Information on marine debris along the Mediterranean coast of Israel, especially on the seafloor, is limited. Many recreational divers are enthusiasts of marine conservation and can thus contribute to data collection which does not require highly specialized training. The Society for the Protection of Nature in Israel together with The Israeli Diving Federation established the diver volunteer program “Sea Guard” (“Mishmar Hayam” in Hebrew), which supports marine conservation through citizen science. The divers were trained in marine ecology and survey methods to conduct independent surveys and lead underwater cleanups. For the first time, we have described the patterns of benthic debris density and composition in the nearshore environment of the southeastern part of the Mediterranean Sea. We found that benthic marine debris in the nearshore along the Israeli Mediterranean coast is primarily plastic, likely originating from the use of local beaches. Fishing, boating and domestic activities also play an important role as sources for marine debris. The currents' regime prevented the debris from accumulating on the seafloor in the nearshore environment, with the exception of several “debris traps”. Our findings will be useful for the development of programs to improve coastal waste management.
The Point-Source Load Estimation Tool (PSLoadEsT) provides a user-friendly interface for generating reproducible load calculations for point source dischargers while managing common data challenges including duplicates, incompatible input tables, and incomplete or missing nutrient concentration or effluent flow data. Maintaining a consistent method across an entire study area is important when estimating loads to be used as calibration data for regional water-quality models. PSLoadEsT is written using the open-source programming language R and has an easy-to-use interface written in Visual Basic for Applications® within a Microsoft Access® database file that guides the user through the necessary steps to estimate point source loads. The purpose of this report is to provide a detailed user guide for PSLoadEsT.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191025","collaboration":"National Water Quality Assessment Program","usgsCitation":"Gorman Sanisaca, L.E., Skinner, K.D., and Maupin, M.A., 2019, Annual wastewater nutrient data preparation and load estimation using the Point Source Load Estimation Tool (PSLoadEsT): U.S. Geological Survey Open-File Report 2019-1025, 48 p., https://doi.org/10.3133/ofr20191025.","productDescription":"Report: vi, 48 p.; Additional Report Piece","onlineOnly":"Y","ipdsId":"IP-099356","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":437510,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QWVZ4L","text":"USGS data release","linkHelpText":"Point-Source Load Estimation Tool (PSLoadEsT)"},{"id":362728,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1025/coverthb.jpg"},{"id":362733,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9QWVZ4L","text":"PSLoadEsT Software release","description":"PSLoadEsT Software release"},{"id":362729,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1025/ofr20191025.pdf","text":"Report","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1025"},{"id":362732,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1101","text":"Data Series 1101","description":"Data Series 1101","linkHelpText":"Point-Source Nutrient Loads to Streams of the Conterminous United States, 2012"}],"contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Total nitrogen and phosphorous loads were estimated for 5,430 major point-source facilities (all types) and 11,537 minor wastewater treatment facilities discharging to streams in the conterminous United States during 2012. Facilities classified as a major discharger are typically a facility that discharges greater than one million gallons of water per day however some industrial facilities are classified as a major based on specific criteria developed by the U.S. Environmental Protection Agency (EPA) and the National Pollutant Discharge Elimination System state program. Data documenting discharge information from point sources were obtained from the EPA’s Integrated Compliance Information System (ICIS) and Permit Compliance System (PCS). When available, actual nutrient concentration measurements were used to calculate point-source loads. In the many cases in which concentration data were not available in either the ICIS or PCS databases, typical pollutant concentrations (TPCs) were developed using data from similar facilities. A new method for calculating TPCs was implemented that allows varying amounts of nutrient concentration data and (or) varying numbers of facilities to determine TPCs. This new method minimized the effect that any single facility discharging extremely large nutrient concentrations had on resultant TPC values. Because of the smaller TPC values from this new TPC method, the total nutrient load for many states was reduced compared to previous TPC methods.
Major wastewater treatment facilities are the largest contributor of nutrient loads to streams even though there are almost three times as many minor wastewater treatment facilities. Specifically, 4,218 major wastewater treatment facilities account for 94 percent of the total nitrogen load for the conterminous United States, whereas 11,397 minor wastewater treatment facilities contribute 6 percent of the total nitrogen load. Total phosphorous loads are similarly divided among major (93 percent) and minor (7 percent) wastewater treatment facilities. Total nitrogen loads, including all facility types, primarily are from wastewater treatment facilities and some petroleum refining facilities. Total phosphorous loads also are primarily from wastewater treatment facilities, but several manufacturing facilities such as corn milling, pulp and paper mills, and industrial chemical facilities also contribute to total phosphorous loads.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1101","usgsCitation":"Skinner, K.D., and Maupin, M.A., 2019, Point-source nutrient loads to streams of the conterminous United States, 2012: U.S. Geological Survey Data Series 1101, 13 p., https://doi.org/10.3133/ds1101.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-080332","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":362734,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191025","text":"OFR 2019-1025","description":"OFR 2019-1025","linkHelpText":"Annual Wastewater Nutrient Data Preparation and Load Estimation Using the Point-Source Load Estimation Tool 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U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
To better understand recent population growth of the Double-crested Cormorant Phalacrocorax auritus along the Pacific coast of North America, we assessed long-term breeding population trends in the Humboldt Bay area, California, using aerial photographic survey data collected since 1989 as well as available prior data. The earliest documentations of breeding (but without nest counts) are from 1924, 1943, and 1947 on the outer coast near Trinidad, and from 1959 in Humboldt Bay at Old Arcata Wharf. The breeding population increased from 188 nests (376 breeding birds) at one colony in 1961 to ~ 350 nests (700 breeding birds) at four colonies by 1980, and then to peaks of nearly 1,700 nests (3,400 breeding birds) in 1997 and 2004 at eight colonies. Breeding was documented at 13 coastal colonies through 2017. The population increased 100% (9 % per annum) from 1989 to 1997, decreased during the strong 1998 El Niño, and rebounded by 2004. After
the 2004 peak, three years of available data indicated slight population decline. For the entire 1989–2017 period, the population increased by 91% (2% per annum). Artificial habitats in Humboldt Bay allowed most of the population growth, especially Teal Island, which was colonized in 1993 and became the largest colony in all but one year thereafter. Nest totals on the outer coast decreased, likely because of movements to the Humboldt Bay colonies, which are closer to main foraging areas, and because of competition for nesting space with Common Murres Uria aalge at one colony (False Cape Rocks). Future growth of the population in the Humboldt Bay area appears limited by the availability of disturbance-free breeding habitat. Declines may occur if artificial habitats are lost.
On February 14, 2019, just before 2:56 am local time (Pacific Standard Time), a landslide initiated from the hillslopes above the Hurricane Gulch section of the City of Sausalito, Marin County, California. The landslide, specifically classified as a debris flow, overran a road (Sausalito Boulevard) immediately below the landslide source area and impacted a residential structure that subsequently toppled downslope and collided with another residential structure. The second structure then crossed a lower road (Crescent Avenue) that runs along the base of the slope before the mixture of soil and structural debris came to rest in and near the valley axis that drains the lower area of Hurricane Gulch.
The U.S. Geological Survey responded to this event within hours of the landslide and provided situational awareness of possible secondary landslide hazards associated with the unstable slope. The USGS also rapidly mobilized its topographic surveying capabilities (specifically, GPS and terrestrial lidar devices) and collected a three-dimensional point cloud model of the landslide source area and surrounding terrain to capture the as-failed condition of the slope for use in potential future studies. This report summarizes the methods and available data collected during this response.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1112","usgsCitation":"Collins, B.D. and Corbett, S.C., 2019. Terrestrial lidar data of the February 14, 2019 Sausalito Boulevard Landslide, Sausalito, California: U.S. Geological Survey Data Series 1112, 12 p., https://doi.org/10.3133/ds1112.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106637","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":362641,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AQRCTJ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Terrestrial LIDAR Data Set of the February 14, 2019 Sausalito Boulevard Landslide, Sausalito, California"},{"id":362639,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1112/coverthb.jpg"},{"id":362640,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1112/ds1112_.pdf","text":"Report","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1112"}],"country":"United States","state":"California","city":"Sausalito","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5074291229248,\n 37.87376937332855\n ],\n [\n -122.50828742980956,\n 37.86299605572604\n ],\n [\n -122.48854637145995,\n 37.84422368363511\n ],\n [\n -122.47670173645018,\n 37.84564702731293\n ],\n [\n -122.47756004333496,\n 37.859540129644195\n ],\n [\n -122.50288009643553,\n 37.87715688349197\n ],\n [\n -122.5074291229248,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
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Situated in the Mississippi Alluvial Plain of the Gulf Coast Prairie Landscape Conservation Cooperative (GCP LCC), the Chitimacha Tribe is one of four federally recognized tribes in Louisiana. The Tribal seat, trust lands/ reservation, and adjacent Tribal owned lands are located near Charenton, Louisiana, totaling nearly 1,000 acres. The Chitimacha, with a population of approximately 1,400 people, are currently impacted by storm surge, which is expected to increase with climate change. The additional stress from storms will likewise increase the vulnerability to catastrophic impact in the event of a breach in the Atchafalaya Basin Spillway levee. A collaborative effort between the U.S. Geological Survey (USGS) and the Chitimacha Tribe has been initiated to provide resources and expertise to increase the Tribe’s ability to prevent, plan, and prepare for these environmental challenges. By enhancing technical skills, providing access to environmental data, and increasing awareness of environmental issues, the Chitimacha will be better prepared to plan and adapt to the environmental impacts facing their lands related to land use and climate change.
For this project, USGS researchers asked how Chitimacha Tribal Lands might be impacted by future sea level rise scenario projections. These models illustrate some flooding within the northernmost boundary of Chitimacha Tribal Lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191030","collaboration":"Prepared in cooperation with The Chitimacha Tribe of Louisiana; Gulf Coast Prairie Landscape Conservation Cooperative","usgsCitation":"Spear, K.A., Jones, W., Griffith, K., Tirpak, B.E., and Walden, K., 2019, Potential sea level rise on Chitimacha Tribal Lands in Louisiana: U.S. Geological Survey Open-File Report 2019–1030, 1 sheet, https://doi.org/10.3133/ofr20191030.","productDescription":"1 Sheet: 24.0 x 36.0 inches","onlineOnly":"Y","ipdsId":"IP-090045","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":502765,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_108491.htm","linkFileType":{"id":5,"text":"html"}},{"id":362387,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1030/ofr20191030.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1030"},{"id":362386,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1030/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chitimacha Tribal Lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.5833,\n 29.8167\n ],\n [\n -91.5,\n 29.8167\n ],\n [\n -91.5,\n 29.9167\n ],\n [\n -91.5833,\n 29.9167\n ],\n [\n -91.5833,\n 29.8167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
Recently introduced unmarked spatial capture–recapture (SCR), spatial mark–resight (SMR), and 2‐flank spatial partial identity models (SPIMs) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single‐flank photographs. Unmarked SCR, SMR, and SPIM share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations because a sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is related to two key ecological concepts, population density and home range size, which we quantify using a proposed Identity Diversity Index (IDI). We show that latent and partial identity SCR models produce imprecise and biased density estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate categorical, partially identifying covariates, which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this “categorical SPIM” via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. We then discuss how the categorical SPIM can be applied to other wildlife sampling scenarios such as remote camera surveys, where natural or researcher‐applied partial marks can be observed in photographs. Finally, we discuss how the categorical SPIM can be added to SMR, 2‐flank SPIM, or other latent identity SCR models.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2627","usgsCitation":"Augustine, B., Royle, J.A., Murphy, S.M., Chandler, R.B., Cox, J., and Kelly, M., 2019, Spatial capture–recapture for categorically marked populations with an application to genetic capture–recapture: Ecosphere, v. 10, no. 4, e02627, 22 p., https://doi.org/10.1002/ecs2.2627.","productDescription":"e02627, 22 p.","ipdsId":"IP-095923","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2627","text":"Publisher Index Page"},{"id":377199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Augustine, Ben C.","contributorId":237797,"corporation":false,"usgs":false,"family":"Augustine","given":"Ben C.","affiliations":[{"id":38081,"text":"Cornell Univ.","active":true,"usgs":false}],"preferred":false,"id":795329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sean M.","contributorId":140195,"corporation":false,"usgs":false,"family":"Murphy","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":795330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chandler, Richard B. rchandler@usgs.gov","contributorId":63524,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":795332,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cox, John J.","contributorId":140196,"corporation":false,"usgs":false,"family":"Cox","given":"John J.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":795334,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, Marcella","contributorId":237800,"corporation":false,"usgs":false,"family":"Kelly","given":"Marcella","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":795333,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203001,"text":"70203001 - 2019 - Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data","interactions":[],"lastModifiedDate":"2019-04-11T13:46:12","indexId":"70203001","displayToPublicDate":"2019-04-01T13:45:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1760,"text":"Geoderma","active":true,"publicationSubtype":{"id":10}},"title":"Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data","docAbstract":"Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual depths of interest separately as ‘2D’ models, or to create models that incorporate depth as a predictor variable creating a ‘3D’ model that can make pre-dictions for all depths. It is unclear how choice of 2D or 3D approach influences model accuracy and uncertainty due to lack of direct comparison and inconsistent presentation of results in past studies. This study compares 2D and 3D methods for mapping soil electrical conductivity (salinity), pH, sum of fine and very fine sands, and organic carbon at 30 m resolution for the upper 432,000 km 2 of the Colorado River Watershed of the United States of America. A new, simple, model-agnostic relative prediction interval (RPI) approach to report un-certainty is presented that scales prediction interval width to the 95% interquantile width of the original training sample distribution. The RPI approach enables direct comparison of uncertainty between properties and depths and is easily interpretable by end users. Results indicate that 3D mapping of soil properties with strong variation with depth can result in substantial areas with much higher uncertainty that coincide with unrealistic predictions relative to 2D models, even though 3D models had slightly better global cross-validation scores. Maps and global model summaries of RPI proved helpful in identifying these issues with 3D models. These results suggest that the use of RPI or similar approaches to evaluate models can identify accuracy problems not evident in global va-lidation diagnostics.","language":"English","publisher":"ELsevier","doi":"10.1016/j.geoderma.2019.03.037","usgsCitation":"Nauman, T., and Duniway, M.C., 2019, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma, v. 347, p. 170-184, https://doi.org/10.1016/j.geoderma.2019.03.037.","productDescription":"15 p.","startPage":"170","endPage":"184","ipdsId":"IP-102589","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":467743,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geoderma.2019.03.037","text":"Publisher Index Page"},{"id":437517,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YBAKC2","text":"USGS data release","linkHelpText":"Predictive maps of 2D and 3D surface soil properties and associated uncertainty for the Upper Colorado River Basin, USA"},{"id":362917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"347","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis","contributorId":214769,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760737,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760738,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202948,"text":"70202948 - 2019 - The contribution of road-based citizen science to the conservation of pond-breeding amphibians","interactions":[],"lastModifiedDate":"2019-04-08T15:24:14","indexId":"70202948","displayToPublicDate":"2019-04-01T12:15:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The contribution of road-based citizen science to the conservation of pond-breeding amphibians","docAbstract":"Roadside amphibian citizen science (CS) programmes bring together volunteers focused on collecting scientific data while working to mitigate population declines by reducing road mortality of pond‐breeding amphibians. Despite the international popularity of these movement‐based, roadside conservation efforts (i.e. “big nights,” “bucket brigades” and “toad patrols”), direct benefits to conservation have rarely been quantified or evaluated.
As a case study, we used a population simulation approach to evaluate how volunteer intensity, frequency and distribution influence three conservation outcomes (minimum population size, population growth rate and years to extinction) of the spotted salamander (Ambystoma maculatum), often a focal pond‐breeding amphibian of CS and conservation programmes in the United States.
Sensitivity analysis supported the expectation that spotted salamander populations were primarily recruitment‐driven. Thus, conservation outcomes were highest when volunteers focused on metamorph outmigration as opposed to adult in‐migration—contrary to the typical timing of such volunteer events.
Almost every volunteer strategy resulted in increased conservation outcomes compared to a no‐volunteer strategy. Specifically, volunteer frequency during metamorph migration increased outcomes more than the same increases in volunteer effort during adult migration. Small population sizes resulted in a negligible effect of volunteer intensity. Volunteers during the first adult in‐migration had a relatively small effect compared to most other strategies.
Synthesis and applications. Although citizen science (CS)‐focused conservation actions could directly benefit declining populations, additional conservation measures are needed to halt or reverse local amphibian declines. This study demonstrates a need to evaluate the effectiveness of focusing CS mitigation efforts on the metamorph stage, as opposed to the adult stage. This may be challenging, compared to other management actions such as road‐crossing infrastructure. Current amphibian CS programmes will be challenged to balance implementing evidence‐based conservation measures on the most limiting life stage, while retaining social and community benefits for volunteers.
Salmon populations spawning in the Lake Ozette watershed of northwestern Washington were once sufficiently abundant to support traditional Tribal fisheries, and were later harvested by settlers. However, in 1974 and 1975, the sockeye salmon (Oncorhynchus nerka) harvest decreased to 0 from a high of more than 17,500 in 1949, thus stimulating research into the causes of decrease, which resulted in eventual listing of the population as threatened under the Endangered Species Act in 1999. The listing status was upheld in 2005 and 2014 following 5-year reviews. Meanwhile, research results were compiled in a limiting factors analysis (LFA) and a recovery plan was developed. Although there has been some improvement in sockeye abundance since listing, the numbers remain too low to allow harvest and it is not yet clear which of the many potential limiting factors are most consequential.
As part of the LFA process, a population model was developed to determine values of life-history parameters that would enable the population to survive for 100 years. The model was based on the best available data, but data are limited for the Lake Ozette system. Results informed the qualitative assessment of the importance of limiting factors used to develop the recovery plan for Lake Ozette sockeye. The model was built in Microsoft Excel® and is difficult to use. The purpose of the model described herein is to synthesize the results of the LFA in a form that can be manipulated by resource managers and the public to create scenarios, test hypotheses, and observe sensitivities of results to changes in parameters. The goal is to provide a tool that enables research, monitoring and management to be focused on the most impactful elements and processes, including identifying the information gaps that are most critical to fill.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191031","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Woodward, A., Haggerty, M., and Crain, P., 2019, Life-history model for sockeye salmon (Oncorhynchus nerka) at Lake Ozette, northwestern Washington—Users' guide: U.S. Geological Survey Open-File Report 2019-1031, 79 p., https://doi.org/10.3133/ofr20191031.","productDescription":"viii, 79 p.","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-101934","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":362633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1031/coverthb.jpg"},{"id":362634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1031/ofr20191031.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1031"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Ozette","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.68074798583986,\n 48.033560004128255\n ],\n [\n -124.59320068359374,\n 48.033560004128255\n ],\n [\n -124.59320068359374,\n 48.15509285476017\n ],\n [\n -124.68074798583986,\n 48.15509285476017\n ],\n [\n -124.68074798583986,\n 48.033560004128255\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Occupancy models are widely applied to estimate species distributions, but few methods exist for model checking. Thorough model assessments can uncover inadequacies and allow for deeper ecological insight by exploring structure in the observed data not accounted for by a model. We introduce occupancy model residual definitions that utilize the posterior distribution of the partially latent occupancy states. Residual‐based assessments are valuable because they can target specific assumptions and identify ways to improve a model, such as adding spatial correlation or meaningful covariates. Our approach defines separate residuals for occupancy and detection, and we use simulation to examine whether missing structure for modeling detection probabilities can be distinguished from that for occupancy probabilities. In many scenarios, our residual diagnostics were able to successfully separate inadequacies at the different model levels, but we describe other situations when this may not be the case. Applying Moran's I residual diagnostics to assess models for silver‐haired (Lasionycteris noctivagans) and little brown (Myotis lucifugus) bats only provided evidence of residual spatial correlation among detections. Targeting specific model assumptions using carefully chosen residual diagnostics is valuable for any analysis, and we remove previous barriers for occupancy analyses — lack of examples and practical advice.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2703","usgsCitation":"Wright, W., Irvine, K., and Higgs, M.D., 2019, Identifying occupancy model inadequacies: Can residuals separately assess detection and presence?: Ecology, v. 100, no. 6, e02703, https://doi.org/10.1002/ecy.2703.","productDescription":"e02703","ipdsId":"IP-088414","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467749,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecy.2703","text":"External Repository"},{"id":362650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Wilson 0000-0003-4276-3850","orcid":"https://orcid.org/0000-0003-4276-3850","contributorId":214592,"corporation":false,"usgs":true,"family":"Wright","given":"Wilson","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":760332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":214591,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":760331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Higgs, Megan D.","contributorId":127365,"corporation":false,"usgs":false,"family":"Higgs","given":"Megan","email":"","middleInitial":"D.","affiliations":[{"id":6916,"text":"Department of Mathematical Sciences, Montana State University, Bozeman, USA","active":true,"usgs":false}],"preferred":false,"id":760333,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219176,"text":"70219176 - 2019 - Status of pelagic prey fishes in Lake Michigan, 2018","interactions":[],"lastModifiedDate":"2021-04-16T12:21:47.47692","indexId":"70219176","displayToPublicDate":"2019-04-01T10:39:36","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Status of pelagic prey fishes in Lake Michigan, 2018","docAbstract":"Acoustic surveys were conducted in late summer/early fall during the years 2004-2018 to estimate pelagic prey fish biomass in Lake Michigan. Midwater trawling during the surveys as well as acoustic target strength provided a measure of species and size composition of the fish community for use in scaling acoustic data and providing species-specific abundance estimates. The 2018 survey consisted of 33 acoustic transects 648 km total (403 miles) and 52 midwater trawl tows. Bottom depth at sampling sites ranged from 5 to 245 m (16-804 ft). Mean prey fish biomass density was 8.5 kg/ha, which was 1.9 times higher than in 2017 and 2.4 times the long-term (15 years) mean. The numeric density of the 2018 alewife year-class was 52% of the time series average and 1.8 times the 2017 density. The 2018 cohort was 7% of total alewife biomass (5.2 kg/ha). In 2018 alewife comprised 61% of total prey fish biomass, while rainbow smelt and bloater were 2% and 37% of total biomass, respectively. Small bloater were extremely rare in 2018 and were only caught near Frankfort, Michigan. Their density (< 1 fish/ha) in 2018 was the lowest observed in the 2004-2018 period. Biomass density of rainbow smelt and bloater remain well below observed in the 1980s-1990s. Cisco are infrequently caught in this survey, including the past two years. In 2018 three adult fish were caught (> 400 mm), with two in Grand Traverse Bay and one south of Manistique, Michigan. These results indicate that cisco density is very low at the lake level.","conferenceTitle":"Great Lakes Fishery Commission, Lake Michigan Committee Meeting","conferenceDate":"March 25, 2019","conferenceLocation":"Ypsilanti, MI","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Warner, D., Phillips, K., Turschak, B., Hanson, D., and Smith, J., 2019, Status of pelagic prey fishes in Lake Michigan, 2018, Great Lakes Fishery Commission, Lake Michigan Committee Meeting, Ypsilanti, MI, March 25, 2019, 15 p.","productDescription":"15 p.","ipdsId":"IP-106761","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385126,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/lake-michigan-committee.php"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n 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Center","active":true,"usgs":true}],"preferred":true,"id":813136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Kristy","contributorId":256722,"corporation":false,"usgs":false,"family":"Phillips","given":"Kristy","affiliations":[],"preferred":false,"id":813137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turschak, Ben","contributorId":257454,"corporation":false,"usgs":false,"family":"Turschak","given":"Ben","email":"","affiliations":[],"preferred":false,"id":814304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanson, Dale","contributorId":190498,"corporation":false,"usgs":false,"family":"Hanson","given":"Dale","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":814305,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Jason","contributorId":215444,"corporation":false,"usgs":false,"family":"Smith","given":"Jason","affiliations":[{"id":39249,"text":"Little Traverse Band of Odawa Indians","active":true,"usgs":false}],"preferred":false,"id":814306,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199964,"text":"70199964 - 2019 - Geospatial data mining for digital raster mapping","interactions":[],"lastModifiedDate":"2024-05-17T15:09:45.727773","indexId":"70199964","displayToPublicDate":"2019-04-01T10:38:33","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Geospatial data mining for digital raster mapping","docAbstract":"We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2018.1517445","usgsCitation":"Wylie, B.K., Pastick, N.J., Picotte, J.J., and Deering, C., 2019, Geospatial data mining for digital raster mapping: GIScience and Remote Sensing, v. 56, no. 3, p. 406-429, https://doi.org/10.1080/15481603.2018.1517445.","productDescription":"14 p.","startPage":"406","endPage":"429","ipdsId":"IP-094736","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499974,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/7c16e86b33fd456cb54a7bd63a3e2985","text":"External Repository"},{"id":358204,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f76e4b0fc368eb53837","contributors":{"authors":[{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":747499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":747500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":747501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Deering, Carol 0000-0003-3565-6264 cdeering@usgs.gov","orcid":"https://orcid.org/0000-0003-3565-6264","contributorId":3001,"corporation":false,"usgs":true,"family":"Deering","given":"Carol","email":"cdeering@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":747502,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203005,"text":"70203005 - 2019 - Upper mantle earth structure in Africa from full-wave ambient noise tomography","interactions":[],"lastModifiedDate":"2019-04-11T11:37:56","indexId":"70203005","displayToPublicDate":"2019-04-01T09:20:02","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Upper mantle earth structure in Africa from full-wave ambient noise tomography","docAbstract":"Our understanding of the tectonic development of the African continent and the interplay between its geological provinces is hindered by unevenly distributed seismic instrumentation. In order to better understand the continent, we used long-period ambient noise full waveform tomography on data collected from 186 broadband seismic stations throughout Africa and surrounding regions to better image the upper mantle structure. We extracted empirical Green’s functions from ambient seismic noise using a frequency-time normalization method and retrieved coherent signal at periods of 7-340 seconds. We simulated wave propagation through a heterogeneous Earth using a spherical finite-difference approach to obtain synthetic waveforms, measured the misfit as phase delay between the data and synthetics, calculated numerical sensitivity kernels using the scattering integral approach, and iteratively inverted for structure. The resulting images of isotropic, shear wavespeed for the continent reveal segmented, low-velocity upper mantle beneath the highly magmatic northern and eastern sections of the East African Rift System (EARS). In the southern and western sections, high-velocity upper mantle dominates, and distinct, low-velocity anomalies are restricted to regions of current volcanism. At deeper depths, the southern and western EARS transitions to low-velocities. In addition to the EARS, several low-velocity anomalies are scattered through the shallow upper mantle beneath Angola and North Africa, and some of these low-velocity anomalies may be connected to a deeper feature. Distinct upper mantle high-velocity anomalies are imaged throughout the continent and suggest multiple cratonic roots within the Congo region, and possible cratonic roots within the Sahara Metacraton.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018GC007804","usgsCitation":"Emry, E.L., Shen, Y., Nyblade, A.A., Flinders, A.F., and Bao, X., 2019, Upper mantle earth structure in Africa from full-wave ambient noise tomography: Geochemistry, Geophysics, Geosystems, v. 20, no. 1, p. 120-147, https://doi.org/10.1029/2018GC007804.","productDescription":"28 p.","startPage":"120","endPage":"147","ipdsId":"IP-102538","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":460423,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018gc007804","text":"Publisher Index Page"},{"id":362907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -15.820312499999988,\n 26.43122806450644\n ],\n [\n -19.335937499999986,\n 17.644022027872712\n ],\n [\n -15.468749999999986,\n 5.9657536710655235\n ],\n [\n -2.1093749999999862,\n 1.0546279422758869\n ],\n [\n 7.3828125,\n 2.4601811810210052\n ],\n [\n 11.6015625,\n -10.833305983642491\n ],\n [\n 10.8984375,\n -19.31114335506464\n ],\n [\n 13.7109375,\n -27.371767300523032\n ],\n [\n 16.875,\n -34.30714385628803\n ],\n [\n 22.8515625,\n -36.87962060502676\n ],\n [\n 52.03125,\n -27.68352808378776\n ],\n [\n 51.328125,\n 2.8113711933311403\n ],\n [\n 51.67968749999999,\n 12.211180191503997\n ],\n [\n 43.06640625,\n 12.554563528593656\n ],\n [\n 42.01171875,\n 15.623036831528264\n ],\n [\n 36.035156250000014,\n 25.799891182088334\n ],\n [\n 33.046875000000014,\n 31.503629305773003\n ],\n [\n 21.445312500000014,\n 33.43144133557529\n ],\n [\n 19.33593750000001,\n 30.751277776257812\n ],\n [\n 10.371093750000012,\n 37.857507156252\n ],\n [\n -8.78906249999999,\n 35.17380831799956\n ],\n [\n -15.820312499999988,\n 26.43122806450644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Emry, Erica L.","contributorId":214774,"corporation":false,"usgs":false,"family":"Emry","given":"Erica","email":"","middleInitial":"L.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":760749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shen, Yang","contributorId":206754,"corporation":false,"usgs":false,"family":"Shen","given":"Yang","affiliations":[{"id":37391,"text":"University of Rhode Island, Graduate School of Oceanography","active":true,"usgs":false}],"preferred":false,"id":760750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nyblade, Andrew A.","contributorId":214775,"corporation":false,"usgs":false,"family":"Nyblade","given":"Andrew","email":"","middleInitial":"A.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":760751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flinders, Ashton F. 0000-0003-2483-4635 aflinders@usgs.gov","orcid":"https://orcid.org/0000-0003-2483-4635","contributorId":196960,"corporation":false,"usgs":true,"family":"Flinders","given":"Ashton","email":"aflinders@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":153,"text":"California Volcano Observatory","active":false,"usgs":true}],"preferred":false,"id":760748,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bao, Xueyang","contributorId":214776,"corporation":false,"usgs":false,"family":"Bao","given":"Xueyang","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":760752,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203198,"text":"70203198 - 2019 - Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps","interactions":[],"lastModifiedDate":"2019-04-29T08:57:06","indexId":"70203198","displayToPublicDate":"2019-04-01T08:56:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps","docAbstract":"Monitoring ichthyoplankton is useful for identifying reproductive fronts and spawning locations of bigheaded carps (Hypophthalmichthys spp.). Unfortunately, sorting and identifying ichthyoplankton to monitor for bigheaded carp reproduction is time consuming and expensive. Traditional methods require frequent egg-larvae sampling, sorting of all samples to obtain presumptively identified bigheaded carp, and genetic validation of presumptively identified eggs. Quantitative PCR (qPCR) has the potential to streamline this process by identifying samples that likely do or do not contain a target species. Our objective was to develop a genetic screening tool using qPCR with the duplex assays SCTM4/5 and BHTM1/2 to prioritize samples that have a higher likelihood of containing bigheaded carp eggs or larvae. We used tandem ichthyoplankton samples collected for monitoring bigheaded carps in the Upper Mississippi, Illinois, and St. Croix rivers to evaluate the effectiveness of qPCR as a screening tool. Samples with > 10,000 copies of DNA had 100% occurrence of bigheaded carp eggs or larvae in the traditionally sorted samples, whereas samples with < 10 copies of DNA had 0% occurrence of ichthyoplankton from these invasive species. We used a logistic regression model to calculate the probability of finding bigheaded carp eggs or larvae based upon the number of DNA copies; 406 copies corresponded with a 50% probability of having bigheaded carp ichthyoplankton present in a sample. These data can be used to inform management actions (i.e., control, containment) for these invasive fishes, and this tool could be adapted for monitoring for reproduction of other aquatic invasive species.","language":"English","publisher":"Springer","doi":"10.1007/s10530-018-1887-9","usgsCitation":"Fritts, A.K., Knights, B.C., Larson, J.H., Amberg, J., Merkes, C.M., Tajjioui, T., Butler, S.E., Diana, M.J., Wahl, D.H., Weber, M.J., and Waters, J.D., 2019, Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps: Biological Invasions, v. 21, no. 4, p. 1143-1153, https://doi.org/10.1007/s10530-018-1887-9.","productDescription":"11 p.","startPage":"1143","endPage":"1153","ipdsId":"IP-100744","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":467750,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10530-018-1887-9","text":"Publisher Index Page"},{"id":437518,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96BTBUH","text":"USGS data release","linkHelpText":"Bigheaded carp ichthyoplankton qPCR screening tool: data"},{"id":363288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.3388671875,\n 35.92464453144099\n ],\n [\n -87.0556640625,\n 35.92464453144099\n ],\n [\n -87.0556640625,\n 49.32512199104001\n ],\n [\n -97.3388671875,\n 49.32512199104001\n ],\n [\n -97.3388671875,\n 35.92464453144099\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences 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0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761605,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tajjioui, Tariq 0000-0002-0113-0451","orcid":"https://orcid.org/0000-0002-0113-0451","contributorId":215091,"corporation":false,"usgs":true,"family":"Tajjioui","given":"Tariq","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761606,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Butler, Steven E.","contributorId":206527,"corporation":false,"usgs":false,"family":"Butler","given":"Steven","email":"","middleInitial":"E.","affiliations":[{"id":37336,"text":"Illinois Natural History Survey, Kaskaskia Biological Station","active":true,"usgs":false}],"preferred":false,"id":761607,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Diana, Matthew J.","contributorId":206528,"corporation":false,"usgs":false,"family":"Diana","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":761608,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wahl, David H.","contributorId":206529,"corporation":false,"usgs":false,"family":"Wahl","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37336,"text":"Illinois Natural History Survey, Kaskaskia Biological Station","active":true,"usgs":false}],"preferred":false,"id":761609,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weber, Michael J. 0000-0003-0430-3087","orcid":"https://orcid.org/0000-0003-0430-3087","contributorId":210835,"corporation":false,"usgs":false,"family":"Weber","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":761610,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Waters, John D.","contributorId":215092,"corporation":false,"usgs":false,"family":"Waters","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":761611,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70206560,"text":"70206560 - 2019 - A novel method to characterise levels of pharmaceutical pollution in large scale aquatic monitoring campaigns","interactions":[],"lastModifiedDate":"2019-11-08T08:55:07","indexId":"70206560","displayToPublicDate":"2019-04-01T08:50:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5841,"text":"Applied Sciences","onlineIssn":"2076-3417","active":true,"publicationSubtype":{"id":10}},"title":"A novel method to characterise levels of pharmaceutical pollution in large scale aquatic monitoring campaigns","docAbstract":"Much of the current understanding of pharmaceutical pollution in the aquatic environment is based on research conducted in Europe, North America and other select high-income nations. One reason for this geographic disparity of data globally is the high cost and analytical intensity of the research, limiting accessibility to necessary equipment. To reduce the impact of such disparities, we present a novel method to support large-scale monitoring campaigns of pharmaceuticals at different geographical scales. The approach employs the use of a miniaturised sampling and shipping approach with a high throughput and fully validated direct-injection High-Performance Liquid Chromatography-Tandem Mass Spectrometry method for the quantification of 61 active pharmaceutical ingredients (APIs) and their metabolites in tap, surface, wastewater treatment plant (WWTP) influent and WWTP effluent water collected globally. A 7-day simulated shipping and sample stability assessment was undertaken demonstrating no significant degradation over the 1–3 days which is typical for global express shipping. Linearity (r2) was consistently ≥0.93 (median = 0.99 ± 0.02), relative standard deviation of intra- and inter-day repeatability and precision was <20% for 75% and 68% of the determinations made at three concentrations, respectively, and recovery from Liquid Chromatography Mass Spectrometry grade water, tap water, surface water and WWTP effluent were within an acceptable range of 60–130% for 87%, 76%, 77% and 63% of determination made at three concentrations respectively. Limits of detection and quantification were determined in all validated matrices and were consistently in the ng/L level needed for environmentally relevant API research. Independent validation of method results was obtained via an interlaboratory comparison of three surface-water samples and one WWTP effluent sample collected in North Liberty, Iowa (USA). Samples used for the interlaboratory validation were analysed at the University of York Centre of Excellence in Mass Spectrometry (York, UK) and the U.S. Geological Survey National Water Quality Laboratory in Denver (Colorado, USA). These results document the robustness of using this method on a global scale. Such application of this method would essentially eliminate the interlaboratory analytical variability typical of such large-scale datasets where multiple methods were used.
","language":"English","publisher":"MDPI","doi":"10.3390/app9071368","usgsCitation":"Wilkinson, J.W., Boxall, A., and Kolpin, D., 2019, A novel method to characterise levels of pharmaceutical pollution in large scale aquatic monitoring campaigns: Applied Sciences, v. 9, no. 7, 1368, 14 p., https://doi.org/10.3390/app9071368.","productDescription":"1368, 14 p.","ipdsId":"IP-106171","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":467751,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/app9071368","text":"Publisher Index Page"},{"id":369080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilkinson, John W.","contributorId":147014,"corporation":false,"usgs":false,"family":"Wilkinson","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":774939,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boxall, Alistair","contributorId":152697,"corporation":false,"usgs":false,"family":"Boxall","given":"Alistair","affiliations":[],"preferred":false,"id":774940,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kolpin, Dana 0000-0002-3529-6506","orcid":"https://orcid.org/0000-0002-3529-6506","contributorId":220448,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":774938,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204653,"text":"70204653 - 2019 - Assessing seasonal changes in microgravity at Yellowstone caldera","interactions":[],"lastModifiedDate":"2019-08-09T10:45:59","indexId":"70204653","displayToPublicDate":"2019-04-01T07:51:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Assessing seasonal changes in microgravity at Yellowstone caldera","docAbstract":"Microgravity time series at active volcanoes can provide an indication of mass change related to subsurface magmatic processes, but uncertainty is often introduced by hydrologic variations and other noise sources that cannot easily be isolated. We empirically assessed seasonality and noise by conducting four surveys over the course of May-October 2017 at Yellowstone caldera, Wyoming. Yellowstone experiences frequent changes in the rates and styles of seismicity and surface deformation, but the mechanisms of these changes are poorly understood because the characteristics of the driving fluids are not clear. Past gravity data from the caldera have yielded ambiguous results, possibly due to hydrologic noise. Given the strong visually observable changes in surface water and snow conditions over the course of our surveys, we expected to see significant variations in gravity. The net change in gravity, however, was less than 20 µGal at most sites, and there was no strong correlation with river and lake levels or snow conditions. Seasonal changes in gravity are therefore small compared to those that would be expected from magmatic activity, although they may be on the same order as those associated with Yellowstone’s hydrothermal system. We did find that noise levels in gravity data were highly dependent on site characteristics, with bedrock sites away from trees yielding the lowest levels of noise, and thin concrete pads in forested areas the highest. These results can be used to plan future surveys at Yellowstone and to reinterpret past data, and they provide guidance in terms of best practices for repeat gravity work on volcanoes worldwide.","language":"English","publisher":"Wiley","doi":"10.1029/2018JB017061","usgsCitation":"Poland, M.P., and de Zeeuw-van Dalfsen, E., 2019, Assessing seasonal changes in microgravity at Yellowstone caldera: Journal of Geophysical Research, v. 124, no. 4, p. 4174-4188, https://doi.org/10.1029/2018JB017061.","productDescription":"15 p.","startPage":"4174","endPage":"4188","ipdsId":"IP-103468","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467754,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2018jb017061","text":"Publisher Index Page"},{"id":366351,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.28051757812499,\n 43.79488907226601\n ],\n [\n -109.3304443359375,\n 43.79488907226601\n ],\n [\n -109.3304443359375,\n 45.14717913418674\n ],\n [\n -111.28051757812499,\n 45.14717913418674\n ],\n [\n -111.28051757812499,\n 43.79488907226601\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Poland, Michael P. 0000-0001-5240-6123 mpoland@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":146118,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","email":"mpoland@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":767930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Zeeuw-van Dalfsen, Elske 0000-0003-2527-4932","orcid":"https://orcid.org/0000-0003-2527-4932","contributorId":217967,"corporation":false,"usgs":false,"family":"de Zeeuw-van Dalfsen","given":"Elske","email":"","affiliations":[{"id":39727,"text":"KNMI","active":true,"usgs":false}],"preferred":false,"id":767931,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203230,"text":"70203230 - 2019 - Simulating the effects of climate variability on waterbodies and wetland-dependent birds in the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2019-05-02T08:07:59","indexId":"70203230","displayToPublicDate":"2019-04-01T07:46:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Simulating the effects of climate variability on waterbodies and wetland-dependent birds in the Prairie Pothole Region","docAbstract":"Understanding how bird populations respond to changes in waterbody availability in the climatically variable Prairie Pothole Region (PPR) of North America hinges on being able to couple hydrological and climate modeling to represent potential future landscapes. Model experiments run with the Pothole Complex Hydrologic Model using downscaled climate data (variables relating to precipitation, temperature, and potential evapotranspiration at 1/8° spatial resolution under four general circulation climate models and two gas emissions scenarios) were used to forecast the abundances of six focal wetland‐dependent bird species in the Missouri Coteau portion of the PPR, providing ensemble scenarios at a spatial scale relevant to resource management. Although the projected number of May ponds (waterbodies present during bird breeding season) fluctuated through time with some decadal periodicity (and with the number present in a given year reflecting abundance over the previous three years), the ensemble model average indicated an increase in the average number of waterbodies present by the turn of the next century. Overall, the model experiments conservatively projected an 11.75% increase in the number of waterbodies present by 2090–2099 compared to a baseline period from 1967 to 2005 in the PPR. Wetland‐dependent bird occurrence and abundance were significantly associated with temporal patterns and decadal periodicity in waterbody dynamics. Because of the strong associations between wetland‐dependent bird occurrence and abundance and the number of prairie potholes, projected waterbody increases are forecasted to result in an 11.97% overall increase in occurrence and 8.63% increase in abundance of the six focal species by the end of the 21st century; these results contrast with forecasted drought‐associated declines in waterbodies and birds in the PPR. This integrated hydrological–climatological approach offers a means of assessing how wetland‐dependent bird populations may respond to changes in wetland habitat availability due to a changing climate. Our results provide information that can help managers decide how to mitigate the effects of climate shifts on the distribution of wetland habitat and biota.
Beginning in the late 1970s, 10- to 15-year cyclical oscillations in salinity were observed at lower Colorado River monitoring sites, moving upstream from the international border with Mexico, above Imperial Dam, below Hoover Dam, and at Lees Ferry. The cause of these cyclical trends in salinity was unknown. These salinity cycles complicate the U.S. Bureau of Reclamation's (Reclamation) responsibility for managing salinity in the river for delivery of water to Mexico to meet treaty obligations. This study develops a conceptual model of the salinity cycles from time-series water quality, streamflow, and precipitation data in both the lower and upper Colorado River Basins in order to provide Reclamation the ability to understand, anticipate, and manage future salinity cycles in the lower river. Compared with the Lees Ferry record, both maximum and minimum salinity levels increase downstream by about 25% at Hoover Dam, by about 49% at Imperial Dam, and by about 69% at the northern international boundary with Mexico. In the upper basin, cyclical salinity trends are evident at the outflow of three major tributaries, where salinity is also noted to be inversely related to streamflow. Time series trends in precipitation within the catchments of the three upper basin tributaries indicate cyclical periods with above normal precipitation and periods with below normal precipitation. Periods of greater than normal precipitation in the contributing areas correspond with declines in salinity at the catchment monitoring sites and periods of less than normal precipitation correspond with rising salinity at the sites. Based on the conceptual model developed in this investigation, a multiple linear regression model was developed using a stepwise variable selection procedure to simulate salinity in Lake Powell inflow. Important variables in the explanation of salinity entering Lake Powell include flow from the three upper basin tributaries, seasonality, and mean precipitation in the upper basin, among others. The root mean square error of prediction for the MLR model was 31.48 mg/L (5.7%).
RANGELAND HEALTH IN A CHANGING WORLD Rangeland health is an integrated metric that describes a complex suite of ecosystem properties and processes as applied to resource management. While the concept of “healthy” landscapes has a long history, the term “rangeland health” was codified in the US in 1994 as part of an effort to move towards a national, data driven, rangeland condition assessment (National Research Council 1994). Rangeland health encompasses the status of both soils and ecological processes as well as the condition of those ecosystems relative to ecological thresholds (e.g. “healthy”, “at-risk”, or “unhealthy\"; National Research Council 1994). This latter application ensures that rangeland health assessments not only evaluate the conditions of plants and soils, but also gauge those conditions with respect to known or hypothesized ecological dynamics (Bestelmeyer et al. 2013) for a given set of abiotic constraints (climate, soil, and topographic setting). Thus, an assessment of rangeland health should identify the “degree to which the integrity of the soil and the ecological processes are sustained” (National Research Council 1994, Us Department of Agriculture 1997). Rangeland health attributes and assessment procedures have become widely utilized tools for measuring and monitoring dryland ecosystems, and they may provide valuable perspectives on rangeland response to climate change, although long-term directional change in environmental conditions represents a challenge for traditional rangeland health assessment frameworks.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Grasslands and climate change","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Cambridge University Press","doi":"10.1017/9781108163941","isbn":"9781316646779","usgsCitation":"Bradford, J.B., Duniway, M.C., and Munson, S.M., 2019, Assessing rangeland health under climate variability and change, chap. 17 of Grasslands and climate change, p. 293-309, https://doi.org/10.1017/9781108163941.","productDescription":"17 p.","startPage":"293","endPage":"309","ipdsId":"IP-092717","costCenters":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":365037,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":362504,"type":{"id":15,"text":"Index Page"},"url":"https://www.cambridge.org/us/academic/subjects/life-sciences/ecology-and-conservation/grasslands-and-climate-change?format=PB"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760191,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760192,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760193,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204415,"text":"70204415 - 2019 - A revised continuous surface elevation model for modeling","interactions":[],"lastModifiedDate":"2019-09-20T12:48:37","indexId":"70204415","displayToPublicDate":"2019-03-31T12:48:27","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"title":"A revised continuous surface elevation model for modeling","docAbstract":"A digital elevation model (DEM) is an essential component of any hydrodynamic model. The Delta Modeling Section (Section) has maintained a database of bathymetry soundings and levee surveys for decades and published a 10-meter (10m) DEM for the San Francisco Bay and Sacramento-San Joaquin Delta (Delta) (California Department of Water Resources 2012). In collaboration with the U.S. Geological Survey (USGS) Pacific Coastal and Marine Science Center, the California Department of Water Resources (DWR) has continued to upgrade these DEMs based on newer survey data and improved interpolation methodologies. An updated San Francisco Bay-Delta bathymetric/topographic digital elevation model was published by the USGS (Fregoso, Wang, Ateljevich, and Jaffe 2017). \n\nBoth DWR and USGS continue to work on the elevation models for several reasons. First, high-resolution multibeam bathymetry data continues to become available. A good portion of the newer collections are performed at locations where bathymetry data is lacking, or of poor quality, or where model sensitivity to bathymetry is known to be high, so the effort has a high return on investment. Recent high-resolution multibeam datasets easily support the development of accurate 2-meter (2m) DEMs, although shallow water, turbidity, vegetation, and the gap between the multibeam data and terrestrial data from light detection and ranging (LiDAR) remain vexing issues that fuel development of enhanced techniques. \n\nIn some locations, migration toward 2m resolution models is motivated by geographical structure even where there has been little improvement in the underlying data. DEMs at 10m resolution are insufficient to adequately describe small-scale terrain features, such as levee crests or the main conveyance channel through a narrow reach, such as Middle River. As a result, elevation modelers have traditionally needed to perform feature enforcement for 10m DEMs, but not for the Section’s 2m DEMs which do resolve these features (California Department of Resources 2012). There are several other problems associated with a coarse target resolution, including reduced conveyance and rough contours where channels run oblique to the elevation model. In places where the topography demands a finer elevation model, the Section and others have adopted improved interpolation techniques for single-beam data to produce reasonable 2m DEMs that preserve features on appropriate scales.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Methodology for flow and salinity estimates in the Sacramento-San Joaquin Delta and Suisun Marsh, 39th Annual Progress Report to the State Water Resources Control Board","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"California Department of Water Resources, Bay-Delta Office","usgsCitation":"Wang, R., Ateljevich, E., Fregoso, T.A., and Jaffe, B.E., 2019, A revised continuous surface elevation model for modeling, chap. of Methodology for flow and salinity estimates in the Sacramento-San Joaquin Delta and Suisun Marsh, 39th Annual Progress Report to the State Water Resources Control Board, v. 39, p. 5-1-5-40.","productDescription":"40 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