Young gold systems in the Great Basin (£ 7 Ma), though not as well studied as their older counterparts, comprise a rapidly growing and in some ways controversial group. The gold inventory for these systems has more than doubled in the last 5 years from roughly 370 tonnes (12 Moz) to 890 tonnes (29 Moz). Although these deposits are characterized by low grades, tonnages can be high and stripping ratios low, and they have been mined profitably, as exemplified by Florida Canyon and Hycroft. Active geothermal systems in the Great Basin also comprise a rapidly growing group, as evidenced by a number of recent discoveries of geothermal groundwater and a more than 50% increase in electricity production capacity from these systems in the last 5 years.
Many young gold deposits are closely associated with active geothermal systems, suggesting that gold deposits may be forming today in the Great Basin. Measured or estimated geothermal reservoir temperatures commonly approach or exceed 200∞C, and other characteristics and processes (advanced argillic caps, hydrothermal eruption breccias) of these young deposits resemble those of nearby Tertiary precious metal deposits. Nonetheless, many young gold systems, especially in Nevada, are not associated with coeval igneous rocks. Similarly, almost all electricity-grade geothermal systems in Nevada are not associated with Quaternary silicic volcanic rocks, and have lower temperature gradients, lower 3He/4He ratios, and lower dissolved trace element concentrations than most magmatic-heated geothermal systems elsewhere in the world.
The increasing economic significance of young gold deposits and active geothermal systems justifies more research to better understand their origins, particularly because in some aspects they remain enigmatic and controversial. Are young gold deposits in Nevada truly amagmatic, or have they received metal and fluid contributions from magmas deeper within the crust? Has gold in these deposits been remobilized from older gold mineralization? Current research is investigating these and other questions to improve our genetic understanding of these young gold systems, which in turn can lead to improved exploration targeting.
The recent rapid growth in resources for both young gold deposits and geothermal systems underscores their underdeveloped exploration potential. Even though many young gold deposits exhibit relatively shallow hot-springs-style mineralization, their young age may preclude exposure by erosion. Uplift along active normal faults has exposed some deposits (e.g., Florida Canyon, Dixie Comstock, Wind Mountain), but in other areas, such as the Walker Lane, where strike-slip faulting is prevalent, the opportunities for exposure can be limited. Many active geothermal systems are also concealed below the surface in that hot springs or steam vents may be absent above areas of thermal groundwater.
With sources of energy to support mine production becoming more problematic, the potential advantages of simultaneously exploring for young gold deposits and spatially associated geothermal systems are becoming more apparent. Exploration methods recently proven effective in geothermal exploration that can be adapted to gold exploration include temperature surveys, hyperspectral remote sensing, geophysical surveys, water analyses, and detailed mapping of geothermal-related features and related fault systems.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geological Society of Nevada Symposium 2010: Great Basin Evolution and Metallogeny","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Geological Society of Nevada Symposium 2010: Great Basin Evolution and Metallogeny","conferenceDate":"May 14-22, 2010","conferenceLocation":"Reno, NV","language":"English","publisher":"Geological Society of Nevada","usgsCitation":"Coolbaugh, M.F., Vikre, P., and Faulds, J., 2011, Young (<7 Ma) gold deposits and active geothermal systems of the Great Basin: Enigmas, questions, and exploration potential, in Geological Society of Nevada Symposium 2010: Great Basin Evolution and Metallogeny, Reno, NV, May 14-22, 2010, p. 845-859.","productDescription":"15 p.","startPage":"845","endPage":"859","ipdsId":"IP-022789","costCenters":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":350792,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.atlasgeoinc.com/services/geothermal-exploration-and-assessment/geology/"},{"id":350793,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a719270e4b0a9a2e9dbde1c","contributors":{"authors":[{"text":"Coolbaugh, Mark F.","contributorId":193870,"corporation":false,"usgs":false,"family":"Coolbaugh","given":"Mark","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":726187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vikre, Peter G. pvikre@usgs.gov","contributorId":1800,"corporation":false,"usgs":true,"family":"Vikre","given":"Peter G.","email":"pvikre@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":726188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faulds, James E.","contributorId":184258,"corporation":false,"usgs":false,"family":"Faulds","given":"James E.","affiliations":[],"preferred":false,"id":726189,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192886,"text":"70192886 - 2011 - Nitrogen contamination of surficial aquifers - A growing legacy","interactions":[],"lastModifiedDate":"2021-04-06T19:01:05.958661","indexId":"70192886","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Nitrogen contamination of surficial aquifers - A growing legacy","docAbstract":"The virtual ubiquity of fertilizer-fed agriculture, increasing over several decades, has become necessary to support the global human population. Ironically, widespread use of nitrogen (N) has contaminated another vital resource: surficial fresh groundwater. Further, as nitrous oxide (N2O) is a potent greenhouse gas, anthropogenic manipulation of N budgets has ramifications that can extend far beyond national borders. To get a handle on the size of the problem, Puckett et al. present an approach to track historical contamination and thus analyze trends now and in the past with implications for the future.
An intense winter fishery for sauger Sander canadensis exists in the lower Tennessee River, and the objective of this study was to estimate the survival of angled saugers. In February 2008 and January–March 2009, 81 angled saugers (72 live plus 9 euthanized) were affixed with ultrasonic tags. The movements (or lack thereof) by saugers released alive were compared with those of euthanized fish to assess survival. Sixty-eight percent of the tagged saugers that were released alive exhibited maximum daily movements exceeding the greatest movement of any euthanized fish (0.5 km/d), and those fish were subsequently classified as survivors. The upstream movements of several euthanized fish indicated that their carcasses were ingested by piscivorous scavengers. In logistic models, the probability of mortality was significantly and inversely related to total length but not to capture depth, water temperature, handling time, or ascent rate. In 2 × 2 contingency tables, the fate of released saugers was not found to be associated with either the presence or absence of bleeding from the hooking wound or whether or not the fish displayed gastric distension. Most released fish survived despite the fact that gastric distension was observed in 72% of the angled saugers.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2011.598395","usgsCitation":"Kitterman, C.L., and Bettoli, P.W., 2011, Survival of angled saugers in the lower Tennessee River: North American Journal of Fisheries Management, v. 31, no. 3, p. 567-573, https://doi.org/10.1080/02755947.2011.598395.","productDescription":"7 p.","startPage":"567","endPage":"573","ipdsId":"IP-014953","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":349482,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Tennessee River","volume":"31","issue":"3","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2011-07-14","publicationStatus":"PW","scienceBaseUri":"5a6107fee4b06e28e9c25642","contributors":{"authors":[{"text":"Kitterman, Christy L.","contributorId":200947,"corporation":false,"usgs":false,"family":"Kitterman","given":"Christy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":723899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bettoli, Phillip William pbettoli@usgs.gov","contributorId":1919,"corporation":false,"usgs":true,"family":"Bettoli","given":"Phillip","email":"pbettoli@usgs.gov","middleInitial":"William","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":716111,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192484,"text":"70192484 - 2011 - Disaster risk assessment case study: Recent drought on the Navajo Nation, USA","interactions":[],"lastModifiedDate":"2018-03-02T12:53:43","indexId":"70192484","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Disaster risk assessment case study: Recent drought on the Navajo Nation, USA","docAbstract":"The Navajo Nation is an ecologically sensitive semi-arid to arid section of the southern Colorado Plateau. In this remote part of the United States, located at the Four Corners (Arizona, New Mexico, Colorado, and Utah), traditional people live a subsistence lifestyle that is inextricably tied to, and dependent upon, landscape conditions and water supplies. Soft bedrock lithologies and sand dunes dominate the region, making it highly sensitive to fluctuations in precipitation intensity, percent vegetation cover, and local land use practices. However, this region has sparse and discontinuous meteorological monitoring records. As a complement to the scant long-term meteorological records and historical documentation, we conducted interviews with 50 Native American elders from the Navajo Nation and compiled their lifetime observations on the changes in water availability, weather, and sand or dust storms. We then used these observations to further refine our understanding of the historical trends and impacts of climate change and drought for the region. In addition to altered landscape conditions due to climatic change, drought, and varying land use practices over the last 130 years, the Navajo people have been affected by federal policies and harsh economic conditions which weaken their cultural fabric. We conclude that a long-term drying trend and decreasing snowpack, superimposed on regional drought cycles, will magnify drought impacts on the Navajo Nation and leave its people increasingly vulnerable.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2011 Global Assessment Report on Disaster Risk Reduction","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"United Nations","usgsCitation":"Hiza, M., Kelley, K.B., Francis, H., and Block, D., 2011, Disaster risk assessment case study: Recent drought on the Navajo Nation, USA, 19 p.","productDescription":"19 p.","ipdsId":"IP-026900","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":352184,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347430,"type":{"id":15,"text":"Index Page"},"url":"https://www.preventionweb.net/english/hyogo/gar/2011/en/what/drought.html"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afef55ee4b0da30c1bfc8fb","contributors":{"authors":[{"text":"Hiza, Margaret 0000-0003-2851-2502 mhiza@usgs.gov","orcid":"https://orcid.org/0000-0003-2851-2502","contributorId":198449,"corporation":false,"usgs":true,"family":"Hiza","given":"Margaret","email":"mhiza@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":716053,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelley, Klara B.","contributorId":198451,"corporation":false,"usgs":false,"family":"Kelley","given":"Klara","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":716055,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Francis, Harris","contributorId":198450,"corporation":false,"usgs":false,"family":"Francis","given":"Harris","email":"","affiliations":[],"preferred":false,"id":716054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Block, Debra 0000-0001-7348-3064 dblock@usgs.gov","orcid":"https://orcid.org/0000-0001-7348-3064","contributorId":198448,"corporation":false,"usgs":true,"family":"Block","given":"Debra","email":"dblock@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":716052,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70033997,"text":"70033997 - 2011 - Understanding interaction effects of climate change and fire management on bird distributions through combined process and habitat models","interactions":[],"lastModifiedDate":"2026-01-29T14:30:04.625608","indexId":"70033997","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Understanding interaction effects of climate change and fire management on bird distributions through combined process and habitat models","docAbstract":"Avian conservation efforts must account for changes in vegetation composition and structure associated with climate change. We modeled vegetation change and the probability of occurrence of birds to project changes in winter bird distributions associated with climate change and fire management in the northern Chihuahuan Desert (southwestern U.S.A.). We simulated vegetation change in a process-based model (Landscape and Fire Simulator) in which anticipated climate change was associated with doubling of current atmospheric carbon dioxide over the next 50 years. We estimated the relative probability of bird occurrence on the basis of statistical models derived from field observations of birds and data on vegetation type, topography, and roads. We selected 3 focal species, Scaled Quail (Callipepla squamata), Loggerhead Shrike (Lanius ludovicianus), and Rock Wren (Salpinctes obsoletus), that had a range of probabilities of occurrence for our study area. Our simulations projected increases in relative probability of bird occurrence in shrubland and decreases in grassland and Yucca spp. and ocotillo (Fouquieria splendens) vegetation. Generally, the relative probability of occurrence of all 3 species was highest in shrubland because leaf-area index values were lower in shrubland. This high probability of occurrence likely is related to the species' use of open vegetation for foraging. Fire suppression had little effect on projected vegetation composition because as climate changed there was less fuel and burned area. Our results show that if future water limits on plant type are considered, models that incorporate spatial data may suggest how and where different species of birds may respond to vegetation changes.
","language":"English, Spanish","publisher":"Society for Conservation Biology","doi":"10.1111/j.1523-1739.2011.01684.x","issn":"08888892","usgsCitation":"White, J., Gutzwiller, K.J., Barrow, W., Johnson-Randall, L., Zygo, L., and Swint, P., 2011, Understanding interaction effects of climate change and fire management on bird distributions through combined process and habitat models: Conservation Biology, v. 25, no. 3, p. 536-546, https://doi.org/10.1111/j.1523-1739.2011.01684.x.","productDescription":"11 p.","startPage":"536","endPage":"546","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":216808,"rank":2,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/j.1523-1739.2011.01684.x"},{"id":244700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chihuahuan Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.533203125,\n 28.92163128242129\n ],\n [\n -111.533203125,\n 34.379712580462204\n ],\n [\n -101.337890625,\n 34.379712580462204\n ],\n [\n -101.337890625,\n 28.92163128242129\n ],\n [\n -111.533203125,\n 28.92163128242129\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"3","noUsgsAuthors":false,"publicationDate":"2011-04-28","publicationStatus":"PW","scienceBaseUri":"505bbc4be4b08c986b328b52","contributors":{"authors":[{"text":"White, Joseph D.","contributorId":56077,"corporation":false,"usgs":true,"family":"White","given":"Joseph D.","affiliations":[],"preferred":false,"id":443575,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gutzwiller, Kevin J.","contributorId":101923,"corporation":false,"usgs":true,"family":"Gutzwiller","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":443576,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barrow, Wylie C. 0000-0003-4671-2823 barroww@usgs.gov","orcid":"https://orcid.org/0000-0003-4671-2823","contributorId":1988,"corporation":false,"usgs":true,"family":"Barrow","given":"Wylie C.","email":"barroww@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":443571,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson-Randall, Lori 0000-0003-0100-994X","orcid":"https://orcid.org/0000-0003-0100-994X","contributorId":43604,"corporation":false,"usgs":true,"family":"Johnson-Randall","given":"Lori","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":443573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zygo, Lisa","contributorId":9898,"corporation":false,"usgs":true,"family":"Zygo","given":"Lisa","affiliations":[],"preferred":false,"id":443572,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Swint, Pamela","contributorId":32765,"corporation":false,"usgs":true,"family":"Swint","given":"Pamela","email":"","affiliations":[],"preferred":false,"id":443574,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70192879,"text":"70192879 - 2011 - The regionalization of national-scale SPARROW models for stream nutrients","interactions":[],"lastModifiedDate":"2018-03-15T10:26:55","indexId":"70192879","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"The regionalization of national-scale SPARROW models for stream nutrients","docAbstract":"This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national-scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national-scale and regional-scale information to estimate a hybrid model that imposes cross-region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.
","language":"English","publisher":"Wiley","doi":"10.1111/j.1752-1688.2011.00581.x","usgsCitation":"Schwarz, G., Alexander, R.B., Smith, R.A., and Preston, S.D., 2011, The regionalization of national-scale SPARROW models for stream nutrients: Journal of the American Water Resources Association, v. 47, no. 5, p. 1151-1172, https://doi.org/10.1111/j.1752-1688.2011.00581.x.","productDescription":"22 p.","startPage":"1151","endPage":"1172","ipdsId":"IP-023218","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":475095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1752-1688.2011.00581.x","text":"Publisher Index Page"},{"id":348671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2011-08-22","publicationStatus":"PW","scienceBaseUri":"5a6107fee4b06e28e9c25640","contributors":{"authors":[{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":543,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory E.","email":"gschwarz@usgs.gov","affiliations":[{"id":5067,"text":"Northeast Regional Director's Office","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":false,"id":717280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alexander, Richard B. 0000-0001-9166-0626 ralex@usgs.gov","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":541,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","email":"ralex@usgs.gov","middleInitial":"B.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":717281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Richard A. 0000-0003-2117-2269 rsmith1@usgs.gov","orcid":"https://orcid.org/0000-0003-2117-2269","contributorId":580,"corporation":false,"usgs":true,"family":"Smith","given":"Richard","email":"rsmith1@usgs.gov","middleInitial":"A.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":717282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Preston, Stephen D. 0000-0003-1515-6692 spreston@usgs.gov","orcid":"https://orcid.org/0000-0003-1515-6692","contributorId":1463,"corporation":false,"usgs":true,"family":"Preston","given":"Stephen","email":"spreston@usgs.gov","middleInitial":"D.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":717283,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192883,"text":"70192883 - 2011 - Inverse modeling with RZWQM2 to predict water quality","interactions":[],"lastModifiedDate":"2018-02-20T13:32:42","indexId":"70192883","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Inverse modeling with RZWQM2 to predict water quality","docAbstract":"This chapter presents guidelines for autocalibration of the Root Zone Water Quality Model (RZWQM2) by inverse modeling using PEST parameter estimation software (Doherty, 2010). Two sites with diverse climate and management were considered for simulation of N losses by leaching and in drain flow: an almond [Prunus dulcis (Mill.) D.A. Webb] orchard in the San Joaquin Valley, California and the Walnut Creek watershed in central Iowa, which is predominantly in corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation. Inverse modeling provides an objective statistical basis for calibration that involves simultaneous adjustment of model parameters and yields parameter confidence intervals and sensitivities. We describe operation of PEST in both parameter estimation and predictive analysis modes. The goal of parameter estimation is to identify a unique set of parameters that minimize a weighted least squares objective function, and the goal of predictive analysis is to construct a nonlinear confidence interval for a prediction of interest by finding a set of parameters that maximizes or minimizes the prediction while maintaining the model in a calibrated state. We also describe PEST utilities (PAR2PAR, TSPROC) for maintaining ordered relations among model parameters (e.g., soil root growth factor) and for post-processing of RZWQM2 outputs representing different cropping practices at the Iowa site. Inverse modeling provided reasonable fits to observed water and N fluxes and directly benefitted the modeling through: (i) simultaneous adjustment of multiple parameters versus one-at-a-time adjustment in manual approaches; (ii) clear indication by convergence criteria of when calibration is complete; (iii) straightforward detection of nonunique and insensitive parameters, which can affect the stability of PEST and RZWQM2; and (iv) generation of confidence intervals for uncertainty analysis of parameters and model predictions. Composite scaled sensitivities, which reflect the total information provided by the observations for a parameter, indicated that most of the RZWQM2 parameters at the California study site (CA) and Iowa study site (IA) could be reliably estimated by regression. Correlations obtained in the CA case indicated that all model parameters could be uniquely estimated by inverse modeling. Although water content at field capacity was highly correlated with bulk density (−0.94), the correlation is less than the threshold for nonuniqueness (0.95, absolute value basis). Additionally, we used truncated singular value decomposition (SVD) at CA to mitigate potential problems with highly correlated and insensitive parameters. Singular value decomposition estimates linear combinations (eigenvectors) of the original process-model parameters. Parameter confidence intervals (CIs) at CA indicated that parameters were reliably estimated with the possible exception of an organic pool transfer coefficient (R45), which had a comparatively wide CI. However, the 95% confidence interval for R45 (0.03–0.35) is mostly within the range of values reported for this parameter. Predictive analysis at CA generated confidence intervals that were compared with independently measured annual water flux (groundwater recharge) and median nitrate concentration in a collocated monitoring well as part of model evaluation. Both the observed recharge (42.3 cm yr−1) and nitrate concentration (24.3 mg L−1) were within their respective 90% confidence intervals, indicating that overall model error was within acceptable limits.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Methods of introducing system models into agricultural research","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, Soil Science Society of America","doi":"10.2134/advagricsystmodel2.c12","usgsCitation":"Nolan, B.T., Malone, R.W., Ma, L., Green, C.T., Fienen, M., and Jaynes, D.B., 2011, Inverse modeling with RZWQM2 to predict water quality, chap. of Methods of introducing system models into agricultural research, p. 327-363, https://doi.org/10.2134/advagricsystmodel2.c12.","productDescription":"37 p.","startPage":"327","endPage":"363","ipdsId":"IP-019987","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":351827,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-26","publicationStatus":"PW","scienceBaseUri":"5afef55ee4b0da30c1bfc8f9","contributors":{"authors":[{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":717287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Malone, Robert W.","contributorId":198835,"corporation":false,"usgs":false,"family":"Malone","given":"Robert","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":717291,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ma, Liwang","contributorId":29140,"corporation":false,"usgs":true,"family":"Ma","given":"Liwang","email":"","affiliations":[],"preferred":false,"id":717290,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":717288,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":177065,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","email":"mnfienen@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":729024,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jaynes, Dan B.","contributorId":192368,"corporation":false,"usgs":false,"family":"Jaynes","given":"Dan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":717289,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191968,"text":"70191968 - 2011 - Seasonal habitat shifts by benthic fishes in headwater streams","interactions":[],"lastModifiedDate":"2018-01-23T14:32:05","indexId":"70191968","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3896,"text":"Proceedings of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal habitat shifts by benthic fishes in headwater streams","docAbstract":"Fish-habitat associations in streams have been widely studied; however, temporal considerations have been neglected, particularly during the winter. We quantitatively sampled perennial headwater streams in the Missouri Ozarks during the summer (n = 13) and winter (n = 4) to evaluate possible habitat shifts by three benthic fishes at two spatial scales: channel unit and microhabitat. Density of all three headwater species in streams was generally lower in winter than summer, with some species being ubiquitous in channel units of streams during the summer and almost entirely absent from the same streams during winter. Presence of each of three species during the summer varied by stream and channel unit, but patterns of channelunit use did not change depending on stream sampled. Ozark sculpin (Cottus hypselurus) was more likely to be present (> 50% probability) in riffles and runs, but not pools. Fantail darter (Etheostoma flabellare) was much more likely to be found in riffles than other channel units whereas rainbow darter (Etheostoma caeruleum) was more likely to occur in runs or pools than riffles. During winter, each of the three species was equally likely to be present or absent from any of the channel units indicating a more general use of channel units. However, each of the three species used deeper microhabitats within pools and slower-velocity areas of riffles during winter compared to summer. Results of this study indicate benthic, headwater species used habitat more generally during cold-water periods compared to warm-water periods, but density estimates indicated changes in channel unit use occurred in some streams and patterns of fine-scale microhabitat shifts did occur.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Rettig, A.V., and Brewer, S.K., 2011, Seasonal habitat shifts by benthic fishes in headwater streams: Proceedings of the Southeastern Association of Fish and Wildlife Agencies, v. 65, p. 105-111.","productDescription":"7 p.","startPage":"105","endPage":"111","ipdsId":"IP-029941","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":350541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":350540,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.seafwa.org/publications/proceedings/?id=77197"}],"volume":"65","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a6857dfe4b06e28e9c65e5a","contributors":{"authors":[{"text":"Rettig, Adam V.","contributorId":201468,"corporation":false,"usgs":false,"family":"Rettig","given":"Adam","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":725627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":713795,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70157324,"text":"70157324 - 2011 - Simulating effects of microtopography on wetland specific yield and hydroperiod","interactions":[],"lastModifiedDate":"2021-11-09T16:57:11.626621","indexId":"70157324","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Simulating effects of microtopography on wetland specific yield and hydroperiod","docAbstract":"Specific yield and hydroperiod have proven to be useful parameters in hydrologic analysis of wetlands. Specific yield is a critical parameter to quantitatively relate hydrologic fluxes (e.g., rainfall, evapotranspiration, and runoff) and water level changes. Hydroperiod measures the temporal variability and frequency of land-surface inundation. Conventionally, hydrologic analyses used these concepts without considering the effects of land surface microtopography and assumed a smoothly-varying land surface. However, these microtopographic effects could result in small-scale variations in land surface inundation and water depth above or below the land surface, which in turn affect ecologic and hydrologic processes of wetlands. The objective of this chapter is to develop a physically-based approach for estimating specific yield and hydroperiod that enables the consideration of microtopographic features of wetlands, and to illustrate the approach at sites in the Florida Everglades. The results indicate that the physically-based approach can better capture the variations of specific yield with water level, in particular when the water level falls between the minimum and maximum land surface elevations. The suggested approach for hydroperiod computation predicted that the wetlands might be completely dry or completely wet much less frequently than suggested by the conventional approach neglecting microtopography. One reasonable generalization may be that the hydroperiod approaches presented in this chapter can be a more accurate prediction tool for water resources management to meet the specific hydroperiod threshold as required by a species of plant or animal of interest.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Modeling hydrologic effects of microtopographic features","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Nova Science Publishers","publisherLocation":"New York City, NY","usgsCitation":"Summer, D.M., 2011, Simulating effects of microtopography on wetland specific yield and hydroperiod, chap. of Modeling hydrologic effects of microtopographic features, p. 59-82.","productDescription":"24 p.","startPage":"59","endPage":"82","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-011991","costCenters":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"links":[{"id":308286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"55fd35b9e4b05d6c4e502c71","contributors":{"editors":[{"text":"Wang, Xixi","contributorId":147799,"corporation":false,"usgs":false,"family":"Wang","given":"Xixi","email":"","affiliations":[],"preferred":false,"id":572691,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Summer, David M.","contributorId":147798,"corporation":false,"usgs":false,"family":"Summer","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":572690,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70036698,"text":"70036698 - 2011 - Chromium(VI) generation in vadose zone soils and alluvial sediments of the southwestern Sacramento Valley, California: a potential source of geogenic Cr(VI) to groundwater","interactions":[],"lastModifiedDate":"2013-04-02T11:28:31","indexId":"70036698","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Chromium(VI) generation in vadose zone soils and alluvial sediments of the southwestern Sacramento Valley, California: a potential source of geogenic Cr(VI) to groundwater","docAbstract":"Concentrations of geogenic Cr(VI) in groundwater that exceed the World Health Organization’s maximum contaminant level for drinking water (50 μg L−1) occur in several locations globally. The major mechanism for mobilization of this Cr(VI) at these sites is the weathering of Cr(III) from ultramafic rocks and its subsequent oxidation on Mn oxides. This process may be occurring in the southern Sacramento Valley of California where Cr(VI) concentrations in groundwater can approach or exceed 50 μg L−1. To characterize Cr geochemistry in the area, samples from several soil auger cores (approximately 4 m deep) and drill cores (approximately 25 m deep) were analyzed for total concentrations of 44 major, minor and trace elements, Cr associated with labile Mn and Fe oxides, and Cr(VI). Total concentrations of Cr in these samples ranged from 140 to 2220 mg per kg soil. Between 9 and 70 mg per kg soil was released by selective extractions that target Fe oxides, but essentially no Cr was associated with the abundant reactive Mn oxides (up to ~1000 mg hydroxylamine-reducible Mn per kg soil was present). Both borehole magnetic susceptibility surveys performed at some of the drill core sites and relative differences between Cr released in a 4-acid digestion versus total Cr (lithium metaborate fusion digestion) suggest that the majority of total Cr in the samples is present in refractory chromite minerals transported from ultramafic exposures in the Coast Range Mountains. Chromium(VI) in the samples studied ranged from 0 to 42 μg kg−1, representing a minute fraction of total Cr. Chromium(VI) content was typically below detection in surface soils (top 10 cm) where soil organic matter was high, and increased with increasing depth in the soil auger cores as organic matter decreased. Maximum concentrations of Cr(VI) were up to 3 times greater in the deeper drill core samples than the shallow auger cores. Although Cr(VI) in these vadose zone soils and sediments was only a very small fraction of the total solid phase Cr, they are a potentially important source for Cr(VI) to groundwater. Enhanced groundwater recharge through the vadose zone due to irrigation could carry Cr(VI) from the vadose zone to the groundwater and may be the mechanism responsible for the correlation observed between elevated Cr(VI) and NO3- source concentrations in previously published data for valley groundwaters. Incubation of a valley subsoil showed a Cr(VI) production rate of 24 μg kg−1 a−1 suggesting that field Cr(VI) concentrations could be regenerated annually. Increased Cr(VI) production rates in H+-amended soil incubations indicate that soil acidification processes such as nitrification of ammonium in fertilizers could potentially increase the occurrence of geogenic Cr(VI) in groundwater. Thus, despite the natural origin of the Cr, Cr(VI) generation in the Sacramento Valley soils and sediments has the potential to be influenced by human activities.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Applied Geochemistry","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.apgeochem.2011.05.023","issn":"08832927","usgsCitation":"Mills, C., Morrison, J.M., Goldhaber, M.B., and Ellefsen, K.J., 2011, Chromium(VI) generation in vadose zone soils and alluvial sediments of the southwestern Sacramento Valley, California: a potential source of geogenic Cr(VI) to groundwater: Applied Geochemistry, v. 26, no. 8, p. 1488-1501, https://doi.org/10.1016/j.apgeochem.2011.05.023.","productDescription":"14 p.","startPage":"1488","endPage":"1501","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":245457,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":217506,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.apgeochem.2011.05.023"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.8,35.0 ], [ -122.8,40.7 ], [ -118.8,40.7 ], [ -118.8,35.0 ], [ -122.8,35.0 ] ] ] } } ] }","volume":"26","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059f5e7e4b0c8380cd4c4a0","contributors":{"authors":[{"text":"Mills, Christopher T. 0000-0001-8414-1414","orcid":"https://orcid.org/0000-0001-8414-1414","contributorId":93308,"corporation":false,"usgs":true,"family":"Mills","given":"Christopher T.","affiliations":[],"preferred":false,"id":457420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morrison, Jean M. 0000-0002-6614-8783 jmorrison@usgs.gov","orcid":"https://orcid.org/0000-0002-6614-8783","contributorId":994,"corporation":false,"usgs":true,"family":"Morrison","given":"Jean","email":"jmorrison@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":457418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goldhaber, Martin B. 0000-0002-1785-4243 mgold@usgs.gov","orcid":"https://orcid.org/0000-0002-1785-4243","contributorId":1339,"corporation":false,"usgs":true,"family":"Goldhaber","given":"Martin","email":"mgold@usgs.gov","middleInitial":"B.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":457419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellefsen, Karl J. 0000-0003-3075-4703 ellefsen@usgs.gov","orcid":"https://orcid.org/0000-0003-3075-4703","contributorId":789,"corporation":false,"usgs":true,"family":"Ellefsen","given":"Karl","email":"ellefsen@usgs.gov","middleInitial":"J.","affiliations":[{"id":82803,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":false}],"preferred":true,"id":457417,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70146327,"text":"70146327 - 2011 - Book review: Nonlinear ocean waves and the inverse scattering transform","interactions":[],"lastModifiedDate":"2015-12-11T12:20:27","indexId":"70146327","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3208,"text":"Pure and Applied Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Nonlinear ocean waves and the inverse scattering transform","docAbstract":"Nonlinear Ocean Waves and the Inverse Scattering Transform is a comprehensive examination of ocean waves built upon the theory of nonlinear Fourier analysis. The renowned author, Alfred R. Osborne, is perhaps best known for the discovery of internal solitons in the Andaman Sea during the 1970s. In this book, he provides an extensive treatment of nonlinear water waves based on a nonlinear spectral theory known as the inverse scattering transform. The writing is exceptional throughout the book, which is particularly useful in explaining some of the more difficult mathematical concepts.
\nReview info: Nonlinear Ocean Waves and the Inverse Scattering Transform. By Alfred R. Osborne, 2010. ISBN: 978-125286299, 917 pp.
","language":"English","publisher":"Springer","publisherLocation":"Berlin, Germany","doi":"10.1007/s00024-010-0260-4","usgsCitation":"Geist, E.L., 2011, Book review: Nonlinear ocean waves and the inverse scattering transform: Pure and Applied Geophysics, v. 168, no. 10, p. 1889-1890, https://doi.org/10.1007/s00024-010-0260-4.","productDescription":"2 p.","startPage":"1889","endPage":"1890","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-026440","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":299959,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"168","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2011-01-19","publicationStatus":"PW","scienceBaseUri":"554200c6e4b0a658d793b2de","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":544968,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70148134,"text":"70148134 - 2011 - Identification of American shad spawning sites and habitat use in the Pee Dee River, North Carolina and South Carolina","interactions":[],"lastModifiedDate":"2015-06-03T09:51:21","indexId":"70148134","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Identification of American shad spawning sites and habitat use in the Pee Dee River, North Carolina and South Carolina","docAbstract":"We examined spawning site selection and habitat use by American shad Alosa sapidissima in the Pee Dee River, North Carolina and South Carolina, to inform future management in this flow-regulated river. American shad eggs were collected in plankton tows, and the origin (spawning site) of each egg was estimated; relocations of radio-tagged adults on spawning grounds illustrated habitat use and movement in relation to changes in water discharge rates. Most spawning was estimated to occur in the Piedmont physiographic region within a 25-river-kilometer (rkm) section just below the lowermost dam in the system; however, some spawning also occurred downstream in the Coastal Plain. The Piedmont region has a higher gradient and is predicted to have slightly higher current velocities and shallower depths, on average, than the Coastal Plain. The Piedmont region is dominated by large substrates (e.g., boulders and gravel), whereas the Coastal Plain is dominated by sand. Sampling at night (the primary spawning period) resulted in the collection of young eggs (≤1.5 h old) that more precisely identified the spawning sites. In the Piedmont region, most radio-tagged American shad remained in discrete areas (average linear range = 3.6 rkm) during the spawning season and generally occupied water velocities between 0.20 and 0.69 m/s, depths between 1.0 and 2.9 m, and substrates dominated by boulder or bedrock and gravel. Tagged adults made only small-scale movements with changes in water discharge rates. Our results demonstrate that the upstream extent of migration and an area of concentrated spawning occur just below the lowermost dam. If upstream areas have similar habitat, facilitating upstream access for American shad could increase the spawning habitat available and increase the population's size.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/02755947.2011.633686","usgsCitation":"Harris, J., and Hightower, J.E., 2011, Identification of American shad spawning sites and habitat use in the Pee Dee River, North Carolina and South Carolina: North American Journal of Fisheries Management, v. 31, no. 6, p. 1019-1033, https://doi.org/10.1080/02755947.2011.633686.","productDescription":"15 p.","startPage":"1019","endPage":"1033","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-026273","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":301000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, South Carolina","otherGeospatial":"Pee Dee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.8651123046875,\n 35.077212628195525\n ],\n [\n -79.9310302734375,\n 35.068221159859256\n ],\n [\n -79.91455078125,\n 35.036743220175275\n ],\n [\n -79.9310302734375,\n 35.000753578642396\n ],\n [\n -79.8980712890625,\n 34.96474810049312\n ],\n [\n -79.87884521484375,\n 34.89944783005726\n ],\n [\n -79.9365234375,\n 34.85212898939811\n ],\n [\n -79.95849609375,\n 34.81605826221895\n ],\n [\n -79.94476318359375,\n 34.75289647745205\n ],\n [\n -79.90631103515625,\n 34.694202959295495\n ],\n [\n -79.8321533203125,\n 34.6241677899049\n ],\n [\n -79.85687255859375,\n 34.511083202999714\n ],\n [\n -79.81292724609375,\n 34.465806327688526\n ],\n [\n -79.78271484375,\n 34.470335121217495\n ],\n [\n -79.76898193359375,\n 34.42730166315869\n ],\n [\n -79.749755859375,\n 34.3955786154917\n ],\n [\n -79.73602294921875,\n 34.347971491244955\n ],\n [\n -79.68658447265625,\n 34.288991865037524\n ],\n [\n -79.59869384765625,\n 34.250405862125\n ],\n [\n -79.6014404296875,\n 34.20725938207231\n ],\n [\n -79.58221435546875,\n 34.11407854333859\n ],\n [\n -79.54376220703125,\n 34.00713506435885\n ],\n [\n -79.52728271484375,\n 33.92740869431181\n ],\n [\n -79.365234375,\n 33.80425580201489\n ],\n [\n -79.332275390625,\n 33.747180448149855\n ],\n [\n -79.26910400390624,\n 33.678639851675555\n ],\n [\n -79.20318603515625,\n 33.67635422308637\n ],\n [\n -79.189453125,\n 33.5963189611327\n ],\n [\n -79.2059326171875,\n 33.53681606773302\n ],\n [\n -79.22515869140625,\n 33.47727218776036\n ],\n [\n -79.2938232421875,\n 33.392466071900756\n ],\n [\n -79.32403564453124,\n 33.32364354794104\n ],\n [\n -79.3212890625,\n 33.268546361901954\n ],\n [\n -79.24163818359375,\n 33.211116472416855\n ],\n [\n -79.19219970703125,\n 33.18813395605041\n ],\n [\n -79.1729736328125,\n 33.22949814144951\n ],\n [\n -79.18121337890625,\n 33.32364354794104\n ],\n [\n -79.22515869140625,\n 33.33970700424026\n ],\n [\n -79.1729736328125,\n 33.417687357334934\n ],\n [\n -79.134521484375,\n 33.502468829034314\n ],\n [\n -79.1180419921875,\n 33.6420625047537\n ],\n [\n -79.1510009765625,\n 33.72890830547334\n ],\n [\n -79.2388916015625,\n 33.742612777346885\n ],\n [\n -79.28558349609375,\n 33.8339199536547\n ],\n [\n -79.35150146484375,\n 33.89093747081252\n ],\n [\n -79.4696044921875,\n 33.99575015925125\n ],\n [\n -79.5245361328125,\n 34.18454183141728\n ],\n [\n -79.53277587890625,\n 34.277644878733824\n ],\n [\n -79.6234130859375,\n 34.338900400404995\n ],\n [\n -79.70306396484375,\n 34.44542371620849\n ],\n [\n -79.7222900390625,\n 34.52918706954935\n ],\n [\n -79.76898193359375,\n 34.54502472496434\n ],\n [\n -79.79919433593749,\n 34.52466147177172\n ],\n [\n -79.7882080078125,\n 34.59930237746266\n ],\n [\n -79.76898193359375,\n 34.65128519895413\n ],\n [\n -79.8211669921875,\n 34.69194468425019\n ],\n [\n -79.87884521484375,\n 34.755153088189324\n ],\n [\n -79.89257812499999,\n 34.81605826221895\n ],\n [\n -79.82940673828125,\n 34.88367790965999\n ],\n [\n -79.8321533203125,\n 34.96024630257349\n ],\n [\n -79.8486328125,\n 35.0120020431607\n ],\n [\n -79.8651123046875,\n 35.077212628195525\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"6","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2011-12-14","publicationStatus":"PW","scienceBaseUri":"5570253ae4b0d9246a9fd1a3","contributors":{"authors":[{"text":"Harris, Julianne E.","contributorId":57687,"corporation":false,"usgs":true,"family":"Harris","given":"Julianne E.","affiliations":[],"preferred":false,"id":548119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hightower, Joseph E. jhightower@usgs.gov","contributorId":835,"corporation":false,"usgs":true,"family":"Hightower","given":"Joseph","email":"jhightower@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":547463,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70136242,"text":"70136242 - 2011 - Status and distribution of the Kittlitz's Murrelet Brachyramphus brevirostris along the Alaska Peninsula and Kodiak and Aleutian Islands, Alaska","interactions":[],"lastModifiedDate":"2018-04-04T11:19:22","indexId":"70136242","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2675,"text":"Marine Ornithology: Journal of Seabird Research and Conservation","onlineIssn":"2074-1235","printIssn":"1018-3337","active":true,"publicationSubtype":{"id":10}},"title":"Status and distribution of the Kittlitz's Murrelet Brachyramphus brevirostris along the Alaska Peninsula and Kodiak and Aleutian Islands, Alaska","docAbstract":"The Kittlitz's Murrelet Brachyramphus brevirostris is adapted for life in glacial-marine ecosystems, being concentrated in the belt of glaciated fjords in the northern Gulf of Alaska from Glacier Bay to Cook Inlet. Most of the remaining birds are scattered along coasts of the Alaska Peninsula and Aleutian Islands, where they reside in protected bays and inlets, often in proximity to remnant glaciers or recently deglaciated landscapes. We summarize existing information on Kittlitz's Murrelet in this mainly unglaciated region, extending from Kodiak Island in the east to the Near Islands in the west. From recent surveys, we estimated that ~2400 Kittlitz's Murrelets were found in several large embayments along the Alaska Peninsula, where adjacent ice fields feed silt-laden water into the bays. On Kodiak Island, where only remnants of ice remain today, observations of Kittlitz's Murrelets at sea were uncommon. The species has been observed historically around the entire Kodiak Archipelago, however, and dozens of nest sites were found in recent years. We found Kittlitz's Murrelets at only a few islands in the Aleutian chain, notably those with long complex shorelines, high mountains and remnant glaciers. The largest population (~1600 birds) of Kittlitz's Murrelet outside the Gulf of Alaska was found at Unalaska Island, which also supports the greatest concentration of glacial ice in the Aleutian Islands. Significant populations were found at Atka (~1100 birds), Attu (~800) and Adak (~200) islands. Smaller numbers have been reported from Unimak, Umnak, Amlia, Kanaga, Tanaga, Kiska islands, and Agattu Island, where dozens of nest sites have been located in recent years. Most of those islands have not been thoroughly surveyed, and significant pockets of Kittlitz's Murrelets may yet be discovered. Our estimate of ~6000 Kittlitz's Murrelets along the Alaska Peninsula and Aleutian Islands is also likely to be conservative because of the survey protocols we employed (i.e. early seasonal timing of surveys, strip transects).
","language":"English","publisher":"African Seabird Group","usgsCitation":"Madison, E.N., Piatt, J.F., Arimitsu, M.L., Romano, M.D., van Pelt, T.I., Nelson, S.K., Williams, J.C., and DeGange, A.R., 2011, Status and distribution of the Kittlitz's Murrelet Brachyramphus brevirostris along the Alaska Peninsula and Kodiak and Aleutian Islands, Alaska: Marine Ornithology: Journal of Seabird Research and Conservation, v. 39, no. 1, p. 111-122.","productDescription":"12 p.","startPage":"111","endPage":"122","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-028350","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":296962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Peninsula, Aleutian Islands, Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -196.171875,\n 48.40003249610685\n ],\n [\n -196.171875,\n 59.489726035537075\n ],\n [\n -153.6328125,\n 59.489726035537075\n ],\n [\n -153.6328125,\n 48.40003249610685\n ],\n [\n -196.171875,\n 48.40003249610685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"54dd2c62e4b08de9379b376a","contributors":{"authors":[{"text":"Madison, Erica N. emadison@usgs.gov","contributorId":3409,"corporation":false,"usgs":true,"family":"Madison","given":"Erica","email":"emadison@usgs.gov","middleInitial":"N.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":537232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":537233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arimitsu, Mayumi L. 0000-0001-6982-2238 marimitsu@usgs.gov","orcid":"https://orcid.org/0000-0001-6982-2238","contributorId":140501,"corporation":false,"usgs":true,"family":"Arimitsu","given":"Mayumi","email":"marimitsu@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":537234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romano, Marc D.","contributorId":73528,"corporation":false,"usgs":true,"family":"Romano","given":"Marc","email":"","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":537487,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Pelt, Thomas I.","contributorId":13392,"corporation":false,"usgs":true,"family":"van Pelt","given":"Thomas","email":"","middleInitial":"I.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":537488,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nelson, S. Kim","contributorId":86680,"corporation":false,"usgs":false,"family":"Nelson","given":"S.","email":"","middleInitial":"Kim","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":537491,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, Jeffrey C.","contributorId":126882,"corporation":false,"usgs":false,"family":"Williams","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":537492,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"DeGange, Anthony R. tdegange@usgs.gov","contributorId":139765,"corporation":false,"usgs":true,"family":"DeGange","given":"Anthony","email":"tdegange@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":537235,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70036984,"text":"70036984 - 2011 - Thermal erosion of a permafrost coastline: Improving process-based models using time-lapse photography","interactions":[],"lastModifiedDate":"2012-03-12T17:21:59","indexId":"70036984","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":899,"text":"Arctic, Antarctic, and Alpine Research","active":true,"publicationSubtype":{"id":10}},"title":"Thermal erosion of a permafrost coastline: Improving process-based models using time-lapse photography","docAbstract":"Coastal erosion rates locally exceeding 30 m y-1 have been documented along Alaska's Beaufort Sea coastline, and a number of studies suggest that these erosion rates have accelerated as a result of climate change. However, a lack of direct observational evidence has limited our progress in quantifying the specific processes that connect climate change to coastal erosion rates in the Arctic. In particular, while longer ice-free periods are likely to lead to both warmer surface waters and longer fetch, the relative roles of thermal and mechanical (wave) erosion in driving coastal retreat have not been comprehensively quantified. We focus on a permafrost coastline in the northern National Petroleum Reserve-Alaska (NPR-A), where coastal erosion rates have averaged 10-15 m y-1 over two years of direct monitoring. We take advantage of these extraordinary rates of coastal erosion to observe and quantify coastal erosion directly via time-lapse photography in combination with meteorological observations. Our observations indicate that the erosion of these bluffs is largely thermally driven, but that surface winds play a crucial role in exposing the frozen bluffs to the radiatively warmed seawater that drives melting of interstitial ice. To first order, erosion in this setting can be modeled using formulations developed to describe iceberg deterioration in the open ocean. These simple models provide a conceptual framework for evaluating how climate-induced changes in thermal and wave energy might influence future erosion rates in this setting.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Arctic, Antarctic, and Alpine Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","doi":"10.1657/1938-4246-43.3.474","issn":"15230430","usgsCitation":"Wobus, C., Anderson, R., Overeem, I., Matell, N., Clow, G., and Urban, F., 2011, Thermal erosion of a permafrost coastline: Improving process-based models using time-lapse photography: Arctic, Antarctic, and Alpine Research, v. 43, no. 3, p. 474-484, https://doi.org/10.1657/1938-4246-43.3.474.","startPage":"474","endPage":"484","numberOfPages":"11","costCenters":[],"links":[{"id":475285,"rank":10000,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.bioone.org/doi/10.1657/1938-4246-43.3.474","text":"External Repository"},{"id":245808,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":217836,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1657/1938-4246-43.3.474"}],"volume":"43","issue":"3","noUsgsAuthors":false,"publicationDate":"2018-01-17","publicationStatus":"PW","scienceBaseUri":"505bb21ee4b08c986b3255ea","contributors":{"authors":[{"text":"Wobus, C.","contributorId":65305,"corporation":false,"usgs":true,"family":"Wobus","given":"C.","email":"","affiliations":[],"preferred":false,"id":458848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, R.","contributorId":104191,"corporation":false,"usgs":false,"family":"Anderson","given":"R.","affiliations":[],"preferred":false,"id":458852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overeem, I.","contributorId":92087,"corporation":false,"usgs":true,"family":"Overeem","given":"I.","affiliations":[],"preferred":false,"id":458850,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matell, N.","contributorId":89751,"corporation":false,"usgs":true,"family":"Matell","given":"N.","email":"","affiliations":[],"preferred":false,"id":458849,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clow, G.","contributorId":92088,"corporation":false,"usgs":true,"family":"Clow","given":"G.","email":"","affiliations":[],"preferred":false,"id":458851,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Urban, F. 0000-0002-1329-1703","orcid":"https://orcid.org/0000-0002-1329-1703","contributorId":9501,"corporation":false,"usgs":true,"family":"Urban","given":"F.","affiliations":[],"preferred":false,"id":458847,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70043293,"text":"70043293 - 2011 - On the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR)","interactions":[],"lastModifiedDate":"2013-04-30T14:11:09","indexId":"70043293","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"On the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR)","docAbstract":"The spectral vegetation index (ρNIR – ρSWIR)/(ρNIR + ρSWIR), where ρNIR and ρSWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI), and normalized burn ratio (NBR), etc. After reviewing each term’s definition, associated sensors, and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2–1.3 µm, 1.55–1.75 µm, or 2.05–2.45 µm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the term’s popularity, and the “rule of priority” in scientific nomenclature, NDWI, NDII, and NBR, each corresponding to the three SWIR regions, are more preferable terms.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"International Journal of Remote Sensing","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2010.510811","usgsCitation":"Ji, L., Zhang, L., Wylie, B.K., and Rover, J.R., 2011, On the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR): International Journal of Remote Sensing, v. 32, no. 21, p. 6901-6909, https://doi.org/10.1080/01431161.2010.510811.","productDescription":"9 p.","startPage":"6901","endPage":"6909","numberOfPages":"9","additionalOnlineFiles":"N","ipdsId":"IP-022003","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":271677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271676,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1080/01431161.2010.510811"}],"volume":"32","issue":"21","noUsgsAuthors":false,"publicationDate":"2011-09-26","publicationStatus":"PW","scienceBaseUri":"5180e7eae4b0df838b924d84","contributors":{"authors":[{"text":"Ji, Lel","contributorId":98609,"corporation":false,"usgs":true,"family":"Ji","given":"Lel","email":"","affiliations":[],"preferred":false,"id":473312,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Li","contributorId":98139,"corporation":false,"usgs":true,"family":"Zhang","given":"Li","affiliations":[],"preferred":false,"id":473311,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":473309,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rover, Jennifer R. 0000-0002-3437-4030 jrover@usgs.gov","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":2941,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"jrover@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":false,"id":473310,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70036985,"text":"70036985 - 2011 - Modeling of hydroecological feedbacks predicts distinct classes of landscape pattern, process, and restoration potential in shallow aquatic ecosystems","interactions":[],"lastModifiedDate":"2017-05-03T13:37:14","indexId":"70036985","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling of hydroecological feedbacks predicts distinct classes of landscape pattern, process, and restoration potential in shallow aquatic ecosystems","docAbstract":"It is widely recognized that interactions between vegetation and flow cause the emergence of channel patterns that are distinct from the standard Schumm classification of river channels. Although landscape pattern is known to be linked to ecosystem services such as habitat provision, pollutant removal, and sustaining biodiversity, the mechanisms responsible for the development and stability of different landscape patterns in shallow, vegetated flows have remained poorly understood. Fortunately, recent advances have made possible large-scale models of flow through vegetated environments that can be run over a range of environmental variables and over timescales of millennia. We describe a new, quasi-3D cellular automata model that couples simulations of shallow-water flow, bed shear stresses, sediment transport, and vegetation dynamics in an efficient manner. That efficiency allowed us to apply the model widely in order to determine how different hydroecological feedbacks control landscape pattern and process in various types of wetlands and floodplains. Distinct classes of landscape pattern were uniquely associated with specific types of allogenic and autogenic drivers in wetland flows. Regular, anisotropically patterned wetlands were dominated by allogenic processes (i.e., processes driven by periodic high water levels and flow velocities that redistribute sediment), relative to autogenic processes (e.g., vegetation production, peat accretion, and gravitational erosion). These anistropically patterned wetlands are therefore particularly prone to hydrologic disturbance. Other classes of wetlands that emerged from simulated interactions included maze-patterned, amorphous, and topographically noisy marshes, open marsh with islands, banded string-pool sequences perpendicular to flow, parallel deep and narrow channels flanked by marsh, and ridge-and-slough patterned marsh oriented parallel to flow. Because vegetation both affects and responds to the balance between the transport capacity of the flow and sediment supply, these vegetated systems exhibit a feedback that is not dominant in most rivers. Consequently, unlike in most rivers, it is not possible to predict the “channel pattern” of a vegetated landscape based only on discharge characteristics and sediment supply; the antecedent vegetation pattern and vegetation dynamics must also be known.
\nIn general, the stability of different wetland pattern types is most strongly related to factors controlling the erosion and deposition of sediment at vegetation patch edges, the magnitude of sediment redistribution by flow, patch elevation relative to water level, and the variability of erosion rates in vegetation patches with low flow-resistance. As we exemplify in our case-study of the Everglades ridge and slough landscape, feedback between flow and vegetation also causes hysteresis in landscape evolution trajectories that will affect the potential for landscape restoration. Namely, even if the hydrologic conditions that historically produced higher flows are restored, degraded portions of the ridge and slough landscape are unlikely to revert to their former patterning. As wetlands and floodplains worldwide become increasingly threatened by climate change and urbanization, the greater mechanistic understanding of landscape pattern and process that our analysis provides will improve our ability to forecast and manage the behavior of these ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2010.03.015","issn":"0169555X","usgsCitation":"Larsen, L., and Harvey, J.W., 2011, Modeling of hydroecological feedbacks predicts distinct classes of landscape pattern, process, and restoration potential in shallow aquatic ecosystems: Geomorphology, v. 126, no. 3-4, p. 279-296, https://doi.org/10.1016/j.geomorph.2010.03.015.","productDescription":"18 p.","startPage":"279","endPage":"296","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-014837","costCenters":[],"links":[{"id":245809,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":217837,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.geomorph.2010.03.015"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.50094604492186,\n 25.759082934951692\n ],\n [\n -80.49957275390625,\n 25.684850188749582\n ],\n [\n -80.54763793945311,\n 25.63781217093439\n ],\n [\n -80.54523468017578,\n 25.578988578695007\n ],\n [\n -80.5678939819336,\n 25.579917597109258\n ],\n [\n -80.56514739990234,\n 25.484810693165663\n ],\n [\n -80.59158325195312,\n 25.46559428893416\n ],\n [\n -80.59295654296875,\n 25.418470119273117\n ],\n [\n -80.58059692382812,\n 25.41474900498932\n ],\n [\n -80.57647705078125,\n 25.289404556494823\n ],\n [\n -80.44326782226562,\n 25.28692117716442\n ],\n [\n -80.3924560546875,\n 25.18505888358067\n ],\n [\n -80.51742553710938,\n 25.025884063244828\n ],\n [\n -80.83465576171875,\n 24.856534339310674\n ],\n [\n -80.8868408203125,\n 24.87646991083154\n ],\n [\n -81.08322143554688,\n 25.07440458233748\n ],\n [\n -81.20819091796875,\n 25.224820176765036\n ],\n [\n -81.17660522460938,\n 25.340302620496118\n ],\n [\n -81.23153686523438,\n 25.5114606200392\n ],\n [\n -81.3482666015625,\n 25.671235828577043\n ],\n [\n -81.52542114257812,\n 25.828324988459716\n ],\n [\n -81.4581298828125,\n 25.890114046690094\n ],\n [\n -81.41143798828125,\n 25.888878582127084\n ],\n [\n -81.40731811523438,\n 25.843157307531634\n ],\n [\n -81.39976501464844,\n 25.833269301382515\n ],\n [\n -81.36062622070312,\n 25.832651273562607\n ],\n [\n -81.36062622070312,\n 25.864784439396765\n ],\n [\n -81.26518249511717,\n 25.863548709865864\n ],\n [\n -81.09832763671875,\n 25.712074241522732\n ],\n [\n -81.05026245117188,\n 25.64895443060557\n ],\n [\n -81.04820251464844,\n 25.615524531842528\n ],\n [\n -80.85868835449219,\n 25.618001141542337\n ],\n [\n -80.85800170898438,\n 25.760319754713887\n ],\n [\n -80.50094604492186,\n 25.759082934951692\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"3-4","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5c15e4b0c8380cd6fa01","contributors":{"authors":[{"text":"Larsen, Laurel G. lglarsen@usgs.gov","contributorId":1987,"corporation":false,"usgs":true,"family":"Larsen","given":"Laurel G.","email":"lglarsen@usgs.gov","affiliations":[],"preferred":false,"id":458854,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Judson W. 0000-0002-2654-9873 jwharvey@usgs.gov","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":1796,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","email":"jwharvey@usgs.gov","middleInitial":"W.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":458853,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70036961,"text":"70036961 - 2011 - Nitrous oxide emission from denitrification in stream and river networks","interactions":[],"lastModifiedDate":"2020-12-18T15:34:05.169289","indexId":"70036961","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Nitrous oxide emission from denitrification in stream and river networks","docAbstract":"Nitrous oxide (N2O) is a potent greenhouse gas that contributes to climate change and stratospheric ozone destruction. Anthropogenic nitrogen (N) loading to river networks is a potentially important source of N2O via microbial denitrification that converts N to N2O and dinitrogen (N2). The fraction of denitrified N that escapes as N2O rather than N2 (i.e., the N2O yield) is an important determinant of how much N2O is produced by river networks, but little is known about the N2O yield in flowing waters. Here, we present the results of whole-stream 15N-tracer additions conducted in 72 headwater streams draining multiple land-use types across the United States. We found that stream denitrification produces N2O at rates that increase with stream water nitrate (NO3−) concentrations, but that <1% of denitrified N is converted to N2O. Unlike some previous studies, we found no relationship between the N2O yield and stream water NO3−. We suggest that increased stream NO3− loading stimulates denitrification and concomitant N2O production, but does not increase the N2O yield. In our study, most streams were sources of N2O to the atmosphere and the highest emission rates were observed in streams draining urban basins. Using a global river network model, we estimate that microbial N transformations (e.g., denitrification and nitrification) convert at least 0.68 Tg·y−1 of anthropogenic N inputs to N2O in river networks, equivalent to 10% of the global anthropogenic N2O emission rate. This estimate of stream and river N2O emissions is three times greater than estimated by the Intergovernmental Panel on Climate Change.
Humans have more than doubled the availability of fixed nitrogen (N) in the biosphere, particularly through the production of N fertilizers and the cultivation of N-fixing crops (1). Increasing N availability is producing unintended environmental consequences including enhanced emissions of nitrous oxide (N2O), a potent greenhouse gas (2) and an important cause of stratospheric ozone destruction (3). The Intergovernmental Panel on Climate Change (IPCC) estimates that the microbial conversion of agriculturally derived N to N2O in soils and aquatic ecosystems is the largest source of anthropogenic N2O to the atmosphere (2). The production of N2O in agricultural soils has been the focus of intense investigation (i.e., >1,000 published studies) and is a relatively well constrained component of the N2O budget (4). However, emissions of anthropogenic N2O from streams, rivers, and estuaries have received much less attention and remain a major source of uncertainty in the global anthropogenic N2O budget.
Microbial denitrification is a large source of N2O emissions in terrestrial and aquatic ecosystems. Most microbial denitrification is a form of anaerobic respiration in which nitrate (NO3−, the dominant form of inorganic N) is converted to dinitrogen (N2) and N2O gases (5). The proportion of denitrified NO3− that is converted to N2O rather than N2 (hereafter referred to as the N2O yield and expressed as the mole ratio) partially controls how much N2O is produced via denitrification (6), but few studies provide information on the N2O yield in streams and rivers because of the difficulty of measuring N2 and N2O production in these systems. Here we report rates of N2 and N2O production via denitrification measured using whole-stream 15NO3−-tracer experiments in 72 headwater streams draining different land-use types across the United States. This project, known as the second Lotic Intersite Nitrogen eXperiment (LINX II), provides unique whole-system measurements of the N2O yield in streams.
Although N2O emission rates have been reported for streams and rivers (7, 8), the N2O yield has been studied mostly in lentic freshwater and marine ecosystems, where it generally ranges between 0.1 and 1.0%, although yields as high as 6% have been observed (9). These N2O yields are low compared with observations in soils (0–100%) (10), which may be a result of the relatively lower oxygen (O2) availability in the sediments of lakes and estuaries. However, dissolved O2 in headwater streams is commonly near atmospheric equilibrium and benthic algal biofilms can produce O2 at the sediment–water interface, resulting in strong redox gradients more akin to those in partially wetted soils. Thus, streams may have variable and often high N2O yields, similar to those in soils (11). The N2O yield in headwater streams is of particular interest because much of the NO3− input to rivers is derived from groundwater upwelling into headwater streams. Furthermore, headwater streams compose the majority of stream length within a drainage network and have high ratios of bioreactive benthic surface area to water volume (12).
Movement of dissolved inorganic carbon (DIC) through the hydrologic cycle is an important component of global carbon budgets, but there is considerable uncertainty about the controls of DIC transmission from landscapes to streams, and through river networks to the oceans. In this study, diel measurements of DIC, d13C-DIC, dissolved oxygen (O2), d18O-O2, alkalinity, pH, and other parameters were used to assess the relative magnitudes of biological and geochemical controls on DIC cycling and flux in a nutrient-rich, net autotrophic stream. Rates of photosynthesis (P), respiration (R), groundwater discharge, air–water exchange of CO2, and carbonate precipitation/dissolution were quantified through a time-stepping chemical/isotope (12C and 13C, 16O and 18O) mass balance model. Groundwater was the major source of DIC to the stream. Primary production and carbonate precipitation were equally important sinks for DIC removed from the water column. The stream was always super-saturated with respect to carbonate minerals, but carbonate precipitation occurred mainly during the day when P increased pH. We estimated more than half (possibly 90%) of the carbonate precipitated during the day was retained in the reach under steady baseflow conditions. The amount of DIC removed from the overlying water through carbonate precipitation was similar to the amount of DIC generated from R. Air–water exchange of CO2 was always from the stream to the atmosphere, but was the smallest component of the DIC budget. Overall, the in-stream DIC reactions reduced the amount of CO2 evasion and the downstream flux of groundwater-derived DIC by about half relative to a hypothetical scenario with groundwater discharge only. Other streams with similar characteristics are widely distributed in the major river basins of North America. Data from USGS water quality monitoring networks from the 1960s to the 1990s indicated that 40% of 652 stream monitoring stations in the contiguous USA were at or above the equilibrium saturation state for calcite, and 77% of all stations exhibited apparent increases in saturation state from the 1960/70s to the 1980/90s. Diel processes including partially irreversible carbonate precipitation may affect net carbon fluxes from many such watersheds.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2010.12.012","usgsCitation":"Tobias, C., and Bohlke, J., 2011, Biological and geochemical controls on diel dissolved inorganic carbon cycling in a low-order agricultural stream: Implications for reach scales and beyond: Chemical Geology, v. 283, no. 1-2, p. 18-30, https://doi.org/10.1016/j.chemgeo.2010.12.012.","productDescription":"13 p.","startPage":"18","endPage":"30","ipdsId":"IP-022716","costCenters":[{"id":146,"text":"Branch of Regional Research-Eastern Region","active":false,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":265319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.21093749999999,\n 49.49667452747045\n ],\n [\n -124.98046874999999,\n 46.07323062540835\n ],\n [\n -125.68359374999999,\n 42.032974332441405\n ],\n [\n -125.33203125,\n 39.232253141714885\n ],\n [\n -122.87109375,\n 36.1733569352216\n ],\n [\n -119.53125,\n 33.43144133557529\n ],\n [\n -116.3671875,\n 32.69486597787505\n ],\n [\n -111.4453125,\n 31.50362930577303\n ],\n [\n -106.875,\n 31.653381399664\n ],\n [\n -95.97656249999999,\n 25.005972656239187\n ],\n [\n -95.625,\n 27.68352808378776\n ],\n [\n -92.98828125,\n 29.38217507514529\n ],\n [\n -88.59374999999999,\n 28.613459424004414\n ],\n [\n -88.24218749999999,\n 29.84064389983441\n ],\n [\n -84.90234375,\n 28.613459424004414\n ],\n [\n -80.68359375,\n 24.046463999666567\n ],\n [\n -79.1015625,\n 25.48295117535531\n ],\n [\n -78.92578124999999,\n 30.751277776257812\n ],\n [\n -76.46484375,\n 34.59704151614417\n ],\n [\n -74.70703125,\n 37.020098201368114\n ],\n [\n -73.30078125,\n 38.8225909761771\n ],\n [\n -70.48828125,\n 40.84706035607122\n ],\n [\n -67.5,\n 43.83452678223682\n ],\n [\n -67.5,\n 47.27922900257082\n ],\n [\n -69.78515625,\n 47.27922900257082\n ],\n [\n -75.76171875,\n 45.82879925192134\n ],\n [\n -81.73828125,\n 42.16340342422401\n ],\n [\n -80.85937499999999,\n 45.089035564831036\n ],\n [\n -84.19921875,\n 46.92025531537451\n ],\n [\n -93.8671875,\n 49.38237278700955\n ],\n [\n -126.21093749999999,\n 49.49667452747045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"283","issue":"1-2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"50ebfc72e4b07f1501afcfc4","contributors":{"authors":[{"text":"Tobias, Craig","contributorId":90612,"corporation":false,"usgs":true,"family":"Tobias","given":"Craig","affiliations":[],"preferred":false,"id":471455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":471454,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70036987,"text":"70036987 - 2011 - Successful integration efforts in water quality from the integrated Ocean Observing System Regional Associations and the National Water Quality Monitoring Network","interactions":[],"lastModifiedDate":"2020-12-17T17:09:36.700553","indexId":"70036987","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2678,"text":"Marine Technology Society Journal","active":true,"publicationSubtype":{"id":10}},"title":"Successful integration efforts in water quality from the integrated Ocean Observing System Regional Associations and the National Water Quality Monitoring Network","docAbstract":"The Integrated Ocean Observing System (IOOS®) Regional Associations and Interagency Partners hosted a water quality workshop in January 2010 to discuss issues of nutrient enrichment and dissolved oxygen depletion (hypoxia), harmful algal blooms (HABs), and beach water quality. In 2007, the National Water Quality Monitoring Council piloted demonstration projects as part of the National Water Quality Monitoring Network (Network) for U.S. Coastal Waters and their Tributaries in three IOOS Regional Associations, and these projects are ongoing. Examples of integrated science-based solutions to water quality issues of major concern from the IOOS regions and Network demonstration projects are explored in this article. These examples illustrate instances where management decisions have benefited from decision-support tools that make use of interoperable data. Gaps, challenges, and outcomes are identified, and a proposal is made for future work toward a multiregional water quality project for beach water quality.
","language":"English","publisher":"Ingenta Connect","doi":"10.4031/MTSJ.45.1.3","issn":"00253324","usgsCitation":"Ragsdale, R., Vowinkel, E., Porter, D., Hamilton, P., Morrison, R., Kohut, J., Connell, B., Kelsey, H., and Trowbridge, P., 2011, Successful integration efforts in water quality from the integrated Ocean Observing System Regional Associations and the National Water Quality Monitoring Network: Marine Technology Society Journal, v. 45, no. 1, p. 19-28, https://doi.org/10.4031/MTSJ.45.1.3.","productDescription":"10 p.","startPage":"19","endPage":"28","costCenters":[],"links":[{"id":475200,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4031/mtsj.45.1.3","text":"Publisher Index Page"},{"id":245840,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b9da3e4b08c986b31d981","contributors":{"authors":[{"text":"Ragsdale, R.","contributorId":46343,"corporation":false,"usgs":true,"family":"Ragsdale","given":"R.","email":"","affiliations":[],"preferred":false,"id":458865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vowinkel, E.","contributorId":51134,"corporation":false,"usgs":true,"family":"Vowinkel","given":"E.","affiliations":[],"preferred":false,"id":458866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porter, D.","contributorId":13470,"corporation":false,"usgs":true,"family":"Porter","given":"D.","email":"","affiliations":[],"preferred":false,"id":458861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamilton, P.","contributorId":42034,"corporation":false,"usgs":true,"family":"Hamilton","given":"P.","affiliations":[],"preferred":false,"id":458863,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morrison, R.","contributorId":39953,"corporation":false,"usgs":true,"family":"Morrison","given":"R.","affiliations":[],"preferred":false,"id":458862,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kohut, J.","contributorId":105152,"corporation":false,"usgs":true,"family":"Kohut","given":"J.","affiliations":[],"preferred":false,"id":458868,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Connell, B.","contributorId":44013,"corporation":false,"usgs":true,"family":"Connell","given":"B.","email":"","affiliations":[],"preferred":false,"id":458864,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kelsey, H.","contributorId":84556,"corporation":false,"usgs":true,"family":"Kelsey","given":"H.","email":"","affiliations":[],"preferred":false,"id":458867,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Trowbridge, P.","contributorId":12296,"corporation":false,"usgs":true,"family":"Trowbridge","given":"P.","email":"","affiliations":[],"preferred":false,"id":458860,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70036837,"text":"70036837 - 2011 - Interannual variation of rare earth element abundances in corals from northern coast of the South China Sea and its relation with sea-level change and human activities","interactions":[],"lastModifiedDate":"2020-12-18T19:17:49.416737","indexId":"70036837","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2664,"text":"Marine Environmental Research","active":true,"publicationSubtype":{"id":10}},"title":"Interannual variation of rare earth element abundances in corals from northern coast of the South China Sea and its relation with sea-level change and human activities","docAbstract":"Here we present interannual rare earth element (REE) records spanning the last two decades of the 20th century in two living Porites corals, collected from Longwan Bay, close to the estuarine zones off Wanquan River of Hainan Island and Hong Kong off the Pearl River Delta of Guangdong Province in the northern South China Sea. The results show that both coral REE contents (0.5–40 ng g−1 in Longwan Bay and 2–250 ng g−1 in Hong Kong for La–Lu) are characterized with a declining trend, which are significantly negative correlated with regional sea-level rise (9.4 mm a−1 from 1981 to 1996 in Longwan Bay, 13.7 mm a−1 from 1991 to 2001 in Hong Kong). The REE features are proposed to be resulted from seawater intrusion into the estuaries in response to contemporary sea-level rise. However, the tendency for the coral Er/Nd time series at Hong Kong site is absent and there is no significant relation between Er/Nd and total REEs as found for the coral at Longwan Bay site. The observations are likely attributed to changes of the water discharge and sediment load of Pearl River, which have been significantly affected by intense human activities, such as the construction of dams/reservoirs and riverbed sediment mining, in past decades. The riverine sediment load/discharge ratio of the Pearl River decreased sharply with a rate of 0.02 kg m−3 a−1, which could make significant contribution to the declining trend of coral REE. We propose that coastal corals in Longwan Bay and similar unexplored sites with little influences of river discharge and anthropogenic disruption are ideal candidates to investigate the influence of sea-level change on seawater/coral REE.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.marenvres.2010.10.003","issn":"01411136","usgsCitation":"Liu, Y., Peng, Z., Wei, G., Chen, T., Sun, W., He, J., Liu, G., Chou, C.L., and Shen, C., 2011, Interannual variation of rare earth element abundances in corals from northern coast of the South China Sea and its relation with sea-level change and human activities: Marine Environmental Research, v. 71, no. 1, p. 62-69, https://doi.org/10.1016/j.marenvres.2010.10.003.","productDescription":"8 p.","startPage":"62","endPage":"69","costCenters":[],"links":[{"id":245769,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":217797,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.marenvres.2010.10.003"}],"country":"China","otherGeospatial":"South China Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 108.19335937499999,\n 16.88865978738161\n ],\n [\n 111.884765625,\n 16.88865978738161\n ],\n [\n 111.884765625,\n 21.289374355860424\n ],\n [\n 108.19335937499999,\n 21.289374355860424\n ],\n [\n 108.19335937499999,\n 16.88865978738161\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 112.8515625,\n 20.797201434307\n ],\n [\n 115.48828125000001,\n 20.797201434307\n ],\n [\n 115.48828125000001,\n 23.483400654325642\n ],\n [\n 112.8515625,\n 23.483400654325642\n ],\n [\n 112.8515625,\n 20.797201434307\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"71","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a3ceae4b0c8380cd63152","contributors":{"authors":[{"text":"Liu, Yajing","contributorId":16553,"corporation":false,"usgs":true,"family":"Liu","given":"Yajing","affiliations":[],"preferred":false,"id":458088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peng, Z.","contributorId":95598,"corporation":false,"usgs":true,"family":"Peng","given":"Z.","affiliations":[],"preferred":false,"id":458092,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wei, G.","contributorId":105415,"corporation":false,"usgs":true,"family":"Wei","given":"G.","email":"","affiliations":[],"preferred":false,"id":458094,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, T.","contributorId":107836,"corporation":false,"usgs":true,"family":"Chen","given":"T.","email":"","affiliations":[],"preferred":false,"id":458095,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sun, W.","contributorId":69692,"corporation":false,"usgs":true,"family":"Sun","given":"W.","email":"","affiliations":[],"preferred":false,"id":458091,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"He, J.","contributorId":95993,"corporation":false,"usgs":true,"family":"He","given":"J.","email":"","affiliations":[],"preferred":false,"id":458093,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Liu, Gaisheng","contributorId":15158,"corporation":false,"usgs":true,"family":"Liu","given":"Gaisheng","email":"","affiliations":[],"preferred":false,"id":458087,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chou, C. L.","contributorId":32655,"corporation":false,"usgs":false,"family":"Chou","given":"C.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":458090,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shen, C.-C.","contributorId":25018,"corporation":false,"usgs":true,"family":"Shen","given":"C.-C.","email":"","affiliations":[],"preferred":false,"id":458089,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70036838,"text":"70036838 - 2011 - Does small-perimeter fencing inhibit mule deer or pronghorn use of water developments?","interactions":[],"lastModifiedDate":"2020-12-18T19:04:51.190749","indexId":"70036838","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","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":"Does small-perimeter fencing inhibit mule deer or pronghorn use of water developments?","docAbstract":"Wildlife water development can be an important habitat management strategy in western North America for many species, including both pronghorn (Antilocapra americana) and mule deer (Odocoileus hemionus). In many areas, water developments are fenced (often with small-perimeter fencing) to exclude domestic livestock and feral horses. Small-perimeter exclosures could limit wild ungulate use of fenced water sources, as exclosures present a barrier pronghorn and mule deer must negotiate to gain access to fenced drinking water. To evaluate the hypothesis that exclosures limit wild ungulate access to water sources, we compared use (photo counts) of fenced versus unfenced water sources for both pronghorn and mule deer between June and October 2002–2008 in western Utah. We used model selection to identify an adequate distribution and best approximating model. We selected a zero-inflated negative binomial distribution for both pronghorn and mule deer photo counts. Both pronghorn and mule deer photo counts were positively associated with sampling time and average daily maximum temperature in top models. A fence effect was present in top models for both pronghorn and mule deer, but mule deer response to small-perimeter fencing was much more pronounced than pronghorn response. For mule deer, we estimated that presence of a fence around water developments reduced photo counts by a factor of 0.25. We suggest eliminating fencing of water developments whenever possible or fencing a big enough area around water sources to avoid inhibiting mule deer. More generally, our results provide additional evidence that water development design and placement influence wildlife use. Failure to account for species-specific preferences will limit effectiveness of management actions and could compromise research results.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.163","issn":"0022541X","usgsCitation":"Larsen, R., Bissonette, J., Flinders, J., and Robinson, A., 2011, Does small-perimeter fencing inhibit mule deer or pronghorn use of water developments?: Journal of Wildlife Management, v. 75, no. 6, p. 1417-1425, https://doi.org/10.1002/jwmg.163.","productDescription":"9 p.","startPage":"1417","endPage":"1425","costCenters":[{"id":609,"text":"Utah Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"links":[{"id":245799,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":217827,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jwmg.163"}],"country":"United States","state":"Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.97216796875,\n 40.27952566881291\n ],\n [\n -111.86279296875,\n 40.36328834091583\n ],\n [\n -112.06054687499999,\n 41.244772343082076\n ],\n [\n -112.1044921875,\n 41.60722821271717\n ],\n [\n -112.763671875,\n 41.918628865183045\n ],\n [\n -113.37890625,\n 41.78769700539063\n ],\n [\n -113.5986328125,\n 41.29431726315258\n ],\n [\n -114.06005859375,\n 41.261291493919884\n ],\n [\n -113.97216796875,\n 40.27952566881291\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"6","noUsgsAuthors":false,"publicationDate":"2011-07-13","publicationStatus":"PW","scienceBaseUri":"505a0396e4b0c8380cd50561","contributors":{"authors":[{"text":"Larsen, R.T.","contributorId":6693,"corporation":false,"usgs":true,"family":"Larsen","given":"R.T.","email":"","affiliations":[],"preferred":false,"id":458096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bissonette, John","contributorId":62914,"corporation":false,"usgs":true,"family":"Bissonette","given":"John","affiliations":[],"preferred":false,"id":458097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flinders, J.T.","contributorId":43703,"corporation":false,"usgs":true,"family":"Flinders","given":"J.T.","email":"","affiliations":[],"preferred":false,"id":458098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Robinson, A.C.","contributorId":70409,"corporation":false,"usgs":true,"family":"Robinson","given":"A.C.","email":"","affiliations":[],"preferred":false,"id":458099,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70036988,"text":"70036988 - 2011 - Geochemical analysis of Atlantic rim water, Carbon County, Wyoming: New applications for characterizing coalbed natural gas reservoirs","interactions":[],"lastModifiedDate":"2020-12-21T13:09:17.614014","indexId":"70036988","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":701,"text":"American Association of Petroleum Geologists Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical analysis of Atlantic rim water, Carbon County, Wyoming: New applications for characterizing coalbed natural gas reservoirs","docAbstract":"Coalbed natural gas (CBNG) production typically requires the extraction of large volumes of water from target formations, thereby influencing any associated reservoir systems. We describe isotopic tracers that provide immediate data on the presence or absence of biogenic natural gas and the identify methane-containing reservoirs are hydrologically confined. Isotopes of dissolved inorganic carbon and strontium, along with water quality data, were used to characterize the CBNG reservoirs and hydrogeologic systems of Wyoming’s Atlantic Rim. Water was analyzed from a stream, springs, and CBNG wells.
Strontium isotopic composition and major ion geochemistry identify two groups of surface water samples. Muddy Creek and Mesaverde Group spring samples are Ca-Mg-SO4–type water with higher 87Sr/86Sr, reflecting relatively young groundwater recharged from precipitation in the Sierra Madre. Groundwaters emitted from the Lewis Shale springs are Na-HCO3–type waters with lower 87Sr/86Sr, reflecting sulfate reduction and more extensive water-rock interaction.
To distinguish coalbed waters, methanogenically enriched d13CDIC was used from other natural waters. Enriched d13CDIC, between −3.6 and +13.3‰, identified spring water that likely originates from Mesaverde coalbed reservoirs. Strongly positive d13CDIC, between +12.6 and +22.8‰, identified those coalbed reservoirs that are confined, whereas lower d13CDIC, between +0.0 and +9.9‰, identified wells within unconfined reservoir systems
","language":"English","publisher":"American Association of Petroleum Geologists","publisherLocation":"Tulsa, OK","doi":"10.1306/06301009190","usgsCitation":"McLaughlin, J., Frost, C., and Sharma, S., 2011, Geochemical analysis of Atlantic rim water, Carbon County, Wyoming: New applications for characterizing coalbed natural gas reservoirs: American Association of Petroleum Geologists Bulletin, v. 95, no. 2, p. 191-217, https://doi.org/10.1306/06301009190.","productDescription":"27 p.","startPage":"191","endPage":"217","numberOfPages":"27","costCenters":[],"links":[{"id":245868,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","county":"Carbon County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-106.0749,42.4325],[-106.0747,42.4179],[-106.0745,42.4038],[-106.0747,42.3748],[-106.0756,42.3189],[-106.076,42.3039],[-106.0752,42.2893],[-106.0756,42.2748],[-106.0753,42.2612],[-106.0734,42.1735],[-106.0738,42.1135],[-106.0744,41.9581],[-106.0748,41.9436],[-106.0746,41.9291],[-106.075,41.915],[-106.0741,41.9005],[-106.0739,41.8859],[-106.0743,41.8714],[-106.0747,41.8569],[-106.0745,41.8423],[-106.0743,41.8278],[-106.0735,41.8119],[-106.0745,41.7974],[-106.0747,41.7683],[-106.0745,41.7538],[-106.0728,41.6593],[-106.072,41.6407],[-106.0718,41.6257],[-106.071,41.5676],[-106.0708,41.3951],[-106.0977,41.3955],[-106.1155,41.3953],[-106.3243,41.3936],[-106.3251,41.2851],[-106.3241,41.2252],[-106.3237,41.2162],[-106.3233,41.1785],[-106.3227,41.1036],[-106.3227,41.075],[-106.3223,41.0446],[-106.3215,41.001],[-106.3257,41.0023],[-106.3263,41.0025],[-106.3318,41.0025],[-106.3519,41.0025],[-106.3793,41.0026],[-106.4481,41.0035],[-106.456,41.0035],[-106.4864,41.0033],[-106.5436,41.0038],[-106.5723,41.0038],[-106.582,41.0037],[-106.5911,41.0035],[-106.8639,41.0041],[-107.0021,41.0044],[-107.0259,41.0043],[-107.1355,41.0037],[-107.2299,41.0035],[-107.306,41.0034],[-107.3181,41.0035],[-107.3437,41.0033],[-107.3674,41.0032],[-107.3948,41.003],[-107.4137,41.0029],[-107.4575,41.0027],[-107.4947,41.0026],[-107.5093,41.0026],[-107.5136,41.0026],[-107.5288,41.0026],[-107.6049,41.0028],[-107.6767,41.0028],[-107.7078,41.0028],[-107.7845,41.0028],[-107.8131,41.0028],[-107.8206,41.0028],[-107.8326,41.0028],[-107.8391,41.0028],[-107.8521,41.0029],[-107.8801,41.0029],[-107.888,41.0029],[-107.9154,41.0029],[-107.9176,41.2244],[-107.9168,41.3996],[-107.9308,41.3996],[-107.9308,41.4123],[-107.9314,41.4272],[-107.9314,41.4418],[-107.932,41.4567],[-107.9326,41.4713],[-107.9326,41.4862],[-107.9326,41.5017],[-107.9326,41.5162],[-107.9319,41.5312],[-107.9319,41.5457],[-107.9319,41.5607],[-107.9319,41.572],[-107.9319,41.587],[-107.9319,41.6015],[-107.9318,41.6161],[-107.9318,41.631],[-107.9318,41.6456],[-107.9318,41.6592],[-107.9128,41.6592],[-107.8931,41.6592],[-107.874,41.6591],[-107.8544,41.6591],[-107.8353,41.6591],[-107.8169,41.6591],[-107.7991,41.659],[-107.78,41.659],[-107.7604,41.659],[-107.7407,41.659],[-107.721,41.6589],[-107.702,41.6589],[-107.6799,41.6588],[-107.6608,41.6588],[-107.6418,41.6582],[-107.6221,41.6582],[-107.6037,41.6581],[-107.5858,41.6581],[-107.5668,41.658],[-107.5477,41.6575],[-107.5287,41.6574],[-107.509,41.6574],[-107.5004,41.6573],[-107.501,41.6691],[-107.5008,41.6837],[-107.5007,41.6986],[-107.5013,41.7132],[-107.5012,41.7277],[-107.5011,41.74],[-107.501,41.7549],[-107.5009,41.7695],[-107.5007,41.7849],[-107.5,41.7994],[-107.4999,41.814],[-107.4998,41.8285],[-107.4997,41.8435],[-107.4996,41.858],[-107.4995,41.873],[-107.4994,41.8871],[-107.4999,41.9021],[-107.4998,41.9148],[-107.4997,41.9275],[-107.5002,41.942],[-107.5001,41.957],[-107.5,41.9715],[-107.5005,41.9861],[-107.5005,41.9992],[-107.5003,42.0147],[-107.5002,42.0288],[-107.5001,42.0437],[-107.5,42.0583],[-107.4999,42.0737],[-107.4998,42.0878],[-107.5221,42.0883],[-107.5196,42.1742],[-107.5195,42.1887],[-107.521,42.2459],[-107.5209,42.2596],[-107.5208,42.2741],[-107.5209,42.3473],[-107.5208,42.3618],[-107.5206,42.3904],[-107.5211,42.4054],[-107.5209,42.4336],[-107.4426,42.4342],[-107.4234,42.4341],[-107.4041,42.4345],[-107.3873,42.4344],[-107.3457,42.4346],[-107.3084,42.4349],[-107.2885,42.4343],[-107.1916,42.4347],[-107.173,42.4346],[-107.0934,42.4345],[-107.0742,42.4343],[-106.9704,42.4331],[-106.9574,42.433],[-106.9455,42.4334],[-106.9412,42.4333],[-106.9387,42.4333],[-106.8747,42.4323],[-106.8642,42.4322],[-106.8492,42.4321],[-106.8362,42.4324],[-106.8219,42.4328],[-106.7958,42.4325],[-106.7834,42.4324],[-106.738,42.4324],[-106.7169,42.4322],[-106.7076,42.4317],[-106.6864,42.4315],[-106.6187,42.4312],[-106.6088,42.4311],[-106.6007,42.4315],[-106.592,42.4314],[-106.5336,42.4312],[-106.5137,42.431],[-106.4814,42.4306],[-106.474,42.4305],[-106.4435,42.4306],[-106.4323,42.4305],[-106.3658,42.431],[-106.3546,42.4309],[-106.3434,42.4312],[-106.3235,42.4314],[-106.2651,42.4315],[-106.2471,42.4317],[-106.239,42.4321],[-106.1464,42.4326],[-106.1265,42.4328],[-106.1228,42.4323],[-106.1054,42.4325],[-106.0749,42.4325]]]},\"properties\":{\"name\":\"Carbon\",\"state\":\"WY\"}}]}","volume":"95","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a15b7e4b0c8380cd54f0c","contributors":{"authors":[{"text":"McLaughlin, J.F.","contributorId":41683,"corporation":false,"usgs":true,"family":"McLaughlin","given":"J.F.","email":"","affiliations":[],"preferred":false,"id":458871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Frost, C.D.","contributorId":20900,"corporation":false,"usgs":true,"family":"Frost","given":"C.D.","email":"","affiliations":[],"preferred":false,"id":458869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sharma, Shruti","contributorId":34088,"corporation":false,"usgs":true,"family":"Sharma","given":"Shruti","email":"","affiliations":[],"preferred":false,"id":458870,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70036816,"text":"70036816 - 2011 - New aerial survey and hierarchical model to estimate manatee abundance","interactions":[],"lastModifiedDate":"2020-12-16T19:11:32.387533","indexId":"70036816","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","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":"New aerial survey and hierarchical model to estimate manatee abundance","docAbstract":"Monitoring the response of endangered and protected species to hydrological restoration is a major component of the adaptive management framework of the Comprehensive Everglades Restoration Plan. The endangered Florida manatee (Trichechus manatus latirostris) lives at the marine-freshwater interface in southwest Florida and is likely to be affected by hydrologic restoration. To provide managers with prerestoration information on distribution and abundance for postrestoration comparison, we developed and implemented a new aerial survey design and hierarchical statistical model to estimate and map abundance of manatees as a function of patch-specific habitat characteristics, indicative of manatee requirements for offshore forage (seagrass), inland fresh drinking water, and warm-water winter refuge. We estimated the number of groups of manatees from dual-observer counts and estimated the number of individuals within groups by removal sampling. Our model is unique in that we jointly analyzed group and individual counts using assumptions that allow probabilities of group detection to depend on group size. Ours is the first analysis of manatee aerial surveys to model spatial and temporal abundance of manatees in association with habitat type while accounting for imperfect detection. We conducted the study in the Ten Thousand Islands area of southwestern Florida, USA, which was expected to be affected by the Picayune Strand Restoration Project to restore hydrology altered for a failed real-estate development. We conducted 11 surveys in 2006, spanning the cold, dry season and warm, wet season. To examine short-term and seasonal changes in distribution we flew paired surveys 1–2 days apart within a given month during the year. Manatees were sparsely distributed across the landscape in small groups. Probability of detection of a group increased with group size; the magnitude of the relationship between group size and detection probability varied among surveys. Probability of detection of individual manatees within a group also differed among surveys, ranging from a low of 0.27 on 11 January to a high of 0.73 on 8 August. During winter surveys, abundance was always higher inland at Port of the Islands (POI), a manatee warm-water aggregation site, than in the other habitat types. During warm-season surveys, highest abundances were estimated in offshore habitat where manatees forage on seagrass. Manatees continued to use POI in summer, but in lower numbers than in winter, possibly to drink freshwater. Abundance in other inland systems and inshore bays was low compared to POI in winter and summer, possibly because of low availability of freshwater. During cold weather, maps of patch abundance of paired surveys showed daily changes in manatee distribution associated with rapid changes in air and water temperature as manatees sought warm water with falling temperatures and seagrass areas with increasing temperatures. Within a habitat type, some patches had higher manatee abundance suggesting differences in quality, possibly due to freshwater flow. If hydrological restoration alters the location of quality habitat, postrestoration comparisons using our methods will document how manatees adjust to new resources, providing managers with information on spatial needs for further monitoring or management. Total abundance for the entire area was similar among survey dates. Credible intervals however were large on a few surveys, and may limit our ability to statistically detect trends in total abundance. Additional modeling of abundance with time- and patch-specific covariates of salinity, water temperature, and seagrass abundance will directly link manatee abundance with physical and biological changes due to restoration and should decrease uncertainty of estimates
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.41","issn":"0022541X","usgsCitation":"Langtimm, C.A., Dorazio, R., Stith, B., and Doyle, T., 2011, New aerial survey and hierarchical model to estimate manatee abundance: Journal of Wildlife Management, v. 75, no. 2, p. 399-412, https://doi.org/10.1002/jwmg.41.","productDescription":"14 p.","startPage":"399","endPage":"412","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":245407,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":217457,"rank":9999,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jwmg.41"}],"country":"United States","state":"Florida","otherGeospatial":"Ten Thousand Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.76986694335938,\n 25.81101826700782\n ],\n [\n -81.45263671875,\n 25.81101826700782\n ],\n [\n -81.45263671875,\n 26.061717616104055\n ],\n [\n -81.76986694335938,\n 26.061717616104055\n ],\n [\n -81.76986694335938,\n 25.81101826700782\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"2","noUsgsAuthors":false,"publicationDate":"2011-03-29","publicationStatus":"PW","scienceBaseUri":"505a6550e4b0c8380cd72b68","contributors":{"authors":[{"text":"Langtimm, Catherine A. 0000-0001-8499-5743 clangtimm@usgs.gov","orcid":"https://orcid.org/0000-0001-8499-5743","contributorId":3045,"corporation":false,"usgs":true,"family":"Langtimm","given":"Catherine","email":"clangtimm@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":457980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":172151,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":457978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stith, B.M.","contributorId":53741,"corporation":false,"usgs":true,"family":"Stith","given":"B.M.","email":"","affiliations":[],"preferred":false,"id":457979,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doyle, T.J.","contributorId":103489,"corporation":false,"usgs":true,"family":"Doyle","given":"T.J.","email":"","affiliations":[],"preferred":false,"id":457981,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70043971,"text":"70043971 - 2011 - Wind River watershed restoration, annual report November 2009 to October 2010.","interactions":[],"lastModifiedDate":"2017-02-16T10:20:18","indexId":"70043971","displayToPublicDate":"2011-01-01T00:00:00","publicationYear":"2011","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wind River watershed restoration, annual report November 2009 to October 2010.","docAbstract":"This report summarizes work completed by U.S. Geological Survey’s Columbia River Research Laboratory (USGS-CRRL) in the Wind River subbasin during the period November 2009 through October 2010 under Bonneville Power Administration (BPA) contract 46102. Long term research in the Wind River has focused on assessments of steelhead/rainbow trout Oncorhynchus mykiss populations, interactions with introduced populations of spring Chinook salmon O. tshawytscha and brook trout Salvelinus fontinalis, and influences of habitat variables and habitat restoration on fish productivity. During the period covered by this report, we collected water temperature data to characterize variation within and among tributaries and mainstem sections in the Trout Creek watershed, and assisted Washington Department of Fish and Wildlife (WDFW) with smolt trapping and tagging of smolt and parr steelhead with passive integrated transponder (PIT) tags. We also continued to maintain and test efficacy of a passive integrated transponder tag interrogation system (PTIS) in Trout Creek for assessing the adult steelhead runsize. \n\t\nA statement of work (SOW) was submitted to BPA in October 2009 that outlined work to be performed by USGS-CRRL. The SOW was organized by work elements, with each describing a research task. This report summarizes the progress completed under each work element. \n","language":"English","publisher":"Bonneville Power Administration","publisherLocation":"Reston, VA","usgsCitation":"Connolly, P., and Jezorek, I., 2011, Wind River watershed restoration, annual report November 2009 to October 2010.","numberOfPages":"8","ipdsId":"IP-029348","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":335683,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"UNITED STATES","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58a6c834e4b025c46428629c","contributors":{"authors":[{"text":"Connolly, P.J.","contributorId":70141,"corporation":false,"usgs":true,"family":"Connolly","given":"P.J.","email":"","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":669563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jezorek, I.G.","contributorId":177887,"corporation":false,"usgs":true,"family":"Jezorek","given":"I.G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":669564,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}