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 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Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
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
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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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.
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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.
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U.S. Geological Survey
1217 Biltmore Dr.
Lawrence, KS 66049
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.
Air pollution has been identified as one of the most challenging health issues in urban areas worldwide.
The aim of this study was to investigate the association between short-term exposure to ambient air pollution and respiratory disease over a long-term period in Shiraz, one of the largest cities in Iran. Methods: hospital admissions due to respiratory diseases (asthma, pneumonia, chronic obstructive pulmonary disease(COPD) and pleural effusion) in residents of Shiraz between March 21, 2009 and March 20, 2015 were included. Demographics for each patient and meta data to include principal meteorological variables (temperature and relative humidity) and five ambient pollutants (CO, O3, SO2, NO2, and PM10) were also included. Statistical analysis was performed by Poisson regression in single-pollutant generalized linear model with principal component analysis (GLPCA) to analyze the relationship between pollutants and hospital admissions at the 0–9 cumulative lag day period. Pearson correlation test was used to determine the relationship between different pollutants, temperature and humidity.
It was found that the highest increase in asthma admission was related to PM10(relative risk (RR) = 1.31, 95% CI = 1.17, 1.47). For COPD, the rate of hospital visits significantly increased with the increase in NO2 concentration (RR = 1.17, 95% CI = 1.09.1.27). In the children's hospital, O3 (RR = 1.25, 95% CI = 1.06, 1.47) and SO2(RR = 1.17, 95% CI = 1.07, 1.28) affected the asthma admissions and all contaminants (highest RR observed was for NO2 (RR = 1.28 95% CI = 1.18, 1.40) affected pneumonia admissions on cumulative lag days of 0–9. Conclusions: These data confirm an association between ambient air pollutants and hospital admissions due to respiratory disease.
Digital flood-inundation maps for a 6.7-mile reach of Joachim Creek, De Soto, Missouri, were created by the U.S. Geological Survey (USGS) in cooperation with the city of De Soto and Jefferson County, Missouri. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500). Near-real-time stages at this streamgage may be obtained on the internet from the USGS National Water Information System at https://waterdata.usgs.gov/nwis or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps2/hydrograph.php?wfo=lsx&gage=desm7, which also forecasts flood hydrographs at this site (site DESM7).
Flood profiles were computed for the stream reach using a one-dimensional model for simulation of water-surface profiles with steady-state (gradually varied) or unsteady-state flow computation options. The model was calibrated by using the theoretical stage-discharge relation at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500), and documented high-water marks from the flood of April 18, 2013.
The hydraulic model was then used to compute 10 water surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum. The profiles ranged from 8.0 ft, or near bankfull, to 17.0 ft, which exceeds the stage that corresponds to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and 1.97-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures and for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195068","collaboration":"Prepared in cooperation with the city of De Soto, Missouri, and Jefferson County, Missouri","usgsCitation":"Heimann, D.C., Voss, J.D., and Rydlund, P.H., Jr., 2019, Flood-inundation maps for Joachim Creek, De Soto, Missouri, 2018: U.S. Geological Survey Scientific Investigations Report 2019–5068, 10 p., https://doi.org/10.3133/sir20195068.","productDescription":"Report: vi, 10 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-105218","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":366556,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5068/sir20195068.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Viability selection yields adult populations that are more genetically variable than those of juveniles, producing a positive correlation between heterozygosity and survival. Viability selection could be the result of decreased heterozygosity across many loci in inbred individuals and a subsequent decrease in survivorship resulting from the expression of the deleterious alleles. Alternatively, locus-specific differences in genetic variability between adults and juveniles may be driven by forms of balancing selection, including heterozygote advantage, frequency-dependent selection, or selection across temporal and spatial scales. We use a pooled-sequencing approach to compare genome-wide and locus-specific genetic variability between 74 golden eagle (Aquila chrysaetos), 62 imperial eagle (Aquila heliaca), and 69 prairie falcon (Falco mexicanus) juveniles and adults. Although genome-wide genetic variability is comparable between juvenile and adult golden eagles and prairie falcons, imperial eagle adults are significantly more heterozygous than juveniles. This evidence of viability selection may stem from a relatively smaller imperial eagle effective population size and potentially greater genetic load. We additionally identify ~2000 single-nucleotide polymorphisms across the 3 species with extreme differences in heterozygosity between juveniles and adults. Many of these markers are associated with genes implicated in immune function or olfaction. These loci represent potential targets for studies of how heterozygote advantage, frequency-dependent selection, and selection over spatial and temporal scales influence survivorship in avian species. Overall, our genome-wide data extend previous studies that used allozyme or microsatellite markers and indicate that viability selection may be a more common evolutionary phenomenon than often appreciated.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jhered/esz048","usgsCitation":"Doyle, J.M., Willoughby, J.R., Bell, D.A., Bloom, P.H., Bragin, E.A., Fernandez, N.B., Katzner, T., Leonard, K., and DeWoody, J., 2019, Elevated heterozygosity in adults relative to juveniles provides evidence of viability selection on eagles and falcons: Journal of Heredity, v. 110, no. 6, p. 696-706, https://doi.org/10.1093/jhered/esz048.","productDescription":"11 p.","startPage":"696","endPage":"706","ipdsId":"IP-105731","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":467368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jhered/esz048","text":"Publisher Index Page"},{"id":370601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"110","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Doyle, Jacqueline M.","contributorId":175099,"corporation":false,"usgs":false,"family":"Doyle","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":778350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Willoughby, Janna R 0000-0002-0176-1878","orcid":"https://orcid.org/0000-0002-0176-1878","contributorId":221484,"corporation":false,"usgs":false,"family":"Willoughby","given":"Janna","email":"","middleInitial":"R","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":778351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Douglas A.","contributorId":199739,"corporation":false,"usgs":false,"family":"Bell","given":"Douglas","email":"","middleInitial":"A.","affiliations":[{"id":24634,"text":"East Bay Regional Park District","active":true,"usgs":false}],"preferred":false,"id":778352,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bloom, Peter H.","contributorId":191356,"corporation":false,"usgs":false,"family":"Bloom","given":"Peter","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":778353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bragin, Evgeny A.","contributorId":194894,"corporation":false,"usgs":false,"family":"Bragin","given":"Evgeny","email":"","middleInitial":"A.","affiliations":[{"id":35656,"text":"Science Department, Naurzum National Nature Reserve, Kostanay Oblast, Naurzumski Raijon, Karamendy, Kazakhstan","active":true,"usgs":false}],"preferred":false,"id":778354,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fernandez, Nadia B.","contributorId":175100,"corporation":false,"usgs":false,"family":"Fernandez","given":"Nadia","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":778355,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778349,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Leonard, Kolbe","contributorId":204166,"corporation":false,"usgs":false,"family":"Leonard","given":"Kolbe","email":"","affiliations":[{"id":36867,"text":"Department of Computer and Information Sciences, Towson University","active":true,"usgs":false}],"preferred":false,"id":778356,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeWoody, J Andrew","contributorId":221485,"corporation":false,"usgs":false,"family":"DeWoody","given":"J Andrew","affiliations":[{"id":33107,"text":"Towson University","active":true,"usgs":false}],"preferred":false,"id":778357,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211576,"text":"70211576 - 2019 - Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future","interactions":[],"lastModifiedDate":"2020-07-31T15:01:25.745893","indexId":"70211576","displayToPublicDate":"2019-08-15T09:24:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future","docAbstract":"The Rio Grande Cutthroat Trout Oncorhynchus clarkii virginalis (RGCT ) occupies just 12% of its ancestral range. As the southernmost subspecies of Cutthroat Trout, we expect a warming climate to bring additional stressors to RGCT populations, such as increased stream temperatures, reduced streamflows, and increased incidence of wildfire. We developed a Bayesian network (BN ) model using site‐specific data, empirical research, and expert knowledge to estimate the probability of persistence for each of the 121 remaining RGCT conservation populations and to rank the severity of the threats they face. These inputs quantified the genetic risks (e.g., inbreeding risk and hybridization risk), population demographics (disease risk, habitat suitability, and survival), and probability of stochastic disturbances (stream drying risk and wildfire risk) in an uncertain future. We also created stream temperature and base flow discharge models coupled with regionally downscaled climate projections to predict future abiotic conditions at short‐term (2040s) and long‐term (2080s) time horizons. In the absence of active management, we predicted a decrease in the average probability of population persistence from 0.53 (current) to 0.31 (2040s) and 0.26 (2080s). Only 11% of these populations were predicted to have a greater than 75% chance of persisting to the 2080s. Threat of invasion by nonnative trout had the strongest effect on population persistence. Of the 78 populations that are already invaded or lacking complete barriers, 60% were estimated to be extirpated by 2080 and the remainder averaged only a 10% chance of persistence. In contrast, the effects of increased stream temperatures were predicted to affect the future persistence of only 9% of the 121 RGCT populations remaining, as most have been restricted to high‐elevation habitats that are cold enough to buffer against some stream warming. Our BN model provides a framework for evaluating threats and will be useful to guide management actions that are likely to provide the most benefit for long‐term conservation.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10320","usgsCitation":"Zeigler, M.P., Rogers, K., Roberts, J., Todd, A., and Fausch, K., 2019, Predicting persistence of Rio Grande Cutthroat Trout populations in an uncertain future: North American Journal of Fisheries Management, v. 39, no. 5, p. 819-848, https://doi.org/10.1002/nafm.10320.","productDescription":"30 p.","startPage":"819","endPage":"848","ipdsId":"IP-080586","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.677001953125,\n 33.578014746143985\n ],\n [\n -103.7548828125,\n 33.578014746143985\n ],\n [\n -103.7548828125,\n 38.47079371120379\n ],\n [\n -107.677001953125,\n 38.47079371120379\n ],\n [\n -107.677001953125,\n 33.578014746143985\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeigler, Mathew P.","contributorId":91006,"corporation":false,"usgs":true,"family":"Zeigler","given":"Mathew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":794698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Kevin B.","contributorId":220104,"corporation":false,"usgs":false,"family":"Rogers","given":"Kevin B.","affiliations":[],"preferred":false,"id":794699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794673,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Todd, Andrew atodd@usgs.gov","contributorId":149790,"corporation":false,"usgs":true,"family":"Todd","given":"Andrew","email":"atodd@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":794700,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fausch, Kurt D. 0000-0001-5825-7560","orcid":"https://orcid.org/0000-0001-5825-7560","contributorId":198488,"corporation":false,"usgs":false,"family":"Fausch","given":"Kurt D.","affiliations":[],"preferred":false,"id":794701,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215168,"text":"70215168 - 2019 - Phytoplankton community structure response to groundwater-borne nutrients in the inland bays, Delaware","interactions":[],"lastModifiedDate":"2020-10-09T12:48:32.877319","indexId":"70215168","displayToPublicDate":"2019-08-15T07:46:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Phytoplankton community structure response to groundwater-borne nutrients in the inland bays, Delaware","docAbstract":"To determine the impacts of groundwater-borne nutrients on phytoplankton biomass and community structure, we conducted a series of mesocosm experiments in the Inland Bays of Delaware. Four treatments were tested, including mesocosms coupled directly to submarine groundwater seepage, mesocosms with the addition of pumped submarine groundwater, mesocosms with the addition of phosphate, and control mesocosms with no nutrient addition. We measured chlorophyll a concentrations as a proxy for overall biomass and used genetic sequencing techniques to characterize the phytoplankton community structure. Groundwater carried a high N load to the estuary with NO3- up to 295 µmol l-1 and NH4+ up to 55 µmol l-1. As a result, treatment mesocosms had elevated NO3- and NH4+, while control mesocosms were relatively low in nutrients. In June, the highest chlorophyll a concentrations occurred in mesocosms attached to seepage meters after 3.5 d, with significant differences across all treatments. In August, groundwater-amended mesocosms reached the highest biomass concentrations, which peaked after 3 d. There were significant differences across all treatments, except control and phosphate-amended mesocosms which remained unchanged. Community sequence data showed that species assemblage was also impacted by availability of nutrients, with significant differences in community structure for mesocosms receiving nutrients vs. control mesocosms in both June and August experiments. Harmful algal species proliferated in high nutrient treatments, including Cylindrotheca closterium, Karlodinium veneficum, Nitzschia spp., and Heterocapsa spp. While the general relationship between nutrient supply and biomass production is well known, we demonstrate the role groundwater-borne nutrients and sediment processes play in shaping community structure in estuarine primary producers and in promoting harmful algal blooms.
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\"}}]}","volume":"624","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Torre, Daniel","contributorId":242900,"corporation":false,"usgs":false,"family":"Torre","given":"Daniel","email":"","affiliations":[{"id":48572,"text":"University of Delaware, School of Marine Science and Policy, Lewes, Delaware, 19958, USA","active":true,"usgs":false}],"preferred":false,"id":801059,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coyne, Kathryn","contributorId":242901,"corporation":false,"usgs":false,"family":"Coyne","given":"Kathryn","email":"","affiliations":[{"id":48572,"text":"University of Delaware, School of Marine Science and Policy, Lewes, Delaware, 19958, USA","active":true,"usgs":false}],"preferred":false,"id":801060,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":801061,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"York, Joanna K.","contributorId":140023,"corporation":false,"usgs":false,"family":"York","given":"Joanna","email":"","middleInitial":"K.","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":801062,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207595,"text":"70207595 - 2019 - Predicting surf zone injuries along the Delaware coast using a Bayesian network","interactions":[],"lastModifiedDate":"2019-12-30T16:30:44","indexId":"70207595","displayToPublicDate":"2019-08-14T16:28:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Predicting surf zone injuries along the Delaware coast using a Bayesian network","docAbstract":"Personnel at Beebe Healthcare in Lewes, Delaware, collected surf zone injury (SZI) data for eight summer seasons from 2010 through 2017. Data include, but are not limited to, time of injury, gender, age, and activity. More than 2000 SZI events, including 196 spinal injuries and 6 fatalities, occurred at the five most populated beaches along the 25 miles of Atlantic-fronting coast. SZI are predominantly wave related incidents associated with wading (50.1%), body surfing (18.4%), and body boarding (13.3%). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf zone to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) were constructed to model SZI and predict changes in injury rate (proportion of injuries to bathers) and injury likelihood (probability of at least one injury occurrence) on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood during any specified hour with more skill than prior predictions with the best performing model improving prediction 69.1% of the time (LLR = 69.1%). An alternative BN predicting injury rate performed worse with the prior probability model out predicting the injury rate network (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.","language":"English","publisher":"Springer","doi":"10.1007/s11069-019-03697-y","usgsCitation":"Doelp, M., Puleo, J., and Plant, N.G., 2019, Predicting surf zone injuries along the Delaware coast using a Bayesian network: Natural Hazards, v. 98, no. 2, p. 379-401, https://doi.org/10.1007/s11069-019-03697-y.","productDescription":"22 p.","startPage":"379","endPage":"401","ipdsId":"IP-100096","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":370880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":778661,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204738,"text":"sir20195082 - 2019 - Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotelluric surveys","interactions":[],"lastModifiedDate":"2019-10-07T16:51:39","indexId":"sir20195082","displayToPublicDate":"2019-08-14T09:51:12","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":"2019-5082","displayTitle":"Characterization of Big Chino Subbasin Hydrogeology near Paulden, Arizona, Using Controlled Source Audio-Frequency Magnetotelluric Surveys","title":"Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotelluric surveys","docAbstract":"The Big Chino subbasin is located in central-northwest Arizona in the transition zone between the Colorado Plateau and the Basin and Range Province. The controlled source audio-frequency magnetotelluric (CSAMT) geophysical method, a low-impact, non-intrusive, electrical resistance sounding technique, was used to evaluate the subsurface hydrogeology of the southern third of the Big Chino subbasin. The Big Chino subbasin is a northwest-trending, late Tertiary graben bordered by the Big Chino Fault along its northeast flank where there is as much as 1,100 meters of displacement. The main water-bearing stratigraphic unit of the basin is Tertiary alluvial-fill sediment. The Devonian Martin Formation provides water to wells near Drake and the Mississippian Redwall Limestone provides water to wells east of the basin and in the Paulden area.
The purpose of the CSAMT surveys was to improve the conceptual model of the aquifer by constraining the basin geometry and identifying stratigraphic units and their subsurface extents. CSAMT methods were used to map the subsurface along 100 kilometers (62 miles) of survey lines across the southern third of the subbasin. Of 21 survey lines, 14 were west of the town of Paulden and another 7 were east of Paulden. Data were cleaned and prepared for entry into Zonge SCS2D software and then inverted to provide a two-dimensional resistivity profile for each survey line. Final inversion models representing the best fit to measured data were compared to driller’s logs or borehole data where present.
Data from the CSAMT lines west and north of Paulden are consistent with thicker alluvial basin deposits that range from 100 meters thick to a few hundred meters thick. Data from the CSAMT lines east of Paulden are consistent with thinner alluvial and basalt deposits overlying Paleozoic Martin Formation and Redwall Limestone, Tapeats Sandstone, and Precambrian granite and schist.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195082","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and Salt River Project","usgsCitation":"Macy, J.P., Gungle, B., and Mason, J.P., 2019, Characterization of Big Chino subbasin hydrogeology near Paulden, Arizona, using controlled source audio-frequency magnetotellursurveys: U.S. Geological Survey Scientific Investigations Report 2019–5082, 39 p., https://doi.org/10.3133/sir20195082.\nic ","productDescription":"vii, 39 p.","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-098264","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":366547,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5082/sir20195082.pdf","text":"Report","size":"50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
From fine-scale foraging to broad-scale migration, animal movement is shaped by the distribution of resources. There is mounting evidence, however, that learning and memory also guide movement. Although migratory mammals commonly track resource waves, how resource tracking and memory guide long-distance migration has not been reconciled. We examined these hypotheses using movement data from four populations of migratory mule deer (n = 91). Spatial memory had an extraordinary influence on migration, affecting movement 2–28 times more strongly than tracking spring green-up or autumn snow depth. Importantly, with only an ability to track resources, simulated deer were unable to recreate empirical migratory routes. In contrast, simulated deer with memory of empirical routes used those routes and obtained higher foraging benefits. For migratory terrestrial mammals, spatial memory provides knowledge of where seasonal ranges and migratory routes exist, whereas resource tracking determines when to beneficially move within those areas.
The use of data generated from long term monitoring efforts necessitates accurate authoritative source citations of those data to ensure credit for data collected, and accountability for the data quality to enable repeated retrieval of a given data set. Data sets used in published reports and articles are increasingly being considered objects that are required to be published and cited. Aggregating data into open access databases is becoming common and is the focus of the Coordinated Assessment for Salmon and Steelhead project (CA; https://www.pnamp.org/project/coordinated-assessments-for-salmon-and-steelhead; http://www.streamnet.org/data/coordinated-assessments/) and National Marine Fisheries Service, National Oceanic and Atmospheric Administration Salmon Population Summary (SPS; https://www.webapps.nwfsc.noaa.gov/apex/f?p=261:home:0) among others. Guidelines are needed for citing these long-term dynamic data sets that have many contributors. We explore best practices and provide recommendations for including robust metadata attributes within data sets to enable data publication and citation using the CA and SPS data repositories as case studies. From reviewing the current citations possible from the CA and the SPS we recommend at minimum that natural resource monitoring databases contain: metadata to identify organizations that generated the data; contact persons for each organization that contributes data to an aggregated data set; and that metadata be incorporated into databases to enable auto-generated citations that recognize all contributing organizations with time-stamped versions of the data delivered. Beyond those minimums, additional best practice recommendations include this suite of metadata elements that identify a given data set upon citation or publication: author(s); publication date; description of data; file format(s) of data - e.g. tiles, shapefile sets, images, text files; dates data were collected; locations where data were collected; producers/contributors to the data set version cited; date data set was downloaded; original data repository from which the data were obtained; version identifier to note significant change to a data set; and a persistent identifier that can be used to locate that version of the data.
","language":"English","publisher":"Pacific Northwest Aquatic Monitoring Partnership","usgsCitation":"Olson, S.J., Barnas, K.A., Williams, M., Wheaton, C., Banach, M., and Bayer, J.M., 2019, Citing aquatic monitoring data sets: Best practice recommendations for authoritative data citation, 38 p.","productDescription":"38 p.","ipdsId":"IP-096434","costCenters":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":367206,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":367184,"type":{"id":15,"text":"Index Page"},"url":"https://www.pnamp.org/document/15001"}],"publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Olson, Sheryn J. 0000-0002-6751-9013","orcid":"https://orcid.org/0000-0002-6751-9013","contributorId":204736,"corporation":false,"usgs":true,"family":"Olson","given":"Sheryn","email":"","middleInitial":"J.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":770136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnas, Katie A","contributorId":218747,"corporation":false,"usgs":false,"family":"Barnas","given":"Katie","email":"","middleInitial":"A","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":770137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Margaret","contributorId":218748,"corporation":false,"usgs":false,"family":"Williams","given":"Margaret","email":"","affiliations":[{"id":39899,"text":"NOAA - Affiliate","active":true,"usgs":false}],"preferred":false,"id":770138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wheaton, Christopher","contributorId":218749,"corporation":false,"usgs":false,"family":"Wheaton","given":"Christopher","email":"","affiliations":[{"id":39900,"text":"PSMFC","active":true,"usgs":false}],"preferred":false,"id":770139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Banach, Michael","contributorId":218750,"corporation":false,"usgs":false,"family":"Banach","given":"Michael","email":"","affiliations":[{"id":39900,"text":"PSMFC","active":true,"usgs":false}],"preferred":false,"id":770141,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bayer, Jennifer M. 0000-0001-9564-3110 jbayer@usgs.gov","orcid":"https://orcid.org/0000-0001-9564-3110","contributorId":3393,"corporation":false,"usgs":true,"family":"Bayer","given":"Jennifer","email":"jbayer@usgs.gov","middleInitial":"M.","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":770140,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70206000,"text":"70206000 - 2019 - Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption","interactions":[],"lastModifiedDate":"2019-10-17T07:00:50","indexId":"70206000","displayToPublicDate":"2019-08-13T14:44:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption","docAbstract":"The 9 month long 2011-2012 eruption of Cordon Caulle (Southern Andes, Chile) is the best instrumentally recorded rhyolitic eruption to date and the first time that the effusion of a rhyolitic flow has been observed in detail. We use Interferometric Synthetic Aperture Radar (InSAR), with time-lapse DEMs and numerical models to study the dynamics of coupled magma reservoir deflation and lava effusion. InSAR recorded ~2.2-2.5 m of deflation after the first three days of the eruption, which can be modeled using a spheroidal magma reservoir at a depth of ∼5 km, ∼20 km long, and with a pressure drop of ~20-30 MPa. The source is elongated in the NW-SE direction and its large dimensions imply a large plumbing system spanning neighboring volcanoes and active throughout the eruption, with a slight change halfway through the effusive phase. TanDEM-X DEMs record the extrusion of both the rhyolitic lava flow and the intrusion of a shallow laccolith around the eruptive vent, with a total volume of ~1.2 km3 DRE. The laccolith was emplaced during the first month of the eruption, during both the eruption explosive and effusive stages. Both the reservoir pressure drop and the extruded volume time series follow quasi-exponential trends, and can be explained by a model that couples the reservoir pres- sure decrease, time- and pressured ependent variations in the magma properties inside of the reservoir, and conduit flow. This model predicts both the temporal evolution and amplitude of both time series, and a magma compressibility of ∼10^−10 Pa−1, half the compressibility of the magma of the sub-Plinian explosive phase. Further, we estimate that the reservoir contained 1-3 wt.% dissolved H2O at the onset of lava effusion, with no exsolved CO2 and H2O in the reservoir throughout the eruption. This is in accord with a magma that was significantly degassed after the explosive phase. These remaining volatiles might have been responsible for magma fragmentation, consistent with the hybrid explosive and effusive style observed during the waning of the eruption.","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.115736","usgsCitation":"Delgado, F., Julia Kubanek, Anderson, K.R., Paul Lundgren, and Pritchard, M.E., 2019, Physicochemical models of effusive rhyolitic eruptions constrained with InSAR and DEM data: A case study of the 2011-2012 Cordon Caulle eruption: Earth and Planetary Science Letters, v. 524, p. 1-14, https://doi.org/10.1016/j.epsl.2019.115736.","productDescription":"115736, 14p.","startPage":"1","endPage":"14","ipdsId":"IP-102193","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2019.115736","text":"Publisher Index Page"},{"id":368337,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0012821X19304285"},{"id":368348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Cordón Caulle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.59765625,\n -42.779275360241904\n ],\n [\n -71.69677734375,\n -42.779275360241904\n ],\n [\n -71.69677734375,\n -41.4262531950727\n ],\n [\n -72.59765625,\n -41.4262531950727\n ],\n [\n -72.59765625,\n -42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"524","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Delgado, Francisco","contributorId":219817,"corporation":false,"usgs":false,"family":"Delgado","given":"Francisco","email":"","affiliations":[{"id":40075,"text":"Institute de Physique du Globe de Paris","active":true,"usgs":false}],"preferred":false,"id":773252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Julia Kubanek","contributorId":219818,"corporation":false,"usgs":false,"family":"Julia Kubanek","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":773253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":773251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paul Lundgren","contributorId":219819,"corporation":false,"usgs":false,"family":"Paul Lundgren","affiliations":[{"id":32904,"text":"Jet Propulsion Lab, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":773254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pritchard, Matthew E.","contributorId":219820,"corporation":false,"usgs":false,"family":"Pritchard","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":773255,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208120,"text":"70208120 - 2019 - Global positioning system tracking devices can decrease Greater Sage-Grouse survival","interactions":[],"lastModifiedDate":"2020-01-29T16:27:32","indexId":"70208120","displayToPublicDate":"2019-08-13T13:06:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3551,"text":"The Condor","active":true,"publicationSubtype":{"id":10}},"title":"Global positioning system tracking devices can decrease Greater Sage-Grouse survival","docAbstract":"Reliable demographic estimates hinge on the assumption that marking animals does not alter their behavior, reproduction, or survival. Violations can bias inference and are especially egregious for species of high conservation concern. Global positioning system (GPS) devices represent a recent technological advancement that has contributed greatly to avian ecological studies compared with traditionally used very high frequency (VHF) radio transmitters, but may affect demographic rates differently than VHF transmitters. We compared survival between VHF (necklace attachment) and GPS (rump-mounted attachment) devices from >1,100 Greater Sage-Grouse (Centrocercus urophasianus), a species of high conservation concern, across multiple populations within California and Nevada. We found lower survival for GPS-marked compared to VHF-marked sage-grouse across most sex, age, and seasonal comparisons. Estimates of annual survival for GPS-marked sage-grouse were 0.55–0.86 times that of VHF-marked birds with considerable variation among sex and age classes. Differences in survival could be attributed to features associated with GPS devices, including greater weight, position of attachment (e.g., rump-mount harness), and a semi-reflective solar panel. In a post hoc analysis, we evaluated additive and interactive effects between device type (GPS vs. VHF) and transmitter mass as a proportion of body mass (PBM). While the device type effect alone was the best model, the PBM interaction also had support. For GPS devices, survival decreased with increasing PBM, whereas PBM effects were not found for VHF. We attributed differences in PBM effect to placement of transmitters on sage-grouse, as weight of GPS devices was positioned rearward. This information can help managers and researchers weigh costs and benefits of GPS-based monitoring. Our results indicate demographic data collected from GPS devices should be interpreted with caution, and use of these devices should be tailored to specific ecological questions. Future research aimed at investigating behavioral impacts and GPS designs that reduce adverse impacts on survival would be beneficial.
","language":"English","publisher":"American Ornithological Society","doi":"10.1093/condor/duz032","usgsCitation":"Severson, J.P., Coates, P.S., Prochazka, B.G., Ricca, M.A., Casazza, M.L., and Delahunty, D.J., 2019, Global positioning system tracking devices can decrease Greater Sage-Grouse survival: The Condor, v. 121, no. 3, duz032, 15 p., https://doi.org/10.1093/condor/duz032.","productDescription":"duz032, 15 p.","ipdsId":"IP-106346","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":460307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz032","text":"Publisher Index Page"},{"id":371649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, 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Here, we provide an update on the state of the knowledge of OHC, and also include mercury, exposure and/or associated effects in key Arctic marine and terrestrial mammal and bird species as well as in fish by reviewing the literature published since the last AMAP assessment in 2010. We aimed at updating the knowledge of how single but also combined health effects are or can be associated to the exposure to single compounds or mixtures of OHCs. We also focussed on assessing both potential individual as well as population health impacts using population-specific exposure data post 2000. We have identified quantifiable effects on vitamin metabolism, immune functioning, thyroid and steroid hormone balances, oxidative stress, tissue pathology, and reproduction. As with the previous assessment, a wealth of documentation is available for biological effects in marine mammals and seabirds, and sentinel species such as the sledge dog and Arctic fox, but information for terrestrial vertebrates and fish remain scarce. While hormones and vitamins are thoroughly studied, oxidative stress, immunotoxic and reproductive effects need further investigation. Depending on the species and population, some OHCs and mercury tissue contaminant burdens post 2000 were observed to be high enough to exceed putative risk threshold levels that have been previously estimated for non-target species or populations outside the Arctic. In this assessment, we made use of risk quotient calculations to summarize the cumulative effects of different OHC classes and mercury for which critical body burdens can be estimated for wildlife across the Arctic. As our ultimate goal is to better predict or estimate the effects of OHCs and mercury in Arctic wildlife at the individual, population and ecosystem level, there remain numerous knowledge gaps on the biological effects of exposure in Arctic biota. These knowledge gaps include the establishment of concentration thresholds for individual compounds as well as for realistic cocktail mixtures that in fact indicate biologically relevant, and not statistically determined, health effects for specific species and subpopulations. Finally, we provide future perspectives on understanding Arctic wildlife health using new in vivo, in vitro, and in silico techniques, and provide case studies on multiple stressors to show that future assessments would benefit from significant efforts to integrate human health, wildlife ecology and retrospective and forecasting aspects into assessing the biological effects of OHC and mercury exposure in Arctic wildlife and fish.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.133792","usgsCitation":"Dietz, R., Letcher, R.J., Desforges, J., Eulaers, I., Sonne, C., Wilson, S., Andersen-Ranberg, E., Basu, N., Barst, B.D., Bustnes, J.O., Bytingsvik, J., Ciesielski, T.M., Drevnick, P.E., Gabrielsen, G.W., Haarr, A., Hylland, K., Jenssen, B.M., Levin, M., McKinney, M.A., Norregaard, R.D., Pedersen, K.E., Provencher, J., Styrishave, B., Tartu, S., Aars, J., Ackerman, J., Rosing-Asvid, A., Barrett, R., Bignert, A., Born, E.W., Branigan, M., Braune, B., Bryan, C.E., Dam, M., Eagles-Smith, C.A., Evans, M.S., Evans, T., Fisk, A.T., Gamberg, M., Gustavson, K., Hartman, C.A., Helander, B., Herzog, M.P., Hoekstra, P.F., Houde, M., Hoydal, K., Jackson, A., Kucklick, J., Lie, E., Loseto, L., Mallory, M.L., Miljeteig, C., Mosbech, A., Muir, D.C., Nielsen, S.T., Peacock, E., Pedro, S., Peterson, S.H., Polder, A., Riget, F.F., Roach, P., Saunes, H., Sinding, M.S., Skaare, J.U., Sondergaard, J., Stenson, G., Stern, G., Treu, G., Schuur, S.S., and Vikingsson, G., 2019, Current state of knowledge on biological effects from contaminants on arctic wildlife and fish: Science of the Total Environment, v. 696, 133792, 40 p., https://doi.org/10.1016/j.scitotenv.2019.133792.","productDescription":"133792, 40 p.","ipdsId":"IP-107367","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":467371,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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for Zoo and Wildlife Research","active":true,"usgs":false}],"preferred":false,"id":769121,"contributorType":{"id":1,"text":"Authors"},"rank":68},{"text":"Schuur, Stacy S.","contributorId":218386,"corporation":false,"usgs":false,"family":"Schuur","given":"Stacy","email":"","middleInitial":"S.","affiliations":[{"id":39825,"text":"National Institute of Standards and Technology, Chemical Sciences Division, Hollings Marine Laboratory","active":true,"usgs":false}],"preferred":false,"id":769122,"contributorType":{"id":1,"text":"Authors"},"rank":69},{"text":"Vikingsson, Gisli","contributorId":218387,"corporation":false,"usgs":false,"family":"Vikingsson","given":"Gisli","email":"","affiliations":[{"id":39837,"text":"Marine and Freshwater Research Institute","active":true,"usgs":false}],"preferred":false,"id":769123,"contributorType":{"id":1,"text":"Authors"},"rank":70}]}} ,{"id":70204877,"text":"70204877 - 2019 - A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge","interactions":[],"lastModifiedDate":"2019-08-21T10:31:44","indexId":"70204877","displayToPublicDate":"2019-08-13T10:22:39","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge","docAbstract":"Worldwide, eutrophication is threatening lake ecosystems. To support lake management numerous eutrophication models have been developed. Diverse research questions in a wide range of lake ecosystems are addressed by these models. The established models are based on three key approaches: the empirical approach that employs field surveys, the theoretical approach in which models based on first principles are tested against lab experiments, and the process-based approach that uses parameters and functions representing detailed biogeochemical processes. These approaches have led to an accumulation of field-, lab- and model-based knowledge, respectively. Linking these sources of knowledge would benefit lake management by exploiting complementary information; however, the development of a simple tool that links these approaches was hampered by their large differences in scale and complexity. Here we propose a Generically Parameterized Lake eutrophication model (GPLake) that links field-, lab- and model-based knowledge and can be used to make a first diagnosis of lake water quality. We derived GPLake from consumer-resource theory by the principle that lacustrine phytoplankton is typically limited by two resources: nutrients and light. These limitations are captured in two generic parameters that shape the nutrient to chlorophyll-a relations. Next, we parameterized GPLake, using knowledge from empirical, theoretical, and process-based approaches. GPLake generic parameters were found to scale in a comparable manner across data sources. Finally, we show that GPLake can be applied as a simple tool that provides lake managers with a first diagnosis of the limiting factor and lake water quality, using only the parameters for lake depth, residence time and current nutrient loading. With this first-order assessment, lake managers can easily assess measures such as reducing nutrient load, decreasing residence time or changing depth before spending money on field-, lab- or model- experiments to support lake management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.133887","usgsCitation":"Chang, M., Teurlincx, S., DeAngelis, D.L., Janse, J.H., Troost, T.A., van Wijk, D., Mooij, W.M., and Janssen, A., 2019, A generically parameterized model of lake eutrophication (GPLake) that links field-, lab- and model-based knowledge: Science of the Total Environment, v. 695, 133887, 11 p., https://doi.org/10.1016/j.scitotenv.2019.133887.","productDescription":"133887, 11 p.","ipdsId":"IP-104765","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.133887","text":"Publisher Index Page"},{"id":366781,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"695","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chang, Manqi","contributorId":218274,"corporation":false,"usgs":false,"family":"Chang","given":"Manqi","email":"","affiliations":[],"preferred":false,"id":768853,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teurlincx, Sven","contributorId":218275,"corporation":false,"usgs":false,"family":"Teurlincx","given":"Sven","email":"","affiliations":[],"preferred":false,"id":768854,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":768855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janse, Jan H.","contributorId":215555,"corporation":false,"usgs":false,"family":"Janse","given":"Jan","email":"","middleInitial":"H.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Troost, Tineke A.","contributorId":218276,"corporation":false,"usgs":false,"family":"Troost","given":"Tineke","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":768857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Wijk, Dianneke","contributorId":215557,"corporation":false,"usgs":false,"family":"van Wijk","given":"Dianneke","email":"","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mooij, Wolf M.","contributorId":215556,"corporation":false,"usgs":false,"family":"Mooij","given":"Wolf","email":"","middleInitial":"M.","affiliations":[{"id":39277,"text":"Dept. of Aquatic Ecology, Netherlands Institute of Ecology, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":768859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Janssen, Annette B. G.","contributorId":200076,"corporation":false,"usgs":false,"family":"Janssen","given":"Annette B. G.","affiliations":[],"preferred":false,"id":768860,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}