guam","otherGeospatial":"Anderson Air Force Base","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              144.591064453125,\n              13.186468107797188\n            ],\n            [\n              144.9700927734375,\n              13.186468107797188\n            ],\n            [\n              144.9700927734375,\n              13.662000579724483\n            ],\n            [\n              144.591064453125,\n              13.662000579724483\n            ],\n            [\n              144.591064453125,\n              13.186468107797188\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"26","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-22","publicationStatus":"PW","scienceBaseUri":"59f83a2fe4b063d5d30980a2","contributors":{"authors":[{"text":"Nafus, Melia G. 0000-0002-7325-3055 mnafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":197462,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia","email":"mnafus@usgs.gov","middleInitial":"G.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":715804,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":715805,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":715807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christy, Michelle T.","contributorId":197463,"corporation":false,"usgs":false,"family":"Christy","given":"Michelle","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":715806,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":715808,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70192852,"text":"70192852 - 2018 - Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management","interactions":[],"lastModifiedDate":"2018-08-10T13:45:32","indexId":"70192852","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management","docAbstract":"<p><span>Understanding the causes and consequences of component change in sagebrush steppe is crucial for evaluating ecosystem sustainability. The sagebrush (</span><i class=\"EmphasisTypeItalic \">Artemisia</i><span><span>&nbsp;</span>spp.) steppe ecosystem of the northwest USA has been impacted by the invasion of exotic grasses, increasing fire return intervals, changing land management practices, and fragmentation, often lowering the overall resilience to change. We utilized contemporary and historical Landsat imagery, field data, and regression tree models to produce fractional cover maps of rangeland components (shrub, sagebrush, herbaceous, bare ground, and litter) through the last 30&nbsp;years. Our main goals were to (1) investigate rangeland component trends over 30&nbsp;years, (2) evaluate the magnitude and direction of trends in components and climate drivers and their relationship, and (3) assess component trends influenced by climate. Results indicated that over the study period, shrub, sage, herbaceous, and litter cover decreased, while bare ground cover increased. Measured rates of change ranged from −&nbsp;0.14%&nbsp;decade</span><sup>−1</sup><span><span>&nbsp;</span>for shrub cover to 0.05%&nbsp;decade</span><sup>−1</sup><span><span>&nbsp;</span>for bare ground, whereas herbaceous and litter cover trends were negligible. Net landscape cover changes were consistent with expectations of climate change and disturbance producing a loss of biotic cover, and converting a portion of shrub and sagebrush to herbaceous cover. Overall, fire and related successional recovery was the greatest change agent for all components in terms of area and cover change, while increasing minimum temperature, at a rate of 0.66°C&nbsp;decade</span><sup>−1</sup><span>, was found to be the most significant climate driver.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s10021-017-0191-3","usgsCitation":"Shi, H., Rigge, M.B., Homer, C.G., Xian, G.Z., Meyer, D., and Bunde, B., 2018, Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management: Ecosystems, v. 21, no. 5, p. 913-929, https://doi.org/10.1007/s10021-017-0191-3.","productDescription":"17 p.","startPage":"913","endPage":"929","ipdsId":"IP-079676","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":347727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada, Oregon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -120.1904296875,\n              40.97575093157534\n            ],\n            [\n              -118.16894531249999,\n              40.97575093157534\n            ],\n            [\n              -118.16894531249999,\n              42.7349091465156\n            ],\n            [\n              -120.1904296875,\n              42.7349091465156\n            ],\n            [\n              -120.1904296875,\n              40.97575093157534\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"21","issue":"5","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59f83a2de4b063d5d309808a","contributors":{"authors":[{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Homer, Collin G. 0000-0003-4755-8135 homer@usgs.gov","orcid":"https://orcid.org/0000-0003-4755-8135","contributorId":2262,"corporation":false,"usgs":true,"family":"Homer","given":"Collin","email":"homer@usgs.gov","middleInitial":"G.","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":717210,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717211,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meyer, Debbie 0000-0002-8841-697X debbie.meyer.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":192361,"corporation":false,"usgs":true,"family":"Meyer","given":"Debbie","email":"debbie.meyer.ctr@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":717212,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bunde, Brett 0000-0003-0228-779X brett.bunde.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":198821,"corporation":false,"usgs":true,"family":"Bunde","given":"Brett","email":"brett.bunde.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":717213,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70191451,"text":"fs20173069 - 2018 - Everglades Depth Estimation Network (EDEN)—A decade of serving hydrologic information to scientists and resource managers","interactions":[],"lastModifiedDate":"2021-10-26T16:14:19.190775","indexId":"fs20173069","displayToPublicDate":"2017-10-30T00:00:00","publicationYear":"2018","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":"2017-3069","title":"Everglades Depth Estimation Network (EDEN)—A decade of serving hydrologic information to scientists and resource managers","docAbstract":"<h1>Introduction</h1><p>The Everglades Depth Estimation Network (EDEN) provides scientists and resource managers with regional maps of daily water levels and depths in the freshwater part of the Greater Everglades landscape. The EDEN domain includes all or parts of five Water Conservation Areas, Big Cypress National Preserve, Pennsuco Wetlands, and Everglades National Park. Daily water-level maps are interpolated from water-level data at monitoring gages, and depth is estimated by using a digital elevation model of the land surface. Online datasets provide time series of daily water levels at gages and rainfall and evapotranspiration data (<a href=\"https://sofia.usgs.gov/eden/\" data-mce-href=\"https://sofia.usgs.gov/eden/\">https://sofia.usgs.gov/eden/</a>). These datasets are used by scientists and resource managers to guide large-scale field operations, describe hydrologic changes, and support biological and ecological assessments that measure ecosystem response to the implementation of the Comprehensive Everglades Restoration Plan. EDEN water-level data have been used in a variety of biological and ecological studies including (1) the health of American alligators as a function of water depth, (2) the variability of post-fire landscape dynamics in relation to water depth, (3) the habitat quality for wading birds with dynamic habitat selection, and (4) an evaluation of the habitat of the Cape Sable seaside sparrow.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173069","collaboration":"Prepared as part of the U.S. Geological Survey Greater Everglades Priority Ecosystems Science and in collaboration with the<br />U.S. Army Corps of Engineers as part of the Comprehensive Everglades Restoration Plan REstoration COordination and VERification (RECOVER) Program","usgsCitation":"Patino, Eduardo, Conrads, Paul, Swain, Eric, and Beerens, James, 2018, Everglades Depth Estimation Network (EDEN)—A decade of serving hydrologic information to scientists and resource managers (ver. 1.1, January 2018): U.S. Geological Survey Fact Sheet 2017–3069, 6 p., https://doi.org/10.3133/fs20173069.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-071266","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":347419,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3069/fs20173069.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3069"},{"id":347418,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3069/coverthb1.jpg"},{"id":350307,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2017/3069/versionHist.txt","size":"1 MB","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -80.29083251953124,\n              26.684275490019488\n            ],\n            [\n              -80.45013427734375,\n              26.686729520004036\n            ],\n            [\n              -80.56549072265625,\n              26.350036674507894\n            ],\n            [\n              -81.68609619140624,\n              26.33280692289788\n            ],\n            [\n              -81.70257568359375,\n              26.143110637100634\n            ],\n            [\n              -81.91955566406249,\n              26.06418490332395\n            ],\n            [\n              -81.134033203125,\n              25.008461758688334\n            ],\n            [\n              -80.41168212890625,\n              25.17760219565174\n            ],\n            [\n              -80.49407958984375,\n              25.693513062561056\n            ],\n            [\n              -80.35400390625,\n              26.115985925333536\n            ],\n            [\n              -80.2935791015625,\n              26.185018250078308\n            ],\n            [\n              -80.233154296875,\n              26.362342068998764\n            ],\n            [\n              -80.2056884765625,\n              26.524650377182763\n            ],\n            [\n              -80.29083251953124,\n              26.684275490019488\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Version 1.0: October 30, 2017; Version 1.1","contact":"<p>Director, <a href=\"https://www2.usgs.gov/water/caribbeanflorida/\" data-mce-href=\"https://www2.usgs.gov/water/caribbeanflorida/\">Caribbean-Florida Water Science Center</a><br>U.S. Geological Survey<br>4446 Pet Lane, Suite 108<br>Lutz, FL 33559<br></p>","tableOfContents":"<ul><li>Introduction<br></li><li>EDEN Water-Level Model<br></li><li>EDEN Web Applications<br></li><li>Looking Forward to the Next Decade<br></li><li>References<br></li><li>Special Acknowledgment</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2017-10-30","revisedDate":"2018-01-05","noUsgsAuthors":false,"publicationDate":"2017-10-30","publicationStatus":"PW","scienceBaseUri":"59f83a30e4b063d5d30980ab","contributors":{"authors":[{"text":"Patino, Eduardo 0000-0003-1016-3658 epatino@usgs.gov","orcid":"https://orcid.org/0000-0003-1016-3658","contributorId":1743,"corporation":false,"usgs":true,"family":"Patino","given":"Eduardo","email":"epatino@usgs.gov","affiliations":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true},{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"preferred":true,"id":712329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conrads, Paul 0000-0003-0408-4208 pconrads@usgs.gov","orcid":"https://orcid.org/0000-0003-0408-4208","contributorId":764,"corporation":false,"usgs":true,"family":"Conrads","given":"Paul","email":"pconrads@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":712331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swain, Eric D. 0000-0001-7168-708X edswain@usgs.gov","orcid":"https://orcid.org/0000-0001-7168-708X","contributorId":1538,"corporation":false,"usgs":true,"family":"Swain","given":"Eric","email":"edswain@usgs.gov","middleInitial":"D.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":712330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beerens, James M. 0000-0001-8143-916X jbeerens@usgs.gov","orcid":"https://orcid.org/0000-0001-8143-916X","contributorId":143722,"corporation":false,"usgs":true,"family":"Beerens","given":"James","email":"jbeerens@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":725420,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192839,"text":"70192839 - 2018 - Landscape-scale processes influence riparian plant composition along a regulated river","interactions":[],"lastModifiedDate":"2017-11-29T16:15:31","indexId":"70192839","displayToPublicDate":"2017-10-27T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2183,"text":"Journal of Arid Environments","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale processes influence riparian plant composition along a regulated river","docAbstract":"<p>Hierarchical frameworks are useful constructs when exploring landscape- and local-scale factors affecting patterns of vegetation in riparian areas. In drylands, which have steep environmental gradients and high habitat heterogeneity, landscape-scale variables, such as climate, can change rapidly along a river's course, affecting the relative influence of environmental variables at different scales. To assess how landscape-scale factors change the structure of riparian vegetation, we measured riparian vegetation composition along the Colorado River through Grand Canyon, determined which factors best explain observed changes, identified how richness and functional diversity vary, and described the implications of our results for river management. Cluster analysis identified three divergent floristic groups that are distributed longitudinally along the river. These groups were distributed along gradients of elevation, temperature and seasonal precipitation, but were not associated with annual precipitation or local-scale factors. Species richness and functional diversity decreased as a function of distance downstream showing that changing landscape-scale factors result in changes to ecosystem characteristics. Species composition and distribution remain closely linked to seasonal precipitation and temperature. These patterns in floristic composition in a semiarid system inform management and provide insights into potential future changes as a result of shifts in climate and changes in flow management.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2017.10.001","usgsCitation":"Palmquist, E.C., Ralston, B., Merritt, D.M., and Shafroth, P.B., 2018, Landscape-scale processes influence riparian plant composition along a regulated river: Journal of Arid Environments, v. 148, p. 54-64, https://doi.org/10.1016/j.jaridenv.2017.10.001.","productDescription":"11 p.","startPage":"54","endPage":"64","ipdsId":"IP-084012","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":438081,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V986X3","text":"USGS data release","linkHelpText":"Riparian Vegetation and Environmental Variables, Colorado River, 2014Data"},{"id":347595,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Grand Canyon","volume":"148","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f44593e4b063d5d306f2a3","contributors":{"authors":[{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":717147,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralston, Barbara 0000-0001-9991-8994 bralston@usgs.gov","orcid":"https://orcid.org/0000-0001-9991-8994","contributorId":195797,"corporation":false,"usgs":true,"family":"Ralston","given":"Barbara","email":"bralston@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":717148,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merritt, David M.","contributorId":192229,"corporation":false,"usgs":false,"family":"Merritt","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":24595,"text":"USDA Forest Service, Fort Collins CO","active":true,"usgs":false}],"preferred":false,"id":717149,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":717150,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192505,"text":"70192505 - 2018 - A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing","interactions":[],"lastModifiedDate":"2021-08-12T14:44:56.393391","indexId":"70192505","displayToPublicDate":"2017-10-26T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing","docAbstract":"<ol><li>The development and use of automated species-detection technologies, such as acoustic recorders, for monitoring wildlife are rapidly expanding. Automated classification algorithms provide a cost- and time-effective means to process information-rich data, but often at the cost of additional detection errors. Appropriate methods are necessary to analyse such data while dealing with the different types of detection errors.<br></li><li>We developed a hierarchical modelling framework for estimating species occupancy from automated species-detection data. We explore design and optimization of data post-processing procedures to account for detection errors and generate accurate estimates. Our proposed method accounts for both imperfect detection and false positive errors and utilizes information about both occurrence and abundance of detections to improve estimation.<br></li><li>Using simulations, we show that our method provides much more accurate estimates than models ignoring the abundance of detections. The same findings are reached when we apply the methods to two real datasets on North American frogs surveyed with acoustic recorders.<br></li><li>When false positives occur, estimator accuracy can be improved when a subset of detections produced by the classification algorithm is post-validated by a human observer. We use simulations to investigate the relationship between accuracy and effort spent on post-validation, and found that very accurate occupancy estimates can be obtained with as little as 1% of data being validated.<br></li><li>Automated monitoring of wildlife provides opportunity and challenges. Our methods for analysing automated species-detection data help to meet key challenges unique to these data and will prove useful for many wildlife monitoring programs.<br></li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.12910","usgsCitation":"Chambert, T.A., Waddle, J.H., Miller, D., Walls, S.C., and Nichols, J.D., 2018, A new framework for analysing automated acoustic species detection data: Occupancy estimation and optimization of recordings post-processing: Methods in Ecology and Evolution, v. 9, no. 3, p. 560-570, https://doi.org/10.1111/2041-210X.12910.","productDescription":"11 p.","startPage":"560","endPage":"570","ipdsId":"IP-085481","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":469175,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.12910","text":"Publisher Index Page"},{"id":438082,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MP51H4","text":"USGS data release","linkHelpText":"Computer automated frog vocalization results from Picayune Strand State Forest, Florida 2011-2012"},{"id":347438,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-09","publicationStatus":"PW","scienceBaseUri":"5a07e85ae4b09af898c8cb58","contributors":{"authors":[{"text":"Chambert, Thierry A. 0000-0002-9450-9080 tchambert@usgs.gov","orcid":"https://orcid.org/0000-0002-9450-9080","contributorId":5973,"corporation":false,"usgs":true,"family":"Chambert","given":"Thierry","email":"tchambert@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":716089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waddle, J. Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":138953,"corporation":false,"usgs":true,"family":"Waddle","given":"J.","email":"waddleh@usgs.gov","middleInitial":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":716088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, David A.W.","contributorId":198461,"corporation":false,"usgs":false,"family":"Miller","given":"David A.W.","affiliations":[],"preferred":false,"id":716090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walls, Susan C. 0000-0001-7391-9155 swalls@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":138952,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","email":"swalls@usgs.gov","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":716092,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":716091,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70192411,"text":"70192411 - 2018 - A probe-based quantitative PCR assay for detecting Tetracapsuloides bryosalmonae in fish tissue and environmental DNA water samples","interactions":[],"lastModifiedDate":"2018-08-31T11:07:48","indexId":"70192411","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1325,"text":"Conservation Genetics Resources","active":true,"publicationSubtype":{"id":10}},"title":"A probe-based quantitative PCR assay for detecting Tetracapsuloides bryosalmonae in fish tissue and environmental DNA water samples","docAbstract":"<p><span>A probe-based quantitative real-time PCR assay was developed to detect&nbsp;</span><i class=\"EmphasisTypeItalic \">Tetracapsuloides bryosalmonae</i><span>, which causes proliferative kidney disease in salmonid fish, in kidney tissue and environmental DNA (eDNA) water samples. The limits of detection and quantification were 7 and 100 DNA copies for calibration standards and<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">T. bryosalmonae</i><span><span>&nbsp;</span>was reliably detected down to 100 copies in tissue and eDNA samples. The assay presented here is a highly sensitive and quantitative tool for detecting<span>&nbsp;</span></span><i class=\"EmphasisTypeItalic \">T. bryosalmonae</i><span><span>&nbsp;</span>with potential applications for tissue diagnostics and environmental detection.</span></p>","language":"English","publisher":"Springer","doi":"10.1007/s12686-017-0812-3","usgsCitation":"Hutchins, P.R., Sepulveda, A.J., Martin, R., and Hopper, L., 2018, A probe-based quantitative PCR assay for detecting Tetracapsuloides bryosalmonae in fish tissue and environmental DNA water samples: Conservation Genetics Resources, v. 10, no. 3, p. 317-319, https://doi.org/10.1007/s12686-017-0812-3.","productDescription":"3 p.","startPage":"317","endPage":"319","ipdsId":"IP-085334","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":347384,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-19","publicationStatus":"PW","scienceBaseUri":"59f1a298e4b0220bbd9d9ec0","contributors":{"authors":[{"text":"Hutchins, Patrick R. 0000-0001-5232-0821 phutchins@usgs.gov","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":198337,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick","email":"phutchins@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715738,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":715737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Renee","contributorId":198339,"corporation":false,"usgs":false,"family":"Martin","given":"Renee","affiliations":[],"preferred":false,"id":715740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hopper, Lacey","contributorId":13139,"corporation":false,"usgs":true,"family":"Hopper","given":"Lacey","affiliations":[],"preferred":false,"id":715739,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70192408,"text":"70192408 - 2018 - Novel application of explicit dynamics occupancy models to ongoing aquatic invasions","interactions":[],"lastModifiedDate":"2018-02-14T14:24:19","indexId":"70192408","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Novel application of explicit dynamics occupancy models to ongoing aquatic invasions","docAbstract":"<ol><li>Identification of suitable habitats, where invasive species can establish, is an important step towards controlling their spread. Accurate identification is difficult for new or slow invaders because unoccupied habitats may be suitable, given enough time for dispersal, while occupied habitats may prove to be unsuitable for establishment.<br></li><li>To identify the suitable habitat of a recent invader, I used an explicit dynamics occupancy modelling framework to evaluate habitat covariates related to successful and failed establishments of American bullfrogs (<i>Lithobates catesbeianus</i>) within the Yellowstone River floodplain of Montana, USA from 2012-2016.<br></li><li>During this five-year period, bullfrogs failed to establish at most sites they colonized. Bullfrog establishment was most likely to occur and least likely to fail at sites closest to human-modified ponds and lakes and those with emergent vegetation. These habitat covariates were generally associated with the presence of permanent water.<br></li><li>Suitable habitat for bullfrog establishment is abundant in the Yellowstone River floodplain, though many sites with suitable habitat remain uncolonized. Thus, the maximum distribution of bullfrogs is much greater than their current distribution.<br></li><li>Synthesis and applications. Focused control efforts on habitats with or proximate to permanent waters are most likely to reduce the potential for invasive bullfrog establishment and spread in the Yellowstone River. The novel application of explicit dynamics occupancy models is a useful and widely applicable tool for guiding management efforts towards those habitats where new or slow invaders are most likely to establish and persist.<br></li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13002","usgsCitation":"Sepulveda, A., 2018, Novel application of explicit dynamics occupancy models to ongoing aquatic invasions: Journal of Applied Ecology, v. 55, no. 2, p. 917-925, https://doi.org/10.1111/1365-2664.13002.","productDescription":"9 p.","startPage":"917","endPage":"925","ipdsId":"IP-085768","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13002","text":"Publisher Index Page"},{"id":347372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -109.149169921875,\n              45.556371735883125\n            ],\n            [\n              -107.6495361328125,\n              45.556371735883125\n            ],\n            [\n              -107.6495361328125,\n              46.12274903582433\n            ],\n            [\n              -109.149169921875,\n              46.12274903582433\n            ],\n            [\n              -109.149169921875,\n              45.556371735883125\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"55","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-31","publicationStatus":"PW","scienceBaseUri":"59f1a299e4b0220bbd9d9ecc","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":715725,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70191897,"text":"70191897 - 2018 - At the end of the line: independent overwater colonizations of the Solomon Islands by a hyperdiverse trans-Wallacean lizard lineage (Cyrtodactylus: Gekkota: Squamata)","interactions":[],"lastModifiedDate":"2018-03-19T11:32:27","indexId":"70191897","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3810,"text":"Zoological Journal of the Linnean Society","active":true,"publicationSubtype":{"id":10}},"displayTitle":"At the end of the line: independent overwater colonizations of the Solomon Islands by a hyperdiverse trans-Wallacean lizard lineage (<i>Cyrtodactylus</i>: Gekkota: Squamata)","title":"At the end of the line: independent overwater colonizations of the Solomon Islands by a hyperdiverse trans-Wallacean lizard lineage (Cyrtodactylus: Gekkota: Squamata)","docAbstract":"<p><span>The islands of East Melanesia have generated key insights into speciation processes and community assembly. However, when and how these islands began to form, emerge and accumulate endemic taxa remains poorly understood. Here, we show that two divergent lineages within the world’s most diverse genus of geckos (</span><i>Cyrtodactylus</i><span>) occur in the Solomon Islands. One large-bodied species is nested within a radiation from far eastern New Guinea, with inferred colonization, spread and diversification since the late Miocene. In contrast, a newly sampled and relatively small species with a restricted distribution on Guadalcanal Island is a relict that diverged from extant congeners around the early to mid-Miocene. Similar Miocene divergences from extralimital relatives have been inferred for other endemic bird, bat and lizard lineages in East Melanesia. In contrast, across all lineages (including divergent relictual lineages), there is little evidence for endemic<span>&nbsp;</span></span><i>in situ</i><span><span>&nbsp;</span>diversification within East Melanesia predating the Pliocene (especially in the Solomon Islands). While some East Melanesian endemic lineages may have origins on progenitor islands during the Miocene or even earlier, current evidence suggests the<span>&nbsp;</span></span><i>in situ</i><span><span>&nbsp;</span>diversification and assembly of extant biological communities commenced around the end of the Miocene.</span></p>","language":"English","publisher":"The Linnean Society","doi":"10.1093/zoolinnean/zlx047","usgsCitation":"Oliver, P.M., Travers, S.L., Richmond, J.Q., Pikacha, P., and Fisher, R.N., 2018, At the end of the line: independent overwater colonizations of the Solomon Islands by a hyperdiverse trans-Wallacean lizard lineage (Cyrtodactylus: Gekkota: Squamata): Zoological Journal of the Linnean Society, v. 182, no. 3, p. 681-694, https://doi.org/10.1093/zoolinnean/zlx047.","productDescription":"14 p.","startPage":"681","endPage":"694","ipdsId":"IP-085994","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":490032,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/zoolinnean/zlx047","text":"Publisher Index Page"},{"id":347320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              68.5546875,\n              -22.512556954051437\n            ],\n            [\n              153.6328125,\n              -22.512556954051437\n            ],\n            [\n              153.6328125,\n              25.799891182088334\n            ],\n            [\n              68.5546875,\n              25.799891182088334\n            ],\n            [\n              68.5546875,\n              -22.512556954051437\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"182","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-13","publicationStatus":"PW","scienceBaseUri":"59f1a2a2e4b0220bbd9d9f23","contributors":{"authors":[{"text":"Oliver, Paul M.","contributorId":178111,"corporation":false,"usgs":false,"family":"Oliver","given":"Paul","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":713579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Travers, Scott L","contributorId":197478,"corporation":false,"usgs":false,"family":"Travers","given":"Scott","email":"","middleInitial":"L","affiliations":[],"preferred":false,"id":713580,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":713581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pikacha, Patrick","contributorId":197479,"corporation":false,"usgs":false,"family":"Pikacha","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":713582,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":713578,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70192340,"text":"70192340 - 2018 - Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning","interactions":[],"lastModifiedDate":"2018-03-23T12:06:28","indexId":"70192340","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning","docAbstract":"<p><span>Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R</span><sup>2</sup><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>0.77, RMSE</span><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>7%), annual grasses (R</span><sup>2</sup><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>0.70, RMSE</span><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>21%), perennial grasses (R</span><sup>2</sup><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>0.36, RMSE</span><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>12%), forbs (R</span><sup>2</sup><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>0.52, RMSE</span><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>6%), bare earth or litter (R</span><sup>2</sup><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>0.49, RMSE</span><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>19%), and the biomass of shrubs (R</span><sup>2</sup><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>0.71, RMSE</span><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>175</span><span>&nbsp;</span><span>g) and herbaceous vegetation (R</span><sup>2</sup><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>0.61, RMSE</span><span>&nbsp;</span><span>=</span><span>&nbsp;</span><span>99</span><span>&nbsp;</span><span>g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2017.09.034","usgsCitation":"Anderson, K.E., Glenn, N.F., Spaete, L.P., Shinneman, D.J., Pilliod, D.S., Arkle, R., McIlroy, S., and Derryberry, D.R., 2018, Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning: Ecological Indicators, v. 84, p. 793-802, https://doi.org/10.1016/j.ecolind.2017.09.034.","productDescription":"10 p.","startPage":"793","endPage":"802","ipdsId":"IP-066377","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469177,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2017.09.034","text":"Publisher Index Page"},{"id":347311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Morley Nelson Snake River Birds of Prey National Conservation Area","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -116.65557861328124,\n              43.24520272203356\n            ],\n            [\n              -116.63635253906249,\n              43.19516498456403\n            ],\n            [\n              -116.46331787109375,\n              43.04480541304369\n            ],\n            [\n              -116.24359130859375,\n              42.96245265666877\n            ],\n            [\n              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,{"id":70192395,"text":"70192395 - 2018 - Pneumonia in bighorn sheep: Risk and resilience","interactions":[],"lastModifiedDate":"2017-12-11T13:26:39","indexId":"70192395","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","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":"Pneumonia in bighorn sheep: Risk and resilience","docAbstract":"Infectious disease was an important driver of historic declines and extirpations of bighorn sheep (Ovis canadensis) in North America and continues to impede population restoration and management.  Domestic sheep have long been linked to pneumonia outbreaks in bighorn sheep and this association has now been confirmed in 13 captive commingling experiments. However, ecological and etiological complexities still hinder our understanding and control of the disease.  We provide an overview of the current state of knowledge about the biology and management of respiratory disease in bighorn sheep and propose strategies for moving forward.  Epizootic pneumonia in bighorn sheep is polymicrobial.  Mycoplasma ovipneumoniae, a bacterium host-specific to Caprinae and commonly carried by healthy domestic sheep and goats appears to be a necessary primary agent.  All-age epizootics following introduction of M. ovipneumoniae along with other pathogens into bighorn sheep populations are usually severe (median mortality 47%) but fatality rates vary widely, from 15 – 100%.  Disease severity may be influenced by the strain of M. ovipneumoniae, by secondary bacterial and viral pathogens, and by factors affecting transmission and host immunity.  Once introduced, M. ovipneumoniae can persist in bighorn sheep populations for decades. Carrier dams transmit the pathogen to their susceptible lambs, triggering fatal pneumonia outbreaks in nursery groups, which limits recruitment and slows or prevents population recovery. The result is that demographic costs of pathogen persistence often outweigh the impacts of the initial invasion and die-off.  There is currently no effective vaccine or antibiotic for domestic or wild sheep and to date, no management actions have been successful in reducing morbidity, mortality, or disease spread once pathogen invasion has occurred.  Molecular-based strain typing suggests that spillover of M. ovipneumoniae into bighorn sheep populations from domestic small ruminants is ongoing, and that consequences of pathogen invasion are amplified by movements of infected bighorn sheep.  Therefore, current disease management strategies focus on reducing risk of spillover from reservoir populations of domestic small ruminants and on limiting transmission among bighorn sheep.  A broad array of approaches has been tried and more are needed to prevent pathogen introduction, induce disease fadeout in persistently infected populations, and promote population resilience across the diverse landscapes bighorn sheep inhabit.  A comprehensive examination of disease dynamics across populations could help elucidate how disease fades out naturally and if population resilience can be increased in the face of infection.  Cross-jurisdictional adaptive management experiments and transdisciplinary collaboration, including partnerships with members of the domestic sheep and goat community, are needed to facilitate innovation and speed progress towards sustainable solutions for managing pneumonia to protect and restore bighorn sheep populations.","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21309","usgsCitation":"Cassirer, E.F., Manlove, K.R., Almberg, E.S., Kamath, P., Cox, M., Wolff, P.L., Roug, A., Shannon, J.M., Robinson, R., Harris, R.B., Gonzales, B.J., Plowright, R.K., Hudson, P.J., Cross, P.C., Dobson, A., and Besser, T., 2018, Pneumonia in bighorn sheep: Risk and resilience: Journal of Wildlife Management, v. 82, no. 1, p. 32-45, https://doi.org/10.1002/jwmg.21309.","productDescription":"14 p.","startPage":"32","endPage":"45","ipdsId":"IP-081940","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science 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,{"id":70192375,"text":"70192375 - 2018 - Divergent migration within lake sturgeon (Acipenser fulvescens) populations: Multiple distinct patterns exist across an unrestricted migration corridor","interactions":[],"lastModifiedDate":"2018-03-27T11:16:27","indexId":"70192375","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Divergent migration within lake sturgeon (<i>Acipenser fulvescens</i>) populations: Multiple distinct patterns exist across an unrestricted migration corridor","title":"Divergent migration within lake sturgeon (Acipenser fulvescens) populations: Multiple distinct patterns exist across an unrestricted migration corridor","docAbstract":"<ol><li>Population structure, distribution, abundance, and dispersal arguably underpin the entire field of animal ecology, with consequences for regional species persistence, and provision of ecosystem services. Divergent migration behaviours among individuals or among populations is an important aspect of the ecology of highly-mobile animals, allowing populations to exploit spatially- or temporally-distributed food and space resources.<br></li><li>This study investigated the spatial ecology of lake sturgeon (<i>Acipenser fulvescens</i>) within the barrier free Huron-Erie Corridor (HEC), which connects Lake Huron and Lake Erie of the North American Laurentian Great Lakes.<br></li><li>Over six years (2011 – 2016), movements of 268 lake sturgeon in the HEC were continuously monitored across the Great Lakes using acoustic telemetry (10 yr battery life acoustic transmitters). Five distinct migration behaviours were identified with hierarchical cluster analysis, based on the phenology and duration of river and lake use.<br></li><li>Lake sturgeon in the HEC were found to contain a high level of intraspecific divergent migration, including partial migration with the existence of residents. Specific behaviours included year-round river residency and multiple lake-migrant behaviours that involved movements between lakes and rivers. Over 85% of individuals were assign to migration behaviours as movements were consistently repeated over the study, which suggested migration behaviours were consistent and persistent in lake sturgeon. Differential use of specific rivers or lakes by acoustic-tagged lake sturgeon further subdivided individuals into 14 “contingents” (spatiotemporally segregated subgroups).<br></li><li>Contingents associated with one river (Detroit or St. Clair) were rarely detected in the other river, which confirmed that lake sturgeon in the Detroit and St. Clair represent two semi-independent populations that could require separate management consideration for their conservation. The distribution of migration behaviours did not vary between populations, sexes, body size, or among release locations, which indicated that intrapopulation variability in migratory behaviour is a general feature of the spatial ecology of lake sturgeon in un-fragmented landscapes.<br></li></ol>","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.12772","usgsCitation":"Kessel, S.T., Hondorp, D.W., Holbrook, C., Boase, J., Chiotti, J., Thomas, M.V., Wills, T.C., Roseman, E.F., Drouin, R., and Krueger, C., 2018, Divergent migration within lake sturgeon (Acipenser fulvescens) populations: Multiple distinct patterns exist across an unrestricted migration corridor: Journal of Animal Ecology, v. 87, no. 1, p. 259-273, https://doi.org/10.1111/1365-2656.12772.","productDescription":"15 p.","startPage":"259","endPage":"273","ipdsId":"IP-090744","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":461139,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.12772","text":"Publisher Index Page"},{"id":438084,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F769722Z","text":"USGS data release","linkHelpText":"Acoustic telemetry detection data for acoustic-tagged lake sturgeon (Acipenser fulvescens) in the Lake Huron-to-Lake Erie corridor"},{"id":347308,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Huron-Erie Corridor, Lake Eerie, Lake Huron","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -84.61669921875,\n              40.78054143186033\n            ],\n            [\n              -80.5517578125,\n              40.78054143186033\n            ],\n            [\n              -80.5517578125,\n              45.84410779560204\n            ],\n            [\n              -84.61669921875,\n              45.84410779560204\n            ],\n            [\n              -84.61669921875,\n              40.78054143186033\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"87","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-05","publicationStatus":"PW","scienceBaseUri":"59f1a29ce4b0220bbd9d9ee0","contributors":{"authors":[{"text":"Kessel, Steven T.","contributorId":195403,"corporation":false,"usgs":false,"family":"Kessel","given":"Steven","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":715542,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":715541,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":715543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boase, James C.","contributorId":38077,"corporation":false,"usgs":false,"family":"Boase","given":"James C.","affiliations":[{"id":12428,"text":"U. 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,{"id":70188643,"text":"tm7C16 - 2018 - Overview of a compre­hensive resource database for the assessment of recoverable hydrocarbons produced by carbon dioxide enhanced oil recovery","interactions":[],"lastModifiedDate":"2022-04-26T19:09:23.347144","indexId":"tm7C16","displayToPublicDate":"2017-10-24T10:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C16","title":"Overview of a compre­hensive resource database for the assessment of recoverable hydrocarbons produced by carbon dioxide enhanced oil recovery","docAbstract":"<p>A database called the “Comprehensive Resource Database” (CRD) was prepared to support U.S. Geological Survey (USGS) assessments of technically recoverable hydrocarbons that might result from the injection of miscible or immiscible carbon dioxide (CO<sub>2</sub>) for enhanced oil recovery (EOR). The CRD was designed by INTEK Inc., a consulting company under contract to the USGS. The CRD contains data on the location, key petrophysical properties, production, and well counts (number of wells) for the major oil and gas reservoirs in onshore areas and State waters of the conterminous United States and Alaska. The CRD includes proprietary data on petrophysical properties of fields and reservoirs from the “Significant Oil and Gas Fields of the United States Database,” prepared by Nehring Associates in 2012, and proprietary production and drilling data from the “Petroleum Information Data Model Relational U.S. Well Data,” prepared by IHS Inc. in 2012. This report describes the CRD and the computer algorithms used to (1) estimate missing reservoir property values in the Nehring Associates (2012) database, and to (2) generate values of additional properties used to characterize reservoirs suitable for miscible or immiscible CO<sub>2</sub> flooding for EOR. Because of the proprietary nature of the data and contractual obligations, the CRD and actual data from Nehring Associates (2012) and IHS Inc. (2012) cannot be presented in this report.</p>","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7: <i>Automated data processing and computations</i>","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C16","usgsCitation":"Carolus, Marshall, Biglarbigi, Khosrow, Warwick, P.D., Attanasi, E.D., Freeman, P.A., and Lohr, C.D., 2018, Overview of a compre­hensive resource database for the assessment of recoverable hydrocarbons produced by carbon dioxide enhanced oil recovery (ver. 1.1 June 2018): U.S. Geological Survey Techniques and Methods, book 7, chap. 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data-mce-href=\"https://energy.usgs.gov/\">https://energy.usgs.gov/</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Program Structure</li><li>Model Methodology </li><li>Data Sources<br></li><li>Data Preparation</li><li>Screening Module</li><li>Outputs</li><li>Additional Fluid Properties in Oil Reservoirs</li><li>Gas Reservoir and Fluid Properties</li><li>Summary</li><li>Acknowledgments</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2017-10-24","revisedDate":"2018-06-12","noUsgsAuthors":false,"publicationDate":"2017-10-24","publicationStatus":"PW","scienceBaseUri":"59f0511be4b0220bbd9a1d4c","contributors":{"authors":[{"text":"Carolus, Marshall","contributorId":192606,"corporation":false,"usgs":false,"family":"Carolus","given":"Marshall","email":"","affiliations":[],"preferred":false,"id":698713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biglarbigi, Khosrow","contributorId":192607,"corporation":false,"usgs":false,"family":"Biglarbigi","given":"Khosrow","email":"","affiliations":[],"preferred":false,"id":698714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warwick, Peter D. 0000-0002-3152-7783 pwarwick@usgs.gov","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":762,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter","email":"pwarwick@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":698712,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":193092,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil","email":"attanasi@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science 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,{"id":70192227,"text":"70192227 - 2018 - Coherence between coastal and river flooding along the California coast","interactions":[],"lastModifiedDate":"2018-03-12T13:23:26","indexId":"70192227","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"title":"Coherence between coastal and river flooding along the California coast","docAbstract":"<p><span>Water levels around river mouths are intrinsically determined by sea level and river discharge. If storm-associated coastal water-level anomalies coincide with extreme river discharge, landscapes near river mouths will be flooded by the hydrodynamic interactions of these two water masses. Unfortunately, the temporal relationships between ocean and river water masses are not well understood. The coherence between extreme river discharge and coastal water levels at six California river mouths across different climatic and geographic regions was examined. Data from river gauges, wave buoys, and tide gauges from 2007 to 2014 were integrated to investigate the relationships between extreme river discharge and coastal water levels near the mouths of the Eel, Russian, San Lorenzo, Ventura, Arroyo Trabuco, and San Diego rivers. Results indicate that mean and extreme coastal water levels during extreme river discharge are significantly higher compared with background conditions. Elevated coastal water levels result from the combination of nontidal residuals (NTRs) and wave setups. Mean and extreme (&gt;99th percentile of observations) NTRs are 3–20 cm and ∼30 cm higher during extreme river discharge conditions, respectively. Mean and extreme wave setups are up to 40 cm and ∼20–90 cm higher during extreme river discharge than typical conditions, respectively. These water-level anomalies were generally greatest for the northern rivers and least for the southern rivers. Time-series comparisons suggest that increases in NTRs are largely coherent with extreme river discharge, owing to the low atmospheric pressure systems associated with storms. The potential flooding risks of the concurrent timing of these water masses are tempered by the mixed, semidiurnal tides of the region that have amplitudes of 2–2.5 m. In summary, flooding hazard assessments for floodplains near California river mouths for current or future conditions with sea-level rise should include the temporal coherence of fluvial and oceanic water levels.</span></p>","language":"English","publisher":"Coastal Education and Research Foundation","doi":"10.2112/JCOASTRES-D-16-00226.1","usgsCitation":"Odigie, K.O., and Warrick, J.A., 2018, Coherence between coastal and river flooding along the California coast: Journal of Coastal Research, v. 34, no. 2, p. 308-317, https://doi.org/10.2112/JCOASTRES-D-16-00226.1.","productDescription":"10 p.","startPage":"308","endPage":"317","ipdsId":"IP-084158","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469178,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/5065674c","text":"External Repository"},{"id":347226,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"34","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f0511fe4b0220bbd9a1d66","contributors":{"authors":[{"text":"Odigie, Kingsley O.","contributorId":198037,"corporation":false,"usgs":false,"family":"Odigie","given":"Kingsley","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":714880,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":714879,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70192041,"text":"70192041 - 2018 - Research note: Mapping spatial patterns in sewer age, material, and proximity to surface waterways to infer sewer leakage hotspots","interactions":[],"lastModifiedDate":"2017-12-11T13:31:28","indexId":"70192041","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2603,"text":"Landscape and Urban Planning","active":true,"publicationSubtype":{"id":10}},"title":"Research note: Mapping spatial patterns in sewer age, material, and proximity to surface waterways to infer sewer leakage hotspots","docAbstract":"<p><span>Identifying areas where deteriorating sewer infrastructure is in close proximity to surface waterways is needed to map likely connections between sewers and streams. We present a method to estimate sewer installation year and deterioration status using historical maps of the sewer network, parcel-scale property assessment data, and pipe material. Areas where streams were likely buried into the sewer system were mapped by intersecting the historical stream network derived from a 10-m resolution digital elevation model with sewer pipe locations. Potential sewer leakage hotspots were mapped by identifying where aging sewer pipes are in close proximity (50-m) to surface waterways. Results from Pittsburgh, Pennsylvania (USA), indicated 41% of the historical stream length was lost or buried and the potential interface between sewers and streams is great. The co-location of aging sewer infrastructure (&gt;75</span><span>&nbsp;</span><span>years old) near stream channels suggests that 42% of existing streams are located in areas with a high potential for sewer leakage if sewer infrastructure fails. Mapping the sewer-stream interface provides an approach to better understand areas were failing sewers may contribute a disproportional amount of nutrients and other pathogens to surface waterways.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2017.04.011","usgsCitation":"Hopkins, K.G., and Bain, D., 2018, Research note: Mapping spatial patterns in sewer age, material, and proximity to surface waterways to infer sewer leakage hotspots: Landscape and Urban Planning, v. 170, p. 320-324, https://doi.org/10.1016/j.landurbplan.2017.04.011.","productDescription":"5 p.","startPage":"320","endPage":"324","ipdsId":"IP-077253","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469179,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.landurbplan.2017.04.011","text":"Publisher Index Page"},{"id":347268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":" Allegheny 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f0511fe4b0220bbd9a1d6c","contributors":{"authors":[{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":713958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bain, Daniel J.","contributorId":29276,"corporation":false,"usgs":true,"family":"Bain","given":"Daniel J.","affiliations":[],"preferred":false,"id":713959,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70192138,"text":"70192138 - 2018 - Groundwater development stress: Global-scale indices compared to regional modeling","interactions":[],"lastModifiedDate":"2018-09-12T16:08:09","indexId":"70192138","displayToPublicDate":"2017-10-23T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater development stress: Global-scale indices compared to regional modeling","docAbstract":"<p><span>The increased availability of global datasets and technologies such as global hydrologic models and the Gravity Recovery and Climate Experiment (GRACE) satellites have resulted in a growing number of global-scale assessments of water availability using simple indices of water stress. Developed initially for surface water, such indices are increasingly used to evaluate global groundwater resources. We compare indices of groundwater development stress for three major agricultural areas of the United States to information available from regional water budgets developed from detailed groundwater modeling. These comparisons illustrate the potential value of regional-scale analyses to supplement global hydrological models and GRACE analyses of groundwater depletion. Regional-scale analyses allow assessments of water stress that better account for scale effects, the dynamics of groundwater flow systems, the complexities of irrigated agricultural systems, and the laws, regulations, engineering, and socioeconomic factors that govern groundwater use. Strategic use of regional-scale models with global-scale analyses would greatly enhance knowledge of the global groundwater depletion problem.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12578","usgsCitation":"Alley, W., Clark, B.R., Ely, M., and Faunt, C., 2018, Groundwater development stress: Global-scale indices compared to regional modeling: Groundwater, v. 56, no. 2, p. 266-275, https://doi.org/10.1111/gwat.12578.","productDescription":"10 p.","startPage":"266","endPage":"275","ipdsId":"IP-088279","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":347137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-15","publicationStatus":"PW","scienceBaseUri":"59eeffa2e4b0220bbd988f5a","contributors":{"authors":[{"text":"Alley, William 0000-0001-7286-3938 walley@usgs.gov","orcid":"https://orcid.org/0000-0001-7286-3938","contributorId":140175,"corporation":false,"usgs":true,"family":"Alley","given":"William","email":"walley@usgs.gov","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":714370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Brian R. 0000-0001-6611-3807 brclark@usgs.gov","orcid":"https://orcid.org/0000-0001-6611-3807","contributorId":1502,"corporation":false,"usgs":true,"family":"Clark","given":"Brian","email":"brclark@usgs.gov","middleInitial":"R.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":714371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ely, Matt 0000-0003-3190-2907 mely@usgs.gov","orcid":"https://orcid.org/0000-0003-3190-2907","contributorId":1641,"corporation":false,"usgs":true,"family":"Ely","given":"Matt","email":"mely@usgs.gov","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faunt, Claudia C. 0000-0001-5659-7529 ccfaunt@usgs.gov","orcid":"https://orcid.org/0000-0001-5659-7529","contributorId":150147,"corporation":false,"usgs":true,"family":"Faunt","given":"Claudia C.","email":"ccfaunt@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":714372,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
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