The unsaturated zone (UZ) extends across the Earth’s terrestrial surface and is central to many problems related to land and water resource management. Flow of water through the UZ is typically thought to be slow and diffusive, such that it could attenuate fluxes and dampen variability between atmospheric inputs and underlying aquifer systems. This would reduce water resource vulnerability to contaminants and water-related hazards. Reducing or negating that effect, however, spatially concentrated and rapid flow and transport through the unsaturated zone is surprisingly common and becoming more so with the increasing frequency and magnitude of extreme hydroclimatic events. Arising from the wide range in the rates and complex modes of nonlinear flow processes, these effects are among the most poorly characterized hydrologic phenomena. Issues of scale present additional difficulties. Equations representing unsaturated processes have been developed and tested on the basis of field and laboratory measurements typically made at scales from pore size to plot size. In contrast, related problems of significant interest to society, including floods, aquifer recharge, landslides, and groundwater contamination, range from watershed to regional scales. The disparity between the scale of our understanding and the scale of interest for societal problems has spurred application of these model equations at increasingly coarse resolutions over larger areas than can be justified by existing measurements or theory. This mismatch in scales requires an assumption that spatially averaging slow diffusive flow and rapid preferential flow can effectively represent the influence of both processes across vast areas. Given the currently inadequate recognition and quantitative characterization of focused and rapid processes in unsaturated flow, these phenomena are critically in need of expanded attention and effort.
A key assumption in species distribution modeling (SDM) with presence‐background (PB) methods is that sampling of occurrence localities is unbiased and that any sampling bias is proportional to the background distribution of environmental covariates. This assumption is rarely met when SDM practitioners rely on federated museum records from natural history collections for geo‐located occurrences due to inherent sampling bias found in these collections. We use a simulation approach to explore the effectiveness of three methods developed to account for sampling bias in SDM with PB frameworks. Two of the methods rely on careful filtering of observation data—geographic thinning (G‐Filter) and environmental thinning (E‐Filter)—while a third, FactorBiasOut, creates selection weights for background data to bias locations toward areas where the observation dataset was sampled. While these methods have been assessed previously, evaluation has emphasized spatial predictions of habitat potential. Here, we dig deeper into the effectiveness of these methods by exploring how sampling bias not only affects predictions of habitat potential, but also our understanding of niche characteristics such as which explanatory variables and response curves best represent species–environment relationships. We simulate 100 virtual species ranging from generalist to specialist in their habitat preferences and introduce geographic and environmental bias at three intensity levels to measure the effectiveness of each correction method to (1) predict true probability of occurrence across a study area, (2) recover true species–environment relationships, and (3) identify true explanatory variables. We find that the FactorBiasOut most often showed the greatest improvement in recreating known distributions but did no better at correctly identifying environmental covariates or recreating species–environment relationships than G‐Filter or E‐Filter methods. Narrow niche species are most problematic for biased calibration datasets, such that correction methods can, in some cases, make predictions worse.
Despite the importance of species–area relationships (SARs) to conservation, SARs in human‐fragmented rivers have received little attention. Our aim was to test for the presence and strength of SARs for littoral fish assemblages of an extensively dammed river in south‐central Ontario, Canada, and to examine long‐running hypotheses for the drivers of SARs. Twenty‐six navigational dams with locks built between 1837 and 1913 occur along the 160 km length of the Trent River examined in this study. We evaluated the relationship between richness and fragment area, and then used linear models to test whether the area per se, habitat diversity, or other hypotheses were best supported by the data. A power–function relationship with area explained 46% of the variation in fish species richness, and the slope (z = 0.4) was high compared with SARs reported from other ecosystems, indicating that species accumulated rapidly with an increase in fragment area. Multi‐predictor models suggested that area was significantly related to richness, but that vegetation cover diversity had a stronger relative effect. The slope of our SAR may indicate that there is a high degree of isolation between populations in different fragments, even though the lock system reportedly allows some passage of organisms. Our findings also suggest that mitigating against local extinction due to small population sizes (i.e., area effects), and enhancing aquatic vegetation cover may be viable strategies for promoting species diversity in the study river. Studies of SARs in fragmented rivers may offer additional benefits to supporting restoration planning where efforts are being made to increase species diversity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3411","usgsCitation":"Carl, L.M., Esselman, P., Sparks-Jackson, B.L., and Wilson, C.C., 2021, The species–area relationship for a highly fragmented temperate river system: Ecosphere, v. 12, no. 3, e03411, 17 p., https://doi.org/10.1002/ecs2.3411.","productDescription":"e03411, 17 p.","ipdsId":"IP-074620","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":490069,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3411","text":"Publisher Index Page"},{"id":436433,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L1AYLW","text":"USGS data release","linkHelpText":"Habitat and fish assemblages along four river mainstems in Ontario, Canada, 1997 to 2001, with supporting spatial data"},{"id":384698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Ontario","otherGeospatial":"Rice Lake, Trent River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.99468994140625,\n 44.43476045009948\n ],\n [\n -78.41629028320312,\n 44.173339873464684\n ],\n [\n -78.28582763671875,\n 43.94537239244209\n ],\n [\n -78.01803588867188,\n 43.95822503841972\n ],\n [\n -77.86972045898438,\n 43.982933852960805\n ],\n [\n -77.73101806640625,\n 43.982933852960805\n ],\n [\n -77.57858276367188,\n 44.049102784014536\n ],\n [\n -77.574462890625,\n 44.26683800273895\n ],\n [\n -77.99468994140625,\n 44.43476045009948\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Carl, Leon M. 0000-0001-6419-2214 lcarl@usgs.gov","orcid":"https://orcid.org/0000-0001-6419-2214","contributorId":256693,"corporation":false,"usgs":true,"family":"Carl","given":"Leon","email":"lcarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":813065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":813066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sparks-Jackson, Beth L 0000-0002-1726-1480","orcid":"https://orcid.org/0000-0002-1726-1480","contributorId":256695,"corporation":false,"usgs":false,"family":"Sparks-Jackson","given":"Beth","email":"","middleInitial":"L","affiliations":[{"id":51831,"text":"Contractor to USGS Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":813067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Christopher C. 0000-0002-9528-0652","orcid":"https://orcid.org/0000-0002-9528-0652","contributorId":256696,"corporation":false,"usgs":false,"family":"Wilson","given":"Christopher","email":"","middleInitial":"C.","affiliations":[{"id":51832,"text":"Aquatic Biodiversity and Conservation Unit, Ontario Ministry of Natural Resources, Peterborough, ON, Canada","active":true,"usgs":false}],"preferred":false,"id":813068,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219512,"text":"70219512 - 2021 - Drivers of methane flux differ between lakes and reservoirs, complicating global upscaling efforts","interactions":[],"lastModifiedDate":"2021-04-12T14:57:05.766282","indexId":"70219512","displayToPublicDate":"2021-03-25T09:53:38","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8116,"text":"Journal of Geophysical Research-Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of methane flux differ between lakes and reservoirs, complicating global upscaling efforts","docAbstract":"Methane is an important greenhouse gas with growing atmospheric concentrations. Freshwater lakes and reservoirs contribute substantially to atmospheric methane concentrations, but the magnitude of this contribution is poorly constrained. Uncertainty stems partially from whether the sites currently sampled represent the global population as well as incomplete knowledge of which environmental variables predict methane flux. Thus, determining the main drivers of methane flux across diverse waterbody types will inform more accurate upscaling approaches. Here we use a new database of total, diffusive, and ebullitive areal methane emissions from 313 lakes and reservoirs (ranging in surface area from 6 m2 to 5,400 km2) to identify the best predictors of methane emission. We found that the best predictors of methane emission differed by waterbody type (lakes vs. reservoirs), and that ecosystem morphometric variables (e.g., surface area and maximum depth) were more important predictors in lakes whereas metrics of autochthonous production (e.g., chlorophyll a) were more important in reservoirs. We also found that productivity strongly predicted methane ebullition, whereas ecosystem morphometry and waterbody type were more important predictors of diffusive methane flux. Finally, we identify several knowledge gaps that limit upscaling efforts. First, we need more methane emission measurements in small reservoirs, large lakes, and both natural and artificial ponds. Additionally, more accurate upscaling efforts require improved global information about waterbody surface area, waterbody type (lake vs. reservoir), ice phenology, and the distribution of productivity‐related predictor variables such as total phosphorus, DOC, and chlorophyll a.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JG005600","usgsCitation":"Deemer, B., and Holgerson, M.A., 2021, Drivers of methane flux differ between lakes and reservoirs, complicating global upscaling efforts: Journal of Geophysical Research-Biogeosciences, v. 126, no. 4, e2019JG005600, 15 p., https://doi.org/10.1029/2019JG005600.","productDescription":"e2019JG005600, 15 p.","ipdsId":"IP-112962","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":385017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holgerson, Meredith A.","contributorId":257243,"corporation":false,"usgs":false,"family":"Holgerson","given":"Meredith","email":"","middleInitial":"A.","affiliations":[{"id":51986,"text":"Departments of Biology and Environmental Studies, St. Olaf College, Northfield, Minnesota, USA","active":true,"usgs":false}],"preferred":false,"id":813863,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219125,"text":"sir20205140 - 2021 - Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017","interactions":[],"lastModifiedDate":"2021-03-25T15:53:32.129298","indexId":"sir20205140","displayToPublicDate":"2021-03-25T09:45:00","publicationYear":"2021","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":"2020-5140","displayTitle":"Evaluation and Application of the Purge Analyzer Tool (PAT) To Determine In-Well Flow and Purge Criteria for Sampling Monitoring Wells at the Stringfellow Superfund Site in Jurupa Valley, California, in 2017","title":"Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017","docAbstract":"The U.S. Geological Survey and U.S. Environmental Protection Agency are developing analytical tools to assess the representativeness of groundwater samples from fractured-rock aquifers. As part of this effort, monitoring wells from the Stringfellow Superfund site in Jurupa Valley in Riverside County, California, approximately 50 miles east of Los Angeles, were field tested to collect information to assist in the evaluation and application of in-well flow as computed by the analytical model called the Purge Analyzer Tool, which computes in-well groundwater travel times for simple piston transport of inflowing groundwater from open intervals of a monitoring well to the pump intake and can provide insight into optimal purging parameters (duration, rate, and pump position) needed for the collection of representative groundwater samples. Field testing of wells included hydraulic, chemistry, and dye tracer analysis to investigate travel times in wells under pumping conditions. The Purge Analyzer Tool was able to replicate dye velocities (travel times) for one of three wells that had appreciable inflow from the aquifer but not the other two wells, which are screened in low-permeability sediments and rock, where flow was dominated by borehole storage. A set of criteria was established to help assess the ability to collect representative groundwater chemistry from monitoring wells; criteria included understanding the height of the static well water column and relative exchange rate between the aquifer and the well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205140","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Harte, P.T., Perina, T., Becher, K., Levine, H., Rojas-Mickelson, D., Walther, L., and Brown, A., 2021, Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017: U.S. Geological Survey Scientific Investigations Report 2020–5140, 54 p., https://doi.org/10.3133/sir20205140.","productDescription":"Report: ix, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103064","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":384640,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191104","text":"Open-File Report 2019–1104","linkHelpText":"- Instructions for Running the Analytical Code PAT (Purge Analyzer Tool) for Computation of In-Well Time of Travel of Groundwater under Pumping Conditions"},{"id":384639,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CGINH0","text":"USGS data release","linkHelpText":"Data associated with the evaluation of the PAT (Purge Analyzer Tool), Stringfellow Superfund site, Jurupa Valley, California, 2017"},{"id":384641,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5140/coverthb.jpg"},{"id":384642,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5140/sir20205140.pdf","text":"Report","size":"5.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5140"}],"country":"United States","state":"California","city":"Jurupa Valley","otherGeospatial":"Stringfellow Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.47066974639893,\n 34.019585556900935\n ],\n [\n -117.45178699493408,\n 34.019585556900935\n ],\n [\n -117.45178699493408,\n 34.03078943899101\n ],\n [\n -117.47066974639893,\n 34.03078943899101\n ],\n [\n -117.47066974639893,\n 34.019585556900935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Slow-moving landslides are destabilized by accumulated precipitation and consequent soil moisture. Yet, the continuous high-resolution soil-moisture measurements needed to aid the understanding of landslide processes are generally absent in steep terrain. Here, we produce soil-moisture time-series maps for a seasonally active grassland landslide in the northern California coast ranges, USA, using backscattering coefficients from NASA's uninhabited aerial vehicle synthetic aperture radar at 6-m resolution. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture for the landslide. Both forward modeling (backscattering estimation) and the retrieval (soil-moisture validation) show good agreement. The root-mean-square errors (RMSE) for vertical transmit vertical receive (VV) and horizontal transmit horizontal receive (HH) polarizations in forward model comparison are 1.93 dB and 1.88 dB, respectively. The soil-moisture retrieval shows unbiased RMSE of 0.054 m 3 /m 3 . Our successful retrieval benefits from the surface and double-bounce scattering, which is common in grasslands. The retrieved maps show saturated wetness conditions within the active landslide boundaries. We also performed sensitivity tests for incidence angle and found that the retrieval is weakly dependent on the angle, especially while using copolarized HH and VV together. Using the two copolarized inputs, the retrieval is also not sensitive to the change of orientation angles of grass cylinders. The physical model inversion presented here can be generally applied for soil-moisture retrieval in areas with the same vegetation cover types in California.
","language":"English","publisher":"IEEE","doi":"10.1109/JSTARS.2021.3069010","usgsCitation":"Liao, T., Kim, S., Handwerger, A.L., Fielding, E.J., Cosh, M.H., and Schulz, W.H., 2021, High-resolution soil-moisture maps over landslide regions in northern California grassland derived From SAR backscattering coefficients: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 14, p. 4547-4560, https://doi.org/10.1109/JSTARS.2021.3069010.","productDescription":"14 p.","startPage":"4547","endPage":"4560","ipdsId":"IP-126334","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":452942,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/jstars.2021.3069010","text":"Publisher Index Page"},{"id":436434,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NSUMOY","text":"USGS data release","linkHelpText":"Precipitation and soil-moisture data from the Two Towers landslide, Trinity County, California"},{"id":394381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Eel River catchment, Two Towers landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46761703491211,\n 40.09832911511634\n ],\n [\n -123.45680236816408,\n 40.09832911511634\n ],\n [\n -123.45680236816408,\n 40.10669965638878\n ],\n [\n -123.46761703491211,\n 40.10669965638878\n ],\n [\n -123.46761703491211,\n 40.09832911511634\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liao, Tien-Hao","contributorId":271098,"corporation":false,"usgs":false,"family":"Liao","given":"Tien-Hao","email":"","affiliations":[{"id":56278,"text":"California Institute of Technology, Division Office Geological and Planetary Sciences","active":true,"usgs":false}],"preferred":false,"id":830812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kim, Seung-bum","contributorId":244954,"corporation":false,"usgs":false,"family":"Kim","given":"Seung-bum","email":"","affiliations":[{"id":27365,"text":"NASA Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":830813,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Handwerger, Alexander L.","contributorId":218095,"corporation":false,"usgs":false,"family":"Handwerger","given":"Alexander","email":"","middleInitial":"L.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":830814,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fielding, Eric J.","contributorId":218096,"corporation":false,"usgs":false,"family":"Fielding","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":830815,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cosh, Michael H.","contributorId":146998,"corporation":false,"usgs":false,"family":"Cosh","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":830816,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":830817,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228855,"text":"70228855 - 2021 - Fipronil pellets reduce flea abundance on black-tailed prairie dogs: Potential tool for plague management and black-footed ferret conservation","interactions":[],"lastModifiedDate":"2022-02-23T15:51:09.254874","indexId":"70228855","displayToPublicDate":"2021-03-25T09:24:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2507,"text":"Journal of Wildlife Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Fipronil pellets reduce flea abundance on black-tailed prairie dogs: Potential tool for plague management and black-footed ferret conservation","docAbstract":"In western North America, sylvatic plague (a flea-borne disease) poses a significant risk to endangered black-footed ferrets (Mustela nigripes) and their primary prey, prairie dogs (Cynomys spp.). Pulicides (flea-killing agents) can be used to suppress fleas and thereby manage plague. In South Dakota, US, we tested edible “FipBit” pellets, each containing 0.84 mg fipronil, on free-living black-tailed prairie dogs (Cynomys ludivicianus). FipBits were applied along transects at 125 per ha and nearly eliminated fleas for 2 mo. From 9–14 mo post-treatment, we found only 10 fleas on FipBit sites versus 1,266 fleas on nontreated sites. This degree and duration of flea control should suppress plague transmission. FipBits are effective, inexpensive, and easily distributed but require federal approval for operational use.
","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/JWD-D-20-00161","usgsCitation":"Eads, D.A., Livieri, T.M., Dobesh, P., Childers, E., Noble, L., Vasquez, M., and Biggins, D.E., 2021, Fipronil pellets reduce flea abundance on black-tailed prairie dogs: Potential tool for plague management and black-footed ferret conservation: Journal of Wildlife Diseases, v. 57, no. 2, p. 434-438, https://doi.org/10.7589/JWD-D-20-00161.","productDescription":"6 p.","startPage":"434","endPage":"438","ipdsId":"IP-122271","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":436435,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KOQEUX","text":"USGS data release","linkHelpText":"Flea abundance and body condition data for black-tailed prairie dogs on sites treated and not treated with "FipBit" fipronil pellets, South Dakota, 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M.","contributorId":198977,"corporation":false,"usgs":false,"family":"Livieri","given":"Travis","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":835704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dobesh, Phillip","contributorId":279889,"corporation":false,"usgs":false,"family":"Dobesh","given":"Phillip","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":835705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Childers, Eddie","contributorId":279890,"corporation":false,"usgs":false,"family":"Childers","given":"Eddie","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":835706,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Noble, Lauren","contributorId":279891,"corporation":false,"usgs":false,"family":"Noble","given":"Lauren","email":"","affiliations":[{"id":57385,"text":"Previously USGS 180GG technician","active":true,"usgs":false}],"preferred":false,"id":835707,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vasquez, Michele","contributorId":279892,"corporation":false,"usgs":false,"family":"Vasquez","given":"Michele","email":"","affiliations":[{"id":57385,"text":"Previously USGS 180GG technician","active":true,"usgs":false}],"preferred":false,"id":835708,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835709,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219451,"text":"70219451 - 2021 - Physics‐based evaluation of the maximum magnitude of potential earthquakes induced by the Hutubi (China) underground gas storage","interactions":[],"lastModifiedDate":"2021-04-22T17:57:05.216862","indexId":"70219451","displayToPublicDate":"2021-03-25T08:05:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Physics‐based evaluation of the maximum magnitude of potential earthquakes induced by the Hutubi (China) underground gas storage","docAbstract":"The world’s largest underground gas storage facility in Hutubi (HUGS), China, is a unique case where cyclic gas injection‐extraction induced both seismicity and ground deformation. To assess the potential for future induced seismicity, we develop a framework physically based on a well‐constrained hydro‐geomechanical model and on fully coupled poroelastic simulations. We first interpret the spatiotemporal distribution and focal mechanisms of induced earthquakes and use these to estimate the magnitude and location of the largest potential earthquake. The sharp increase in seismicity was controlled by poroelastic loading on secondary southwest‐dipping thrust faults with spatial scales too small to be resolved by 3D seismic surveys. Both operational and local geological factors affect the seismic productivity at the HUGS site, distinguishing it from most cases of seismicity induced by wastewater disposal and hydraulic fracturing. We then conduct slip tendency analyses for major faults imaged by the seismic data, including the largest reservoir‐bounding Hutubi fault hydraulically connected to injection wells. The reactivation potentials of these imaged faults are estimated to be extremely low. Accordingly, future seismicity would most likely occur on failure‐prone secondary faults in regions with positive stress perturbation due to poroelastic loading. The maximum magnitude likely depends on the spatial scales of the secondary faults. As the occurrence of detected earthquakes is spatially and temporally consistent with the simulated evolution of Coulomb stress perturbation, the location of the largest potential earthquake probably depends on the sizes of the poroelastic stressing regions.
The United States (US) National Park Service (NPS) manages protected public lands to preserve biodiversity. Exposure to and effects of bioactive organic contaminants in NPS streams are challenges for resource managers. Recent assessment of pesticides and pharmaceuticals in protected-streams within the urbanized NPS Southeast Region (SER) indicated the importance of fluvial inflows from external sources as drivers of aquatic contaminant-mixture exposures. Great Smoky Mountains National Park (GRSM), lies within SER, has the highest biodiversity and annual visitation of NPS parks, but, in contrast to the previously studied systems, straddles a high-elevation hydrologic divide; this setting limits fluvial-inflows of contaminants but potentially increases visitation-driven contaminant deliveries. We leveraged the unique characteristics of GRSM to test further the importance of fluvial contaminant inflows as drivers of protected-stream exposures and to inform the relative importance of potential additional contaminant transport mechanisms, by comparing the estimated risks of 328 pesticides and pharmaceuticals in water at 16 GRSM stream locations to those estimated previously in SER streams. Extensive mixtures (31 compounds) were only observed in an atypical reach on the boundary of GRSM downstream of a wastewater discharge, while limited mixtures (2–5 compounds) were observed in one stream with elevated visitation pressure (recreational “tube floating”). The insecticide, imidacloprid, used to eradicate hemlock woolly adelgid, was detected in 8 (50%) streams. Infrequent exceedances of a cumulative ToxCast-based, exposure-activity ratio (ΣEAR) 0.001 screening-level of concern suggested limited risk to non-target, aquatic vertebrates, whereas exceedances of a cumulative benchmark-based, invertebrate toxicity quotient (ΣTQ) 0.1 screening level at 8 locations indicated generally high risk to invertebrates. The results are consistent with the importance of fluvial transport from extra-park sources as a driver of bioactive-contaminant mixture exposures in protected streams and illustrate the potential additional risks from visitation-driven and tactical-use-pesticides.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.146711","usgsCitation":"Bradley, P., Kulp, M.A., Huffman, B.J., Romanok, K., Smalling, K., Breitmeyer, S.E., Clark, J., and Journey, C., 2021, Reconnaissance of cumulative risk of pesticides and pharmaceuticals in Great Smoky Mountains National Park streams: Science of the Total Environment, v. 781, 146711, 9 p., https://doi.org/10.1016/j.scitotenv.2021.146711.","productDescription":"146711, 9 p.","onlineOnly":"N","ipdsId":"IP-117880","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":436436,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GUEIMD","text":"USGS data release","linkHelpText":"Pesticide and Pharmaceutical Exposure Data for Select Streams within Great Smoky Mountains National Park, 2019"},{"id":384693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Great Smokey Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.166259765625,\n 35.03899204678081\n ],\n [\n -82.81494140625,\n 35.03899204678081\n ],\n [\n -82.81494140625,\n 35.782170703266075\n ],\n [\n -84.166259765625,\n 35.782170703266075\n ],\n [\n -84.166259765625,\n 35.03899204678081\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"781","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":813009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huffman, Bradley J. 0000-0003-2827-8074","orcid":"https://orcid.org/0000-0003-2827-8074","contributorId":220344,"corporation":false,"usgs":true,"family":"Huffman","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breitmeyer, Sara E. 0000-0003-0609-1559 sbreitmeyer@usgs.gov","orcid":"https://orcid.org/0000-0003-0609-1559","contributorId":172622,"corporation":false,"usgs":true,"family":"Breitmeyer","given":"Sara","email":"sbreitmeyer@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":813012,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Clark, Jimmy 0000-0002-3138-5738","orcid":"https://orcid.org/0000-0002-3138-5738","contributorId":221235,"corporation":false,"usgs":true,"family":"Clark","given":"Jimmy","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813013,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Journey, Celeste A. 0000-0002-2284-5851","orcid":"https://orcid.org/0000-0002-2284-5851","contributorId":221232,"corporation":false,"usgs":true,"family":"Journey","given":"Celeste A.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813014,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70219192,"text":"70219192 - 2021 - Embryo deformities and nesting trends in Kemp’s ridley sea turtles Lepidochelys kempii before and after the Deepwater Horizon oil spill","interactions":[],"lastModifiedDate":"2021-03-30T12:41:09.774633","indexId":"70219192","displayToPublicDate":"2021-03-25T07:37:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Embryo deformities and nesting trends in Kemp’s ridley sea turtles Lepidochelys kempii before and after the Deepwater Horizon oil spill","docAbstract":"Kemp’s ridley sea turtles Lepidochelys kempii were disproportionately affected by the Deepwater Horizon (DWH) oil spill, which began on 20 April 2010. Embryo deformities were documented in inviable L. kempii eggs before (2008-2010) and after (2011-2013) the DWH spill in 2 Texas (USA) nesting areas (Upper Texas Coast and Padre Island National Seashore). Additional nesting trends, including clutch size and hatching success, were also investigated. Total and late-stage embryo deformity prevalence were 1.5 times greater after 2010 than before, but low in all nesting seasons (mean ± SD: 0.7 ± 8.5% total; 0.6 ± 8.0% late-stage) and did not differ between locations. Craniofacial and carapace deformities were the most frequently observed deformity types. Documented nests in both areas declined in 2010 relative to previous years, ending an exponential increase observed beginning in 1995. Clutch size remained consistent before and after the spill. Hatching success averaged 87.0 ± 33.3% in all years, but no effects from DWH were determined. Collectively, these data represent useful benchmarks against which to judge impacts of future crude oil spills and other catastrophic events.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01107","usgsCitation":"Shaver, D.J., Gredzens, C., Walker, J.S., Godard-Codding, C., Yacabucci, J.E., Frey, A., Dutton, P., and Schmitt, C.J., 2021, Embryo deformities and nesting trends in Kemp’s ridley sea turtles Lepidochelys kempii before and after the Deepwater Horizon oil spill: Endangered Species Research, v. 44, p. 277-289, https://doi.org/10.3354/esr01107.","productDescription":"13 p.","startPage":"277","endPage":"289","ipdsId":"IP-122895","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":452949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01107","text":"Publisher Index Page"},{"id":384756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Padre Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.23724365234375,\n 27.63487379134253\n ],\n [\n -97.0751953125,\n 27.63487379134253\n ],\n [\n -97.0751953125,\n 27.715141756723987\n ],\n [\n -97.23724365234375,\n 27.715141756723987\n ],\n [\n -97.23724365234375,\n 27.63487379134253\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":813157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gredzens, Christian","contributorId":209784,"corporation":false,"usgs":false,"family":"Gredzens","given":"Christian","email":"","affiliations":[{"id":37980,"text":"Marine Turtle Research, Ecology and Conservation Group, Florida State University, Tallahassee, FL, USA 32306","active":true,"usgs":false}],"preferred":false,"id":813158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walker, J. Shelby","contributorId":256733,"corporation":false,"usgs":false,"family":"Walker","given":"J.","email":"","middleInitial":"Shelby","affiliations":[{"id":33240,"text":"National Park Service, Padre Island National Seashore, Corpus Christi, TX","active":true,"usgs":false}],"preferred":false,"id":813159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Godard-Codding, Céline A. J.","contributorId":256734,"corporation":false,"usgs":false,"family":"Godard-Codding","given":"Céline A. J.","affiliations":[{"id":36344,"text":"The Institute of Environmental and Human Health, Texas Tech University, Lubbock, TX","active":true,"usgs":false}],"preferred":false,"id":813160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yacabucci, Janet E.","contributorId":256736,"corporation":false,"usgs":false,"family":"Yacabucci","given":"Janet","email":"","middleInitial":"E.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":813161,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frey, Amy","contributorId":196390,"corporation":false,"usgs":false,"family":"Frey","given":"Amy","email":"","affiliations":[],"preferred":false,"id":813163,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dutton, Peter H.","contributorId":256741,"corporation":false,"usgs":false,"family":"Dutton","given":"Peter H.","affiliations":[{"id":51846,"text":"NOAA Fisheries, Southwest Fisheries Science Center, La Jolla, CA","active":true,"usgs":false}],"preferred":false,"id":813164,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmitt, Christopher J. 0000-0001-6804-2360 cjschmitt@usgs.gov","orcid":"https://orcid.org/0000-0001-6804-2360","contributorId":491,"corporation":false,"usgs":true,"family":"Schmitt","given":"Christopher","email":"cjschmitt@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":813162,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229521,"text":"70229521 - 2021 - Hydroacoustic survey standardization: Inter-vessel differences in fish densities and potential effects of vessel avoidance","interactions":[],"lastModifiedDate":"2022-03-11T13:06:11.432321","indexId":"70229521","displayToPublicDate":"2021-03-25T07:03:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Hydroacoustic survey standardization: Inter-vessel differences in fish densities and potential effects of vessel avoidance","docAbstract":"Hydroacoustics is used broadly to assess fish populations in marine and freshwater systems. Large-scale surveys often employ multiple vessels to complete a survey. Vessels can be a source of variation in multi-vessel surveys, and accounting for this variation is critical to precise and accurate assessments, whether as indices or measures of absolute abundance. We examined areal and volumetric density estimates, area backscattering coefficient (ABC), and mean target strength (TS) produced by three vessels in Lake Erie along three cross-lake transects in July 2014. Our study showed positive correlation in areal ABC, mean TS, and density among vessels; however, there was an almost 30 % higher density obtained with the largest vessel, on average. As a result, vessel choice could contribute to variation and/or bias in forage fish surveys deploying multi-vessels conducted on Lake Erie. Additionally, in some situations, differential depth patterns of fish contributed to vessel related differences in survey results suggesting a difference in vessel avoidance behaviors among targeted forage species, primarily emerald shiner Notropis atherinoides and rainbow smelt Osmerus mordax. This work demonstrates the potential magnitude of differences among vessels and highlights the need to identify and account for or otherwise control for these effects when monitoring or managing fisheries through standardized hydroacoustic assessments surveys.
Between 2014 and 2017, widespread seabird mortality events were documented annually in the Bering and Chukchi seas, concurrent with dramatic reductions of sea ice, warmer than average ocean temperatures, and rapid shifts in marine ecosystems. Among other changes in the marine environment, harmful algal blooms (HABs) that produce the neurotoxins saxitoxin (STX) and domoic acid (DA) have been identified as a growing concern in this region. Although STX and DA have been documented in Alaska (US) for decades, current projections suggest that the incidence of HABs is likely to increase with climate warming and may pose a threat to marine birds and other wildlife. In 2017, a multispecies die-off consisting of primarily Northern Fulmars (Fulmarus glacialis) and Short-tailed Shearwaters (Ardenna tenuirostris) occurred in the Bering and Chukchi seas. To evaluate whether algal toxins may have contributed to bird mortality, we tested carcasses collected from multiple locations in western and northern Alaska for STX and DA. We did not detect DA in any samples, but STX was present in 60% of all individuals tested and in 88% of Northern Fulmars. Toxin concentrations in Northern Fulmars were within the range of those reported from other STX-induced bird die-offs, suggesting that STX may have contributed to mortalities. However, direct neurotoxic action by STX could not be confirmed and starvation appeared to be the proximate cause of death among birds examined in this study.
","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/JWD-D-20-00057","usgsCitation":"Van Hemert, C.R., Dusek, R.J., Smith, M.M., Kaler, R., Sheffield, G., Divine, L.M., Kuletz, K.J., Knowles, S., Lankton, J.S., Hardison, D.R., Litaker, R.W., Jones, T., Burgess, H.K., and Parrish, J.K., 2021, Investigation of algal toxins in a multispecies seabird die-off in the Bering and Chukchi seas: Journal of Wildlife Diseases, v. 57, no. 2, p. 399-407, https://doi.org/10.7589/JWD-D-20-00057.","productDescription":"9 p.","startPage":"399","endPage":"407","ipdsId":"IP-118232","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":467249,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/50778","text":"External Repository"},{"id":436439,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OK4I8M","text":"USGS data release","linkHelpText":"SUPERSEDED: Data Associated with Algal Toxin Testing of Seabirds from the Bering and Chukchi Seas, 2017"},{"id":385215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Van Hemert, Caroline R. 0000-0002-6858-7165 cvanhemert@usgs.gov","orcid":"https://orcid.org/0000-0002-6858-7165","contributorId":3592,"corporation":false,"usgs":true,"family":"Van Hemert","given":"Caroline","email":"cvanhemert@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":814496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dusek, Robert J. 0000-0001-6177-7479 rdusek@usgs.gov","orcid":"https://orcid.org/0000-0001-6177-7479","contributorId":174374,"corporation":false,"usgs":true,"family":"Dusek","given":"Robert","email":"rdusek@usgs.gov","middleInitial":"J.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":814497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Matthew M. 0000-0002-2259-5135 mmsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-2259-5135","contributorId":5115,"corporation":false,"usgs":true,"family":"Smith","given":"Matthew","email":"mmsmith@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":814498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kaler, Robert","contributorId":199324,"corporation":false,"usgs":false,"family":"Kaler","given":"Robert","email":"","affiliations":[],"preferred":false,"id":814499,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sheffield, Gay","contributorId":257533,"corporation":false,"usgs":false,"family":"Sheffield","given":"Gay","email":"","affiliations":[{"id":52049,"text":"Alaska Sea Grant","active":true,"usgs":false}],"preferred":false,"id":814500,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Divine, Lauren M.","contributorId":257534,"corporation":false,"usgs":false,"family":"Divine","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":52051,"text":"Aleut Community of St. Paul Island","active":true,"usgs":false}],"preferred":false,"id":814501,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kuletz, Kathy J.","contributorId":257535,"corporation":false,"usgs":false,"family":"Kuletz","given":"Kathy","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":814502,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":814503,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":814504,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hardison, D. Ransom","contributorId":222038,"corporation":false,"usgs":false,"family":"Hardison","given":"D.","email":"","middleInitial":"Ransom","affiliations":[{"id":40480,"text":"NOAA National Centers for Coastal Ocean Science, Beaufort, NC","active":true,"usgs":false}],"preferred":false,"id":814505,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Litaker, R. Wayne","contributorId":202495,"corporation":false,"usgs":false,"family":"Litaker","given":"R.","email":"","middleInitial":"Wayne","affiliations":[{"id":36460,"text":"National Oceanic and Atmospheric Administration, National Ocean Service","active":true,"usgs":false}],"preferred":false,"id":814506,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Jones, Timothy","contributorId":220052,"corporation":false,"usgs":false,"family":"Jones","given":"Timothy","email":"","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":814507,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Burgess, Hillary K.","contributorId":220053,"corporation":false,"usgs":false,"family":"Burgess","given":"Hillary","email":"","middleInitial":"K.","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":814508,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Parrish, Julia K.","contributorId":220055,"corporation":false,"usgs":false,"family":"Parrish","given":"Julia","email":"","middleInitial":"K.","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":814509,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70262318,"text":"70262318 - 2021 - Suitability of an upper Mississippi River tributary for invasive carp reproduction","interactions":[],"lastModifiedDate":"2025-01-22T16:06:39.189719","indexId":"70262318","displayToPublicDate":"2021-03-25T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Suitability of an upper Mississippi River tributary for invasive carp reproduction","docAbstract":"Invasive carp are expanding throughout the upper Mississippi River basin and are of great concern due to their potential economic and ecological impacts. Identification of spawning locations provides critical information on recruitment sources to evaluate potential management strategies. Our objective was to create and validate a spawning habitat suitability model of the Des Moines River, Iowa, during low-, average-, and high-water-level conditions. Backwater availability, abundance of hardpoints (structures that create turbulence), river gradient and sinuosity, water temperature, and continuously free-flowing river lengths were used as model parameters. The model was compared to back-calculated spawning locations from invasive carp eggs collected in 2014–2015. Turbulent hardpoints, river sinuosity, and gradient were not significant predictors of invasive carp spawning locations, and backwater availability in the 25 river kilometers downstream of each reach was inversely correlated with invasive carp spawning locations. Invasive carp eggs were not caught in 2014 despite optimal spawning conditions, revealing that spawning may have high interannual variation. This study suggests that predicting invasive carp reproduction may require variables in addition to those currently proposed.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10551","usgsCitation":"Camacho, C., Sullivan, C., Weber, M., and Pierce, C., 2021, Suitability of an upper Mississippi River tributary for invasive carp reproduction: North American Journal of Fisheries Management, v. 43, no. 1, p. 12-24, https://doi.org/10.1002/nafm.10551.","productDescription":"13 p.","startPage":"12","endPage":"24","ipdsId":"IP-081177","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","otherGeospatial":"Des Moines 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","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20203015","usgsCitation":"Schenk, C.J., Mercier, T.J., Woodall, C.A., Finn, T.M., Le, P.A., Brownfield, M.E., Marra, K.R., Gaswirth, S.B., Leathers-Miller, H.M., and Pitman, J.K., 2021, Assessment of continuous gas resources in the Horn River Basin, Cordova Embayment, and Liard Basin, Canada, 2019: U.S. Geological Survey Fact Sheet 2020–3015, 4 p., https://doi.org/10.3133/fs20203015.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"N","ipdsId":"IP-109723","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":384643,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3015/coverthb.jpg"},{"id":384644,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3015/fs20203015.pdf","text":"Report","size":"1.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3015"},{"id":384645,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P923XB6L","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project -- Horn River Basin, Cordova Embayment, and Liard Basin, Canada, Assessment Unit Boundaries and Assessment Input Forms"}],"country":"Canada","state":"Alberta, British Columbia, Northwest Territories, Yukon","otherGeospatial":"Cordova Embayment, Horn River Basin, Liard Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.3857421875,\n 55.25407706707272\n ],\n [\n -117.8173828125,\n 55.25407706707272\n ],\n [\n -117.8173828125,\n 62.12443624549497\n ],\n [\n -130.3857421875,\n 62.12443624549497\n ],\n [\n -130.3857421875,\n 55.25407706707272\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The U.S. Geological Survey, Cape Cod National Seashore of the National Park Service, and Friends of Herring River cooperated from 2015 to 2017 to assess nutrient concentrations and fluxes across the ocean-estuary boundary at a dike on the Herring River in Wellfleet, Massachusetts. The purpose of this assessment was to characterize environmental conditions prior to a future removal of the dike, which has restricted saltwater inputs into the Herring River watershed for more than 100 years. Water temperature, dissolved oxygen, pH, and specific conductance were monitored continuously, and flow-weighted composite samples were collected approximately twice per month at the ocean-estuary boundary. Bidirectional discharge was computed for the U.S. Geological Survey Herring River at Chequessett Neck Road at Wellfleet, Massachusetts, streamgage (011058798) by using a stage-area rating and index-velocity ratings developed with acoustic Doppler current profile measurements made upstream and downstream from the dike. LOADEST regression modeling software was used to estimate nutrient fluxes (loads) from composite, paired nutrient concentration and discharge data in conjunction with continuous discharge data. Temperature, dissolved oxygen, pH, and specific conductance were also monitored continuously on two tributaries to the Herring River, Pole Dike Creek and Bound Brook, from late-May 2016 to mid-June 2017. Composite or discrete water samples were collected from the tributaries approximately twice per month in most months from late-May 2016 to mid-June 2017 and analyzed for total nitrogen, total phosphorus, and dissolved organic carbon.
Flow-weighted concentrations of ammonium, nitrate, and total nitrogen on the Herring River at the dike on the ebb tide generally varied between 0.01 and 0.1, 0.003 and 0.03, and 0.3 and 0.7 milligram per liter as nitrogen, respectively. Flow-weighted concentrations of orthophosphate, total dissolved phosphorus, and total phosphorus generally varied between 0.002 and 0.02, 0.003 and 0.06, and 0.03 and 0.1 milligram per liter as phosphorus, respectively, on the ebb tide. Flow-weighted concentrations of silicate and dissolved organic carbon on the ebb tide generally varied between 0.08 and 3.0 milligrams per liter of silica (silicon dioxide), and 1.7 and 5.6 milligrams per liter of carbon, respectively. Ebb tide concentrations of nitrate were highest in winter and lowest in summer. By contrast, ebb tide concentrations of phosphorus species were highest in late summer and early fall and lowest in winter. Silica and dissolved organic carbon did not exhibit systematic variation in seasonal concentrations. There was uncertainty in estimates of nutrient fluxes, but the LOADEST-estimated fluxes indicated that annual (and in almost all cases seasonal) exports (ebb tides) exceeded inputs (flood tides). Ebb tide concentrations of ammonium, nitrate, total nitrogen, and silica were positively correlated with antecedent cumulative 7-day precipitation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205120","collaboration":"Prepared in cooperation with the National Park Service and Friends of Herring River","usgsCitation":"Huntington, T.G., Spaetzel, A.B., Colman, J.A., Kroeger, K.D., and Bradley, R.T., 2021, Assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017: U.S. Geological Survey Scientific Investigations Report 2020–5120, 59 p., https://doi.org/10.3133/sir20205120.","productDescription":"Report: x, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106718","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":384601,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5120/coverthb.jpg"},{"id":384603,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BKW4BX","text":"USGS data release","linkHelpText":"Tidal daily discharge and quality assurance data supporting an assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015–September 2017"},{"id":384602,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5120/sir20205120.pdf","text":"Report","size":"3.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5120"}],"country":"United States","state":"Massachusetts","city":"Wellfleet","otherGeospatial":"Herring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.07801055908203,\n 41.93318868195924\n ],\n [\n -69.99870300292969,\n 41.93318868195924\n ],\n [\n -69.99870300292969,\n 41.98833256890643\n ],\n [\n -70.07801055908203,\n 41.98833256890643\n ],\n [\n -70.07801055908203,\n 41.93318868195924\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Groundwater is the primary source of municipal water for Saipan. Nearly all groundwater for the municipal water supply is withdrawn from a freshwater-lens system with a limited amount of freshwater that is susceptible to saltwater intrusion. The status of Saipan’s groundwater resources has not been thoroughly assessed since 2003. The U.S. Geological Survey—in cooperation with the Office of Grants Management, Commonwealth of the Northern Mariana Islands, and in collaboration with the Commonwealth Utilities Corporation—assessed the status and characteristics of Saipan’s groundwater resources by (1) evaluating groundwater withdrawals from municipal production wells during 2014–19, (2) evaluating chloride concentrations of municipal groundwater withdrawals during 2009–19, and (3) collecting salinity profiles at selected groundwater-monitoring wells during 2018–19. At the time of preparation of this report (2019), the periods of groundwater-withdrawal and chloride-concentration data represent the only periods of data available since 2003.
During 2014–19, groundwater for the municipal water supply was withdrawn from about 143 production wells. Most of the wells are drilled into limestone formations in the southern plateau and the Kagman Peninsula and generally have withdrawal rates of about 40–60 gallons per minute. Records of monthly groundwater withdrawals from municipal production wells were available for May 2014–March 2019; during that period, monthly withdrawals ranged from 5.7 to 12.8 million gallons per day (Mgal/d) and averaged 9.3 Mgal/d, although records were unavailable for 9 months (May 2015–January 2016). Private wells, mainly located on the western coastal plain, currently are permitted to withdraw a total of about 7 Mgal/d of groundwater. Actual groundwater withdrawals from private wells, however, are uncertain because withdrawal records for private wells are not available.
The Commonwealth Utilities Corporation measured the chloride concentration of groundwater pumped from each of its production wells about twice a year from 2009–19; during this period, 146 production wells were active and sampled. Only 32 of the 146 (22 percent) municipal production wells had median chloride concentrations less than or equal to 250 milligrams per liter (mg/L), the secondary drinking water standard set by the U.S. Environmental Protection Agency. Eighty-one wells (55 percent) pumped water with median chloride concentrations above 500 mg/L.
The Mann-Kendall test was used to determine if chloride concentrations of groundwater withdrawals at 146 municipal production wells had statistically significant trends during December 2009–February 2019. Trends were considered statistically significant for probability values (p-values) less than or equal to 0.05. Test results indicate an upward trend at 9 wells, a downward trend at 52 wells, and no trend at 85 wells.
Salinity profiles were measured in 12 selected monitor wells during July–August 2018 and were measured in six of the twelve selected monitor wells during March 2019. The salinity profiles were used to estimate the thickness of the freshwater lens at 10 monitor wells; freshwater-lens thickness was greatest (46 ft) in a monitor well in the Dan Dan well field near the northern part of the southern plateau. Freshwater-lens-thickness estimates elsewhere were (1) between 0 and 28 ft for the remaining monitor wells on the southern plateau, (2) between 19 and 21 ft for monitor wells on the Kagman Peninsula, (3) 2 ft for a monitor well in the Sablan Quarry well field on west-central Saipan, and (4) 8 ft for a monitor well in the Marpi Quarry well field on northern Saipan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205129","collaboration":"Prepared in cooperation with the Office of Grants Management and in collaboration with the Commonwealth Utilities Corporation, Commonwealth of the Northern Mariana Islands","usgsCitation":"Mitchell, J.N., Presley, T.K., and Carruth, R.L., 2021, Groundwater conditions and trends, 2009–19, Saipan, Commonwealth of the Northern Mariana Islands: U.S. Geological Survey Scientific Investigations Report 2020–5129, 51 p., https://doi.org/10.3133/sir20205129.","productDescription":"vii, 51 p.","numberOfPages":"51","onlineOnly":"Y","ipdsId":"IP-111052","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":384606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5129/sir20205129.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5129/covrthb.jpg"}],"country":"United States","otherGeospatial":"Commonwealth of the Norhtern Marianas Islands, Saipan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 145.67665100097656,\n 15.083394661897604\n ],\n [\n 145.83595275878906,\n 15.083394661897604\n ],\n [\n 145.83595275878906,\n 15.339153696147529\n ],\n [\n 145.67665100097656,\n 15.339153696147529\n ],\n [\n 145.67665100097656,\n 15.083394661897604\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Proliferative kidney disease (PKD) is an emerging disease that recently resulted in a large mortality event of salmonids in the Yellowstone River (Montana, USA). Total PKD fish mortalities in the Yellowstone River were estimated in the tens of thousands, which resulted in a multi‐week river closure and an estimated economic loss of US$500,000. This event shocked scientists, managers, and the public, as this was the first occurrence of the disease in the Yellowstone River, the only reported occurrence of the disease in Montana in the past 25 yr, and arguably the largest wild PKD fish kill in the world. To understand why the Yellowstone River fish kill occurred, we used molecular and historical data to evaluate evidence for several hypotheses: Was the causative parasite Tetracapsuloides bryosalmonae a novel invader, was the fish kill associated with a unique parasite strain, and/or was the outbreak caused by unprecedented environmental conditions? We found that T. bryosalmonae is widely distributed in Montana and have documented occurrence of this parasite in archived fish collected in the Yellowstone River prior to the fish kill. T. bryosalmonae had minimal phylogeographic population structure, as the DNA of parasites sampled from the Yellowstone River and distant water bodies were very similar. These results suggest that T. bryosalmonae could be endemic in Montana. Due to data limitations, we could not reject the hypothesis that the fish kill was caused by a novel and more virulent genetic strain of the parasite. Finally, we found that single‐year environmental conditions are insufficient to explain the cause of the 2016 Yellowstone River PKD outbreak. Other regional rivers where we documented T. bryosalmonae had similar or even more extreme conditions than the Yellowstone River and similar or more extreme conditions have occurred in the Yellowstone River in the recent past, yet mass PKD mortalities have not been documented in either instance. We conclude by placing these results and unresolved hypotheses into the broader context of international research on T. bryosalmonae and PKD, which strongly suggests that a better understanding of bryozoans, the primary host of T. bryosalmonae, is required for better ecosystem understanding.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3436","usgsCitation":"Hutchins, P., Sepulveda, A., Hartikainen, H., Staigmiller, K.D., Opitz, S.T., Yamamoto, R.M., Huttinger, A., Cordes, R.J., Weiss, T., Hopper, L.R., Purcell, M.K., and Okamura, B., 2021, Exploration of the 2016 Yellowstone River fish kill and proliferative kidney disease in wild fish populations: Ecosphere, v. 3, no. 12, e03436, 20 p., https://doi.org/10.1002/ecs2.3436.","productDescription":"e03436, 20 p.","ipdsId":"IP-120532","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":452955,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3436","text":"Publisher Index Page"},{"id":436440,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95XKXE1","text":"USGS data release","linkHelpText":"T. bryosalmonae detection in fish and water, DNA sequence, and simple sequence repeat data collected in the Inter-Mountain West from 2011 to 2019"},{"id":384695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Madison River, Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.423095703125,\n 45.40616374516014\n ],\n [\n -111.368408203125,\n 45.40616374516014\n ],\n [\n -111.368408203125,\n 46.27103747280261\n ],\n [\n -112.423095703125,\n 46.27103747280261\n ],\n [\n -112.423095703125,\n 45.40616374516014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-03-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Hutchins, Patrick Ross 0000-0001-5232-0821","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":256658,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick Ross","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hartikainen, Hanna","contributorId":256659,"corporation":false,"usgs":false,"family":"Hartikainen","given":"Hanna","email":"","affiliations":[{"id":39130,"text":"University of Nottingham","active":true,"usgs":false}],"preferred":false,"id":812993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staigmiller, Ken D.","contributorId":256661,"corporation":false,"usgs":false,"family":"Staigmiller","given":"Ken","email":"","middleInitial":"D.","affiliations":[{"id":40948,"text":"Montana Fish Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":812994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Opitz, Scott T.","contributorId":256663,"corporation":false,"usgs":false,"family":"Opitz","given":"Scott","email":"","middleInitial":"T.","affiliations":[{"id":40948,"text":"Montana Fish Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":812995,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yamamoto, Renee M.","contributorId":256665,"corporation":false,"usgs":false,"family":"Yamamoto","given":"Renee","email":"","middleInitial":"M.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":812996,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huttinger, Amberly","contributorId":256668,"corporation":false,"usgs":false,"family":"Huttinger","given":"Amberly","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":812997,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cordes, Rick J.","contributorId":256670,"corporation":false,"usgs":false,"family":"Cordes","given":"Rick","email":"","middleInitial":"J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":812998,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Weiss, Tammy","contributorId":256672,"corporation":false,"usgs":false,"family":"Weiss","given":"Tammy","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":812999,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hopper, Lacey R.","contributorId":206813,"corporation":false,"usgs":false,"family":"Hopper","given":"Lacey","email":"","middleInitial":"R.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":813000,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Purcell, Maureen K. 0000-0003-0154-8433 mpurcell@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8433","contributorId":168475,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen","email":"mpurcell@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":813001,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Okamura, Beth","contributorId":256676,"corporation":false,"usgs":false,"family":"Okamura","given":"Beth","email":"","affiliations":[{"id":51827,"text":"Natural History Museum","active":true,"usgs":false}],"preferred":false,"id":813002,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70218832,"text":"sir20205106 - 2021 - Assessment of contaminant trends in plumes and wells and monitoring network optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin","interactions":[],"lastModifiedDate":"2021-03-24T21:57:54.814314","indexId":"sir20205106","displayToPublicDate":"2021-03-24T09:50:00","publicationYear":"2021","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":"2020-5106","displayTitle":"Assessment of Contaminant Trends in Plumes and Wells and Monitoring Network Optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin","title":"Assessment of contaminant trends in plumes and wells and monitoring network optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin","docAbstract":"Soil and groundwater at the Badger Army Ammunition Plant (BAAP), Sauk County, Wisconsin, were affected by several contaminants as a result of production and waste disposal practices common during its operation from 1942 to 1975. Three distinct plumes of contaminated groundwater originate on BAAP property and extend off-site, as identified by previous studies. Routine sampling of groundwater quality from a network of monitoring wells and off-site private wells has been performed since 1990, although the number of wells monitored and the monitoring frequency have varied as the approved monitoring plan was modified. During the period of monitoring from 1990 to 2018, numerous site investigations and remedial actions were conducted to address the sources of contamination, contaminated soils, and groundwater. Concentrations of contaminants reportedly decreased between 2000 and 2012 within all three plumes. Five or six contaminants of concern (COCs) were identified for each of the three plumes. An independent assessment of the contaminant plumes and of the monitoring network was conducted using groundwater-quality data collected from more than 600 wells between 2000 and 2018.
In a study conducted by the U.S. Geological Survey (USGS), in cooperation with the Army Environmental Command, a consistent data aggregation and interpolation scheme was applied to derive the likely maximum groundwater plume extents in four 3-year time periods between 2000 and 2018. The plume extent was defined by the Enforcement Standard for each COC and represents the maximum concentration observed in each 3-year time period. The plume boundary analysis shows that the spatial extent of groundwater contamination decreased for most COCs during the study period. Some plume boundaries are not well delineated by the existing monitoring network, particularly the downgradient edge of the Propellant Burning Ground plume. Maps identify the plume boundary in each time period, the sampling well network used to delineate the plume, and wells that were sampled in the 2010–12 period but not sampled in the 2015–18 period.
A series of statistical analyses using the Monitoring and Remediation Optimization System, version 3.0, program were applied to the available COC concentration data for two distinct periods, 2000 to 2012 and 2013 to 2018, with the break between periods coinciding with changes to the monitoring network in 2013. Trends in the concentration of COCs in individual wells varied, although generally more wells had decreasing than had increasing concentrations for most COCs in both time periods. The exceptions were ethyl ether in the 2004–12 period and 2,6-dinitrotoluene in the 2013–18 period, for which more wells had an increasing trend. Spatial moment analysis of concentration data from the well network was used to assess the stability of each plume for the COCs. During the 2000–12 period, most of the contaminant plumes for which data were sufficient to complete the analysis were either decreasing or stable in mass and size. The exceptions were carbon tetrachloride (associated solely with the Propellant Burning Ground plume) and 2,4-dinitrotoluene and 2,6-dinitrotoluene (in the Deterrent Burning Ground plume), which showed an increasing trend in mass. No COCs showed an increasing trend in plume mass in the 2013–18 period. Some wells with increasing trends in concentration or with concentrations greater than the enforcement standard are near the tail of a plume, where increased monitoring may be of value to better define future plume boundaries. A spatial optimization analysis covering the 2013–18 period identified six wells that provided information redundant to that from other wells. A temporal optimization analysis identified optimal sampling frequencies for 125 wells. Remedial actions directed at the Propellant Burning Ground plume coincided with a general decrease in plume mass and size, although in specific areas and depths, the plume size for specific contaminants may still be increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205106","collaboration":"Prepared in cooperation with the Army Environmental Command","usgsCitation":"Pajerowski, M., Goodling, P., and Metes, M., 2021, Assessment of contaminant trends in plumes and wells and monitoring network optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5106, 80 p., https://doi.org/10.3133/sir20205106.","productDescription":"Report: x, 80 p.; Data Release; 16 Plates","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118955","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":384411,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5106/sir20205106_plates.pdf","text":"Plates 1 through 16","size":"189 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5106/coverthb.jpg"},{"id":384402,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5106/sir20205106.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5106"},{"id":384403,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97UKYNR","text":"USGS data release","linkHelpText":"Groundwater quality and plume boundaries for select contaminants of concern at Badger Army Ammunition Plant, Wisconsin (2000–2018)"}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.77375030517578,\n 43.30694264971061\n ],\n [\n -89.67041015625,\n 43.30694264971061\n ],\n [\n -89.67041015625,\n 43.420634784134876\n ],\n [\n -89.77375030517578,\n 43.420634784134876\n ],\n [\n -89.77375030517578,\n 43.30694264971061\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
The San Andreas fault has the highest calculated time-dependent probability for large-magnitude earthquakes in southern California. However, where the fault is multistranded east of the Los Angeles metropolitan area, it has been uncertain which strand has the fastest slip rate and, therefore, which has the highest probability of a destructive earthquake. Reconstruction of offset Pleistocene-Holocene landforms dated using the uranium-thorium soil carbonate and beryllium-10 surface exposure techniques indicates slip rates of 24.1 ± 3 millimeter per year for the San Andreas fault, with 21.6 ± 2 and 2.5 ± 1 millimeters per year for the Mission Creek and Banning strands, respectively. These data establish the Mission Creek strand as the primary fault bounding the Pacific and North American plates at this latitude and imply that 6 to 9 meters of elastic strain has accumulated along the fault since the most recent surface-rupturing earthquake, highlighting the potential for large earthquakes along this strand.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.aaz5691","usgsCitation":"Blisniuk, K., Scharer, K., Sharp, W., Burgmann, R., Amos, C., and Rymer, M., 2021, A revised position for the primary strand of the Pleistocene-Holocene San Andreas fault in southern California: Science Advances, v. 7, eaaz5691, 15 p., https://doi.org/10.1126/sciadv.aaz5691.","productDescription":"eaaz5691, 15 p.","ipdsId":"IP-111194","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":452956,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.aaz5691","text":"External Repository"},{"id":406838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.21612548828124,\n 33.527658137677335\n ],\n [\n -116.004638671875,\n 33.71748624018193\n ],\n [\n -116.8011474609375,\n 34.21634468843463\n ],\n [\n -116.971435546875,\n 33.93880275084578\n ],\n [\n -116.21612548828124,\n 33.527658137677335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blisniuk, Kim","contributorId":296614,"corporation":false,"usgs":false,"family":"Blisniuk","given":"Kim","email":"","affiliations":[{"id":24620,"text":"San Jose State University","active":true,"usgs":false}],"preferred":false,"id":851987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":851988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sharp, Warren","contributorId":295386,"corporation":false,"usgs":false,"family":"Sharp","given":"Warren","affiliations":[{"id":38176,"text":"Berkeley Geochronology Center","active":true,"usgs":false}],"preferred":false,"id":851989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burgmann, Roland 0000-0002-3560-044X","orcid":"https://orcid.org/0000-0002-3560-044X","contributorId":264610,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","email":"","affiliations":[{"id":54514,"text":"Berkeley Seismological Laboratory, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":851990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amos, Colin","contributorId":196408,"corporation":false,"usgs":false,"family":"Amos","given":"Colin","affiliations":[],"preferred":false,"id":851991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rymer, Michael 0000-0002-5429-5073 mrymer@usgs.gov","orcid":"https://orcid.org/0000-0002-5429-5073","contributorId":220757,"corporation":false,"usgs":true,"family":"Rymer","given":"Michael","email":"mrymer@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":851992,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220559,"text":"70220559 - 2021 - Across borders: External factors and prior behaviour influence North Pacific albatross associations with fishing vessels","interactions":[],"lastModifiedDate":"2021-06-30T18:56:32.323965","indexId":"70220559","displayToPublicDate":"2021-03-24T07:45:58","publicationYear":"2021","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":"Across borders: External factors and prior behaviour influence North Pacific albatross associations with fishing vessels","docAbstract":"The Everglades wetland was once a river of grass, with water flowing slowly through the sawgrass, southward across the landscape. As developers took hold of south Florida, water was sent away from the heart of the Everglades through canals and levees to protect the former wetland for residential and agricultural development. In the 1990s, planning began to restore the Everglades in what is the largest hydrologic restoration undertaking in the world. With billions of taxpayer dollars at stake, restoration planners benefit from forecasting tools to inform restoration planning. To meet this need, scientists developed predictive ecological models and other decision support tools tailored to this dynamic ecosystem as well as to the needs of restoration planning teams. Predictive modeling has been able to take advantage of well-understood relationships between species of interest and hydrologic dynamics in the Everglades. Recent modeling advances include multi-species approaches that consider interactions among species as well as explicit consideration of trade-offs among species from potential water management actions. Scientists are also starting to look at ecosystem-wide vulnerabilities with explicit consideration of future change such as sea level rise. Modeling tools and approaches continue to be refined to meet decision making needs for Everglades restoration. However, more work is needed to consider additional complexities of this dynamic wetland as well as to consider the broader socio-environmental system.
Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13061210","usgsCitation":"Benedict, T.D., Brown, J.F., Boyte, S., Howard, D., Fuchs, B., Wardlow, B.D., Tadesse, T., and Evenson, K., 2021, Exploring VIIRS continuity with MODIS in an expedited capability for monitoring drought-related vegetation conditions: Remote Sensing, v. 13, no. 6, 1210, 17 p., https://doi.org/10.3390/rs13061210.","productDescription":"1210, 17 p.","ipdsId":"IP-126651","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13061210","text":"Publisher Index Page"},{"id":384696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 37.68382032669382\n ],\n [\n -94.833984375,\n 37.68382032669382\n ],\n [\n -94.833984375,\n 39.977120098439634\n ],\n [\n -98.61328125,\n 39.977120098439634\n ],\n [\n -98.61328125,\n 37.68382032669382\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Benedict, Trenton D 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":256662,"corporation":false,"usgs":false,"family":"Benedict","given":"Trenton","email":"","middleInitial":"D","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","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":812984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":812985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":256667,"corporation":false,"usgs":false,"family":"Howard","given":"Daniel","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812986,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuchs, Brian","contributorId":192359,"corporation":false,"usgs":false,"family":"Fuchs","given":"Brian","email":"","affiliations":[],"preferred":false,"id":812987,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wardlow, Brian D. 0000-0002-4767-581X","orcid":"https://orcid.org/0000-0002-4767-581X","contributorId":191403,"corporation":false,"usgs":false,"family":"Wardlow","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":812988,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tadesse, Tsegaye 0000-0002-4102-1137","orcid":"https://orcid.org/0000-0002-4102-1137","contributorId":147617,"corporation":false,"usgs":false,"family":"Tadesse","given":"Tsegaye","email":"","affiliations":[],"preferred":false,"id":812989,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Evenson, Kirk","contributorId":256674,"corporation":false,"usgs":false,"family":"Evenson","given":"Kirk","email":"","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812990,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223101,"text":"70223101 - 2021 - Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces","interactions":[],"lastModifiedDate":"2021-08-11T14:40:29.799032","indexId":"70223101","displayToPublicDate":"2021-03-23T09:32:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces","title":"Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces","docAbstract":"Sea lamprey (Petromyzon marinus) are invasive in the Laurentian Great Lakes, parasitize large-bodied fishes, and therefore are the focus of an international control program. However, damage caused by sea lamprey to modern day fish stocks remains uncertain because diet analysis of juvenile sea lamprey has been challenging; they feed on blood and are difficult to randomly sample in the lakes. Here, both challenges were addressed by showing that DNA metabarcoding of fecal material can be used to identify the diet of actively feeding juvenile sea lamprey, and can also be used to determine what non-feeding adult sea lamprey captured in streams fed on while parasitizing fish. Fecal samples from juvenile sea lamprey that were feeding on lake trout in northern Lake Huron overwhelmingly contained lake trout (Salvelinus namaycush) DNA (90%), while smaller percentages contained lake whitefish (Coregonus clupeaformis; 5%) and longnose sucker (Catostomus catostomus; 5%) DNA. Fecal samples from adult sea lamprey captured from a tributary to northern Lake Huron overwhelmingly contained longnose and white sucker DNA (Catostomus spp.; 80%), while a smaller percentage contained lake trout DNA (10%). Diet composition of adult sea lamprey sampled in the tributary (Black Mallard Creek) was more diverse than juvenile diet composition. DNA metabarcoding suggests that Catostomus spp. may be an important host fish in northern Lake Huron for sea lamprey prior to spawning. Future research could investigate how diet varies across years and lakes and the prevalence and sources of DNA contamination. Application of DNA metabarcoding for diet assessment may be practical for identifying populations of invasive sea lamprey that feed on highly valued fishes and help guide restoration of lampreys worldwide.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107605","usgsCitation":"Johnson, N.S., Lewandoski, S.A., and Merkes, C.M., 2021, Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces: Ecological Indicators, v. 125, 107605, 9 p., https://doi.org/10.1016/j.ecolind.2021.107605.","productDescription":"107605, 9 p.","ipdsId":"IP-127616","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":452962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107605","text":"Publisher Index Page"},{"id":387856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Hammond Bay, Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.99459838867188,\n 45.49431506693786\n ],\n [\n -83.9520263671875,\n 45.53232688809725\n ],\n [\n -83.97880554199219,\n 45.58761466314763\n ],\n [\n -84.06257629394531,\n 45.6029894122523\n ],\n [\n -84.11819458007811,\n 45.58569252333191\n ],\n [\n -84.12574768066406,\n 45.558294879426235\n ],\n [\n -84.11338806152344,\n 45.511640093571096\n ],\n [\n -84.07424926757812,\n 45.49238973487207\n ],\n [\n -84.02549743652344,\n 45.50057194157223\n ],\n [\n -83.99459838867188,\n 45.49431506693786\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":820952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lewandoski, Sean A.","contributorId":221007,"corporation":false,"usgs":false,"family":"Lewandoski","given":"Sean","email":"","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":820953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merkes, Christopher M. 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":820954,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222609,"text":"70222609 - 2021 - Geological constraints on the mechanisms of slow earthquakes","interactions":[],"lastModifiedDate":"2021-08-09T13:57:48.415744","indexId":"70222609","displayToPublicDate":"2021-03-23T08:54:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9136,"text":"Nature Reviews Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Geological constraints on the mechanisms of slow earthquakes","docAbstract":"The recognition of slow earthquakes in geodetic and seismological data has transformed the understanding of how plate motions are accommodated at major plate boundaries. Slow earthquakes, which slip more slowly than regular earthquakes but faster than plate motion velocities, occur in a range of tectonic and metamorphic settings. They exhibit spatiotemporal associations with large seismic events that indicate a causal relation between modes of slip at different slip rates. Defining the physical controls on slow earthquakes is, therefore, critical for understanding fault and shear zone mechanics. In this Review, we synthesize geological observations of a suite of ancient structures that were active in tectonic settings comparable to where slow earthquakes are observed today. At inferred slow earthquake regions, a range of grain-scale deformation mechanisms accommodated slip at low effective stresses. Material heterogeneity and the geometric complexity of structures that formed at different inferred strain rates are common to faults and shear zones in multiple tectonic environments, and might represent key limiting factors of slow earthquake slip rates. Further geological work is needed to resolve how the spectrum of slow earthquake slip rates can arise from different grain-scale deformation mechanisms and whether there is one universal rate-limiting mechanism that defines slow earthquake slip.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s43017-021-00148-w","usgsCitation":"Kirkpatrick, J.D., Fagereng, A., and Shelly, D.R., 2021, Geological constraints on the mechanisms of slow earthquakes: Nature Reviews Earth and Environment, v. 2, p. 285-301, https://doi.org/10.1038/s43017-021-00148-w.","productDescription":"17 p.","startPage":"285","endPage":"301","ipdsId":"IP-124320","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":467251,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orca.cardiff.ac.uk/id/eprint/140270/","text":"External Repository"},{"id":387780,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirkpatrick, James D.","contributorId":202603,"corporation":false,"usgs":false,"family":"Kirkpatrick","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":820736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fagereng, Ake","contributorId":261903,"corporation":false,"usgs":false,"family":"Fagereng","given":"Ake","email":"","affiliations":[{"id":53076,"text":"School of Earth & Ocean Sciences, Cardiff University, Cardiff, CF10 3AT, UK","active":true,"usgs":false}],"preferred":false,"id":820737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820803,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222461,"text":"70222461 - 2021 - Genetic structure of Maryland Brook Trout populations: Management implications for a threatened species","interactions":[],"lastModifiedDate":"2021-09-14T16:37:31.761678","indexId":"70222461","displayToPublicDate":"2021-03-23T08:49:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Genetic structure of Maryland Brook Trout populations: Management implications for a threatened species","docAbstract":"Brook Trout Salvelinus fontinalis have declined across their native range due to multiple anthropogenic factors, including landscape alteration and climate change. Although coldwater streams in Maryland (eastern United States) historically supported significant Brook Trout populations, only fragmented remnant populations remain, with the exception of the upper Savage River watershed in western Maryland. Using microsatellite data from 38 collections, we defined genetic relationships of Brook Trout populations in Maryland drainages. Microsatellite analyses of Brook Trout indicated the presence of five major discrete units defined as the Youghiogheny (Ohio), Susquehanna, Patapsco/Gunpowder, Catoctin, and Upper Potomac, with a distinct genetic subunit present in the Savage River (upper Potomac). We did not observe evidence for widespread hatchery introgression with native Brook Trout. However, genetic effects due to fragmentation were evident in several Maryland Brook Trout populations, resulting in erosion of diversity that may have negative implications for their future persistence. Our current study supplements an increasing body of evidence that Brook Trout populations in Maryland are highly susceptible to multiple anthropogenic stresses, and many populations may be extirpated in the near future. Future management efforts focused on habitat protection and potential stream restoration, coupled with a comprehensive assessment framework that includes genetic considerations, may provide the best outlook for Brook Trout populations in Maryland.