During July 2015, water samples were collected from 18 wetlands on the Lake Traverse Indian Reservation in northeastern South Dakota and southeastern North Dakota and analyzed for physical properties and 54 pesticides. This study by the U.S. Geological Survey in cooperation with the Sisseton-Wahpeton Oyate was designed to provide an update on pesticide concentrations of the same 18 wetlands that were sampled for a reconnaissance-level assessment during July 2006. The purpose of this report is to present the results of the assessment of pesticide concentrations in selected Lake Traverse Indian Reservation wetlands during July 2015 and provide a comparison of pesticide concentrations between 2006 and 2015.
Of the 54 pesticides that were analyzed for in the samples collected during July 2015, 47 pesticides were not detected in any samples. Seven pesticides—2-chloro-4-isopropylamino-6-amino-s-triazine (CIAT); 2,4–D; acetachlor; atrazine; glyphosate; metolachlor; and prometon—were detected in the 2015 samples with estimated concentrations or concentrations greater than the laboratory reporting level, and most pesticides were detected at low concentrations in only a few samples. Samples from all wetlands contained at least one detected pesticide. The maximum number of pesticides detected in a wetland sample was six, and the median number of pesticides detected was three.
The most commonly detected pesticides in the 2015 samples were atrazine and the atrazine degradate CIAT (also known as deethylatrazine), which were detected in 14 and 13 of the wetlands sampled, respectively. Glyphosate was detected in samples from 11 wetlands, and metolachlor was detected in samples from 10 wetlands. The other detected pesticides were 2,4–D (4 wetlands), acetochlor (3 wetlands), and prometon (1 wetland).
The same pesticides that were detected in the 2006 samples were detected in the 2015 samples, with the exception of simazine, which was detected only in one sample in 2006. Atrazine and CIAT were the most commonly detected pesticides in both sampling years; however, atrazine and CIAT were detected in fewer wetlands in 2015 (14 and 13 wetlands, respectively) than in 2006 (17 wetlands for both pesticides). The pesticides 2,4–D and prometon also were detected in fewer wetlands in 2015 than 2006, and simazine was only detected in 2006. In contrast, acetochlor, glyphosate, and metolachlor were detected in samples from more wetlands in 2015 than in 2006. In samples from individual wetlands, the number of pesticides detected was similar between 2006 and 2015. At least one pesticide was detected in all wetlands in 2015, and all but one wetland had pesticide detections in 2006.
Concentrations of pesticides detected in samples from wetlands were compared to selected water-quality (human-health and aquatic-life) benchmarks. None of the concentrations in either 2006 or 2015 were greater than water-quality benchmarks, with the exception of atrazine. All detections of atrazine in the 2006 and 2015 samples were greater than the acute benchmark of 0.001 microgram per liter (μg/L) for vascular plants. In addition, some concentrations of 2,4–D and atrazine were within an order of magnitude of a water-quality benchmark. The 2,4–D concentrations in the 2015 samples from three wetlands were within an order of magnitude of the U.S. Environmental Protection Agency’s Maximum Contaminant Level of 70 μg/L (that is, sample concentrations were greater than 7.0 μg/L). The maximum dissolved atrazine concentration of 0.185 μg/L in the 2015 samples along with the concentrations in 2006 samples from two wetlands were within an order of magnitude of the acute benchmark of less than 1 μg/L for nonvascular plants (that is, concentrations were greater than 0.1 μg/L).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds984","collaboration":"Prepared in cooperation with the Sisseton-Wahpeton Oyate","usgsCitation":"Carter, J.M., and Thompson, R.F., 2016, Pesticide concentrations in wetlands on the Lake Traverse Indian Reservation, South and North Dakota, July 2015: U.S. Geological Survey Data Series Report 984, 32 p., https://dx.doi.org/10.3133/ds984.","productDescription":"vi, 32 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-072207","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":320926,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0984/coverthb.jpg"},{"id":320927,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0984/ds984.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 984"}],"country":"United States","state":"North Dakota, South Dakota","otherGeospatial":"Lake Traverse Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.56021118164062,\n 45.93778073466329\n ],\n [\n -97.5146484375,\n 46.02462129598765\n ],\n [\n -97.18505859374999,\n 44.97645666320777\n ],\n [\n -96.85272216796875,\n 45.60058738537025\n ],\n [\n -96.85684204101562,\n 45.622682153628226\n ],\n [\n -96.84173583984374,\n 45.64188792039229\n ],\n [\n -96.78543090820312,\n 45.68123916702059\n ],\n [\n -96.70989990234374,\n 45.71864517367924\n ],\n [\n -96.66320800781249,\n 45.74261022090537\n ],\n [\n -96.61102294921875,\n 45.79625461321962\n ],\n [\n -96.5753173828125,\n 45.84602106744846\n ],\n [\n -96.56021118164062,\n 45.93778073466329\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Dakota Water Science Center
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U.S. Fish and Wildlife Service (USFWS) staff in the Pacific Southwest Region and at the Hopper Mountain National Wildlife Refuge Complex requested technical assistance to improve their global positioning system (GPS) data acquisition, management, and archive in support of the California Condor Recovery Program. The USFWS deployed and maintained GPS units on individual Gymnogyps californianus (California condor) in support of long-term research and daily operational monitoring and management of California condors. The U.S. Geological Survey (USGS) obtained funding through the Science Support Program to provide coordination among project participants, provide GPS Global System for Mobile Communication (GSM) transmitters for testing, and compare GSM/GPS with existing Argos satellite GPS technology. The USFWS staff worked with private companies to design, develop, and fit condors with GSM/GPS transmitters. The Movebank organization, an online database of animal tracking data, coordinated with each of these companies to automatically stream their GPS data into Movebank servers and coordinated with USFWS to improve Movebank software for managing transmitter data, including proofing/error checking of incoming GPS data. The USGS arranged to pull raw GPS data from Movebank into the USGS California Condor Management and Analysis Portal (CCMAP) (https://my.usgs.gov/ccmap) for production and dissemination of a daily map of condor movements including various automated alerts. Further, the USGS developed an automatic archiving system for pulling raw and proofed Movebank data into USGS ScienceBase to comply with the Federal Information Security Management Act of 2002. This improved data management system requires minimal manual intervention resulting in more efficient data flow from GPS data capture to archive status. As a result of the project’s success, Pinnacles National Park and the Ventana Wildlife Society California condor programs became partners and adopted the same workflow, tracking, and data archive system. This GPS tracking data management model and workflow should be applicable and beneficial to other wildlife tracking programs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161030","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service, Region 8","usgsCitation":"Waltermire, R.G., Emmerich, C.U., Mendenhall, L.C., Bohrer, Gil, Weinzierl, R.P., McGann, A.J., Lineback, P.K., Kern, T.J., and Douglas, D.C., 2016, Improve wildlife species tracking—Implementing an enhanced global positioning system data management system for California condors: U.S. Geological Survey Open-File Report 2016–1030, 46 p., https://dx.doi.org/10.3133/ofr20161030.","productDescription":"vi, 46 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066019","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":320820,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1030/ofr20161030.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1032"},{"id":320819,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1030/coverthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.29980468749999,\n 37.07271048132946\n ],\n [\n -122.1240234375,\n 36.86204269508728\n ],\n [\n -121.79443359375,\n 36.87962060502676\n ],\n [\n -121.88232421875,\n 36.686041276581925\n ],\n [\n -122.05810546875,\n 36.63316209558658\n ],\n [\n -121.9482421875,\n 36.2265501474709\n ],\n [\n -121.53076171875,\n 35.871246850027966\n ],\n [\n -121.201171875,\n 35.60371874069731\n ],\n [\n -120.9814453125,\n 35.31736632923788\n ],\n [\n -120.95947265624999,\n 35.137879119634185\n ],\n [\n -120.76171875,\n 35.10193405724606\n ],\n [\n -120.65185546875,\n 34.66935854524543\n ],\n [\n -120.80566406250001,\n 34.52466147177172\n ],\n [\n -120.25634765624999,\n 34.34343606848294\n ],\n [\n -119.94873046875,\n 34.379712580462204\n ],\n [\n -119.59716796875,\n 34.34343606848294\n ],\n [\n -119.2236328125,\n 34.21634468843465\n ],\n [\n -118.91601562499999,\n 33.88865750124075\n ],\n [\n -118.5205078125,\n 33.88865750124075\n ],\n [\n -118.19091796875,\n 33.63291573870476\n ],\n [\n -117.83935546874999,\n 33.486435450999885\n ],\n [\n -117.6416015625,\n 33.87041555094183\n ],\n [\n -116.23535156249999,\n 34.03445260967645\n ],\n [\n -116.25732421875,\n 35.7286770448517\n ],\n [\n -116.89453125,\n 35.60371874069731\n ],\n [\n -117.22412109375,\n 36.38591277287651\n ],\n [\n -117.97119140625,\n 37.212831514455964\n ],\n [\n -118.47656249999999,\n 37.35269280367274\n ],\n [\n -118.95996093749999,\n 37.09023980307208\n ],\n [\n -119.77294921874999,\n 37.19533058280065\n ],\n [\n -120.34423828125,\n 37.28279464911045\n ],\n [\n -121.06933593749999,\n 37.23032838760387\n ],\n [\n -121.31103515625,\n 37.45741810262938\n ],\n [\n -121.55273437499999,\n 37.24782120155428\n ],\n [\n -121.9482421875,\n 37.125286284966805\n ],\n [\n -122.29980468749999,\n 37.07271048132946\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, USGS Fort Collins Science Center
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Dams impound the majority of rivers and provide important societal benefits, especially daily water releases that enable on-peak hydroelectricity generation. Such “hydropeaking” is common worldwide, but its downstream impacts remain unclear. We evaluated the response of aquatic insects, a cornerstone of river food webs, to hydropeaking using a life history–hydrodynamic model. Our model predicts that aquatic-insect abundance will depend on a basic life-history trait—adult egg-laying behavior—such that open-water layers will be unaffected by hydropeaking, whereas ecologically important and widespread river-edge layers, such as mayflies, will be extirpated. These predictions are supported by a more-than-2500-sample, citizen-science data set of aquatic insects from the Colorado River in the Grand Canyon and by a survey of insect diversity and hydropeaking intensity across dammed rivers of the Western United States. Our study reveals a hydropeaking-related life history bottleneck that precludes viable populations of many aquatic insects from inhabiting regulated rivers.
","language":"English","publisher":"American Institute of Biological Sciences","publisherLocation":"Washington, D.C.","doi":"10.1093/biosci/biw059","usgsCitation":"Kennedy, T.A., Muehlbauer, J.D., Yackulic, C.B., Lytle, D., Miller, S., Dibble, K.L., Kortenhoeven, E.W., Metcalfe, A.N., and Baxter, C., 2016, Flow management for hydropower extirpates aquatic insects, undermining river food webs: BioScience, v. 66, no. 7, p. 561-575, https://doi.org/10.1093/biosci/biw059.","productDescription":"15 p.","startPage":"561","endPage":"575","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069041","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":471029,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biw059","text":"Publisher Index Page"},{"id":438616,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7WM1BH4","text":"USGS data release","linkHelpText":"Flow management for hydropower extirpates aquatic insects, undermining river food websData"},{"id":320875,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"66","issue":"7","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-02","publicationStatus":"PW","scienceBaseUri":"5729cbb2e4b0b13d3919a342","contributors":{"authors":[{"text":"Kennedy, Theodore A. 0000-0003-3477-3629 tkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":167537,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","email":"tkennedy@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muehlbauer, Jeffrey D. 0000-0003-1808-580X jmuehlbauer@usgs.gov","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":5045,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","email":"jmuehlbauer@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lytle, D.A.","contributorId":85422,"corporation":false,"usgs":true,"family":"Lytle","given":"D.A.","email":"","affiliations":[],"preferred":false,"id":628491,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, S.A.","contributorId":66389,"corporation":false,"usgs":true,"family":"Miller","given":"S.A.","email":"","affiliations":[],"preferred":false,"id":628492,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dibble, Kimberly L. 0000-0003-0799-4477 kdibble@usgs.gov","orcid":"https://orcid.org/0000-0003-0799-4477","contributorId":5174,"corporation":false,"usgs":true,"family":"Dibble","given":"Kimberly","email":"kdibble@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628500,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kortenhoeven, Eric W. ekortenhoeven@usgs.gov","contributorId":5046,"corporation":false,"usgs":true,"family":"Kortenhoeven","given":"Eric","email":"ekortenhoeven@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628493,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Metcalfe, Anya N. 0000-0002-6286-4889 ametcalfe@usgs.gov","orcid":"https://orcid.org/0000-0002-6286-4889","contributorId":5271,"corporation":false,"usgs":true,"family":"Metcalfe","given":"Anya","email":"ametcalfe@usgs.gov","middleInitial":"N.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":628494,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Baxter, Colden V.","contributorId":47334,"corporation":false,"usgs":false,"family":"Baxter","given":"Colden V.","affiliations":[{"id":13656,"text":"Idaho State Univ.","active":true,"usgs":false}],"preferred":false,"id":628501,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70170789,"text":"70170789 - 2016 - Spectrally based mapping of riverbed composition","interactions":[],"lastModifiedDate":"2016-05-03T10:52:50","indexId":"70170789","displayToPublicDate":"2016-05-01T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Spectrally based mapping of riverbed composition","docAbstract":"Remote sensing methods provide an efficient means of characterizing fluvial systems. This study evaluated the potential to map riverbed composition based on in situ and/or remote measurements of reflectance. Field spectra and substrate photos from the Snake River, Wyoming, USA, were used to identify different sediment facies and degrees of algal development and to quantify their optical characteristics. We hypothesized that accounting for the effects of depth and water column attenuation to isolate the reflectance of the streambed would enhance distinctions among bottom types and facilitate substrate classification. A bottom reflectance retrieval algorithm adapted from coastal research yielded realistic spectra for the 450 to 700 nm range; but bottom reflectance-based substrate classifications, generated using a random forest technique, were no more accurate than classifications derived from above-water field spectra. Additional hypothesis testing indicated that a combination of reflectance magnitude (brightness) and indices of spectral shape provided the most accurate riverbed classifications. Convolving field spectra to the response functions of a multispectral satellite and a hyperspectral imaging system did not reduce classification accuracies, implying that high spectral resolution was not essential. Supervised classifications of algal density produced from hyperspectral data and an inferred bottom reflectance image were not highly accurate, but unsupervised classification of the bottom reflectance image revealed distinct spectrally based clusters, suggesting that such an image could provide additional river information. We attribute the failure of bottom reflectance retrieval to yield more reliable substrate maps to a latent correlation between depth and bottom type. Accounting for the effects of depth might have eliminated a key distinction among substrates and thus reduced discriminatory power. Although further, more systematic study across a broader range of fluvial environments is needed to substantiate our initial results, this case study suggests that bed composition in shallow, clear-flowing rivers potentially could be mapped remotely.
","language":"English","publisher":"Elsevier Science Pub. Co.","publisherLocation":"New York, NY","doi":"10.1016/j.geomorph.2016.04.006","usgsCitation":"Legleiter, C.J., Stegman, T.K., and Overstreet, B.T., 2016, Spectrally based mapping of riverbed composition: Geomorphology, v. 264, p. 61-79, https://doi.org/10.1016/j.geomorph.2016.04.006.","productDescription":"19 p.","startPage":"61","endPage":"79","numberOfPages":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073537","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":471034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2016.04.006","text":"Publisher Index Page"},{"id":320883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"264","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5729cbbae4b0b13d3919a3c6","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":628405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stegman, Tobin K.","contributorId":169087,"corporation":false,"usgs":false,"family":"Stegman","given":"Tobin","email":"","middleInitial":"K.","affiliations":[{"id":6656,"text":"University of Wyoming, Renewable Resources","active":true,"usgs":false}],"preferred":false,"id":628406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon T. 0000-0001-7845-6671","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":63257,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"","middleInitial":"T.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":628407,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70170763,"text":"70170763 - 2016 - Developing population models with data from marked individuals","interactions":[],"lastModifiedDate":"2016-05-03T10:55:39","indexId":"70170763","displayToPublicDate":"2016-05-01T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Developing population models with data from marked individuals","docAbstract":"Population viability analysis (PVA) is a powerful tool for biodiversity assessments, but its use has been limited because of the requirements for fully specified population models such as demographic structure, density-dependence, environmental stochasticity, and specification of uncertainties. Developing a fully specified population model from commonly available data sources – notably, mark–recapture studies – remains complicated due to lack of practical methods for estimating fecundity, true survival (as opposed to apparent survival), natural temporal variability in both survival and fecundity, density-dependence in the demographic parameters, and uncertainty in model parameters. We present a general method that estimates all the key parameters required to specify a stochastic, matrix-based population model, constructed using a long-term mark–recapture dataset. Unlike standard mark–recapture analyses, our approach provides estimates of true survival rates and fecundities, their respective natural temporal variabilities, and density-dependence functions, making it possible to construct a population model for long-term projection of population dynamics. Furthermore, our method includes a formal quantification of parameter uncertainty for global (multivariate) sensitivity analysis. We apply this approach to 9 bird species and demonstrate the feasibility of using data from the Monitoring Avian Productivity and Survivorship (MAPS) program. Bias-correction factors for raw estimates of survival and fecundity derived from mark–recapture data (apparent survival and juvenile:adult ratio, respectively) were non-negligible, and corrected parameters were generally more biologically reasonable than their uncorrected counterparts. Our method allows the development of fully specified stochastic population models using a single, widely available data source, substantially reducing the barriers that have until now limited the widespread application of PVA. This method is expected to greatly enhance our understanding of the processes underlying population dynamics and our ability to analyze viability and project trends for species of conservation concern.
","language":"English","publisher":"Elsevier Science Ltd.","publisherLocation":"Kidlington, Oxford","doi":"10.1016/j.biocon.2016.02.031","collaboration":"Stony Brook University; University of Wisconsin-Madison; U.S. Fish and Wildlife Service","usgsCitation":"Ryu, H.Y., Kevin T. Shoemaker, Kneip, E., Anna Pidgeon, Heglund, P., Bateman, B., Thogmartin, W.E., and Akcakaya, R., 2016, Developing population models with data from marked individuals: Biological Conservation, v. 197, p. 190-199, https://doi.org/10.1016/j.biocon.2016.02.031.","productDescription":"10 p.","startPage":"190","endPage":"199","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-066416","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":320884,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"197","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5729cbafe4b0b13d3919a2ef","contributors":{"authors":[{"text":"Ryu, Hae Yeong","contributorId":169059,"corporation":false,"usgs":false,"family":"Ryu","given":"Hae","email":"","middleInitial":"Yeong","affiliations":[{"id":25401,"text":"Stony Brook University, Stony Brook, NY","active":true,"usgs":false}],"preferred":false,"id":628323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kevin T. Shoemaker","contributorId":169060,"corporation":false,"usgs":false,"family":"Kevin T. Shoemaker","affiliations":[{"id":25401,"text":"Stony Brook University, Stony Brook, NY","active":true,"usgs":false}],"preferred":false,"id":628324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kneip, Eva","contributorId":169062,"corporation":false,"usgs":false,"family":"Kneip","given":"Eva","email":"","affiliations":[{"id":25401,"text":"Stony Brook University, Stony Brook, NY","active":true,"usgs":false}],"preferred":false,"id":628326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anna Pidgeon","contributorId":169061,"corporation":false,"usgs":false,"family":"Anna Pidgeon","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":628325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heglund, Patricia","contributorId":169063,"corporation":false,"usgs":false,"family":"Heglund","given":"Patricia","email":"","affiliations":[{"id":5128,"text":"U.S. Fish and Wildlife Service, University of Montana, Missoula, MT 59812","active":true,"usgs":false}],"preferred":false,"id":628327,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bateman, Brooke","contributorId":169064,"corporation":false,"usgs":false,"family":"Bateman","given":"Brooke","email":"","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":628328,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":628322,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Akcakaya, Resit","contributorId":169065,"corporation":false,"usgs":false,"family":"Akcakaya","given":"Resit","email":"","affiliations":[{"id":25401,"text":"Stony Brook University, Stony Brook, NY","active":true,"usgs":false}],"preferred":false,"id":628329,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70171438,"text":"70171438 - 2016 - Regional patterns of total nitrogen concentrations in the National Rivers and Streams Assessment","interactions":[],"lastModifiedDate":"2017-01-18T09:20:26","indexId":"70171438","displayToPublicDate":"2016-05-01T01:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Regional patterns of total nitrogen concentrations in the National Rivers and Streams Assessment","docAbstract":"Patterns of nitrogen (N) concentrations in streams sampled by the National Rivers and Streams Assessment (NRSA) were examined semiquantitatively to identify regional differences in stream N levels. The data were categorized and analyzed by watershed size classes to reveal patterns of the concentrations that are consistent with the spatial homogeneity in natural and anthropogenic characteristics associated with regional differences in N levels. Ecoregions and mapped information on human activities including agricultural practices were used to determine the resultant regions. Marked differences in N levels were found among the nine aggregations of ecoregions used to report the results of the NRSA. We identified distinct regional patterns of stream N concentrations within the reporting regions that are associated with the characteristics of specific Level III ecoregions, groups of Level III ecoregions, groups of Level IV ecoregions, certain geographic characteristics within ecoregions, and/or particular watershed size classes. We described each of these regions and illustrated their areal extent and median and range in N concentrations. Understanding the spatial variability of nutrient concentrations in flowing waters and the apparent contributions that human and nonhuman factors have on different sizes of streams and rivers is critical to the development of effective water quality assessment and management plans. This semi-quantitative analysis is also intended to identify areas within which more detailed quantitative work can be conducted to determine specific regional factors associated with variations in stream N concentrations.
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.71.3.167","usgsCitation":"Omernik, J.M., Paulsen, S.G., Griffith, G.E., and Weber, M.H., 2016, Regional patterns of total nitrogen concentrations in the National Rivers and Streams Assessment: Journal of Soil and Water Conservation, v. 71, no. 3, p. 167-181, https://doi.org/10.2489/jswc.71.3.167.","productDescription":"15 p.","startPage":"167","endPage":"181","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057215","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":322053,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"71","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-09","publicationStatus":"PW","scienceBaseUri":"57500771e4b0ee97d51bb70e","contributors":{"authors":[{"text":"Omernik, James M.","contributorId":169740,"corporation":false,"usgs":false,"family":"Omernik","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":25578,"text":"USGS -Volunteer","active":true,"usgs":false}],"preferred":false,"id":630982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paulsen, Steven G.","contributorId":169741,"corporation":false,"usgs":false,"family":"Paulsen","given":"Steven","email":"","middleInitial":"G.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":630983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Griffith, Glenn E. 0000-0001-7966-4720 ggriffith@usgs.gov","orcid":"https://orcid.org/0000-0001-7966-4720","contributorId":4053,"corporation":false,"usgs":true,"family":"Griffith","given":"Glenn","email":"ggriffith@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":630981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weber, Marc H.","contributorId":169742,"corporation":false,"usgs":false,"family":"Weber","given":"Marc","email":"","middleInitial":"H.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":630984,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178062,"text":"70178062 - 2016 - Developing fish trophic interaction indicators of climate change for the Great Lakes","interactions":[],"lastModifiedDate":"2016-11-01T15:10:34","indexId":"70178062","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Developing fish trophic interaction indicators of climate change for the Great Lakes","docAbstract":"This project addressed regional climate change effects on aquatic food webs in the Great Lakes. We sought insights by examining Lake Erie as a representative system with a high level of anthropogenic impacts, strong nutrient gradients, seasonal hypoxia, and spatial overlap of cold- and cool-water fish guilds. In Lake Erie and in large embayments throughout the Great Lakes basin, this situation is a concern for fishery managers, as climate change may exacerbate hypoxia and reduce habitat volume for some species. We examined fish community composition, fine-scale distribution, prey availability, diets, and biochemical tracers for dominant fishes from study areas with medium-high nutrient levels (mesotrophic, Fairport study area), and low nutrient levels (oligotrophic, Erie study area). This multi-year database (2011-2013) provides the ability to contrast years with wide variation in rainfall, winter ice-cover, and thermal stratification. In addition, multiple indicators of dietary and distributional responses to environmental variability will allow resource managers to select the most informative approach for addressing specific climate change questions. Our results support the incorporation of some relatively simple and cost-efficient approaches into existing agency monitoring programs to track the near-term condition status of fish and fish community composition by functional groupings. Other metrics appear better suited for understanding longer-term changes, and may take more resources to implement on an ongoing basis. Although we hypothesized that dietary overlap and similarity in selected species would be sharply different during thermal stratification and hypoxic episodes, we found little evidence of this. Instead, to our surprise, this study found that fish tended to aggregate at the edges of hypoxia, highlighting potential spatial changes in catch efficiency of the fishery. This work has had several positive impacts on a wide range of resource management and stakeholder activities, most notably in Lake Erie. The results were instrumental in the development of an interim decision rule for dealing with data collected during hypoxic events to improve stock assessment of Yellow Perch. In addition, novel findings from this study regarding spatial and temporal variability in hypoxia have aided US-Environmental Protection Agency in the development of a modified sampling protocol to more accurately quantify the central basin hypoxic zone, and this directly addressed a goal of the Great Lakes Water Quality Agreement of 2012 to reduce the extent and severity of hypoxia. Finally, the study areas developed in this project formed the basis for food web sampling in the 2014 bi-national Coordinated Science and Monitoring Initiative work in Lake Erie.
","language":"English","publisher":"Northeast Climate Science Center","usgsCitation":"Kraus, R.T., Knight, C.T., Gorman, A.M., Kocovsky, P.M., Weidel, B., and Rogers, M.W., 2016, Developing fish trophic interaction indicators of climate change for the Great Lakes, 70 p.","productDescription":"70 p.","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":330639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":330638,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://necsc.umass.edu/biblio/developing-fish-trophic-interaction-indicators-climate-change-great-lakes"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5819a9c4e4b0bb36a4c91029","contributors":{"authors":[{"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":652683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Carey T.","contributorId":56529,"corporation":false,"usgs":true,"family":"Knight","given":"Carey","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":652684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman, Ann Marie","contributorId":145525,"corporation":false,"usgs":false,"family":"Gorman","given":"Ann","email":"","middleInitial":"Marie","affiliations":[],"preferred":false,"id":652685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":652686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":652687,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":652688,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70176812,"text":"70176812 - 2016 - To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors","interactions":[],"lastModifiedDate":"2016-10-11T15:27:47","indexId":"70176812","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors","docAbstract":"Roads are significant barriers to landscape-scale movements of individuals or populations of many wildlife taxa. The decision by an animal near a road to either cross or not cross may be influenced by characteristics of the road, environmental conditions, traits of the individual animal, and other aspects of the context within which the decision is made. We considered such factors in a mixed-effects logistic regression model describing the nightly road crossing probabilities of invasive nocturnal Brown Treesnakes (Boiga irregularis) through short-term radiotracking of 691 snakes within close proximity to 50 road segments across the island of Guam. All measures of road magnitude (traffic volume, gap width, surface type, etc.) were significantly negatively correlated with crossing probabilities. Snake body size was the only intrinsic factor associated with crossing rates, with larger snakes crossing roads more frequently. Humidity was the only environmental variable affecting crossing rate. The distance of the snake from the road at the start of nightly movement trials was the most significant predictor of crossings. The presence of snake traps with live mouse lures during a portion of the trials indicated that localized prey cues reduced the probability of a snake crossing the road away from the traps, suggesting that a snake's decision to cross roads is influenced by local foraging opportunities. Per capita road crossing rates of Brown Treesnakes were very low, and comparisons to historical records suggest that crossing rates have declined in the 60+ yr since introduction to Guam. We report a simplified model that will allow managers to predict road crossing rates based on snake, road, and contextual characteristics. Road crossing simulations based on actual snake size distributions demonstrate that populations with size distributions skewed toward larger snakes will result in a higher number of road crossings. Our method of modeling per capita road crossing probabilities as a binary response variable, influenced by contextual factors, may be useful for describing or predicting road crossings by individuals of other taxa provided that appropriate spatial and temporal resolution can be achieved and that potentially influential covariate data can be obtained.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1292","usgsCitation":"Siers, S.R., Reed, R., and Savidge, J.A., 2016, To cross or not to cross: modeling wildlife road crossings as a binary response variable with contextual predictors: Ecosphere, v. 7, no. 5, e01292; 19 p., https://doi.org/10.1002/ecs2.1292.","productDescription":"e01292; 19 p.","ipdsId":"IP-069215","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":471035,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1292","text":"Publisher Index Page"},{"id":329465,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-31","publicationStatus":"PW","scienceBaseUri":"57fe679ee4b0824b2d143713","contributors":{"authors":[{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Robert N. reedr@usgs.gov","contributorId":1686,"corporation":false,"usgs":true,"family":"Reed","given":"Robert N.","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":650394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":650396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178545,"text":"70178545 - 2016 - Assessing the role of seabirds in the ecology of influenza A viruses","interactions":[],"lastModifiedDate":"2018-07-15T18:33:20","indexId":"70178545","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the role of seabirds in the ecology of influenza A viruses","docAbstract":"Wild waterbirds, specifically waterfowl, gulls, and shorebirds, are recognized as the primordial reservoir of influenza A viruses (IAVs). However, the role of seabirds, an abundant, diverse, and globally distributed group of birds, in the perpetuation and transmission of IAVs is less clear. Here we summarize published and publicly available data for influenza viruses in seabirds, which for the purposes of this study are defined as birds that exhibit a largely or exclusively pelagic lifestyle and exclude waterfowl, gulls, and shorebirds, and we review this collective dataset to assess the role of seabirds in the influenza A ecology. Since 1961, more than 40,000 samples have been collected worldwide from the seabirds considered here and screened, using a variety of techniques, for evidence of active or past IAV infection. From these data, the overall prevalence of active infection has been estimated to be very low; however, serological data provide evidence that some seabird species are more frequently exposed to IAVs. Sequence data for viruses from seabirds are limited, except for murres (common murre, Uria aalge, and thick-billed murre, Uria lomvia; family Alcidae) for which there are full or partial genome sequences available for more than 80 viruses. Characterization of these viruses suggests that murres are infected with Group 1 hemagglutinin subtype viruses more frequently as compared to Group 2 and also indicates that these northern, circumpolar birds are frequently infected by intercontinental reassortant viruses. Greater temporal and spatial sampling and characterization of additional viruses are required to better understand the role of seabirds in global IAV dynamics.
","language":"English","publisher":"American Association of Avian Pathologists","doi":"10.1637/11135-050815-RegR","usgsCitation":"Lang, A.S., Lebarbenchon, C., Robertson, G.J., Ramey, A.M., Waldenstrom, J., and Wille, M., 2016, Assessing the role of seabirds in the ecology of influenza A viruses: Avian Diseases, v. 60, no. 1s, p. 378-386, https://doi.org/10.1637/11135-050815-RegR.","productDescription":"9 p.","startPage":"378","endPage":"386","ipdsId":"IP-065494","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":331238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"1s","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583d5034e4b0d9329c80c59f","contributors":{"authors":[{"text":"Lang, Andrew S.","contributorId":177028,"corporation":false,"usgs":false,"family":"Lang","given":"Andrew","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":654331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lebarbenchon, Camille","contributorId":140670,"corporation":false,"usgs":false,"family":"Lebarbenchon","given":"Camille","email":"","affiliations":[],"preferred":false,"id":654332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","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":654336,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robertson, Gregory J.","contributorId":173883,"corporation":false,"usgs":false,"family":"Robertson","given":"Gregory","email":"","middleInitial":"J.","affiliations":[{"id":27311,"text":"Wildlife Research Division, Science and Technology Branch, Environment and Climate","active":true,"usgs":false}],"preferred":false,"id":654333,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waldenstrom, Jonas","contributorId":42891,"corporation":false,"usgs":true,"family":"Waldenstrom","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":654334,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wille, Michelle","contributorId":173881,"corporation":false,"usgs":false,"family":"Wille","given":"Michelle","email":"","affiliations":[{"id":27309,"text":"Memorial University of Newfoundland, St. John’s, NL A1B 3X9, Canada","active":true,"usgs":false}],"preferred":false,"id":654335,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70171382,"text":"70171382 - 2016 - Federal interagency nature‐like fishway passage design guidelines for Atlantic coast diadromous fishes","interactions":[],"lastModifiedDate":"2022-11-03T16:08:51.655969","indexId":"70171382","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Federal interagency nature‐like fishway passage design guidelines for Atlantic coast diadromous fishes","docAbstract":"The National Marine Fisheries Service (NMFS), the U.S. Geological Survey (USGS) and the U.S. Fish and Wildlife Service (USFWS) have collaborated to develop passage design guidance for use by engineers and other restoration practitioners considering and designing nature‐like fishways (NLFs). The primary purpose of these guidelines is to provide a summary of existing fish swimming and leaping performance data and the best available scientific information on safe, timely and effective passage for 14 diadromous fish species using Atlantic Coast rivers and streams. These guidelines apply to passage sites where complete barrier removal is not possible. This technical memorandum presents seven key physical design parameters based on the biometrics and swimming mode and performance of each target fishes for application in the design of NLFs addressing passage of a species or an assemblage of these species. The passage parameters include six dimensional guidelines recommended for minimum weir opening width and depth, minimum pool length, width and depth, and maximum channel slope, along with a maximum flow velocity guideline for each species. While\r\nthese guidelines are targeted for the design of step‐pool NLFs, the information may also have application in the design of other NLF types being considered at passage restoration sites and grade control necessary for infrastructure protection upstream of some dam removals, and in considering passage performance at sites such as natural bedrock features.","language":"English","publisher":"NOAA National Marine Fisheries Service","usgsCitation":"Turek, J., Haro, A.J., and Towler, B., 2016, Federal interagency nature‐like fishway passage design guidelines for Atlantic coast diadromous fishes, iii., 48 p.","productDescription":"iii., 48 p.","ipdsId":"IP-064934","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":336275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":321862,"type":{"id":15,"text":"Index Page"},"url":"https://repository.library.noaa.gov/view/noaa/28919"}],"country":"United States","otherGeospatial":"Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.53543042272173,\n 25.604386278077925\n ],\n [\n -78.9738695738202,\n 26.2643242769976\n ],\n [\n -77.65563187117027,\n 27.236312736346505\n ],\n [\n -77.71124176588978,\n 30.200566890880694\n ],\n [\n -75.63764563171657,\n 31.546671317157745\n ],\n [\n -72.88778473754125,\n 34.107153920647505\n ],\n [\n -71.80667946744646,\n 38.30485849898287\n ],\n [\n -68.12544832929673,\n 40.622781401735494\n ],\n [\n -66.51825275575436,\n 43.557615229438966\n ],\n [\n -66.93056853663478,\n 44.9924864367367\n ],\n [\n -70.50837479109403,\n 44.130583189469974\n ],\n [\n -71.56583190151437,\n 42.4531846455987\n ],\n [\n -71.04268393869013,\n 41.91302433499763\n ],\n [\n -73.12852497871336,\n 41.628116403040906\n ],\n [\n -74.47837257632011,\n 40.75996139502365\n ],\n [\n -74.85717819557229,\n 39.49615977300374\n ],\n [\n -75.36275241829512,\n 40.14818872121907\n ],\n [\n -76.70262042023853,\n 39.66328529391899\n ],\n [\n -77.26910128913559,\n 38.52431672198591\n ],\n [\n -76.80124506434308,\n 36.52548541174258\n ],\n [\n -77.48886662326828,\n 34.86340904706914\n ],\n [\n -78.19352283187868,\n 34.321846961811204\n ],\n [\n -79.02627770062944,\n 34.25764313694468\n ],\n [\n -79.67952029102847,\n 33.43439936645508\n ],\n [\n -81.32614282245044,\n 32.564707556457805\n ],\n [\n -82.03773749487134,\n 30.379248385899615\n ],\n [\n -80.44494631692717,\n 26.744769560981013\n ],\n [\n -80.53543042272173,\n 25.604386278077925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58b548c1e4b01ccd54fddfb8","contributors":{"authors":[{"text":"Turek, James","contributorId":169721,"corporation":false,"usgs":false,"family":"Turek","given":"James","email":"","affiliations":[{"id":25574,"text":"NOAA/NMFS Restoration Center","active":true,"usgs":false}],"preferred":false,"id":630824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haro, Alexander J. 0000-0002-7188-9172 aharo@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-9172","contributorId":2917,"corporation":false,"usgs":true,"family":"Haro","given":"Alexander","email":"aharo@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":630823,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Towler, Brett","contributorId":141164,"corporation":false,"usgs":false,"family":"Towler","given":"Brett","email":"","affiliations":[{"id":6927,"text":"USFWS, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":630825,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70161743,"text":"70161743 - 2016 - Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes","interactions":[],"lastModifiedDate":"2016-06-28T16:09:00","indexId":"70161743","displayToPublicDate":"2016-05-01T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes","docAbstract":"We present a revised ground‐motion prediction equation (GMPE) for computing medians and standard deviations of peak ground acceleration (PGA) and 5% damped pseudospectral acceleration (PSA) response ordinates of the horizontal component of randomly oriented ground motions to be used for seismic‐hazard analyses and engineering applications. This GMPE is derived from the expanded Next Generation Attenuation (NGA)‐West 1 database (see Data and Resources; Chiou et al., 2008). The revised model includes an anelastic attenuation term as a function of quality factor (Q0) to capture regional differences in far‐source (beyond 150 km) attenuation, and a new frequency‐dependent sedimentary‐basin scaling term as a function of depth to the 1.5 km/s shear‐wave velocity isosurface to improve ground‐motion predictions at sites located on deep sedimentary basins. The new Graizer–Kalkan 2015 (GK15) model, developed to be simple, is applicable for the western United States and other similar shallow crustal continental regions in active tectonic environments for earthquakes with moment magnitudes (M) 5.0–8.0, distances 0–250 km, average shear‐wave velocities in the upper 30 m (VS30) 200–1300 m/s, and spectral periods (T) 0.01–5 s. Our aleatory variability model captures interevent (between‐event) variability, which decreases with magnitude and increases with distance. The mixed‐effect residuals analysis reveals that the GK15 has no trend with respect to the independent predictor parameters. Compared to our 2007–2009 GMPE, the PGA values are very similar, whereas spectral ordinates predicted are larger at T<0.2 s and they are smaller at longer periods.
","language":"English","publisher":"Seismological Society of America","publisherLocation":"El Cerito, CA","doi":"10.1785/0120150194","usgsCitation":"Graizer, V., and Kalkan, E., 2016, Summary of the GK15 ground‐motion prediction equation for horizontal PGA and 5% damped PSA from shallow crustal continental earthquakes: Bulletin of the Seismological Society of America, v. 106, no. 2, p. 687-707, https://doi.org/10.1785/0120150194.","productDescription":"21 p.","startPage":"687","endPage":"707","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065326","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":324563,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Armenia, Georgia, Italy, Taiwan, Turkey, United States, Uzbekistan","state":"Alaska, California, Nevada","volume":"106","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-06","publicationStatus":"PW","scienceBaseUri":"57739fb7e4b07657d1a90d78","contributors":{"authors":[{"text":"Graizer, Vladimir;","contributorId":152040,"corporation":false,"usgs":false,"family":"Graizer","given":"Vladimir;","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":587625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":587624,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170355,"text":"fs20163025 - 2016 - Urban infrastructure and water management—Science capabilities of the U.S. Geological Survey","interactions":[],"lastModifiedDate":"2016-04-29T13:41:25","indexId":"fs20163025","displayToPublicDate":"2016-04-29T12:00:00","publicationYear":"2016","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":"2016-3025","title":"Urban infrastructure and water management—Science capabilities of the U.S. Geological Survey","docAbstract":"Managing the urban-water cycle has increasingly become a challenge for water-resources planners and regulators faced with the problem of providing clean drinking water to urban residents. Sanitary and combined sanitary and storm sewer networks convey wastewater to centralized treatment plants. Impervious surfaces, which include roads, parking lots, and buildings, increase stormwater runoff and the efficiency by which runoff is conveyed to nearby stream channels; therefore, impervious surfaces increase the risk of urban flooding and alteration of natural ecosystems. These challenges will increase with the expansion of urban centers and the probable effects of climate change on precipitation patterns. Understanding the urban-water cycle is critical to effectively manage water resources and to protect people, infrastructure, and urban-stream ecosystems. As a leader in water-supply, wastewater, and stormwater assessments, the U.S. Geological Survey has the expertise and resources needed to monitor, model, and interpret data related to the urban-water cycle and thereby enable water-resources managers to make informed decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163025","collaboration":"Northeast Region Urban Landscape Capabilities Team","usgsCitation":"U.S. Geological Survey, 2016, Urban infrastructure and water management—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3025, 2 p., https://dx.doi.org/10.3133/fs20163025.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074177","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":320439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3025/fs20163025.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3025"},{"id":320438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3025/coverthb.jpg"},{"id":320440,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163023","text":"Fact Sheet 2016-3023"},{"id":320441,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163024","text":"Fact Sheet 2016-3024"},{"id":320442,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163026","text":"Fact Sheet 2016-3026"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Urbanization affects streamflow characteristics, coastal flooding, and groundwater recharge. Increasing impervious areas, streamflow diversions, and groundwater pumpage are some of the ways that the natural water cycle is affected by urbanization. Assessment of the relations among these factors and changes in land use helps water-resource managers with issues such as stormwater management and vulnerability to flood and drought. Scientists with the U.S. Geological Survey (USGS) have the expertise to monitor and model urban hydrologic systems. Streamflow and groundwater data are available in national databases, and analyses of these data, including identification of long-term streamflow trends and the efficacy of management practices, are published in USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163023","collaboration":"Northeast Region Urban Landscape Capabilities Team ","usgsCitation":"U.S. Geological Survey, 2016, Urban hydrology—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3023, 2 p., https://dx.doi.org/10.3133/fs20163023.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074174","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":320426,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163025","text":"Fact Sheet 2016-3025","description":"FS 2016-3023"},{"id":320425,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163024","text":"Fact Sheet 2016-3024","description":"FS 2016-3023"},{"id":320423,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3023/coverthb.jpg"},{"id":320427,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163026","text":"Fact Sheet 2016-3026","description":"FS 2016-3023"},{"id":320424,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3023/fs20163023.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3023"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Streams and estuaries with urban watersheds commonly exhibit increased streamflow and decreased base flow; diminished stream-channel stability; excessive amounts of contaminants such as pesticides, metals, industrial and municipal waste, and combustion products; and alterations to biotic community structure. Collectively, these detrimental effects have been termed the “urban-stream syndrome.” Water-resource managers seek to lessen the effects on receiving water bodies of new urban development and remediate the effects in areas of existing urbanization. Similarly, the scientific community has produced extensive research on these topics, with researchers from the U.S. Geological Survey (USGS) leading many studies of urban streams and the processes responsible for the urban-stream syndrome. Increasingly, USGS studies are evaluating the effects of management and restoration activities to better understand how urban waters respond to the implementation of management practices. The USGS has expertise in collecting and interpreting data for many physical, chemical, and ecological processes in urban waters and, thus, provides holistic assessments to inform managers of urban water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163024","collaboration":"Northeast Region Urban Landscape Capabilities Team","usgsCitation":"U.S. Geological Survey, 2016, Contaminants in urban waters—Science capabilities of the U.S. Geological Survey: U.S. Geological Survey Fact Sheet 2016–3024, 2 p., https://dx.doi.org/10.3133/fs20163024.","productDescription":"2 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074176","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":320432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3024/coverthb.jpg"},{"id":320433,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3024/fs20163024.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016-3024"},{"id":320434,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163023","text":"Fact Sheet 2016-3023","description":"FS 2016-3024"},{"id":320435,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163025","text":"Fact Sheet 2016-3025","description":"FS 2016-3024"},{"id":320436,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/fs20163026","text":"Fact Sheet 2016-3026","description":"FS 2016-3024"}],"contact":"U.S. Geological Survey
Northeast Region Urban Landscapes Capability Team
Email: GS-NE_ULCT@usgs.gov
Evidence suggests that lakes are important sites for atmospheric CO2 exchange and so play a substantial role in the global carbon budget. Previous research has 2 weaknesses: (1) most data have been collected only during the open-water or summer seasons, and (2) data are concentrated principally on natural lakes in northern latitudes. Here, we report on the full annual cycle of atmospheric CO2 exchanges of 15 oligotrophic to eutrophic reservoirs in the Glacial Till Plains of the United States. With one exception, these reservoirs showed an overall loss of CO2 during the year, with most values within the lower range reported for temperate lakes. There was a strong cross-system seasonal pattern: an average of 70% of total annual CO2 efflux occurred by the end of spring mixis; some 20% of annual flux was reabsorbed during summer stratification; and the remaining 50% of efflux was lost during autumnal mixing. Net annual flux was negatively correlated with depth and positively correlated with both water residence time and DOC, with the smallest annual CO2 efflux measured in shallow fertile impoundments. Strong correlations yield relationships allowing regional up-scaling of CO2 evasion. Understanding lacustrine CO2 uptake and evasion requires seasonal analyses across the full range of lake trophic states and morphometric attributes.
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2008","interactions":[],"lastModifiedDate":"2016-04-28T13:22:20","indexId":"ds990","displayToPublicDate":"2016-04-28T13:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"990","title":"Post-Hurricane Ike coastal oblique aerial photographs collected along the Alabama, Mississippi, and Louisiana barrier islands and the north Texas coast, September 14-15, 2008","docAbstract":"The U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On September 14-15, 2008, the USGS conducted an oblique aerial photographic survey along the Alabama, Mississippi, and Louisiana barrier islands and the north Texas coast, aboard a Beechcraft Super King Air 200 (aircraft) at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was flown to collect post-Hurricane Ike data for assessing incremental changes in the beach and nearshore area since the last survey, flown on September 9-10, 2008, and the data can be used in the assessment of future coastal change.
The photographs provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML file was created using the photographic navigation files. The KML file can be found in the kml folder.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds990","usgsCitation":"Morgan, K.L.M., Krohn, M.D., and Guy, K.K., 2016, Post-Hurricane Ike coastal oblique aerial photographs collected along the Alabama, Mississippi, and Louisiana barrier islands and the north Texas coast, September 14-15, 2008: U.S. Geological Survey Data Series 990, https://dx.doi.org/10.3133/ds990.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070992","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":320553,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0990/index.html"}],"country":"United States","state":"Alabama, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.82421875,\n 29.075375179558346\n ],\n [\n -89.82421875,\n 30.637912028341123\n ],\n [\n -87.857666015625,\n 30.637912028341123\n ],\n [\n -87.857666015625,\n 29.075375179558346\n ],\n [\n -89.82421875,\n 29.075375179558346\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.417236328125,\n 28.83504997263515\n ],\n [\n -91.417236328125,\n 30.51494904517773\n ],\n [\n -89.835205078125,\n 30.51494904517773\n ],\n [\n -89.835205078125,\n 28.83504997263515\n ],\n [\n -91.417236328125,\n 28.83504997263515\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.284423828125,\n 28.9023972285585\n ],\n [\n -95.284423828125,\n 30.268556249047727\n ],\n [\n -92.955322265625,\n 30.268556249047727\n ],\n [\n -92.955322265625,\n 28.9023972285585\n ],\n [\n -95.284423828125,\n 28.9023972285585\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov
The U.S. Geological Survey (USGS), as part of the National Assessment of Coastal Change Hazards project, conducts baseline and storm-response photography missions to document and understand the changes in vulnerability of the Nation's coasts to extreme storms (Morgan, 2009). On September 9-10, 2008, the USGS conducted an oblique aerial photographic survey from Calcasieu Lake, Louisiana, to Brownsville, Texas, aboard a Cessna C-210 (aircraft) at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was flown to collect baseline data for assessing incremental changes of the beach and nearshore area, and the data can be used in the assessment of future coastal change.
The photographs provided in this report are Joint Photographic Experts Group (JPEG) images. ExifTool was used to add the following to the header of each photo: time of collection, Global Positioning System (GPS) latitude, GPS longitude, keywords, credit, artist (photographer), caption, copyright, and contact information. The photograph locations are an estimate of the position of the aircraft at the time the photograph was taken and do not indicate the location of any feature in the images (see the Navigation Data page). These photographs document the state of the barrier islands and other coastal features at the time of the survey. Pages containing thumbnail images of the photographs, referred to as contact sheets, were created in 5-minute segments of flight time. These segments can be found on the Photos and Maps page. Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet.
In addition to the photographs, a Google Earth Keyhole Markup Language (KML) file is provided and can be used to view the images by clicking on the marker and then clicking on either the thumbnail or the link above the thumbnail. The KML file was created using the photographic navigation files. The KML file can be found in the kml folder.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds991","usgsCitation":"Morgan, K.L.M., and Westphal, K.A., 2016, Baseline coastal oblique aerial photographs collected from Calcasieu Lake, Louisiana, to Brownsville, Texas, September 9-10, 2008: U.S. Geological Survey Data Series 991, https://dx.doi.org/10.3133/ds991.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2008-09-09","ipdsId":"IP-072328","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":320621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":320527,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0991"}],"country":"United States","state":"Louisiana, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.657470703125,\n 25.849336891707605\n ],\n [\n -97.657470703125,\n 30.221101852485987\n ],\n [\n -92.63671875,\n 30.221101852485987\n ],\n [\n -92.63671875,\n 25.849336891707605\n ],\n [\n -97.657470703125,\n 25.849336891707605\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
(727) 502–8000
http://coastal.er.usgs.gov
Methylmercury contamination of fish is a global threat to environmental health. Mercury (Hg) monitoring programs are valuable for generating data that can be compiled for spatially broad syntheses to identify emergent ecosystem properties that influence fish Hg bioaccumulation. Fish total Hg (THg) concentrations were evaluated across the Western United States (US) and Canada, a region defined by extreme gradients in habitat structure and water management. A database was compiled with THg concentrations in 96,310 fish that comprised 206 species from 4262 locations, and used to evaluate the spatial distribution of fish THg across the region and effects of species, foraging guilds, habitats, and ecoregions. Areas of elevated THg exposure were identified by developing a relativized estimate of fish mercury concentrations at a watershed scale that accounted for the variability associated with fish species, fish size, and site effects. THg concentrations in fish muscle ranged between 0.001 and 28.4 (μg/g wet weight (ww)) with a geometric mean of 0.17. Overall, 30% of individual fish samples and 17% of means by location exceeded the 0.30 μg/g ww US EPA fish tissue criterion. Fish THg concentrations differed among habitat types, with riverine habitats consistently higher than lacustrine habitats. Importantly, fish THg concentrations were not correlated with sediment THg concentrations at a watershed scale, but were weakly correlated with sediment MeHg concentrations, suggesting that factors influencing MeHg production may be more important than inorganic Hg loading for determining fish MeHg exposure. There was large heterogeneity in fish THg concentrations across the landscape; THg concentrations were generally higher in semi-arid and arid regions such as the Great Basin and Desert Southwest, than in temperate forests. Results suggest that fish mercury exposure is widespread throughout Western US and Canada, and that species, habitat type, and region play an important role in influencing ecological risk of mercury in aquatic ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2016.03.229","usgsCitation":"Eagles-Smith, C.A., Ackerman, J., Willacker, J.J., Tate, M., Lutz, M.A., Fleck, J., Stewart, A.R., Wiener, J.G., Evers, D.C., Lepak, J.M., Davis, J., and Pritz, C.F., 2016, Spatial and temporal patterns of mercury concentrations in freshwater fish across the Western United States and Canada: Science of the Total Environment, v. 568, p. 1171-1184, https://doi.org/10.1016/j.scitotenv.2016.03.229.","productDescription":"14 p.","startPage":"1171","endPage":"1184","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070592","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":482076,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2016.03.229","text":"Publisher Index Page"},{"id":320593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":354415,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ja/70170374/70170374_appendix.pdf","text":"USGS open-access version of article appendix","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-access journal article appendix"},{"id":354414,"rank":2,"type":{"id":42,"text":"Open Access USGS Document"},"url":"https://pubs.usgs.gov/ja/70170374/70170374.pdf","text":"USGS open-access version of article","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-access journal article"}],"volume":"568","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5721d4a4e4b0b13d3912914b","chorus":{"doi":"10.1016/j.scitotenv.2016.03.229","url":"http://dx.doi.org/10.1016/j.scitotenv.2016.03.229","publisher":"Elsevier BV","authors":"Eagles-Smith Collin A., Ackerman Joshua T., Willacker James J., Tate Michael T., Lutz Michelle A., Fleck Jacob A., Stewart A. Robin, Wiener James G., Evers David C., Lepak Jesse M., Davis Jay A., Pritz Colleen Flanagan","journalName":"Science of The Total Environment","publicationDate":"10/2016"},"contributors":{"authors":[{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285 ceagles-smith@usgs.gov","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":505,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin","email":"ceagles-smith@usgs.gov","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":627015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":627016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willacker, James J. jwillacker@usgs.gov","contributorId":5614,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"jwillacker@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":627017,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":627018,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lutz, Michelle A. malutz@usgs.gov","contributorId":167259,"corporation":false,"usgs":true,"family":"Lutz","given":"Michelle","email":"malutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":627019,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fleck, Jacob 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":168694,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":627020,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stewart, A. Robin 0000-0003-2918-546X arstewar@usgs.gov","orcid":"https://orcid.org/0000-0003-2918-546X","contributorId":1482,"corporation":false,"usgs":true,"family":"Stewart","given":"A.","email":"arstewar@usgs.gov","middleInitial":"Robin","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":40553,"text":"WMA - Office of the Chief Operating Officer","active":true,"usgs":true}],"preferred":true,"id":627021,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wiener, James G.","contributorId":93853,"corporation":false,"usgs":false,"family":"Wiener","given":"James","email":"","middleInitial":"G.","affiliations":[{"id":17913,"text":"River Studies Center, University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":627022,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Evers, David C.","contributorId":96160,"corporation":false,"usgs":false,"family":"Evers","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":627023,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lepak, Jesse M.","contributorId":168695,"corporation":false,"usgs":false,"family":"Lepak","given":"Jesse","email":"","middleInitial":"M.","affiliations":[{"id":13449,"text":"Colorado Division of Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":627024,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Davis, Jay A.","contributorId":168696,"corporation":false,"usgs":false,"family":"Davis","given":"Jay A.","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":627025,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pritz, Colleen Flanagan","contributorId":67422,"corporation":false,"usgs":true,"family":"Pritz","given":"Colleen","email":"","middleInitial":"Flanagan","affiliations":[],"preferred":false,"id":627026,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70170564,"text":"70170564 - 2016 - Will changes in phenology track climate change? A study of growth initiation timing in coast Douglas-fir","interactions":[],"lastModifiedDate":"2016-10-21T13:41:54","indexId":"70170564","displayToPublicDate":"2016-04-27T10:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Will changes in phenology track climate change? A study of growth initiation timing in coast Douglas-fir","docAbstract":"Under climate change, the reduction of frost risk, onset of warm temperatures and depletion of soil moisture are all likely to occur earlier in the year in many temperate regions. The resilience of tree species will depend on their ability to track these changes in climate with shifts in phenology that lead to earlier growth initiation in the spring. Exposure to warm temperatures (“forcing”) typically triggers growth initiation, but many trees also require exposure to cool temperatures (“chilling”) while dormant to readily initiate growth in the spring. If warming increases forcing and decreases chilling, climate change could maintain, advance or delay growth initiation phenology relative to the onset of favorable conditions. We modeled the timing of height- and diameter-growth initiation in coast Douglas-fir (an ecologically and economically vital tree in western North America) to determine whether changes in phenology are likely to track changes in climate using data from field-based and controlled-environment studies, which included conditions warmer than those currently experienced in the tree's range. For high latitude and elevation portions of the tree's range, our models predicted that warming will lead to earlier growth initiation and allow trees to track changes in the onset of the warm but still moist conditions that favor growth, generally without substantially greater exposure to frost. In contrast, towards lower latitude and elevation range limits, the models predicted that warming will lead to delayed growth initiation relative to changes in climate due to reduced chilling, with trees failing to capture favorable conditions in the earlier parts of the spring. This maladaptive response to climate change was more prevalent for diameter-growth initiation than height-growth initiation. The decoupling of growth initiation with the onset of favorable climatic conditions could reduce the resilience of coast Douglas-fir to climate change at the warm edges of its distribution.
","language":"English","publisher":"Blackwell Science","doi":"10.1111/gcb.13328","usgsCitation":"Ford, K.R., Harrington, C.A., Bansal, S., Gould, P.J., and St. Clair, B., 2016, Will changes in phenology track climate change? 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We quantified wave-height evolution across 7 km of mudflat in San Pablo Bay (northern San Francisco Bay), an environment where tidal mixing prevents the formation of fluid mud. Wave height was measured along a cross shore transect (elevation range−2mto+0.45mMLLW) in winter 2011 and summer 2012. Wave height decreased more than 50% across the transect. The exponential decay coefficient λ was inversely related to depth squared (λ=6×10−4h−2). The physical roughness length scale kb, estimated from near-bed turbulence measurements, was 3.5×10−3 m in winter and 1.1×10−2 m in summer. Estimated wave friction factor fw determined from wave-height data suggests that bottom friction dominates dissipation at high Rew but not at low Rew. Predictions of near-shore wave height based on offshore wave height and a rough formulation for fw were quite accurate, with errors about half as great as those based on the smooth formulation for fw. Researchers often assume that the wave boundary layer is smooth for settings with fine-grained sediments. At this site, use of a smooth fw results in an underestimate of wave shear stress by a factor of 2 for typical waves and as much as 5 for more energetic waves. It also inadequately captures the effectiveness of the mudflats in protecting the shoreline through wave attenuation.
","language":"English","publisher":"World Scientific Pub.","doi":"10.1016/j.coastaleng.2016.03.008","usgsCitation":"Lacy, J.R., and MacVean, L.J., 2016, Wave attenuation in the shallows of San Francisco Bay: Coastal Engineering, v. 114, p. 159-168, https://doi.org/10.1016/j.coastaleng.2016.03.008.","productDescription":"10 p.","startPage":"159","endPage":"168","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069928","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":321455,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay, San Pablo Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.56622314453124,\n 37.81032643553478\n ],\n [\n -122.56622314453124,\n 39\n ],\n [\n -122.22015380859374,\n 39\n ],\n [\n -122.22015380859374,\n 37.81032643553478\n ],\n [\n -122.56622314453124,\n 37.81032643553478\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"114","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57403560e4b07e28b65e9732","contributors":{"authors":[{"text":"Lacy, Jessica R. 0000-0002-2797-6172 jlacy@usgs.gov","orcid":"https://orcid.org/0000-0002-2797-6172","contributorId":3158,"corporation":false,"usgs":true,"family":"Lacy","given":"Jessica","email":"jlacy@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":629943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"MacVean, Lissa J. lmacvean@usgs.gov","contributorId":4698,"corporation":false,"usgs":true,"family":"MacVean","given":"Lissa","email":"lmacvean@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":629944,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70170331,"text":"fs20163027 - 2016 - U.S. Geological Survey response to flooding in Texas, May–June 2015","interactions":[],"lastModifiedDate":"2016-04-26T11:33:30","indexId":"fs20163027","displayToPublicDate":"2016-04-26T00:00:00","publicationYear":"2016","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":"2016-3027","title":"U.S. Geological Survey response to flooding in Texas, May–June 2015","docAbstract":"As a Federal science agency within the Department of the Interior, the U.S. Geological Survey (USGS) collects and disseminates streamflow stage and discharge information along with other types of water information as a major part of its Water mission area. Data collected at USGS streamflow-gaging stations (hereinafter referred to as “streamgages”) are used for a variety of purposes including flood warning, engineering design, management of water resources, and scientific research.
During flood events, the need for timely, accurate, and complete streamflow data is underscored because these data are relied on by local, State, and Federal emergency management personnel for flood response purposes. For example, the National Weather Service uses the data from USGS streamgages to develop flood forecasts for specific locations on a river. Tasks that the USGS performs in response to floods include monitoring the operation of gages and responding to any interruptions in data collection, calibrating and verifying stage-discharge ratings, and documenting extreme events including peak stage and peak discharge.
Frequent, severe storms during May and June 2015 caused widespread flooding in Texas. By various measures, the storms that caused the flooding were extreme and persistent. May 2015 was the wettest month on record for Texas, with a statewide average precipitation of 9.06 inches. In comparison, the long-term statewide average monthly precipitation is 3.37 inches, with the previous record average monthly precipitation reported as 6.66 inches during June 2004. The Office of the Texas State Climatologist compiled monthly precipitation amounts for 19 selected cities throughout Texas and for 1 city in Louisiana; the total monthly precipitation amounts exceeded the monthly normal precipitation for 18 of the 19 selected cities in Texas, with 5 of these cities exceeding their previous record for the month of May.
The onset of abundant precipitation in May 2015 resulted in the National Weather Service flood stage being exceeded at USGS streamgages on numerous rivers. The widespread and prolonged nature of the flooding was unusual; most flood events in Texas are localized, typically affecting only one or two river basins and generally lasting only a few days. With the exception of the Rio Grande, flooding was widespread in all of the major rivers in Texas during May–June 2015.
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\"}}]}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4733
Data specific to the Harlem River, New York, have been summarized and are presented in this report. The data illustrate improvements in the quality of water for the past 65 years and emphasize the importance of a continuous water-quality record for establishing trends in environmental conditions. Although there is a paucity of sediment-quality data, the New York City Department of Environmental Protection (NYCDEP) Bureau of Wastewater Treatment has maintained a water-quality monitoring network in the Harlem River (and throughout the harbor of New York City) to which 61 combined sewer outfalls discharge effluent. In cooperation with the NYCDEP, the U.S. Geological Survey evaluated water-quality data collected by the NYCDEP dating back to 1945, which indicate trends in water quality and reveal improvement following the 1972 passage of the Clean Water Act. These improvements are indicated by the steady increase in median dissolved oxygen concentrations and an overall decrease in fecal indicator bacteria concentrations starting in the late 1970s. Further, the magnitude of the highest fecal indicator bacteria concentrations (that is, the 90th percentile) in samples collected from the Harlem River have decreased significantly over the past four decades. Other parameters of water quality used to gauge the health of a water body include total suspended solids and nutrient (inorganic forms of nitrogen and phosphorus) concentrations—mean concentrations for these indicators have also decreased in the past decades. The limited sediment data available for one sample in the Harlem River indicate concentrations of copper, zinc, and lead are above sediment-quality thresholds set by the New York State Department of Environmental Conservation. However, more data are needed to better understand the changes in both sediment and water quality in the Harlem River, both as the tide cycles and during precipitation events. As a partner in the Urban Waters Federal Partnership, the U.S. Geological Survey has worked to address the chronic water-quality concerns of the Harlem River by compiling relevant data and studies, which is an important component for understanding and rectifying water-quality problems within a watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165044","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Fisher, S.C., 2016, Historical water-quality data from the Harlem River, New York: U.S. Geological Survey Scientific Investigations Report 2016–5044, 21 p., appendix, https://dx.doi.org/10.3133/sir20165044.","productDescription":"Report: viii, 19 p.; Water-quality data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055014","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":320361,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5044/sir20165044_appendix1.xlsx","text":"Water-quality data - Tables 1-1 through 1-9 ","size":"3.4 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5044"},{"id":320327,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5044/coverthb.jpg"},{"id":320328,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5044/sir20165044.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5044"}],"country":"United States","state":"New York","otherGeospatial":"Harlem River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.9657974243164,\n 40.76520144280567\n ],\n [\n -73.9657974243164,\n 40.883928811599326\n ],\n [\n -73.89335632324219,\n 40.883928811599326\n ],\n [\n -73.89335632324219,\n 40.76520144280567\n ],\n [\n -73.9657974243164,\n 40.76520144280567\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
2045 Route 112, Building 4
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Or visit our Web site at:
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Eelgrass (Zostera marina) populations occupying coastal waters of Alaska are separated by a peninsula and island archipelago into two Large Marine Ecosystems (LMEs). From populations in both LMEs, we characterize genetic diversity, population structure, and polarity in gene flow using nuclear microsatellite fragment and chloroplast and nuclear sequence data. An inverse relationship between genetic diversity and latitude was observed (heterozygosity: R2 = 0.738, P < 0.001; allelic richness: R2 = 0.327, P = 0.047), as was significant genetic partitioning across most sampling sites (θ = 0.302, P < 0.0001). Variance in allele frequency was significantly partitioned by region only in cases when a population geographically in the Gulf of Alaska LME (Kinzarof Lagoon) was instead included with populations in the Eastern Bering Sea LME (θp = 0.128–0.172; P < 0.003), suggesting gene flow between the two LMEs in this region. Gene flow among locales was rarely symmetrical, with notable exceptions generally following net coastal ocean current direction. Genetic data failed to support recent proposals that multiple Zostera species (i.e. Z. japonica and Z. angustifolia) are codistributed with Z. marina in Alaska. Comparative analyses also failed to support the hypothesis that eelgrass populations in the North Atlantic derived from eelgrass retained in northeastern Pacific Last Glacial Maximum refugia. These data suggest northeastern Pacific populations are derived from populations expanding northward from temperate populations following climate amelioration at the terminus of the last Pleistocene glaciation.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0152701","usgsCitation":"Talbot, S.L., Sage, G.K., Rearick, J.R., Fowler, M., Muñiz-Salazar, R., Baibak, B., Wyllie-Echeverria, S., Cabello-Pasini, A., and Ward, D.H., 2016, The structure of genetic diversity in eelgrass (Zostera marina L.) along the North Pacific and Bering Sea coasts of Alaska: PLoS ONE, v. 11, no. 4, Article e0152701; 31 p., https://doi.org/10.1371/journal.pone.0152701.","productDescription":"Article e0152701; 31 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061219","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":471053,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0152701","text":"Publisher Index Page"},{"id":438617,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GQ6VTK","text":"USGS data 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large benthic foraminifera","docAbstract":"Large benthic foraminifera (LBF) are prolific producers of calcium carbonate sediments in shallow, tropical environments that are being influenced by ocean acidification (OA). Two LBF species, Amphistegina gibbosa (Order Rotaliida) with low-Mg calcite tests and Archaias angulatus (Order Miliolida) with high-Mg calcite tests, were studied to assess the effects of pH 7.6 on oxygen and carbon isotopic fractionation between test calcite and ambient seawater. The δ18O and δ13C values of terminal chambers and of whole adult tests of both species after 6 weeks were not significantly different between pH treatments of 8.0 and 7.6. However, tests of juveniles produced during the 6-week treatments showed significant differences between δ18O and δ13C values from control (pH 8.0) when compared with the treatment (pH 7.6) for both species. Although each individual's growth was photographed and measured, difficulty in distinguishing and manually extracting newly precipitated calcite from adult specimens likely confounded any differences in isotopic signals. However, juvenile specimens that resulted from asexual reproduction that occurred during the experiments did not contain old carbonate that could confound the new isotopic signals. These data reveal a potential bias in the design of OA experiments if only adults are used to investigate changes in test chemistries. Furthermore, the results reaffirm that different calcification mechanisms in these two foraminiferal orders control the fractionation of stable isotopes in the tests and will reflect decreasing pH in seawater somewhat differently. .
","language":"English","publisher":"Oxford Journals","doi":"10.1093/icesjms/fsw056","collaboration":"Knorr, P.O.;Wynn, J.G.;Hallock, P.;Harries, P.","usgsCitation":"Robbins, L.L., Knorr, P.O., Wynn, J., Hallock, P., and Harries, P., 2016, Interpreting the role of pH on stable isotopes in large benthic foraminifera: ICES Journal of Marine Science, fsw056: 10 p., https://doi.org/10.1093/icesjms/fsw056.","productDescription":"fsw056: 10 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065880","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":471054,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsw056","text":"Publisher Index Page"},{"id":328091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-22","publicationStatus":"PW","scienceBaseUri":"57c7ffb5e4b0f2f0cebfc28e","contributors":{"authors":[{"text":"Robbins, Lisa L. 0000-0003-3681-1094 lrobbins@usgs.gov","orcid":"https://orcid.org/0000-0003-3681-1094","contributorId":422,"corporation":false,"usgs":true,"family":"Robbins","given":"Lisa","email":"lrobbins@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":647469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knorr, P. O.","contributorId":103485,"corporation":false,"usgs":true,"family":"Knorr","given":"P.","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":647470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wynn, J.G.","contributorId":16215,"corporation":false,"usgs":true,"family":"Wynn","given":"J.G.","email":"","affiliations":[],"preferred":false,"id":647471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hallock, P.","contributorId":91263,"corporation":false,"usgs":false,"family":"Hallock","given":"P.","email":"","affiliations":[],"preferred":false,"id":647473,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harries, P.","contributorId":174140,"corporation":false,"usgs":false,"family":"Harries","given":"P.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":647472,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70170351,"text":"ds994 - 2016 - Biotelemetry data for golden eagles (Aquila chrysaetos) captured in coastal southern California, November 2014–February 2016","interactions":[],"lastModifiedDate":"2017-12-06T11:03:33","indexId":"ds994","displayToPublicDate":"2016-04-21T17:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"994","title":"Biotelemetry data for golden eagles (Aquila chrysaetos) captured in coastal southern California, November 2014–February 2016","docAbstract":"The status of golden eagles (Aquila chrysaetos) in coastal southern California is unclear. To address this knowledge gap, the U.S. Geological Survey (USGS) in collaboration with local, State, and other Federal agencies began a multi-year survey and tracking program of golden eagles to address questions regarding habitat use, movement behavior, nest occupancy, genetic population structure, and human impacts on eagles. Golden eagle trapping and tracking efforts began in October 2014 and continued until early March 2015. During the first trapping season that focused on San Diego County, we captured 13 golden eagles (8 females and 5 males). During the second trapping season that began in November 2015, we focused on trapping sites in San Diego, Orange, and western Riverside Counties. By February 23, 2016, we captured an additional 14 golden eagles (7 females and 7 males). In this report, biotelemetry data were collected between November 22, 2014, and February 23, 2016. The location data for eagles ranged as far north as San Luis Obispo, California, and as far south as La Paz, Baja California, Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds994","collaboration":"Prepared for San Diego Association of Governments (SANDAG), California Department of Fish and Wildlife, Bureau of Land Management, and U.S. Fish and Wildlife Service","usgsCitation":"Tracey, J.A., Madden, M.C., Sebes, J.B., Bloom, P.H., Katzner, T.E., and Fisher, R.N., 2016, Biotelemetry data for golden eagles (Aquila chrysaetos) captured in coastal southern California, November 2014–February 2016: U.S. Geological Survey Data Series 994, 32 p., https://dx.doi.org/10.3133/ds994.","productDescription":"iv, 32 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073516","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":320383,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0994/ds994.pdf","text":"Report","size":"19.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 994"},{"id":320382,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0994/coverthb.jpg"}],"country":"Mexico, United States","state":"Baja California, Baja Californa Sur, California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.83886718750001,\n 34.470335121217495\n ],\n [\n -120.36621093749999,\n 34.939985151560435\n ],\n [\n -120.52001953124999,\n 35.35321610123821\n ],\n [\n -120.34423828125,\n 35.65729624809628\n ],\n [\n -119.53125,\n 35.496456056584165\n ],\n [\n -118.6083984375,\n 35.08395557927643\n ],\n [\n -117.39990234375,\n 34.74161249883172\n ],\n [\n -116.82861328125001,\n 34.57895241036945\n ],\n [\n -116.03759765625,\n 34.45221847282654\n ],\n [\n -115.72998046875,\n 33.925129700072\n ],\n [\n -115.37841796874999,\n 33.284619968887704\n ],\n [\n -115.400390625,\n 32.75032260780972\n ],\n [\n -115.31249999999999,\n 32.15701248607008\n ],\n [\n -114.9609375,\n 31.541089879585808\n ],\n [\n -114.85107421875,\n 30.977609093348686\n ],\n [\n -114.6533203125,\n 30.751277776257812\n ],\n [\n -114.63134765625001,\n 30.183121842195515\n ],\n [\n -114.27978515625,\n 29.859701442126756\n ],\n [\n -113.6865234375,\n 29.305561325527698\n ],\n [\n -113.35693359375,\n 28.9023972285585\n ],\n [\n -113.22509765625,\n 28.767659105691255\n ],\n [\n -112.9833984375,\n 28.401064827220896\n ],\n [\n -112.87353515625,\n 28.459033019728043\n ],\n [\n -112.82958984375,\n 28.130127737874005\n ],\n [\n -112.60986328125,\n 27.664068965384516\n ],\n [\n -112.32421875,\n 27.46928747369202\n ],\n [\n -112.08251953125,\n 27.176469131898898\n ],\n [\n -111.8408203125,\n 26.941659545381516\n ],\n [\n -111.533203125,\n 26.70635985763354\n ],\n [\n -111.4013671875,\n 26.23430203240673\n ],\n [\n -111.3134765625,\n 25.918526162075153\n ],\n [\n -111.09374999999999,\n 25.522614647623293\n ],\n [\n -110.72021484375,\n 25.045792240303445\n ],\n [\n -110.58837890625,\n 25.025884063244828\n ],\n [\n -110.72021484375,\n 24.746831298412058\n ],\n [\n -110.61035156249999,\n 24.347096633808512\n ],\n [\n -110.36865234374999,\n 24.226928664976377\n ],\n [\n -110.1708984375,\n 23.926013033021203\n ],\n [\n -110.478515625,\n 23.765236889758672\n ],\n [\n -110.80810546875,\n 24.126701958681682\n ],\n [\n -111.09374999999999,\n 24.587090339209634\n ],\n [\n -111.357421875,\n 25.18505888358067\n ],\n [\n -111.533203125,\n 25.62171595984575\n ],\n [\n -112.0166015625,\n 25.958044673317843\n ],\n [\n -112.412109375,\n 26.175158990178133\n ],\n [\n -113.00537109375,\n 27.196014383173306\n ],\n [\n -113.53271484375,\n 27.858503954841247\n ],\n [\n -113.75244140624999,\n 28.285033294640684\n ],\n [\n -114.10400390625,\n 28.536274512989916\n ],\n [\n -114.43359375,\n 28.748396571187406\n ],\n [\n -114.71923828124999,\n 29.113775395114416\n ],\n [\n -115.42236328124999,\n 30.164126343161097\n ],\n [\n -115.59814453125001,\n 30.619004797647808\n ],\n [\n -116.21337890625,\n 31.29732799140429\n ],\n [\n -116.69677734375,\n 31.840232667909365\n ],\n [\n -116.96044921875,\n 32.02670629333614\n ],\n [\n -117.13623046874999,\n 32.36140331527543\n ],\n [\n -117.333984375,\n 32.97180377635759\n ],\n [\n -117.44384765625,\n 33.22949814144951\n ],\n [\n -117.83935546874999,\n 33.578014746143985\n ],\n [\n -118.125,\n 33.76088200086917\n ],\n [\n -118.43261718749999,\n 33.797408767572485\n ],\n [\n -118.67431640625,\n 33.925129700072\n ],\n [\n -118.95996093749999,\n 33.99802726234877\n ],\n [\n -119.3115234375,\n 34.08906131584996\n ],\n [\n -119.42138671875,\n 34.21634468843465\n ],\n [\n -119.64111328125,\n 34.34343606848294\n ],\n [\n -119.83886718750001,\n 34.470335121217495\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State Unviersity Drive East
Sacramento, CA 95819
http://www.werc.usgs.gov/