The clupeid gizzard shad Dorosoma cepedianum is often the most abundant fish species in North American reservoirs, and this dominance can have cascading trophic effects on entire fish assemblages. Accordingly, a key aspect of managing reservoir fish assemblages involves controlling gizzard shad densities. We used a 33-year time series to evaluate the relative importance of parental stock density, winter temperature, and water regime on recruitment of age-0 gizzard shad in a large reservoir. Recruitment modeled with a Ricker-type curve increased with the size of the adult stock, peaked, and then decreased at high stock densities. This over-compensatory stock-recruitment relationship was made more dynamic by fluctuations in inflow, with recruitment increasing in years of high inflow, however there was no temperature effect at the latitude of the study site. The influence of stock size on recruitment was roughly twice as high as the influence of inflow. This study is the first to report stock-recruitment relationships for a clupeid species in a reservoir and concurs with analyses of marine fishes that have shown that most clupeids exhibit compensatory or over-compensatory patterns in their stock-recruitment relationships.
More than a century of dam construction and water development in the western United States has led to extensive ecological alteration of rivers. Growing interest in improving river function is compelling practitioners to consider ecological restoration when managing dams and water extraction. We developed an Ecological Response Model (ERM) for the Cache la Poudre River, northern Colorado, USA, to illuminate effects of current and possible future water management and climate change. We used empirical data and modeled interactions among multiple ecosystem components to capture system-wide insights not possible with the unintegrated models commonly used in environmental assessments. The ERM results showed additional flow regime modification would further alter the structure and function of Poudre River aquatic and riparian ecosystems due to multiple and interacting stressors. Model predictions illustrated that specific peak flow magnitudes in spring and early summer are critical for substrate mobilization, dynamic channel morphology, and overbank flows, with strong subsequent effects on instream and riparian biota that varied seasonally and spatially, allowing exploration of nuanced management scenarios. Instream biological indicators benefitted from higher and more stable base flows and high peak flows, but stable base flows with low peak flows were only half as effective to increase indicators. Improving base flows while reducing peak flows, as currently proposed for the Cache la Poudre River, would further reduce ecosystem function. Modeling showed that even presently depleted annual flow volumes can achieve substantially different ecological outcomes in designed flow scenarios, while still supporting social demands. Model predictions demonstrated that implementing designed flows in a natural pattern, with attention to base and peak flows, may be needed to preserve or improve ecosystem function of the Poudre River. Improved regulatory policies would include preservation of ecosystem-level, flow-related processes and adaptive management when water development projects are considered.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2005","usgsCitation":"Bestgen, K.R., Poff, N.L., Baker, D.W., Bledsoe, B.P., Merritt, D.M., Lorie, M., Auble, G.T., Sanderson, J.S., and Kondratieff, B.C., 2020, Designing flows to enhance ecosystem functioning in heavily altered rivers: Ecological Applications, v. 30, no. 1, e02005, 19 p., https://doi.org/10.1002/eap.2005.","productDescription":"e02005, 19 p.","ipdsId":"IP-104612","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":458644,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2005","text":"Publisher Index Page"},{"id":387388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Cache la Poudre River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.029052734375,\n 40.32351403031129\n ],\n [\n -104.48272705078124,\n 40.32351403031129\n ],\n [\n -104.48272705078124,\n 40.81796653313175\n ],\n [\n -106.029052734375,\n 40.81796653313175\n ],\n [\n -106.029052734375,\n 40.32351403031129\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bestgen, Kevin R. 0000-0001-8691-2227","orcid":"https://orcid.org/0000-0001-8691-2227","contributorId":171573,"corporation":false,"usgs":false,"family":"Bestgen","given":"Kevin","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":819651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poff, N. LeRoy","contributorId":261271,"corporation":false,"usgs":false,"family":"Poff","given":"N.","email":"","middleInitial":"LeRoy","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":819652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, Daniel W","contributorId":261272,"corporation":false,"usgs":false,"family":"Baker","given":"Daniel","email":"","middleInitial":"W","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":819654,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bledsoe, Brian P.","contributorId":140605,"corporation":false,"usgs":false,"family":"Bledsoe","given":"Brian","email":"","middleInitial":"P.","affiliations":[{"id":13538,"text":"Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":819653,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":819655,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lorie, Mark","contributorId":172964,"corporation":false,"usgs":false,"family":"Lorie","given":"Mark","email":"","affiliations":[],"preferred":false,"id":819749,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Auble, Gregor T. 0000-0002-0843-2751 aubleg@usgs.gov","orcid":"https://orcid.org/0000-0002-0843-2751","contributorId":2187,"corporation":false,"usgs":true,"family":"Auble","given":"Gregor","email":"aubleg@usgs.gov","middleInitial":"T.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":819656,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sanderson, John S.","contributorId":210638,"corporation":false,"usgs":false,"family":"Sanderson","given":"John","email":"","middleInitial":"S.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":819657,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kondratieff, Boris C.","contributorId":24868,"corporation":false,"usgs":false,"family":"Kondratieff","given":"Boris","email":"","middleInitial":"C.","affiliations":[{"id":17860,"text":"Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":819658,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70205866,"text":"70205866 - 2020 - Quantifying hydrologic controls on local- and landscape-scale indicators of coastal wetland loss","interactions":[],"lastModifiedDate":"2020-02-06T10:54:20","indexId":"70205866","displayToPublicDate":"2019-09-18T17:02:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":789,"text":"Annals of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying hydrologic controls on local- and landscape-scale indicators of coastal wetland loss","docAbstract":"Coastal wetlands have evolved to withstand stressful abiotic conditions through the maintenance of hydrologic feedbacks among vegetation production and flooding. However, disruption of these feedbacks can lead to ecosystem collapse, or a regime shift from vegetated wetland to open water. To prevent the loss of critical coastal wetland habitat, we must improve understanding of the abiotic-biotic linkages among flooding and wetland stability. The aim of this research was to identify characteristic landscape patterns and thresholds of wetland degradation that can be used to identify areas of vulnerability, reduce flooding threats, and improve habitat quality.
We measured local- and landscape-scale responses of coastal wetland vegetation to flooding stress in healthy and degrading coastal wetlands. We hypothesized that conversion of Spartina patens wetlands to open water could be defined by a distinct change in landscape configuration pattern, and that this change would occur at a discrete elevation threshold.
Despite similarities in total land and water cover, we observed differences in the landscape configuration of vegetated and open water pixels in healthy and degrading wetlands. Healthy wetlands were more aggregated, and degrading wetlands were more fragmented. Generally, greater aggregation was associated with higher wetland elevation and better drainage, compared to fragmented wetlands, which had lower elevation and poor drainage. The relationship between vegetation cover and elevation was non-linear, and the conversion from vegetated wetland to open water occurred beyond an elevation threshold of hydrologic stress.
The elevation threshold defined a transition zone where healthy, aggregated, wetland converted to a degrading, fragmented, wetland beyond an elevation threshold of 0.09 m NAVD88 (0.27 m MSL), and complete conversion to open water occurred beyond 0.03 m NAVD88 (0.21 m MSL). This work illustrates that changes in landscape configuration can be used as an indicator of wetland loss, with specific elevation thresholds to inform restoration and conservation planning to maximize wetland stability in anticipation of flooding threats.
","language":"English","publisher":"Oxford Academic Press","doi":"10.1093/aob/mcz144","usgsCitation":"Stagg, C., Osland, M., Moon, J.A., Hall, C., Feher, L., Jones, W.R., Couvillion, B., Hartley, S.B., and Vervaeke, W., 2020, Quantifying hydrologic controls on local- and landscape-scale indicators of coastal wetland loss: Annals of Botany, v. 125, no. 2, p. 365-376, https://doi.org/10.1093/aob/mcz144.","productDescription":"12 p.","startPage":"365","endPage":"376","ipdsId":"IP-106464","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458646,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/aob/mcz144","text":"Publisher Index Page"},{"id":437216,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SXJX2T","text":"USGS data release","linkHelpText":"Local and landscape-scale data describing patterns of coastal wetland loss in the Texas Chenier Plain, U.S.A."},{"id":437215,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7736Q51","text":"USGS data release","linkHelpText":"Land-water classification for selected sites in McFaddin NWR and J.D. Murphree WMA"},{"id":368133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana, Texas","otherGeospatial":"Chenier Plain, Gulf of Mexico, McFaddin National Wildlife Refuge, J.D. Murphree Wildlife Management Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.42675781249999,\n 28.478348692223165\n ],\n [\n -91.219482421875,\n 28.478348692223165\n ],\n [\n -91.219482421875,\n 31.68143311662596\n ],\n [\n -97.42675781249999,\n 31.68143311662596\n ],\n [\n -97.42675781249999,\n 28.478348692223165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":206252,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":206246,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moon, Jena A.","contributorId":171483,"corporation":false,"usgs":false,"family":"Moon","given":"Jena","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":772715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hall, Courtney 0000-0003-0990-5212","orcid":"https://orcid.org/0000-0003-0990-5212","contributorId":218912,"corporation":false,"usgs":true,"family":"Hall","given":"Courtney","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":214841,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, William R. 0000-0002-5493-4138 jonesb@usgs.gov","orcid":"https://orcid.org/0000-0002-5493-4138","contributorId":463,"corporation":false,"usgs":true,"family":"Jones","given":"William","email":"jonesb@usgs.gov","middleInitial":"R.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772718,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Couvillion, Brady 0000-0001-5323-1687 couvillionb@usgs.gov","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":146832,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","email":"couvillionb@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772719,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hartley, Stephen B. 0000-0003-1380-2769 hartleys@usgs.gov","orcid":"https://orcid.org/0000-0003-1380-2769","contributorId":4164,"corporation":false,"usgs":true,"family":"Hartley","given":"Stephen","email":"hartleys@usgs.gov","middleInitial":"B.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":772720,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vervaeke, William 0000-0002-1518-5197 vervaekew@usgs.gov","orcid":"https://orcid.org/0000-0002-1518-5197","contributorId":3265,"corporation":false,"usgs":true,"family":"Vervaeke","given":"William","email":"vervaekew@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772721,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209105,"text":"70209105 - 2020 - Apatite trace element geochemistry and cathodoluminescent textures—Acomparison between regional magmatism and the Pea Ridge IOA-REE andBoss IOCG deposits, southeastern Missouri iron metallogenic province, USA","interactions":[],"lastModifiedDate":"2020-03-16T16:43:43","indexId":"70209105","displayToPublicDate":"2019-09-17T16:37:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Apatite trace element geochemistry and cathodoluminescent textures—Acomparison between regional magmatism and the Pea Ridge IOA-REE andBoss IOCG deposits, southeastern Missouri iron metallogenic province, USA","docAbstract":"The southeast Missouri iron metallogenic province contains a remarkable wealth of historically important Fe, Cu, Au, and rare earth element (REE) deposits including the Pea Ridge iron oxide-apatite-rare earth element (IOA-REE) deposit and the Boss iron oxide-copper-gold (IOCG) deposit. These deposits are coeval with silicic and intermediate composition magmatism in the St. Francois Mountains terrane. Magmatism, iron-oxide (±Cu, Au, Co) and apatite formation, and REE mineralization overlapped in space and time, but the specific role of regional magmatism in the metallogenesis of these deposits remains unclear and basic petrogenetic models are still debated. \nWe report results from high-spatial resolution textural and geochemical analyses of apatite from regional igneous and ore rocks to elucidate their petrogenetic histories and evaluate deposit models. Backscattered electron and spectral cathodoluminescence imaging of apatite reveal no primary igneous zoning, but show different domains with intricate rims and dissolution/reprecipitation textures, each with distinctive REE patterns in many samples. Apatite from all samples are nearly endmember fluorapatite containing up to ~1.3 wt% Cl and F/Cl ratios span nearly three orders of magnitude. Fresh igneous fluorapatite contain low Na2O (0.15 wt%) while most Pea Ridge ore samples contain higher Na2O (up to ~0.45 wt%), and concentrations of sulfur in fluorapatite of all types are generally moderate to low (0.3 wt% SO3). Significant amounts of Fe (60,000 ppm), Mg (30,000 ppm), Mn (7,000 ppm), and Sr (12,000 ppm) are contained in fluorapatite of all sample types, and they also have moderate amounts of As (4,000 ppm), Ba (2,000 ppm), Th (400 ppm), and U (80 ppm). Fluorapatite show an extraordinarily large range of REE (~0.1-2.0 wt%) and Y (~100-7000 ppm) concentrations. While fresh igneous fluorapatite share many geochemical features with metasomatized igneous fluorapatite and ore-stage fluorapatite from the Pea Ridge IOA and Boss IOCG ore zones, they also have distinct geochemical signatures that are indicative of unique trace element partitioning and substitution mechanisms. These distinguishing textural and geochemical signatures preclude ore-zone fluorapatite genesis directly from a magma (i.e., crystallization directly from a silicate melt) but are permissive of ore-zone fluorapatite formation by magmatic-hydrothermal fluids derived from the regional magmas. Basinal brines may play an important role in the formation of fluorapatite, especially from the Pea Ridge hematite and Boss magnetite-rich zones. Fluorapatite from different ore zones likely formed by crystallization during pulses of hydrothermal fluids with varying Cl-, Na-, and F-contents, which fundamentally controlled the carrying capacity and solubility of REE+Y and generated geochemically distinctive generations of fluorapatite. \nExploration geologists using fluorapatite trace element geochemistry to identify IOA and IOCG deposits should proceed with caution, as more high-quality data from these deposits are needed to improve multivariate discrimination analysis. Fluorapatite from IOA/IOCG deposits can be reasonably discriminated from that of other mineral deposit types (e.g., porphyry/epithermal, skarn, orogenic), but no criteria successfully discriminate yet between IOA and IOCG deposits.","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2019.103129","usgsCitation":"Mercer, C.N., Watts, K., and Gross, J., 2020, Apatite trace element geochemistry and cathodoluminescent textures—Acomparison between regional magmatism and the Pea Ridge IOA-REE andBoss IOCG deposits, southeastern Missouri iron metallogenic province, USA: Ore Geology Reviews, v. 116, 103129, 22 p., https://doi.org/10.1016/j.oregeorev.2019.103129.","productDescription":"103129, 22 p.","ipdsId":"IP-102053","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":458647,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2019.103129","text":"Publisher Index Page"},{"id":437217,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YIHMO8","text":"USGS data release","linkHelpText":"Geochemical data supporting a comparison of apatite between regional magmatism and the Pea Ridge Iron Oxide-Apatite-Rare Earth Element (IOA-REE) and Boss Iron Oxide-Copper-Cobalt-Gold-REE Deposits (IOCG) deposits, southeastern Missouri, USA"},{"id":373299,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"St. Francois Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.944580078125,\n 36.62434536776987\n ],\n [\n -90.120849609375,\n 36.62434536776987\n ],\n [\n -90.120849609375,\n 38.363195134453846\n ],\n [\n -91.944580078125,\n 38.363195134453846\n ],\n [\n -91.944580078125,\n 36.62434536776987\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"116","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mercer, Celestine N. 0000-0001-8359-4147 cmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-8359-4147","contributorId":4006,"corporation":false,"usgs":true,"family":"Mercer","given":"Celestine","email":"cmercer@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":784950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watts, Kathryn E. 0000-0002-6110-7499","orcid":"https://orcid.org/0000-0002-6110-7499","contributorId":204344,"corporation":false,"usgs":true,"family":"Watts","given":"Kathryn E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":784951,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gross, Juliane 0000-0002-5288-0981","orcid":"https://orcid.org/0000-0002-5288-0981","contributorId":223401,"corporation":false,"usgs":false,"family":"Gross","given":"Juliane","email":"","affiliations":[{"id":40711,"text":"Rutgers State University of New Jersey","active":true,"usgs":false}],"preferred":false,"id":784953,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211968,"text":"70211968 - 2020 - Late Quaternary sea-level history of Saipan, Commonwealth of the Northern Mariana Islands, USA: A test of tectonic uplift and glacial isostatic adjustment models","interactions":[],"lastModifiedDate":"2020-08-12T20:53:20.311416","indexId":"70211968","displayToPublicDate":"2019-09-17T15:50:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Late Quaternary sea-level history of Saipan, Commonwealth of the Northern Mariana Islands, USA: A test of tectonic uplift and glacial isostatic adjustment models","docAbstract":"In 1979, S. Uyeda and H. Kanamori proposed a tectonic model with two end members of a subduction-boundary continuum: the “Chilean” type (shallow dip of the subducting plate, great thrust events, compression, and uplift of the overriding plate) and a “Mariana” type (steep dip of the subducting plate, no great thrust events, tension, and no uplift). This concept has been used to explain variable rates of Quaternary uplift around the Pacific Rim, yet no uplift rates have been determined for the Mariana Islands themselves, one of the end members in this model. We studied the late Quaternary Tanapag Limestone, which rims much of the eastern and southern coasts of Saipan, Northern Mariana Islands, with elevations of ∼13 m to ∼30 m. Samples from 12 well-preserved corals (Acropora, Porites, and Goniastrea) yielded U-series ages ranging from ca. 134 ka to ca. 126 ka. These ages correlate the emergent reef of the Tanapag Limestone with the last interglacial period, when sea level was several meters above present. Ages and measured reef elevations from the Tanapag Limestone, along with paleo–sea-level data, yield relatively low late Quaternary uplift rates of 0.002–0.19 m/k.y., consistent with the Uyeda-Kanamori model. A review of data from other localities near subduction zones around the Pacific Basin, however, indicates that many coastlines do not fit the model. Uplift rates along the Chilean coast are predicted to be relatively high, but field studies indicate they are low. On some coastlines, relatively high uplift rates are better explained by subduction of seamounts or submarine ridges rather than subduction zone geometry. Despite the low long-term uplift rate on Saipan, the island also hosts an emergent, low-elevation (+3.9–4.0 m) reef with corals in growth position below a notch (+4.2 m). The corals are dated to 3.9–3.1 ka. The occurrence of this young, emergent reef is likely not due to tectonic uplift; instead, it is interpreted to be the result of glacial isostatic adjustment processes after the end of the last glacial period. Our findings are consistent with similar observations on tectonically stable or slowly uplifting islands elsewhere in the equatorial Pacific Ocean and agree with numerical models of a higher-than-present Holocene sea level in this region due to glacial isostatic adjustment processes.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35162.1","usgsCitation":"Muhs, D., Schweig, E.S., and Simmons, K., 2020, Late Quaternary sea-level history of Saipan, Commonwealth of the Northern Mariana Islands, USA: A test of tectonic uplift and glacial isostatic adjustment models: Geological Society of America Bulletin, v. 132, p. 863-883, https://doi.org/10.1130/B35162.1.","productDescription":"21 p.","startPage":"863","endPage":"883","ipdsId":"IP-102631","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":377440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Mariana Islands, Saipan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 145.79887390136716,\n 15.166914868426344\n ],\n [\n 145.78445434570312,\n 15.205349599759678\n ],\n [\n 145.83663940429688,\n 15.268950303672504\n ],\n [\n 145.81260681152344,\n 15.298094191660693\n ],\n [\n 145.70960998535156,\n 15.223901791042142\n ],\n [\n 145.68214416503906,\n 15.117867306000468\n ],\n [\n 145.71578979492188,\n 15.097316980284674\n ],\n [\n 145.7549285888672,\n 15.088698509791715\n ],\n [\n 145.79887390136716,\n 15.166914868426344\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"132","noUsgsAuthors":false,"publicationDate":"2019-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Muhs, Daniel R. 0000-0001-7449-251X dmuhs@usgs.gov","orcid":"https://orcid.org/0000-0001-7449-251X","contributorId":168575,"corporation":false,"usgs":true,"family":"Muhs","given":"Daniel R.","email":"dmuhs@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796005,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schweig, Eugene S. 0000-0003-3669-9741 schweig@usgs.gov","orcid":"https://orcid.org/0000-0003-3669-9741","contributorId":1271,"corporation":false,"usgs":true,"family":"Schweig","given":"Eugene","email":"schweig@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796006,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simmons, Kathleen R. 0000-0002-7920-094X","orcid":"https://orcid.org/0000-0002-7920-094X","contributorId":229460,"corporation":false,"usgs":false,"family":"Simmons","given":"Kathleen R.","affiliations":[{"id":12608,"text":"USGS, retired","active":true,"usgs":false}],"preferred":false,"id":796007,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205814,"text":"70205814 - 2020 - Winter climate change and the poleward range expansion of a tropical invasive tree (Brazilian pepper ‐ Shinus terebinthifolius)","interactions":[],"lastModifiedDate":"2020-02-06T10:51:02","indexId":"70205814","displayToPublicDate":"2019-09-17T11:52:00","publicationYear":"2020","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}},"displayTitle":"Winter climate change and the poleward range expansion of a tropical invasive tree (Brazilian pepper ‐ Shinus terebinthifolius)","title":"Winter climate change and the poleward range expansion of a tropical invasive tree (Brazilian pepper ‐ Shinus terebinthifolius)","docAbstract":"Winter climate change is expected to lead to the tropicalization of temperate ecosystems, where tropical species expand poleward in response to a decrease in the intensity and duration of winter temperature extremes (i.e., freeze events). In the southeastern United States, freezing temperatures control the northern range limits of many invasive non‐native species. Here, we examine the influence of freezing temperatures and winter climate change on the northern range limits of an invasive non‐native tree — Schinus terebenthifolius (Brazilian pepper). Since introduction in the 1800s, Brazilian pepper has invaded ecosystems throughout south and central Florida to become the state's most widespread non‐native plant species. Although Brazilian pepper is sensitive to freezing temperatures, temperature controls on its northern distribution have not been adequately quantified. We used temperature and plant occurrence data to quantify the sensitivity of Brazilian pepper to freezing temperatures. Then, we examined the potential for range expansion under three alternative future climate scenarios (+2°C, +4°C, and +6°C). Our analyses identify a strong nonlinear sigmoidal relationship between minimum temperature and Brazilian pepper presence, with a discrete threshold temperature occurring near ‐11°C. Our future scenario analyses indicate that, in response to warming winter temperatures, Brazilian pepper is expected to expand northward and transform ecosystems in north Florida and across much of the Gulf of Mexico and south Atlantic coasts of the United States. These results underscore the importance of early detection and rapid response efforts to identify and manage the northward invasion of Brazilian pepper in response to climate change. Looking more broadly, our work highlights the need to anticipate and prepare for the tropicalization of temperate ecosystems by tropical invasive species.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14842","usgsCitation":"Osland, M., and Feher, L., 2020, Winter climate change and the poleward range expansion of a tropical invasive tree (Brazilian pepper ‐ Shinus terebinthifolius): Global Change Biology, v. 26, no. 2, p. 607-615, https://doi.org/10.1111/gcb.14842.","productDescription":"9 p.","startPage":"607","endPage":"615","ipdsId":"IP-108778","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":368005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida, Georgia, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.662109375,\n 33.90689555128866\n ],\n [\n -79.87060546875,\n 34.813803317113155\n ],\n [\n -80.79345703125,\n 34.903952965590065\n ],\n [\n -81.80419921875,\n 35.28150065789119\n ],\n [\n -82.72705078125,\n 35.137879119634185\n ],\n [\n -83.12255859375,\n 35.02099970111467\n ],\n [\n -85.60546875,\n 35.003003395276714\n ],\n [\n -85.045166015625,\n 32.36140331527543\n ],\n [\n -85.242919921875,\n 31.70947636001935\n ],\n [\n -85.001220703125,\n 30.93050081760779\n ],\n [\n -87.56103515625,\n 30.977609093348686\n ],\n [\n -87.637939453125,\n 30.855079286968596\n ],\n [\n -87.36328125,\n 30.581179257386985\n ],\n [\n -87.4951171875,\n 30.306503259848835\n ],\n [\n -86.275634765625,\n 30.29701788337205\n ],\n [\n -85.75927734375,\n 30.06909396443887\n ],\n [\n -85.26489257812499,\n 29.640320395351402\n ],\n [\n -84.9462890625,\n 29.554345125748267\n ],\n [\n -83.990478515625,\n 30.012030680358613\n ],\n [\n -82.99072265625,\n 29.036960648558267\n ],\n [\n -82.73803710937499,\n 28.8927788645183\n ],\n [\n -82.891845703125,\n 27.955591004642553\n ],\n [\n -82.540283203125,\n 26.853479438420024\n ],\n [\n -81.793212890625,\n 25.78999956287362\n ],\n [\n -81.309814453125,\n 25.085598897064752\n ],\n [\n -82.342529296875,\n 24.686952411999155\n ],\n [\n -82.33154296875,\n 24.337086982410497\n ],\n [\n -81.090087890625,\n 24.337086982410497\n ],\n [\n -79.87060546875,\n 25.393660521998022\n ],\n [\n -79.82666015625,\n 26.95145308349826\n ],\n [\n -80.39794921875,\n 28.738763971370293\n ],\n [\n -81.090087890625,\n 29.935895213372444\n ],\n [\n -81.199951171875,\n 30.56226095049944\n ],\n [\n -80.96923828125,\n 31.484893386890164\n ],\n [\n -79.69482421875,\n 32.63937487360669\n ],\n [\n -78.98071289062499,\n 33.30298618122413\n ],\n [\n -78.662109375,\n 33.90689555128866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":205379,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":215075,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":772468,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70207539,"text":"70207539 - 2020 - Local abundance of Ixodes scapularis in forests: Effects of environmental moisture, vegetation characteristics, and host abundance","interactions":[],"lastModifiedDate":"2019-12-25T08:45:05","indexId":"70207539","displayToPublicDate":"2019-09-14T11:28:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5082,"text":"Ticks and Tick-borne Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Local abundance of Ixodes scapularis in forests: Effects of environmental moisture, vegetation characteristics, and host abundance","title":"Local abundance of Ixodes scapularis in forests: Effects of environmental moisture, vegetation characteristics, and host abundance","docAbstract":"Ixodes scapularis is the primary vector of Lyme disease spirochetes in eastern and central North America, and local densities of this tick can affect human disease risk. We sampled larvae and nymphs from sites in Massachusetts and Wisconsin, USA, using flag/drag devices and by collecting ticks from hosts, and measured environmental variables to evaluate the environmental factors that affect local distribution and abundance of I. scapularis. Our sites were all forested areas with known I. scapularis populations. Environmental variables included those associated with weather (e.g., temperature and relative humidity), vegetation characteristics (at canopy, shrub, and ground levels), and host abundance (small and medium-sized mammals and reptiles). The numbers of larvae on animals at a given site and season showed a logarithmic relationship to the numbers in flag/drag samples, suggesting limitation in the numbers on host animals. The numbers of nymphs on animals showed no relationship to the numbers in flag/drag samples. These results suggest that only a small proportion of larvae and nymphs found hosts because in neither stage did the numbers of host-seeking ticks decline with increased numbers on hosts. Canopy cover was predictive of larval and nymphal numbers in flag/drag samples, but not of numbers on hosts. Numbers of small and medium-sized mammal hosts the previous year were generally not predictive of the current year’s tick numbers, except that mouse abundance predicted log numbers of nymphs on all hosts the following year. Some measures of larval abundance were predictive of nymphal numbers the following year. The mean number of larvae per mouse was well predicted by measures of overall larval abundance (based on flag/drag samples and samples from all hosts), and some environmental factors contributed significantly to the model. In contrast, the mean numbers of nymphs per mouse were not well predicted by environmental variables, only by overall nymphal abundance on hosts. Therefore, larvae respond differently than nymphs to environmental factors. Furthermore, flag/drag samples provide different information about nymphal numbers than do samples from hosts. Flag/drag samples can provide information about human risk of acquiring nymph-borne pathogens because they provide information on the densities of ticks that might encounter humans, but to understand the epizootiology of tick-borne agents both flag/drag and host infestation data are needed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ttbdis.2019.101271","usgsCitation":"Ginsberg, H., Rulison, E.L., Miller, J.L., Pang, G., Arsnoe, I.M., Hickling, G.J., Ogden, N.H., LeBrun, R.A., and Tsao, J.I., 2020, Local abundance of Ixodes scapularis in forests: Effects of environmental moisture, vegetation characteristics, and host abundance: Ticks and Tick-borne Diseases, v. 11, no. 1, 101271, 12 p., https://doi.org/10.1016/j.ttbdis.2019.101271.","productDescription":"101271, 12 p.","ipdsId":"IP-100963","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458652,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://digitalcommons.uri.edu/pls_facpubs/136","text":"Publisher Index Page"},{"id":370668,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts, Wisconsin","otherGeospatial":"Cape Cod National Seashore, Fort McCoy","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.7470703125,\n 43.909765943908\n ],\n [\n -90.59326171875,\n 43.909765943908\n ],\n [\n -90.59326171875,\n 44.16841480642917\n ],\n [\n -90.7470703125,\n 44.16841480642917\n ],\n [\n -90.7470703125,\n 43.909765943908\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.23834228515625,\n 41.611335399441735\n ],\n [\n -69.90600585937499,\n 41.611335399441735\n ],\n [\n -69.90600585937499,\n 42.09007006868398\n ],\n [\n -70.23834228515625,\n 42.09007006868398\n ],\n [\n -70.23834228515625,\n 41.611335399441735\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rulison, Eric L.","contributorId":87478,"corporation":false,"usgs":false,"family":"Rulison","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":778395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Jasmine L.","contributorId":221487,"corporation":false,"usgs":false,"family":"Miller","given":"Jasmine","email":"","middleInitial":"L.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":778396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pang, Genevieve","contributorId":221488,"corporation":false,"usgs":false,"family":"Pang","given":"Genevieve","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":778397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arsnoe, Isis M.","contributorId":140902,"corporation":false,"usgs":false,"family":"Arsnoe","given":"Isis","email":"","middleInitial":"M.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":778398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hickling, Graham J.","contributorId":140903,"corporation":false,"usgs":false,"family":"Hickling","given":"Graham","email":"","middleInitial":"J.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":778400,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ogden, Nicholas H.","contributorId":147667,"corporation":false,"usgs":false,"family":"Ogden","given":"Nicholas","email":"","middleInitial":"H.","affiliations":[{"id":16890,"text":"Public Health Agency of Canada","active":true,"usgs":false}],"preferred":false,"id":778401,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LeBrun, Roger A.","contributorId":70907,"corporation":false,"usgs":false,"family":"LeBrun","given":"Roger","email":"","middleInitial":"A.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":778402,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Tsao, Jean I.","contributorId":140905,"corporation":false,"usgs":false,"family":"Tsao","given":"Jean","email":"","middleInitial":"I.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":778399,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240961,"text":"70240961 - 2020 - Geoacoustic inversion for a New England mud patch sediment using the silt-suspension theory of marine mud","interactions":[],"lastModifiedDate":"2023-03-02T16:34:20.050478","indexId":"70240961","displayToPublicDate":"2019-09-13T10:28:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1941,"text":"IEEE Journal of Oceanic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Geoacoustic inversion for a New England mud patch sediment using the silt-suspension theory of marine mud","docAbstract":"This article provides an application of the silt-suspension theory to a Bayesian-inference inversion for the geo-acoustic parameters in marine mud. The theory, with consequences that have been developed recently, postulates a suspension of water and clay mineral card-houses that supports moderately dilute concentrations of silt particles. The approach is an example of a physically based model inversion, in which parameters representing physical mud-layer properties are obtained by inversion and used to produce estimates of geoacoustic properties, including their frequency dependence. The acoustic data are from a combustive source signal propagated along a track, located over several meters of fine-grained mud in the New England Mud Patch, to a single hydrophone on a receiver array during the 2017 Seabed Characterization Experiment. Data extracted from a nearby piston core inform the physical modeling, with selections of inversion parameters guided by both sensitivity analyses and bounds from archival and core measurements. Results show the feasibility of this inversion approach. The estimates of mud density and sound speed are close to values obtained independently. The frequency dependence of attenuation is estimated over the full low-frequency source band and has an approximate power exponent of 1.72.
","language":"English","publisher":"IEEE","doi":"10.1109/JOE.2019.2934604","usgsCitation":"Brown, E.M., Lin, Y., Chaytor, J., and Siegmann, W.L., 2020, Geoacoustic inversion for a New England mud patch sediment using the silt-suspension theory of marine mud: IEEE Journal of Oceanic Engineering, v. 45, no. 1, p. 144-160, https://doi.org/10.1109/JOE.2019.2934604.","productDescription":"17 p.","startPage":"144","endPage":"160","ipdsId":"IP-106813","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":413624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brown, Elisabeth M.","contributorId":302803,"corporation":false,"usgs":false,"family":"Brown","given":"Elisabeth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":865499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lin, Ying-Tsong","contributorId":302804,"corporation":false,"usgs":false,"family":"Lin","given":"Ying-Tsong","email":"","affiliations":[],"preferred":false,"id":865500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chaytor, Jason 0000-0001-8135-8677 jchaytor@usgs.gov","orcid":"https://orcid.org/0000-0001-8135-8677","contributorId":140095,"corporation":false,"usgs":true,"family":"Chaytor","given":"Jason","email":"jchaytor@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":865501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siegmann, William L.","contributorId":302805,"corporation":false,"usgs":false,"family":"Siegmann","given":"William","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":865502,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218268,"text":"70218268 - 2020 - Spatial variability of phytoplankton in a shallow tidal freshwater system reveals complex controls on abundance and community structure","interactions":[],"lastModifiedDate":"2021-02-23T13:23:17.461665","indexId":"70218268","displayToPublicDate":"2019-09-13T07:20:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatial variability of phytoplankton in a shallow tidal freshwater system reveals complex controls on abundance and community structure","docAbstract":"Estuaries worldwide are undergoing changes to patterns of aquatic productivity because of human activities that alter flow, impact sediment delivery and thus the light field, and contribute nutrients and contaminants like pesticides and metals. These changes can influence phytoplankton communities, which in turn can alter estuarine food webs. We used multiple approaches-including high-resolution water quality mapping, synoptic sampling, productivity and nitrogen uptake rates, Lagrangian parcel tracking, enclosure experiments and bottle incubations-over a short time period to take a “spatial snapshot” of conditions in the northern region of the San Francisco Estuary (California, USA) to examine how environmental drivers like light availability, nutrients, water residence time, and contaminants affect phytoplankton abundance and community attributes like size distribution, taxonomic structure, and nutrient uptake rates. Zones characterized by longer residence time (15–60 days) had higher chlorophyll-a concentrations (9 ± 4 µg L−1) and were comprised primarily of small phytoplankton cells (<5 µm, 74 ± 8%), lower ammonium concentrations (1 ± 0.8 µM), higher nitrate uptake rates, and higher rates of potential carbon productivity. Conversely, zones characterized by shorter residence time (1–14 days) had higher ammonium concentration (13 ± 5 µM) and lower chlorophyll-a concentration (5 ± 1 µg L−1) with diatoms making up a larger percent contribution. Longer residence time, however, did not result in the accumulation of large (>5 µm) cells considered important to pelagic food webs. Rather, longer residence time zones had a phytoplankton community comprised primarily of small cells, particularly picocyanobacteria that made up 38 ± 17% of the chlorophyll-a – nearly double the concentration seen in shorter residence time zones (22 ± 7% picocyanobacterial of chlorophyll-a). Our results suggest that water residence time in estuaries may have an effect as large or larger than that experimentally demonstrated for light, contaminants, or nutrients.
Wildlife repatriation is an important tool to decrease extinction risk for imperiled species, but successful repatriations require significant time, resources and planning. Because repatriations can be long and expensive processes, clear release strategies and monitoring programs are essential to efficiently use resources and evaluate success. However, monitoring can be challenging and surrounded by significant uncertainty, particularly for secretive species with extremely low detection probability. Here, we simulated how alternative repatriation strategies influence repatriation success for the eastern indigo snake Drymarchon couperi, a federally-Threatened species that is currently being repatriated in Alabama and Florida. Critically, we demonstrate how observed population growth can differ from true population growth when detection probabilities are low and mark-recapture analyses are not an option. Specifically, we built a stochastic stage-based population model to predict population growth and extinction risk under different release strategies and use information from ongoing repatriations to predict success and guide future releases. Because D. couperi is difficult to monitor, we modeled how detection probability influenced perceptions of abundance and population growth by monitoring programs. Simulated repatriation strategies releasing older, head-started snakes in greater abundance and frequency created wild populations with decreased extinction risk relative to scenarios releasing fewer and younger snakes less frequently. Ongoing repatriations currently have a 0.23 (Alabama) and 0.61 (Florida) probability of quasi-extinction, but extinction risk decreased to 0.07 and 0.10 at sites upon achieving the targeted number of releases. Abundances observed under realistic detection thresholds for D. couperi did not always predict true population growth; specifically, we demonstrate that monitoring programs during repatriations of secretive species may indicate that efforts have been unsuccessful when populations are actually growing. Overall, our modeling framework informs release strategies to maximize repatriation success while demonstrating the need to consider how detection processes influence assessment of success during conservation interventions.
Inducing biological soil crust (biocrust) development is an appealing approach for dust mitigation in drylands due to the resistance biocrusts can provide against erosion. Using a portable device, we evaluated dust emissions from surfaces either inoculated with biocrust, amended with a plant‐based soil stabilizer, or both at varying wind friction velocities. Four months after application, emissions from all treatments were either indistinguishable from or greater than controls, despite evidence of biocrust establishment. All treatments had greater surface roughness and showed more evidence of entrapment of windblown sediment than controls, factors which may have been partially responsible for elevated emissions. There was a synergistic effect of inoculation and stabilizer addition, resulting in a nearly two‐fold reduction in estimated emissions compared to either treatment alone. Stepwise regression analysis indicated that variables associated with surface crust strength (aggregate stability, penetration resistance) were negatively associated with emissions and variables associated with sediment supply (sand content, loose sediment cover) were positively associated with emissions. With more time to develop, the soil‐trapping activity and surface integrity of biocrust inoculum and soil stabilizer mixtures is expected to increase with the accumulation of surface biomass and enhancement of roughness through freeze–thaw cycles.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.4731","usgsCitation":"Fick, S.E., Barger, N.N., Tatarko, J., and Duniway, M.C., 2020, Induced biological soil crust controls on wind erodibility and dust (PM10) emissions: Earth Surface Processes and Landforms, v. 45, no. 1, p. 224-236, https://doi.org/10.1002/esp.4731.","productDescription":"13 p.","startPage":"224","endPage":"236","ipdsId":"IP-104178","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":372918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Canyonlands Research Center, Dugout Ranch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.67239379882812,\n 37.91549263478459\n ],\n [\n -109.39498901367188,\n 37.91549263478459\n ],\n [\n -109.39498901367188,\n 38.11781187396181\n ],\n [\n -109.67239379882812,\n 38.11781187396181\n ],\n [\n -109.67239379882812,\n 37.91549263478459\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Fick, Stephen E. 0000-0002-3548-6966","orcid":"https://orcid.org/0000-0002-3548-6966","contributorId":214319,"corporation":false,"usgs":true,"family":"Fick","given":"Stephen","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":783899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barger, Nichole N.","contributorId":193039,"corporation":false,"usgs":false,"family":"Barger","given":"Nichole","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":783900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tatarko, John","contributorId":169778,"corporation":false,"usgs":false,"family":"Tatarko","given":"John","email":"","affiliations":[{"id":25584,"text":"USDA-ARS Agricultural Systems Research Unit, Fort Collins, CO 80526","active":true,"usgs":false}],"preferred":false,"id":783901,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":783902,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205560,"text":"70205560 - 2020 - Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States","interactions":[],"lastModifiedDate":"2020-01-20T12:22:35","indexId":"70205560","displayToPublicDate":"2019-09-09T10:19:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States","docAbstract":"Streamwater quality can be affected by climate-related variability in hydrologic state, which controls flow paths and affects biogeochemical processes. Thirty-one years of input/output solute fluxes at Panola Mountain Research Watershed, a small, forested, seasonally water-limited watershed near Atlanta, Georgia, were used to quantify the effects of climatic-related variability in storage on streamwater solute concentrations and fluxes. Streamwater fluxes were estimated for ten solutes from weekly and event sample concentrations using regression-based methods. The most pertinent storage attribute (current or antecedent watershed, shallow, and deep storage) for each solute was determined by fitting separate concentration relationships. The concentration-discharge relationships varied more for reactive solutes such as potassium, sulfate, and DOC and less for weathering products (base cations and dissolved silica) and conservative chloride. Many solutes exhibited higher concentrations when storage levels were lower or wetting up, which was likely the result of the concentrating effects of evapotranspiration and/or the buildup and flushing of weathering products associated with longer residence times. The impacts of storage modeling on annual fluxes varied by solute, ranging from about 5% (magnesium) to 52% (nitrate) as relative standard deviations, and sufficiently removed climate-related patterns observed in streamwater concentrations. Sulfate was particularly mobilized following growing season droughts but only if deep storage was sufficiently recharged, possibly indicating that sulfides in the deep storage pool were oxidized to sulfate during droughts and mobilized when re-wetted. The lack of streamwater sulfate response to 61% declines in atmospheric deposition indicates the importance of watershed biogeochemical processes on controls of streamwater export of sulfate. The approach of explicitly incorporating storage in the streamwater concentration modeling elucidated the effects of climate on streamwater water-quality and may provide insight into the effects of climatic change on future fluxes.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13589","usgsCitation":"Aulenbach, B.T., 2020, Effects of climate-related variability in storage on streamwater solute concentrations and fluxes in a small forested watershed in the Southeastern United States: Hydrological Processes, v. 34, no. 2, p. 189-208, https://doi.org/10.1002/hyp.13589.","productDescription":"20 p.","startPage":"189","endPage":"208","ipdsId":"IP-104585","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":367690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Panola Mountain Research Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.18994903564453,\n 33.61976556057674\n ],\n [\n -84.13021087646484,\n 33.61976556057674\n ],\n [\n -84.13021087646484,\n 33.64627826509988\n ],\n [\n -84.18994903564453,\n 33.64627826509988\n ],\n [\n -84.18994903564453,\n 33.61976556057674\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Aulenbach, Brent T. 0000-0003-2863-1288 btaulenb@usgs.gov","orcid":"https://orcid.org/0000-0003-2863-1288","contributorId":3057,"corporation":false,"usgs":true,"family":"Aulenbach","given":"Brent","email":"btaulenb@usgs.gov","middleInitial":"T.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771652,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70215988,"text":"70215988 - 2020 - Reproductive ecology and movement of pallid sturgeon in the upper Missouri River, Montana","interactions":[],"lastModifiedDate":"2020-11-03T14:16:28.31736","indexId":"70215988","displayToPublicDate":"2019-09-07T08:13:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"title":"Reproductive ecology and movement of pallid sturgeon in the upper Missouri River, Montana","docAbstract":"Successful recruitment of endangered pallid sturgeon has not been documented in the upper Missouri River basin for decades, and research on the reproductive ecology of pallid sturgeon has been hindered by low sample size. A conservation propagation program was initiated in the 1990s, and the oldest age class of hatchery‐origin pallid sturgeon are becoming sexually mature increasing the number of reproductively‐active fish in the system. However, it is currently unknown how the reproductive ecology of hatchery‐origin pallid sturgeon relates to the few remaining wild fish. Following spring reproductive assessments, weekly relocations were recorded for each individual from late‐May to mid‐July to facilitate comparisons of spawning season movements among reproductive classifications and between spring hydrographs (2015 and 2016) for male pallid sturgeon. Mean total movement distances (±SE) were 104.5 km (18.9) for reproductively‐active wild males, 116.0 km (18.1) for reproductively‐active 1997‐year class males, and 20.6 km (3.0) for non‐reproductively‐active fish of unconfirmed sex. Movement characteristics of reproductively‐active males did not differ between 2015 and 2016 despite a difference of eight days in the timing of peak discharge and a difference of 79 m3/s (16.7%) in magnitude. Male aggregations were observed on the descending limb of the hydrograph in 2016 during temperatures suitable for spawning, but female pallid sturgeon underwent follicular atresia, similar to the other years of the study. Hatchery‐origin pallid sturgeon from the conservation propagation program appear to have retained reproductive characteristics from the wild broodstock, a key finding for a population where local extirpation of the wild stock is imminent.
","language":"English","publisher":"Wiley Blackwell","doi":"10.1111/jai.13962","usgsCitation":"Holmquist, L.M., Guy, C.S., Tews, A., Trimpe, D.J., and Webb, M.A., 2020, Reproductive ecology and movement of pallid sturgeon in the upper Missouri River, Montana, https://doi.org/10.1111/jai.13962.","ipdsId":"IP-107826","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":458670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jai.13962","text":"Publisher Index Page"},{"id":380076,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.39038085937499,\n 47.07012182383309\n ],\n [\n -105.88623046874999,\n 47.07012182383309\n ],\n [\n -105.88623046874999,\n 47.99727386804474\n ],\n [\n -111.39038085937499,\n 47.99727386804474\n ],\n [\n -111.39038085937499,\n 47.07012182383309\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2019-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Holmquist, Luke M. 0000-0002-9282-8897","orcid":"https://orcid.org/0000-0002-9282-8897","contributorId":244286,"corporation":false,"usgs":false,"family":"Holmquist","given":"Luke","email":"","middleInitial":"M.","affiliations":[{"id":36244,"text":"MSU","active":true,"usgs":false}],"preferred":false,"id":803688,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":803689,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tews, Anne","contributorId":244287,"corporation":false,"usgs":false,"family":"Tews","given":"Anne","email":"","affiliations":[{"id":48627,"text":"mtfwp","active":true,"usgs":false}],"preferred":false,"id":803690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trimpe, David J.","contributorId":244288,"corporation":false,"usgs":false,"family":"Trimpe","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":12646,"text":"BOR","active":true,"usgs":false}],"preferred":false,"id":803691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webb, Molly A. H.","contributorId":152118,"corporation":false,"usgs":false,"family":"Webb","given":"Molly","email":"","middleInitial":"A. H.","affiliations":[{"id":18870,"text":"Bozeman Fish Technology Center, U.S. Fish and Wildlife Service, Bozeman, Montana 59715","active":true,"usgs":false}],"preferred":false,"id":803692,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210920,"text":"70210920 - 2020 - Applying spatially explicit capture–recapture models to estimate black bear density in South Carolina","interactions":[],"lastModifiedDate":"2020-07-03T14:02:08.90055","indexId":"70210920","displayToPublicDate":"2019-09-05T08:59:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Applying spatially explicit capture–recapture models to estimate black bear density in South Carolina","docAbstract":"Population density is an important component of wildlife management decisions, but can be difficult to estimate directly for an itinerant, wide‐ranging species such as the American black bear (Ursus americanus ). In South Carolina, USA, where there has been growth in black bear populations and bear–human‐conflict reports during the past several decades, managers need robust estimates of population size to inform management strategies. We used maximum‐likelihood capture–recapture models, using hair snares to collect DNA samples, to estimate density and abundance for a harvested population of black bear in northwestern South Carolina during 2013 to 2014. Models were tested in a spatially explicit framework using the secr package in Program R. Black bear density was estimated at 0.133 bears/km2 (SE = 0.034) in 2013 and 0.179 bears/km2 (SE = 0.043) in 2014. Black bear abundance in our study area was estimated to be 586 bears (SE = 95) in 2013 and 680 bears (SE = 128) in 2014, which are 2–3‐fold lower than previous estimates. We suggest that these estimates be considered a baseline for state biologists to employ in the population's management and in developing future harvest‐regulation strategies. Overall our study highlighted the potential for model choice to influence density estimates, and we concluded that spatially explicit models were appropriate for this study because geographic closure could not be assumed.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1007","usgsCitation":"Azad, S., McFadden, K., Clark, J.D., Wactor, T., and Jachowski, D., 2020, Applying spatially explicit capture–recapture models to estimate black bear density in South Carolina: Wildlife Society Bulletin, v. 43, no. 3, p. 500-507, https://doi.org/10.1002/wsb.1007.","productDescription":"8 p.","startPage":"500","endPage":"507","ipdsId":"IP-108028","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":499857,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/1934029e1d5c411d8e18f9f7abaa7f57","text":"External Repository"},{"id":376120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"south Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.936279296875,\n 35.10193405724606\n ],\n [\n -82.430419921875,\n 35.191766965947394\n ],\n [\n -83.1005859375,\n 35.003003395276714\n ],\n [\n -83.353271484375,\n 34.71452466170392\n ],\n [\n -83.023681640625,\n 34.49750272138159\n ],\n [\n -82.7490234375,\n 34.27083595165\n ],\n [\n -81.23291015625,\n 34.31621838080741\n ],\n [\n -81.03515625,\n 34.334364487026306\n ],\n [\n -80.936279296875,\n 35.10193405724606\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Azad, Shefali","contributorId":228811,"corporation":false,"usgs":false,"family":"Azad","given":"Shefali","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":792137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McFadden, Katherine kwmcfadden@usgs.gov","contributorId":228812,"corporation":false,"usgs":false,"family":"McFadden","given":"Katherine","email":"kwmcfadden@usgs.gov","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":792138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wactor, Tammy","contributorId":228813,"corporation":false,"usgs":false,"family":"Wactor","given":"Tammy","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":792140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jachowski, David S.","contributorId":228814,"corporation":false,"usgs":false,"family":"Jachowski","given":"David S.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":792141,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206074,"text":"70206074 - 2020 - Historical changes in fish communities in urban streams of the southeastern U.S. and the relative importance of water-quality stressors","interactions":[],"lastModifiedDate":"2020-01-05T14:01:25","indexId":"70206074","displayToPublicDate":"2019-09-04T10:54:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1471,"text":"Ecology of Freshwater Fish","active":true,"publicationSubtype":{"id":10}},"title":"Historical changes in fish communities in urban streams of the southeastern U.S. and the relative importance of water-quality stressors","docAbstract":"A total of 71 stream sites representing a gradient of urban land use was sampled across the Piedmont of the southeastern U.S. in 2014. Fish data collected (observed) at each site were compared to an expected community based on georeferenced historical (~1950 - ~1990) species occurrence records for stream segments (1:100,000 scale) containing the sampled stream sites. Loss of expected fish species (percent of fish species expected to occur but not observed) and homogenization (difference in Jaccard’s similarity of the fish community among sites observed and expected) were determined. On average, there was a 13.2% increase in the similarity of fish communities across sites, demonstrating evidence of community homogenization. Occurrence of Redbreast Sunfish (Lepomis auritus), Green Sunfish (L. cyanellus), and Bluegill (L. macrochirus) increased more than 50% over time (between observed and expected). Species loss increased significantly with urbanization whereas homogenization was not related to urbanization. Random forest analysis indicated that herbicides, insecticides, and centrarchid species richness were significant predictors of species loss. Of these, generalized additive model regression indicated that herbicides represented the most parsimonious model based on a single predictor. Stream base flow, elevation, and total nitrogen were significant predictors of homogenization. Generalized additive model regression indicated that decreased stream base flow was the single most important factor associated with increased homogenization. Chemical contaminants and associated ecosystem alteration and changes in stream flow may represent important regional influences on changes in fish communities in urban streams in the southeastern U.S.","language":"English","publisher":"Wiley","doi":"10.1111/eff.12503","usgsCitation":"Meador, M.R., 2020, Historical changes in fish communities in urban streams of the southeastern U.S. and the relative importance of water-quality stressors: Ecology of Freshwater Fish, v. 29, no. 1, p. 156-169, https://doi.org/10.1111/eff.12503.","productDescription":"14 p.","startPage":"156","endPage":"169","ipdsId":"IP-092961","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":368448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States ","state":"Alabama, Georgia, North Carolina, South Carolina, Tennessee, Virginia, Washington DC.","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.431396484375,\n 39.487084981687495\n ],\n [\n -79.266357421875,\n 38.70265930723801\n ],\n [\n -79.771728515625,\n 38.634036452919226\n ],\n [\n -80.343017578125,\n 37.90953361677018\n ],\n [\n -80.628662109375,\n 37.58811876638322\n ],\n [\n -81.090087890625,\n 37.49229399862877\n ],\n [\n -81.89208984375,\n 37.52715361723378\n ],\n [\n -84.1552734375,\n 36.65079252503471\n ],\n [\n -85.60546875,\n 34.994003757575776\n ],\n [\n -86.5283203125,\n 33.137551192346145\n ],\n [\n -87.29736328125,\n 32.0639555946604\n ],\n [\n -85.10009765625,\n 31.690781806136822\n ],\n [\n -83.78173828125,\n 32.1570124860701\n ],\n [\n -81.9580078125,\n 33.46810795527896\n ],\n [\n -80.947265625,\n 33.797408767572485\n ],\n [\n -80.04638671875,\n 34.17999758688084\n ],\n [\n -79.60693359375,\n 34.867904962568716\n ],\n [\n -78.22265625,\n 35.7286770448517\n ],\n [\n -77.113037109375,\n 36.54494944148322\n ],\n [\n -77.16796875,\n 37.96152331396614\n ],\n [\n -76.849365234375,\n 38.92522904714054\n ],\n [\n -77.18994140625,\n 39.06184913429154\n ],\n [\n -77.991943359375,\n 38.496593518947584\n ],\n [\n -77.82714843749999,\n 39.13006024213511\n ],\n [\n -78.431396484375,\n 39.487084981687495\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Meador, Michael R. 0000-0001-5956-3340 mrmeador@usgs.gov","orcid":"https://orcid.org/0000-0001-5956-3340","contributorId":219878,"corporation":false,"usgs":true,"family":"Meador","given":"Michael","email":"mrmeador@usgs.gov","middleInitial":"R.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":773484,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70263567,"text":"70263567 - 2020 - Regional Global Navigation Satellite System networks for crustal deformation monitoring","interactions":[],"lastModifiedDate":"2025-02-13T16:47:47.502631","indexId":"70263567","displayToPublicDate":"2019-09-04T10:45:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Regional Global Navigation Satellite System networks for crustal deformation monitoring","docAbstract":"Regional networks of Global Navigation Satellite System (GNSS) stations cover seismically and volcanically active areas throughout the United States. Data from these networks have been used to produce high‐precision, three‐component velocity fields covering broad geographic regions as well as position time series that track time‐varying crustal deformation. This information has contributed to assessing interseismic strain accumulation and related seismic hazard, revealed previously unknown occurrences of aseismic fault slip, constrained coseismic slip estimates, and enabled monitoring of volcanic unrest and postseismic deformation. In addition, real‐time GNSS data are now widely available. Such observations proved invaluable for tracking the rapidly evolving eruption of Kīlauea in 2018. Real‐time earthquake source modeling using GNSS data is being incorporated into tsunami warning systems, and a vigorous research effort is focused on quantifying the contribution that real‐time GNSS can make to improve earthquake early warnings as part of the Advanced National Seismic System ShakeAlert system. Real‐time GNSS data can also aid in the tracking of ionospheric disturbances and precipitable water vapor for weather forecasting. Although regional GNSS and seismic networks generally have been established independently, their spatial footprints often overlap, and in some cases the same institution operates both types of networks. Further integration of GNSS and seismic networks would promote joint use of the two data types to better characterize earthquake sources and ground motion as well as offer opportunities for more efficient network operations. Looking ahead, upgrading network stations to leverage new GNSS technology could enable more precise positioning and robust real‐time operations. New computational approaches such as machine learning have the potential to enable full utilization of the large amounts of data generated by continuous GNSS networks. Development of seafloor Global Positioning System‐acoustic networks would provide unique information for fundamental and applied research on subduction zone seismic hazard and, potentially, monitoring.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220190113","usgsCitation":"Murray, J.R., Bartlow, N., Bock, Y., Brooks, B.A., Foster, J.H., Freymueller, J.T., Hammond, W.C., Hodgkinson, K., Johanson, I.A., Lopez-Venegas, A., Mann, D., Mattioli, G., Melbourne, T., Mencin, D., Montgomery-Brown, E.K., Murray, M.H., Smalley, R., and Thomas, V., 2020, Regional Global Navigation Satellite System networks for crustal deformation monitoring: Seismological Research Letters, v. 91, no. 2A, p. 552-572, https://doi.org/10.1785/0220190113.","productDescription":"21 p.","startPage":"552","endPage":"572","ipdsId":"IP-108083","costCenters":[{"id":237,"text":"Earthquake Science 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breadth in endocrine active compound detection","interactions":[],"lastModifiedDate":"2019-11-08T08:48:22","indexId":"70206562","displayToPublicDate":"2019-09-04T08:44:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"De facto water reuse: Bioassay suite approach delivers depth and breadth in endocrine active compound detection","docAbstract":"Although endocrine disrupting compounds (EDCs) have been detected in wastewater and surface waters worldwide using a variety of in vitro effects-based screening tools, e.g. bioassays, few have examined potential attenuation of environmental contaminants by both natural (sorption, degradation, etc) and anthropogenic (water treatment practices) processes. This study used several bioassays and quantitative chemical analyses to assess residence-time weighted samples at six sites along a river in the northeastern United States beginning upstream of a waste water treatment plant (WWTP) outfall and proceeding downstream along the stream reach to a drinking water treatment plant (DWTP). Known steroidal estrogens were quantified and changes in signaling pathway molecular initiating events (activation of estrogen, androgen, glucocorticoid, peroxisome proliferator-activated, pregnane X receptor, and aryl hydrocarbon receptor signaling networks) were identified in water extracts. In initial multi-endpoint assays geographic and receptor-specific endocrine activity patterns in transcription factor signatures and nuclear receptor activation were discovered. In subsequent single endpoint receptor-specific bioassays, estrogen (16 of 18 samples; 0.01 to 28 ng estradiol equivalents [E2Eqs]/L) glucocorticoid (3 of 18 samples; 1.8 to 21 ng dexamethasone equivalents [DexEqs]/L), and androgen (2 of 18 samples; 0.95 to 2.1 ng dihydrotestosterone equivalents [DHTEqs]/L) receptor transcriptional activation occurred above respective assay method detection limits (0.04 ng E2Eqs/L, 1.2 ng DexEqs/L, and 0.77 ng DHTEqs/L) in multiple sampling events. Estrogen activity, the most often detected, correlated well with measured concentrations of known steroidal estrogens (R2 = 0.890). Overall, activity indicative of multiple types of endocrine active compounds was highest in wastewater effluent samples, while activity downstream was progressively lower, and negligible in (unfinished) treated water. This multiple bioassay approach, in conjunction with targeted analytical chemistry methods, has gained acceptance among water quality screening programs. Not only was estrogenic and glucocorticoid activity confirmed in the effluent by utilizing multiple methods concurrently, but other activated signaling networks that historically received less attention (i.e. peroxisome proliferator-activated receptor) were also detected.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.134297","usgsCitation":"Medlock Kakaley, E.K., Blackwell, B., Cardon, M.C., Conley, J.M., Evans, N., Feifarek, D.J., Furlong, E., Glassmeyer, S.T., Gray, L.E., Hartig, P.C., Kolpin, D., Mills, M.A., Rosenblum, L., Villeneuve, D.L., and Wilson, V.S., 2020, De facto water reuse: Bioassay suite approach delivers depth and breadth in endocrine active compound detection: Science of the Total Environment, v. 699, https://doi.org/10.1016/j.scitotenv.2019.134297.","productDescription":"134297, 12 p.","startPage":"1-12","ipdsId":"IP-111008","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":458674,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1703348","text":"Publisher Index Page"},{"id":369079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"699","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Medlock Kakaley, Elizabeth K","contributorId":220449,"corporation":false,"usgs":false,"family":"Medlock Kakaley","given":"Elizabeth","email":"","middleInitial":"K","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":774944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blackwell, Brett R.","contributorId":173601,"corporation":false,"usgs":false,"family":"Blackwell","given":"Brett R.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":774945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cardon, Mary C.","contributorId":190792,"corporation":false,"usgs":false,"family":"Cardon","given":"Mary","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":774946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conley, Justin M.","contributorId":184086,"corporation":false,"usgs":false,"family":"Conley","given":"Justin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":774947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Evans, Nicola","contributorId":184087,"corporation":false,"usgs":false,"family":"Evans","given":"Nicola","email":"","affiliations":[],"preferred":false,"id":774948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Feifarek, David J.","contributorId":198057,"corporation":false,"usgs":false,"family":"Feifarek","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774949,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Furlong, Edward 0000-0002-7305-4603","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":213730,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":774950,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Glassmeyer, Susan T.","contributorId":184135,"corporation":false,"usgs":false,"family":"Glassmeyer","given":"Susan","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":774951,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gray, L. 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Models that only approximate habitat potential are incomplete because areas of high habitat potential may be isolated, whereas intermixed areas of lower habitat potential may still be critical for maintaining connectivity between and among populations. We developed a range-wide, omnidirectional (‘coreless’) connectivity model and map for the threatened Mojave desert tortoise at a high spatial resolution (30 m), based on empirical movement data and a circuit-theoretic approach to estimating connectivity. Specifically, we first estimated habitat potential (i.e., quality) for tortoise movement (as distinct from habitat potential more generally) across its range using hypotheses based on the published literature, linear mixed models, multiple environmental factors derived from remotely sensed data, and recent solar and wind development footprints. The resultant raster output was used to represent landscape conductance in a circuit-theoretic model of connectivity, which relates the flow of electrical current through a circuit to the movement of tortoises through the landscape. We then modeled potential connectivity across the range of the tortoise using Circuitscape software and the Julia numerical programming language. Intermediate distances from minor roads, intermediate values of annual average maximum temperature, and increasing density of desert washes were among the strongest predictors of movement habitat quality. There was also strong evidence for increased habitat quality for movement with increasing amounts of vegetation cover. The resulting connectivity model and map was determined to accurately reflect important areas for tortoise movement, but we encourage others to do their own evaluation of the model within local areas of interest and as more data become available. Accordingly, the map can provide an important component to improve management decisions that have the potential to influence the conservation of connected desert tortoise populations throughout the range.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2847","usgsCitation":"Gray, M.E., Dickson, B.G., Nussear, K., Esque, T., and Chang, T., 2020, A range-wide model of contemporary, omnidirectional connectivity for the threatened Mojave desert tortoise: Ecosphere, v. 10, no. 9, e02847, https://doi.org/10.1002/ecs2.2847.","productDescription":"e02847","ipdsId":"IP-109686","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2847","text":"Publisher Index Page"},{"id":371617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.3946533203125,\n 33.65578083204094\n ],\n [\n -114.70275878906249,\n 33.280027811732154\n ],\n [\n -114.40612792968749,\n 35.14686290675633\n ],\n [\n -115.77941894531249,\n 35.92464453144099\n ],\n [\n -116.70227050781249,\n 35.420391545750746\n ],\n [\n -117.32299804687499,\n 34.985003130171066\n ],\n [\n -116.83959960937499,\n 34.347971491244955\n ],\n [\n -116.3946533203125,\n 33.65578083204094\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"9","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Miranda E","contributorId":221848,"corporation":false,"usgs":false,"family":"Gray","given":"Miranda","email":"","middleInitial":"E","affiliations":[{"id":40441,"text":"Conservation Science Partners, Truckee, CA","active":true,"usgs":false}],"preferred":false,"id":780491,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dickson, Brett G.","contributorId":221849,"corporation":false,"usgs":false,"family":"Dickson","given":"Brett","email":"","middleInitial":"G.","affiliations":[{"id":40442,"text":"Conservation Science Partners, Truckee, CA; Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":780492,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nussear, Kenneth","contributorId":194538,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","affiliations":[{"id":24618,"text":"Department of Geography, University of Nevada, Reno, Reno, NV","active":true,"usgs":false}],"preferred":false,"id":780493,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chang, Tony","contributorId":191992,"corporation":false,"usgs":false,"family":"Chang","given":"Tony","email":"","affiliations":[],"preferred":false,"id":780494,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211191,"text":"70211191 - 2020 - Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic","interactions":[],"lastModifiedDate":"2020-07-16T18:49:40.296564","indexId":"70211191","displayToPublicDate":"2019-09-03T13:44:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1478,"text":"Ecosystems","active":true,"publicationSubtype":{"id":10}},"title":"Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic","docAbstract":"Permafrost thaw in the Arctic is mobilizing old carbon (C) from soils to aquatic ecosystems and the atmosphere. Little is known, however, about the assimilation of old C by aquatic food webs in Arctic watersheds. Here, we used C isotopes (δ13C, Δ14C) to quantify C assimilation by biota across 12 streams in arctic Alaska. Streams spanned watersheds with varying permafrost hydrology, from ice-poor bedrock to ice-rich loess (that is, yedoma). We measured isotopic content of (1) C sources including dissolved organic C (DOC), dissolved inorganic C (DIC), and soil C, and (2) stream biota, including benthic biofilm and macroinvertebrates, and resident fish species (Arctic Grayling (Thymallus arcticus) and Dolly Varden (Salvelinus malma)). Findings document the assimilation of old C by stream biota, with depleted Δ14C values observed at multiple trophic levels, including benthic biofilm (14C ages = 5255 to 265 years before present (y BP)), macroinvertebrates (4490 y BP to modern), and fish (3195 y BP to modern). Mixing model results indicate that DOC and DIC contribute to benthic biofilm composition, with relative contributions differing across streams draining ice-poor and ice-rich terrain. DOC originates primarily from old terrestrial C sources, including deep peat horizons (39–47%; 530 y BP) and near-surface permafrost (12–19%; 5490 y BP). DOC also accounts for approximately half of fish isotopic composition. Analyses suggest that as the contribution of old C to fish increases, fish growth and nutritional status decline. We anticipate increases in old DOC delivery to streams under projected warming, which may further alter food web function in Arctic watersheds.
","language":"English","publisher":"Springer","doi":"10.1007/s10021-019-00413-6","usgsCitation":"O'Donnell, J., Carey, M.P., Koch, J.C., Xu, X., Poulin, B., Walker, J., and Zimmerman, C.E., 2020, Permafrost hydrology drives the assimilation of old carbon by stream food webs in the Arctic: Ecosystems, v. 23, p. 435-453, https://doi.org/10.1007/s10021-019-00413-6.","productDescription":"19 p.","startPage":"435","endPage":"453","ipdsId":"IP-102831","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":437218,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NAUIQR","text":"USGS data release","linkHelpText":"Carbon Isotope Concentrations in Stream Food Webs of the Arctic Network National Parks, Alaska, 2014-2016"},{"id":376449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Land Bridge and Noatak National Preserves","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165.58593749999997,\n 65.4217295985527\n ],\n [\n -156.09375,\n 65.4217295985527\n ],\n [\n -156.09375,\n 68.12248241161676\n ],\n [\n -165.58593749999997,\n 68.12248241161676\n ],\n [\n -165.58593749999997,\n 65.4217295985527\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","noUsgsAuthors":false,"publicationDate":"2019-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"O'Donnell, Jonathon A 0000-0001-7031-9808","orcid":"https://orcid.org/0000-0001-7031-9808","contributorId":222968,"corporation":false,"usgs":false,"family":"O'Donnell","given":"Jonathon A","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":793044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"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":793045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":793046,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Xu, Xiaomei","contributorId":139915,"corporation":false,"usgs":false,"family":"Xu","given":"Xiaomei","email":"","affiliations":[{"id":13312,"text":"University of California-Irvine","active":true,"usgs":false}],"preferred":false,"id":793047,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poulin, Brett 0000-0002-5555-7733 bpoulin@usgs.gov","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":194253,"corporation":false,"usgs":true,"family":"Poulin","given":"Brett","email":"bpoulin@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":793048,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walker, Jennifer","contributorId":201558,"corporation":false,"usgs":false,"family":"Walker","given":"Jennifer","affiliations":[],"preferred":false,"id":793049,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","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":793050,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227104,"text":"70227104 - 2020 - Attack of the PCR clones: Rates of clonality have little effect on RAD-seq genotype calls","interactions":[],"lastModifiedDate":"2021-12-29T14:00:37.386552","indexId":"70227104","displayToPublicDate":"2019-09-03T07:57:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Attack of the PCR clones: Rates of clonality have little effect on RAD-seq genotype calls","docAbstract":"Interpretation of high-throughput sequence data requires an understanding of how decisions made during bioinformatic data processing can influence results. One source of bias that is often cited is PCR clones (or PCR duplicates). PCR clones are common in restriction site-associated sequencing (RAD-seq) data sets, which are increasingly being used for molecular ecology. To determine the influence PCR clones and the bioinformatic handling of clones have on genotyping, we evaluate four RAD-seq data sets. Data sets were compared before and after clones were removed to estimate the number of clones present in RAD-seq data, quantify how often the presence of clones in a data set causes genotype calls to change compared to when clones were removed, investigate the mechanisms that lead to genotype call changes and test whether clones bias heterozygosity estimates. Our RAD-seq data sets contained 30%–60% PCR clones, but 95% of RAD-tags had five or fewer clones. Relatively few genotypes changed once clones were removed (5%–10%), and the vast majority of these changes (98%) were associated with genotypes switching from a called to no-call state or vice versa. PCR clones had a larger influence on genotype calls in individuals with low read depth but appeared to influence genotype calls at all loci similarly. Removal of PCR clones reduced the number of called genotypes by 2% but had almost no influence on estimates of heterozygosity. As such, while steps should be taken to limit PCR clones during library preparation, PCR clones are likely not a substantial source of bias for most RAD-seq studies.
In river systems, high‐head dams may increase the distance‐decay of fish community similarity by creating nearly impermeable dispersal barriers to certain species from upstream reaches. Substantial evidence suggests that migratory species are impacted by dams, and most previous studies in stream/river networks have focused on small streams and headwaters. Here, we assess whether a high‐head dam (Lock and Dam 19; LD 19) on a large river, the Upper Mississippi River (UMR), substantially alters fish community structure relative to variability expected to occur independent of the dam's effect as a fish dispersal barrier. Using fish catch per unit effort data, we modelled the distance‐decay function for the UMR fish community and then estimated the similarity that would be expected to occur across LD19 and compared it with measured similarity. Measured similarity in the fish community above and below LD19 was close to the expected value based on the distance‐decay function, suggesting LD19 does not create an abrupt transition in the fish community. Although some migratory fish species no longer occur above LD19 (e.g., skipjack herring, Alosa chrysochloris), these species do not occur in high abundance below the dam and so do not drive variation in fish community structure. Instead, much of the variation in species structure is driven by the loss/gain of species across the latitudinal gradient. Lock and Dam 19 does not appear to be a clear transition point in the river's fish community, although it may function as a meaningful barrier for particular species (e.g., invasive species) and warrant future attention from a management perspective.
","language":"English","publisher":"John Wiley and Sons, Inc.","doi":"10.1002/rra.3534","usgsCitation":"Anderson, R.L., Anderson, C.A., Larson, J.H., Knights, B.C., Vallazza, J.M., Jenkins, S.E., and Lamer, J.T., 2020, Influence of a high-head dam as a dispersal barrier to fish community structure of the Upper Mississippi River: River Research and Applications, v. 36, no. 1, p. 47-56, https://doi.org/10.1002/rra.3534.","productDescription":"10 p.","startPage":"47","endPage":"56","ipdsId":"IP-095942","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":458682,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.3534","text":"Publisher Index Page"},{"id":372095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.8125,\n 37.52715361723378\n ],\n [\n -88.79150390625,\n 37.52715361723378\n ],\n [\n -88.79150390625,\n 44.68427737181225\n ],\n [\n -92.8125,\n 44.68427737181225\n ],\n [\n -92.8125,\n 37.52715361723378\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Rebekah L.","contributorId":218832,"corporation":false,"usgs":false,"family":"Anderson","given":"Rebekah","email":"","middleInitial":"L.","affiliations":[{"id":39921,"text":"Illinois Deptment of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":781660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Cory A.","contributorId":196305,"corporation":false,"usgs":false,"family":"Anderson","given":"Cory","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":781661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":781659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":781662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vallazza, Jonathan M. 0000-0003-2367-4887 jvallazza@usgs.gov","orcid":"https://orcid.org/0000-0003-2367-4887","contributorId":149362,"corporation":false,"usgs":true,"family":"Vallazza","given":"Jonathan","email":"jvallazza@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":781663,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jenkins, Sean E.","contributorId":199666,"corporation":false,"usgs":false,"family":"Jenkins","given":"Sean","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":781665,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lamer, James T. 0000-0003-1155-1548","orcid":"https://orcid.org/0000-0003-1155-1548","contributorId":196307,"corporation":false,"usgs":false,"family":"Lamer","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":48847,"text":"Illinois River Biological Station, Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":781664,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212604,"text":"70212604 - 2020 - Porphyry copper potential of the U.S. Southern Basin and Range using ASTER data integrated with geochemical and geologic datasets to assess potential near-surface deposits in well-explored permissive tracts","interactions":[],"lastModifiedDate":"2020-08-24T12:21:59.418909","indexId":"70212604","displayToPublicDate":"2019-09-01T15:21:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Porphyry copper potential of the U.S. Southern Basin and Range using ASTER data integrated with geochemical and geologic datasets to assess potential near-surface deposits in well-explored permissive tracts","docAbstract":"ArcGIS was used to spatially assess and rank potential porphyry copper deposits using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data together with geochemical and geologic datasets in order to estimate undiscovered deposits in the southern Basin and Range Province in the southwestern United States. The assessment was done using a traditional expert opinion three-part method and a prospectivity model developed using weights of evidence and logistic regression techniques to determine if ASTER data integrated with other geologic datasets can be used to find additional areas of prospectivity in well-explored permissive tracts. ASTER hydrothermal alteration data were expressed as 457 alteration polygons defined from a low-pass filtered alteration density map of combined argillic, phyllic, and propylitic rock units. Sediment stream samples were plotted as map grid data and used as spatial information in ASTER polygons. Gravity and magnetic data were also used to define basins greater than 1 km in depth. Each ASTER alteration polygon was ranked for porphyry copper potential using alteration types, spatial amounts of alteration, stream sediment geochemistry, lithology, polygon shape, proximity to other alteration polygons, and deposit and prospects data. Permissive tracts defined for the assessment in the southern Basin and Range Province include the Laramide Northwest, Laramide Southeast, Jurassic, and Tertiary tracts. Expert opinion estimates using the three-part assessment method resulted in a mean estimate of 17 undiscovered porphyry copper deposits, whereas the prospectivity modeling predicted a mean estimate of nine undiscovered deposits. In the well-explored Laramide Southeast tract, which contains the most deposits and has been explored for over 100 years, an average of 4.3 undiscovered deposits was estimated using ASTER alteration polygon data versus 2.8 undiscovered deposits without ASTER data. The Tertiary tract, which contains the largest number of ASTER alteration polygons not associated with known Tertiary deposits, was predicted to contain the most undiscovered resources in the southern Basin and Range Province.
","language":"English","publisher":"Economic Geology","doi":"10.5382/econgeo.4675","usgsCitation":"Mars, J.C., Robinson, Hammarstrom, J.M., Zurcher, L., Whitney, H.A., Solano, F., Gettings, M.E., and Ludington, S., 2020, Porphyry copper potential of the U.S. Southern Basin and Range using ASTER data integrated with geochemical and geologic datasets to assess potential near-surface deposits in well-explored permissive tracts: Economic Geology, v. 114, no. 6, p. 1095-1121, https://doi.org/10.5382/econgeo.4675.","productDescription":"27 p.","startPage":"1095","endPage":"1121","ipdsId":"IP-096385","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":458683,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5382/econgeo.4675","text":"Publisher Index 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managers","docAbstract":"Invasive alien species are likely to interact with climate change, thus necessitating management that proactively addresses both global changes. However, invasive species managers’ concerns about the effects of climate change, the degree to which they incorporate climate change into their management, and what stops them from doing so remain unknown. Therefore, we surveyed natural resource managers addressing invasive species across the U.S. about their priorities, concerns, and management strategies in a changing climate. Of the 211 managers we surveyed, most were very concerned about the influence of climate change on invasive species management, but their organizations were significantly less so. Managers reported that lack of funding and personnel limited their ability to effectively manage invasive species, while lack of information limited their consideration of climate change in decision-making. Additionally, managers prioritized research that identifies range-shifting invasive species and native communities resilient to invasions and climate change. Managers also reported that this information would be most effectively communicated through conversations, research summaries, and meetings/symposia. Despite the need for more information, 65% of managers incorporate climate change into their invasive species management through strategic planning, preventative management, changing treatment and control, and increasing education and outreach. These results show the potential for incorporating climate change into management, but also highlight a clear and pressing need for more targeted research, accessible science communication, and two-way dialogue between researchers and managers focused on invasive species and climate change.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-019-02087-6","usgsCitation":"Beaury, E.M., Fusco, E.J., Jackson, M.R., Laginhas, B.B., Morelli, T.L., Allen, J.M., Pasquarella, V.J., and Bradley, B.A., 2020, Incorporating climate change into invasive species management: Insights from managers: Biological Invasions, v. 22, p. 233-252, https://doi.org/10.1007/s10530-019-02087-6.","productDescription":"20 p.","startPage":"233","endPage":"252","ipdsId":"IP-109690","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":458686,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10530-019-02087-6","text":"Publisher Index Page"},{"id":376816,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","noUsgsAuthors":false,"publicationDate":"2019-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaury, Evelyn M.","contributorId":236820,"corporation":false,"usgs":false,"family":"Beaury","given":"Evelyn","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":794354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fusco, Emily J.","contributorId":236821,"corporation":false,"usgs":false,"family":"Fusco","given":"Emily","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":794355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Michelle R.","contributorId":236822,"corporation":false,"usgs":false,"family":"Jackson","given":"Michelle","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":794356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laginhas, Brittany B.","contributorId":236823,"corporation":false,"usgs":false,"family":"Laginhas","given":"Brittany","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":794357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":794358,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allen, Jenica M.","contributorId":146420,"corporation":false,"usgs":false,"family":"Allen","given":"Jenica","email":"","middleInitial":"M.","affiliations":[{"id":13006,"text":"Department of Ecology and Evolutionary Biology, University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":794359,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pasquarella, Valerie J.","contributorId":236824,"corporation":false,"usgs":false,"family":"Pasquarella","given":"Valerie","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":794360,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bradley, Bethany A.","contributorId":40117,"corporation":false,"usgs":true,"family":"Bradley","given":"Bethany","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":794361,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70206109,"text":"70206109 - 2020 - Temporal patterns of induced seismicity in Oklahoma revealed from multi-station template matching","interactions":[],"lastModifiedDate":"2020-10-13T22:50:25.42041","indexId":"70206109","displayToPublicDate":"2019-08-29T08:05:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2453,"text":"Journal of Seismology","active":true,"publicationSubtype":{"id":10}},"title":"Temporal patterns of induced seismicity in Oklahoma revealed from multi-station template matching","docAbstract":"Over the past decade, Oklahoma became the most seismically active region of the mid-Continental USA as a result of industry operations. However, seismic network limitations and completeness of earthquake catalogs have restricted the types of analyses that can be performed. By applying multi-station template matching on the 23,889 cataloged earthquakes in Oklahoma and Southern Kansas between late-2008 and 2016, we increased the number of detected earthquakes to 209,409 events. While the improved catalog produced an order of magnitude events than the original catalog, the frequency-magnitude distribution remains similar to the original catalog. We found that the coefficient of variation of interevent times in small spatial bins tends to spatially correlate with the location of M ≥ 4 earthquakes. The improved catalog reveals the pervasiveness of swarm-like patterns in seismicity across the entire study region. The rapid increase in seismicity rate of these swarms in 2013 coincided with a reduction in the calculated p values (power law decay rates) before and after larger events. We also used the catalog to revisit the temporal patterns in the four M ≥ 5 sequences, finding more active foreshock behavior than previously recognized and variations in aftershock behavior. When compared against poroelastic stress models for the Pawnee and Fairview sequences, the catalog shows an improved correlation with stress that accounts for variable-rate injection, supporting the conclusion that injection rate is an important contributor to seismic hazard.
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University","active":true,"usgs":false}],"preferred":false,"id":773611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Currie, Brian S.","contributorId":207881,"corporation":false,"usgs":false,"family":"Currie","given":"Brian","email":"","middleInitial":"S.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":773612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ries, Rosamiel","contributorId":211773,"corporation":false,"usgs":false,"family":"Ries","given":"Rosamiel","email":"","affiliations":[{"id":38316,"text":"Miami University, Oxford, Ohio","active":true,"usgs":false}],"preferred":false,"id":773613,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206445,"text":"70206445 - 2020 - A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades","interactions":[],"lastModifiedDate":"2020-08-05T13:58:56.739844","indexId":"70206445","displayToPublicDate":"2019-08-28T15:41:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2035,"text":"International Journal of Digital Earth","active":true,"publicationSubtype":{"id":10}},"title":"A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades","docAbstract":"The overarching goal of this study was to perform a comprehensive meta-analysis of irrigated agricultural Crop Water Productivity (CWP) of the world’s three leading crops: wheat, corn, and rice based on three decades of remote sensing and non-remote sensing-based studies. Overall, CWP data from 148 crop growing study sites (60 wheat, 43 corn, and 45 rice) spread across the world were gathered from published articles spanning 31 different countries. There was overwhelming evidence of a significant increase in CWP with an increase in latitude for predominately northern hemisphere datasets. For example, corn grown in latitude 40–50° had much higher mean CWP (2.45 kg/m³) compared to mean CWP of corn grown in other latitudes such as 30–40° (1.67 kg/m³) or 20–30° (0.94 kg/m³). The same trend existed for wheat and rice as well. For soils, none of the CWP values, for any of the three crops, were statistically different. However, mean CWP in higher latitudes for the same soil was significantly higher than the mean CWP for the same soil in lower latitudes. This applied for all three crops studied. For wheat, the global CWP categories were low (≤0.75 kg/m³), medium (>0.75 to <1.10 kg/m³), and high CWP (≥1.10 kg/m³). For corn the global CWP categories were low (≤1.25 kg/m³), medium (>1.25 to ≤1.75 kg/m³), and high (>1.75 kg/m³). For rice the global CWP categories were low (≤0.70 kg/m³), medium (>0.70 to ≤1.25 kg/m³), and high (>1.25 kg/m³). USA and China are the only two countries that have consistently high CWP for wheat, corn, and rice. Australia and India have medium CWP for wheat and rice. India’s corn, however, has low CWP. Egypt, Turkey, Netherlands, Mexico, and Israel have high CWP for wheat. Romania, Argentina, and Hungary have high CWP for corn, and Philippines has high CWP for rice. All other countries have either low or medium CWP for all three crops. Based on data in this study, the highest consumers of water for crop production also have the most potential for water savings. These countries are USA, India, and China for wheat; USA, China, and Brazil for corn; India, China, and Pakistan for rice. For example, even just a 10% increase in CWP of wheat grown in India can save 6974 billion liters of water. This is equivalent to creating 6974 lakes each of 100 m³ in volume that leads to many benefits such as acting as ‘water banks’ for lean season, recreation, and numerous ecological services. This study establishes the volume of water that can be saved for each crop in each country when there is an increase in CWP by 10%, 20%, and 30%.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/17538947.2019.1651912","usgsCitation":"Daniel J. Foley, Thenkabail, P., Aneece, I., Pardhasaradhi Teluguntla, and Oliphant, A., 2020, A meta-analysis of global crop water productivity of three leading world crops (wheat, corn, and rice) in the irrigated areas over three decades: International Journal of Digital Earth, v. 13, no. 8, p. 939-975, https://doi.org/10.1080/17538947.2019.1651912.","productDescription":"37 p.","startPage":"939","endPage":"975","ipdsId":"IP-105160","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":458691,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/17538947.2019.1651912","text":"Publisher Index Page"},{"id":368938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Daniel J. Foley 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":220240,"corporation":false,"usgs":false,"family":"Daniel J. Foley","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":774571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":774570,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aneece, Itiya 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":220241,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":774572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardhasaradhi Teluguntla 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":214457,"corporation":false,"usgs":false,"family":"Pardhasaradhi Teluguntla","affiliations":[{"id":39046,"text":"Bay Area Environmental Research Institute at USGS","active":true,"usgs":false}],"preferred":false,"id":774573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":774574,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}