The effect of nutrients on phytoplankton biomass in lakes continues to be a subject of debate by aquatic scientists. However, determining whether or not chlorophyll a (CHL) is limited by phosphorus (P) and/or nitrogen (N) is rarely considered using a probabilistic method in studies of hundreds of lakes across broad spatial extents. Several studies have applied a unified CHL-nutrient relationship to determine nutrient limitation, but pose a risk of ecological fallacy because they neglect spatial heterogeneity in ecological contexts. To examine whether or not CHL is limited by P, N, or both nutrients in hundreds of lakes and across diverse ecological settings, a probabilistic machine learning method, Bayesian Network, was applied. Spatial heterogeneity in ecological context was accommodated by the probabilistic nature of the results. We analyzed data from 1382 lakes in 17 US states to evaluate the cause-effect relationships between CHL and nutrients. Observations of CHL, total phosphorus (TP), and total nitrogen (TN) were discretized into three trophic states (oligo-mesotrophic, eutrophic, and hypereutrophic) to train the model. We found that although both nutrients were related to CHL trophic state, TP was more related to CHL than TN, especially under oligo-mesotrophic and eutrophic CHL conditions. However, when the CHL trophic state was hypereutrophic, both TP and TN were important. These results provide additional evidence that P-limitation is more likely under oligo-mesotrophic or eutrophic CHL conditions and that co-limitation of P and N occurs under hypereutrophic CHL conditions. We also found a decreasing pattern of the TN/TP ratio with increasing CHL concentrations, which might be a key driver for the role change of nutrients. Previous work performed at smaller scales support our findings, indicating potential for extension of our findings to other regions. Our findings enhance the understanding of nutrient limitation at macroscales and revealed that the current debate on the limiting nutrient might be caused by failure to consider CHL trophic state. Our findings also provide prior information for the site-specific eutrophication management of unsampled or data-limited lakes.
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Forestry","active":true,"usgs":false}],"preferred":false,"id":793898,"contributorType":{"id":1,"text":"Authors"},"rank":32}]}} ,{"id":70215808,"text":"70215808 - 2020 - Thiamine concentrations in lake trout and Atlantic salmon eggs during 14 years following the invasion of alewife in Lake Champlain","interactions":[],"lastModifiedDate":"2020-10-30T13:53:42.308177","indexId":"70215808","displayToPublicDate":"2020-07-27T08:48:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Thiamine concentrations in lake trout and Atlantic salmon eggs during 14 years following the invasion of alewife in Lake Champlain","docAbstract":"Thiamine (vitamin B1) deficiency in Great Lakes salmonines has been linked to consumption of alewife Alosa pseudoharengus. Thiamine deficiency has been recognized as a possible impediment to lake trout Salvelinus namaycush recruitment in the Great Lakes and Atlantic salmon Salmo salar recruitment in the Finger Lakes and Baltic Sea. Alewife invaded Lake Champlain in 2003 which provided an opportunity to investigate changes in thiamine concentrations in salmonine predators during an alewife invasion. We monitored egg unphosphorylated and total thiamine concentrations in lake trout and Atlantic salmon in 2004 and 2007–2019, assessed whether concentrations were associated with mortality, and examined thiaminase activity in alewife. Total thiamine concentrations in lake trout and Atlantic salmon were significantly lower than in 2004 for seven of the ten collection years for lake trout and for nine of the 12 collection years for Atlantic salmon. Mortality and signs of thiamine deficiency were observed in laboratory-reared Atlantic salmon free embryos but not in lake trout. Average thiaminase activity in adult alewife declined from 5200 pmol/g/min in 2006 to 1500 pmol/g/min in 2012. Our results provide further evidence that a diet that includes alewife reduces egg thiamine concentrations in salmonines. This effect was observed within four years of the invasion of alewife.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.06.018","usgsCitation":"Ladago, B.J., Futia, M.H., Ardren, W.R., Honeyfield, D.C., Kelsey, K.P., Kozel, C.L., Riley, S., Rinchard, J., Tillitt, D.E., Zajicek, J., and Marsden, J., 2020, Thiamine concentrations in lake trout and Atlantic salmon eggs during 14 years following the invasion of alewife in Lake Champlain: Journal of Great Lakes Research, v. 45, no. 5, p. 1340-1348, https://doi.org/10.1016/j.jglr.2020.06.018.","productDescription":"9 p.","startPage":"1340","endPage":"1348","ipdsId":"IP-111546","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":379963,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","otherGeospatial":"Lake Champlain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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H.","contributorId":208498,"corporation":false,"usgs":false,"family":"Futia","given":"Matthew","email":"","middleInitial":"H.","affiliations":[{"id":37810,"text":"Department of Environmental Science and Ecology, The College at Brockport – State University of New York, 350 New Campus Drive, Brockport, New York","active":true,"usgs":false}],"preferred":false,"id":803510,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ardren, William R.","contributorId":184180,"corporation":false,"usgs":false,"family":"Ardren","given":"William","email":"","middleInitial":"R.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":803511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Honeyfield, Dale C. 0000-0003-3034-2047 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Ellen","contributorId":238544,"corporation":false,"usgs":false,"family":"Marsden","given":"J. Ellen","affiliations":[{"id":47733,"text":"Wildlife and Fisheries Biology Program, University of Vermont, Burlington, VT","active":true,"usgs":false}],"preferred":false,"id":803519,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70211893,"text":"70211893 - 2020 - Sediment delivery to marsh platforms minimized by source decoupling and flux convergence","interactions":[],"lastModifiedDate":"2020-08-11T13:50:54.908128","indexId":"70211893","displayToPublicDate":"2020-07-27T08:44:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Sediment delivery to marsh platforms minimized by source decoupling and flux convergence","docAbstract":"Sediment supply is a primary factor in determining marsh response to sea level rise and is typically approximated through high‐resolution measurements of suspended sediment concentrations (SSCs) from adjacent tidal channels. However, understanding sediment transport across the marsh itself remains limited by discontinuous measurements of SSC over individual tidal cycles. Here, we use an array of optical turbidity sensors to build a long‐term, continuous record of SSC across a marsh platform and adjacent tidal channel. We find that channel and marsh concentrations are correlated (i.e., coupled) within tidal cycles but are largely decoupled over longer time scales. We also find that net sediment fluxes decline to near zero within 10 m of the marsh edge. Together, these results suggest that large sections of the marsh platform receive minimal sediment independent of flooding frequency or channel sediment supply. Marsh‐centric, as opposed to channel‐centric, measures of sediment supply may better characterize marsh platform vulnerability.
Ice cover plays a critical role in physical, biogeochemical, and ecological processes in lakes. Despite its importance, winter limnology remains relatively understudied. Here, we provide a primer on the predominant drivers of freshwater lake ice cover and the current methodologies used to study lake ice, including in situ and remote sensing observations, physical based models, and experiments. We highlight opportunities for future research by integrating these four disciplines to address key knowledge gaps in our understanding of lake ice dynamics in changing winters. Advances in technology, data integration, and interdisciplinary collaboration will allow the field to move toward developing global forecasts of lake ice cover for small to large lakes across broad spatial and temporal scales, quantifying ice quality and ice thickness, moving from binary to continuous ice records, and determining how winter ice conditions and quality impact ecosystem processes in lakes over winter. Ultimately, integrating disciplines will improve our ability to understand the impacts of changing winters on lake ice.
The Winnebago Pool is a chain of 4 shallow lakes in Wisconsin. Because of high external phosphorus (P) inputs to the lakes, the lakes became highly eutrophic, with much P contained in their sediments. In developing a total maximum daily load (TMDL) for these lakes, it is important to determine how their phosphorus concentrations should respond to changes in external P loading. In many TMDLs, internal P loading is assumed to be negligible or it is estimated based on sediment release rates and dissolved oxygen conditions in the lake, and each lake is considered independently. To evaluate these assumptions, internal P loading and external P loading were quantified by developing detailed P budgets for the Winnebago Pool chain of lakes. This information was then inputted into 2 eutrophication models (BATHTUB and Jensen models), which were used to simulate the steady-state and transient effects of various P reduction strategies. The importance of internal P loading varied among lakes, from being a minor source to representing almost 60% of the summer P input. Model results indicate that each lake responds to external P reductions, but internal loading can delay the lake responses, especially in the most downstream lake, Lake Winnebago, where internal P loading was most important to its summer P budget. Accurately quantifying net internal P loading and using this information in lake models are important in evaluating how large shallow lakes should respond to P reduction strategies, setting realistic expectations from watershed P reductions, and guiding TMDL efforts.
In order to comply with a current U.S. Environmental Protection Agency watershed-based National Pollutant Discharge Elimination System permit, the City of Albuquerque required a better understanding of the rainfall-runoff processes in its small urban watersheds. That requirement prompted the initiation of the assessment of three existing watershed models that were developed to simulate those processes. Three existing rainfall-runoff modeling software packages—Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) (using two sets of methods), Program for Predicting Polluting Particle Passage Through Pits, Puddles, and Ponds (P8), and Arid-Lands Hydrologic Model (AHYMO)—were compared to determine which provided the best balance of accuracy and usability for simulating storm runoff in small watersheds in the urbanized area of Albuquerque, New Mexico. Additionally, results of this study could help inform model users who have interest in simulating storm runoff in similar urban areas throughout the United States. Each model was used to simulate storm runoff in the Hahn Arroyo watershed, an urbanized watershed with concrete-lined arroyo channels in the northeastern quadrant of Albuquerque that exhibits flashy, monsoonal-driven storm runoff. Model results were compared to observed discharge data, according to literature-recommended performance measures and performance evaluation criteria. The HEC-HMS model using the Soil Conservation Service (SCS) curve number (CN) and SCS unit hydrograph methods ranked the highest when averaging the individual performance measures (Nash-Sutcliffe Efficiency, percent bias, and coefficient of determination) rankings together across the hourly calibration and validation periods, followed by P8, which was tied with the HEC-HMS initial and constant approach. For daily rankings using the same rank-averaging approach, the HEC-HMS CN-based model and P8 were tied for the highest ranking, followed by the HEC-HMS initial and constant approach. Alternatively, rating performance using validation period results as an indication of the expected confidence in forecasted results for future conditions, the P8 model performed best for both hourly and daily time-steps, followed by the HEC-HMS CN-based model and the HEC-HMS initial and constant-based model. However, based on the literature performance evaluation criteria, the HEC-HMS and P8 models overall had marginally satisfactory performance only for operation at the daily time-step. Direct comparison of the HEC-HMS and P8 models to the AHYMO is difficult, given the different performance assessment criteria used to assess these models separately in this study, as recommended by the literature. The AHYMO results generally lacked precision, given the wide range in the performance assessment values across events in percent error in peak discharge, difference in timing of peak discharge, percent error in total runoff volume, and difference in duration of event relative to observed data. For some events, however, the AHYMO results were fairly accurate, and AHYMO was likely a good predictor of the timing of storm runoff and the shape of the hydrograph. This study did not assess the results for all potential applications of the models in the Albuquerque urbanized area. Further study may be required to assess the model performance capabilities in other modeling applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205058","collaboration":"Prepared in cooperation with the City of Albuquerque","usgsCitation":"Shephard, Z.M., and Douglas-Mankin, K.R., 2020, Comparison of storm runoff models for a small watershed in an urban metropolitan area, Albuquerque, New Mexico: U.S. Geological Survey Scientific Investigations Report 2020–5058, 30 p., https://doi.org/10.3133/sir20205058.","productDescription":"Report: viii, 30 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-113457","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":376561,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5058/coverthb.jpg"},{"id":376562,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5058/sir20205058.pdf","text":"Report","size":"1.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5058"},{"id":376563,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P930WKCH","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Input and output data used to compare storm runoff models for a small watershed in an urban metropolitan area, Albuquerque, New Mexico"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.71844482421874,\n 35.08395557927643\n ],\n [\n -106.44996643066406,\n 35.08395557927643\n ],\n [\n -106.45545959472653,\n 35.19345038573419\n ],\n [\n -106.66694641113278,\n 35.19232810975203\n ],\n [\n -106.71844482421874,\n 35.08395557927643\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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6700 Edith Blvd NE
Albuquerque, New Mexico 87113
Seagrasses influence local hydrodynamics by inducing drag on the flow and dampening near-bed velocities and wave energy. When seagrasses are lost, near-bed currents and wave energy can increase, which enhances bottom shear stresses, destabilizes sediment, and promotes suspension and erosion. Though seagrasses are being lost rapidly globally, the magnitude of change in sediment stabilization following ecosystem-wide eelgrass loss has rarely been measured. In this study, we explored the geomorphological changes associated with an unprecedented estuary-wide collapse of a seagrass (eelgrass, Zostera marina) in Morro Bay, CA, USA. Morro Bay has historically suffered from accelerated sedimentation and accretion. However, following massive eelgrass loss since 2010, over 90% of locations that previously had eelgrass experienced erosion. Elevation losses (erosion) reached 0.50 m in some places (mean loss of 0.10 m) with as much as a 50% decrease (median decrease of 13.6%) in elevation (i.e., increase in depth) compared to pre-decline levels. In comparison, the mouth of the estuary, where eelgrass was largely retained, had only 27.7% of the locations with prior eelgrass experiencing erosion and underwent a mean elevation increase (accretion) of 0.32 m. Thus, the loss of eelgrass appears to have altered dynamics at the seabed and transitioned large regions of the estuary from an environment that promotes deposition and accretion to one that promotes suspension and erosion. Large-scale erosion following seagrass loss may be predictive of future shoreline and coastal habitat changes and is likely to be exacerbated by increased storm surge and sea level rise expected with climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2020.106910","usgsCitation":"Walter, R.K., O’Leary, J.K., Vitousek, S., Taherkhani, M., Geraghty, C., and Kitajima, A., 2020, Large-scale erosion driven by intertidal eelgrass loss in an estuarine environment: Estuarine, Coastal and Shelf Science, v. 243, 106910, 7 p., https://doi.org/10.1016/j.ecss.2020.106910.","productDescription":"106910, 7 p.","ipdsId":"IP-120131","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455874,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2020.106910","text":"Publisher Index Page"},{"id":378671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Morro Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.87329864501952,\n 35.306160014550784\n ],\n [\n -120.81287384033205,\n 35.306160014550784\n ],\n [\n -120.81287384033205,\n 35.38121266833199\n ],\n [\n -120.87329864501952,\n 35.38121266833199\n ],\n [\n -120.87329864501952,\n 35.306160014550784\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"243","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Walter, Ryan K.","contributorId":241045,"corporation":false,"usgs":false,"family":"Walter","given":"Ryan","email":"","middleInitial":"K.","affiliations":[{"id":16725,"text":"California Polytechnic State University, San Luis Obispo","active":true,"usgs":false}],"preferred":false,"id":799408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Leary, Jenifer K.","contributorId":241046,"corporation":false,"usgs":false,"family":"O’Leary","given":"Jenifer","email":"","middleInitial":"K.","affiliations":[{"id":48194,"text":"California Sea Grant, San Luis Obispo; Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":799409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taherkhani, Mohsen","contributorId":223951,"corporation":false,"usgs":false,"family":"Taherkhani","given":"Mohsen","affiliations":[{"id":18137,"text":"University of Illinois at Chicago","active":true,"usgs":false}],"preferred":false,"id":799411,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geraghty, Carolyn","contributorId":241048,"corporation":false,"usgs":false,"family":"Geraghty","given":"Carolyn","email":"","affiliations":[{"id":48196,"text":"Morro Bay National Estuary Program","active":true,"usgs":false}],"preferred":false,"id":799412,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kitajima, Ann","contributorId":241049,"corporation":false,"usgs":false,"family":"Kitajima","given":"Ann","email":"","affiliations":[{"id":48196,"text":"Morro Bay National Estuary Program","active":true,"usgs":false}],"preferred":false,"id":799413,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70214668,"text":"70214668 - 2020 - DNA metabarcoding of feces to infer summer diet of Pacific walruses","interactions":[],"lastModifiedDate":"2020-10-12T17:36:30.613311","indexId":"70214668","displayToPublicDate":"2020-07-24T11:56:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2671,"text":"Marine Mammal Science","active":true,"publicationSubtype":{"id":10}},"title":"DNA metabarcoding of feces to infer summer diet of Pacific walruses","docAbstract":"Environmental conditions in the Chukchi Sea are changing rapidly and may alter the abundance and distribution of marine species and their benthic prey. We used a metabarcoding approach to identify potentially important prey taxa from Pacific walrus (Odobenus rosmarus divergens) fecal samples (n = 87). Bivalvia was the most dominant class of prey (66% of all normalized counts) and occurred in 98% of the samples. Polychaeta and Gastropoda occurred in 70% and 62% of the samples, respectively. The remaining nine invertebrate classes comprised <21% of all normalized counts. The common occurrence of these three prey classes is consistent with examinations of walrus stomach contents. Despite these consistencies, biases in the metabarcoding approach to determine diet from feces have been highlighted in other studies and require further study, in addition to biases that may have arisen from our opportunistic sampling. However, this noninvasive approach provides accurate identification of prey taxa from degraded samples and could yield much‐needed information on shifts in walrus diet in a rapidly changing Arctic.
","language":"English","publisher":"Wiley","doi":"10.1111/mms.12717","usgsCitation":"Sonsthagen, S.A., Jay, C.V., Cornman, R.S., Fischbach, A.S., Grebmeier, J.M., and Talbot, S.L., 2020, DNA metabarcoding of feces to infer summer diet of Pacific walruses: Marine Mammal Science, v. 36, no. 4, p. 1196-1211, https://doi.org/10.1111/mms.12717.","productDescription":"6 p.","startPage":"1196","endPage":"1211","ipdsId":"IP-107736","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":436860,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WWMPLT","text":"USGS data release","linkHelpText":"Metabarcoding of Feces of Pacific Walruses and Autosomal DNA Sequence Data of Marine Invertebrates, 2012-2015, Alaska"},{"id":378959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bering Sea, Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -170.33203125,\n 65.29346780107583\n ],\n [\n -156.9287109375,\n 65.29346780107583\n ],\n [\n -156.9287109375,\n 72.64648585149378\n ],\n [\n -170.33203125,\n 72.64648585149378\n ],\n [\n -170.33203125,\n 65.29346780107583\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":800365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jay, Chadwick V. 0000-0002-9559-2189 cjay@usgs.gov","orcid":"https://orcid.org/0000-0002-9559-2189","contributorId":192736,"corporation":false,"usgs":true,"family":"Jay","given":"Chadwick","email":"cjay@usgs.gov","middleInitial":"V.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":800366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":800367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fischbach, Anthony S. 0000-0002-6555-865X afischbach@usgs.gov","orcid":"https://orcid.org/0000-0002-6555-865X","contributorId":2865,"corporation":false,"usgs":true,"family":"Fischbach","given":"Anthony","email":"afischbach@usgs.gov","middleInitial":"S.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":800368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grebmeier, Jacqueline M.","contributorId":48815,"corporation":false,"usgs":false,"family":"Grebmeier","given":"Jacqueline","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":800369,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":800370,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215152,"text":"70215152 - 2020 - Key components and contrasts in the nitrogen budget across a US-Canadian transboundary watershed","interactions":[],"lastModifiedDate":"2020-10-08T12:16:54.101245","indexId":"70215152","displayToPublicDate":"2020-07-24T07:12:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Key components and contrasts in the nitrogen budget across a US-Canadian transboundary watershed","docAbstract":"Watershed nitrogen (N) budgets provide insights into drivers and solutions for groundwater and surface water N contamination. We constructed a comprehensive N budget for the transboundary Nooksack River Watershed (British Columbia, Canada, and Washington, USA) using locally derived data, national statistics, and standard parameters. Feed imports for dairy (mainly in the United States) and poultry (mainly in Canada) accounted for 30% and 29% of the total N input to the watershed, respectively. Synthetic fertilizer was the next largest source contributing 21% of inputs. Food imports for humans and pets together accounted for 9% of total inputs, lower than atmospheric deposition (10%). N imported by returning salmon representing marine‐derived nutrients accounted for <0.06% of total N input. Quantified N export was 80% of total N input, driven by ammonia emission (32% of exports). Animal product export was the second largest output of N (31%) as milk and cattle in the United States and poultry products in Canada. Riverine export of N was estimated at 28% of total N export. The commonly used crop nitrogen use efficiency (NUE) metric alone did not provide sufficient information on farming activities but in combination with other criteria such as farm‐gate NUE may better represent management efficiency. Agriculture was the primary driver of N inputs to the environment as a result of its regional importance; the N budget information can inform management to minimize N losses. The N budget provides key information for stakeholders across sectors and borders to create environmentally and economically viable and effective solutions.
The Chattahoochee River National Recreation Area (CRNRA) is a National Park Service unit/park with 48 miles of urban waterway in the Atlanta metropolitan area. The Chattahoochee River within the CRNRA is a popular place for water-based recreation but is known to periodically experience elevated levels of fecal-coliform bacteria associated with warm-blooded animals that can result in a variety of pathogen-related human illnesses. In 2000, the National Park Service entered into a public-private partnership with the U.S. Geological Survey (USGS) and the Chattahoochee Riverkeeper, called the Chattahoochee River BacteriALERT program, to monitor Escherichia coli (E. coli), which is a fecal indicator bacteria and a proxy for human health risk from waterborne pathogens. The BacteriALERT network monitors E. coli densities at three stations on the Chattahoochee River within the CRNRA, at Norcross (USGS station 02335000), Powers Ferry (USGS station 02335880), and Atlanta (USGS station 02336000). E. coli densities determined from water samples were compared to the U.S. Environmental Protection Agency’s Beach Action Value (BAV) of 235 colony forming units per 100 milliliters to assess whether conditions were considered safe for freshwater, primary contact recreational use. Sample E. coli densities exceeded the BAV for 15.5 percent of the samples collected at Norcross (n = 1,969) and 30.3 percent of the samples at Atlanta (n = 1,938) for the study period October 23, 2000, to May 23, 2019, and 33.6 percent of the samples from Powers Ferry (n = 134) for the study period May 5, 2016, to May 23, 2019.
Models to predict E. coli densities in near real-time were developed for the three BacteriALERT stations. Models were developed using forward-stepwise multiple linear regression with the Bayesian Information Criteria and were calibrated with samples collected between October 4, 2007, and May 23, 2019. Explanatory variables included season, turbidity, water temperature, streamflow, upstream tributary streamflows, and temporal trend. The most statistically significant explanatory variables in the models were turbidity, upstream tributary streamflows, and season. The Norcross model had an increasing trend in E. coli densities of 2.3 percent per year. A significant trend was not detected for the Atlanta station, while trends were not assessed for Powers Ferry models due to the short (3-year) calibration period. Model adjusted R2s ranged from 0.686 (Atlanta) to 0.795 (Norcross with time trend) indicating that the models explained a substantial portion of the variations in E. coli densities. Evaluation of model predictions and residuals indicated that models were well posed and exhibited little bias. The models performed well in accurately determining compliance and exceedance of the BAV with low misidentification rates ranging from 3.5 percent (Norcross) to 11.3 percent (Powers Ferry). Misidentification was most common for densities near the BAV, and misidentification rates in the study were low despite fairly low model precisions because E. coli densities were infrequently near the BAV. The precisions of the models developed herein were comparable to the more complex models developed by Lawrence (2012) that were never implemented in the BacteriALERT program due to their computational complexity. The predictive E. coli models developed herein will improve the ability to assess the health risks of water-based recreational activities in the CRNRA in near real-time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201048","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Aulenbach, B.T., and McKee, A.M., 2020, Monitoring and real-time modeling of Escherichia coli bacteria for the Chattahoochee River, Chattahoochee River National Recreation Area, Georgia, 2000–2019: U.S. Geological Survey Open-File Report 2020–1048, 43 p., https://doi.org/10.3133/ofr20201048.","productDescription":"x, 43 p.","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-113124","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":376643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1048/coverthb.jpg"},{"id":376663,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1048/ofr20201048.pdf","text":"Report","size":"5.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1048"}],"country":"United States","state":"Georgia","city":"Atlanta","otherGeospatial":"Chattahoochee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.1168212890625,\n 34.17772537282446\n ],\n [\n -84.48211669921875,\n 34.01396527491264\n ],\n [\n -84.44915771484375,\n 33.747180448149855\n ],\n [\n -84.29809570312499,\n 33.76544869849223\n ],\n [\n -84.1387939453125,\n 33.902336404480685\n ],\n [\n -84.0509033203125,\n 34.075412438417395\n ],\n [\n -84.04541015625,\n 34.14363482031264\n ],\n [\n -84.1168212890625,\n 34.17772537282446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
Surface-water and suspended-sediment samples were collected and analyzed by the U.S. Geological Survey for multiple current-use pesticides and pesticide degradates approximately every 2 weeks at up to five sites in the Yolo Bypass and Cache Slough Complex before, during, and after augmented flow pulses in summer and fall 2016 and 2018 as well as during ambient flow conditions in summer and fall 2017 (no flow pulse). In 2016, augmented flows occurred during the summer (July) and required the pumping of Sacramento River water by local Reclamation Districts into the Colusa Basin Drain and Yolo Bypass Toe Drain. In contrast, augmented flows in 2018 occurred in the fall (August–September) and used agricultural tailwater (primarily rice field discharge water) to create the flow pulse. Water samples were analyzed by the U.S. Geological Survey for a suite of 175 current-use pesticides and pesticide degradates using gas chromatography with mass spectrometry and liquid chromatography with tandem mass spectrometry laboratory methods. Suspended sediments filtered from the water samples were analyzed for 143 pesticides and degradates by gas chromatography with mass spectrometry.
During the study, 53 pesticides were detected, and all the samples contained mixtures of multiple pesticides at concentrations ranging from below method detection limits to 8,780 nanograms per liter. Pesticides used in growing rice were the dominant pesticides present at four of the five sites sampled and urban-use pesticides dominated at the remaining site. Overall, total pesticide concentrations tended to be higher at sites in the northern part of the Yolo Bypass and lower at southern sites, except for the farthest downstream site which received additional pesticide inputs from the Sacramento River. Flow-pulse water source influenced total pesticide concentrations in the Yolo Bypass and Cache Slough Complex, and the highest total pesticide concentrations at each site were detected either immediately before or during the flow pulse generated with agricultural tailwater in 2018. Data gathered during this study will aid the California Department of Water Resources and other agencies working in the region in adaptively managing pulse flows in the Yolo Bypass and Cache Slough Complex, as one of several California Natural Resources Agency’s Delta Smelt Resiliency strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201076","collaboration":"Prepared in cooperation with the California Department of Water Resources and the State and Federal Contractors Water Agency","usgsCitation":"Orlando, J.L., De Parsia, M., Sanders, C., Hladik, M., and Frantzich, J., 2020, Pesticide concentrations associated with augmented flow pulses in the Yolo Bypass and Cache Slough Complex, California: U.S. Geological Survey Open-File Report 2020–1076, 101 p., https://doi.org/10.3133/ofr20201076.","productDescription":"Report: vi, 101 p.; Data release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-109449","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376629,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"U.S. Geological Survey, 2019, National Water Information System: U.S. Geological Survey Web interface"},{"id":376628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1076/ofr20201076.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1076/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yolo Bypass and Cache Slough Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.85760498046875,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 38.45789034424927\n ],\n [\n -121.35772705078125,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 39.06184913429154\n ],\n [\n -121.85760498046875,\n 38.45789034424927\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
A disease can be a source of disturbance, causing population declines or extirpations, altering species interactions, and affecting habitat structure. This is particularly relevant for diseases that affect keystone species or ecosystem engineers, leading to potentially cascading effects on ecosystems.
We investigated the invasion of a non-native disease, plague, to a keystone species, prairie dogs, and documented the resulting extent of fragmentation and habitat loss in western grasslands. Specifically, we assessed how the arrival of plague in the Conata Basin, South Dakota, United States, affected the size, shape, and aggregation of prairie dog colonies, an animal species known to be highly susceptible to plague.
Colonies in the prairie dog complex were mapped every 1 to 3 years from 1993 to 2015. Plague was first confirmed in 2008 and we compared prairie dog complex and colony characteristics before and after the arrival of plague.
As expected the colony complex and the patches in colonies became smaller and more fragmented after the arrival of plague; the total area of each colony and the mean area per patch within a colony decreased, the number of patches per colony increased, and mean contiguity of each patch decreased, leading to habitat fragmentation.
We demonstrate how an emerging infectious disease can act as a source of disturbance to natural systems and lead to potentially permanent alteration of habitat characteristics. While perhaps not traditionally thought of as a source of ecosystem disturbances, in recent years emerging infectious diseases have shown to be able to have large effects on ecosystems if they affect keystone species.
Information garnered from the capture and handling of free-ranging animals helps advance understanding of wildlife ecology and can aid in decisions on wildlife management. Unfortunately, animals may experience increased levels of stress, injuries, and death resulting from captures (e.g., exertional myopathy, trauma). Partial sedation is a technique proposed to alleviate stress in animals during capture, yet efficacy of partial sedation for reducing stress and promoting survival post-capture remains unclear. We evaluated the effects of partial sedation on physiological, biochemical, and behavioral indicators of acute stress and probability of survival post-capture for mule deer (Odocoileus hemionus) that were captured via helicopter net-gunning in the eastern Greater Yellowstone Ecosystem, Wyoming, USA. We administered 10–30 mg of midazolam and 15 mg of azaperone intramuscularly (IM) to 32 mule deer in 2016 and 53 mule deer in 2017, and maintained a control group (captured but not sedated) of 38 mule deer in 2016 and 54 mule deer in 2017. To evaluate indicators of acute stress, we measured heart rate, blood-oxygen saturation, body temperature, respiration rate, and levels of serum cortisol. We recorded number of kicks and vocalizations of deer during handling and evaluated behavior during release. We also measured levels of fecal glucocorticoids as an indicator of baseline stress. Midazolam and azaperone did not reduce physiological, biochemical, or behavioral indicators of acute stress or influence probability of survival post-capture. Mule deer that were administered midazolam and azaperone, however, were more likely to hesitate, stumble or fall, and walk during release compared with individuals in the control group, which were more likely to trot, stot, or run without stumbling or falling. Our findings suggest that midazolam (10–30 mg IM) and azaperone (15 mg IM) may not yield physiological or demographic benefits for captured mule deer as previously assumed and may pose adverse effects that can complicate safety for captured animals, including drug-induced lethargy. Although we failed to find efficacy of midazolam and azaperone as a method for reducing stress in captured mule deer, the efficacy of midazolam and azaperone or other combinations of partial sedatives in reducing stress may depend on the dose of tranquilizer, study animal, capture setting, and how stress is defined.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21929","usgsCitation":"Ortega, A.C., Dwinnell, S., Lasharr, T.N., Jakopak, R., Denryter, K., Huggler, K.S., Hayes, M.M., Aikens, E., Verzuh, T.L., May, A.B., Kauffman, M., and Monteith, K., 2020, Effectiveness of partial sedation to reduce stress in captured mule deer: Journal of Wildlife Management, v. 84, no. 8, p. 1445-1456, https://doi.org/10.1002/jwmg.21929.","productDescription":"12 p.","startPage":"1445","endPage":"1456","ipdsId":"IP-119840","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","city":"Cody, Dubois, Lander, Meeteetse","otherGeospatial":"eastern Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.9951171875,\n 42.68243539838623\n ],\n [\n -108.1988525390625,\n 42.68243539838623\n ],\n [\n -108.1988525390625,\n 44.68427737181225\n ],\n [\n -109.9951171875,\n 44.68427737181225\n ],\n [\n -109.9951171875,\n 42.68243539838623\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ortega, Anna C.","contributorId":280169,"corporation":false,"usgs":false,"family":"Ortega","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836167,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dwinnell, Samantha P.","contributorId":280147,"corporation":false,"usgs":false,"family":"Dwinnell","given":"Samantha P.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lasharr, Tayler N.","contributorId":280148,"corporation":false,"usgs":false,"family":"Lasharr","given":"Tayler","email":"","middleInitial":"N.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jakopak, Rhiannon P.","contributorId":280150,"corporation":false,"usgs":false,"family":"Jakopak","given":"Rhiannon P.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Denryter, Kristin","contributorId":280152,"corporation":false,"usgs":false,"family":"Denryter","given":"Kristin","email":"","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Huggler, Katey S.","contributorId":280155,"corporation":false,"usgs":false,"family":"Huggler","given":"Katey","email":"","middleInitial":"S.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836074,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hayes, Matthew M.","contributorId":280158,"corporation":false,"usgs":false,"family":"Hayes","given":"Matthew","email":"","middleInitial":"M.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836075,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Aikens, Ellen O.","contributorId":280161,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen O.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836076,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Verzuh, Tana L","contributorId":280170,"corporation":false,"usgs":false,"family":"Verzuh","given":"Tana","email":"","middleInitial":"L","affiliations":[],"preferred":false,"id":836168,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"May, Alexander B.","contributorId":280164,"corporation":false,"usgs":false,"family":"May","given":"Alexander","email":"","middleInitial":"B.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836077,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836069,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Monteith, Kevin L.","contributorId":280167,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin L.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836078,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70211324,"text":"70211324 - 2020 - Application of empirical land-cover changes to construct climate change scenarios in federally managed lands","interactions":[],"lastModifiedDate":"2020-07-24T15:29:27.866317","indexId":"70211324","displayToPublicDate":"2020-07-23T10:16:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Application of empirical land-cover changes to construct climate change scenarios in federally managed lands","docAbstract":"Sagebrush-dominant ecosystems in the western United States are highly vulnerable to climatic variability. To understand how these ecosystems will respond under potential future conditions, we correlated changes in National Land Cover Dataset “Back-in-Time” fractional cover maps from 1985-2018 with Daymet climate data in three federally managed preserves in the sagebrush steppe ecosystem: Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Wildlife Refuge. Future (2018 to 2050) abundance and distribution of vegetation cover were modeled at a 300-m resolution under a business-as-usual climate (BAU) scenario and a Representative Concentration Pathway (RCP) 8.5 climate change scenario. Spatially explicit map projections suggest that climate influences may make the landscape more homogeneous in the near future. Specifically, projections indicate that pixels with high bare ground cover become less bare ground dominant, pixels with moderate herbaceous cover contain less herbaceous cover, and pixels with low shrub cover contain more shrub cover. General vegetation patterns and composition do not differ dramatically between scenarios despite RCP 8.5 projections of + 1.2 °C mean annual minimum temperatures and +7.6 mm total annual precipitation. Hart Mountain National Antelope Refuge is forecast to undergo the most change, with both models projecting larger declines in bare ground and larger increases in average herbaceous and shrub cover compared to Beaty Butte Herd Management Area and Sheldon National Wildlife Refuge. These scenarios present plausible future outcomes intended to guide federal land managers to identify vegetation cover changes that may affect habitat condition and availability for species of interest.","language":"English","publisher":"MDPI","doi":"10.3390/rs12152360","usgsCitation":"Soulard, C.E., and Rigge, M.B., 2020, Application of empirical land-cover changes to construct climate change scenarios in federally managed lands: Remote Sensing, v. 12, no. 15, 22 p., https://doi.org/10.3390/rs12152360.","productDescription":"22 p.","ipdsId":"IP-118423","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455891,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12152360","text":"Publisher Index Page"},{"id":436862,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LJ1FI4","text":"USGS data release","linkHelpText":"Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin, 2018-2050"},{"id":376686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":793783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":793784,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211299,"text":"fs20203038 - 2020 - Flowering plants preferred by bees of the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2020-07-23T15:47:00.200578","indexId":"fs20203038","displayToPublicDate":"2020-07-23T09:26:34","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3038","displayTitle":"Flowering Plants Preferred by Bees of the Prairie Pothole Region","title":"Flowering plants preferred by bees of the Prairie Pothole Region","docAbstract":"Land managers have stressed the need for improved pollinator habitat on private and public lands of the Prairie Pothole Region. Understanding flowering plant preferences of pollinators will improve the cost-effectiveness of conservation seeding mixes. The purpose of this fact sheet is to assist conservation planners and producers with developing seed mixes by highlighting flowering plants that are preferred by honey bees and wild bees across a variety of grassland cover types in the Prairie Pothole Region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203038","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture, Farm Service Agency; U.S. Department of Agriculture, Natural Resources Conservation Service; and Honey Bee Health Coalition","usgsCitation":"Simanonok, S., and Otto, C.R.V., 2020, Flowering plants preferred by bees of the Prairie Pothole Region: U.S. Geological Survey Fact Sheet 2020–3038, 2 p., https://doi.org/10.3133/fs20203038.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-119539","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":376624,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3038/fs20203038.pdf","text":"Report","size":"1.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3038"},{"id":376625,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O61BCB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Dataset—Plant and bee transects in the Northern Great Plains, USA 2015–2019"},{"id":376623,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3038/coverthb.jpg"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
The history of Earth observation from space is well reflected through the Landsat program. With data collection beginning with Landsat-1 in 1972, the program has evolved technical capabilities while maintaining continuity of land observations. In so doing, Landsat has provided a critical reference for assessing long-term changes to Earth's land environment due to both natural and human forcing. Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. Landsat-9 will maintain the current data acquisition rate of up to 740 scenes per day, with these scenes available from the Landsat archive at no cost to users. In this communication, we provide background and rationale for the Landsat 9 mission, describe the instrument payloads and ground system, and discuss data products available from the Landsat 9 mission through USGS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111968","usgsCitation":"Masek, J.G., Wulder, M.A., Markham, B., McCorkel, J., Crawford, C., Storey, J.C., and Jenstrom, D., 2020, Landsat 9: Empowering open science and applications through continuity: Remote Sensing of Environment, v. 248, 111968, 13 p., https://doi.org/10.1016/j.rse.2020.111968.","productDescription":"111968, 13 p.","ipdsId":"IP-118603","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":378748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Masek, Jeffery G.","contributorId":87438,"corporation":false,"usgs":true,"family":"Masek","given":"Jeffery","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":799592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wulder, Michael A.","contributorId":103584,"corporation":false,"usgs":true,"family":"Wulder","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":799593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markham, Brian 0000-0002-9612-8169","orcid":"https://orcid.org/0000-0002-9612-8169","contributorId":139286,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":799594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCorkel, Joel","contributorId":192459,"corporation":false,"usgs":false,"family":"McCorkel","given":"Joel","email":"","affiliations":[],"preferred":false,"id":799595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":799596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Storey, James C. 0000-0002-6664-7232 storey@usgs.gov","orcid":"https://orcid.org/0000-0002-6664-7232","contributorId":5333,"corporation":false,"usgs":true,"family":"Storey","given":"James","email":"storey@usgs.gov","middleInitial":"C.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":799597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jenstrom, Del","contributorId":241119,"corporation":false,"usgs":false,"family":"Jenstrom","given":"Del","email":"","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":799598,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212502,"text":"70212502 - 2020 - Reconstructing the velocity and deformation of a rapid landslide using multiview video","interactions":[],"lastModifiedDate":"2020-08-18T14:18:20.660046","indexId":"70212502","displayToPublicDate":"2020-07-23T09:13:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6454,"text":"Journal of Geophysical Research - Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Reconstructing the velocity and deformation of a rapid landslide using multiview video","docAbstract":"Noncontact measurements of spatially varied ground surface deformation during landslide motion can provide important constraints on landslide mechanics. Here, we present and test a new method for extracting measurements of rapid landslide surface displacement and velocity (accelerations of approximately 1 m/s2) using sequences of stereo images obtained from a pair of inexpensive, stationary 4K video cameras with nominal frame rates of 29.97 Hz. The method combines elements of Structure from Motion with those of optical flow to extract data on 3‐D evolution of the ground surface during slope failure. We apply the method to an experiment at the U.S. Geological Survey debris‐flow flume in which a high‐speed, liquefying landslide was triggered by gradually adding water to a 6‐m3 prism of loosely packed sediment on a 31° slope. Strip‐scanning lidar measurements made during the experiment corroborate our video‐based measurements, but the latter encompassed the entire landslide surface and were much lower in cost. Our video‐based measurements enabled computation of depth‐integrated landslide dilation/contraction rates. The range of computed rates was within the ranges inferred from independent measurements of evolving pore water pressures and reasonable estimates of the hydraulic permeability of the sediment. Dilation and contraction rates play a crucial role in landslide mechanics. The dilation and contraction we observe contradict the incompressible flow assumption used in many studies that have employed noncontact methods to infer landslide properties.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JF005348","collaboration":"Colorado School of Mines; Oregon State University","usgsCitation":"Rapstine, T.D., Rengers, F.K., Allstadt, K.E., Iverson, R.M., Smith, J.B., Obryk, M., Logan, M., and Olsen, M.J., 2020, Reconstructing the velocity and deformation of a rapid landslide using multiview video: Journal of Geophysical Research - Earth Surface, v. 125, e2019JF005348, 18 p., https://doi.org/10.1029/2019JF005348.","productDescription":"e2019JF005348, 18 p.","ipdsId":"IP-116947","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Blue River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.42340087890624,\n 44.08758502824516\n ],\n [\n -122.20642089843749,\n 44.08758502824516\n ],\n [\n -122.20642089843749,\n 44.209772586984485\n ],\n [\n -122.42340087890624,\n 44.209772586984485\n ],\n [\n -122.42340087890624,\n 44.08758502824516\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationDate":"2020-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rapstine, Thomas D 0000-0001-5939-9587","orcid":"https://orcid.org/0000-0001-5939-9587","contributorId":224777,"corporation":false,"usgs":true,"family":"Rapstine","given":"Thomas","email":"","middleInitial":"D","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796608,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796609,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796610,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iverson, Richard M. 0000-0002-7369-3819 riverson@usgs.gov","orcid":"https://orcid.org/0000-0002-7369-3819","contributorId":536,"corporation":false,"usgs":true,"family":"Iverson","given":"Richard","email":"riverson@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":796611,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":796612,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Obryk, Maciej K. 0000-0002-8182-8656","orcid":"https://orcid.org/0000-0002-8182-8656","contributorId":203477,"corporation":false,"usgs":true,"family":"Obryk","given":"Maciej","middleInitial":"K.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":796613,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Logan, M. 0000-0002-3558-2405","orcid":"https://orcid.org/0000-0002-3558-2405","contributorId":238816,"corporation":false,"usgs":true,"family":"Logan","given":"M.","affiliations":[{"id":47793,"text":"USGS - Cascades Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":796614,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Olsen, M. 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Field observations on water levels, wave heights, and wave runup were used to drive and evaluate both models, which were subsequently used to determine the effect of sea-level rise and extreme wave conditions on wave runup and its components. Results show that specifically modelling the sea-swell component (using XB-NH) provides a better approximation of the observed runup than XB-SB (which only models the time-variation of the sea-swell wave height), despite good model performance of both models on reef flat water levels and wave heights. XB-SB has a bias of −0.108 – 0.057 m and scatter index of 0.083–0.639, whereas XB-NH has bias of −0.132 – 0.055 m and 0.122–0.490, respectively. However, both models under-predict runup peaks. The difference between XB-SB and XB-NH increases for more extreme wave events and higher sea levels, as XB-NH resolves individual waves and therefore captures SS-wave motions in runup. However, for even larger forcing conditions with offshore wave heights of 6 m, the island is flooded in both XB-SB and XB-NH computations, regardless the sea-swell wave energy contribution. In such cases XB-SB would be adequate to model flooding depths and extents on the island while requiring 4–5 times less computational effort.","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2020.103704","usgsCitation":"Quataert, E., Storlazzi, C., van Dongeren, A., and McCall, R.T., 2020, The importance of explicitly modelling sea-swell waves for runup on reef-lined coasts: Coastal Engineering, v. 160, 103704, 11 p., https://doi.org/10.1016/j.coastaleng.2020.103704.","productDescription":"103704, 11 p.","ipdsId":"IP-108391","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455896,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2020.103704","text":"Publisher Index Page"},{"id":436863,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XUI9Y1","text":"USGS data release","linkHelpText":"Model parameter input files to compare wave-averaged versus wave-resolving XBeach coastal flooding models for coral reef-lined coasts"},{"id":376660,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Republic of the Marshall Islands","otherGeospatial":"Roi-Namur Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 167.4605655670166,\n 9.386572562434718\n ],\n [\n 167.49506950378418,\n 9.386572562434718\n ],\n [\n 167.49506950378418,\n 9.40727655830451\n ],\n [\n 167.4605655670166,\n 9.40727655830451\n ],\n [\n 167.4605655670166,\n 9.386572562434718\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"160","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Quataert, Ellen","contributorId":193834,"corporation":false,"usgs":false,"family":"Quataert","given":"Ellen","email":"","affiliations":[],"preferred":false,"id":793666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":229614,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":793667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Dongeren, Ap","contributorId":149002,"corporation":false,"usgs":false,"family":"van Dongeren","given":"Ap","email":"","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":793668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCall, Robert T.","contributorId":148986,"corporation":false,"usgs":false,"family":"McCall","given":"Robert","email":"","middleInitial":"T.","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":793669,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211278,"text":"gip205 - 2020 - Brianna postcard","interactions":[],"lastModifiedDate":"2020-07-27T13:59:47.768622","indexId":"gip205","displayToPublicDate":"2020-07-23T07:08:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"205","displayTitle":"Brianna Postcard","title":"Brianna postcard","docAbstract":"Brianna is a hydrologist in the Hydrologic Investigations (Studies) Unit. She received a bachelor of science degree in chemical engineering and a master’s degree in civil engineering from the University of Kansas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip205","usgsCitation":"U.S. Geological Survey, 2020, Brianna postcard: U.S. Geological Survey General Information Product 205, 2 p., https://doi.org/10.3133/gip205.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117274","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376599,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0205/gip205.pdf","text":"Postcard","size":"393 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 205"},{"id":376598,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0205/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Brad is a hydrologist in the Surface Water Investigation Unit. He received his bachelor of science degree in natural sciences from Concordia University in Wisconsin and his master’s degree in freshwater sciences from the University of Wisconsin-Milwaukee.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip204","usgsCitation":"U.S. Geological Survey, 2020, Brad postcard: U.S. Geological Survey General Information Product 204, 2 p., https://doi.org/10.3133/gip204.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117269","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376607,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0204/gip204.pdf","text":"Postcard","size":"367 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 204"},{"id":376606,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0204/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Hydrologic technicians collect water data related to water quantity, quality, availability, and movement in surface-water and groundwater environments.
For more information, visit https://www.usajobs.gov.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip203","usgsCitation":"U.S. Geological Survey, 2020, Hydrologic technican postcard: U.S. Geological Survey General Information Product 203, 2 p., https://doi.org/10.3133/gip203.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-117273","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":376605,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0203/gip203.pdf","text":"Postcard","size":"326 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 203"},{"id":376604,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0203/coverthb.jpg"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049