Anthropogenic climate change presents challenges and opportunities to the growth, reproduction, and survival of individuals throughout their life cycles. Demographic compensation among life‐history stages has the potential to buffer populations from decline, but alternatively, compounding negative effects can lead to accelerated population decline and extinction. In montane ecosystems of the U.S. Pacific Northwest, increasing temperatures are resulting in a transition from snow‐dominated to rain‐dominated precipitation events, reducing snowpack. For ectotherms such as amphibians, warmer winters can reduce the frequency of critical minimum temperatures and increase the length of summer growing seasons, benefiting post‐metamorphic stages, but may also increase metabolic costs during winter months, which could decrease survival. Lower snowpack levels also result in wetlands that dry sooner or more frequently in the summer, increasing larval desiccation risk. To evaluate how these challenges and opportunities compound within a species’ life history, we collected demographic data on Cascades frog (Rana cascadae) in Olympic National Park in Washington state to parameterize stage‐based stochastic matrix population models under current and future (A1B, 2040s, and 2080s) environmental conditions. We estimated the proportion of reproductive effort lost each year due to drying using watershed‐specific hydrologic models, and coupled this with an analysis that relates 15 yr of R. cascadae abundance data with a suite of climate variables. We estimated the current population growth (λs) to be 0.97 (95% CI 0.84–1.13), but predict that λs will decline under continued climate warming, resulting in a 62% chance of extinction by the 2080s because of compounding negative effects on early and late life history stages. By the 2080s, our models predict that larval mortality will increase by 17% as a result of increased pond drying, and adult survival will decrease by 7% as winter length and summer precipitation continue to decrease. We find that reduced larval survival drives initial declines in the 2040s, but further declines in the 2080s are compounded by decreases in adult survival. Our results demonstrate the need to understand the potential for compounding or compensatory effects within different life history stages to exacerbate or buffer the effects of climate change on population growth rates through time.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.1832","usgsCitation":"Kissel, A.M., Palen, W.J., Ryan, M.E., and Adams, M.J., 2019, Compounding effects of climate change reduce population viability of a montane amphibian: Ecological Applications, v. 29, no. 2, p. 1-12, https://doi.org/10.1002/eap.1832.","productDescription":"e01832; 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-092187","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":360793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kissel, Amanda M.","contributorId":211917,"corporation":false,"usgs":false,"family":"Kissel","given":"Amanda","email":"","middleInitial":"M.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":755199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palen, Wendy J.","contributorId":211918,"corporation":false,"usgs":false,"family":"Palen","given":"Wendy","email":"","middleInitial":"J.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":755200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ryan, Maureen E.","contributorId":208314,"corporation":false,"usgs":false,"family":"Ryan","given":"Maureen","email":"","middleInitial":"E.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":755201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":755198,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201760,"text":"70201760 - 2019 - Behavioral effects of copper on larval white sturgeon","interactions":[],"lastModifiedDate":"2019-01-29T12:25:13","indexId":"70201760","displayToPublicDate":"2019-01-29T12:25:09","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral effects of copper on larval white sturgeon","docAbstract":"Early–life stage white sturgeon are sensitive to copper (Cu), with adverse behavioral responses observed during previous studies. The objectives of the present study were to quantify the effects of Cu exposure on white sturgeon swimming and feeding behaviors and determine their time to response. Larval sturgeon (1–2, 28, or 35 d posthatch [dph]) were exposed to Cu (0.5–8 μg/L) for 4 to 14 d. Abnormal behavioral changes were observed within the first few days of exposure including loss of equilibrium and immobilization. Digital video tracking software revealed decreased swimming activity with increasing Cu concentration. Significant changes in behavior and mortality occurred at concentrations of Cu between 1 and 8 μg/L. Juvenile white sturgeon, 58 dph, exposed to 12 μg/L Cu consumed 37 to 60% less food than controls after 3 d of exposure. The present results indicate that behavioral endpoints were more sensitive than some standard toxicity test endpoints and can effectively expand the sensitivity of standard toxicity tests for white sturgeon. Swimming behavior was impaired to the extent that survival in the field would likely be jeopardized. Such data would provide managers a useful metric for characterizing the risks of Cu contamination to white sturgeon.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.4293","usgsCitation":"Puglis, H.J., Calfee, R.D., and Little, E.E., 2019, Behavioral effects of copper on larval white sturgeon: Environmental Toxicology and Chemistry, v. 38, no. 1, p. 132-144, https://doi.org/10.1002/etc.4293.","productDescription":"13 p.","startPage":"132","endPage":"144","ipdsId":"IP-095133","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":437598,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QB60EV","text":"USGS data release","linkHelpText":"Behavioral Effects of Copper on Larval White Sturgeon"},{"id":437597,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QB60EV","text":"USGS data release","linkHelpText":"Behavioral Effects of Copper on Larval White Sturgeon"},{"id":360782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Puglis, Holly J. 0000-0002-3090-6597 hpuglis@usgs.gov","orcid":"https://orcid.org/0000-0002-3090-6597","contributorId":4686,"corporation":false,"usgs":true,"family":"Puglis","given":"Holly","email":"hpuglis@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":755264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calfee, Robin D. 0000-0001-6056-7023 rcalfee@usgs.gov","orcid":"https://orcid.org/0000-0001-6056-7023","contributorId":1841,"corporation":false,"usgs":true,"family":"Calfee","given":"Robin","email":"rcalfee@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":755265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Little, Edward E. 0000-0003-0034-3639 elittle@usgs.gov","orcid":"https://orcid.org/0000-0003-0034-3639","contributorId":1746,"corporation":false,"usgs":true,"family":"Little","given":"Edward","email":"elittle@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":755266,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215867,"text":"70215867 - 2019 - Iterative models for early detection of invasive species across spread pathways","interactions":[],"lastModifiedDate":"2020-11-02T13:00:31.325277","indexId":"70215867","displayToPublicDate":"2019-01-29T12:10:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Iterative models for early detection of invasive species across spread pathways","docAbstract":"Species distribution models can be used to direct early detection of invasive species, if they include proxies for invasion pathways. Due to the dynamic nature of invasion, these models violate assumptions of stationarity across space and time. To compensate for issues of stationarity, we iteratively update regionalized species distribution models annually for European gypsy moth (Lymantria dispar dispar) to target early detection surveys for the USDA APHIS gypsy moth program. We defined regions based on the distances from the invasion spread front where shifts in variable importance occurred and included models for the non-quarantine portion of the state of Maine, a short-range region, an intermediate region, and a long-range region. We considered variables that represented potential gypsy moth movement pathways within each region, including transportation networks, recreational activities, urban characteristics, and household movement data originating from gypsy moth infested areas (U.S. Postal Service address forwarding data). We updated the models annually, linked the models to an early detection survey design, and validated the models for the following year using predicted risk at new positive detection locations. Human-assisted pathways data, such as address forwarding, became increasingly important predictors of gypsy moth detection in the intermediate-range geographic model as more predictor data accumulated over time (relative importance = 5.9%, 17.36%, and 35.76% for 2015, 2016, and 2018, respectively). Receiver operating curves showed increasing performance for iterative annual models (area under the curve (AUC) = 0.63, 0.76, and 0.84 for 2014, 2015, and 2016 models, respectively), and boxplots of predicted risk each year showed increasing accuracy and precision of following year positive detection locations. The inclusion of human-assisted pathway predictors combined with the strategy of iterative modeling brings significant advantages to targeting early detection of invasive species. We present the first published example of iterative species distribution modeling for invasive species in an operational context.
","language":"English","publisher":"MDPI","doi":"10.3390/f10020108","usgsCitation":"Cook, G., Jarnevich, C.S., Warden, M., Downing, M., Withrow, J., and Leinwand, I., 2019, Iterative models for early detection of invasive species across spread pathways: Forests, v. 10, no. 2, 108, 21 p., https://doi.org/10.3390/f10020108.","productDescription":"108, 21 p.","ipdsId":"IP-013042","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467967,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f10020108","text":"Publisher Index Page"},{"id":379981,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin, Illinois, Indiana, Ohio, West Virginia, Virginia, Pennsylvania, Delaware, Maryland, New Jersey, Connecticut, Rhode Island, Massachusetts, Maine, New Hampshire, Vermont, New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.59277343749999,\n 46.6795944656402\n ],\n [\n -93.779296875,\n 45.49094569262732\n ],\n [\n -91.7578125,\n 43.197167282501276\n ],\n [\n -88.9013671875,\n 41.73852846935917\n ],\n [\n -86.5283203125,\n 39.90973623453719\n ],\n [\n -82.8369140625,\n 39.67337039176558\n ],\n [\n -80.8154296875,\n 37.33522435930639\n ],\n [\n -77.51953125,\n 37.26530995561875\n ],\n [\n -76.0693359375,\n 36.63316209558658\n ],\n [\n -67.1044921875,\n 44.809121700077355\n ],\n [\n -67.763671875,\n 45.767522962149876\n ],\n [\n -68.02734375,\n 47.368594345213374\n ],\n [\n -69.12597656249999,\n 47.45780853075031\n ],\n [\n -71.3232421875,\n 45.336701909968134\n ],\n [\n -75.1025390625,\n 44.96479793033101\n ],\n [\n -76.5966796875,\n 43.99281450048989\n ],\n [\n -79.1015625,\n 43.16512263158296\n ],\n [\n -81.5185546875,\n 41.60722821271717\n ],\n [\n -82.96875,\n 42.16340342422401\n ],\n [\n -82.2216796875,\n 43.54854811091286\n ],\n [\n -82.79296874999999,\n 45.55252525134013\n ],\n [\n -84.462890625,\n 46.37725420510028\n ],\n [\n -88.505859375,\n 48.28319289548349\n ],\n [\n -92.59277343749999,\n 46.6795944656402\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Gericke","contributorId":197522,"corporation":false,"usgs":false,"family":"Cook","given":"Gericke","email":"","affiliations":[],"preferred":false,"id":803599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":803545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warden, Melissa","contributorId":244250,"corporation":false,"usgs":false,"family":"Warden","given":"Melissa","email":"","affiliations":[],"preferred":false,"id":803600,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downing, Marla","contributorId":244251,"corporation":false,"usgs":false,"family":"Downing","given":"Marla","email":"","affiliations":[],"preferred":false,"id":803601,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Withrow, John","contributorId":244252,"corporation":false,"usgs":false,"family":"Withrow","given":"John","affiliations":[],"preferred":false,"id":803602,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leinwand, I.","contributorId":70300,"corporation":false,"usgs":true,"family":"Leinwand","given":"I.","affiliations":[],"preferred":false,"id":803603,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237804,"text":"70237804 - 2019 - Size distributions of Arctic waterbodies reveal consistent relations in their statistical moments in space and time","interactions":[],"lastModifiedDate":"2022-10-24T14:56:05.970198","indexId":"70237804","displayToPublicDate":"2019-01-29T09:39:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9121,"text":"Frontiers Earth Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Size distributions of Arctic waterbodies reveal consistent relations in their statistical moments in space and time","docAbstract":"Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km2 (100 m2) to 1 km2. We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size (R2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2019.00005","usgsCitation":"Muster, S., Riley, W.J., Roth, K., Langer, M., Cresto Aleina, F., Koven, C.D., Lange, S., Bartsch, A., Grosse, G., Wilson, C.J., Jones, B.M., and Boike, J., 2019, Size distributions of Arctic waterbodies reveal consistent relations in their statistical moments in space and time: Frontiers Earth Science Journal, v. 7, 5,15 p., https://doi.org/10.3389/feart.2019.00005.","productDescription":"5,15 p.","ipdsId":"IP-084407","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":467968,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2019.00005","text":"Publisher Index Page"},{"id":408644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Russia, United States","state":"Alaska","geographicExtents":"{\n \"type\": 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J.","contributorId":88242,"corporation":false,"usgs":true,"family":"Wilson","given":"C.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":855699,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"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":855700,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Boike, Julia","contributorId":194646,"corporation":false,"usgs":false,"family":"Boike","given":"Julia","email":"","affiliations":[],"preferred":false,"id":855701,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70201744,"text":"70201744 - 2019 - Elevated manganese concentrations in United States groundwater, role of land surface–soil–aquifer connections","interactions":[],"lastModifiedDate":"2019-01-28T14:36:31","indexId":"70201744","displayToPublicDate":"2019-01-28T14:36:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Elevated manganese concentrations in United States groundwater, role of land surface–soil–aquifer connections","docAbstract":"Chemical data from 43 334 wells were used to examine the role of land surface–soil–aquifer connections in producing elevated manganese concentrations (>300 μg/L) in United States (U.S.) groundwater. Elevated concentrations of manganese and dissolved organic carbon (DOC) in groundwater are associated with shallow, anoxic water tables and soils enriched in organic carbon, suggesting soil-derived DOC supports manganese reduction and mobilization in shallow groundwater. Manganese and DOC concentrations are higher near rivers than farther from rivers, suggesting river-derived DOC also supports manganese mobilization. Anthropogenic nitrogen may also affect manganese concentrations in groundwater. In parts of the northeastern U.S. containing poorly buffered soils, ∼40% of the samples with elevated manganese concentrations have pH values < 6 and elevated concentrations of nitrate relative to samples with pH ≥ 6, suggesting acidic recharge produced by the oxidation of ammonium in fertilizer helps mobilize manganese. An estimated 2.6 million people potentially consume groundwater with elevated manganese concentrations, the highest densities of which occur near rivers and in areas with organic carbon rich soil. Results from this study indicate land surface–soil–aquifer connections play an important role in producing elevated manganese concentrations in groundwater used for human consumption.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b04055","usgsCitation":"McMahon, P.B., Belitz, K., Reddy, J.E., and Johnson, T., 2019, Elevated manganese concentrations in United States groundwater, role of land surface–soil–aquifer connections: Environmental Science & Technology, v. 53, no. 1, p. 29-38, https://doi.org/10.1021/acs.est.8b04055.","productDescription":"10 p.","startPage":"29","endPage":"38","ipdsId":"IP-098153","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":437599,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y4GOFQ","text":"USGS data release","linkHelpText":"Data for Elevated Manganese Concentrations in United States Groundwater, Role of Land Surface-Soil-Aquifer Connections"},{"id":360761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"53","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-12","publicationStatus":"PW","scienceBaseUri":"5c5022c0e4b0708288f7e7c8","contributors":{"authors":[{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":755153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reddy, James E. 0000-0002-6998-7267","orcid":"https://orcid.org/0000-0002-6998-7267","contributorId":202976,"corporation":false,"usgs":true,"family":"Reddy","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":201888,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755155,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70201728,"text":"70201728 - 2019 - Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska","interactions":[],"lastModifiedDate":"2022-04-14T19:31:06.616565","indexId":"70201728","displayToPublicDate":"2019-01-28T13:57:34","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska","docAbstract":"Lakes in boreal lowlands cycle carbon and supply an important source of freshwater for wildlife and migratory waterfowl. The abundance and distribution of these lakes are supported, in part, by permafrost distribution, which is subject to change. Relationships between permafrost thaw and lake dynamics remain poorly known in most boreal regions. Here, new airborne electromagnetic (AEM) data collected during June 2010 and February 2016 were used to constrain deep permafrost distribution. AEM data were coupled with Landsat-derived lake surface-area data from 1979 through 2011 to inform temporal lake behavior changes in the 35 500- km2 Yukon Flats ecoregion of Alaska. Together, over 1500 km of AEM data, and roughly 30 years of Landsat data were used to explore processes that drive lake dynamics across a variety of permafrost thaw states not possible in studies conducted with satellite imagery or field measurements alone. Clustered time-series data identified lakes with similar temporal dynamics. Clusters possessed similarities in lake permanence (i.e. ephemeral versus perennial), subsurface permafrost distribution, and proximity to rivers and streams. Of the clustered lakes, ~66% are inferred to have at least intermittent connectivity with other surface-water features, ~19% are inferred to have shallow subsurface connectivity to other surface water features that served as a low-pass filter for hydroclimatic fluctuations, and ~15% appear to be isolated by surrounding permafrost (i.e. no connectivity). Integrated analysis of AEM and Landsat data reveals a progression from relatively synchronous lake dynamics among disconnected lakes in the most spatially continuous, thick permafrost to quite high spatiotemporal heterogeneity in lake behavior among variably-connected lakes in regions with notably less continuous permafrost. Variability can be explained by the preferential development of thawed permeable gravel pathways for lateral water redistribution in this area. The general spatial progression in permafrost thaw state and lake area behavior may be extended to the temporal dimension. However, extensive permafrost thaw, beyond what is currently observed, is expected to promote ubiquitous subsurface connectivity, eventually evolving to a state of increased lake synchronicity.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/aaf06f","usgsCitation":"Rey, D., Walvoord, M., Minsley, B., Rover, J., and Singha, K., 2019, Investigating lake-area dynamics across a permafrost-thaw spectrum using airborne electromagnetic surveys and remote sensing time-series data in Yukon Flats, Alaska: Environmental Research Letters, v. 14, no. 2, p. 1-13, https://doi.org/10.1088/1748-9326/aaf06f.","productDescription":"Article 025001; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-098493","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":467970,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aaf06f","text":"Publisher Index Page"},{"id":360756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats","volume":"14","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-21","publicationStatus":"PW","scienceBaseUri":"5c5022c1e4b0708288f7e7cc","contributors":{"authors":[{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":755036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walvoord, Michelle Ann 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211847,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle Ann","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":755035,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minsley, Burke 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":211849,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":false,"id":755037,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":755038,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":755039,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201734,"text":"70201734 - 2019 - Ecosystem service flows from a migratory species: Spatial subsidies of the northern pintail","interactions":[],"lastModifiedDate":"2020-09-01T20:11:07.096526","indexId":"70201734","displayToPublicDate":"2019-01-28T13:32:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":698,"text":"Ambio","active":true,"publicationSubtype":{"id":10}},"title":"Ecosystem service flows from a migratory species: Spatial subsidies of the northern pintail","docAbstract":"Migratory species provide important benefits to society, but their cross-border conservation poses serious challenges. By quantifying the economic value of ecosystem services (ESs) provided across a species’ range and ecological data on a species’ habitat dependence, we estimate spatial subsidies—how different regions support ESs provided by a species across its range. We illustrate this method for migratory northern pintail ducks in North America. Pintails support over \\$101 million USD annually in recreational hunting and viewing and subsistence hunting in the U.S. and Canada. Pintail breeding regions provide nearly \\$30 million in subsidies to wintering regions, with the “Prairie Pothole” region supplying over \\$24 million in annual benefits to other regions. This information can be used to inform conservation funding allocation among migratory regions and nations on which the pintail depends. We thus illustrate a transferrable method to quantify migratory species-derived ESs and provide information to aid in their transboundary conservation.
","language":"English","publisher":"Springer","doi":"10.1007/s13280-018-1049-4","usgsCitation":"Bagstad, K.J., Semmens, D.J., Diffendorfer, J., Mattsson, B., Dubovsky, J.A., Thogmartin, W.E., Wiederholt, R., Loomis, J.B., Bieri, J., Sample, C., Goldstein, J., and Lopez-Hoffman, L., 2019, Ecosystem service flows from a migratory species: Spatial subsidies of the northern pintail: Ambio, v. 48, no. 1, p. 61-73, https://doi.org/10.1007/s13280-018-1049-4.","productDescription":"13 p.","startPage":"61","endPage":"73","ipdsId":"IP-089020","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":467972,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s13280-018-1049-4","text":"External Repository"},{"id":437600,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q23ZFC","text":"USGS data release","linkHelpText":"Data release for ecosystem service flows from a migratory species: spatial subsidies of the northern pintail"},{"id":360747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5c5022c2e4b0708288f7e7db","contributors":{"authors":[{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":755066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":755067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mattsson, Brady J.","contributorId":171612,"corporation":false,"usgs":false,"family":"Mattsson","given":"Brady J.","affiliations":[{"id":26928,"text":"Univ. of Vienna","active":true,"usgs":false}],"preferred":false,"id":755068,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dubovsky, James A.","contributorId":201247,"corporation":false,"usgs":false,"family":"Dubovsky","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":755069,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":755070,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wiederholt, Ruscena","contributorId":171611,"corporation":false,"usgs":false,"family":"Wiederholt","given":"Ruscena","email":"","affiliations":[{"id":12738,"text":"U of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":755071,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Loomis, John B.","contributorId":197268,"corporation":false,"usgs":false,"family":"Loomis","given":"John","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":755072,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bieri, Joanna A.","contributorId":201599,"corporation":false,"usgs":false,"family":"Bieri","given":"Joanna A.","affiliations":[{"id":36213,"text":"University of Redlands","active":true,"usgs":false}],"preferred":false,"id":755074,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sample, Christine","contributorId":201060,"corporation":false,"usgs":false,"family":"Sample","given":"Christine","email":"","affiliations":[],"preferred":false,"id":755076,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Goldstein, Joshua","contributorId":197267,"corporation":false,"usgs":false,"family":"Goldstein","given":"Joshua","affiliations":[],"preferred":false,"id":755073,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lopez-Hoffman, Laura","contributorId":149127,"corporation":false,"usgs":false,"family":"Lopez-Hoffman","given":"Laura","affiliations":[{"id":17654,"text":"School of Natural Resources & the Environment and Udall Center for Studies in Public Policy, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":755075,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70201738,"text":"70201738 - 2019 - Landscape genetics reveal broad and fine‐scale population structure due to landscape features and climate history in the northern leopard frog (Rana pipiens) in North Dakota","interactions":[],"lastModifiedDate":"2019-02-21T14:45:11","indexId":"70201738","displayToPublicDate":"2019-01-28T13:10:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Landscape genetics reveal broad and fine‐scale population structure due to landscape features and climate history in the northern leopard frog (Rana pipiens) in North Dakota","title":"Landscape genetics reveal broad and fine‐scale population structure due to landscape features and climate history in the northern leopard frog (Rana pipiens) in North Dakota","docAbstract":"Prehistoric climate and landscape features play large roles structuring wildlife populations. The amphibians of the northern Great Plains of North America present an opportunity to investigate how these factors affect colonization, migration, and current population genetic structure. This study used 11 microsatellite loci to genotype 1,230 northern leopard frogs (Rana pipiens) from 41 wetlands (30 samples/wetland) across North Dakota. Genetic structure of the sampled frogs was evaluated using Bayesian and multivariate clustering methods. All analyses produced concordant results, identifying a major east–west split between two R. pipiens population clusters separated by the Missouri River. Substructuring within the two major identified population clusters was also found. Spatial principal component analysis (sPCA) and variance partitioning analysis identified distance, river basins, and the Missouri River as the most important landscape factors differentiating R. pipiens populations across the state. Bayesian reconstruction of coalescence times suggested the major east–west split occurred ~13–18 kya during a period of glacial retreat in the northern Great Plains and substructuring largely occurred ~5–11 kya during a period of extreme drought cycles. A range‐wide species distribution model (SDM) for R. pipiens was developed and applied to prehistoric climate conditions during the Last Glacial Maximum (21 kya) and the mid‐Holocene (6 kya) from the CCSM4 climate model to identify potential refugia. The SDM indicated potential refugia existed in South Dakota or further south in Nebraska. The ancestral populations of R. pipiens in North Dakota may have inhabited these refugia, but more sampling outside the state is needed to reconstruct the route of colonization. Using microsatellite genotype data, this study determined that colonization from glacial refugia, drought dynamics in the northern Great Plains, and major rivers acting as barriers to gene flow were the defining forces shaping the regional population structure of R. pipiens in North Dakota.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4745","usgsCitation":"Waraniak, J.M., Fisher, J., Purcell, K., Mushet, D.M., and Stockwell, C.A., 2019, Landscape genetics reveal broad and fine‐scale population structure due to landscape features and climate history in the northern leopard frog (Rana pipiens) in North Dakota: Ecology and Evolution, v. 9, no. 3, p. 1041-1060, https://doi.org/10.1002/ece3.4745.","productDescription":"20 p.","startPage":"1041","endPage":"1060","ipdsId":"IP-098834","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467975,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4745","text":"Publisher Index Page"},{"id":360742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","volume":"9","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-15","publicationStatus":"PW","scienceBaseUri":"5c5022c2e4b0708288f7e7e7","contributors":{"authors":[{"text":"Waraniak, Justin M.","contributorId":211882,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","email":"","middleInitial":"M.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":755121,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, Justin D. L.","contributorId":211883,"corporation":false,"usgs":false,"family":"Fisher","given":"Justin D. L.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":755122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Purcell, Kevin","contributorId":211884,"corporation":false,"usgs":false,"family":"Purcell","given":"Kevin","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":755123,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":755120,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stockwell, Craig A.","contributorId":194252,"corporation":false,"usgs":false,"family":"Stockwell","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":755124,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70201716,"text":"70201716 - 2019 - Evidence for interactions among environmental stressors in the Laurentian Great Lakes","interactions":[],"lastModifiedDate":"2019-01-28T11:37:28","indexId":"70201716","displayToPublicDate":"2019-01-28T11:37:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for interactions among environmental stressors in the Laurentian Great Lakes","docAbstract":"Co-occurrence of environmental stressors is ubiquitous in ecosystems, but cumulative effects are difficult to predict for effective indicator development. Individual stressors can amplify (synergies) or lessen (antagonisms) each other's impacts or have fully independent effects (additive). Here we use the Laurentian Great Lakes, where a multitude of stressors have been studied for decades, as a case study for considering insights from both a systematic literature review and an expert elicitation (or structured expert judgment) to identify stressor interactions. In our literature search for pairs of stressors and interaction-related keywords, relatively few studies (9%, or 6/65) supported additive interactions with independent stressor effects. Instead, both antagonisms (42%, or 27/65) and synergies (49%, or 32/65) were common. We found substantial evidence for interactions of invasive dreissenid mussels with nutrient loading and between pairs of invasive species (predominantly dreissenids × round goby), yet both sets of records included mixtures of synergies and antagonisms. Complete quantification of individual and joint effects of stressors was rare, but effect sizes for dreissenid mussels × nutrient loading supported an antagonism. Our expert elicitation included discussion in focus groups and a follow-up survey. This process highlighted the potential for synergies of nutrient loading with dreissenid mussels and climate change as seen from the literature review. The elicitation also identified additional potential interactions less explored in the literature, particularly synergies of nutrient loading with hypoxia and wetland loss. To stimulate future research, we built a conceptual model describing interactions among dreissenid mussels, climate change, and nutrient loading. Our case study illustrates the value of considering results from both elicitations and systematic reviews to overcome data limitations. The simultaneous occurrence of synergies and antagonisms in a single ecosystem underscores the challenge of predicting the cumulative effects of stressors to guide indicator development and other management and restoration decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2019.01.010","usgsCitation":"Smith, S.D., Bunnell, D.B., Burton, G., Ciborowski, J.J., Davidson, A.D., Dickinson, C.E., Eaton, L.A., Esselman, P.C., Evans, M.A., Kashian, D.R., Manning, N., McIntyre, P.B., Nalepa, T.F., Perez-Fuentetaja, A., Steinman, A.D., Uzarski, D.G., and Allan, J.D., 2019, Evidence for interactions among environmental stressors in the Laurentian Great Lakes: Ecological Indicators, v. 101, p. 203-211, https://doi.org/10.1016/j.ecolind.2019.01.010.","productDescription":"9 p.","startPage":"203","endPage":"211","ipdsId":"IP-093690","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":360726,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Laurentian Great Lakes","volume":"101","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c5022c3e4b0708288f7e7ee","contributors":{"authors":[{"text":"Smith, Sigrid D. P.","contributorId":211810,"corporation":false,"usgs":false,"family":"Smith","given":"Sigrid","email":"","middleInitial":"D. P.","affiliations":[],"preferred":false,"id":754961,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bunnell, David B. 0000-0003-3521-7747 dbunnell@usgs.gov","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":195888,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","email":"dbunnell@usgs.gov","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":754960,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burton, G.A. Jr.","contributorId":91959,"corporation":false,"usgs":true,"family":"Burton","given":"G.A.","suffix":"Jr.","email":"","affiliations":[],"preferred":false,"id":754962,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ciborowski, Jan J. H.","contributorId":211812,"corporation":false,"usgs":false,"family":"Ciborowski","given":"Jan","email":"","middleInitial":"J. H.","affiliations":[],"preferred":false,"id":754963,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davidson, Alisha D.","contributorId":211813,"corporation":false,"usgs":false,"family":"Davidson","given":"Alisha","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":754964,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dickinson, Caitlin E.","contributorId":211814,"corporation":false,"usgs":false,"family":"Dickinson","given":"Caitlin","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":754965,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eaton, Lauren A.","contributorId":211815,"corporation":false,"usgs":false,"family":"Eaton","given":"Lauren","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":754966,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Esselman, Peter C. 0000-0002-0085-903X pesselman@usgs.gov","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":5965,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter","email":"pesselman@usgs.gov","middleInitial":"C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":754967,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":149358,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":754968,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kashian, Donna R.","contributorId":205602,"corporation":false,"usgs":false,"family":"Kashian","given":"Donna","email":"","middleInitial":"R.","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":754969,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Manning, Nathan F.","contributorId":211818,"corporation":false,"usgs":false,"family":"Manning","given":"Nathan F.","affiliations":[],"preferred":false,"id":754970,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"McIntyre, Peter B.","contributorId":166828,"corporation":false,"usgs":false,"family":"McIntyre","given":"Peter","email":"","middleInitial":"B.","affiliations":[{"id":24540,"text":"Center for Limnology, University of Wisconsin, Madison, Wisconsin, 53706, USA.","active":true,"usgs":false}],"preferred":false,"id":754971,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Nalepa, Thomas F.","contributorId":211819,"corporation":false,"usgs":false,"family":"Nalepa","given":"Thomas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":754972,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Perez-Fuentetaja, Alicia","contributorId":211820,"corporation":false,"usgs":false,"family":"Perez-Fuentetaja","given":"Alicia","email":"","affiliations":[],"preferred":false,"id":754973,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Steinman, Alan D.","contributorId":190417,"corporation":false,"usgs":false,"family":"Steinman","given":"Alan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":754974,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Uzarski, Donald G.","contributorId":211821,"corporation":false,"usgs":false,"family":"Uzarski","given":"Donald","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":754975,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Allan, J. David","contributorId":211822,"corporation":false,"usgs":false,"family":"Allan","given":"J.","email":"","middleInitial":"David","affiliations":[],"preferred":false,"id":754976,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70215503,"text":"70215503 - 2019 - Estimating lake–climate responses from sparse data: An application to high elevation lakes","interactions":[],"lastModifiedDate":"2020-10-21T14:54:27.846192","indexId":"70215503","displayToPublicDate":"2019-01-28T09:49:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Estimating lake–climate responses from sparse data: An application to high elevation lakes","docAbstract":"Although many studies demonstrate lake warming, few document trends from lakes with sparse data. Diel and seasonal variability of surface temperatures limit conventional trend analyses to datasets with frequent repeated observations. Thus, remote lakes, including many high elevation lakes, are underrepresented in trend analyses. We used a Bayesian technique to analyze sparse data that explicitly incorporated diel and seasonal variability. This approach allowed us to estimate lake warming in a region of limited knowledge: high elevation lakes (> 2100 m ASL) of the Southern Rocky Mountains, U.S.A. The analysis allowed for inclusion of lakes with few repeated measurements, and observations made before 1980 when more intensive lake monitoring began. We accumulated the largest dataset of high elevation lake temperatures analyzed to date. Data from 590 high elevation lakes in the Southern Rocky Mountains showed a 0.13°C decade−1 increase in surface temperatures and a 14% increase in seasonal degree days since 1955. This result is lower than other regional and global estimates of lake warming; however, it is similar to other high elevation lake studies. Our approach can be applied to other understudied regions, increasing our overall understanding of the effects of climate change on lakes and their temporal dynamics.
The genomics revolution has initiated a new era of population genetics where genome-wide data are frequently used to understand complex patterns of population structure and selection. However, the application of genomic tools to inform management and conservation has been somewhat rare outside a few well studied species. Fortunately, two recently developed approaches, amplicon sequencing and sequence capture, have the potential to significantly advance the field of conservation genomics. Here, amplicon sequencing refers to highly multiplexed PCR followed by high-throughput sequencing (e.g., GTseq), and sequence capture refers to using capture probes to isolate loci from reduced-representation libraries (e.g., Rapture). Both approaches allow sequencing of thousands of individuals at relatively low costs, do not require any specialized equipment for library preparation, and generate data that can be analyzed without sophisticated computational infrastructure. Here, we discuss the advantages and disadvantages of each method and provide a decision framework for geneticists who are looking to integrate these methods into their research programme. While it will always be important to consider the specifics of the biological question and system, we believe that amplicon sequencing is best suited for projects aiming to genotype <500 loci on many individuals (>1,500) or for species where continued monitoring is anticipated (e.g., long-term pedigrees). Sequence capture, on the other hand, is best applied to projects including fewer individuals or where >500 loci are required. Both of these techniques should smooth the transition from traditional genetic techniques to genomics, helping to usher in the conservation genomics era.
The forthcoming Surface Water and Ocean Topography (SWOT) satellite mission will provide global measurements of the free surface of large rivers, providing new opportunities for remote sensing‐derived estimates of river discharge in gaged and ungaged basins. SWOT discharge algorithms have been developed and benchmarked using synthetic data but remain untested on real‐world swath altimetry observations. We present the first discharge estimates from AirSWOT, a SWOT‐like airborne Ka‐band radar, using 6 days of measurements over a 40‐km segment of the Willamette River in Oregon, USA. The three evaluated discharge algorithms estimated discharge with normalized root‐mean‐square errors of 10–31% when compared with in situ gage data but were sensitive to an initial estimate of mean annual discharge. Our results show that these discharge algorithms provide reliable discharge estimates on remotely sensed data at SWOT‐like spatial scales while highlighting the need for further algorithm sensitivity tests.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018GL080771","usgsCitation":"Tuozzolo, S., Lind, G.D., Overstreet, B., Mangano, J.F., Fonstad, M.A., Hagemann, M., Frasson, R., Larnier, K., Garambois, P., Monnier, J., and Durand, M., 2019, Estimating river discharge with swath altimetry: A proof of concept using AirSWOT observations: Geophysical Research Letters, v. 46, no. 3, p. 1459-1466, https://doi.org/10.1029/2018GL080771.","productDescription":"8 p.","startPage":"1459","endPage":"1466","ipdsId":"IP-103081","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":499858,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/528a9d5c4cdf4081a5ffc133acacf87b","text":"External Repository"},{"id":379940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.40966796874999,\n 43.82263823180498\n ],\n [\n -122.80517578125,\n 44.008620115415354\n ],\n [\n -122.991943359375,\n 44.07574700247845\n ],\n [\n -123.02490234375,\n 43.937461690316646\n ],\n [\n -122.47009277343749,\n 43.64800079902171\n ],\n [\n -122.40966796874999,\n 43.82263823180498\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"46","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-02-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Tuozzolo, Stephen","contributorId":244142,"corporation":false,"usgs":false,"family":"Tuozzolo","given":"Stephen","email":"","affiliations":[{"id":48856,"text":"Byrd Polar and Climate Research Center, Ohio State University","active":true,"usgs":false}],"preferred":false,"id":803406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lind, Greg D. 0000-0001-5385-2117 glind@usgs.gov","orcid":"https://orcid.org/0000-0001-5385-2117","contributorId":5514,"corporation":false,"usgs":true,"family":"Lind","given":"Greg","email":"glind@usgs.gov","middleInitial":"D.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon 0000-0001-7845-6671 boverstreet@usgs.gov","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":169201,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"boverstreet@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803408,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangano, Joseph F. 0000-0003-4213-8406 jmangano@usgs.gov","orcid":"https://orcid.org/0000-0003-4213-8406","contributorId":4722,"corporation":false,"usgs":true,"family":"Mangano","given":"Joseph","email":"jmangano@usgs.gov","middleInitial":"F.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803409,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fonstad, Mark A","contributorId":169202,"corporation":false,"usgs":false,"family":"Fonstad","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":803410,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hagemann, M.","contributorId":244143,"corporation":false,"usgs":false,"family":"Hagemann","given":"M.","email":"","affiliations":[{"id":48857,"text":"Byrd Polar and Climate Research Center, Ohio State University, Columbus, Ohio, USA","active":true,"usgs":false}],"preferred":false,"id":803411,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frasson, R.P.M.","contributorId":244144,"corporation":false,"usgs":false,"family":"Frasson","given":"R.P.M.","affiliations":[{"id":48857,"text":"Byrd Polar and Climate Research Center, Ohio State University, Columbus, Ohio, USA","active":true,"usgs":false}],"preferred":false,"id":803412,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Larnier, K","contributorId":244145,"corporation":false,"usgs":false,"family":"Larnier","given":"K","affiliations":[{"id":48858,"text":"CS corporation, Business Unit Espace, Toulouse, France; 5Institut de Mathématiques de Toulouse (IMT), France; INSA Strasbourg, France; INSA Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":803413,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Garambois, P.-A.","contributorId":244146,"corporation":false,"usgs":false,"family":"Garambois","given":"P.-A.","affiliations":[{"id":48859,"text":"INSA Strasbourg, France; ICUBE, Strasbourg, France","active":true,"usgs":false}],"preferred":false,"id":803414,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Monnier, J.","contributorId":244147,"corporation":false,"usgs":false,"family":"Monnier","given":"J.","email":"","affiliations":[{"id":48860,"text":"Institut de Mathématiques de Toulouse (IMT), France; INSA Toulouse, France","active":true,"usgs":false}],"preferred":false,"id":803415,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Durand, M.","contributorId":244148,"corporation":false,"usgs":false,"family":"Durand","given":"M.","email":"","affiliations":[{"id":48857,"text":"Byrd Polar and Climate Research Center, Ohio State University, Columbus, Ohio, USA","active":true,"usgs":false}],"preferred":false,"id":803416,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70223291,"text":"70223291 - 2019 - Does incorporating gear selectivity during macroscale investigations of fish growth reduce size-selective sampling bias in parameter estimates?","interactions":[],"lastModifiedDate":"2021-08-20T13:30:36.240789","indexId":"70223291","displayToPublicDate":"2019-01-19T08:28:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6455,"text":"Canadian Journal Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Does incorporating gear selectivity during macroscale investigations of fish growth reduce size-selective sampling bias in parameter estimates?","docAbstract":"After the pallid sturgeon (Scaphirhynchus albus) was listed as endangered in 1990, a variety of management actions focusing on early life history needs have been implemented to aid species recovery. Given the scarcity of age-0 pallid sturgeon, managers and scientists have relied on sympatric congeners to evaluate the effectiveness of management actions in the short term; however, increased understanding of habitat requirements for age-0 Scaphirhynchus sturgeon is still needed to appropriately focus management efforts. Recently, a lack of food-producing and foraging habitats were proposed as potential limiting factors for pallid sturgeon, and the purpose of this study was to evaluate the current definition of these habitats at multiple spatial scales using data from age-0 Scaphirhynchus sturgeon (shovelnose sturgeon [Scaphirhynchus platyrhynchus] or hybrid [shovelnose sturgeon x pallid sturgeon]). Results showed the water depths and velocities that currently define age-0 pallid sturgeon foraging habitat had little effect on age-0 Scaphirhynchus sturgeon prey consumption. Similar results occurred when evaluating the relationship between prey consumption and food-producing habitat present 10, 20, and 30 days before capture. Assuming that individuals captured during this study were a valid surrogate, these results suggest that increasing foraging and food-producing habitat as defined by the current depth and velocity criteria is unlikely to result in the desired benefits of increased growth and survival of age-0 pallid sturgeon.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3395","usgsCitation":"Gemeinhardt, T.R., Gosch, N.J., Civiello, A., Chrisman, N., Shaughnessy, H., Brown, T.L., Long, J.M., and Bonneau, J.L., 2019, The influence of depth and velocity on age-0 Scaphirhynchus sturgeon prey consumption: Implications for aquatic habitat restoration: River Research and Applications, v. 35, no. 3, p. 205-215, https://doi.org/10.1002/rra.3395.","productDescription":"11 p.","startPage":"205","endPage":"215","ipdsId":"IP-098842","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"lower Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.76806640624999,\n 37.92686760148135\n ],\n [\n -90.164794921875,\n 37.92686760148135\n ],\n [\n -90.164794921875,\n 39.52099229357195\n ],\n [\n -94.76806640624999,\n 39.52099229357195\n ],\n [\n -94.76806640624999,\n 37.92686760148135\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Gemeinhardt, T. R.","contributorId":275284,"corporation":false,"usgs":false,"family":"Gemeinhardt","given":"T.","email":"","middleInitial":"R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":833927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gosch, N. J. C.","contributorId":272518,"corporation":false,"usgs":false,"family":"Gosch","given":"N.","email":"","middleInitial":"J. C.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":833928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Civiello, A. P.","contributorId":272519,"corporation":false,"usgs":false,"family":"Civiello","given":"A. P.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":833929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chrisman, N.","contributorId":275285,"corporation":false,"usgs":false,"family":"Chrisman","given":"N.","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":833930,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shaughnessy, H.","contributorId":275286,"corporation":false,"usgs":false,"family":"Shaughnessy","given":"H.","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":833931,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, T. L.","contributorId":275287,"corporation":false,"usgs":false,"family":"Brown","given":"T.","email":"","middleInitial":"L.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":833932,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833933,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bonneau, J. L.","contributorId":275288,"corporation":false,"usgs":false,"family":"Bonneau","given":"J.","email":"","middleInitial":"L.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":833934,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216037,"text":"70216037 - 2019 - Temporal variability in nitrate – discharge relationships in large rivers as revealed by high frequency data","interactions":[],"lastModifiedDate":"2020-11-03T17:07:07.577689","indexId":"70216037","displayToPublicDate":"2019-01-17T11:03:13","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Temporal variability in nitrate – discharge relationships in large rivers as revealed by high frequency data","docAbstract":"Little is known about temporal variability in nitrate concentration responses to changes in discharge on intraannual time scales in large rivers. To investigate this knowledge gap, we used a six‐year data set of daily surface water nitrate concentration and discharge averaged from near‐continuous monitoring at U.S. Geological Survey gaging stations on the Connecticut, Potomac, and Mississippi Rivers, three large rivers that contribute substantial nutrient pollution to important estuaries. Interannually, a comparison of nitrate concentration‐discharge (c‐Q) relationships between a traditional discrete grab sample data set and the near‐continuous data set revealed differing c‐Q slopes, which suggests that sample frequency can impact how we ultimately characterize hydrologic systems. Intraannually, we conducted correlation analyses over 30‐day windows to isolate the strength and direction of monthly c‐Q relationships. Monthly c‐Q slopes in the Potomac were positive (enrichment/mobilization response) in summer and fall and negative (dilution response) and weakly chemostatic (nonsignificant near‐zero c‐Q slope) in winter and spring, respectively. The Connecticut displayed a dilution response year‐round, except summer when it was weakly chemostatic. Mississippi c‐Q slopes were weakly chemostatic in all seasons and showed inconsistent responses to discharge fluctuations. The c‐Q dynamics in the Potomac and Connecticut were correlated (R > 0.3) to river temperature, flow percentile, and calendar day. Minimal correlation in the Mississippi suggests that the large basin area coupled with spatiotemporally variable anthropogenic forcings from substantial land use development created stochastic short‐term c‐Q relationships. Additional work using high‐frequency sensors across large river networks can improve our understanding of spatial source input dynamics in these natural‐human coupled systems.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023478","usgsCitation":"Zimmer, M., Pellerin, B., Burns, D., and Petrochenkov, G.P., 2019, Temporal variability in nitrate – discharge relationships in large rivers as revealed by high frequency data: Water Resources Research, v. 55, no. 2, p. 973-989, https://doi.org/10.1029/2018WR023478.","productDescription":"17 p.","startPage":"973","endPage":"989","ipdsId":"IP-092633","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":467990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doaj.org/article/82eeaf2c310645d18b234ef435a83b9c","text":"Publisher Index Page"},{"id":380080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmer, Margaret 0000-0001-8287-1923","orcid":"https://orcid.org/0000-0001-8287-1923","contributorId":225158,"corporation":false,"usgs":false,"family":"Zimmer","given":"Margaret","affiliations":[{"id":41054,"text":"Earth and Planetary Sciences, University of California, Santa Cruz, CA, 95064, USA","active":true,"usgs":false}],"preferred":false,"id":803845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pellerin, Brian A. 0000-0003-3712-7884","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":204324,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":803846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803847,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petrochenkov, Gregory Paul 0000-0001-9247-821X","orcid":"https://orcid.org/0000-0001-9247-821X","contributorId":244356,"corporation":false,"usgs":true,"family":"Petrochenkov","given":"Gregory","email":"","middleInitial":"Paul","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":803848,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203149,"text":"70203149 - 2019 - Sediment oxygen demand: A review of in situ methods","interactions":[],"lastModifiedDate":"2019-04-24T08:49:15","indexId":"70203149","displayToPublicDate":"2019-01-17T08:47:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Sediment oxygen demand: A review of in situ methods","docAbstract":"Sediment oxygen demand (SOD) plays a fundamental role in biological and chemical processes within the benthic layer of a water body. Land use, including agricultural land use, can affect SOD. However, a wide variety of approaches have been used for in situ SOD chamber construction and data collection, and modelers frequently use SOD values from the literature, without consideration of the differences in methods. Here, we review existing literature on SOD chambers (32 papers, 1974–2016), compare the differences between in situ and laboratory methods, evaluate the effects of in situ chamber mixing, and discuss common challenges associated with deployment. A cohesive in situ sealed chamber design for use with a multiparameter water-quality instrument is presented as an effort toward standardizing SOD methodology, an important consideration that may facilitate integration of SOD data sets among multiple research efforts.
","language":"English","publisher":"ACSESS","doi":"10.2134/jeq2018.06.0251","usgsCitation":"Coenen, E., Christensen, V.G., Bartsch, L., Kreiling, R.M., and Richardson, W.B., 2019, Sediment oxygen demand: A review of in situ methods: Journal of Environmental Quality, v. 48, no. 2, p. 403-411, https://doi.org/10.2134/jeq2018.06.0251.","productDescription":"9 p.","startPage":"403","endPage":"411","ipdsId":"IP-091254","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":363167,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coenen, Erin N. 0000-0003-2470-3854","orcid":"https://orcid.org/0000-0003-2470-3854","contributorId":211159,"corporation":false,"usgs":true,"family":"Coenen","given":"Erin N.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christensen, Victoria G. 0000-0003-4166-7461 vglenn@usgs.gov","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":2354,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","email":"vglenn@usgs.gov","middleInitial":"G.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartsch, Lynn 0000-0002-1483-4845 lbartsch@usgs.gov","orcid":"https://orcid.org/0000-0002-1483-4845","contributorId":214995,"corporation":false,"usgs":true,"family":"Bartsch","given":"Lynn","email":"lbartsch@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kreiling, Rebecca M. 0000-0002-9295-4156","orcid":"https://orcid.org/0000-0002-9295-4156","contributorId":202193,"corporation":false,"usgs":true,"family":"Kreiling","given":"Rebecca","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761398,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202726,"text":"70202726 - 2019 - Absence of PCB hot spot effect in walleye Sander vitreus from lower Green Bay of Lake Michigan","interactions":[],"lastModifiedDate":"2019-03-21T16:24:54","indexId":"70202726","displayToPublicDate":"2019-01-15T16:15:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":887,"text":"Archives of Environmental Contamination and Toxicology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Absence of PCB hot spot effect in walleye Sander vitreus from lower Green Bay of Lake Michigan","title":"Absence of PCB hot spot effect in walleye Sander vitreus from lower Green Bay of Lake Michigan","docAbstract":"Under certain conditions, polychlorinated biphenyl (PCB) concentration in individuals of one sex of an adult fish population may exceed that of the other sex by more than a factor of two. This phenomenon, known as the PCB hot spot effect, has been postulated to be contingent upon the following two conditions: (1) presence of a PCB hot spot in the bottom sediments of the aquatic ecosystem such that prey PCB concentrations in the hot spot region are substantially higher than prey PCB concentrations in locations distant from the hot spot, and (2) habitat use varying between the sexes such that individuals of one sex inhabit the hot spot region to a considerably greater degree than individuals of the other sex. To test whether PCB concentrations in walleye Sander vitreus from lower Green Bay of Lake Michigan displayed a PCB hot spot effect, whole-fish PCB concentrations were determined in 10 female and 10 male adult walleye from the population spawning in the Fox River, the main tributary to lower Green Bay. In addition, mark-recapture data for the Fox River walleye population were analyzed to determine differences in spatial distributions between the sexes. Results revealed that the ratio of mean PCB concentration in males to mean PCB concentration in females was only 1.13, indicating the absence of a PCB hot spot effect. This result was corroborated by the mark-recapture data analysis, which showed no significant difference in habitat use between the sexes. Thus, although condition 1 was met, condition 2 was not met, and consequently the PCB hot spot effect was not observed in the Fox River walleye population. Lack of a significant difference in PCB congener distributions between the sexes further corroborated our conclusions.","language":"English","publisher":"Springer","doi":"10.1007/s00244-018-00591-9","usgsCitation":"Madenjian, C.P., Dembkowski, D.J., Isermann, D.A., Batterman, S.A., Chernyak, S.C., Cogswell, S.F., and Holey, M.E., 2019, Absence of PCB hot spot effect in walleye Sander vitreus from lower Green Bay of Lake Michigan: Archives of Environmental Contamination and Toxicology, v. 76, no. 3, p. 442-452, https://doi.org/10.1007/s00244-018-00591-9.","productDescription":"11 p.","startPage":"442","endPage":"452","ipdsId":"IP-100952","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":437602,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90P3Q6F","text":"USGS data release","linkHelpText":"Polychlorinated biphenyl concentrations in adult walleye from the Fox River (Wisconsin) population, 2014"},{"id":362253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"De Pere Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.06576251983643,\n 44.447935720634085\n ],\n [\n -88.06196451187134,\n 44.447935720634085\n ],\n [\n -88.06196451187134,\n 44.448487178796235\n ],\n [\n -88.06576251983643,\n 44.448487178796235\n ],\n [\n -88.06576251983643,\n 44.447935720634085\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-08","publicationStatus":"PW","contributors":{"authors":[{"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":759659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel J.","contributorId":210893,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":759660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":759661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Batterman, Stuart A.","contributorId":199915,"corporation":false,"usgs":false,"family":"Batterman","given":"Stuart","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":759662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chernyak, Sergei C.","contributorId":214332,"corporation":false,"usgs":false,"family":"Chernyak","given":"Sergei","email":"","middleInitial":"C.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":759663,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cogswell, Stewart F.","contributorId":212698,"corporation":false,"usgs":false,"family":"Cogswell","given":"Stewart","email":"","middleInitial":"F.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":759664,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holey, Mark E.","contributorId":212699,"corporation":false,"usgs":false,"family":"Holey","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":759665,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202161,"text":"70202161 - 2019 - Reconstructing precipitation in the tropical South Pacific from dinosterol 2H/1H ratios in lake sediment","interactions":[],"lastModifiedDate":"2019-02-12T11:13:01","indexId":"70202161","displayToPublicDate":"2019-01-15T11:12:47","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reconstructing precipitation in the tropical South Pacific from dinosterol 2H/1H ratios in lake sediment","title":"Reconstructing precipitation in the tropical South Pacific from dinosterol 2H/1H ratios in lake sediment","docAbstract":"The South Pacific Convergence Zone (SPCZ) is the Southern Hemisphere’s largest precipitation feature supplying freshwater to 11 million people. Despite its significance, little is known about the location and intensity of SPCZ precipitation prior to instrumental records, hindering attempts to predict precipitation changes in a warming world. Here we use sedimentary molecular fossils to establish a tool for extending the historical record of precipitation. Freshwater lake sediments and water samples were collected from 30 lakes that span a 4.6 mm d−1 range in precipitation rates from the Global Precipitation Climatology Project (GPCP). δ2Hlakewater values from 29 lakes ranged from −29 to +23‰ and were inversely correlated (r = −0.51, p < 0.001) with precipitation rates, likely due to the combination of the amount of precipitation plus evaporation. δ2H values of the dinoflagellate sterol dinosterol in surficial sediments from 21 lakes ranged from −316‰ in the Solomon Islands to −245‰ in French Polynesia. These δ2Hdinosterolvalues were significantly correlated (r = 0.71, p < 0.001) with δ2Hlakewaterand inversely correlated (r = −0.77, p < 0.001) with mean annual precipitation rates with a sensitivity of −12.1 ± 2.6‰ (mm d−1)−1. Fractionation between dinosterol and lake water (εdinosterol/lakewater) decreased at the driest lake sites (r = − 0.70, p < 0.001). The empirical relationship between δ2Hdinosterol and GPCP rainfall, although indirect, provides a means of quantitatively reconstructing past precipitation in the SPCZ region with an uncertainty of less than 3.1 mm d−1, which compares favorably to the 1.5 mm d−1 uncertainty for the satellite-gauge based GPCP precipitation data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2018.10.028","usgsCitation":"Maloney, A.E., Nelson, D.B., Richey, J.N., Prebble, M., Sear, D.A., Hassall, J.D., Langdon, P.G., Croudace, I.W., Zawadzki, A., and Sachs, J.P., 2019, Reconstructing precipitation in the tropical South Pacific from dinosterol 2H/1H ratios in lake sediment: Geochimica et Cosmochimica Acta, v. 245, p. 190-206, https://doi.org/10.1016/j.gca.2018.10.028.","productDescription":"17 p.","startPage":"190","endPage":"206","ipdsId":"IP-101195","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467994,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gca.2018.10.028","text":"Publisher Index Page"},{"id":361167,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"245","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Maloney, Ashley E.","contributorId":213177,"corporation":false,"usgs":false,"family":"Maloney","given":"Ashley","email":"","middleInitial":"E.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":757044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nelson, Daniel B.","contributorId":213178,"corporation":false,"usgs":false,"family":"Nelson","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":38710,"text":"University of Basel","active":true,"usgs":false}],"preferred":false,"id":757045,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richey, Julie N. 0000-0002-2319-7980 jrichey@usgs.gov","orcid":"https://orcid.org/0000-0002-2319-7980","contributorId":174046,"corporation":false,"usgs":true,"family":"Richey","given":"Julie","email":"jrichey@usgs.gov","middleInitial":"N.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":757043,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prebble, Matthew","contributorId":213179,"corporation":false,"usgs":false,"family":"Prebble","given":"Matthew","email":"","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":757046,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sear, David A.","contributorId":213180,"corporation":false,"usgs":false,"family":"Sear","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":37955,"text":"University of Southampton","active":true,"usgs":false}],"preferred":false,"id":757047,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hassall, Jonathan D.","contributorId":213181,"corporation":false,"usgs":false,"family":"Hassall","given":"Jonathan","email":"","middleInitial":"D.","affiliations":[{"id":37955,"text":"University of Southampton","active":true,"usgs":false}],"preferred":false,"id":757048,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Langdon, Peter G.","contributorId":213182,"corporation":false,"usgs":false,"family":"Langdon","given":"Peter","email":"","middleInitial":"G.","affiliations":[{"id":37955,"text":"University of Southampton","active":true,"usgs":false}],"preferred":false,"id":757049,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Croudace, Ian W.","contributorId":213183,"corporation":false,"usgs":false,"family":"Croudace","given":"Ian","email":"","middleInitial":"W.","affiliations":[{"id":37955,"text":"University of Southampton","active":true,"usgs":false}],"preferred":false,"id":757050,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Zawadzki, Atun","contributorId":213184,"corporation":false,"usgs":false,"family":"Zawadzki","given":"Atun","email":"","affiliations":[{"id":38711,"text":"Australian Nuclear Science an Technology Organization","active":true,"usgs":false}],"preferred":false,"id":757051,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sachs, Julian P.","contributorId":174047,"corporation":false,"usgs":false,"family":"Sachs","given":"Julian","email":"","middleInitial":"P.","affiliations":[{"id":27348,"text":"School of Oceanography, University of Washington, Seattle, WA 98195, USA","active":true,"usgs":false}],"preferred":false,"id":757052,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70202670,"text":"70202670 - 2019 - Vertical habitat use of adult Walleye conflicts with expectations from fishery-independent surveys","interactions":[],"lastModifiedDate":"2019-06-18T10:58:04","indexId":"70202670","displayToPublicDate":"2019-01-14T14:16:10","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":"Vertical habitat use of adult Walleye conflicts with expectations from fishery-independent surveys","docAbstract":"Stock assessments of Walleyes Sander vitreus in Lake Erie rely on a combination of suspended and bottom overnight gill‐net surveys to provide population and demographic information. However, the assumption that Walleyes undertake diel vertical migrations and become available to the suspended gill nets at night has never been validated. To understand how vertical habitat use affects the availability of Walleyes to fishery‐independent surveys, we compared individual behaviors observed by means of acoustic depth telemetry tags (20 individuals; 2013–2016) with catches in paired suspended and bottom gill‐net sets (273 paired sets; 2013–2016) used by management agencies. In contrast to our expectations and observations in other lakes, the mean depths for Walleyes most often occurred in the lower one‐half to one‐third of the water column, and at lake depths <25 m the fish tended to be close to the bottom. The relationship between fish and lake depth was dependent on year and season. At lake depths >15 m, Walleyes were found at shallower depths during stratified periods (i.e., summer) than during unstratified periods (autumn). They were also found at shallower depths in 2015 and 2016 than in 2013 and 2014. In paired autumn gill‐net surveys, (1) the overall proportion of Walleye catch was nearly equal in suspended and bottom gill nets and (2) the proportion of the catch in suspended gill nets declined with fish length. The pattern of decline was dependent on how deep the suspended net was fished and the year in which the data were collected. These results provide evidence that the suspended gill‐net surveys currently being used to assess Walleye demographics in Lake Erie are biased toward the capture small Walleyes, while bottom nets are biased toward the capture of large ones. Future telemetry investigations will be needed to understand whether these biases reflect differences in the depth of habitat between small and large Walleyes.","language":"English","doi":"10.1002/tafs.10150","usgsCitation":"Ann Marie Gorman, Kraus, R.T., Gutowsky, L., Vandergoot, C., Yingming Zhao, Knight, C., Faust, M., Hayden, T., and Charles Krueger, 2019, Vertical habitat use of adult Walleye conflicts with expectations from fishery-independent surveys: Transactions of the American Fisheries Society, v. 148, no. 3, p. 592-604, https://doi.org/10.1002/tafs.10150.","productDescription":"13 p.","startPage":"592","endPage":"604","ipdsId":"IP-097146","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467995,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10150","text":"Publisher Index Page"},{"id":362144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.19921875,\n 41.27780646738183\n ],\n [\n -78.75,\n 41.27780646738183\n ],\n [\n -78.75,\n 43.068887774169625\n ],\n [\n -84.19921875,\n 43.068887774169625\n ],\n [\n -84.19921875,\n 41.27780646738183\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"148","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Ann Marie Gorman","contributorId":214228,"corporation":false,"usgs":false,"family":"Ann Marie Gorman","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":759401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Richard T. 0000-0003-4494-1841 rkraus@usgs.gov","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":2609,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","email":"rkraus@usgs.gov","middleInitial":"T.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":759400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gutowsky, Lee","contributorId":214229,"corporation":false,"usgs":false,"family":"Gutowsky","given":"Lee","email":"","affiliations":[{"id":16762,"text":"Ontario Ministry of Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":759402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vandergoot, Christopher 0000-0003-4128-3329 cvandergoot@usgs.gov","orcid":"https://orcid.org/0000-0003-4128-3329","contributorId":178356,"corporation":false,"usgs":true,"family":"Vandergoot","given":"Christopher","email":"cvandergoot@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":759403,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yingming Zhao","contributorId":140825,"corporation":false,"usgs":false,"family":"Yingming Zhao","affiliations":[{"id":6780,"text":"Ontario Ministry of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":759404,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knight, Carey","contributorId":214230,"corporation":false,"usgs":false,"family":"Knight","given":"Carey","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":759405,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Faust, Matt","contributorId":214231,"corporation":false,"usgs":false,"family":"Faust","given":"Matt","email":"","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":759406,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hayden, Todd","contributorId":214232,"corporation":false,"usgs":false,"family":"Hayden","given":"Todd","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":759407,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Charles Krueger","contributorId":203268,"corporation":false,"usgs":false,"family":"Charles Krueger","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":759408,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70203580,"text":"70203580 - 2019 - Simulating runoff quality with the highway-runoff database and the Stochastic Empirical Loading and Dilution Model","interactions":[],"lastModifiedDate":"2019-05-24T08:14:50","indexId":"70203580","displayToPublicDate":"2019-01-13T07:45:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3647,"text":"Transportation Research Record","active":true,"publicationSubtype":{"id":10}},"title":"Simulating runoff quality with the highway-runoff database and the Stochastic Empirical Loading and Dilution Model","docAbstract":"Stormwater practitioners need quantitative information about the quality and volume of highway runoff to assess and mitigate potential adverse effects of runoff on the Nation’s receiving waters. The U.S. Geological Survey developed the Highway Runoff Database (HRDB) in cooperation with the FHWA to provide practice-ready information to meet these information needs on the local or national scale. This paper describes the datasets that are available in version 1.1 of the HRDB and demonstrates how data and statistics from the HRDB can be used with the Stochastic Empirical Loading and Dilution Model (SELDM) to simulate highway runoff. The HRDB includes 249 sites, 6,849 runoff events, and 106,869 event mean concentrations (EMCs) collected during the 1975–2017 period. It includes data from 16 States in the conterminous United States and from Hawaii. The EMCs in the HRDB include measurements for 415 different water-quality constituents. These water-quality measurements include 32,944 trace-metal; 27,496 organic; 15,684 nutrient; 13,016 physical property; 10,307 major inorganic; 6,773 sediment; and 649 other constituent values. There are large variations in the data. For example, EMCs for total suspended solids and total phosphorus range from 0.4 to 5,440 mg/L and 0.004 to 22 mg/L, respectively; geometric means range from 1.58 to 1,379 mg/L and 0.017 to 2.82 mg/L for these constituents, respectively. The example simulations indicate that risks for adverse effects of runoff can vary by orders of magnitude; the HRDB and SELDM facilitate selection of representative statistics from available datasets.
The outbreak and transmission of disease-causing pathogens are contributing to the unprecedented rate of biodiversity decline. Recent advances in genomics have coalesced into powerful tools to monitor, detect, and reconstruct the role of pathogens impacting wildlife populations. Wildlife researchers are thus uniquely positioned to merge ecological and evolutionary studies with genomic technologies to exploit unprecedented ‘Big Data’ tools in disease research; however, many researchers lack the training and expertise required to use these computationally intensive methodologies. To address this disparity, the inaugural ‘Genomics of Disease in Wildlife’ workshop assembled early to mid-career professionals with expertise across scientific disciplines (e.g., genomics, wildlife biology, veterinary sciences, and conservation management) for training in the application of genomic tools to wildlife disease research. A horizon scanning-like exercise, an activity to identify forthcoming trends and challenges, performed by the workshop participants identified and discussed five themes considered to be the most pressing to the application of genomics in wildlife disease research: i) “Improving Communication”, ii) “Methodological and Analytical Advancements”, iii) “Translation into Practice”, iv) “Integrating Landscape Ecology and Genomics”, and v) “Emerging New Questions”. Wide-ranging solutions from the horizon scan were international in scope, itemized both deficiencies and strengths in wildlife genomic initiatives, promoted the use of genomic technologies to unite wildlife and human disease research, and advocated best practices for optimal use of genomic tools in wildlife disease projects. The results offer a glimpse of the potential revolution in human and wildlife disease research possible through multi-disciplinary collaborations at local, regional, and global scales.
","language":"English","publisher":"American Genetic Association","doi":"10.1093/jhered/esz001","usgsCitation":"Fitak, R.R., Antonides, J.D., Baitchman, E.J., Bonaccorso, E., Braun, J., Kubiski, S., Chiu, E., Fagre, A.C., Gagne, R.B., Lee, J.S., Malmberg, J.L., Stenglein, M.D., Dusek, R.J., Forgacs, D., Fountain-Jones, N.M., Gilbertson, M.L., Worsley-Tonks, K.E., Funk, W.C., Trumbo, D.R., Ghersi, B.M., Grimaldi, W., Heisel, S.E., Jardine, C.M., Kamath, P.L., Karmacharya, D., Kozakiewicz, C.P., Kraberger, S., Loisel, D.A., McDonald, C., Miller, S., O’Rourke, D., Ott-Conn, C.N., Páez-Vacas, M., Peel, A.J., Turner, W.C., VanAcker, M.C., VandeWoude, S., and Pecon-Slattery, J., 2019, The expectations and challenges of wildlife disease research in the era of genomics: Forecasting with a horizon scan-like exercise: Journal of Heredity, v. 110, no. 3, p. 261-274, https://doi.org/10.1093/jhered/esz001.","productDescription":"14 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Jennifer D.","contributorId":212103,"corporation":false,"usgs":false,"family":"Antonides","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[{"id":38409,"text":"Department of Forestry & Natural Resources, Purdue University, West Lafayette, IN, USA","active":true,"usgs":false}],"preferred":false,"id":755575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baitchman, Eric J.","contributorId":212104,"corporation":false,"usgs":false,"family":"Baitchman","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":38410,"text":"Zoo New England Division of Animal Health and Conservation, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":755576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonaccorso, Elisa","contributorId":212105,"corporation":false,"usgs":false,"family":"Bonaccorso","given":"Elisa","email":"","affiliations":[{"id":38411,"text":"Instituto BIOSFERA and Colegio de Ciencias Biológicas y Ambientales, 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A total of 71,833 specimens representing 38 species were identified from a gradient of different elevation zones across the study area. We identified five live assemblages and nine biofacies for the dead assemblages from estuarine, low marsh, middle marsh, high marsh, and upland transition environments. Although dissolution of calcareous tests was observed in the dead assemblages, characteristic species and abundance patterns dependent on elevation in the intertidal zone were similar between living assemblages and dead biofacies. The assemblages from the eastern Mississippi Sound estuaries were dominated by Ammonia tepida, Cribroelphidium poeyanum, C. excavatum, and Paratrochammina simplissima. The low marshes were dominated by Ammotium salsum, Ammobaculites exiguus, and Miliammina fusca. The dominant species in the middle marshes was Arenoparrella mexicana. The most abundant species in the high marshes was Entzia macrescens. The upland–marsh transition zones were dominated by Trochamminita irregularis and Pseudothurammina limnetis. Canonical correspondence analysis was applied to assess the relationship between a priori defined biofacies and measured environmental data (elevation, grain size, organic matter, and salinity) to test the hypothesis that distribution of foraminiferal assemblages is driven by elevation and hence flooding frequency. Salinity was the second most important explanatory variable of dead assemblages. Riverine freshwater from the Pascagoula River markedly influenced the live and dead assemblages in the Pascagoula River marsh, which was represented by low diversity and densities and dominance by Ammoastuta inepta. The relationship between the measured environmental variables and assemblage distributions can be used in future Mississippi Sound paleo-environmental studies.","language":"English","publisher":"GeoScienceWorld","doi":"10.2113/gsjfr.49.1.29","usgsCitation":"Haller, C., Smith, C., Hallock, P., Hine, A.C., Osterman, L., and McCloskey, T., 2019, Distribution of modern salt-marsh Foraminifera from the eastern Mississippi Sound, U.S.A.: Journal of Foraminiferal Research, v. 49, no. 1, p. 29-47, https://doi.org/10.2113/gsjfr.49.1.29.","productDescription":"19 p.; Data Release","startPage":"29","endPage":"47","ipdsId":"IP-091945","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437608,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P901T47X","text":"USGS data release","linkHelpText":"Sedimentary data from the lower Pascagoula River, Mississippi, USA"},{"id":366948,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.4124755859375,\n 30.637912028341123\n ],\n [\n -89.7308349609375,\n 30.477082932837682\n ],\n [\n -89.4891357421875,\n 29.954934549656144\n ],\n [\n -88.0224609375,\n 30.012030680358613\n ],\n [\n -88.4124755859375,\n 30.637912028341123\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haller, Christian","contributorId":200685,"corporation":false,"usgs":false,"family":"Haller","given":"Christian","affiliations":[],"preferred":false,"id":769279,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hallock, Pamela 0000-0002-1813-0482","orcid":"https://orcid.org/0000-0002-1813-0482","contributorId":215416,"corporation":false,"usgs":false,"family":"Hallock","given":"Pamela","email":"","affiliations":[{"id":39241,"text":"College of Marine Science, University of South Florida","active":true,"usgs":false}],"preferred":false,"id":769280,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hine, Albert C.","contributorId":218440,"corporation":false,"usgs":false,"family":"Hine","given":"Albert","email":"","middleInitial":"C.","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":769281,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Osterman, Lisa 0000-0002-8603-5217 osterman@usgs.gov","orcid":"https://orcid.org/0000-0002-8603-5217","contributorId":218441,"corporation":false,"usgs":true,"family":"Osterman","given":"Lisa","email":"osterman@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769282,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCloskey, Terrence 0000-0003-3979-3821","orcid":"https://orcid.org/0000-0003-3979-3821","contributorId":218442,"corporation":false,"usgs":true,"family":"McCloskey","given":"Terrence","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":769283,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}