In forests, total belowground carbon (C) flux (TBCF) is a large component of the C budget and represents a critical pathway for delivery of plant C to soil. Reducing uncertainty around regional estimates of forest C cycling may be aided by incorporating knowledge of controls over soil respiration and TBCF. Photosynthesis, and presumably TBCF, declines with advancing tree size and age, and photosynthesis increases yet C partitioning to TBCF decreases in response to high soil fertility. We hypothesized that these causal relationships would result in predictable patterns of TBCF, and partitioning of C to TBCF, with natural variability in leaf area index (LAI), soil nitrogen (N), and tree height in subalpine forests in the Rocky Mountains, USA. Using three consecutive years of soil respiration data collected from 22 0.38-ha locations across three 1-km2 subalpine forested landscapes, we tested three hypotheses: (1) annual soil respiration and TBCF will show a hump-shaped relationship with LAI; (2) variability in TBCF unexplained by LAI will be related to soil nitrogen (N); and (3) partitioning of C to TBCF (relative to woody growth) will decline with increasing soil N and tree height. We found partial support for Hypothesis 1 and full support for Hypotheses 2 and 3. TBCF, but not soil respiration, was explained by LAI and soil N patterns (r2 = 0.49), and the ratio of annual TBCF to TBCF plus aboveground net primary productivity (ANPP) was related to soil N and tree height (r2 = 0.72). Thus, forest C partitioning to TBCF can vary even within the same forest type and region, and approaches that assume a constant fraction of TBCF relative to ANPP may be missing some of this variability. These relationships can aid with estimates of forest soil respiration and TBCF across landscapes, using spatially explicit forest data such as national inventories or remotely sensed data products.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1418","usgsCitation":"Berryman, E.M., Ryan, M., Bradford, J.B., Hawbaker, T., and Birdsey, R., 2016, Total belowground carbon flux in subalpine forests is related to leaf area index, soil nitrogen, and tree height: Ecosphere, v. 7, no. 8, e01418; 16 p., https://doi.org/10.1002/ecs2.1418.","productDescription":"e01418; 16 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069872","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470660,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1418","text":"Publisher Index Page"},{"id":326484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b2d9a8e4b03bcb010287c2","contributors":{"authors":[{"text":"Berryman, Erin Michele 0000-0001-8699-2474 eberryman@usgs.gov","orcid":"https://orcid.org/0000-0001-8699-2474","contributorId":5765,"corporation":false,"usgs":true,"family":"Berryman","given":"Erin","email":"eberryman@usgs.gov","middleInitial":"Michele","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":645447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ryan, Michael G.","contributorId":101580,"corporation":false,"usgs":true,"family":"Ryan","given":"Michael G.","affiliations":[],"preferred":false,"id":645450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":645448,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":645449,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Birdsey, R.","contributorId":14670,"corporation":false,"usgs":true,"family":"Birdsey","given":"R.","email":"","affiliations":[],"preferred":false,"id":645451,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70175749,"text":"70175749 - 2016 - Development of targeted delivery techniques for Zequanox®","interactions":[],"lastModifiedDate":"2016-08-31T10:46:47","indexId":"70175749","displayToPublicDate":"2016-08-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Development of targeted delivery techniques for Zequanox®","docAbstract":"The effects of water temperature and concentration on the physical characteristics of Zequanox®, a dead-cell spray-dried powder formulation of Pseudomonas fluorescens (strain CL145A) used for controlling invasive dreissenid mussels (zebra mussel, Dreissena polymorpha, and quagga mussel, Dreissena bugensis), were investigated to determine optimal temperature-specific concentrations and delivery techniques for use during open-water subsurface Zequanox applications. Temperature-controlled laboratory tests evaluated viscosity, settling, stratification, and buoyancy of various concentrations of Zequanox suspension in water to select an optimal target viscosity for Zequanox applications. A two-step linear regression procedure was used to create a temperature-specific Zequanox prediction model from the viscosity data. The prediction model and subsurface application techniques were validated by conducting three independent outdoor pond trials at temperatures of ~9, 14, and 20°C. During these outdoor trials, subsurface applications of Zequanox at concentrations predicted by the model were performed and water samples were collected at varying depths and analyzed via spectroscopy to determine Zequanox concentration and dispersion. Although the predicted Zequanox concentrations and delivery techniques used resulted in successfully maintaining lethal Zequanox concentrations in the bottom 7.5 cm of the water column for the duration of the exposure, a revised prediction model is also provided for more accurately selecting temperature-specific Zequanox concentrations.","language":"English","publisher":"Legislative-Citizen Commission on Minnesota Resources (LCCMR)","usgsCitation":"Severson, T.J., and Luoma, J.A., 2016, Development of targeted delivery techniques for Zequanox®, 14 p.","productDescription":"14 p.","ipdsId":"IP-077367","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":328102,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":326870,"type":{"id":15,"text":"Index Page"},"url":"https://www.lccmr.leg.mn/projects/2013-index.html#201306f"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57c7ffb0e4b0f2f0cebfc231","contributors":{"authors":[{"text":"Severson, Todd J. 0000-0001-5282-3779 tseverson@usgs.gov","orcid":"https://orcid.org/0000-0001-5282-3779","contributorId":4749,"corporation":false,"usgs":true,"family":"Severson","given":"Todd","email":"tseverson@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":646302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":646303,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70175101,"text":"sir20165109 - 2016 - Network global navigation satellite system survey to harmonize water-surface elevation data for the Rainy River Basin","interactions":[],"lastModifiedDate":"2016-08-15T13:49:05","indexId":"sir20165109","displayToPublicDate":"2016-08-15T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5109","title":"Network global navigation satellite system survey to harmonize water-surface elevation data for the Rainy River Basin","docAbstract":"Continuously recording water-level streamgages in Rainy Lake and Namakan Reservoir are used to regulate water levels according to rule curves established in 2000 by the International Joint Commission; however, water levels at streamgages were referenced to a variety of vertical datums, confounding efforts to model the flow of water through the system, regulate water levels during periods of high inflow, and evaluate the effectiveness of the rule curves. In October 2014, the U.S. Geological Survey, Natural Resources Canada, International Joint Commission, and National Park Service began a joint field study with the goal of obtaining precise elevations referenced to a uniform vertical datum for all reference marks used to set water levels at streamgages throughout Rainy Lake and Namakan Reservoir. This report was prepared by the U.S. Geological Survey in cooperation with Natural Resources Canada, International Joint Commission, and National Park Service.
Three field crews deployed Global Navigation Satellite System receivers statically over 16 reference marks colocated with active and discontinued water-level streamgages throughout Rainy River, Rainy Lake, Namakan Reservoir, and select tributaries of Rainy Lake and Namakan Reservoir. A Global Navigation Satellite System receiver also was deployed statically over a National Geodetic Survey cooperative base network control station for use as a quality-control reference mark. Satellite data were collected simultaneously during a 5-day period and processed independently by the U.S. Geological Survey and Natural Resources Canada to obtain accurate positioning and elevations for the 17 surveyed reference marks. Processed satellite data were used to convert published water levels to elevations above sea level referenced to the Canadian Geodetic Vertical Datum of 2013 in order to compare water-surface elevations referenced to a uniform vertical datum throughout the study area. In this report, an “offset” refers to the correction applied to published data from a particular streamgage to produce elevation data referenced to a specified vertical datum.
Offsets were applied to water-level data from surveyed streamgages to further evaluate the accuracy and utility of updated reference mark elevations presented in this report. Daily mean water levels from active streamgages surveyed in this study were converted to water-surface elevations referenced to the Canadian Geodetic Vertical Datum of 2013. Graphical comparisons of water-surface elevations for streamgages in Namakan Reservoir, Rainy Lake, and selected rivers are presented (referencing the Canadian Geodetic Vertical Datum of 2013). Offsets presented in this report can be used in the evaluation of rule curves and in flood damage curves that fully assess the benefits of one regulation approach over another. In addition, offsets may be used to calibrate hydraulic models developed for four narrows that connect lakes of Namakan Reservoir, refine digital elevation models, and support modeling studies designed to assess the effects of rule curves on aquatic vegetation, benthic invertebrates, northern pike, and walleye.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165109","collaboration":"Prepared in cooperation with Natural Resources Canada, the International Joint Commission, and the National Park Service","usgsCitation":"Ziegeweid, J.R., Silliker, R.J., Densmore, B.K., and Krahulik, Justin, 2016, Network Global Navigation Satellite System survey to harmonize water-surface elevation data for the Rainy River Basin: U.S. Geological Survey Scientific Investigations Report 2016–5109, 20 p., https://dx.doi.org/10.3133/sir20165109.","productDescription":"Report: vi, 20 p.; Appendixes 1-3","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075640","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":326427,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5109/sir20165109_appendix2","text":"Appendix 2","size":"154 MB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2016–5109 Appendix 2"},{"id":326426,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5109/sir20165109_appendix1.xlsx","text":"Appendix 1","size":"269 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5109 Appendix 1"},{"id":326425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5109/sir20165109.pdf","text":"Report","size":"1.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5109"},{"id":326424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5109/coverthb.jpg"},{"id":326428,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5109/sir20165109_appendix3.xlsx","text":"Appendix 3","size":"22 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5109 Appendix 3"}],"country":"Canada, United States","state":"Minnesota, Ontario","otherGeospatial":"Rainy River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.63372802734375,\n 48.17158081783164\n ],\n [\n -93.63372802734375,\n 48.90444878143716\n ],\n [\n -92.20001220703125,\n 48.90444878143716\n ],\n [\n -92.20001220703125,\n 48.17158081783164\n ],\n [\n -93.63372802734375,\n 48.17158081783164\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.82025146484375,\n 48.40732607972984\n ],\n [\n -94.82025146484375,\n 49.0306652257167\n ],\n [\n -93.89190673828125,\n 49.0306652257167\n ],\n [\n -93.89190673828125,\n 48.40732607972984\n ],\n [\n -94.82025146484375,\n 48.40732607972984\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Minnesota Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, Minnesota 55112
\n
\n
","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2016-08-15","noUsgsAuthors":false,"publicationDate":"2016-08-15","publicationStatus":"PW","scienceBaseUri":"57b2d9a7e4b03bcb010287be","contributors":{"authors":[{"text":"Ziegeweid, Jeffrey R. 0000-0001-7797-3044 jrziege@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-3044","contributorId":4166,"corporation":false,"usgs":true,"family":"Ziegeweid","given":"Jeffrey","email":"jrziege@usgs.gov","middleInitial":"R.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":643920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silliker, R. Jason","contributorId":173254,"corporation":false,"usgs":false,"family":"Silliker","given":"R.","email":"","middleInitial":"Jason","affiliations":[{"id":7219,"text":"Natural Resources Canada","active":true,"usgs":false}],"preferred":false,"id":643921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Densmore, Brenda K. 0000-0003-2429-638X bdensmore@usgs.gov","orcid":"https://orcid.org/0000-0003-2429-638X","contributorId":4896,"corporation":false,"usgs":true,"family":"Densmore","given":"Brenda","email":"bdensmore@usgs.gov","middleInitial":"K.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":643922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krahulik, Justin 0000-0003-0917-9468 jkrahuli@usgs.gov","orcid":"https://orcid.org/0000-0003-0917-9468","contributorId":139523,"corporation":false,"usgs":true,"family":"Krahulik","given":"Justin","email":"jkrahuli@usgs.gov","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":643923,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70175467,"text":"70175467 - 2016 - Divergent projections of future land use in the United States arising from different models and scenarios","interactions":[],"lastModifiedDate":"2017-08-29T09:36:55","indexId":"70175467","displayToPublicDate":"2016-08-12T12:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Divergent projections of future land use in the United States arising from different models and scenarios","docAbstract":"
A variety of land-use and land-cover (LULC) models operating at scales from local to global have been developed in recent years, including a number of models that provide spatially explicit, multi-class LULC projections for the conterminous United States. This diversity of modeling approaches raises the question: how consistent are their projections of future land use? We compared projections from six LULC modeling applications for the United States and assessed quantitative, spatial, and conceptual inconsistencies. Each set of projections provided multiple scenarios covering a period from roughly 2000 to 2050. Given the unique spatial, thematic, and temporal characteristics of each set of projections, individual projections were aggregated to a common set of basic, generalized LULC classes (i.e., cropland, pasture, forest, range, and urban) and summarized at the county level across the conterminous United States. We found very little agreement in projected future LULC trends and patterns among the different models. Variability among scenarios for a given model was generally lower than variability among different models, in terms of both trends in the amounts of basic LULC classes and their projected spatial patterns. Even when different models assessed the same purported scenario, model projections varied substantially. Projections of agricultural trends were often far above the maximum historical amounts, raising concerns about the realism of the projections. Comparisons among models were hindered by major discrepancies in categorical definitions, and suggest a need for standardization of historical LULC data sources. To capture a broader range of uncertainties, ensemble modeling approaches are also recommended. However, the vast inconsistencies among LULC models raise questions about the theoretical and conceptual underpinnings of current modeling approaches. Given the substantial effects that land-use change can have on ecological and societal processes, there is a need for improvement in LULC theory and modeling capabilities to improve acceptance and use of regional- to national-scale LULC projections for the United States and elsewhere.
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C.","contributorId":141124,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker","email":"","middleInitial":"C.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":645360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Theobald, David M. 0000-0002-1271-9368","orcid":"https://orcid.org/0000-0002-1271-9368","contributorId":10271,"corporation":false,"usgs":false,"family":"Theobald","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":true,"id":645361,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sleeter, Benjamin M. 0000-0003-2371-9571 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Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"A long-term evaluation of biopsy darts and DNA to estimate cougar density","docAbstract":"Accurately estimating cougar (Puma concolor) density is usually based on long-term research consisting of intensive capture and Global Positioning System collaring efforts and may cost hundreds of thousands of dollars annually. Because wildlife agency budgets rarely accommodate this approach, most infer cougar density from published literature, rely on short-term studies, or use hunter harvest data as a surrogate in their jurisdictions; all of which may limit accuracy and increase risk of management actions. In an effort to develop a more cost-effective long-term strategy, we evaluated a research approach using citizen scientists with trained hounds to tree cougars and collect tissue samples with biopsy darts. We then used the DNA to individually identify cougars and employed spatially explicit capture–recapture models to estimate cougar densities. Overall, 240 tissue samples were collected in northeastern Washington, USA, producing 166 genotypes (including recaptures and excluding dependent kittens) of 133 different cougars (8-25/yr) from 2003 to 2011. Mark–recapture analyses revealed a mean density of 2.2 cougars/100 km2 (95% CI=1.1-4.3) and stable to decreasing population trends (β=-0.048, 95% CI=-0.106–0.011) over the 9 years of study, with an average annual harvest rate of 14% (range=7-21%). The average annual cost per year for field sampling and genotyping was US$11,265 ($422.24/sample or $610.73/successfully genotyped sample). Our results demonstrated that long-term biopsy sampling using citizen scientists can increase capture success and provide reliable cougar-density information at a reasonable cost.
","language":"English","publisher":"Wiley","doi":"10.1002/wsb.675","usgsCitation":"Beausoleil, R.A., Clark, J.D., and Maletzke, B.T., 2016, A long-term evaluation of biopsy darts and DNA to estimate cougar density: Wildlife Society Bulletin, v. 40, no. 3, p. 583-592, https://doi.org/10.1002/wsb.675.","productDescription":"10 p.","startPage":"583","endPage":"592","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-072668","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":500041,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/82b8f68faa4c4491bb97e6ba69823c2e","text":"External Repository"},{"id":332085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","county":"Ferry County","otherGeospatial":"Game Management Unit 101","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.22113037109375,\n 49.00004203215395\n ],\n [\n -118.22113037109375,\n 48.93152205931365\n ],\n [\n -118.19641113281249,\n 48.909864610926675\n ],\n [\n -118.2183837890625,\n 48.89180956320587\n ],\n [\n -118.20465087890625,\n 48.850258199721495\n ],\n [\n -118.16894531249999,\n 48.814098527355746\n ],\n [\n -118.12225341796874,\n 48.75075629617738\n ],\n [\n -118.10577392578124,\n 48.669198799260045\n ],\n [\n -118.14971923828124,\n 48.55661410872126\n ],\n [\n -118.19091796875,\n 48.50386733939323\n ],\n [\n -118.2403564453125,\n 48.472921272487824\n ],\n [\n -118.74298095703124,\n 48.48202501720533\n ],\n [\n -118.74572753906249,\n 48.511146022547344\n ],\n [\n -118.74572753906249,\n 48.5493419587775\n ],\n [\n -118.74572753906249,\n 48.585692256886624\n ],\n [\n -118.73199462890626,\n 48.64016871811908\n ],\n [\n -118.82537841796876,\n 48.65650019559012\n ],\n [\n -118.93524169921875,\n 48.68914728403741\n ],\n [\n -118.96270751953124,\n 48.70908786918211\n ],\n [\n -118.95996093749999,\n 48.725397018130344\n ],\n [\n -118.97644042968749,\n 48.721773219750666\n ],\n [\n -118.9105224609375,\n 48.75437800586312\n ],\n [\n -118.91601562499999,\n 48.7869616455333\n ],\n [\n -118.89129638671875,\n 48.84302835299516\n ],\n [\n -118.86932373046874,\n 48.88639177703194\n ],\n [\n -118.86932373046874,\n 48.90444878143716\n ],\n [\n -118.84735107421875,\n 48.92791313673253\n ],\n [\n -118.82537841796876,\n 48.93693495409401\n ],\n [\n -118.751220703125,\n 48.935130721045326\n ],\n [\n -118.77868652343751,\n 48.96579381461063\n ],\n [\n -118.77593994140624,\n 49.001843917978526\n ],\n [\n -118.22113037109375,\n 49.00004203215395\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-12","publicationStatus":"PW","scienceBaseUri":"585116bbe4b08138bf1abd58","contributors":{"authors":[{"text":"Beausoleil, Richard A.","contributorId":156253,"corporation":false,"usgs":false,"family":"Beausoleil","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":597058,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":597057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maletzke, Benjamin T.","contributorId":156254,"corporation":false,"usgs":false,"family":"Maletzke","given":"Benjamin","email":"","middleInitial":"T.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":597059,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175132,"text":"ds1012 - 2016 - Macroinvertebrate community sample collection methods and data collected from Sand Creek and Medano Creek, Great Sand Dunes National Park and Preserve, Colorado, 2005–07","interactions":[],"lastModifiedDate":"2016-08-11T12:03:51","indexId":"ds1012","displayToPublicDate":"2016-08-11T10:30:00","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":"1012","title":"Macroinvertebrate community sample collection methods and data collected from Sand Creek and Medano Creek, Great Sand Dunes National Park and Preserve, Colorado, 2005–07","docAbstract":"This report provides a table of site descriptions, sample information, and semiquantitative aquatic macroinvertebrate data from 105 samples collected between 2005 and 2007 from 7 stream sites within the Sand Creek and Medano Creek watersheds in Great Sand Dunes National Park and Preserve, Saguache County, Colorado. Additionally, a short description of sample collection methods and laboratory sample processing procedures is presented. These data were collected in anticipation of assessing the potential effects of fish toxicants on macroinvertebrates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1012","collaboration":"Prepared in cooperation with Great Sand Dunes National Park and Preserve","usgsCitation":"Ford, M.A., Zuellig, R.E., Walters, D.M., and Bruce, J.F., 2016, Macroinvertebrate community sample collection methods and data collected from Sand Creek and Medano Creek, Great Sand Dunes National Park and Preserve, Colorado, 2005–07: U.S. Geological Survey Data Series 1012, 3 p., https://dx.doi.org/10.3133/ds1012.","productDescription":"Report: iii, 3 p.; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-076746","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":326377,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1012/ds1012.pdf","text":"Report","size":"796 kB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1012 Report"},{"id":326376,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1012/coverthb.jpg"},{"id":326379,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/1012/ds1012_table1.xlsx","text":"Table 1. Description of sites, samples, and macroinvertebrate data","size":"176 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 1012 Appendix"}],"country":"United States","state":"Colorado","otherGeospatial":"Great Sand Dunes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.75,\n 37.99\n ],\n [\n -105.75,\n 37.65\n ],\n [\n -105.42,\n 37.65\n ],\n [\n -105.42,\n 37.99\n ],\n [\n -105.75,\n 37.99\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director
U.S. Geological Survey Fort Collins Science Center
2150 Centre Ave. Building C
Fort Collins, CO 80526-8118
http://www.fort.usgs.gov
Director
U.S. Geological Survey Colorado Water Science Center
Denver Federal Center, MS-415
Building 53
Denver, CO 80225-0046
http://co.water.usgs.gov
Climate change impacts ecosystems in many ways, from effects on species to phenology to wildfire dynamics. Assessing the potential vulnerability of ecosystems to future changes in climate is an important first step in prioritizing and planning for conservation. Although assessments of climate change vulnerability commonly are done for species, fewer have been done for ecosystems. To aid regional conservation planning efforts, we assessed climate change vulnerability for ecosystems in the Southeastern United States and Caribbean.
First, we solicited input from experts to create a list of candidate ecosystems for assessment. From that list, 12 ecosystems were selected for a vulnerability assessment that was based on a synthesis of available geographic information system (GIS) data and literature related to 3 components of vulnerability—sensitivity, exposure, and adaptive capacity. This literature and data synthesis comprised “Phase I” of the assessment. Sensitivity is the degree to which the species or processes in the ecosystem are affected by climate. Exposure is the likely future change in important climate and sea level variables. Adaptive capacity is the degree to which ecosystems can adjust to changing conditions. Where available, GIS data relevant to each of these components were used. For example, we summarized observed and projected climate, protected areas existing in 2011, projected sea-level rise, and projected urbanization across each ecosystem’s distribution. These summaries were supplemented with information in the literature, and a short narrative assessment was compiled for each ecosystem. We also summarized all information into a qualitative vulnerability rating for each ecosystem.
Next, for 2 of the 12 ecosystems (East Gulf Coastal Plain Near-Coast Pine Flatwoods and Nashville Basin Limestone Glade and Woodland), the NatureServe Habitat Climate Change Vulnerability Index (HCCVI) framework was used as an alternative approach for assessing vulnerability. Use of the HCCVI approach comprised “Phase II” of the assessment. This approach uses summaries of GIS data and models to develop a series of numeric indices for components of vulnerability. We incorporated many of the data sources used in Phase I, but added the results of several other data sources, including climate envelope modeling and vegetation dynamics modeling. The results of Phase II were high and low numeric vulnerability ratings for mid-century and the end of century for each ecosystem. The high and low ratings represented the potential range of vulnerability scores owing to uncertainties in future climate conditions and ecosystem effects.
Of the 12 ecosystems assessed in the first approach, five were rated as having high vulnerability (Caribbean Coastal Mangrove, Caribbean Montane Wet Elfin Forest, East Gulf Coastal Plain Southern Loess Bluff Forest, Edwards Plateau Limestone Shrubland, and Nashville Basin Limestone Glade and Woodland). Six ecosystems had medium vulnerability, and one ecosystem had low vulnerability. For the two ecosystems assessed with both approaches, vulnerability ratings generally agreed. The assessment concluded by comparing the two approaches, identifying critical research needs, and making suggestions for future ecosystem vulnerability assessments in the Southeast and beyond. Research needs include reducing uncertainty in the degree of climate exposure likely in the future, as well as acquiring more information on how climate might affect biotic interactions and hydrologic processes. Ideally, a comprehensive vulnerability assessment would include both the narrative summaries that resulted from the synthesis in Phase I, as well as a numeric index that incorporates uncertainty as in Phase II.
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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
3916 Sunset Ridge Rd
Raleigh, N.C. 27607
http://nc.water.usgs.gov/
Water from the Little Arkansas River is used as source water for artificial recharge of the Equus Beds aquifer, one of the primary water-supply sources for the city of Wichita, Kansas. The U.S. Geological Survey has operated two continuous real-time water-quality monitoring stations since 1995 on the Little Arkansas River in Kansas. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to compute concentrations of those constituents of interest. Site-specific regression models were originally published in 2000 for the near Halstead and near Sedgwick U.S. Geological Survey streamgaging stations and the site-specific regression models were then updated in 2003. This report updates those regression models using discrete and continuous data collected during May 1998 through August 2014. In addition to the constituents listed in the 2003 update, new regression models were developed for total organic carbon. The real-time computations of water-quality concentrations and loads are available at http://nrtwq.usgs.gov. The water-quality information in this report is important to the city of Wichita because water-quality information allows for real-time quantification and characterization of chemicals of concern (including chloride), in addition to nutrients, sediment, bacteria, and atrazine transported in the Little Arkansas River. The water-quality information in this report aids in the decision making for water treatment before artificial recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161057","collaboration":"Prepared in cooperation with the city of Wichita, Kansas","usgsCitation":"Rasmussen, P.P., Eslick, P.J., and Ziegler, A.C., 2016, Relations between continuous real-time physical properties and discrete water-quality constituents in the Little Arkansas River, south-central Kansas, 1998-2014: U.S. Geological Survey Open-File Report 2016–1057, 20 p., https://dx.doi.org/10.3133/ofr20161057.","productDescription":"Report: ii, 16 p.; Appendixes 1-2","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073013","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":326275,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1057/ofr20161057.pdf","text":"Report","size":"783 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1057"},{"id":326274,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1057/coverthb.jpg"},{"id":326280,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1057/ofr20161057_appendix2.pdf","text":"Appendix 2","size":"2.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1057 Appendix 2"},{"id":326276,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1057/ofr20161057_appendix1.pdf","text":"Appendix 1","size":"2.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1057 Appendix 1"}],"country":"United States","state":"Kansas","otherGeospatial":"Little Arkansas River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.2,\n 37.75\n ],\n [\n -98.2,\n 38.6\n ],\n [\n -97.25,\n 38.6\n ],\n [\n -97.25,\n 37.75\n ],\n [\n -98.2,\n 37.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
Dissolved-selenium loading analyses of data collected at 18 water-quality sites in the lower Gunnison River Basin in Colorado were completed through water year (WY) 2014. A WY is defined as October 1–September 30. Selenium is a trace element that bioaccumulates in aquatic food chains and can cause reproductive failure, deformities, and other harmful effects. This report presents information on the dissolved-selenium loads at 18 sites in the lower Gunnison River Basin for WYs 2011–2014. Annual dissolved-selenium loads were calculated at 5 sites with continuous U.S. Geological Survey (USGS) streamflow gages, whereas instantaneous dissolved-selenium loads were calculated for the remaining 13 sites using water-quality samples that had been collected periodically during WYs 2011–2014. Annual dissolved-selenium loads for WY 2014 ranged from 336 pounds (lb) at Uncompahgre River at Colona to 13,300 lb at Gunnison River near Grand Junction (Whitewater). Most sites in the basin had a median instantaneous dissolved-selenium load of less than 20.0 lb per day. In general, dissolved-selenium loads at Gunnison River main-stem sites showed an increase from upstream to downstream.
The State of Colorado water-quality standard for dissolved selenium of 4.6 micrograms per liter (µg/L) was compared to the 85th percentiles for dissolved selenium at selected water-quality sites. Annual 85th percentiles for dissolved selenium were calculated for the five core USGS sites having streamflow gages using estimated dissolved-selenium concentrations from linear regression models. These annual 85th percentiles in WY 2014 ranged from 0.97 µg/L at Uncompahgre River at Colona to 16.7 µg/L at Uncompahgre River at Delta. Uncompahgre River at Delta and Whitewater were the only core sites where water samples exceeded the State of Colorado water-quality standard for dissolved selenium of 4.6 µg/L.
Instantaneous 85th percentiles for dissolved selenium were calculated for sites with sufficient data using water-quality samples collected during WYs 2011–2014. The instantaneous 85th percentiles for samples for WY 2014 ranged from 1.1 µg/L at Uncompahgre River at Colona to 125 µg/L at Loutzenhizer Arroyo at North River Road.
A trend analysis was completed for Whitewater to determine if dissolved-selenium loads are increasing or decreasing. The trend analysis indicates a decrease of 8,000 lb from WY 1986 to WY 2014, a 34.8 percent reduction during the time period, and an additional 6.2 percent reduction from a reported 28.6 percent reduction during WYs 1986–2008. The trend analysis for WY 1992 to WY 2014 indicates a decrease of 5,800 lb per year, or 27.9 percent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161129","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Henneberg, M.F., 2016, 2014 annual summary of the lower Gunnison River Basin Selenium Management Program water-quality monitoring, Colorado: U.S. Geological Survey Open-File Report 2016–1129, 25 p., https://dx.doi.org/10.3133/ofr20161129. ","productDescription":"iv, 26 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-076878","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":326308,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1129/ofr20161129.pdf","text":"Report","size":"3.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1129"},{"id":326307,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1129/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.39935302734375,\n 39.095962936305504\n ],\n [\n -108.270263671875,\n 39.059716474034666\n ],\n [\n -108.160400390625,\n 39.03838632847038\n ],\n [\n -107.99011230468749,\n 39.06824672852526\n ],\n [\n -107.874755859375,\n 39.095962936305504\n ],\n [\n -107.786865234375,\n 39.089567854849314\n ],\n [\n -107.70172119140624,\n 39.0533181067413\n ],\n [\n -107.6055908203125,\n 38.976492485539424\n ],\n [\n -107.6055908203125,\n 38.805470223177466\n ],\n [\n -107.70721435546875,\n 38.62116234642254\n ],\n [\n -107.808837890625,\n 38.43207668538204\n ],\n [\n -107.841796875,\n 38.28131307922969\n ],\n [\n -107.81982421874999,\n 38.048091067457236\n ],\n [\n -107.81982421874999,\n 37.95286091815649\n ],\n [\n -107.92144775390625,\n 37.91820111976663\n ],\n [\n -108.0120849609375,\n 37.91603433975963\n ],\n [\n -108.15216064453125,\n 37.94203148678865\n ],\n [\n -108.26202392578125,\n 38.07404145941957\n ],\n [\n -108.44329833984374,\n 38.47939467327645\n ],\n [\n -108.5723876953125,\n 38.70908932739828\n ],\n [\n -108.6053466796875,\n 38.83542884007303\n ],\n [\n -108.58612060546875,\n 39.04691915968503\n ],\n [\n -108.49822998046875,\n 39.104488809440475\n ],\n [\n -108.39935302734375,\n 39.095962936305504\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225-0046
Knowledge of the factors affecting the vigor of desert riparian trees is important for their conservation and management. I used multiple regression to assess effects of streamflow and climate (12–14 years of data) or climate alone (up to 60 years of data) on radial growth of clonal narrowleaf cottonwood (Populus angustifolia), a foundation species in the arid, Closed Basin portion of the San Luis Valley, Colorado. I collected increment cores from trees (14–90 cm DBH) at four sites along each of Sand and Deadman creeks (total N = 85), including both perennial and ephemeral reaches. Analyses on trees <110 m from the stream channel explained 33–64% of the variation in standardized growth index (SGI) over the period having discharge measurements. Only 3 of 7 models included a streamflow variable; inclusion of prior-year conditions was common. Models for trees farther from the channel or over a deep water table explained 23–71% of SGI variability, and 4 of 5 contained a streamflow variable. Analyses using solely climate variables over longer time periods explained 17–85% of SGI variability, and 10 of 12 included a variable indexing summer precipitation. Three large, abrupt shifts in recent decades from wet to dry conditions (indexed by a seasonal Palmer Drought Severity Index) coincided with dramatically reduced radial growth. Each shift was presumably associated with branch dieback that produced a legacy effect apparent in many SGI series: uncharacteristically low SGI in the year following the shift. My results suggest trees in locations distant from the active channel rely on the regional shallow unconfined aquifer, summer rainfall, or both to meet water demands. The landscape-level differences in the water supplies sustaining these trees imply variable effects from shifts in winter-versus monsoon-related precipitation, and from climate change versus streamflow or groundwater management.
","language":"English","publisher":"Academic Press","publisherLocation":"London","doi":"10.1016/j.jaridenv.2016.07.005","usgsCitation":"Andersen, D., 2016, Climate, streamflow, and legacy effects on growth of riparian Populus angustifolia in the arid San Luis Valley, Colorado: Journal of Arid Environments, v. 134, p. 104-121, https://doi.org/10.1016/j.jaridenv.2016.07.005.","startPage":"104","endPage":"121","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071295","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":470671,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2016.07.005","text":"Publisher Index Page"},{"id":326343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.84983825683594,\n 37.62075814551956\n ],\n [\n -105.84983825683594,\n 38.03078569382294\n ],\n [\n -105.47561645507812,\n 38.03078569382294\n ],\n [\n -105.47561645507812,\n 37.62075814551956\n ],\n [\n -105.84983825683594,\n 37.62075814551956\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"134","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57ac4227e4b0d183567452ec","chorus":{"doi":"10.1016/j.jaridenv.2016.07.005","url":"http://dx.doi.org/10.1016/j.jaridenv.2016.07.005","publisher":"Elsevier BV","authors":"Andersen Douglas C.","journalName":"Journal of Arid Environments","publicationDate":"11/2016"},"contributors":{"authors":[{"text":"Andersen, Douglas doug_andersen@usgs.gov","contributorId":152661,"corporation":false,"usgs":true,"family":"Andersen","given":"Douglas","email":"doug_andersen@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":645133,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70175409,"text":"70175409 - 2016 - Nonlinear relationships can lead to bias in biomass calculations and drift-foraging models when using summaries of invertebrate drift data","interactions":[],"lastModifiedDate":"2016-09-06T13:32:42","indexId":"70175409","displayToPublicDate":"2016-08-09T10:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Nonlinear relationships can lead to bias in biomass calculations and drift-foraging models when using summaries of invertebrate drift data","docAbstract":"Drift-foraging models offer a mechanistic description of how fish feed in flowing water and the application of drift-foraging bioenergetics models to answer both applied and theoretical questions in aquatic ecology is growing. These models typically include nonlinear descriptions of ecological processes and as a result may be sensitive to how model inputs are summarized because of a mathematical property of nonlinear equations known as Jensen’s inequality. In particular, we show that the way in which continuous size distributions of invertebrate prey are represented within foraging models can lead to biases within the modeling process. We begin by illustrating how different equations common to drift-foraging models are sensitive to invertebrate inputs. We then use two case studies to show how different representations of invertebrate prey can influence predictions of energy intake and lifetime growth. Greater emphasis should be placed on accurate characterizations of invertebrate drift, acknowledging that inferences from drift-foraging models may be influenced by how invertebrate prey are represented.
","language":"English","publisher":"Kluwer Academic Publishers","doi":"10.1007/s10641-016-0507-8","usgsCitation":"Dodrill, M.J., and Yackulic, C.B., 2016, Nonlinear relationships can lead to bias in biomass calculations and drift-foraging models when using summaries of invertebrate drift data: Environmental Biology of Fishes, v. 99, no. 8, p. 659-670, https://doi.org/10.1007/s10641-016-0507-8.","productDescription":"12 p.","startPage":"659","endPage":"670","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070151","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":326332,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-09","publicationStatus":"PW","scienceBaseUri":"57ac50dbe4b0d1835674b25c","chorus":{"doi":"10.1007/s10641-016-0507-8","url":"http://dx.doi.org/10.1007/s10641-016-0507-8","publisher":"Springer Nature","authors":"Dodrill Michael J., Yackulic Charles B.","journalName":"Environmental Biology of Fishes","publicationDate":"8/9/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"8/9/2016"},"contributors":{"authors":[{"text":"Dodrill, Michael J. 0000-0002-7038-7170 mdodrill@usgs.gov","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":5468,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","email":"mdodrill@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":645105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":645106,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70174244,"text":"ofr20161061 - 2016 - Demographics and movements of least terns and piping plovers in the Central Platte River Valley, Nebraska","interactions":[],"lastModifiedDate":"2016-08-10T09:08:51","indexId":"ofr20161061","displayToPublicDate":"2016-08-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1061","title":"Demographics and movements of least terns and piping plovers in the Central Platte River Valley, Nebraska","docAbstract":"The Central Platte River Valley provides breeding habitat for a variety of migratory birds, including federally endangered interior least terns (Sternula antillarum; least tern) and threatened piping plovers (Charadrius melodus). Since 2009, researchers have collected demographic data on both species that span their lifecycle (that is, from egg laying through survival of adults). Demographic data were used to estimate vital rates (for example, nest survival, chick survival, and so on) for both species and assess how these vital rates were related to type and age of nesting habitat. Nest survival of both species was unrelated to the age of the site a nest was initiated on. Piping plover chick survival to fledging age was not related to the age of the site it was hatched at, however, the probability of a least tern chick surviving to fledging was higher at older sites. In general there were fewer piping plover nests than least tern nests found at sites created through either the physical construction of a new site or new vegetation management regimes, during 2009–14.
Mean daily least tern nest survival was 0.9742 (95-percent confidence interval [CI]: 0.9692–0.9783) and cumulative nest survival was 0.59 (95-percent CI: 0.53–0.65). Mean daily least tern chick survival was 0.9602 (95-percent CI: 0.9515–0.9673) and cumulative survival to fledging was 0.54 (95-percent CI = 0.48–0.61). Annual apparent survival rates were estimated at 0.42 (95-percent CI = 0.22–0.64) for adult least terns nesting in the Central Platte River Valley and an apparent survival rate of 0.14 (95-pecent CI = 0.04–0.41) for juvenile least terns. The number of least tern nests present at sites created during 2009–14 was associated with the age of the site; more least tern nests were associated with older sites. During 2009–14, there were four (less than 1 percent of all chicks marked) least tern chicks hatched from the Central Platte River Valley that were subsequently captured on nests as adults. Two of these least terns returned to nest at the same site they had hatched from. Ten instances were documented in which an adult least tern could either switch to nest at a new location or remain at the previous location with the onset of a new year. In five (50 percent) of these instances, least terns returned to nest on the site where they had nested in a previous year.
For piping plovers, mean daily apparent nest survival was 0.9880 (95-pecent CI: 0.9836–0.9912) and cumulative nest survival was 0.66 (95-pecent CI: 0.57–0.74). Mean daily piping plover chick survival was 0.9621 (95-pecent CI: 0.9514–0.9706) and cumulative survival to fledging was 0.46 (95-pecent CI = 0.37– 0.56). The annual apparent survival estimate for adult piping plovers nesting in the Central Platte River Valley was 0.76 (95-pecent CI = 0.65–0.85) and was 0.20 (95-pecent CI = 0.14–0.29) for juvenile piping plovers. The number of piping plover nests present at sites created through either the physical construction of a new site or new vegetation management regimes was also associated with site age, with more piping plover nests associated with older sites; however, in general there were fewer piping plover nests found at created sites than least tern nests. Only first-year adult piping plovers were observed on sites in the first year of availability, whereas older sites had a higher proportion of after-first-year adult piping plovers than first-year adult piping plovers. Twelve piping plover chicks (approximately 3 percent of all chicks marked) hatched from the Central Platte River Valley and were subsequently documented on nests as adults. All piping plovers returned to nest on different sites from the one on which they hatched. A total of 45 instances were documented in which an adult plover could either switch to nest at a new location or remain at the previous location with the onset of a new year. In 39 instances (87 percent), the adult nested on the same site as its prior documented nesting attempt and in 6 of these instances the adult switched to a new nesting location between years. There were 13 of 75 uniquely identifiable piping plovers observed to renest (that is, initiate more than one nest in a season) during 2009–14; no renests were observed among uniquely identifiable least terns. In all but one case, piping plover renests were found at the same site as the first nest initiated that season. For birds that renested, the mean initiation date of the first nest was May 6 and the mean initiation date of the second nest was June 8. On average, renests were initiated 7.5 days plus or minus 7.3 (SD [standard deviation]) following the date the initial nesting attempt was ‘fated’ (considered either failed or hatched).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161061","usgsCitation":"Roche, E.A., Sherfy, M.H., Ring, M.M., Shaffer, T.L., Anteau, M.J., and Stucker, J.H., 2016, Demographics and movements of least terns and piping plovers in the Central Platte River Valley, Nebraska: U.S. Geological Survey Open-File Report 2016–1061, 27 p., https://dx.doi.org/10.3133/ofr20161061.","productDescription":"vi, 27 p.","startPage":"1","endPage":"27","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-066073","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":326293,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1061/ofr20161061.pdf","text":"Report","size":"2.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1061"},{"id":326292,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1061/coverthb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Central Platte River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.69680786132812,\n 40.81588791441588\n ],\n [\n -99.78195190429688,\n 40.66813955408042\n ],\n [\n -99.71466064453125,\n 40.63896734381723\n ],\n [\n -99.60617065429688,\n 40.62437645591559\n ],\n [\n -99.49905395507812,\n 40.622291783092706\n ],\n [\n -99.39743041992188,\n 40.629587853312174\n ],\n [\n -99.36309814453125,\n 40.629587853312174\n ],\n [\n -99.283447265625,\n 40.61186744303007\n ],\n [\n -99.217529296875,\n 40.61082491956405\n ],\n [\n -99.07470703125,\n 40.60456943720527\n ],\n [\n -98.95660400390625,\n 40.606654663050485\n ],\n [\n -98.81927490234375,\n 40.614994915836924\n ],\n [\n -98.75198364257811,\n 40.637925243274374\n ],\n [\n -98.6956787109375,\n 40.65355504328842\n ],\n [\n -98.60641479492188,\n 40.67126439151552\n ],\n [\n -98.51852416992186,\n 40.70042247927178\n ],\n [\n -98.44436645507812,\n 40.713955826286046\n ],\n [\n -98.349609375,\n 40.751418432997454\n ],\n [\n -98.3056640625,\n 40.7743018636372\n ],\n [\n -98.2342529296875,\n 40.831475967182925\n ],\n [\n -98.16146850585938,\n 40.898981853950986\n ],\n [\n -98.09005737304688,\n 40.95915977213492\n ],\n [\n -98.00216674804688,\n 40.99959341455489\n ],\n [\n -97.943115234375,\n 41.036894775104436\n ],\n [\n -98.06121826171875,\n 41.12695250600846\n ],\n [\n -98.09692382812499,\n 41.088667173210624\n ],\n [\n -98.16421508789062,\n 41.05243077390835\n ],\n [\n -98.25759887695312,\n 41.01099329360268\n ],\n [\n -98.34686279296874,\n 40.977824533189526\n ],\n [\n -98.37844848632811,\n 40.95189982816191\n ],\n [\n -98.43475341796875,\n 40.90313381465847\n ],\n [\n -98.4814453125,\n 40.861602479810266\n ],\n [\n -98.54461669921875,\n 40.83667117059108\n ],\n [\n -98.63662719726562,\n 40.80861223601287\n ],\n [\n -98.70529174804686,\n 40.78054143186031\n ],\n [\n -98.75885009765625,\n 40.771181859756496\n ],\n [\n -98.84124755859375,\n 40.758700379161034\n ],\n [\n -98.93600463867188,\n 40.74725696280421\n ],\n [\n -99.01702880859374,\n 40.72956780913899\n ],\n [\n -99.11590576171875,\n 40.73268976628568\n ],\n [\n -99.22027587890624,\n 40.73268976628568\n ],\n [\n -99.32327270507812,\n 40.74309523218185\n ],\n [\n -99.40155029296875,\n 40.75245875985305\n ],\n [\n -99.49081420898438,\n 40.75557964275591\n ],\n [\n -99.62677001953125,\n 40.77846164090355\n ],\n [\n -99.69680786132812,\n 40.81588791441588\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, North Dakota 58401
Debris flows are a typical hazard on steep slopes after wildfire, but unlike debris flows that mobilize from landslides, most post-wildfire debris flows are generated from water runoff. The majority of existing debris-flow modeling has focused on landslide-triggered debris flows. In this study we explore the potential for using process-based rainfall-runoff models to simulate the timing of water flow and runoff-generated debris flows in recently burned areas. Two different spatially distributed hydrologic models with differing levels of complexity were used: the full shallow water equations and the kinematic wave approximation. Model parameter values were calibrated in two different watersheds, spanning two orders of magnitude in drainage area. These watersheds were affected by the 2009 Station Fire in the San Gabriel Mountains, CA, USA. Input data for the numerical models were constrained by time series of soil moisture, flow stage, and rainfall collected at field sites, as well as high-resolution lidar-derived digital elevation models. The calibrated parameters were used to model a third watershed in the burn area, and the results show a good match with observed timing of flow peaks. The calibrated roughness parameter (Manning's $n$) was generally higher when using the kinematic wave approximation relative to the shallow water equations, and decreased with increasing spatial scale. The calibrated effective watershed hydraulic conductivity was low for both models, even for storms occurring several months after the fire, suggesting that wildfire-induced changes to soil-water infiltration were retained throughout that time. Overall the two model simulations were quite similar suggesting that a kinematic wave model, which is simpler and more computationally efficient, is a suitable approach for predicting flood and debris flow timing in steep, burned watersheds.
","language":"English","publisher":"AGU Publications","doi":"10.1002/2015WR018176","usgsCitation":"Rengers, F.K., McGuire, L., Kean, J.W., Staley, D.M., and Hobley, D., 2016, Model simulations of flood and debris flow timing in steep catchments after wildfire: Water Resources Research, v. 52, no. 8, p. 6041-6061, https://doi.org/10.1002/2015WR018176.","productDescription":"21 p.","startPage":"6041","endPage":"6061","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073271","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2015wr018176","text":"Publisher Index Page"},{"id":326243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-11","publicationStatus":"PW","scienceBaseUri":"57a99f25e4b05e859bdf4859","contributors":{"authors":[{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":620765,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Luke lmcguire@usgs.gov","contributorId":167018,"corporation":false,"usgs":true,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":620766,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":620767,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":620768,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hobley, D.E.J","contributorId":167019,"corporation":false,"usgs":false,"family":"Hobley","given":"D.E.J","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":620769,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70175160,"text":"70175160 - 2016 - Remote sensing of tamarisk biomass, insect herbivory, and defoliation: Novel methods in the Grand Canyon Region, Arizona","interactions":[],"lastModifiedDate":"2016-08-08T13:19:55","indexId":"70175160","displayToPublicDate":"2016-08-08T14:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing of tamarisk biomass, insect herbivory, and defoliation: Novel methods in the Grand Canyon Region, Arizona","docAbstract":"Tamarisk is an invasive, riparian shrub species in the southwestern USA. The northern tamarisk beetle (Diorhabda carinulata) has been introduced to several states to control tamarisk. We classified tamarisk distribution in the Glen Canyon National Recreation Area, Arizona using a 0.2 m resolution, airborne multispectral data and estimated tamarisk beetle effects (overall accuracy of 86 percent) leading to leaf defoliation in a 49,408 m2 area. We also estimated individual tamarisk tree biomass and their uncertainties using airbonre liday data (100 points/m2). On average, total above ground tamarisk biomass was 8.67 kg/m2 (SD=17.6). The tamarisk beetle defoliation resulted in a mean leaf biomass loss of 0.52 kg/m2 and an equivalent of 25,692 kg across the entire study area. Our defoliated tamarisk map and biomass estimates can help inform restoration treatments to reduce tamarisk. Continued monitoring of tamarisk and tamarisk beetle effects are recommended to understand the currently-unknown eventual equilibrium between the two species and the cascading effects on ecosystem processes.
","language":"English","publisher":"ingenta Connect","doi":"10.14358/PERS.82.8.645","usgsCitation":"Sankey, T.T., Sankey, J.B., Horne, R., and Bedford, A., 2016, Remote sensing of tamarisk biomass, insect herbivory, and defoliation: Novel methods in the Grand Canyon Region, Arizona: Photogrammetric Engineering and Remote Sensing, v. 82, no. 8, p. 645-652, https://doi.org/10.14358/PERS.82.8.645.","productDescription":"8 p.","startPage":"645","endPage":"652","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-069946","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":488513,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.82.8.645","text":"Publisher Index Page"},{"id":326234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a99f25e4b05e859bdf485d","contributors":{"authors":[{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":644152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":644151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horne, Rene","contributorId":173299,"corporation":false,"usgs":false,"family":"Horne","given":"Rene","email":"","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":644154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bedford, Ashton","contributorId":173298,"corporation":false,"usgs":false,"family":"Bedford","given":"Ashton","email":"","affiliations":[{"id":27207,"text":"NAU and NPS","active":true,"usgs":false}],"preferred":false,"id":644153,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188567,"text":"70188567 - 2016 - Modeling streamflow from coupled airborne laser scanning and acoustic Doppler current profiler data","interactions":[],"lastModifiedDate":"2017-08-03T08:41:16","indexId":"70188567","displayToPublicDate":"2016-08-08T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5426,"text":"Hydrology Research","active":true,"publicationSubtype":{"id":10}},"title":"Modeling streamflow from coupled airborne laser scanning and acoustic Doppler current profiler data","docAbstract":"The rating curve enables the translation of water depth into stream discharge through a reference cross-section. This study investigates coupling national scale airborne laser scanning (ALS) and acoustic Doppler current profiler (ADCP) bathymetric survey data for generating stream rating curves. A digital terrain model was defined from these data and applied in a physically based 1-D hydraulic model to generate rating curves for a regularly monitored location in northern Sweden. Analysis of the ALS data showed that overestimation of the streambank elevation could be adjusted with a root mean square error (RMSE) block adjustment using a higher accuracy manual topographic survey. The results of our study demonstrate that the rating curve generated from the vertically corrected ALS data combined with ADCP data had lower errors (RMSE = 0.79 m3/s) than the empirical rating curve (RMSE = 1.13 m3/s) when compared to streamflow measurements. We consider these findings encouraging as hydrometric agencies can potentially leverage national-scale ALS and ADCP instrumentation to reduce the cost and effort required for maintaining and establishing rating curves at gauging station sites similar to the Röån River.
","language":"English","publisher":"IWA","doi":"10.2166/nh.2016.257","usgsCitation":"Norris, L., Kean, J.W., and Lyon, S., 2016, Modeling streamflow from coupled airborne laser scanning and acoustic Doppler current profiler data: Hydrology Research, v. 48, no. 4, p. 981-996, https://doi.org/10.2166/nh.2016.257.","productDescription":"16 p.","startPage":"981","endPage":"996","ipdsId":"IP-075690","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470677,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-135572","text":"External Repository"},{"id":342555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-08","publicationStatus":"PW","scienceBaseUri":"59439c94e4b062508e31a9b8","contributors":{"authors":[{"text":"Norris, Lam","contributorId":192981,"corporation":false,"usgs":false,"family":"Norris","given":"Lam","email":"","affiliations":[],"preferred":false,"id":698369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyon, Steve","contributorId":192971,"corporation":false,"usgs":false,"family":"Lyon","given":"Steve","affiliations":[],"preferred":false,"id":698371,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70175154,"text":"ofr20161125 - 2016 - Effects of climate change on tidal marshes along a latitudinal gradient in California","interactions":[],"lastModifiedDate":"2017-07-19T15:20:47","indexId":"ofr20161125","displayToPublicDate":"2016-08-05T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1125","title":"Effects of climate change on tidal marshes along a latitudinal gradient in California","docAbstract":"The coastal region of California supports a wealth of ecosystem services including habitat provision for wildlife and fisheries. Tidal marshes, mudflats, and shallow bays within coastal estuaries link marine, freshwater and terrestrial habitats, and provide economic and recreational benefits to local communities. Climate change effects such as sea-level rise (SLR) are altering these habitats, but we know little about how these areas will change over the next 50–100 years. Our study examined the projected effects of three recent SLR scenarios produced for the West Coast of North America on tidal marshes in California. We compiled physical and biological data, including coastal topography, tidal inundation, plant composition, and sediment accretion to project how SLR may alter these ecosystems in the future. The goal of our research was to provide results that support coastal management and conservation efforts across California. Under a low SLR scenario, all study sites remained vegetated tidal wetlands, with most sites showing little elevation and vegetation change relative to sea level. At most sites, mid SLR projections led to increases in low marsh habitat at the expense of middle and high marsh habitat. Marshes at Morro Bay and Tijuana River Estuary were the most vulnerable to mid SLR with many areas becoming intertidal mudflat. Under a high SLR scenario, most sites were projected to lose vegetated habitat, eventually converting to intertidal mudflats. Our results suggest that California marshes are vulnerable to major habitat shifts under mid or high rates of SLR, especially in the latter part of the century. Loss of vegetated tidal marshes in California due to SLR is expected to impact ecosystem services that are dependent on coastal wetlands such as wildlife habitat, carbon sequestration, improved water quality, and coastal protection from storms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161125","collaboration":"Prepared in cooperation with the Southwest Climate Science Center","usgsCitation":"Thorne, K.M., MacDonald, G.M., Ambrose, R.F., Buffington, K.J., Freeman, C.M., Janousek, C.N., Brown, L.N., Holmquist, J.R., Guntenspergen, G.R., Powelson, K.W., Barnard, P.L., and Takekawa, J.Y., 2016, Effects of climate change on tidal marshes along a latitudinal gradient in California: U.S. Geological Survey Open-File Report 2016-1125, 75 p., https://dx.doi.org/10.3133/ofr20161125.","productDescription":"Report: viii, 75 p.; Appendixes","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075871","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":326133,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1125/coverthb.jpg"},{"id":326134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1125/ofr20161125.pdf","text":"Report","size":"4.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1125 Report PDF"},{"id":326135,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1125/ofr20161125_appendixes.pdf","text":"Appendixes","size":"11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1125 Appendixes PDF"}],"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 -124.91455078125,\n 40.94671366508002\n ],\n [\n -122.51953124999999,\n 35.99578538642032\n ],\n [\n -120.52001953124999,\n 33.61461929233378\n ],\n [\n -117.92724609375,\n 32.41706632846282\n ],\n [\n -116.05957031249999,\n 32.657875736955305\n ],\n [\n -118.65234374999999,\n 34.63320791137959\n ],\n [\n -120.65185546875,\n 36.58024660149866\n ],\n [\n -122.98095703125,\n 41.0130657870063\n ],\n [\n -124.91455078125,\n 40.94671366508002\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/
Accurate distribution data are critical to the development of conservation and management strategies for imperiled species, particularly for narrow endemics with life history traits that make them vulnerable to extinction. Medionidus walkeri is a rare freshwater mussel endemic to the Suwannee River Basin in southeastern North America. This species was rediscovered in 2012 after a 16-year hiatus between collections and is currently proposed for listing under the Endangered Species Act. Our study fills knowledge gaps regarding changes in distribution and early life history requirements of M. walkeri. Spatiotemporal changes in M. walkeri distribution were displayed using a conservation status assessment map incorporating metadata from 98 historical (1916–1999) and 401 recent (2000–2015) site surveys from museums and field notes representing records for 312 specimens. Recent surveys detected M. walkeri only in the middle Suwannee subbasin (n = 86, 22 locations) and lower Santa Fe subbasin (n = 2, 2 locations), and it appears the species may be extirpated from 67% of historically occupied 10-digit HUCs. In our laboratory experiments, M. walkeri successfully metamorphosed on Percina nigrofasciata (56.2% ± 8.9) and Etheostoma edwini (16.1% ± 7.9) but not on Trinectes maculatus, Lepomis marginatus, Notropis texanus, Noturus leptacanthus, Etheostoma fusiforme, or Gambusia holbrooki. We characterize M. walkeri as a lure-displaying host fish specialist and a long-term brooder (bradytictic), gravid from fall to early summer of the following year. The early life history and distribution data presented here provide the baseline framework for listing decisions and future efforts to conserve and recover the species.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr00752","usgsCitation":"Johnson, N.A., Mcleod, J., Holcomb, J., Rowe, M.T., and Williams, J.D., 2016, Early life history and spatiotemporal changes in distribution of the rediscovered Suwannee moccasinshell Medionidus walkeri (Bivalvia: Unionidae): Endangered Species Research, v. 31, p. 163-175, https://doi.org/10.3354/esr00752.","productDescription":"13 p.","startPage":"163","endPage":"175","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074053","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470679,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr00752","text":"Publisher Index Page"},{"id":326300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57aaff09e4b05e859be0f1f6","contributors":{"authors":[{"text":"Johnson, Nathan A. 0000-0001-5167-1988 najohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":4175,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan","email":"najohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":645067,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mcleod, John 0000-0001-7962-7989 jmmcleod@usgs.gov","orcid":"https://orcid.org/0000-0001-7962-7989","contributorId":173552,"corporation":false,"usgs":true,"family":"Mcleod","given":"John","email":"jmmcleod@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":645068,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holcomb, Jordan","contributorId":173553,"corporation":false,"usgs":false,"family":"Holcomb","given":"Jordan","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":645069,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rowe, Matthew T.","contributorId":150928,"corporation":false,"usgs":false,"family":"Rowe","given":"Matthew","email":"","middleInitial":"T.","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":645070,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, James D.","contributorId":17690,"corporation":false,"usgs":false,"family":"Williams","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":645071,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70176262,"text":"70176262 - 2016 - Cross-seasonal effects on waterfowl productivity: Implications under climate change","interactions":[],"lastModifiedDate":"2016-09-07T12:21:17","indexId":"70176262","displayToPublicDate":"2016-08-05T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Cross-seasonal effects on waterfowl productivity: Implications under climate change","docAbstract":"Previous efforts to relate winter-ground precipitation to subsequent reproductive success as measured by the ratio of juveniles to adults in the autumn failed to account for increased vulnerability of juvenile ducks to hunting and uncertainty in the estimated age ratio. Neglecting increased juvenile vulnerability will positively bias the mean productivity estimate, and neglecting increased vulnerability and estimation uncertainty will positively bias the year-to-year variance in productivity because raw age ratios are the product of sampling variation, the year-specific vulnerability, and year-specific reproductive success. Therefore, we estimated the effects of cumulative winter precipitation in the California Central Valley and the Mississippi Alluvial Valley on pintail (Anas acuta) and mallard (Anas platyrhnchos) reproduction, respectively, using hierarchical Bayesian methods to correct for sampling bias in productivity estimates and observation error in covariates. We applied the model to a hunter-collected parts survey implemented by the United States Fish and Wildlife Service and band recoveries reported to the United States Geological Survey Bird Banding Laboratory using data from 1961 to 2013. We compared our results to previous estimates that used simple linear regression on uncorrected age ratios from a smaller subset of years in pintail (1961–1985). Like previous analyses, we found large and consistent effects of population size and wetland conditions in prairie Canada on mallard productivity, and large effects of population size and mean latitude of the observed breeding population on pintail productivity. Unlike previous analyses, we report a large amount of uncertainty in the estimated effects of wintering-ground precipitation on pintail and mallard productivity, with considerable uncertainty in the sign of the estimated main effect, although the posterior medians of precipitation effects were consistent with past studies. We found more consistent estimates in the sign of an interaction effect between population size and precipitation, suggesting that wintering-ground precipitation has a larger effect in years of high population size, especially for pintail. When we used the estimated effects in a population model to derive a sustainable harvest and population size projection (i.e., a yield curve), there was considerable uncertainty in the effect of increased or decreased wintering-ground precipitation on sustainable harvest potential and population size. These results suggest that the mechanism of cross-seasonal effects between winter habitat and reproduction in ducks occurs through a reduction in the strength of density dependence in years of above-average wintering-ground precipitation. We suggest additional investigation of the underlying mechanisms and that habitat managers and decision-makers consider the level of uncertainty in these estimates when attempting to integrate habitat management and harvest management decisions. Collection of annual data on the status of wintering-ground habitat in a rigorous sampling framework would likely be the most direct way to improve understanding of mechanisms and inform management.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.21124","usgsCitation":"Osnas, E.E., Zhao, Q., Runge, M.C., and Boomer, G., 2016, Cross-seasonal effects on waterfowl productivity: Implications under climate change: Journal of Wildlife Management, v. 80, no. 7, p. 1227-1241, https://doi.org/10.1002/jwmg.21124.","productDescription":"15 p.","startPage":"1227","endPage":"1241","ipdsId":"IP-070276","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":328312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"80","issue":"7","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-05","publicationStatus":"PW","scienceBaseUri":"57d13a39e4b0571647cf8db5","contributors":{"authors":[{"text":"Osnas, Erik E. 0000-0001-9528-0866 eosnas@usgs.gov","orcid":"https://orcid.org/0000-0001-9528-0866","contributorId":5586,"corporation":false,"usgs":true,"family":"Osnas","given":"Erik","email":"eosnas@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":648120,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Qing","contributorId":174370,"corporation":false,"usgs":false,"family":"Zhao","given":"Qing","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":648121,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":648119,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boomer, G Scott","contributorId":172150,"corporation":false,"usgs":false,"family":"Boomer","given":"G Scott","affiliations":[{"id":26994,"text":"Div. of Migratory Bird Management, U.S. Fish and Wildlife Service, MD","active":true,"usgs":false}],"preferred":false,"id":648122,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70174015,"text":"ds1006 - 2016 - Ground-penetrating radar and differential global positioning system data collected from Long Beach Island, New Jersey, April 2015","interactions":[],"lastModifiedDate":"2025-05-13T16:47:43.892357","indexId":"ds1006","displayToPublicDate":"2016-08-04T15:45:00","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":"1006","title":"Ground-penetrating radar and differential global positioning system data collected from Long Beach Island, New Jersey, April 2015","docAbstract":"Scientists from the United States Geological Survey, St. Petersburg Coastal and Marine Science Center, U.S. Geological Survey Pacific Coastal and Marine Science Center, and students from the University of Hawaii at Manoa collected sediment cores, sediment surface grab samples, ground-penetrating radar (GPR) and Differential Global Positioning System (DGPS) data from within the Edwin B. Forsythe National Wildlife Refuge–Holgate Unit located on the southern end of Long Beach Island, New Jersey, in April 2015 (FAN 2015-611-FA). The study’s objective was to identify washover deposits in the stratigraphic record to aid in understanding barrier island evolution. This report is an archive of GPR and DGPS data collected from Long Beach Island in 2015. Data products, including raw GPR and processed DGPS data, elevation corrected GPR profiles, and accompanying Federal Geographic Data Committee metadata can be downloaded from the Data Downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1006","usgsCitation":"Zaremba, N.J., Smith, K.E.L., Bishop, J.M., and Smith, C.G., 2016, Ground-penetrating radar and differential global positioning system data collected from Long Beach Island, New Jersey, April 2015: U.S. Geological Survey Data Series 1006, https://dx.doi.org/10.3133/ds1006.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-073028","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":325529,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":325528,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/1006/index.html"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.18243408203125,\n 39.7789912112384\n ],\n [\n -74.959716796875,\n 39.036252959636606\n ],\n [\n -75.0091552734375,\n 38.91881851059804\n ],\n [\n -74.79766845703125,\n 38.905995699991145\n ],\n [\n -74.68231201171875,\n 39.10875135935859\n ],\n [\n -74.54498291015625,\n 39.26203141523749\n ],\n [\n -74.2840576171875,\n 39.459523110465156\n ],\n [\n -74.03411865234375,\n 39.96870074491696\n ],\n [\n -74.18243408203125,\n 39.7789912112384\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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In 2013, the U.S. Geological Survey initiated a study to update regional skew, annual exceedance probability discharges, and regional regression equations used to estimate annual exceedance probability discharges for ungaged locations on streams in the study area with the use of recent geospatial data, new analytical methods, and available annual peak-discharge data through the 2013 water year. An analysis of regional skew using Bayesian weighted least-squares/Bayesian generalized-least squares regression was performed for Arkansas, Louisiana, and parts of Missouri and Oklahoma. The newly developed constant regional skew of -0.17 was used in the computation of annual exceedance probability discharges for 281 streamgages used in the regional regression analysis. Based on analysis of covariance, four flood regions were identified for use in the generation of regional regression models. Thirty-nine basin characteristics were considered as potential explanatory variables, and ordinary least-squares regression techniques were used to determine the optimum combinations of basin characteristics for each of the four regions. Basin characteristics in candidate models were evaluated based on multicollinearity with other basin characteristics (variance inflation factor < 2.5) and statistical significance at the 95-percent confidence level (p ≤ 0.05). Generalized least-squares regression was used to develop the final regression models for each flood region. Average standard errors of prediction of the generalized least-squares models ranged from 32.76 to 59.53 percent, with the largest range in flood region D. Pseudo coefficients of determination of the generalized least-squares models ranged from 90.29 to 97.28 percent, with the largest range also in flood region D. The regional regression equations apply only to locations on streams in Arkansas where annual peak discharges are not substantially affected by regulation, diversion, channelization, backwater, or urbanization. The applicability and accuracy of the regional regression equations depend on the basin characteristics measured for an ungaged location on a stream being within range of those used to develop the equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165081","collaboration":"Prepared in cooperation with the Arkansas State Highway and Transportation Department and the U.S. Army Corps of Engineers, Little Rock District","usgsCitation":"Wagner, D.M., Krieger, J.D., and Veilleux, A.G., 2016, Methods for estimating annual exceedance probability discharges for streams in Arkansas, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2016–5081, 136 p., https://dx.doi.org/10.3133/sir20165081.","productDescription":"Report: ix, 136 p.; Appendix Tables","numberOfPages":"149","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070399","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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This paper demonstrates geospatial modification of the USGS methodology for assessing geologic CO2 storage resources, and was applied to the Pre-Punta Gorda Composite and Dollar Bay reservoirs of the South Florida Basin. The study provides detailed evaluation of porous intervals within these reservoirs and utilizes GIS to evaluate the potential spatial distribution of reservoir parameters and volume of CO2 that can be stored. This study also shows that incorporating spatial variation of parameters using detailed and robust datasets may improve estimates of storage resources when compared to applying uniform values across the study area derived from small datasets, like many assessment methodologies. Geospatially derived estimates of storage resources presented here (Pre-Punta Gorda Composite = 105,570 MtCO2; Dollar Bay = 24,760 MtCO2) were greater than previous assessments, which was largely attributed to the fact that detailed evaluation of these reservoirs resulted in higher estimates of porosity and net-porous thickness, and areas of high porosity and thick net-porous intervals were incorporated into the model, likely increasing the calculated volume of storage space available for CO2 sequestration. The geospatial method for evaluating CO2 storage resources also provides the ability to identify areas that potentially contain higher volumes of storage resources, as well as areas that might be less favorable.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marpetgeo.2016.06.010","usgsCitation":"Roberts-Ashby, T., and Ashby, B.N., 2016, A method for examining the geospatial distribution of CO2 storage resources applied to the Pre-Punta Gorda Composite and Dollar Bay reservoirs of the South Florida Basin, U.S.A: Marine and Petroleum Geology, v. 77, p. 141-159, https://doi.org/10.1016/j.marpetgeo.2016.06.010.","productDescription":"18 p.","startPage":"141","endPage":"159","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073465","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":470683,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.marpetgeo.2016.06.010","text":"Publisher Index Page"},{"id":326136,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"South Florida Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.94677734375,\n 28.17855984939698\n ],\n [\n -80.419921875,\n 28.430052892335723\n ],\n [\n -79.70581054687499,\n 26.990618722964737\n ],\n [\n -79.617919921875,\n 25.21488107113259\n ],\n [\n -82.4853515625,\n 24.156778233303413\n ],\n [\n -82.99072265625,\n 24.87646991083154\n ],\n [\n -82.803955078125,\n 25.96792222903405\n ],\n [\n -82.94677734375,\n 28.17855984939698\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a5b8b0e4b0ebae89b7881b","contributors":{"authors":[{"text":"Roberts-Ashby, Tina 0000-0003-2940-1740 troberts-ashby@usgs.gov","orcid":"https://orcid.org/0000-0003-2940-1740","contributorId":173458,"corporation":false,"usgs":true,"family":"Roberts-Ashby","given":"Tina","email":"troberts-ashby@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":644776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ashby, Brandon N.","contributorId":173459,"corporation":false,"usgs":false,"family":"Ashby","given":"Brandon","email":"","middleInitial":"N.","affiliations":[{"id":27233,"text":"Washington Gas / WGL Holdings, Inc., Springfield, VA 22151","active":true,"usgs":false}],"preferred":false,"id":644777,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70174887,"text":"sir20165103 - 2016 - Low-flow characteristics for streams on the Islands of Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi","interactions":[],"lastModifiedDate":"2016-09-06T09:04:14","indexId":"sir20165103","displayToPublicDate":"2016-08-03T19:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5103","title":"Low-flow characteristics for streams on the Islands of Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi","docAbstract":"Statistical models were developed to estimate natural streamflow under low-flow conditions for streams with existing streamflow data at measurement sites on the Islands of Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi. Streamflow statistics used to describe the low-flow characteristics are flow-duration discharges that are equaled or exceeded between 50 and 95 percent of the time during the 30-year base period 1984–2013. Record-augmentation techniques were applied to develop statistical models relating concurrent streamflow data at the measurement sites and long-term data from nearby continuous-record streamflow-gaging stations that were in operation during the base period and were selected as index stations. Existing data and subsequent low-flow analyses of the available data help to identify streams in under-represented geographic areas and hydrogeologic settings where additional data collection is suggested.
Low-flow duration discharges were estimated for 107 measurement sites (including long-term and short-term continuous-record streamflow-gaging stations, and partial-record stations) and 27 index stations. The adequacy of statistical models was evaluated with correlation coefficients and modified Nash-Sutcliff coefficients of efficiency, and a majority of the low-flow duration-discharge estimates are satisfactory based on these regression statistics.
Molokaʻi and Hawaiʻi have the fewest number of measurement sites (that are not located on ephemeral stream reaches) at which flow-duration discharges were estimated, which can be partially explained by the limited number of index stations available on these islands that could be used for record augmentation. At measurement sites on some tributary streams, low-flow duration discharges could not be estimated because no adequate correlations could be developed with the index stations. These measurement sites are located on streams where duration-discharge estimates are available at long-term stations at other locations on the main stream channel to provide at least some definition of low-flow characteristics on that stream. In terms of general natural streamflow data availability, data are scarce in the leeward areas for all five islands as many leeward streams are dry or have minimal flow. Other under-represented areas include central Oʻahu, central Maui, and southeastern Maui.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165103","collaboration":"Prepared in cooperation with the State of Hawaiʻi Commission on Water Resource Management, State of Hawaiʻi Department of Hawaiian Home Lands, and Office of Hawaiian Affairs","usgsCitation":"Cheng, C.L., 2016, Low-flow characteristics for streams on the Islands of Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2016-5103, 36 p., https://dx.doi.org/10.3133/sir20165103.","productDescription":"Report: v, 36 p.; Appendixes 1-2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-065440","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":325965,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5103/coverthb.jpg"},{"id":325966,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5103/sir20165103.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5103 Report PDF"},{"id":325967,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5103/sir20165103_appendix1.xlsx","text":"Appendix 1 - ","size":"29 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5103 Appendix 1 spreadsheet","linkHelpText":"Station Numbers, Names, Station Type, Period of Record, and Record Length of Measurement Sites Used in this Study, State of Hawaiʻi."},{"id":325968,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5103/sir20165103_appendix2.xlsx","text":"Appendix 2 - ","size":"56 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U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
http://hi.water.usgs.gov/
Understanding groundwater-budget components, particularly groundwater recharge, is important to sustainably manage both groundwater and surface water supplies in the Colorado River basin now and in the future. This study quantifies projected changes in upper Colorado River basin (UCRB) groundwater recharge from recent historical (1950–2015) through future (2016–2099) time periods, using a distributed-parameter groundwater recharge model with downscaled climate data from 97 Coupled Model Intercomparison Project Phase 5 climate projections. Simulated future groundwater recharge in the UCRB is generally expected to be greater than the historical average in most decades. Increases in groundwater recharge in the UCRB are a consequence of projected increases in precipitation, offsetting reductions in recharge that would result from projected increased temperatures.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016GL069714","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2016, Changes in groundwater recharge under projected climate in the upper Colorado River basin: Geophysical Research Letters, v. 43, no. 13, p. 6968-6974, https://doi.org/10.1002/2016GL069714.","productDescription":"7 p.","startPage":"6968","endPage":"6974","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075002","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":326086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.95068359374999,\n 36.527294814546245\n ],\n [\n -111.95068359374999,\n 42.032974332441405\n ],\n [\n -106.3916015625,\n 42.032974332441405\n ],\n [\n -106.3916015625,\n 36.527294814546245\n ],\n [\n -111.95068359374999,\n 36.527294814546245\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"13","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-07-08","publicationStatus":"PW","scienceBaseUri":"57a307a7e4b006cb455561ad","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":644665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":644666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":644667,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70173919,"text":"ofr20161100 - 2016 - Preliminary results from exploratory sampling of wells for the California oil, gas, and groundwater program, 2014–15","interactions":[],"lastModifiedDate":"2017-02-07T15:35:55","indexId":"ofr20161100","displayToPublicDate":"2016-08-03T12:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1100","title":"Preliminary results from exploratory sampling of wells for the California oil, gas, and groundwater program, 2014–15","docAbstract":"In 2014 and 2015, the U.S. Geological Survey (USGS) sampled water wells in the Los Angeles Basin and southern San Joaquin Valley, California, and oil wells in the San Joaquin Valley for analysis of multiple chemical, isotopic, and groundwater-age tracers. The purpose of this reconnaissance sampling was to evaluate the utility of tracers for assessing the effects of oil and gas production activities on groundwater quality in California. The study was done in cooperation with the California State Water Resources Control Board. Results of the study are intended to help design a regional groundwater-monitoring program to be implemented as part of California Senate Bill 4 (SB 4 statutes of 2013). The regional monitoring program plans to assess the effects of oil and gas production activities on groundwater quality and to provide a regional context for local monitoring of the groundwater-quality effects from well-stimulation treatments, which are techniques used to improve oil and gas production by increasing their rate of flow to the well. California SB 4 mandates that this local monitoring is to be done by oil-well operators in accordance with monitoring criteria established by the State Water Board.
\nThis report evaluates the utility of the chemical, isotopic, and groundwater-age tracers for assessing sources of salinity, methane, and petroleum hydrocarbons in groundwater overlying or near several California oil fields. Tracers of dissolved organic carbon in
oil-field-formation water are also discussed. Tracer data for samples collected from 51 water wells and 4 oil wells are examined.
Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
http://ca.water.usgs.gov
Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center in St. Petersburg, Florida, collected bathymetric data along the upper shoreface and within the wilderness breach at Fire Island, New York, in June 2014. The U.S. Geological Survey is involved in a post-Hurricane Sandy effort to map and monitor the morphologic evolution of the shoreface along Fire Island and model the evolution of the wilderness breach as a part of the Hurricane Sandy Supplemental Project GS2-2B. During this study, bathymetry was collected with single-beam echo sounders and global positioning systems, mounted to personal watercraft, along the Fire Island shoreface and within the wilderness breach. Additional bathymetry was collected using backpack global positioning systems along the flood shoals and shallow channels within the wilderness breach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1007","usgsCitation":"Nelson, T.R., Miselis, J.L., Hapke, C.J., Wilson, K.E., Henderson, R.E., Brenner, O.T., Reynolds, B.J., and Hansen, M.E., 2016, Coastal bathymetry data collected in June 2014 from Fire Island, New York—The wilderness breach and shoreface: U.S. Geological Survey Data Series 1007, https://dx.doi.org/10.3133/ds1007.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071019","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":325940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":325903,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1007"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.35,\n 40.875\n ],\n [\n -73.35,\n 40.6\n ],\n [\n -72.7,\n 40.6\n ],\n [\n -72.7,\n 40.875\n ],\n [\n -73.35,\n 40.875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"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