Prey fish communities in Lake Michigan have been steadily changing, characterized by declines in both the quantity and quality of Alewife Alosa pseudoharengus. To evaluate concurrent changes in the diet of Lake Trout Salvelinus namaycush in northeastern Lake Michigan, we analyzed stomach contents of Lake Trout caught during gill‐net surveys and fishing tournaments from May through October 2016. We then compared the composition, on a wet‐weight basis, of 2016 diets with those previously described in a recent survey conducted in 2011. Overall, we found that Lake Trout diets in 2016 consisted mostly (94% by wet weight) of Alewives and Round Goby Neogobius melanostomus. Averaging across May through October, 61% of the Lake Trout diet consisted of Alewives. A clear seasonal shift was apparent: the diet was dominated by Round Goby (67%) during May–June, whereas Alewives dominated the diet (76%) during July–October. Seasonal dominance of Round Goby in spring Lake Trout diets has not been previously observed in northeastern Lake Michigan as Round Goby represented only 21% of the Lake Trout diet in spring of 2011. Diet composition of Lake Trout caught in gill nets did not significantly differ from diet composition of Lake Trout caught by anglers in either the May–June period or the July–October period. Although Lake Trout showed increased diet flexibility in 2016 compared with 2011, Alewives were still the predominant diet component during 2016, despite reduced Alewife biomass throughout Lake Michigan. Nonetheless, this further evidence of diet plasticity suggests that Lake Trout may be resilient to ongoing and future forage base changes.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10318","usgsCitation":"Luo, M.K., Madenjian, C.P., Diana, J.S., Kornis, M.S., and Bronte, C.R., 2019, Shifting diets of Lake Trout in northeastern Lake Michigan: North American Journal of Fisheries Management, v. 39, no. 4, p. 793-806, https://doi.org/10.1002/nafm.10318.","productDescription":"14 p.","startPage":"793","endPage":"806","ipdsId":"IP-105241","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467355,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/2027.42/151367","text":"External Repository"},{"id":366775,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.29711914062499,\n 41.582579601430346\n ],\n [\n -84.74853515625,\n 41.582579601430346\n ],\n [\n -84.74853515625,\n 46.20264638061019\n ],\n [\n -88.29711914062499,\n 46.20264638061019\n ],\n [\n -88.29711914062499,\n 41.582579601430346\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Luo, Miles K.","contributorId":218263,"corporation":false,"usgs":false,"family":"Luo","given":"Miles","email":"","middleInitial":"K.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":768833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":768832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diana, James S.","contributorId":216547,"corporation":false,"usgs":false,"family":"Diana","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":768834,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kornis, Matthew S.","contributorId":201252,"corporation":false,"usgs":false,"family":"Kornis","given":"Matthew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":768835,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":768836,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203824,"text":"ofr20191072 - 2019 - Offshore shallow structure and sediment distribution, Punta Gorda to Point Arena, Northern California","interactions":[],"lastModifiedDate":"2019-08-21T14:16:36","indexId":"ofr20191072","displayToPublicDate":"2019-08-21T09:26:55","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1072","displayTitle":"Offshore Shallow Structure and Sediment Distribution, Punta Gorda to Point Arena, Northern California","title":"Offshore shallow structure and sediment distribution, Punta Gorda to Point Arena, Northern California","docAbstract":"This publication consists of two map sheets that display shallow geologic structure, along with sediment distribution and thickness, for an approximately 150-km-long offshore section of the northern California coast between Punta Gorda and Point Arena. Each map sheet includes three maps at scales of either 1:100,000 or 1:200,000, and together the sheets include 30 figures that contain representative high-resolution seismic-reflection profiles. The maps and seismic-reflection surveys cover most of the continental shelf in this region. In addition, the maps show the locations of the shelf break and the 3-nautical-mile limit of California’s State Waters.
The seismic-reflection data, which are the primary dataset used to develop the maps, were collected to support the California Seafloor Mapping Program, U.S. Geological Survey Offshore Geologic Hazards projects, and National Oceanic and Atmospheric Administration’s (NOAA’s) Ocean Explorer program. In addition to the two map sheets, this publication includes geographic information system data files of faults, sediment thicknesses, and depths-to-base of sediment
The map area includes the northernmost section of the right-lateral San Andreas Fault, which extends offshore from Point Arena in the south to Point Delgada in the north. The San Andreas Fault is the primary structure in the widely distributed plate boundary between the Pacific Plate and the Sierra Nevada–Great Valley Microplate, with estimates of cumulative right slip of as much as up to 450 km. South of Point Delgada, fault-related transtension has resulted in development of the Noyo Basin. North of Point Delgada, the San Andreas Fault transitions into a complex contractional zone in and (or) south of the King Range, including a possible nearshore fault that may connect with the Mattole Canyon Fault.
Quaternary sediments and bedrock underlie the shelf. On the seismic-reflection profiles, we digitally traced the thickness and depth of the uppermost seismic-stratigraphic unit, which is a focus of this publication. The upper contact of this unit is the seafloor; the lower contact is a transgressive surface of erosion, a commonly angular, wave-cut unconformity characterized by an upward change to lower amplitude, more diffuse reflections. On the basis of this lower contact, this stratigraphic unit is inferred to have been deposited on the shelf in the last about 21,000 years during the sea-level rise that followed the last major lowstand and the Last Glacial Maximum (LGM). Maps in this publication show both the thickness of this upper sediment unit and the depth to the base of the sediment unit. Within the map region, five different “domains” of post-LGM shelf sediment are delineated on the basis of sediment thickness and coastal geomorphology. Maximum sediment thickness (as much as 67 m) is found in the northern part of the region, along the steep south flank of the King Range. Minimum sediment thickness (areas of exposed bedrock) is found on fault-bounded uplifts, which include Tolo bank and Punta Gorda bank. Mean sediment thickness for the entire shelf in the map area between Punta Gorda and Point Arena is 8.9 m, and total sediment volume is 12,824×106 m3.
Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
The U.S. Army Corps of Engineers, Jacksonville District, plans to deepen the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel, beginning at the mouth of the river at the Atlantic Ocean, to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, (1) installed continuous data collection stations to monitor discharge, salinity, and associated parameters at 23 sites prior to the commencement of dredging and (2) monitored stage and discharge at 13 sites and water temperature, specific conductance, and salinity at 16 sites; all parameters were monitored at some sites.
This is the second annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening and contains information pertinent to the data collection sites during the 2017 water year, from October 2016 to September 2017. One data collection site on the St. Johns River below Shands Bridge was added to the network during this timeframe after the previously monitored location was damaged by Hurricane Matthew.
Discharge and salinity varied widely during the data collection period, reflecting the effects of Hurricane Matthew in October 2016 and Hurricane Irma in September 2017. The annual mean discharge at Trout River was greatest among the tributaries, followed by annual mean discharges at Durbin Creek, Ortega River, Julington Creek, Pottsburg Creek, Clapboard Creek, Cedar River, Broward River, and Dunn Creek. Among the tributary sites, annual mean salinity was highest at the site closest to the Atlantic Ocean, Clapboard Creek, and lowest at the site farthest from the ocean, Durbin Creek. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which is expected. Relative to salinity for the 2016 water year, annual mean salinity in the tributaries was higher for the 2017 water year at four monitoring locations, lower at four monitoring locations, and the same at one location. Of the three sites where salinity was calculated on the main stem in the 2016 water year, salinity was higher at one monitoring location in the 2017 water year and lower at two locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191078","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2019, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2017: U.S. Geological Survey Open-File Report 2019–1078, 35 p., https://doi.org/10.3133/ofr20191078.","productDescription":"viii, 35 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-106628","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":366770,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1078/ofr20191078.pdf","text":"Report","size":"11.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1078"},{"id":366769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1078/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Lower St Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.1502685546875,\n 29.156958511360703\n ],\n [\n -81.2109375,\n 29.156958511360703\n ],\n [\n -81.2109375,\n 30.500750980290693\n ],\n [\n -82.1502685546875,\n 30.500750980290693\n ],\n [\n -82.1502685546875,\n 29.156958511360703\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Home to Arches, Grand Canyon, and Saguaro National Parks, among others, the American Southwest’s landscapes are as fragile as they are iconic. Energy development, water security, and grassland restoration are important to the region as it experiences population growth and increased demand for resources. The U.S. Geological Survey’s Southwest Biological Science Center provides sound scientific information to help identify effective management strategies for the Southwest’s abundant natural resources and vast public lands. Research is focused on two key areas—dryland ecology and river science.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193041","usgsCitation":"Southwest Biological Science Center staff, 2019, The U.S. Geological Survey Southwest Biological Science Center—Sound science to serve the American Southwest: U.S. Geological Survey Fact Sheet 2019–3041, 4 p., https://doi.org/10.3133/fs20193041.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-110036","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":366772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3041/fs20193041.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2019-3041"},{"id":366771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3041/coverthb.jpg"}],"country":"United States","state":"Arizona, California, Nevada, Utah","otherGeospatial":"American Southwest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.0478515625,\n 31.50362930577303\n ],\n [\n -109.248046875,\n 31.50362930577303\n ],\n [\n -109.248046875,\n 38.324420427006544\n ],\n [\n -119.0478515625,\n 38.324420427006544\n ],\n [\n -119.0478515625,\n 31.50362930577303\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
United States
The U.S. Geological Survey conducted a study using modern methods of molecular analysis aimed at attempting to identify the source(s) of fecal contamination that had been identified in previous studies conducted by the West Virginia Conservation Agency in the Elk Run watershed, Jefferson County, West Virginia. Water samples from multiple sites showing elevated fecal coliform counts were analyzed using molecular markers associated with general mammalian fecal contamination (AllBac), human Bacteroides (HF183), bovine Bacteroides (BoBac), and human polyomavirus (HPyV). Samples were also analyzed by quantitative polymerase chain reaction (qPCR) for human and bovine cytochrome b (mitochondrial DNA marker). A headwater site (Elk Branch at Shenandoah Junction) was found to be severely affected by both human and bovine contamination in May 2017. Although many of the molecular marker levels as well as Escherichia coli numbers had declined by a repeat sampling in June 2017, total coliform bacterial numbers remained high. Examination of the data indicated that this site had probably been affected by two separate contamination events, an influx of bovine contamination close to the time of the May sampling and a human contamination event that had occurred earlier. Samples from all sites contained bovine mitochondrial DNA, whereas only one revealed relatively high levels of human mitochondrial DNA. The Elk Run watershed appears to be widely affected by bovine influences with human influence episodically playing a role. Surface runoff caused by rain events exacerbates both.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191064","usgsCitation":"Schill, W.B., and Iwanowicz, D.D., 2019, Molecular identification of fecal contamination in the Elks Run watershed, Jefferson County, West Virginia, 2016–17: U.S. Geological Survey Open-File Report 2019–1064, 9 p., https://doi.org/10.3133/ofr20191064.","productDescription":"9 p.","onlineOnly":"Y","ipdsId":"IP-092227","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":366675,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1064/ofr20191064.pdf","text":"Report","size":"6.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1064"},{"id":366674,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1064/coverthb.jpg"}],"country":"United States","state":"West Virginia","county":"Jefferson 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Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Caged mussels used as biomonitors can provide insights about ambient contaminant assemblages and spatial patterns, sources of contaminants, and contaminant exposure risks for consumers of wild and farmed mussels. This study explored the potential role of ambient sediment in the uptake of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and potentially toxic inorganic elements by caged mussels and complements findings from a Puget Sound-wide stormwater-contaminant mussel-monitoring survey in Washington State. In summary, ambient sediment appeared to be related to mussel uptake of lead and possibly copper at all sites, PCBs at industrial sites, and PAHs at Liberty Bay, Eagle Harbor, and, to a lesser extent, Smith Cove. These findings indicate that resuspended bed sediment is one, but not the only, pathway that filter-feeding mussels are exposed to contaminants. Overall, PAHs, PCBs, arsenic, and potentially toxic metals were low in intertidal bed sediment at the nine sites measured in Puget Sound in February 2016 and signify a low risk of sediment-bound contaminant exposure to mussels at those locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191087","usgsCitation":"Takesue, R.K., Campbell, P.L., and Conn, K.E., 2019, Polycyclic aromatic hydrocarbons, polychlorinated biphenyls, and metals in ambient sediment at mussel biomonitoring sites, Puget Sound, Washington: U.S. Geological Survey Open-File Report 2019–1087, 15 p., https://doi.org/10.3133/ofr20191087.","productDescription":"Report: vi, 15 p.","numberOfPages":"15","onlineOnly":"Y","ipdsId":"IP-102107","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":366767,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1087/ofr20191087.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1087"},{"id":366766,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1087/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.3759765625,\n 47.03269459852135\n ],\n [\n -121.83837890625,\n 47.03269459852135\n ],\n [\n -121.83837890625,\n 48.98382212608503\n ],\n [\n -125.3759765625,\n 48.98382212608503\n ],\n [\n -125.3759765625,\n 47.03269459852135\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
Data from a 4-year capture and transport program were used to assess translocation as a management strategy for two long-lived, federally endangered catostomids in the Upper Klamath Basin, Oregon. Lost River (Deltistes luxatus) and shortnose (Chasmistes brevirostris) suckers, two species endemic to the Klamath Basin, were translocated from Lake Ewauna to Upper Klamath Lake in each of 4 years (2014–2017) in an effort to augment existing spawning populations in Upper Klamath Lake. Lake Ewauna, downstream of Upper Klamath Lake and connected to it by the Link River, has small populations of Lost River and shortnose suckers. Upper Klamath Lake has the largest remaining population of Lost River suckers and one of the largest remaining populations of shortnose suckers. Adult suckers were captured in Lake Ewauna, tagged with passive integrated transponder (PIT) tags, and translocated to the Williamson River, a spawning tributary that flows into Upper Klamath Lake. We monitored initial success of translocation efforts with encounters from remote PIT tag antennas and physical recaptures.
A total of 659 suckers were translocated from Lake Ewauna to the Williamson River (40 in 2014, 384 in 2015, 172 in 2016, and 63 in 2017). All individuals that were translocated were assumed to be one of the endangered taxa, but recaptures indicated that some translocated suckers were misidentified and were instead Klamath largescale suckers (Catostomus snyderi), a non-listed species that is also endemic to the Upper Klamath Basin. Other recaptures of translocated individuals revealed conflicts in species identification between the two endangered taxa as well. Due to species identification conflicts, we analyzed translocated individuals by cohort (year of translocation) and sex only. Specifically, we documented encounters of translocated individuals at spawning locations and throughout the Upper Klamath Lake watershed, analyzed frequency of return to spawning sites, assessed fidelity to spawning sites, and monitored migration timing over three full years (2015, 2016, and 2017). Remote PIT tag antennas at 11 sites and 5 physical capture locations were part of a monitoring network to re-encounter translocated individuals. In contrast to other years of the study, high flows in the Williamson River in 2017 prevented the installation of a river-wide weir and upstream trap with associated PIT-tag antennas that routinely detect large numbers of tagged fish. As a result, re-encounter probabilities in 2017 were expected to be lower than 2015 and 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191085","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Banet, N.V., and Hewitt, D.A., 2019, Monitoring of endangered Klamath Basin suckers translocated from Lake Ewauna to Upper Klamath Lake, Oregon, 2014−2017: U.S. Geological Survey Open-File Report 2019–1085, 40 p., https://doi.org/10.3133/ofr20191085.","productDescription":"v, 39 p.","onlineOnly":"Y","ipdsId":"IP-097743","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":366745,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1085/coverthb.jpg"},{"id":366746,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1085/ofr20191085.pdf","text":"Report","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1085"}],"country":"United States","state":"Oregon","otherGeospatial":"Lake Ewauna, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.1240234375,\n 42.1613675328748\n ],\n [\n -121.74224853515625,\n 42.1613675328748\n ],\n [\n -121.74224853515625,\n 42.60970621339408\n ],\n [\n -122.1240234375,\n 42.60970621339408\n ],\n [\n -122.1240234375,\n 42.1613675328748\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Geophysical investigations of Mountain Pass and vicinity were begun as part of an effort to study regional crustal structures as an aid to understanding the geologic framework and mineral resources of the eastern Mojave Desert. The study area encompasses Mountain Pass, host to one of the world’s largest rare earth element carbonatite deposits. The deposit is found along a north-northwest-trending, fault-bounded block that extends along the eastern parts of the Clark Mountain Range, Mescal Range, and Ivanpah Mountains. This Paleoproterozoic block is composed of a 1.7-Ga metamorphic complex of gneiss and schist that underwent widespread metamorphism and associated plutonism during the Ivanpah orogeny. The Paleoproterozoic rocks were intruded by a Mesoproterozoic (1.4 Ga) ultrapotassic alkaline intrusive suite and carbonatite body. The intrusive rocks include, from oldest to youngest, shonkinite, mesosyenite, syenite, quartz syenite, potassic granite, carbonatite, carbonatite dikes, and late shonkinite dikes.
The diverse physical properties of rocks that underlie the study area are well suited to geophysical investigations. Contrasts in radiogenic signatures between Paleoproterozoic crystalline basement, rocks of the Mesoproterozoic carbonatite body and the associated alkaline intrusive suite, Paleozoic carbonate rocks, Mesozoic granitoids, Tertiary volcanic rocks, and unconsolidated alluvium, for example, produce a distinctive pattern of radiometric anomalies that can aid in understanding the geologic framework and mineral resource potential of the eastern Mojave Desert.
A high-resolution radiometric survey of Mountain Pass was flown by helicopter over parts of the Clark Mountain Range, Mescal Range, and Ivanpah Mountains. Aeroradiometric surveys measure the intensity and energy spectrum of gamma-ray radiation from the three most common naturally occurring radioelements: potassium (40K), thorium (232Th), and uranium (238U). For 232Th and 238U, the source of the gamma-rays comes from their thallium (208Tl) and bismuth (214Bi) decay products, respectively, and, thus, concentrations for Th and U are referred to as “equivalent concentration,” assuming radioactive equilibrium. The concentrations of these radioelements can be used together to estimate changes in geochemistry and lithology.
Carbonatite deposits typically have distinctive geophysical signatures because they are relatively dense, magnetic, and radiogenic. Specifically, the carbonatite and alkaline intrusive suite at Mountain Pass is ultrapotassic and contains relatively significant amounts of K, Th, and U, which can be delineated using airborne radiometric surveys.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3412C","usgsCitation":"Ponce, D.A., and Denton, K.M. (D.A. Ponce, ed.), 2019, Airborne radiometric maps of Mountain Pass, California: U.S. Geological Survey Scientific Investigations Map 3412–C, scale 1:62,500, https://doi.org/10.3133/sim3412C.","productDescription":"1 Plate: 39 x 32 inches; Data Release","numberOfPages":"1","onlineOnly":"Y","ipdsId":"IP-100413","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":366599,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ENLS6D","linkHelpText":"High-resolution airborne radiometric survey of Mountain Pass, California"},{"id":366597,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3412/c/coverthb.jpg"},{"id":366598,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3412/c/sim3412C_.pdf","text":"Report","size":"3 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Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Changing Arctic climate may alter freshwater ecosystems as a result of warmer surface waters, longer open-water periods, reduced wintertime lake ice growth, and altered hydrologic connectivity. This study aims to characterize zooplankton community composition and size structure in the context of hydrologic connectivity and ice regimes in Arctic lakes. Between 2011 and 2016, we sampled the phytoplankton, zooplankton, and fish communities from a set of representative lakes on the Arctic Coastal Plain (ACP) of northern Alaska to determine potential food web responses to changing Arctic ecosystems. Multivariate analyses showed that time from ice-out had a strong influence on zooplankton community structure and that seasonal succession of zooplankton differed between lakes with varying hydrologic connectivity. Trends were observed suggesting that large-bodied zooplankton (Daphnia, calanoid copepods) may be more prevalent in poorly connected lakes with low fish diversity. Large-bodied zooplankton displayed higher biomass in lakes with high occurrences of bedfast ice, while small-bodied zooplankton (Bosmina, rotifers) displayed highest biomass in deeper lakes with low occurrences of bedfast ice. Our results contribute to limited knowledge of zooplankton in remote lakes of the ACP and suggest that the anticipated changes to aquatic ecosystems in the Arctic may include energetically less efficient plankton food webs.
","language":"English","publisher":"Taylor and Francis","doi":"10.1080/15230430.2019.1643210","usgsCitation":"Beaver, J.R., Arp, C.D., Tausz, C.E., Jones, B.M., Whitman, M.S., Renicker, T.R., Samples, E.E., Ordosch, D.M., and Scotese, K.C., 2019, Potential shifts in zooplankton community structure in response to changing ice regimes and hydrologic connectivity: Arctic, Antarctic, and Alpine Research, v. 51, no. 1, p. 327-345, https://doi.org/10.1080/15230430.2019.1643210.","productDescription":"19 p.","startPage":"327","endPage":"345","ipdsId":"IP-086427","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":467357,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15230430.2019.1643210","text":"Publisher Index Page"},{"id":408649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -150.4555084446034,\n 70.42307829371518\n ],\n [\n -154.7694812572369,\n 70.42307829371518\n ],\n [\n -154.7694812572369,\n 68.72452581295417\n ],\n [\n -150.4555084446034,\n 68.72452581295417\n ],\n [\n -150.4555084446034,\n 70.42307829371518\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Beaver, John R. 0000-0003-0091-2387","orcid":"https://orcid.org/0000-0003-0091-2387","contributorId":202089,"corporation":false,"usgs":false,"family":"Beaver","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":36339,"text":"BSA Environmental Services, Inc.","active":true,"usgs":false}],"preferred":false,"id":855597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arp, Christopher D.","contributorId":17330,"corporation":false,"usgs":false,"family":"Arp","given":"Christopher","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":855598,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tausz, Claudia E.","contributorId":202091,"corporation":false,"usgs":false,"family":"Tausz","given":"Claudia","email":"","middleInitial":"E.","affiliations":[{"id":36339,"text":"BSA Environmental Services, Inc.","active":true,"usgs":false}],"preferred":false,"id":855715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855599,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitman, Matthew S.","contributorId":67961,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":855600,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Renicker, Thomas R.","contributorId":202090,"corporation":false,"usgs":false,"family":"Renicker","given":"Thomas","email":"","middleInitial":"R.","affiliations":[{"id":36339,"text":"BSA Environmental Services, Inc.","active":true,"usgs":false}],"preferred":false,"id":855601,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Samples, Erin E","contributorId":298429,"corporation":false,"usgs":false,"family":"Samples","given":"Erin","email":"","middleInitial":"E","affiliations":[{"id":64577,"text":"BSA Environmental Services","active":true,"usgs":false}],"preferred":false,"id":855602,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ordosch, David M","contributorId":298430,"corporation":false,"usgs":false,"family":"Ordosch","given":"David","email":"","middleInitial":"M","affiliations":[{"id":64577,"text":"BSA Environmental Services","active":true,"usgs":false}],"preferred":false,"id":855603,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scotese, Kyle C.","contributorId":201592,"corporation":false,"usgs":false,"family":"Scotese","given":"Kyle","email":"","middleInitial":"C.","affiliations":[{"id":36339,"text":"BSA Environmental Services, Inc.","active":true,"usgs":false}],"preferred":true,"id":855604,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70205022,"text":"70205022 - 2019 - The effects of seasonal temperature and photoperiod manipulation on reproduction in the eastern elliptio Elliptio complanata","interactions":[],"lastModifiedDate":"2019-08-28T10:33:59","indexId":"70205022","displayToPublicDate":"2019-08-20T10:30:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2455,"text":"Journal of Shellfish Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The effects of seasonal temperature and photoperiod manipulation on reproduction in the eastern elliptio Elliptio complanata","title":"The effects of seasonal temperature and photoperiod manipulation on reproduction in the eastern elliptio Elliptio complanata","docAbstract":"The eastern elliptio Elliptio complanata is a species of freshwater mussel common to streams and rivers of the Atlantic Coast. Egg fertilization, larval brooding, and glochidial release are reported to occur within a period of several weeks during early to midsummer. In this study, mussels were exposed to manipulated photoperiod and water temperatures to prolong the availability of glochidia for use in artificial propagation and research. Brooding mussels were collected from Pine Creek, Tioga County, PA, in late December and were housed in groups subjected to one of four environmental treatments: natural temperature and photoperiod, 6-wk delay in natural conditions, 12-wk delay in natural conditions, and natural temperature and photoperiod with a winter low of 10°C. Reproductive activity was monitored for 1 y. Mussels subjected to natural conditions released mature glochidia between 16°C and 19°C with a photoperiod of 15 h of light. Temperature and photoperiod delays of 6 and 12 wk delayed reproduction proportional to the treatment, and constant 10°C winter low temperatures slightly shifted the timing of glochidial release. Survival during the study was high (96%–100%). Data indicate that the seasonal availability of E. complanata glochidia can be extended 3-fold using photoperiod and temperature manipulation.
","language":"English","publisher":"BioOne","doi":"10.2983/035.038.0219","usgsCitation":"Blakeslee, C.J., and Lellis, W.A., 2019, The effects of seasonal temperature and photoperiod manipulation on reproduction in the eastern elliptio Elliptio complanata: Journal of Shellfish Research, v. 38, no. 2, p. 379-384, https://doi.org/10.2983/035.038.0219.","productDescription":"6 p.","startPage":"379","endPage":"384","ipdsId":"IP-105915","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":367002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blakeslee, Carrie J. 0000-0002-0801-5325 cblakeslee@usgs.gov","orcid":"https://orcid.org/0000-0002-0801-5325","contributorId":5462,"corporation":false,"usgs":true,"family":"Blakeslee","given":"Carrie","email":"cblakeslee@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":769592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lellis, William A. 0000-0001-7806-2904 wlellis@usgs.gov","orcid":"https://orcid.org/0000-0001-7806-2904","contributorId":2369,"corporation":false,"usgs":true,"family":"Lellis","given":"William","email":"wlellis@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":769593,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208499,"text":"70208499 - 2019 - New insights into the ecology of adfluvial Bull Trout and the population response to the Endangered Species Act in the North Fork Lewis River, Washington","interactions":[],"lastModifiedDate":"2020-02-14T06:31:16","indexId":"70208499","displayToPublicDate":"2019-08-20T08:31:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"New insights into the ecology of adfluvial Bull Trout and the population response to the Endangered Species Act in the North Fork Lewis River, Washington","docAbstract":"Like many other salmonids, Bull Trout Salvelinus confluentus migratory life-history expressions are becoming increasingly rare. A critical step in effectively refining management and conservation strategies is a robust assessment of the effectiveness of such strategies and key biological information used in monitoring and recovery planning. To address this need, we integrated a variety of methods to evaluate the population demographics (abundance), vital rates (survival), and life-history characteristics (ageing, growth, spawning migrations and iteroparity) of an adfluvial Bull Trout population. We also employed our mark-recapture data to quantify if recruitment or adult survival had a greater contribution to population trends from year to year. Our results indicated Bull Trout spawning migrations vary with body size, as a considerable portion of smaller adults (<650 mm) did not spawn each year. Additionally, most spawning individuals made only one spawning migration, while <13% made three or more spawning migrations. Our abundance and survival data, which extends to the early 1990s, illustrated positive responses in survival and abundance following the protection of Bull Trout under the Endangered Species Act (1998). Over this period, we found high interannual variability in both survival and abundance, and adult survival (average = 0.45, SE = 0.04) was surprisingly lower than subadult individuals (average = 0.66, SE = 0.04), suggesting limitations at this important life stage. Our mark-recapture data also suggested the attributes driving the Bull Trout population trend (i.e., recruitment to the adult stage or adult survival) has varied through time, with declining trends in the relative contribution of recruitment. Our results provide new insights into the life-history patterns of adfluvial Bull Trout and can serve as a template to consider factors potentially limiting this and other native trout populations.","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10201","usgsCitation":"Al-Chokhachy, R.K., Doyle, J., and Lampierth, J., 2019, New insights into the ecology of adfluvial Bull Trout and the population response to the Endangered Species Act in the North Fork Lewis River, Washington: Transactions of the American Fisheries Society, v. 148, no. 6, p. 1102-1116, https://doi.org/10.1002/tafs.10201.","productDescription":"15 p.","startPage":"1102","endPage":"1116","ipdsId":"IP-097753","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":372305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"North Fork Lewis River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.12353515624999,\n 45.43700828867391\n ],\n [\n -122.06909179687501,\n 45.43700828867391\n ],\n [\n -122.06909179687501,\n 46.4605655457854\n ],\n [\n -124.12353515624999,\n 46.4605655457854\n ],\n [\n -124.12353515624999,\n 45.43700828867391\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"148","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Al-Chokhachy, Robert K. 0000-0002-2136-5098 ral-chokhachy@usgs.gov","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":1674,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","email":"ral-chokhachy@usgs.gov","middleInitial":"K.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":782173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doyle, Jeremiah","contributorId":214617,"corporation":false,"usgs":false,"family":"Doyle","given":"Jeremiah","email":"","affiliations":[{"id":39086,"text":"PacifiCorp","active":true,"usgs":false}],"preferred":false,"id":782174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lampierth, James","contributorId":222448,"corporation":false,"usgs":false,"family":"Lampierth","given":"James","email":"","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":782175,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222960,"text":"70222960 - 2019 - Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data","interactions":[],"lastModifiedDate":"2021-08-10T13:19:36.379901","indexId":"70222960","displayToPublicDate":"2019-08-20T08:11:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the temperature difference parameter in the SSEBop model with satellite observed land surface temperature data","docAbstract":"The Operational Simplified Surface Energy Balance (SSEBop) model uses the principle of satellite psychrometry to produce spatially explicit actual evapotranspiration (ETa) with remotely sensed and weather data. The temperature difference (dT) in the model is a predefined parameter quantifying the difference between surface temperature at bare soil and air temperature at canopy level. Because dT is derived from the average-sky net radiation based primarily on climate data, validation of the dT estimation is critical for assuring a high-quality ETa product. We used the Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate the SSEBop dT estimation for the conterminous United States. MODIS data (2008–2017) were processed to compute the 10-year average land surface temperature (LST) and normalized difference vegetation index (NDVI) at 1 km resolution and 8-day interval. The observed dT (dTo) was computed from the LST difference between hot (NDVI < 0.25) and cold (NDVI > 0.7) pixels within each 2° × 2° sampling block. There were enough hot and cold pixels within each block to create dTo timeseries in the West Coast and South-Central regions. The comparison of dTo and modeled dT (dTm) showed high agreement, with a bias of 0.8 K and a correlation coefficient of 0.88 on average. This study concludes that the dTm estimation from the SSEBop model is reliable, which further assures the accuracy of the ETa estimation.
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Lakes Michigan and Huron exhibits limited spatial variation but Is temporally linked to alewife abundance","docAbstract":"Chinook Salmon Oncorhynchus tshawytscha were introduced into Lakes Michigan and Huron in the 1960s to diversify recreational fisheries and reduce overabundant, nonnative Alewife Alosa pseudoharengus. Alewife remain the primary prey of Chinook Salmon but have experienced substantial declines in abundance due to reduced food resources and salmonine predation pressure. The movements of Chinook Salmon have been linked to the density and spatial distribution of Alewife, but spatial patterns in Chinook Salmon growth have not been well documented and the temporal relationship between growth and Alewife density has not been evaluated during the current period of low Alewife abundance. We evaluated spatial and temporal variation in growth of Chinook Salmon in Lake Michigan and the U.S. waters of Lake Huron and explored linkages with Alewife density. Von Bertalanffy growth parameters were generally similar for recaptured coded‐wire‐tagged Chinook Salmon from different stocking locations and different recovery locations. Only a few small differences among stocking and recovery regions were evident, with regions divided into two subtly different groups with shared growth parameters. The small regional differences may be attributable to unique habitat and/or stocking characteristics of specific regions. In Lake Michigan average Chinook Salmon length at age also varied across years and was tightly coupled with annual lakewide densities of age‐1 and older Alewife, suggesting that Chinook Salmon growth from 2012 to 2016 was constrained by Alewife density. Our findings are consistent with evidence of lakewide movements associated with foraging and support continued management of Chinook Salmon in Lake Michigan as a single population. Furthermore, similar growth in Chinook Salmon from Lakes Michigan and Huron corroborates evidence that Chinook Salmon move from U.S. waters of Lake Huron to Lake Michigan to feed and reinforces the recent decision to include most fish stocked in northwestern Lake Huron in the Lake Michigan population when managing for predator–prey balance.","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10349","usgsCitation":"Kornis, M., Simpkins, D., Lane, A., Warner, D.M., and Bronte, C., 2019, Growth of hatchery‐reared chinook salmon in Lakes Michigan and Huron exhibits limited spatial variation but Is temporally linked to alewife abundance: North American Journal of Fisheries Management, v. 39, no. 6, p. 1155-1174, https://doi.org/10.1002/nafm.10349.","productDescription":"20 p.","startPage":"1155","endPage":"1174","ipdsId":"IP-101157","costCenters":[{"id":324,"text":"Great Lakes Science 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year","interactions":[],"lastModifiedDate":"2019-12-05T09:57:02","indexId":"sir20195069","displayToPublicDate":"2019-08-19T15:45:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5069","displayTitle":"Estimates of Long-Term Mean Daily Streamflow and Annual Nutrient and Suspended-Sediment Loads Considered for Use in Regional SPARROW Models of the Conterminous United States, 2012 Base Year","title":"Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the Conterminous United States, 2012 base year","docAbstract":"Streamflow, nutrient, and sediment concentration data needed to estimate long-term mean daily streamflow and annual constituent loads were compiled from Federal, State, Tribal, and regional agencies, universities, and nongovernmental organizations. The streamflow and loads are used to develop Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models help describe the distribution, sources, and transport of streamflow, nutrients, and sediment in streams throughout five regions of the conterminous United States. After the data were screened, approximately 5,200 streamflow, 3,000 sediment, and 3,300 nutrient sites, sampled by 137 agencies and organizations were identified as having suitable data for calculating the long-term mean daily streamflow and annual nutrient and sediment loads required for SPARROW model estimation. These sites are representative of a wide range in terms of watershed size, contaminant source types, and land-use and other important watershed characteristics. The methods used to estimate long-term mean annual loads include the Beale ratio estimator and Fluxmaster regression method with Kalman smoothing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195069","collaboration":" ","usgsCitation":"Saad, D.A., Schwarz, G.E., Argue, D.M., Anning, D.W., Ator, S.W., Hoos, A.B., Preston, S.D., Robertson, D.M., and Wise, D.R., 2019, Estimates of long-term mean daily streamflow and annual nutrient and suspended-sediment loads considered for use in regional SPARROW models of the conterminous United States, 2012 base year: U.S. Geological Survey Scientific Investigations Report 2019–5069, 51 p., https://doi.org/10.3133/sir20195069.","productDescription":"Report: vii, 51 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081781","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
This fact sheet describes water quality and geochemistry of the Little Arkansas River and Equus Beds aquifer during 2001 through 2016 as part of the City of Wichita’s Equus Beds aquifer storage and recovery project in south-central Kansas. The Equus Beds aquifer storage and recovery project was developed to help meet future water demand by pumping water out of the Little Arkansas River (during above-base-flow conditions), treating it using National Primary Drinking Water Regulations as a guideline, and injecting it into the aquifer for later use. Water-quality data were collected and analyzed by the U.S. Geological Survey from 2 Little Arkansas River surface-water sites and 63 Equus Beds groundwater sites, including 38 areal assessment index wells, each of which has a shallow well and a deep well. About 4,700 surface and groundwater samples were collected and analyzed for more than 300 water-quality constituents. About 1,300 groundwater chemistry samples were geochemically modeled. Constituents of concern in the Equus Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, Escherichia coli (E. coli), total coliforms, and dissolved iron and arsenic species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193017","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Fact Sheet 2019–3017, 6 p., https://doi.org/10.3133/fs20193017.","productDescription":"Report: 6 p.; Companion Files","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-097042","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364768,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3017/coverthb.jpg"},{"id":364769,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017"},{"id":364770,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"SIR 2019–5026","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and Equus Beds Aquifer, South-Central Kansas, 2001–16"},{"id":364797,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"SIR 2019–5026 Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":"– Table 1.1 through Table 1.14"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.83462524414062,\n 37.884608857503785\n ],\n [\n -97.82844543457031,\n 37.85859141570558\n ],\n [\n -97.76664733886719,\n 37.79296501804014\n ],\n [\n -97.57919311523438,\n 37.66805980224121\n ],\n [\n -97.33749389648438,\n 37.684907136008846\n ],\n [\n -97.33062744140625,\n 37.74248523826606\n ],\n [\n -97.35397338867188,\n 37.859675659210005\n ],\n [\n -97.34230041503906,\n 38.03619406237626\n ],\n [\n -97.3443603515625,\n 38.17829073458205\n ],\n [\n -97.40684509277344,\n 38.17613163876633\n ],\n [\n -97.8826904296875,\n 38.171273439283084\n ],\n [\n -97.89985656738281,\n 38.149137543764894\n ],\n [\n -97.83462524414062,\n 37.884608857503785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Dr.
Lawrence, KS 66049
The city of Wichita’s water supply currently (2019) comes from two primary sources: Cheney Reservoir and the Equus Beds aquifer. The Equus Beds aquifer storage and recovery project was developed to help the city of Wichita meet increasing future water demands. Source water for artificial recharge comes from the Little Arkansas River during above-base-flow conditions, is treated using National Primary Drinking Water Regulations as a guideline, and is injected into the Equus Beds aquifer through recharge wells or surface spreading basins for later use. The Equus Beds aquifer storage and recovery project currently (2019) consists of two coexisting phases. Phase I began in 2007 and captures Little Arkansas River water and indirect streambank diversion well water for aquifer recharge using 4 wells and 2 recharge basins. Phase II began in 2013 and currently (2019) includes a surface-water treatment facility, a river intake facility, eight recharge injection wells, and a third recharge basin. The U.S. Geological Survey, in cooperation with the City of Wichita, completed this study to summarize water-quality and geochemical variability of the Equus Beds aquifer. Data in this report can be used to establish baseline conditions before implementing artificial aquifer recharge further, document groundwater quality, evaluate changing conditions, identify environmental factors affecting groundwater, provide science-based information for decision making, and help meet regulatory monitoring requirements.
Physicochemical properties were measured and water-quality data were collected from 2 Little Arkansas River surface-water sites and 63 Equus Beds aquifer groundwater sites, including 38 areal assessment index wells (IWs) during 2001 through 2016. Data collection included discrete samples and additional continuous measurements at selected sites. Discretely collected samples were analyzed for physicochemical properties, dissolved solids, primary ions, nutrients (nitrogen and phosphorus species), organic carbon, indicator bacteria, trace elements, arsenic species, organic compounds, and radioactivity. This report focuses discussion on aquifer water quality. Federal drinking-water criteria were used to evaluate aquifer water quality. Primary drinking-water criteria are those that are enforceable for public drinking water. Secondary criteria are those that can cause aesthetics or tastes that are unpleasant.
Continuously collected data at a subset of sites included streamflow, groundwater levels, water temperature, specific conductance, pH, oxidation-reduction potential (ORP), dissolved oxygen, turbidity, nitrate plus nitrite, and fluorescent dissolved organic matter. Continuous measurement of physicochemical properties in near-real time allowed characterization of Little Arkansas River surface water and Equus Beds aquifer groundwater during conditions and time scales that would not have been possible otherwise and served as a complement to discrete water-quality sampling. During 2001 through 2016, less than 1 percent of chloride and nitrate plus nitrite, 7 percent of dissolved iron, 48 percent of dissolved manganese, 12 percent of dissolved arsenic, and 39 percent of atrazine detections in surface-water samples exceeded their respective Federal primary or secondary drinking-water criteria. None of the surface-water samples collected exceeded the Federal sulfate criterion, and every sample had detections of total coliform bacteria during the study.
Constituents of concern in the Equus Beds aquifer exceeded their respective Federal criteria throughout the study period and included chloride, sulfate, nitrate plus nitrite, Escherichia coli (E. coli), total coliforms, and dissolved iron and arsenic species. About 5 percent of shallow (less than 80 feet) and 7 percent of deep (greater than 80 feet) IW chloride sample concentrations exceeded the secondary Federal criterion of 250 milligrams per liter (mg/L). Chloride tended to exceed its criterion in shallow and deep wells along the Arkansas River and near Burrton, Kansas, an area with past oil and gas activities. Chloride concentrations near Burrton were larger in the deep parts of the aquifer. About 18 percent of shallow and 13 percent of deep IW sulfate sample concentrations exceeded the secondary Federal criterion of 250 mg/L. Mean sulfate concentrations tended to exceed the criterion in the central part of the study area. Shallow IW mean nitrate plus nitrite (hereafter referred to as “nitrate”) was substantially larger than mean deep IW nitrate. Geochemical conditions in the deeper aquifer reduced forms of nitrogen to species such as ammonia. About 15 percent of shallow and less than 1 percent of deep IW nitrate sample concentrations exceeded the Federal criterion of 10 mg/L. Mean shallow IW nitrate concentrations exceeded the criterion in the northeastern and southeastern parts of the study area; on average, deep IW nitrate concentrations did not exceed the criterion. E. coli and fecal coliform bacteria detections were usually at or near the detection limit. E. coli was detected in 3 percent of shallow and deep IWs, and fecal coliform bacteria were detected in 8 percent of shallow and 6 percent of deep IWs. Total coliforms were detected in 24 percent of shallow and 12 percent of deep IWs. E. coli coliphage was detected in two shallow IW samples (1 percent of samples) at the detection limit and was not detected in deep IW samples.
Dissolved iron was detected in 51 percent of shallow and 62 percent of deep IW samples. Dissolved iron concentrations exceeded the secondary Federal criterion of 0.3 mg/L in 38 percent of shallow and 46 percent of deep IW samples. Mean dissolved iron concentrations were largest mostly in the central and northwest part of the study area corresponding to an area of the aquifer where aquifer material is more clay-rich. The distribution of large dissolved iron concentrations was similar to that of large sulfate concentrations. About 55 percent of shallow and 92 percent of deep IW dissolved manganese samples exceeded the secondary Federal criterion of 0.05 mg/L. Almost all samples from the central and northern parts of the study area had mean dissolved manganese concentrations that exceeded the Federal criterion in the shallow part of the aquifer. Mean dissolved manganese concentrations in the shallow part of the aquifer were substantially large (greater than 1,000 micrograms per liter [μg/L]) in wells near the Little Arkansas River and in the central part of the study area because of chemically reducing conditions in the aquifer that likely related to larger percentages of clay in the aquifer material.
Concentrations of dissolved arsenic species generally were larger in the deep parts of the aquifer. Arsenite was the dominant form of arsenic on average in shallow (52 percent) and deep (55 percent) IWs. About 12 percent of shallow and 34 percent of deep IW dissolved arsenic sample concentrations exceeded the Federal primary drinking criterion of 10 μg/L. Shallow IW dissolved arsenic concentrations were larger near the Little Arkansas River and the center of the study area; large shallow IW dissolved arsenic concentrations (10–50 μg/L) in the center of the study area correspond to areas that have had the most water-level recovery since the historical low in 1993. Mean ORP in shallow IWs generally decreased with increasing water-level depths and were inversely related to mean dissolved arsenic concentrations because of more reducing conditions (smaller ORP) at larger depths below the land surface. Larger dissolved arsenic concentrations in the shallow parts of the aquifer were associated with decreases in water levels and a subsequent decrease in ORP and thus more reducing conditions.
Atrazine was detected in about 58 percent of shallow and 28 percent of deep IWs and did not exceed the primary Federal criterion of 3 μg/L in any groundwater samples. Atrazine concentrations in shallow IWs generally were largest in the northwest part of the study area near the North Branch Kisiwa Creek, and atrazine concentrations in deep IWs generally were largest most often in the southern part of the study area. Gross α radioactivity concentrations exceeded the primary Federal criterion of 15 picocuries per liter in 4 percent of shallow IW samples. Gross α and gross β radioactivity concentrations generally were larger in the southern third of the aquifer.
Most groundwater-sample-simulated minerals saturation indices (SIs) were consistently negative (undersaturated). Minerals that had SI values that were consistently or typically positive (oversaturated) included iron oxide, hydroxide, and quartz-group minerals. Several SI values for arsenic- and manganese-bearing minerals were consistently negative. Some manganese-bearing mineral SI values ranged from undersaturated to oversaturated in shallow and deep IWs during the study. Several carbonate minerals in shallow and deep IWs varied across their equilibrium state. Calcite SI values were larger more often in the deep parts of the aquifer and did not show a clear distributional pattern. Mean and median calcite SI values for shallow and deep IWs were negative (undersaturated) indicating the potential for calcite dissolution if calcite is present for a substantial part of the study period. However, some individual calcite SI values in this study indicated saturation and subsequent calcite precipitation may occur in the study area, potentially resulting in formation of calcite mineral deposits that may reduce efficiency of injection wells. SI values with respect to iron hydroxide varied across their equilibrium states. Mean and median SI values with respect to iron hydroxide were undersaturated in shallow and deep IWs; however, some samples had positive SI values indicating there is potential for iron hydroxide precipitation, possibly caused by leaching and oxidation of iron-containing minerals, like pyrite, in the aquifer material.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195026","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Stone, M.L., Klager, B.J., and Ziegler, A.C., 2019, Water-quality and geochemical variability in the Little Arkansas River and Equus Beds aquifer, south-central Kansas, 2001–16: U.S. Geological Survey Scientific Investigations Report 2019–5026, 79 p., https://doi.org/10.3133/sir20195026.","productDescription":"Report: viii, 79 p.; Appendix Tables: Table 1.1 to Table 1.14; Companion Files","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097040","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":364760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5026/coverthb.jpg"},{"id":364761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026.pdf","text":"Report","size":"11.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5026"},{"id":364771,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5026/sir20195026_appendix01.xlsx","text":"Appendix Tables","size":"236 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5026 Appendix Tables","linkHelpText":" – Table 1.1 through Table 1.14"},{"id":364762,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/fs/2019/3017/fs20193017.pdf","text":"Fact Sheet 2019–3017","size":"4.53 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3017","linkHelpText":" – Water-Quality and Geochemical Variability in the Little Arkansas River and Equus Beds Aquifer, South-Central Kansas, 2001–16"}],"country":"United States","state":"Kansas","otherGeospatial":"Equus Beds Aquifer, Little Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.83462524414062,\n 37.884608857503785\n ],\n [\n -97.82844543457031,\n 37.85859141570558\n ],\n [\n -97.76664733886719,\n 37.79296501804014\n ],\n [\n -97.57919311523438,\n 37.66805980224121\n ],\n [\n -97.33749389648438,\n 37.684907136008846\n ],\n [\n -97.33062744140625,\n 37.74248523826606\n ],\n [\n -97.35397338867188,\n 37.859675659210005\n ],\n [\n -97.34230041503906,\n 38.03619406237626\n ],\n [\n -97.3443603515625,\n 38.17829073458205\n ],\n [\n -97.40684509277344,\n 38.17613163876633\n ],\n [\n -97.8826904296875,\n 38.171273439283084\n ],\n [\n -97.89985656738281,\n 38.149137543764894\n ],\n [\n -97.83462524414062,\n 37.884608857503785\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Dr.
Lawrence, KS 66049
Migration is a widespread but highly diverse component of many animal life histories. Fish migrate throughout the world's oceans, within lakes and rivers, and between the two realms, transporting matter, energy, and other species (e.g., microbes) across boundaries. Migration is therefore a process responsible for myriad ecosystem services. Many human populations depend on the presence of predictable migrations of fish for their subsistence and livelihoods. Although much research has focused on fish migration, many questions remain in our rapidly changing world. We assembled a diverse team of fundamental and applied scientists who study fish migrations in marine and freshwater environments to identify pressing unanswered questions. Our exercise revealed questions within themes related to understanding the migrating individual's internal state, navigational mechanisms, locomotor capabilities, external drivers of migration, the threats confronting migratory fish including climate change, and the role of migration. In addition, we identified key requirements for aquatic animal management, restoration, policy, and governance. Lessons revealed included the difficulties in generalizing among species and populations, and in understanding the levels of connectivity facilitated by migrating fishes. We conclude by identifying priority research needed for assuring a sustainable future for migratory fishes.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2019.00286","usgsCitation":"Lennox, R.J., Paukert, C.P., Aarestrup, K., Auger-Methe, M., Baumgartner, L.J., Birnie-Gauvin, K., Boe, K., Brink, K., Brownscombe, J.W., Chen, Y., Davidsen, J., Eliason, E.J., Filous, A., Gillanders, B., Palm Helland, I., Horodysky, A.Z., Januchowski-Hartley, S.R., Lowerre-Barbieri, S.K., Lucas, M.C., Martins, E.G., Murchie, K.J., Pompeu, P.S., Power, M., Raghavan, R., Rahel, F.J., Secor, D., Thiem, J., Thorstad, E.B., Ueda, H., Whoriskey, F.G., and Cooke, S.J., 2019, One hundred pressing questions on the future of global fish migration science, conservation, and policy: Frontiers in Ecology and Evolution, v. 7, 286, 16 p., https://doi.org/10.3389/fevo.2019.00286.","productDescription":"286, 16 p.","ipdsId":"IP-108920","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00286","text":"Publisher Index Page"},{"id":395049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2019-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Lennox, Robert J.","contributorId":198273,"corporation":false,"usgs":false,"family":"Lennox","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":832044,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aarestrup, Kim","contributorId":203992,"corporation":false,"usgs":false,"family":"Aarestrup","given":"Kim","email":"","affiliations":[{"id":36789,"text":"Danmarks Tekniske Universitet","active":true,"usgs":false}],"preferred":false,"id":832112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auger-Methe, Marie","contributorId":272553,"corporation":false,"usgs":false,"family":"Auger-Methe","given":"Marie","email":"","affiliations":[],"preferred":false,"id":832113,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baumgartner, Lee J.","contributorId":203990,"corporation":false,"usgs":false,"family":"Baumgartner","given":"Lee","email":"","middleInitial":"J.","affiliations":[{"id":36787,"text":"Charles Sturt University, Institute for Land, Water, and Society","active":true,"usgs":false}],"preferred":false,"id":832114,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Birnie-Gauvin, Kim","contributorId":272554,"corporation":false,"usgs":false,"family":"Birnie-Gauvin","given":"Kim","email":"","affiliations":[],"preferred":false,"id":832115,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boe, Kristin","contributorId":272555,"corporation":false,"usgs":false,"family":"Boe","given":"Kristin","email":"","affiliations":[],"preferred":false,"id":832116,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brink, Kerry","contributorId":272556,"corporation":false,"usgs":false,"family":"Brink","given":"Kerry","email":"","affiliations":[],"preferred":false,"id":832117,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brownscombe, Jacob W","contributorId":215060,"corporation":false,"usgs":false,"family":"Brownscombe","given":"Jacob","email":"","middleInitial":"W","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":832118,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Chen, Yushun","contributorId":146569,"corporation":false,"usgs":false,"family":"Chen","given":"Yushun","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":832119,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Davidsen, J. 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J.","contributorId":172747,"corporation":false,"usgs":false,"family":"Cooke","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":832140,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70217322,"text":"70217322 - 2019 - Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems","interactions":[],"lastModifiedDate":"2021-01-19T12:55:34.568699","indexId":"70217322","displayToPublicDate":"2019-08-19T07:07:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems","docAbstract":"Widespread land use change in coastal ecosystems has led to a decline in the amount of habitat available for fish and wildlife, lower production of ecosystem goods and services, and loss of recreational and aesthetic value. This has prompted global efforts to restore the natural hydrologic regimes of developed shorelines, especially resource-rich estuaries, but the resilience of these restored ecosystems in the face of accelerated sea-level rise (SLR) remains uncertain. We implemented a Monitoring-based Simulation of Accretion in Coastal Estuaries (MOSAICS) in R statistical software to address uncertainty in the resilience of modified estuarine habitats, using the Nisqually River Delta in the Pacific Northwest USA as a case study. MOSAICS is a spatially explicit model with a numerical foundation that uses empirical monitoring datasets to forecast habitat change in response to rising tidal levels. Because it accounts for the crucial ecomorphodynamic feedbacks between tidal inundation, vegetative growth, and sediment accretion, MOSAICS can be used to determine whether alternative management scenarios, such as enhanced sediment inputs, will bolster estuarine resilience to SLR. Under moderate SLR (0.62 m), the model predicted that a two-fold increase in mean daily suspended sediment during the rainy season was sufficient to maintain Nisqually’s emergent marshes through 2100, but under high SLR (1.35 m) MOSAICS indicated that greater sediment additions would be necessary to prevent submergence. A comparison between a restored marsh with subsided and high-elevation areas and a relict marsh demonstrated that the subsided restoration area was highly susceptible to SLR. Findings from the MOSAICS model highlight the importance of a site’s initial elevation, capacity for producing above and belowground biomass, and suspended sediment availability when considering management actions in estuaries and other coastal ecosystems.
Ilopango volcano (El Salvador) erupted violently during the Maya Classic Period (250–900 CE) in a densely-populated and intensively-cultivated region of the southern Maya realm, causing regional abandonment of an area covering more than 20,000 km2. However, neither the regional nor global impacts of the Tierra Blanca Joven (TBJ) eruption in Mesoamerica have been well appraised due to limitations in available volcanological, chronological, and archaeological observations. Here we present new evidence of the age, magnitude and sulfur release of the TBJ eruption, establishing it as one of the two hitherto unidentified volcanic triggers of a period of stratospheric aerosol loading that profoundly impacted Northern Hemisphere climate and society between circa 536 and 550 CE. Our chronology is derived from 100 new radiocarbon measurements performed on three subfossil tree trunks enveloped in proximal TBJ pyroclastic deposits. We also reassess the eruption magnitude using terrestrial (El Salvador, Guatemala, Honduras) and near-shore marine TBJ tephra deposit thickness measurements. Together, our new constraints on the age, eruption size (43.6 km3 Dense Rock Equivalent of magma, magnitude = 7.0) and sulfur yield (∼9–90 Tg), along with Ilopango's latitude (13.7° N), squarely frame the TBJ as the major climate-forcing eruption of 539 or 540 CE identified in bipolar ice cores and sourced to the tropics. In addition to deepening appreciation of the TBJ eruption's impacts in Mesoamerica, linking it to the major Northern Hemisphere climatic downturn of the mid-6th century CE offers another piece in the puzzle of understanding Eurasian history of the period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2019.07.037","usgsCitation":"Dull, R.A., Southon, J.R., Kutterolf, S., Anchukaitis, K.J., Freundt, A., Wahl, D., Sheets, P., Amaroli, P., Hernandez, W., Weimann, M.C., and Oppenheimer, C., 2019, Radiocarbon and geologic evidence reveal Ilopango volcano as source of the colossal 'mystery' eruption of 539/40 CE: Quaternary Science Reviews, v. 222, 105855, 17 p., https://doi.org/10.1016/j.quascirev.2019.07.037.","productDescription":"105855, 17 p.","ipdsId":"IP-107480","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467363,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://oceanrep.geomar.de/47558/1/Kopie%20von%20Dull2018_radicarbon_data_TBJ.xlsx","text":"Publisher Index Page"},{"id":366663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"El Salvador","city":"Guazapa","otherGeospatial":"Ilopango Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.26048278808594,\n 13.788070868534126\n ],\n [\n -89.03800964355467,\n 13.788070868534126\n ],\n [\n -89.03800964355467,\n 13.96405503013876\n ],\n [\n -89.26048278808594,\n 13.96405503013876\n ],\n [\n -89.26048278808594,\n 13.788070868534126\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"222","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dull, Robert A.","contributorId":218209,"corporation":false,"usgs":false,"family":"Dull","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":768703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Southon, John R.","contributorId":201538,"corporation":false,"usgs":false,"family":"Southon","given":"John","email":"","middleInitial":"R.","affiliations":[{"id":36191,"text":"Keck Carbon Cycle AMS Laboratory, Department of Earth System Science, University of California Irvine, Irvine, California, USA.","active":true,"usgs":false}],"preferred":false,"id":768704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kutterolf, Steffen","contributorId":218210,"corporation":false,"usgs":false,"family":"Kutterolf","given":"Steffen","email":"","affiliations":[{"id":39780,"text":"GEOMAR, Helmholtz Center for Ocean Research","active":true,"usgs":false}],"preferred":false,"id":768705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anchukaitis, Kevin J.","contributorId":195005,"corporation":false,"usgs":false,"family":"Anchukaitis","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":768706,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freundt, Armin","contributorId":218211,"corporation":false,"usgs":false,"family":"Freundt","given":"Armin","email":"","affiliations":[{"id":39780,"text":"GEOMAR, Helmholtz Center for Ocean Research","active":true,"usgs":false}],"preferred":false,"id":768707,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wahl, David 0000-0002-0451-3554","orcid":"https://orcid.org/0000-0002-0451-3554","contributorId":206113,"corporation":false,"usgs":true,"family":"Wahl","given":"David","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":768702,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sheets, Payson","contributorId":218212,"corporation":false,"usgs":false,"family":"Sheets","given":"Payson","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":768708,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Amaroli, Paul","contributorId":218213,"corporation":false,"usgs":false,"family":"Amaroli","given":"Paul","email":"","affiliations":[{"id":39781,"text":"Fundacion Nacional de Arqueologia de El Salvador","active":true,"usgs":false}],"preferred":false,"id":768709,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hernandez, Walter","contributorId":218214,"corporation":false,"usgs":false,"family":"Hernandez","given":"Walter","email":"","affiliations":[{"id":39782,"text":"Ministerio de Medio Ambiente y Recursos Naturales, San Salvador, El Salvador","active":true,"usgs":false}],"preferred":false,"id":768710,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weimann, Micheal C.","contributorId":218215,"corporation":false,"usgs":false,"family":"Weimann","given":"Micheal","email":"","middleInitial":"C.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":768711,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Oppenheimer, Clive","contributorId":174445,"corporation":false,"usgs":false,"family":"Oppenheimer","given":"Clive","email":"","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":768712,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70203946,"text":"sir20195062 - 2019 - Evaluation of groundwater resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah","interactions":[],"lastModifiedDate":"2020-10-06T20:20:52.39653","indexId":"sir20195062","displayToPublicDate":"2019-08-16T12:14:34","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5062","displayTitle":"Evaluation of Groundwater Resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah","title":"Evaluation of groundwater resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah","docAbstract":"Groundwater resources in the Spanish Valley watershed in southern Utah were quantified for the first time since the early 1970s. The primary objectives of this study were (1) to better understand sources of recharge to, groundwater flow directions within, and discharge points for both the valley-fill and Glen Canyon Group aquifers (VFA and GCGA), and (2) to quantify groundwater budget components of the combined VFA and GCGA, including both recharge and discharge. Based on both groundwater chemistry (stable isotopes, major ions, and noble gases) and environmental tracers in vadose-zone pore water of the Navajo Sandstone outcrop along Sand Flats Road, most recharge to the GCGA occurs high in the La Sal Mountains, and not on the sandstone outcrop area. The same groundwater chemistry and environmental tracer evidence from the saturated zone indicates that Pack Creek, rather than GCGA groundwater, is the primary source of recharge to the VFA. Groundwater recharge in the study area occurs mostly from infiltration of precipitation (in the form of snowmelt) at high altitudes. Additional recharge occurs from the infiltration of runoff along losing reaches of stream channels, or as unconsumed surface-water and groundwater irrigation. Average annual recharge to the Moab-Spanish Valley watershed part of the Spanish Valley study area was estimated to be between 9,550 and 30,000 acre-feet. Based on water-levels collected in the current study, groundwater in both the GCGA and the VFA generally moves downgradient parallel to the topographic slope of the watershed towards the Colorado River. Groundwater discharge measurements, and hydraulic-flux estimates at the lower end of Spanish Valley, provide a more robust estimate of the groundwater budget than evaluating recharge. The primary base-flow discharge components in the study area include groundwater discharge to gaining reaches of streams, groundwater discharge to springs, and well withdrawals. Based on 3 years of measurements (2014–16) and hydraulic-flux calculations at the lower end of Spanish Valley, total groundwater discharge was estimated to be 14,000 to 16,000 acre-feet per year (acre-ft/yr) for the entire watershed, or 13,000 to 15,000 acre-ft/yr, excluding the watershed areas of Grandstaff (formerly Negro Bill) and Ice Box Canyons (compared to the 1971 Sumsion estimate of 22,000 acre-ft/yr). The primary difference is this study’s estimate of subsurface outflow to the Colorado River of only 300 to 1,000 acre-ft/yr, compared to 11,000 acre-ft/yr estimated by Sumsion. Because the study period (2014–16) experienced above average precipitation for 2 of the 3 years, the discharge estimates may be slightly higher than long-term average annual discharge from the groundwater system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195062","collaboration":"Prepared in cooperation with the Utah Division of Water Rights, City of Moab, Grand and San Juan Counties, Grand Water and Sewer Service Agency, Utah School and Institutional Trust Lands Administration, The Nature Conservancy, Utah Division of Wildlife Resources, Living Rivers, San Juan Spanish Valley Special Service District, U.S. Bureau of Land Management, and U.S. Forest Service","usgsCitation":"Masbruch, M.D., Gardner, P.M., Nelson, N.C., Heilweil, V.M., Solder, J.E., Hess, M.D., McKinney, T.S., Briggs, M.A., and Solomon, D.K., 2019, Evaluation of groundwater resources in the Spanish Valley Watershed, Grand and San Juan Counties, Utah: U.S. Geological Survey Scientific Investigations Report 2019–5062, 86 p., https://doi.org/10.3133/sir20195062.","productDescription":"Report: x, 86 p.; 3 Plates: 24.00 x 38.00 inches or smaller","numberOfPages":"86","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080057","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":437367,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z39TII","text":"USGS data release","linkHelpText":"Lumped parameter models of groundwater age, Spanish Valley Watershed, Grand and San Juan Counties, Utah"},{"id":366484,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5062/coverthb.jpg"},{"id":366487,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate2.pdf","text":"Plate 2","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 2","linkHelpText":"- Stiff Diagrams Showing Major-Ion Composition from Hydrogeologic Units in the Spanish Valley Study Area, Utah"},{"id":366485,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062.pdf","text":"Report","size":"14.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062"},{"id":366488,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate3.pdf","text":"Plate 3","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 3","linkHelpText":"- Water-Level Surface Map and General Direction of Groundwater Movement in the Spanish Valley Study Area, Utah"},{"id":366486,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2019/5062/sir20195062_plate1.pdf","text":"Plate 1","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2019–5062 Plate 1","linkHelpText":"- Surficial Extent and Cross Sections of Hydrogeologic Units for Selected Locations in the Spanish Valley Study Area, Utah"}],"country":"United States","state":"Utah","county":"Grand County, San Juan County","otherGeospatial":"Spanish Valley Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.74037170410156,\n 38.357810999675664\n ],\n [\n -109.31259155273438,\n 38.357810999675664\n ],\n [\n -109.31259155273438,\n 38.61043215866372\n ],\n [\n -109.74037170410156,\n 38.61043215866372\n ],\n [\n -109.74037170410156,\n 38.357810999675664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
801-908-5000