Water clarity is a reliable indicator of lake productivity and an ideal metric of regional water quality. Clarity is an indicator of other water quality variables including chlorophyll-a, total phosphorus and trophic status; however, unlike these metrics, clarity can be accurately and efficiently estimated remotely on a regional scale. Remote sensing is useful in regions containing a large number of lakes that are cost prohibitive to monitor regularly using traditional field methods. Field-assessed lakes generally are easily accessible and may represent a spatially irregular, non-random sample of a region. We developed a remote monitoring program for Maine lakes >8 ha (1511 lakes) to supplement existing field monitoring programs. We combined Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) brightness values for TM bands 1 (blue) and 3 (red) to estimate water clarity (secchi disk depth) during 1990–2010. Although similar procedures have been applied to Minnesota and Wisconsin lakes, neither state incorporates physical lake variables or watershed characteristics that potentially affect clarity into their models. Average lake depth consistently improved model fitness, and the proportion of wetland area in lake watersheds also explained variability in clarity in some cases. Nine regression models predicted water clarity (R2 = 0.69–0.90) during 1990–2010, with separate models for eastern (TM path 11; four models) and western Maine (TM path 12; five models that captured differences in topography and landscape disturbance. Average absolute difference between model-estimated and observed secchi depth ranged 0.65–1.03 m. Eutrophic and mesotrophic lakes consistently were estimated more accurately than oligotrophic lakes. Our results show that TM bands 1 and 3 can be used to estimate regional lake water clarity outside the Great Lakes Region and that the accuracy of estimates is improved with additional model variables that reflect physical lake characteristics and watershed conditions.
","language":"English","publisher":"Elsevier ","doi":"10.1016/j.rse.2012.03.006","usgsCitation":"McCullough, I.M., Loftin, C., and Sader, S., 2012, Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity: Remote Sensing of Environment, v. 123, p. 109-115, https://doi.org/10.1016/j.rse.2012.03.006.","productDescription":"7 p.","startPage":"109","endPage":"115","ipdsId":"IP-033562","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.32373046875,\n 48.980216985374994\n ],\n [\n -72.94921875,\n 43.56447158721811\n ],\n [\n -69.169921875,\n 42.147114459220994\n ],\n [\n -65.6103515625,\n 47.84265762816538\n ],\n [\n -69.32373046875,\n 48.980216985374994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425f1e4b0dc0b45b456e5","contributors":{"authors":[{"text":"McCullough, Ian M.","contributorId":149952,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":721311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":720505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sader, Steven A.","contributorId":112282,"corporation":false,"usgs":true,"family":"Sader","given":"Steven A.","affiliations":[],"preferred":false,"id":721312,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70038017,"text":"sir20115216 - 2012 - Status and understanding of groundwater quality in the Tahoe-Martis, Central Sierra, and Southern Sierra study units, 2006-2007--California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2012-04-30T16:43:35","indexId":"sir20115216","displayToPublicDate":"2012-04-11T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2011-5216","title":"Status and understanding of groundwater quality in the Tahoe-Martis, Central Sierra, and Southern Sierra study units, 2006-2007--California GAMA Priority Basin Project","docAbstract":"Groundwater quality in the Tahoe-Martis, Central Sierra, and Southern Sierra study units was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The three study units are located in the Sierra Nevada region of California in parts of Nevada, Placer, El Dorado, Madera, Tulare, and Kern Counties. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board, in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The project was designed to provide statistically robust assessments of untreated groundwater quality within the primary aquifer systems used for drinking water. The primary aquifer systems (hereinafter, primary aquifers) for each study unit are defined by the depth of the screened or open intervals of the wells listed in the California Department of Public Health (CDPH) database of wells used for municipal and community drinking-water supply. The quality of groundwater in shallower or deeper water-bearing zones may differ from that in the primary aquifers; shallower groundwater may be more vulnerable to contamination from the surface. The assessments for the Tahoe-Martis, Central Sierra, and Southern Sierra study units were based on water-quality and ancillary data collected by the USGS from 132 wells in the three study units during 2006 and 2007 and water-quality data reported in the CDPH database. Two types of assessments were made: (1) status, assessment of the current quality of the groundwater resource, and (2) understanding, identification of the natural and human factors affecting groundwater quality. The assessments characterize untreated groundwater quality, not the quality of treated drinking water delivered to consumers by water purveyors. Relative-concentrations (sample concentrations divided by benchmark concentrations) were used for evaluating groundwater quality for those constituents that have Federal or California regulatory or non-regulatory benchmarks for drinking-water quality. A relative-concentration (RC) greater than (>) 1.0 indicates a concentration above a benchmark. RCs for organic constituents (volatile organic compounds and pesticides) and special-interest constituents were classified as \"high\" (RC > 1.0), \"moderate\" (1.0 ≥ RC > 0.1), or \"low\" (RC ≤ 0.1). For inorganic constituents (major ions, trace elements, nutrients, and radioactive constituents), the boundary between low and moderate RCs was set at 0.5. A new metric, aquifer-scale proportion, was used in the status assessment as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the area of the primary aquifers with RC > 1.0 for a particular constituent or class of constituents; moderate and low aquifer-scale proportions are defined as the percentages of the area of the primary aquifer with moderate and low RCs, respectively. Percentages are based on an areal rather than a volumetric basis. Two statistical approaches—grid-based, which used one value per grid cell, and spatially weighted, which used multiple values per grid cell—were used to calculate aquifer-scale proportions for individual constituents and classes of constituents. The spatially weighted estimates of high aquifer-scale proportions were within the 90-percent (%) confidence intervals of the grid-based estimates in all cases. The status assessment showed that inorganic constituents had greater high and moderate aquifer-scale proportions than did organic constituents in all three study units. In the Tahoe-Martis study unit, RCs for inorganic constituents with health-based benchmarks (primarily arsenic) were high in 20% of the primary aquifer, moderate in 13%, and low in 67%. In the Central Sierra study unit, aquifer-scale proportions for inorganic constituents with health-based benchmarks (primarily arsenic, uranium, fluoride, and molybdenum) were 41% high, 36% moderate, and 23% low. In the Southern Sierra study unit, 32, 34, and 34% of the primary aquifer had high, moderate, and low RCs of inorganic constituents with health-based benchmarks (primarily arsenic, uranium, fluoride, boron, and nitrate). The high aquifer-scale proportions for inorganic constituents with non-health-based benchmarks were 14, 34, and 24% for the Tahoe-Martis, Central Sierra, and Southern Sierra study units, respectively, and the primary constituent was manganese for all three study units. Organic constituents with health-based benchmarks were not present at high RCs in the primary aquifers of the Central Sierra and Southern Sierra study units, and were present at high RCs in only 1% of the Tahoe-Martis study unit. Moderate aquifer-scale proportions for organic constituents were < 5% in all three study units. Of the 173 organic constituents analyzed, 22 were detected, and of those 22, 17 have health-based benchmarks. Organic constituents were detected in 20, 27, and 40% of the primary aquifers in the Tahoe-Martis, Central Sierra, and Southern Sierra study units, respectively. Four organic constituents had study-unit detection frequencies of > 10%: the trihalomethane chloroform in the Tahoe-Martis study unit; chloroform and the herbicide simazine in the Central Sierra study unit; and chloroform, simazine, the herbicide atrazine, and the solvent perchloroethene in the Southern Sierra study unit. The second component of this study, the understanding assessment, identified the natural and human factors that may have affected groundwater quality in the three study units by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were land use, septic tank density, climate, relative position in the regional flow system, aquifer lithology, geographic location, well depth and depth to the top of the screened or open interval in the well, groundwater age distribution, pH, and dissolved oxygen concentration. Results of the statistical evaluations were used to explain the occurrence and distribution of constituents in the study units. Aquifer lithology (granitic, metamorphic, sedimentary, or volcanic rocks), groundwater age distribution [modern (recharged since 1952), pre-modern (recharged before 1952), or mixed (containing both modern and pre-modern recharge)], geographic location, pH, and dissolved oxygen were the most significant factors explaining the occurrence patterns of most inorganic constituents. High and moderate RCs of arsenic were associated with pre-modern and mixed-age groundwater and two distinct sets of geochemical conditions: (1) oxic, high-pH conditions, particularly in volcanic rocks, and (2) low-oxygen to anoxic conditions and low- to neutral-pH conditions, particularly in granitic rocks. In granitic and metamorphic rocks, high and moderate RCs of uranium were associated with pre-modern and mixed-age groundwater, low-oxygen to anoxic conditions, and location within parts of the Central Sierra and Southern Sierra study units known to have rocks with anomalously high uranium content compared to other parts of the Sierra Nevada. High and moderate RCs of uranium in sedimentary rocks were associated with pre-modern-age groundwater, oxic and high-pH conditions, and location in the Tahoe Valley South subbasin within the Tahoe-Martis study unit. Land use within 500 meters of the well and groundwater age were the most significant factors explaining occurrence patterns of organic constituents. Herbicide detections were most strongly associated with modern- and mixed-age groundwater from wells with agricultural land use. Trihalomethane detections were most strongly associated with modern- and mixed-age groundwater from wells with > 10% urban land use and (or) septic tank density > 7 tanks per square kilometer. Solvent detections were not significantly related to groundwater age. Eighty-three percent of the wells with modern- or mixed-age groundwater, and 86% of wells with detections of herbicides and (or) THMs had depths to the top of the screened or open interval of < 170 feet. These observations suggest that modern groundwater has infiltrated to depths of approximately 170 feet below land surface. Land use and occurrence of herbicides and solvents were the most significant factors explaining the occurrence of nitrate. Wells with > 5% agricultural land use and detection of a herbicide or solvent had the highest nitrate concentrations. Comparison between observed and predicted detection frequencies of perchlorate suggests that the perchlorate detected at concentrations < 1 microgram per liter likely reflects the distribution of perchlorate under natural conditions, and that the perchlorate detected at higher concentrations may reflect redistribution of originally natural perchlorate salts by irrigation in the agricultural areas of the Southern Sierra study unit.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20115216","collaboration":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Fram, M.S., and Belitz, K., 2012, Status and understanding of groundwater quality in the Tahoe-Martis, Central Sierra, and Southern Sierra study units, 2006-2007--California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2011-5216, xiv, 164 p.; Appendices;, https://doi.org/10.3133/sir20115216.","productDescription":"xiv, 164 p.; Appendices;","startPage":"i","endPage":"222","numberOfPages":"236","additionalOnlineFiles":"N","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":254483,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2011_5216.jpg"},{"id":254479,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2011/5216/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b979ce4b08c986b31bb7a","contributors":{"authors":[{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":463256,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70038023,"text":"ofr20121058 - 2012 - Evaluation of fault-normal/fault-parallel directions rotated ground motions for response history analysis of an instrumented six-story building","interactions":[],"lastModifiedDate":"2012-04-30T16:43:36","indexId":"ofr20121058","displayToPublicDate":"2012-04-11T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1058","title":"Evaluation of fault-normal/fault-parallel directions rotated ground motions for response history analysis of an instrumented six-story building","docAbstract":"According to regulatory building codes in United States (for example, 2010 California Building Code), at least two horizontal ground-motion components are required for three-dimensional (3D) response history analysis (RHA) of buildings. For sites within 5 km of an active fault, these records should be rotated to fault-normal/fault-parallel (FN/FP) directions, and two RHA analyses should be performed separately (when FN and then FP are aligned with the transverse direction of the structural axes). It is assumed that this approach will lead to two sets of responses that envelope the range of possible responses over all nonredundant rotation angles. This assumption is examined here using a 3D computer model of a six-story reinforced-concrete instrumented building subjected to an ensemble of bidirectional near-fault ground motions. Peak responses of engineering demand parameters (EDPs) were obtained for rotation angles ranging from 0° through 180° for evaluating the FN/FP directions. It is demonstrated that rotating ground motions to FN/FP directions (1) does not always lead to the maximum responses over all angles, (2) does not always envelope the range of possible responses, and (3) does not provide maximum responses for all EDPs simultaneously even if it provides a maximum response for a specific EDP.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121058","collaboration":"In cooperation with the University of California Berkeley","usgsCitation":"Kalkan, E., and Kwong, N.S., 2012, Evaluation of fault-normal/fault-parallel directions rotated ground motions for response history analysis of an instrumented six-story building: U.S. Geological Survey Open-File Report 2012-1058, iv, 11 p.; Tables; Figures, https://doi.org/10.3133/ofr20121058.","productDescription":"iv, 11 p.; Tables; Figures","startPage":"i","endPage":"30","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":379,"text":"Menlo Park Science Center","active":false,"usgs":true}],"links":[{"id":254486,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr_2012_1058.gif"},{"id":254482,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1058/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a0c71e4b0c8380cd52b53","contributors":{"authors":[{"text":"Kalkan, Erol 0000-0002-9138-9407 ekalkan@usgs.gov","orcid":"https://orcid.org/0000-0002-9138-9407","contributorId":1218,"corporation":false,"usgs":true,"family":"Kalkan","given":"Erol","email":"ekalkan@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":463263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kwong, Neal S.","contributorId":26279,"corporation":false,"usgs":true,"family":"Kwong","given":"Neal","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":463264,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70037966,"text":"70037966 - 2012 - Bayesian shared frailty models for regional inference about wildlife survival","interactions":[],"lastModifiedDate":"2023-10-13T11:01:50.321221","indexId":"70037966","displayToPublicDate":"2012-04-09T16:01:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian shared frailty models for regional inference about wildlife survival","docAbstract":"One can joke that 'exciting statistics' is an oxymoron, but it is neither a joke nor an exaggeration to say that these are exciting times to be involved in statistical ecology. As Halstead et al.'s (2012) paper nicely exemplifies, recently developed Bayesian analyses can now be used to extract insights from data using techniques that would have been unavailable to the ecological researcher just a decade ago. Some object to this, implying that the subjective priors of the Bayesian approach is the pathway to perdition (e.g. Lele & Dennis, 2009). It is reasonable to ask whether these new approaches are really giving us anything that we could not obtain with traditional tried-and-true frequentist approaches. I believe the answer is a clear yes.","language":"English","publisher":"The Zoological Society of London","publisherLocation":"London, England","doi":"10.1111/j.1469-1795.2012.00532.x","usgsCitation":"Heisey, D., 2012, Bayesian shared frailty models for regional inference about wildlife survival: Animal Conservation, v. 15, no. 2, p. 127-128, https://doi.org/10.1111/j.1469-1795.2012.00532.x.","productDescription":"2 p.","startPage":"127","endPage":"128","numberOfPages":"8","ipdsId":"IP-036681","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":474523,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1469-1795.2012.00532.x","text":"Publisher Index Page"},{"id":254471,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"2","noUsgsAuthors":false,"publicationDate":"2012-03-19","publicationStatus":"PW","scienceBaseUri":"5059f02de4b0c8380cd4a61a","contributors":{"authors":[{"text":"Heisey, D.M.","contributorId":77496,"corporation":false,"usgs":true,"family":"Heisey","given":"D.M.","email":"","affiliations":[],"preferred":false,"id":463181,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70037990,"text":"70037990 - 2012 - Short-term response of Dicamptodon tenebrosus larvae to timber management in southwestern Oregon","interactions":[],"lastModifiedDate":"2017-04-06T15:18:02","indexId":"70037990","displayToPublicDate":"2012-04-04T16:31:00","publicationYear":"2012","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":"Short-term response of Dicamptodon tenebrosus larvae to timber management in southwestern Oregon","docAbstract":"In the Pacific Northwest, previous studies have found a negative effect of timber management on the abundance of stream amphibians, but results have been variable and region specific. These studies have generally used survey methods that did not account for differences in capture probability and focused on stands that were harvested under older management practices. We examined the influences of contemporary forest practices on larval Dicamptodon tenebrosus as part of the Hinkle Creek paired watershed study. We used a mark-recapture analysis to estimate D. tenebrosus density at 100 1-m sites spread throughout the basin and used extended linear models that accounted for correlation resulting from the repeated surveys at sites across years. Density was associated with substrate, but we found no evidence of an effect of harvest. While holding other factors constant, the model-averaged estimates indicated; 1) each 10% increase in small cobble or larger substrate increased median density of D. tenebrosus 1.05 times, 2) each 100-ha increase in the upstream area drained decreased median density of D. tenebrosus 0.96 times, and 3) increasing the fish density in the 40 m around a site by 0.01 increased median salamander density 1.01 times. Although this study took place in a single basin, it suggests that timber management in similar third-order basins of the southwestern Oregon Cascade foothills is unlikely to have short-term effects of D. tenebrosus larvae.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.269","usgsCitation":"Leuthold, N., Adams, M.J., and Hayes, J.P., 2012, Short-term response of Dicamptodon tenebrosus larvae to timber management in southwestern Oregon: Journal of Wildlife Management, v. 76, no. 1, p. 28-37, https://doi.org/10.1002/jwmg.269.","productDescription":"9 p.","startPage":"28","endPage":"37","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":254468,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Hinkle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.0523681640625,\n 43.395069512861355\n ],\n [\n -122.99039840698241,\n 43.395069512861355\n ],\n [\n -122.99039840698241,\n 43.447934055374034\n ],\n [\n -123.0523681640625,\n 43.447934055374034\n ],\n [\n -123.0523681640625,\n 43.395069512861355\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"1","noUsgsAuthors":false,"publicationDate":"2011-11-18","publicationStatus":"PW","scienceBaseUri":"505b8ec1e4b08c986b318b1c","contributors":{"authors":[{"text":"Leuthold, Niels","contributorId":73042,"corporation":false,"usgs":true,"family":"Leuthold","given":"Niels","email":"","affiliations":[],"preferred":false,"id":463221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, M. J. 0000-0001-8844-042X mjadams@usgs.gov","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":3133,"corporation":false,"usgs":false,"family":"Adams","given":"M.","email":"mjadams@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":463219,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, John P.","contributorId":12100,"corporation":false,"usgs":true,"family":"Hayes","given":"John","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":463220,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70037949,"text":"sir20125059 - 2012 - Determination of streamflow of the Arkansas River near Bentley in south-central Kansas","interactions":[],"lastModifiedDate":"2012-04-30T16:43:34","indexId":"sir20125059","displayToPublicDate":"2012-04-04T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5059","title":"Determination of streamflow of the Arkansas River near Bentley in south-central Kansas","docAbstract":"The Kansas Department of Agriculture, Division of Water Resources, requires that the streamflow of the Arkansas River just upstream from Bentley in south-central Kansas be measured or calculated before groundwater can be pumped from the well field. When the daily streamflow of the Arkansas River near Bentley is less than 165 cubic feet per second (ft3/s), pumping must be curtailed. Daily streamflow near Bentley was calculated by determining the relations between streamflow data from two reference streamgages with a concurrent record of 24 years, one located 17.2 miles (mi) upstream and one located 10.9 mi downstream, and streamflow at a temporary gage located just upstream from Bentley (Arkansas River near Bentley, Kansas). Flow-duration curves for the two reference streamgages indicate that during 1988?2011, the mean daily streamflow was less than 165 ft3/s 30 to 35 percent of the time. During extreme low-flow (drought) conditions, the reach of the Arkansas River between Hutchinson and Maize can lose flow to the adjacent alluvial aquifer, with streamflow losses as much as 1.6 cubic feet per second per mile. Three models were developed to calculate the streamflow of the Arkansas River near Bentley, Kansas. The model chosen depends on the data available and on whether the reach of the Arkansas River between Hutchinson and Maize is gaining or losing groundwater from or to the adjacent alluvial aquifer. The first model was a pair of equations developed from linear regressions of the relation between daily streamflow data from the Bentley streamgage and daily streamflow data from either the Arkansas River near Hutchinson, Kansas, station (station number 07143330) or the Arkansas River near Maize, Kansas, station (station number 07143375). The standard error of the Hutchinson-only equation was 22.8 ft3/s, and the standard error of the Maize-only equation was 22.3 ft3/s. The single-station model would be used if only one streamgage was available. In the second model, the flow gradient between the streamflow near Hutchinson and the streamflow near Maize was used to calculate the streamflow at the Bentley streamgage. This equation resulted in a standard error of 26.7 ft3/s. In the third model, a multiple regression analysis between both the daily streamflow of the Arkansas River near Hutchinson, Kansas, and the daily streamflow of the Arkansas River near Maize, Kansas, was used to calculate the streamflow at the Bentley streamgage. The multiple regression equation had a standard error of 21.2 ft3/s, which was the smallest of the standard errors for all the models. An analysis of the number of low-flow days and the number of days when the reach between Hutchinson and Maize loses flow to the adjacent alluvial aquifer indicates that the long-term trend is toward fewer days of losing conditions. This trend may indicate a long-term increase in water levels in the alluvial aquifer, which could be caused by one or more of several conditions, including an increase in rainfall, a decrease in pumping, a decrease in temperature, and an increase in streamflow upstream from the Hutchinson-to-Maize reach of the Arkansas River.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125059","collaboration":"Prepared in cooperation with the City of Wichita, Kansas","usgsCitation":"Perry, C.A., 2012, Determination of streamflow of the Arkansas River near Bentley in south-central Kansas: U.S. Geological Survey Scientific Investigations Report 2012-5059, vi, 7 p.; National Water Information System : Web Interface, https://doi.org/10.3133/sir20125059.","productDescription":"vi, 7 p.; National Water Information System : Web Interface","onlineOnly":"Y","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":254429,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5059.gif"},{"id":254428,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5059/","linkFileType":{"id":5,"text":"html"}}],"scale":"100000","country":"United States","state":"Kansas","county":"Harvey;Kingman;Reno;Sedgwick","city":"Bentley","otherGeospatial":"Arkansas River;Bentley Well Field","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -97.83333333333333,37.666666666666664 ], [ -97.83333333333333,38 ], [ -97.33333333333333,38 ], [ -97.33333333333333,37.666666666666664 ], [ -97.83333333333333,37.666666666666664 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ffcae4b0c8380cd4f3d0","contributors":{"authors":[{"text":"Perry, Charles A. cperry@usgs.gov","contributorId":2093,"corporation":false,"usgs":true,"family":"Perry","given":"Charles","email":"cperry@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":463136,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70146287,"text":"70146287 - 2012 - Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation","interactions":[],"lastModifiedDate":"2015-05-04T13:28:33","indexId":"70146287","displayToPublicDate":"2012-04-03T14:30:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation","docAbstract":"1. In this, the first of a pair of papers which address the simulation and automated measurement of well-sorted natural granular material, a method is presented for simulation of two-phase (solid, void) assemblages of discrete non-cohesive particles. The purpose is to have a flexible, yet computationally and theoretically simple, suite of tools with well constrained and well known statistical properties, in order to simulate realistic granular material as a discrete element model with realistic size and shape distributions, for a variety of purposes. The stochastic modeling framework is based on three-dimensional tessellations with variable degrees of order in particle-packing arrangement. Examples of sediments with a variety of particle size distributions and spatial variability in grain size are presented. The relationship between particle shape and porosity conforms to published data. The immediate application is testing new algorithms for automated measurements of particle properties (mean and standard deviation of particle sizes, and apparent porosity) from images of natural sediment, as detailed in the second of this pair of papers. The model could also prove useful for simulating specific depositional structures found in natural sediments, the result of physical alterations to packing and grain fabric, using discrete particle flow models. While the principal focus here is on naturally occurring sediment and sedimentary rock, the methods presented might also be useful for simulations of similar granular or cellular material encountered in engineering, industrial and life sciences.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Richmond, VA","doi":"10.1029/2011JF001974","usgsCitation":"Buscombe, D., and Rubin, D.M., 2012, Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation: Journal of Geophysical Research F: Earth Surface, v. 117, no. F2, 17 p., https://doi.org/10.1029/2011JF001974.","productDescription":"17 p.","numberOfPages":"17","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-026993","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":300044,"rank":1,"type":{"id":22,"text":"Related Work"},"url":"https://onlinelibrary.wiley.com/doi/10.1029/2011JF001975/abstract"},{"id":300045,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"F2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2012-04-03","publicationStatus":"PW","scienceBaseUri":"55489833e4b0a658d7960d3a","contributors":{"authors":[{"text":"Buscombe, Daniel","contributorId":140252,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":544941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rubin, David M. 0000-0003-1169-1452 drubin@usgs.gov","orcid":"https://orcid.org/0000-0003-1169-1452","contributorId":3159,"corporation":false,"usgs":true,"family":"Rubin","given":"David","email":"drubin@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":544940,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70147543,"text":"70147543 - 2012 - Advances in the simulation and automated measurement of well-sorted granular material: 2. Direct measures of particle properties","interactions":[],"lastModifiedDate":"2015-05-04T13:30:54","indexId":"70147543","displayToPublicDate":"2012-04-03T14:30:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2318,"text":"Journal of Geophysical Research F: Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Advances in the simulation and automated measurement of well-sorted granular material: 2. Direct measures of particle properties","docAbstract":"1. In this, the second of a pair of papers on the structure of well-sorted natural granular material (sediment), new methods are described for automated measurements from images of sediment, of: 1) particle-size standard deviation (arithmetic sorting) with and without apparent void fraction; and 2) mean particle size in material with void fraction. A variety of simulations of granular material are used for testing purposes, in addition to images of natural sediment. Simulations are also used to establish that the effects on automated particle sizing of grains visible through the interstices of the grains at the very surface of a granular material continue to a depth of approximately 4 grain diameters and that this is independent of mean particle size. Ensemble root-mean squared error between observed and estimated arithmetic sorting coefficients for 262 images of natural silts, sands and gravels (drawn from 8 populations) is 31%, which reduces to 27% if adjusted for bias (slope correction between observed and estimated values). These methods allow non-intrusive and fully automated measurements of surfaces of unconsolidated granular material. With no tunable parameters or empirically derived coefficients, they should be broadly universal in appropriate applications. However, empirical corrections may need to be applied for the most accurate results. Finally, analytical formulas are derived for the one-step pore-particle transition probability matrix, estimated from the image's autocorrelogram, from which void fraction of a section of granular material can be estimated directly. This model gives excellent predictions of bulk void fraction yet imperfect predictions of pore-particle transitions.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Richmond, VA","doi":"10.1029/2011JF001975","usgsCitation":"Buscombe, D.D., and Rubin, D.M., 2012, Advances in the simulation and automated measurement of well-sorted granular material: 2. Direct measures of particle properties: Journal of Geophysical Research F: Earth Surface, v. 117, no. F2, 18 p., https://doi.org/10.1029/2011JF001975.","productDescription":"18 p.","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[],"links":[{"id":474526,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2011jf001975","text":"Publisher Index Page"},{"id":300047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":300046,"rank":1,"type":{"id":22,"text":"Related Work"},"url":"https://onlinelibrary.wiley.com/doi/10.1029/2011JF001974/abstract"}],"volume":"117","issue":"F2","noUsgsAuthors":false,"publicationDate":"2012-04-03","publicationStatus":"PW","scienceBaseUri":"55489833e4b0a658d7960d3c","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584 dbuscombe@usgs.gov","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":5020,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","email":"dbuscombe@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":546073,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rubin, David M. 0000-0003-1169-1452 drubin@usgs.gov","orcid":"https://orcid.org/0000-0003-1169-1452","contributorId":3159,"corporation":false,"usgs":true,"family":"Rubin","given":"David","email":"drubin@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":546074,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70177029,"text":"70177029 - 2012 - Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams","interactions":[],"lastModifiedDate":"2016-10-19T14:52:56","indexId":"70177029","displayToPublicDate":"2012-04-03T05:15:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams","docAbstract":"Evaluating water quality and the health of aquatic organisms is challenging in systems with systematic diel (24 hour) or less predictable runoff-induced changes in water composition. To advance our understanding of how to evaluate environmental health in these dynamic systems, field studies of diel cycling were conducted in two streams (Silver Bow Creek and High Ore Creek) affected by historical mining activities in southwestern Montana. A combination of sampling and modeling tools were used to assess the toxicity of metals in these systems. Diffusive Gradients in Thin Films (DGT) samplers were deployed at multiple time intervals during diel sampling to confirm that DGT integrates time-varying concentrations of dissolved metals. Thermodynamic speciation calculations using site specific water compositions, including time-integrated dissolved metal concentrations determined from DGT, and a competitive, multiple-metal biotic ligand model incorporated into the Windemere Humic Aqueous Model Version 6.0 (WHAM VI) were used to determine the chemical speciation of dissolved metals and biotic ligands. The model results were combined with previously collected toxicity data on cutthroat trout to derive a relationship that predicts the relative survivability of these fish at a given site. This integrative approach may prove useful for assessing water quality and toxicity of metals to aquatic organisms in dynamic systems and evaluating whether potential changes in environmental health of aquatic systems are due to anthropogenic activities or natural variability.","language":"English","publisher":"Elsevier Science Direct","doi":"10.1016/j.scitotenv.2012.03.008","usgsCitation":"Balistrieri, L.S., Nimick, D.A., and Mebane, C.A., 2012, Assessing time-integrated dissolved concentrations and predicting toxicity of metals during diel cycling in streams: Science of the Total Environment, v. 425, p. 155-168, https://doi.org/10.1016/j.scitotenv.2012.03.008.","productDescription":"14 p.","startPage":"155","endPage":"168","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-038447","costCenters":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":329763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"425","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58088688e4b0f497e78e24d9","contributors":{"authors":[{"text":"Balistrieri, Laurie S. 0000-0002-6359-3849 balistri@usgs.gov","orcid":"https://orcid.org/0000-0002-6359-3849","contributorId":1406,"corporation":false,"usgs":true,"family":"Balistrieri","given":"Laurie","email":"balistri@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":651046,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nimick, David A. dnimick@usgs.gov","contributorId":421,"corporation":false,"usgs":true,"family":"Nimick","given":"David","email":"dnimick@usgs.gov","middleInitial":"A.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true}],"preferred":true,"id":651048,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":651047,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70037942,"text":"sir20125016 - 2012 - Dispersal of larval suckers at the Williamson River Delta, Upper Klamath Lake, Oregon, 2006-09","interactions":[],"lastModifiedDate":"2012-04-30T16:43:34","indexId":"sir20125016","displayToPublicDate":"2012-04-03T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5016","title":"Dispersal of larval suckers at the Williamson River Delta, Upper Klamath Lake, Oregon, 2006-09","docAbstract":"An advection/diffusion modeling approach was used to simulate the transport of larval suckers from spawning areas in the Williamson River, through the newly restored Williamson River Delta, to Upper Klamath Lake. The density simulations spanned the years of phased restoration, from 2006/2007 prior to any levee breaching, to 2008 when the northern part of the delta was reconnected to the lake, and 2009 when levees on both sides of the delta had been breached. Model simulation results from all four years were compared to field data using rank correlation. Spearman ρ correlation coefficients were usually significant and in the range 0.30 to 0.60, providing moderately strong validation of the model. The correlation coefficients varied with fish size class in a way that suggested that the model best described the distribution of smaller fish near the Williamson River channel, and larger fish away from the channel. When Lost River and shortnose/Klamath largescale suckers were simulated independently, the correlation results suggested that the model better described the transport and dispersal of the latter species. The incorporation of night-time-only drift behavior in the Williamson River channel neither improved nor degraded correlations with field data. The model showed that advection by currents is an important factor in larval dispersal.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125016","collaboration":"Prepared in cooperation with the Bureau of Reclamation?","usgsCitation":"Wood, T.M., Hendrixson, H.A., Markle, D.F., Erdman, C.S., Burdick, S.M., Ellsworth, C.M., and Buccola, N., 2012, Dispersal of larval suckers at the Williamson River Delta, Upper Klamath Lake, Oregon, 2006-09: U.S. Geological Survey Scientific Investigations Report 2012-5016, vi, 28 p.; Animation Downloads 2006-2009, https://doi.org/10.3133/sir20125016.","productDescription":"vi, 28 p.; Animation Downloads 2006-2009","temporalStart":"2006-01-01","temporalEnd":"2009-12-31","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":251619,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5016.jpg"},{"id":251617,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5016/","linkFileType":{"id":5,"text":"html"}}],"projection":"UTM, Zone 10N","datum":"North American Datum of 1927","country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake;Agency Lake;Williamson River Delta","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.16666666666667,42.05 ], [ -122.16666666666667,42.666666666666664 ], [ -121.58333333333333,42.666666666666664 ], [ -121.58333333333333,42.05 ], [ -122.16666666666667,42.05 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a021ae4b0c8380cd4feb0","contributors":{"authors":[{"text":"Wood, Tamara M. 0000-0001-6057-8080 tmwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6057-8080","contributorId":1164,"corporation":false,"usgs":true,"family":"Wood","given":"Tamara","email":"tmwood@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hendrixson, Heather A.","contributorId":43602,"corporation":false,"usgs":true,"family":"Hendrixson","given":"Heather","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":463128,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Markle, Douglas F.","contributorId":14530,"corporation":false,"usgs":true,"family":"Markle","given":"Douglas","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":463126,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erdman, Charles S.","contributorId":66102,"corporation":false,"usgs":true,"family":"Erdman","given":"Charles","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":463129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":463124,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ellsworth, Craig M.","contributorId":14913,"corporation":false,"usgs":true,"family":"Ellsworth","given":"Craig","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":463127,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buccola, Norman L. nbuccola@usgs.gov","contributorId":4295,"corporation":false,"usgs":true,"family":"Buccola","given":"Norman L.","email":"nbuccola@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":463125,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70037935,"text":"70037935 - 2012 - Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States","interactions":[],"lastModifiedDate":"2018-03-08T12:54:26","indexId":"70037935","displayToPublicDate":"2012-04-02T11:18:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":682,"text":"Agriculture, Ecosystems and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States","docAbstract":"The Great Plains of the United States has undergone extensive land-use and land-cover change in the past 150 years, with much of the once vast native grasslands and wetlands converted to agricultural crops, and much of the unbroken prairie now heavily grazed. Future land-use change in the region could have dramatic impacts on ecological resources and processes. A scenario-based modeling framework is needed to support the analysis of potential land-use change in an uncertain future, and to mitigate potentially negative future impacts on ecosystem processes. We developed a scenario-based modeling framework to analyze potential future land-use change in the Great Plains. A unique scenario construction process, using an integrated modeling framework, historical data, workshops, and expert knowledge, was used to develop quantitative demand for future land-use change for four IPCC scenarios at the ecoregion level. The FORE-SCE model ingested the scenario information and produced spatially explicit land-use maps for the region at relatively fine spatial and thematic resolutions. Spatial modeling of the four scenarios provided spatial patterns of land-use change consistent with underlying assumptions and processes associated with each scenario. Economically oriented scenarios were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. Environmentally oriented scenarios experienced modest declines in natural land covers to slight increases. Model results were assessed for quantity and allocation disagreement between each scenario pair. In conjunction with the U.S. Geological Survey's Biological Carbon Sequestration project, the scenario-based modeling framework used for the Great Plains is now being applied to the entire United States.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Agriculture, Ecosystems and Environment","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.agee.2012.02.019","usgsCitation":"Sohl, T.L., Sleeter, B.M., Sayler, K., Bouchard, M., Reker, R.R., Bennett, S.L., Sleeter, R., Kanengieter, R.L., and Zhu, Z., 2012, Spatially explicit land-use and land-cover scenarios for the Great Plains of the United States: Agriculture, Ecosystems and Environment, v. 153, p. 1-15, https://doi.org/10.1016/j.agee.2012.02.019.","productDescription":"15 p.","startPage":"1","endPage":"15","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) 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approach","interactions":[],"lastModifiedDate":"2021-02-09T16:55:47.377688","indexId":"sir20125030","displayToPublicDate":"2012-04-02T11:04:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5030","title":"Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach","docAbstract":"Urban development alters important physical, chemical, and biological processes that define urban stream ecosystems. An approach was developed for quantifying the effects of these processes on aquatic biota, and then linking those effects to endpoints that can be used for environmental management. These complex, interacting systems are challenging to model from a scientific standpoint. A desirable model clearly shows the system, simulates the interactions, and ultimately predicts results of management actions. Traditional regression techniques that calculate empirical relations between pairs of environmental factors do not capture the interconnected web of multiple stressors, but urban development effects are not yet understood at the detailed scales required to make mechanistic modeling approaches feasible. Therefore, in contrast to a fully deterministic or fully statistical modeling approach, a Bayesian network model provides a hybrid approach that can be used to represent known general associations between variables while acknowledging uncertainty in predicted outcomes. It does so by quantifying an expert-elicited network of probabilistic relations between variables. Advantages of this modeling approach include (1) flexibility in accommodating many model specifications and information types; (2) efficiency in storing and manipulating complex information, and to parameterize; and (3) transparency in describing the relations using nodes and arrows and in describing uncertainties with discrete probability distributions for each variable.
\nIn realization of the aforementioned advantages, a Bayesian network model was constructed to characterize the effect of urban development on aquatic macroinvertebrate stream communities through three simultaneous, interacting ecological pathways affecting stream hydrology, habitat, and water quality across watersheds in the Northeastern United States. This model incorporates both empirical data and expert knowledge to calculate the probabilities of attaining desired aquatic ecosystem conditions under different urban stress levels, environmental conditions, and management options. Ecosystem conditions are characterized in terms of standardized Biological Condition Gradient (BCG) management endpoints. This approach to evaluating urban development-induced perturbations in watersheds integrates statistical and mechanistic perspectives, different information sources, and several ecological processes into a comprehensive description of the system that can be used to support decision making. The completed model can be used to infer which management actions would lead to the highest likelihood of desired BCG tier achievement. For example, if best management practices (BMP) were implemented in a highly urbanized watershed to reduce flashiness to medium levels and specific conductance to low levels, the stream would have a 70-percent chance of achieving BCG Tier 3 or better, relative to a 24-percent achievement likelihood for unmanaged high urban land cover. Results are reported probabilistically to account for modeling uncertainty that is inherent in sources such as natural variability and model simplification error.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125030","collaboration":"Prepared in cooperation with Duke University","usgsCitation":"Kashuba, R., McMahon, G., Cuffney, T.F., Qian, S., Reckhow, K., Gerritsen, J., and Davies, S., 2012, Linking urbanization to the Biological Condition Gradient (BCG) for stream ecosystems in the Northeastern United States using a Bayesian network approach: U.S. Geological Survey Scientific Investigations Report 2012-5030, viii, 34 p., https://doi.org/10.3133/sir20125030.","productDescription":"viii, 34 p.","onlineOnly":"Y","ipdsId":"IP-022353","costCenters":[{"id":13634,"text":"South Atlantic Water Science 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Coupled Wave–Ocean–SedimentTransport Model was used to hindcast coastal circulation and fine sedimenttransport on the Mekong shelf in southeastern Asian in 2005. Comparisons with limited observations showed that the model simulation captured the regional patterns and temporal variability of surface wave, sea level, and suspended sediment concentration reasonably well. Significant seasonality in sedimenttransport was revealed. In summer, a large amount of fluvial sediments was delivered and deposited near the MekongRiver mouth. In the following winter, strong ocean mixing, and coastal current lead to resuspension and southwestward dispersal of a small fraction of previously deposited sediments. Model sensitivity experiments (with reduced physics) were performed to investigate the impact of tides, waves, and remotely forced ambient currents on the transport and dispersal of the fluvial sediment. Strong wave mixing and downwelling-favorable coastal current associated with the more energetic northeast monsoon in the winter season are the main factors controlling the southwestward along-shelf transport.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Continental Shelf Research","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.csr.2012.02.010","usgsCitation":"Xue, Z., He, R., Liu, J.P., and Warner, J., 2012, Modeling transport and deposition of the Mekong River sediment: Continental Shelf Research, v. 37, p. 66-78, https://doi.org/10.1016/j.csr.2012.02.010.","productDescription":"13 p.","startPage":"66","endPage":"78","numberOfPages":"12","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":246911,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":246896,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.csr.2012.02.010","linkFileType":{"id":5,"text":"html"}}],"country":"China","otherGeospatial":"Mekong River","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505a5c57e4b0c8380cd6fbfa","contributors":{"authors":[{"text":"Xue, Zuo","contributorId":47216,"corporation":false,"usgs":true,"family":"Xue","given":"Zuo","affiliations":[],"preferred":false,"id":463004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"He, Ruoying","contributorId":68029,"corporation":false,"usgs":true,"family":"He","given":"Ruoying","affiliations":[],"preferred":false,"id":463005,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liu, J. Paul","contributorId":44398,"corporation":false,"usgs":true,"family":"Liu","given":"J.","email":"","middleInitial":"Paul","affiliations":[],"preferred":false,"id":463003,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463002,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70037934,"text":"ds675 - 2012 - Archive of single beam and swath bathymetry data collected nearshore of the Gulf Islands National Seashore, Mississippi, from West Ship Island, Mississippi, to Dauphin Island, Alabama: Methods and data report for USGS Cruises 08CCT01 and 08CCT02, July 2008, and 09CCT03 and 09CCT04, June 2009","interactions":[],"lastModifiedDate":"2012-09-06T17:16:18","indexId":"ds675","displayToPublicDate":"2012-04-02T09:18:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"675","title":"Archive of single beam and swath bathymetry data collected nearshore of the Gulf Islands National Seashore, Mississippi, from West Ship Island, Mississippi, to Dauphin Island, Alabama: Methods and data report for USGS Cruises 08CCT01 and 08CCT02, July 2008, and 09CCT03 and 09CCT04, June 2009","docAbstract":"During the summers of 2008 and 2009 the USGS conducted bathymetric surveys from West Ship Island, Miss., to Dauphin Island, Ala., as part of the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project. The survey area extended from the shoreline out to approximately 2 kilometers and included the adjacent passes (fig. 1). The bathymetry was primarily used to create a topo-bathymetric map and provide a base-level assessment of the seafloor following the 2005 hurricane season. Additionally, these data will be used in conjunction with other geophysical data (chirp and side scan sonar) to construct a comprehensive geological framework of the Mississippi Barrier Island Complex. The culmination of the geophysical surveys will provide baseline bathymetry necessary for scientists to define and interpret seafloor habitat for this area and for scientists to predict future geomorpholocial changes of the islands with respect to climate change, storm impact, and sea-level rise. Furthermore, these data provide information for feasibility of barrier island restoration, particularly in Camille Cut, and for the preservation of historical Fort Massachusetts. For more information refer to http://ngom.usgs.gov/gomsc/mscip/index.html.
\nSince bathymetric surveys have often been conducted for navigational purposes, soundings have traditionally been referenced to a water level datum using tide gages and tide models. Bathymetric measurements referenced to a Global Positioning System (GPS) is a more accurate way of representing water depth and has been implemented in the acquisition and processing procedures for these datasets. Previous single-beam bathymetric studies performed at the USGS Center for Coastal and Marine Science have successfully referenced bathymetric measurements to GPS (DeWitt and others, 2007; Hansen 2008 and 2009). The 2008-2009 bathymetry surveys were conducted as a test to (1) develop acquisition and processing technology utilizing both single beam and swath bathymetry survey methods together, (2) reference both types of measurements to GPS rather than water level, and (3) compare the differences between methods in acquisition and processing. Results of the survey are explained in greater detail within this report.
\nTo acquire suitable coverage of the study area in a limited time frame, the seafloor-elevation survey was conducted using three techniques: single-beam bathymetry, interferometric swath bathymetry, and a walking kinematic survey of the island shorelines. All three techniques utilized GPS measurements. Implementation of these techniques was executed concurrently yet independently aboard two research vessels: the RV Survey Cat, a 26-foot (ft) shallow-draft Glacier Bay Coastal Runner, and the 50-ft RV G.K. Gilbert. A portable push buggy with a rigid antenna mount served as the platform for the kinematic shoreline survey. Data from each survey technique was post-processed and edited independently with proper inclusion of the differentially processed external navigation files. The x,y,z components from each method were then combined and the two survey years (2008 and 2009) were merged into one dataset. The 2008 bathymetry data were processed at the USGS Center for Coastal and Marine Science in St. Petersburg, Fla., and the 2009 bathymetry data were processed at the USGS Coastal and Marine Science Center located in Woods Hole, Mass.
\nThis report serves as an archive of the processed single beam and interferometric swath bathymetry, outlines the methodology, and reports the results. Data products herein include gridded and interpolated digital depth surfaces, and x,y,z data products for both single beam and interferometric swath bathymetry. Additional files include trackline maps, navigation files, geographic information system (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Scanned images of the handwritten FACS logs and digital FACS logs are also provided as PDF files. Refer to the Acronyms page for description of acronyms and abbreviations used in this report or hold the cursor over an acronym for a pop-up explanation.
\nThe USGS St. Petersburg Coastal and Marine Science Center assigns a unique identifier to each cruise or field activity. For example, 08CCT01 indicates that the data were collected in 2008 for the Coastal Change and Transport (CCT) study and the data were collected during the first (01) field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID.
\nSee the digital FACS equipment log for details about the acquisition equipment used. Raw datasets are stored digitally at the USGS St. Petersburg Coastal and Marine Science Center and processed systematically using Novatel's GrafNav version 7.6, SANDS version 3.7, SEA SWATHplus version 3.06.04.03, CARIS HIPS AND SIPS version 3.6, and ESRI ArcGIS version 9.3.1. For more information on processing refer to the Equipment and Processing page. Chirp seismic data were also collected during these surveys and are archived separately.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds675","usgsCitation":"DeWitt, N.T., Flocks, J.G., Pendleton, E., Hansen, M., Reynolds, B., Kelso, K.W., Wiese, D.S., and Worley, C.R., 2012, Archive of single beam and swath bathymetry data collected nearshore of the Gulf Islands National Seashore, Mississippi, from West Ship Island, Mississippi, to Dauphin Island, Alabama: Methods and data report for USGS Cruises 08CCT01 and 08CCT02, July 2008, and 09CCT03 and 09CCT04, June 2009: U.S. Geological Survey Data Series 675, HTML Document; GIS Download, https://doi.org/10.3133/ds675.","productDescription":"HTML Document; GIS Download","costCenters":[],"links":[{"id":246891,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/675/","linkFileType":{"id":5,"text":"html"}},{"id":246894,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds_675.jpg"}],"country":"United States","state":"Mississippi;Alabama","otherGeospatial":"Gulf Islands National Seashore;West Ship Island;Dauphin Island","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -89.21666666666667,30.183333333333334 ], [ -89.21666666666667,30.45 ], [ -88.18333333333334,30.45 ], [ -88.18333333333334,30.183333333333334 ], [ -89.21666666666667,30.183333333333334 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5059ed4de4b0c8380cd49719","contributors":{"authors":[{"text":"DeWitt, Nancy T. 0000-0002-2419-4087 ndewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-2419-4087","contributorId":4095,"corporation":false,"usgs":true,"family":"DeWitt","given":"Nancy","email":"ndewitt@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":463088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flocks, James G. 0000-0002-6177-7433 jflocks@usgs.gov","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":816,"corporation":false,"usgs":true,"family":"Flocks","given":"James","email":"jflocks@usgs.gov","middleInitial":"G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pendleton, Elizabeth A.","contributorId":101312,"corporation":false,"usgs":true,"family":"Pendleton","given":"Elizabeth A.","affiliations":[],"preferred":false,"id":463092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Mark E.","contributorId":49943,"corporation":false,"usgs":true,"family":"Hansen","given":"Mark E.","affiliations":[],"preferred":false,"id":463091,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reynolds, B.J.","contributorId":47874,"corporation":false,"usgs":true,"family":"Reynolds","given":"B.J.","email":"","affiliations":[],"preferred":false,"id":463090,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelso, Kyle W. 0000-0003-0615-242X kkelso@usgs.gov","orcid":"https://orcid.org/0000-0003-0615-242X","contributorId":4307,"corporation":false,"usgs":true,"family":"Kelso","given":"Kyle","email":"kkelso@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463089,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wiese, Dana S. dwiese@usgs.gov","contributorId":2476,"corporation":false,"usgs":true,"family":"Wiese","given":"Dana","email":"dwiese@usgs.gov","middleInitial":"S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463086,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Worley, Charles R. cworley@usgs.gov","contributorId":3063,"corporation":false,"usgs":true,"family":"Worley","given":"Charles","email":"cworley@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":463087,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70037933,"text":"sir20125031 - 2012 - Simulation of streamflows and basin-wide hydrologic variables over several climate-change scenarios, Methow River basin, Washington","interactions":[],"lastModifiedDate":"2012-04-30T16:43:35","indexId":"sir20125031","displayToPublicDate":"2012-04-02T08:54:00","publicationYear":"2012","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-5031","title":"Simulation of streamflows and basin-wide hydrologic variables over several climate-change scenarios, Methow River basin, Washington","docAbstract":"The purpose of this project was to demonstrate the capabilities of an existing watershed model and downscaling procedures to provide simulated hydrological data over various greenhouse gas emission scenarios for use in the Methow River framework prototype. An existing watershed model was used to simulate daily time series of streamflow and basin-wide hydrologic variables for baseline conditions (1990–2000), and then for all combinations of three greenhouse gas emission scenarios and five general circulation models for future conditions (2008–2095). Input data for 18 precipitation and 17 temperature model input sites were generated using statistical techniques to downscale general circulation model data. The simulated results were averaged using an 11-year moving window to characterize the central year of the window to provide simulated data for water years 2008–2095.
\nSimulation results indicate that substantial changes of monthly mean streamflows will occur. For all greenhouse gas emission scenarios, the future streamflows are greater in the winter than baseline conditions because a greater percentage of future precipitation is projected to fall as rain rather than as snow. The simulated future spring streamflows are less than baseline conditions because the spring snowpacks are smaller; therefore, flow contributions from snowmelt are less.
\nA database was developed to automate model execution and to provide users with Internet access to voluminous data products ranging from summary figures to model output timeseries. Database-enabled Internet tools were developed to allow users to create interactive graphs of output results based on their analysis needs. For example, users were able to create graphs by selecting time intervals, greenhouse gas emission scenarios, general circulation models, and specific hydrologic variables.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125031","usgsCitation":"Voss, F.D., and Mastin, M.C., 2012, Simulation of streamflows and basin-wide hydrologic variables over several climate-change scenarios, Methow River basin, Washington: U.S. Geological Survey Scientific Investigations Report 2012-5031, vi, 18 p.; Web tools link, https://doi.org/10.3133/sir20125031.","productDescription":"vi, 18 p.; Web tools link","onlineOnly":"Y","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":246895,"rank":0,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir_2012_5031.jpg"},{"id":246890,"rank":100,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5031/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Methow River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -120.83333333333333,48 ], [ -120.83333333333333,49 ], [ -119.75,49 ], [ -119.75,48 ], [ -120.83333333333333,48 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"505b9097e4b08c986b3195be","contributors":{"authors":[{"text":"Voss, Frank D. fdvoss@usgs.gov","contributorId":1651,"corporation":false,"usgs":true,"family":"Voss","given":"Frank","email":"fdvoss@usgs.gov","middleInitial":"D.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463083,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastin, Mark C. 0000-0003-4018-7861 mcmastin@usgs.gov","orcid":"https://orcid.org/0000-0003-4018-7861","contributorId":1652,"corporation":false,"usgs":true,"family":"Mastin","given":"Mark","email":"mcmastin@usgs.gov","middleInitial":"C.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":463084,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70199539,"text":"70199539 - 2012 - VARBOOT: A spatial bootstrap program for semivariogram uncertainty assessment","interactions":[],"lastModifiedDate":"2018-09-20T15:30:49","indexId":"70199539","displayToPublicDate":"2012-04-01T15:30:37","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"VARBOOT: A spatial bootstrap program for semivariogram uncertainty assessment","docAbstract":"In applied geostatistics, the semivariogram is commonly estimated from experimental data, producing an empirical semivariogram for a specified number of discrete lags. In a second stage, a model defined by a few parameters is fitted to the empirical semivariogram. As the experimental data are usually few and sparsely located, there is considerable uncertainty about the calculated semivariogram values (uncertainty of the empirical semivariogram) and about the parameters of any model fitted to them (uncertainty of the estimated model parameters). In this paper, the uncertainty in the modeling of the empirical semivariogram is numerically assessed by the generalized bootstrap, which is an extension of the classic bootstrap procedure modified for spatially correlated data. A computer program is described and provided for the assessment of those uncertainties. In particular, the program provides for the empirical semivariogram: the standard errors, the bootstrap percentile confidence intervals, the complete variance–covariance matrix, standard deviation correlation matrix. A public domain, natural dataset is used to illustrate the performance of the program. A promising result is that, for any distance, the median of the bootstrap distribution for the empirical semivariogram approximates more closely the underlying semivariogram than the estimate derived from the empirical sample.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2011.09.002","usgsCitation":"Pardo-Iguzquiza, E., and Olea, R., 2012, VARBOOT: A spatial bootstrap program for semivariogram uncertainty assessment: Computers & Geosciences, v. 41, p. 188-198, https://doi.org/10.1016/j.cageo.2011.09.002.","productDescription":"11 p.","startPage":"188","endPage":"198","ipdsId":"IP-021577","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":474530,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/20.500.12468/527","text":"External Repository"},{"id":357566,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c10beb5e4b034bf6a7f08fc","contributors":{"authors":[{"text":"Pardo-Iguzquiza, Eulogio","contributorId":208073,"corporation":false,"usgs":false,"family":"Pardo-Iguzquiza","given":"Eulogio","email":"","affiliations":[{"id":40847,"text":"Instituto Geologico y Minero de Espana","active":true,"usgs":false}],"preferred":false,"id":745816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":26436,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":745815,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70044259,"text":"70044259 - 2012 - Refining the model of barrier island formation along a paraglacial coast in the Gulf of Maine","interactions":[],"lastModifiedDate":"2013-07-11T15:10:28","indexId":"70044259","displayToPublicDate":"2012-04-01T15:03:51","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2667,"text":"Marine Geology","active":true,"publicationSubtype":{"id":10}},"title":"Refining the model of barrier island formation along a paraglacial coast in the Gulf of Maine","docAbstract":"Details of the internal architecture and local geochronology of Plum Island, the longest barrier in the Gulf of Maine, have refined our understanding of barrier island formation in paraglacial settings. Ground-penetrating radar and shallow-seismic profiles coupled with sediment cores and radiocarbon dates provide an 8000-year evolutionary history of this barrier system in response to changes in sediment sources and supply rates as well as variability in the rate of sea-level change. The barrier sequence overlies tills of Wisconsinan and Illinoian glaciations as well as late Pleistocene glaciomarine clay deposited during the post-glacial sea-level highstand at approximately 17 ka. Holocene sediment began accumulating at the site of Plum Island at 7–8 ka, in the form of coarse fluvial channel-lag deposits related to the 50-m wide erosional channel of the Parker River that carved into underlying glaciomarine deposits during a lower stand of sea level. Plum Island had first developed in its modern location by ca. 3.6 ka through onshore migration and vertical accretion of reworked regressive and lowstand deposits. The prevalence of southerly, seaward-dipping layers indicates that greater than 60% of the barrier lithosome developed in its modern location through southerly spit progradation, consistent with a dominantly longshore transport system driven by northeast storms. Thinner sequences of northerly, landward-dipping clinoforms represent the northern recurve of the prograding spit. A 5–6-m-thick inlet-fill sequence was identified overlying the lower stand fluvial deposit; its stratigraphy captures events of channel migration, ebb-delta breaching, onshore bar migration, channel shoaling and inlet infilling associated with the migration and eventual closure of the inlet. This inlet had a maximum cross-sectional area of 2800 m2 and was active around 3.5–3.6 ka. Discovery of this inlet suggests that the tidal prism was once larger than at present. Bay infilling, driven by the import of sediment into the backbarrier environment through tidal inlets, as well as minor sediment contribution from local rivers, led to a vast reduction in the bay tidal prism. This study demonstrates that, prior to about 3 ka, Plum Island and its associated marshes, tidal flats, and inlets were in a paraglacial environment; that is, their main source of sediment was derived from the erosion and reworking of glaciogenic deposits. Since that time, Plum Island has been in a state of dynamic equilibrium with its non-glacial sediment sources and therefore can be largely considered to be in a stable, “post-paraglacial” state. This study is furthermore the first in the Gulf of Maine to show that spit accretion and inlet processes were the dominant mechanisms in barrier-island formation and thus serves as a foundation for future investigations of barrier development in response to backbarrier infilling.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Marine Geology","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2012.03.001","usgsCitation":"Hein, C.J., FitzGerald, D.M., Carruthers, E.A., Stone, B.D., Barnhardt, W., and Gontz, A.M., 2012, Refining the model of barrier island formation along a paraglacial coast in the Gulf of Maine: Marine Geology, v. 307-310, p. 40-57, https://doi.org/10.1016/j.margeo.2012.03.001.","productDescription":"18 p.","startPage":"40","endPage":"57","ipdsId":"IP-028413","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":474532,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/5770","text":"External Repository"},{"id":274896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":274895,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.margeo.2012.03.001"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Gulf Of Maine","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -70.3603,41.526 ], [ -70.3603,44.2315 ], [ -66.4044,44.2315 ], [ -66.4044,41.526 ], [ -70.3603,41.526 ] ] ] } } ] }","volume":"307-310","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51dfd3e5e4b0d332bf22f3ac","contributors":{"authors":[{"text":"Hein, Christopher J.","contributorId":39893,"corporation":false,"usgs":true,"family":"Hein","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":475199,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"FitzGerald, Duncan M.","contributorId":48077,"corporation":false,"usgs":true,"family":"FitzGerald","given":"Duncan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":475200,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carruthers, Emily A.","contributorId":59709,"corporation":false,"usgs":true,"family":"Carruthers","given":"Emily","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":475201,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stone, Byron D. 0000-0001-6092-0798 bdstone@usgs.gov","orcid":"https://orcid.org/0000-0001-6092-0798","contributorId":1702,"corporation":false,"usgs":true,"family":"Stone","given":"Byron","email":"bdstone@usgs.gov","middleInitial":"D.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":475198,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhardt, Walter A.","contributorId":80656,"corporation":false,"usgs":true,"family":"Barnhardt","given":"Walter A.","affiliations":[],"preferred":false,"id":475203,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gontz, Allen M.","contributorId":79784,"corporation":false,"usgs":true,"family":"Gontz","given":"Allen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":475202,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70136251,"text":"70136251 - 2012 - Estimating survival rates with time series of standing age‐structure data","interactions":[],"lastModifiedDate":"2018-03-30T09:24:33","indexId":"70136251","displayToPublicDate":"2012-04-01T11:30:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating survival rates with time series of standing age‐structure data","docAbstract":"It has long been recognized that age‐structure data contain useful information for assessing the status and dynamics of wildlife populations. For example, age‐specific survival rates can be estimated with just a single sample from the age distribution of a stable, stationary population. For a population that is not stable, age‐specific survival rates can be estimated using techniques such as inverse methods that combine time series of age‐structure data with other demographic data. However, estimation of survival rates using these methods typically requires numerical optimization, a relatively long time series of data, and smoothing or other constraints to provide useful estimates. We developed general models for possibly unstable populations that combine time series of age‐structure data with other demographic data to provide explicit maximum likelihood estimators of age‐specific survival rates with as few as two years of data. As an example, we applied these methods to estimate survival rates for female bison (Bison bison) in Yellowstone National Park, USA. This approach provides a simple tool for monitoring survival rates based on age‐structure data.
Northern Goshawks occupying the Alexander Archipelago, Alaska, and coastal British Columbia nest primarily in old-growth and mature forest, which results in spatial heterogeneity in the distribution of individuals across the landscape. We used microsatellite and mitochondrial data to infer genetic structure, gene flow, and fluctuations in population demography through evolutionary time. Patterns in the genetic signatures were used to assess predictions associated with the three population models: panmixia, metapopulation, and isolated populations. Population genetic structure was observed along with asymmetry in gene flow estimates that changed directionality at different temporal scales, consistent with metapopulation model predictions. Therefore, Northern Goshawk assemblages located in the Alexander Archipelago and coastal British Columbia interact through a metapopulation framework, though they may not fit the classic model of a metapopulation. Long-term population sources (coastal mainland British Columbia) and sinks (Revillagigedo and Vancouver islands) were identified. However, there was no trend through evolutionary time in the directionality of dispersal among the remaining assemblages, suggestive of a rescue-effect dynamic. Admiralty, Douglas, and Chichagof island complex appears to be an evolutionarily recent source population in the Alexander Archipelago. In addition, Kupreanof island complex and Kispiox Forest District populations have high dispersal rates to populations in close geographic proximity and potentially serve as local source populations. Metapopulation dynamics occurring in the Alexander Archipelago and coastal British Columbia by Northern Goshawks highlight the importance of both occupied and unoccupied habitats to long-term population persistence of goshawks in this region.
","language":"English","publisher":"Kluwer Academic Publishers","publisherLocation":"Dordrecht","doi":"10.1007/s10592-012-0352-z","usgsCitation":"Sonsthagen, S.A., McClaren, E.L., Doyle, F.I., Titus, K., Sage, G.K., Wilson, R.E., Gust, J.R., and Talbot, S.L., 2012, Identification of metapopulation dynamics among Northern Goshawks of the Alexander Archipelago, Alaska, and Coastal British Columbia: Conservation Genetics, v. 13, no. 4, p. 1045-1057, https://doi.org/10.1007/s10592-012-0352-z.","productDescription":"13 p.","startPage":"1045","endPage":"1057","numberOfPages":"13","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-023923","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":296608,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","otherGeospatial":"Alexander Archipelago","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -140.9765625,\n 60.413852350464936\n ],\n [\n -131.220703125,\n 60.108670463036\n ],\n [\n -120.14648437499999,\n 49.210420445650286\n ],\n [\n -127.35351562499999,\n 46.13417004624326\n ],\n [\n -140.9765625,\n 60.413852350464936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2012-04-19","publicationStatus":"PW","scienceBaseUri":"548ace3de4b00f366bee37b8","contributors":{"authors":[{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":526836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClaren, Erica L.","contributorId":127825,"corporation":false,"usgs":false,"family":"McClaren","given":"Erica","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":526969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doyle, Frank I.","contributorId":127826,"corporation":false,"usgs":false,"family":"Doyle","given":"Frank","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":526970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Titus, K.","contributorId":93865,"corporation":false,"usgs":true,"family":"Titus","given":"K.","email":"","affiliations":[],"preferred":false,"id":526971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":526972,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Robert E. 0000-0003-1800-0183 rewilson@usgs.gov","orcid":"https://orcid.org/0000-0003-1800-0183","contributorId":5718,"corporation":false,"usgs":true,"family":"Wilson","given":"Robert","email":"rewilson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":526973,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gust, Judy R.","contributorId":62458,"corporation":false,"usgs":false,"family":"Gust","given":"Judy","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":526974,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":526975,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70169312,"text":"70169312 - 2012 - Quantity, structure, and habitat selection of natural spawning reefs by walleyes in a north temperate lake: A multiscale analysis","interactions":[],"lastModifiedDate":"2016-03-24T09:54:28","indexId":"70169312","displayToPublicDate":"2012-04-01T04:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Quantity, structure, and habitat selection of natural spawning reefs by walleyes in a north temperate lake: A multiscale analysis","docAbstract":"Spawning habitat, the cornerstone of self-sustaining, naturally reproducing walleyeSander vitreus populations, has received limited quantitative research. Our goal was to quantitatively describe the structure and quantity of natural walleye spawning habitat and evaluate potential selection of habitat in Big Crooked Lake, Wisconsin. In 2004 and 2005, we located and delineated walleye egg deposition polygons through visual snorkel and scuba surveys. We also delineated recently deposited, adhesive egg patches daily along one spawning reef in 2005. To determine habitat selection, we quantified and compared spawning and lakewide available habitat at different scales. In both years, walleyes used similar spawning habitat, including three geomorphic types: linear shorelines, a point bar, and an island. Walleyes used only 14% of the entire lake shoreline and 39% of the shoreline comprised of gravel (6.4–76.0 mm), cobble (76.1–149.9 mm), or coarser substrates for spawning in 2005, indicating selection of specific spawning habitat. Lakewide, walleyes spawned close to shore (outer egg deposition polygon boundary mean distance = 2.7 m), in shallow water (outer egg deposition polygon boundary mean depth = 0.3 m), and over gravel substrate (percent coverage mean = 64.3) having low embeddedness (mean = 1.30). Our best nearshore (0–13-m) resource selection function predicted an increase in the relative probability of egg deposition with the increasing abundance of gravel, cobble, and rubble (150.0–303.9-mm) substrates and a decrease with increasing distance from shore and water depth (89.9% overall correct classification). Adhesive egg patches confirmed that walleyes actively chose nearshore, shallow-water, and coarse-substrate spawning habitat. The quantitative habitat information and predictive models will assist biologists in developing walleye spawning reef protection strategies and potentially aid in designing and evaluating artificial spawning reefs.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/00028487.2012.679017","usgsCitation":"Raabe, J.K., and Bozek, M.A., 2012, Quantity, structure, and habitat selection of natural spawning reefs by walleyes in a north temperate lake: A multiscale analysis: Transactions of the American Fisheries Society, v. 141, no. 4, p. 1097-1108, https://doi.org/10.1080/00028487.2012.679017.","productDescription":"12 p.","startPage":"1097","endPage":"1108","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-032543","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":319347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Big Crooked Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.68276977539062,\n 46.12988744484639\n ],\n [\n -89.68276977539062,\n 46.150345757336574\n ],\n [\n -89.65856552124023,\n 46.150345757336574\n ],\n [\n -89.65856552124023,\n 46.12988744484639\n ],\n [\n -89.68276977539062,\n 46.12988744484639\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"141","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2012-07-02","publicationStatus":"PW","scienceBaseUri":"56f50fcfe4b0f59b85e1eb84","contributors":{"authors":[{"text":"Raabe, Joshua K.","contributorId":140952,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":623503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bozek, Michael A.","contributorId":51030,"corporation":false,"usgs":true,"family":"Bozek","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":623510,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70148132,"text":"70148132 - 2012 - Demographic population model for American shad: will access to additional habitat upstream of dams increase population sizes?","interactions":[],"lastModifiedDate":"2015-06-03T10:06:14","indexId":"70148132","displayToPublicDate":"2012-04-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Demographic population model for American shad: will access to additional habitat upstream of dams increase population sizes?","docAbstract":"American shad Alosa sapidissima are in decline in their native range, and modeling possible management scenarios could help guide their restoration. We developed a density-dependent, deterministic, stage-based matrix model to predict the population-level results of transporting American shad to suitable spawning habitat upstream of dams on the Roanoke River, North Carolina and Virginia. We used data on sonic-tagged adult American shad and oxytetracycline-marked American shad fry both above and below dams on the Roanoke River with information from other systems to estimate a starting population size and vital rates. We modeled the adult female population over 30 years under plausible scenarios of adult transport, effective fecundity (egg production), and survival of adults (i.e., to return to spawn the next year) and juveniles (from spawned egg to age 1). We also evaluated the potential effects of increased survival for adults and juveniles. The adult female population size in the Roanoke River was estimated to be 5,224. With no transport, the model predicted a slow population increase over the next 30 years. Predicted population increases were highest when survival was improved during the first year of life. Transport was predicted to benefit the population only if high rates of effective fecundity and juvenile survival could be achieved. Currently, transported adults and young are less likely to successfully out-migrate than individuals below the dams, and the estimated adult population size is much smaller than either of two assumed values of carrying capacity for the lower river; therefore, transport is not predicted to help restore the stock under present conditions. Research on survival rates, density-dependent processes, and the impacts of structures to increase out-migration success would improve evaluation of the potential benefits of access to additional spawning habitat for American shad.
","language":"English","publisher":"American Fisheries Society","doi":"10.1080/19425120.2012.675969","usgsCitation":"Harris, J., and Hightower, J.E., 2012, Demographic population model for American shad: will access to additional habitat upstream of dams increase population sizes?: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 4, no. 1, p. 262-283, https://doi.org/10.1080/19425120.2012.675969.","productDescription":"22 p.","startPage":"262","endPage":"283","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-028285","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":474538,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/19425120.2012.675969","text":"Publisher Index Page"},{"id":301002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Roanoke River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.18121337890625,\n 36.38149043210595\n ],\n [\n -79.18121337890625,\n 37.084762325442966\n ],\n [\n -77.57720947265624,\n 37.084762325442966\n ],\n [\n -77.57720947265624,\n 36.38149043210595\n ],\n [\n -79.18121337890625,\n 36.38149043210595\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2012-06-18","publicationStatus":"PW","scienceBaseUri":"55702532e4b0d9246a9fd18d","contributors":{"authors":[{"text":"Harris, Julianne E.","contributorId":57687,"corporation":false,"usgs":true,"family":"Harris","given":"Julianne E.","affiliations":[],"preferred":false,"id":548124,"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":547461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70193425,"text":"70193425 - 2012 - Emerging prion disease drives host selection in a wildlife population","interactions":[],"lastModifiedDate":"2017-11-10T18:57:58","indexId":"70193425","displayToPublicDate":"2012-04-01T00:00:00","publicationYear":"2012","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Emerging prion disease drives host selection in a wildlife population","docAbstract":"Infectious diseases are increasingly recognized as an important force driving population dynamics, conservation biology, and natural selection in wildlife populations. Infectious agents have been implicated in the decline of small or endangered populations and may act to constrain population size, distribution, growth rates, or migration patterns. Further, diseases may provide selective pressures that shape the genetic diversity of populations or species. Thus, understanding disease dynamics and selective pressures from pathogens is crucial to understanding population processes, managing wildlife diseases, and conserving biological diversity. There is ample evidence that variation in the prion protein gene (PRNP) impacts host susceptibility to prion diseases. Still, little is known about how genetic differences might influence natural selection within wildlife populations. Here we link genetic variation with differential susceptibility of white-tailed deer to chronic wasting disease (CWD), with implications for fitness and disease-driven genetic selection. We developed a single nucleotide polymorphism (SNP) assay to efficiently genotype deer at the locus of interest (in the 96th codon of the PRNP gene). Then, using a Bayesian modeling approach, we found that the more susceptible genotype had over four times greater risk of CWD infection; and, once infected, deer with the resistant genotype survived 49% longer (8.25 more months). We used these epidemiological parameters in a multi-stage population matrix model to evaluate relative fitness based on genotype-specific population growth rates. The differences in disease infection and mortality rates allowed genetically resistant deer to achieve higher population growth and obtain a long-term fitness advantage, which translated into a selection coefficient of over 1% favoring the CWD-resistant genotype. This selective pressure suggests that the resistant allele could become dominant in the population within an evolutionarily short time frame. Our work provides a rare example of a quantifiable disease-driven selection process in a wildlife population, demonstrating the potential for infectious diseases to alter host populations. This will have direct bearing on the epidemiology, dynamics, and future trends in CWD transmission and spread. Understanding genotype-specific epidemiology will improve predictive models and inform management strategies for CWD-affected cervid populations.
","language":"English","publisher":"Ecological Applications","doi":"10.1890/11-0907.1","usgsCitation":"Robinson, S.J., Samuel, M.D., Johnson, C.J., Adams, M., and McKenzie, D.I., 2012, Emerging prion disease drives host selection in a wildlife population: Ecological Applications, v. 22, no. 3, p. 1050-1059, https://doi.org/10.1890/11-0907.1.","productDescription":"10 p.","startPage":"1050","endPage":"1059","ipdsId":"IP-026545","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":348600,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a06c8d8e4b09af898c86185","contributors":{"authors":[{"text":"Robinson, Stacie J.","contributorId":172022,"corporation":false,"usgs":false,"family":"Robinson","given":"Stacie","email":"","middleInitial":"J.","affiliations":[{"id":12508,"text":"Department of Forest and Wildlife Ecology, University of Wisconsin, 1710 University Ave., Room 285, Madison, WI 53726, USA","active":true,"usgs":false}],"preferred":false,"id":721658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Samuel, Michael D. msamuel@usgs.gov","contributorId":1419,"corporation":false,"usgs":true,"family":"Samuel","given":"Michael","email":"msamuel@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":719000,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Chad J.","contributorId":171369,"corporation":false,"usgs":false,"family":"Johnson","given":"Chad","email":"","middleInitial":"J.","affiliations":[{"id":24576,"text":"University of Wisconsin, Madison, WI","active":true,"usgs":false}],"preferred":false,"id":721659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adams, Marie","contributorId":192488,"corporation":false,"usgs":false,"family":"Adams","given":"Marie","email":"","affiliations":[],"preferred":false,"id":721660,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKenzie, Debbie I.","contributorId":171370,"corporation":false,"usgs":false,"family":"McKenzie","given":"Debbie","email":"","middleInitial":"I.","affiliations":[{"id":12799,"text":"University of Alberta, Edmonton, Alberta, Canada","active":true,"usgs":false}],"preferred":false,"id":721661,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}