The growth hormone (Gh)/insulin-like growth-factor (Igf)/Igf binding protein (Igfbp) system regulates growth and osmoregulation in salmonid fishes, but how this system interacts with other endocrine systems is largely unknown. Given the well-documented consequences of mounting a glucocorticoid stress response on growth, we hypothesized that cortisol inhibits anabolic processes by modulating the expression of hepatic igfbp mRNAs. Atlantic salmon (Salmo salar) parr were implanted intraperitoneally with cortisol implants (0, 10, and 40 μg g−1 body weight) and sampled after 3 or 14 days. Cortisol elicited a dose-dependent reduction in specific growth rate (SGR) after 14 days. While plasma Gh and Igf1 levels were unchanged, hepatic igf1 mRNA was diminished and hepatic igfbp1b1 and -1b2 were stimulated by the high cortisol dose. Plasma Igf1 was positively correlated with SGR at 14 days. Hepatic gh receptor (ghr), igfbp1a, -2a, -2b1, and -2b2 levels were not impacted by cortisol. Muscle igf2, but not igf1 or ghr, levels were stimulated at 3 days by the high cortisol dose. As both cortisol and the Gh/Igf axis promote seawater (SW) tolerance, and particular igfbps respond to SW exposure, we also assessed whether cortisol coordinates the expression of branchial igfbps and genes associated with ion transport. Cortisol stimulated branchial igfbp5b2 levels in parallel with Na+/K+-ATPase (NKA) activity and nka-α1b, Na+/K+/2Cl--cotransporter 1 (nkcc1), and cystic fibrosis transmembrane regulator 1 (cftr1) mRNA levels. The collective results indicate that cortisol modulates the growth of juvenile salmon via the regulation of hepatic igfbp1s whereas no clear links between cortisol and branchial igfbps previously shown to be salinity-responsive could be established.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.mce.2020.110989","usgsCitation":"Breves, J.P., Springer-Miller, R., Chenoweth, D., Paskavitz, A.L., Chang, A.Y., Regish, A.M., Einarsdottir, I., Bjornsson, B., and McCormick, S.D., 2020, Cortisol regulates insulin-like growth-factor binding protein (igfbp) gene expression in Atlantic salmon parr: Molecular and Cellular Endocrinology, v. 518, 110989, 10 p., https://doi.org/10.1016/j.mce.2020.110989.","productDescription":"110989, 10 p.","ipdsId":"IP-120328","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":455576,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.mce.2020.110989","text":"Publisher Index Page"},{"id":378448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"518","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Breves, Jason P.","contributorId":6349,"corporation":false,"usgs":false,"family":"Breves","given":"Jason","email":"","middleInitial":"P.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":798854,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springer-Miller, R.H.","contributorId":240717,"corporation":false,"usgs":false,"family":"Springer-Miller","given":"R.H.","email":"","affiliations":[{"id":35659,"text":"Skidmore College","active":true,"usgs":false}],"preferred":false,"id":798855,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chenoweth, D A","contributorId":240718,"corporation":false,"usgs":false,"family":"Chenoweth","given":"D A","affiliations":[{"id":35659,"text":"Skidmore College","active":true,"usgs":false}],"preferred":false,"id":798856,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paskavitz, A L","contributorId":240719,"corporation":false,"usgs":false,"family":"Paskavitz","given":"A","email":"","middleInitial":"L","affiliations":[{"id":35659,"text":"Skidmore College","active":true,"usgs":false}],"preferred":false,"id":798857,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chang, A Y H","contributorId":240720,"corporation":false,"usgs":false,"family":"Chang","given":"A","email":"","middleInitial":"Y H","affiliations":[{"id":35659,"text":"Skidmore College","active":true,"usgs":false}],"preferred":false,"id":798858,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Regish, Amy M. 0000-0003-4747-4265 aregish@usgs.gov","orcid":"https://orcid.org/0000-0003-4747-4265","contributorId":5415,"corporation":false,"usgs":true,"family":"Regish","given":"Amy","email":"aregish@usgs.gov","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":798859,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Einarsdottir, I E","contributorId":240721,"corporation":false,"usgs":false,"family":"Einarsdottir","given":"I E","affiliations":[{"id":12695,"text":"University of Gothenburg","active":true,"usgs":false}],"preferred":false,"id":798860,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bjornsson, Bjorn","contributorId":240722,"corporation":false,"usgs":false,"family":"Bjornsson","given":"Bjorn","affiliations":[{"id":12695,"text":"University of Gothenburg","active":true,"usgs":false}],"preferred":false,"id":798861,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":798862,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237133,"text":"70237133 - 2020 - Step increase in eastern U.S. precipitation linked to Indian Ocean warming","interactions":[],"lastModifiedDate":"2022-09-30T11:38:19.67307","indexId":"70237133","displayToPublicDate":"2020-08-21T06:35:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Step increase in eastern U.S. precipitation linked to Indian Ocean warming","docAbstract":"A step increase in annual precipitation over the eastern United States in the early 1970s commenced five decades of invigorated hydroclimate, with ongoing impacts on streamflow and water resources. Despite its far-reaching impacts, the dynamical origin of this change is unknown. Here analyses of a century of atmospheric and oceanic data trace the dynamics to changes in the Indian Ocean. Increases in fall precipitation contribute most strongly to the step increase, and the associated mechanism is emergence of a pan-Pacific atmospheric wave emanating from deep convection over the warming Indian Ocean. Documentation of this fall teleconnection draws attention to projected anthropogenic increases in tropical oceanic heat content and their potential impacts on hydroclimate of the midlatitudes.
Digital flood-inundation maps for about an 8-mile reach of the Little Calumet River, Illinois, were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Army Corps of Engineers. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at three USGS streamgages: Little Calumet River at South Holland, Ill. (USGS station 05536290); Little Calumet River at Munster, Indiana (USGS station 05536195); and Thorn Creek at Thornton, Ill. (USGS station 05536275). Near-real-time stages at these streamgages may be obtained on the internet from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/, which also forecasts flood hydrographs at these sites.
Flood profiles were computed for the stream reaches using a one-dimensional unsteady flow step-backwater hydraulic model. The model performance was evaluated using historical streamflow measurements and the most current stage-discharge relations at the USGS streamgages at Little Calumet River at South Holland, Ill.; Little Calumet River at Munster, Ind.; and Thorn Creek at Thornton, Ill. The model was used to compute 24 water-surface profiles at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to about the 0.2-percent annual-exceedance probability flood (500-year recurrence interval flood). The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.6-foot vertical accuracy and a 2-foot horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from USGS streamgages and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205074","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Dunn, A.P., Straub, T.D., and Manaster, A.E., 2020, Flood-inundation maps for the Little Calumet River from Lansing to South Holland, Illinois, 2020: U.S. Geological Survey Scientific Investigations Report 2020–5074, 10 p., https://doi.org/10.3133/sir20205074.","productDescription":"Report: vi, 10 p.; Data Release; Dataset","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-097182","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377581,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99L14DN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geospatial datasets for the flood-inundation study of Little Calumet River from Lansing to South Holland, Illinois, 2020, 2020"},{"id":377582,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":377580,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5074/sir20205074.pdf","text":"Report","size":"2.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5074"},{"id":377579,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5074/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Lansing, South Holland","otherGeospatial":"Little Calumet River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.6295280456543,\n 41.54404730359805\n ],\n [\n -87.52584457397461,\n 41.54404730359805\n ],\n [\n -87.52584457397461,\n 41.62339874820646\n ],\n [\n -87.6295280456543,\n 41.62339874820646\n ],\n [\n -87.6295280456543,\n 41.54404730359805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Light can modify orientation and locomotory behaviors in fish and has been applied to attract or repel migrant fish by inducing positive or negative phototaxis. Here, recently metamorphosed downstream‐migrating Sea Lamprey Petromyzon marinus were exposed to light cues in several orientations and intensities at night under controlled flowing‐water conditions in a laboratory flume. Behaviors and rates of downstream movement were monitored with overhead cameras and nets. When exposed to low‐intensity white light, 16–23% more Sea Lamprey were captured in a net closest to the light cue array compared to a dark control condition, suggesting some degree of positive phototaxis at low light levels (100 lx at a distance of 1 m from the light source). An interaction with the side of the flume (possibly due to varying flow conditions) and light treatment was also observed. At higher light intensities (1,000 lx at 1 m from the source), Sea Lamprey progressed downstream at a lower rate than was observed during dark conditions. After high‐intensity light treatments, fewer Sea Lamprey were observed in the nets at the downstream end of the flume and more Sea Lamprey were observed in the flume or in the release channel compared to dark control trials. Therefore, some photonegative behavior may be expressed at light levels of 1,000 lx or greater, perhaps as an attempt to avoid detection by predators by remaining stationary or seeking shelter. Light may have utility as a cue used for guidance devices to control Sea Lamprey, but further research is needed to define how light intensity and the environment (turbidity, depth, water velocity, and natural habitat features) influence locomotion, changes in swimming depth, and other behavioral responses of downstream‐migrating juvenile Sea Lamprey.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10261","usgsCitation":"Haro, A., Miehls, S.M., Johnson, N., and Wagner, C.M., 2020, Evaluation of visible light as a cue for guiding downstream migrant juvenile Sea Lamprey: Transactions of the American Fisheries Society, v. 149, no. 5, p. 635-647, https://doi.org/10.1002/tafs.10261.","productDescription":"13 p.","startPage":"635","endPage":"647","ipdsId":"IP-115484","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377824,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"149","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Haro, Alexander 0000-0002-7188-9172 aharo@usgs.gov","orcid":"https://orcid.org/0000-0002-7188-9172","contributorId":139198,"corporation":false,"usgs":true,"family":"Haro","given":"Alexander","email":"aharo@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":797217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":797218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":797219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wagner, C. Michael","contributorId":145442,"corporation":false,"usgs":false,"family":"Wagner","given":"C.","email":"","middleInitial":"Michael","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":797220,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212559,"text":"70212559 - 2020 - The influence of climate variability on the accuracy of NHD perennial and non-perennial stream classifications","interactions":[],"lastModifiedDate":"2020-10-12T17:20:59.347945","indexId":"70212559","displayToPublicDate":"2020-08-19T08:49:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"The influence of climate variability on the accuracy of NHD perennial and non-perennial stream classifications","docAbstract":"National Hydrography Dataset (NHD) stream permanence classifications (SPC; perennial, intermittent, and ephemeral) are widely used for data visualization and applied science, and have implications for resource policy and management. NHD SPC were assigned using a combination of topographic field surveys and interviews with local residents. However, previous studies indicate that non‐NHD, in situ streamflow observations (NNO) frequently disagree with NHD SPC. We hypothesized that differences in annual climate conditions between map creation years and the years NNO were collected contributed to disagreement between NNO and NHD SPC. We compared NHD SPC to 10,055 NNO (classified as “wet” or “dry”) collected in the Pacific Northwest between 1977 and 2015. Annual climate conditions were described with the Palmer Drought Severity Index (PDSI). Stream order was added as a covariate to account for different effects along the stream network. NHD SPC agreed with 80.5% of NNO. “Dry” NNO were five times more likely to disagree with NHD than “wet” NNO (p < 0.0001). Disagreement was greatest on first‐order streams. When NHD SPC were collected during a wetter period than NNO the probability of disagreement increased by a factor of 1.17 (p < 0.0001) per unit difference in PDSI. The influence of climate on disagreements between NNO and NHD SPC provides support for the continued development of dynamic models representing SPC as opposed to static NHD classifications.
Changes taking place across the Earth’s land surface have the potential to affect people, economies, and the environment on a daily basis. Our Nation’s economic security and environmental vitality rely on continuous monitoring of the Earth’s continents, islands, and coastal regions to record, study, and understand land change at local, regional, and global scales. The U.S. Geological Survey’s National Land Imaging Program helps meet this need by ensuring the continuous availability of moderate-resolution satellite imagery and other remotely sensed and geospatial data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203034","usgsCitation":"Young, S.M., 2020, National Land Imaging Program: U.S. Geological Survey Fact Sheet 2020–3034, 4 p., https://doi.org/10.3133/fs20203034.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":377618,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3034/fs20203034.pdf","text":"Report","size":"13.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3034"},{"id":377630,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3034/coverthb.jpg"}],"contact":"Land Remote Sensing Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Haskell Lake is a shallow, 89-acre drainage lake in the headwaters of the Squirrel River, on the Lac du Flambeau Reservation in northern Wisconsin. The lake has long been valued by the Lac du Flambeau Band of Lake Superior Chippewa Indians (LDF Tribe) for abundant wild rice and game fish. In recent decades, however, wild rice has mostly disappeared from the lake and the fishery has declined. A petroleum contamination plume discovered in the 1990s in the shallow aquifer upgradient from the northern end of the lake poses a threat to the ecological health of the lake and the aquifer, which is the sole drinking water source for nearby residents and businesses. Understanding of the lake’s hydrology is important to the LDF Tribe as they seek to restore wild rice and maintain the ecological health of the Haskell Lake/Tower Creek watershed. An improved understanding of lithology in the area of the contamination plume, documentation of a contamination pathway from groundwater in the plume source area to Haskell Lake, and an understanding of the plume extent beneath the lake are needed to advance remediation efforts. Evaluation of the fraction of groundwater discharge that is contaminated relative to the overall lake water budget is desired as a first step towards determining the extent of ecological effects from the plume.
A cooperative study between the U.S. Geological Survey and the LDF Tribe was initiated to quantify the lake water budget and the sources of water to the lake, to provide a rough estimate of the maximum quantity of groundwater discharge to the lake that may be contaminated, and to improve the conceptual understanding of the plume extent and subsurface materials in the area of contamination. The results of this study can help inform natural resource management of the Haskell Lake/Tower Creek watershed, including planned wild rice restoration and cleanup of the contaminant plume.
During 2016–17, field data on lake and groundwater levels, gradients, fluxes, and subsurface lithology were collected using a variety of techniques that ranged from basic measurement of water levels and streamflows to distributed temperature sensing, vertical temperature profiling, and several shallow geophysical methods. The data were used to inform a MODFLOW–NWT model that simulated the contributing groundwatershed, including the water budget for Haskell Lake and Tower Creek using the Lake, Streamflow-Routing, and Unsaturated Zone-Flow Packages. Particle tracking with the MODFLOW solution (using MODPATH 6) was used to improve understanding of the downgradient extent of the contamination plume, estimate groundwater flux through the plume area, and delineate the groundwater contributing area (groundwatershed) for the lake/creek system. Linear uncertainty estimates for model results were computed during model parameter estimation using the software package PEST++.
Results indicate groundwater discharge along the perimeter of Haskell Lake, with groundwater accounting for about 22 (± 11.5) percent of the lake water budget. Field data and particle tracking results indicate discharge of the entire contamination plume to Haskell Lake. Although the exact locations where contaminated groundwater enters the lake are unknown, the downgradient extent of the plume beneath Haskell Lake is likely limited to within about 700 feet from the shore. Groundwater flux through the plume accounts for at most about 1.4 percent of total groundwater discharge to Haskell Lake, or about 0.3 percent of the lake water budget. Most groundwater discharging to Haskell Lake and Tower Creek originates as terrestrial recharge. A lesser amount originates in or passes through neighboring lakes, including Buckskin, Crawling Stone, Broken Bow, Tippecanoe, and Jerms Lakes, as well as several unnamed kettles. The average age of simulated groundwater discharge to the lake is about 20 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205024","collaboration":"Prepared in cooperation with the Lac du Flambeau Band of Lake Superior Chippewa Indians","usgsCitation":"Leaf, A.T., and Haserodt, M.J., 2020, Hydrology of Haskell Lake and investigation of a groundwater contamination plume, Lac du Flambeau Reservation, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5024, 79 p., https://doi.org/10.3133/sir20205024.","productDescription":"Report: x, 70 p.; Appendices: 1.1-10.3; Data Release; Companion Report","numberOfPages":"92","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098814","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377617,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZQGGHY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW–NWT and MODPATH models, data from aquifer tests and temperature profilers, and groundwater flux estimates used to assess groundwater/surface-water interactions in Haskell Lake, Wisconsin"},{"id":377616,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table10.1_10.3.xlsx","text":"Appendix Tables 10.1 to 10.3","size":"19.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 10.1 to 10.3"},{"id":377615,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_9.1.xlsx","text":"Appendix Table 9.1","size":"12.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 9.1"},{"id":377614,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_8.1.xlsx","text":"Appendix Table 8.1","size":"17.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 8.1"},{"id":377611,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_5.1.xlsx","text":"Appendix Table 5.1","size":"12.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 5.1"},{"id":377607,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table1.1_1.12.xlsx","text":"Appendix Tables 1.1 to 1.12","size":"35.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 1.1 to 1.12"},{"id":377606,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20205005","text":"SIR 2020–5005","size":"3.67 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"— A distributed temperature sensing investigation of groundwater discharge to Haskell Lake, Lac du Flambeau Reservation, Wisconsin, July 27–August 1, 2016"},{"id":377610,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_4.1.xlsx","text":"Appendix Table 4.1","size":"10.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 4.1"},{"id":377608,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_2.1.xlsx","text":"Appendix Table 2.1","size":"12.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Table 2.1"},{"id":377609,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5024/sir20205024_appendix_table_3.1_3.6.xlsx","text":"Appendix Tables 3.1 to 3.6","size":"24.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5024 Appendix Tables 3.1 to 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8505 Research Way
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Long‐distance migration presents complex conservation challenges, and migratory species often experience shortfalls in conservation due to the difficulty of identifying important locations and resources throughout the annual cycle. In order to prioritize habitats for conservation of migratory wildlife, it is necessary to understand how habitat needs change throughout the annual cycle, as well as to identify key habitat sites and features that concentrate large numbers of individuals and species. Among long‐distance migrants, sea ducks have particularly complex migratory patterns, which often include distinct post‐breeding molt sites as well as breeding, staging and wintering locations. Using a large set of individual tracking data (n = 476 individuals) from five species of sea ducks in eastern North America, we evaluated multi‐species habitat suitability and partitioning across the breeding, post‐breeding migration and molt, wintering and pre‐breeding migration seasons. During breeding, species generally occupied distinct habitat areas, with the highest levels of multi‐species overlap occurring in the Barrenlands west of Hudson Bay. Species generally preferred flatter areas closer to lakes with lower maximum temperatures relative to average conditions, but varied in distance to shore, elevation and precipitation. During non‐breeding, species overlapped extensively during winter but diverged during migration. All species preferred shallow‐water, nearshore habitats with high productivity, but varied in their relationships to salinity, temperature and bottom slope. Sea ducks selected most strongly for preferred habitats during post‐breeding migration, with high partitioning among species; however, both selection and partitioning were weaker during pre‐breeding migration. The addition of tidal current velocity, aquatic vegetation presence and bottom substrate improved non‐breeding habitat models where available. Our results highlight the utility of multi‐species, annual‐cycle habitat assessments in identifying key habitat features and periods of vulnerability in order to optimize conservation strategies for migratory wildlife.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.05003","usgsCitation":"Lamb, J.S., Paton, P.W., Osenkowski, J.E., Badzinski, S.S., Berlin, A., Bowman, T.D., Dwyer, C., Fara, L., Gilliland, S.G., Kenow, K.P., Lepage, C., Mallory, M.L., Olsen, G., Perry, M., Petrie, S.A., Savard, J.L., Savoy, L., Schummer, M.L., Spiegel, C.S., and McWilliams, S.R., 2020, Assessing year‐round habitat use by migratory sea ducks in a multi‐species context reveals seasonal variation in habitat selection and partitioning: Ecography, v. 43, no. 12, p. 1842-1858, https://doi.org/10.1111/ecog.05003.","productDescription":"17 p.","startPage":"1842","endPage":"1858","onlineOnly":"Y","ipdsId":"IP-115137","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.05003","text":"Publisher Index 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Waterfowl","active":true,"usgs":false}],"preferred":false,"id":797141,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Savard, Jean-Pierre L.","contributorId":101776,"corporation":false,"usgs":false,"family":"Savard","given":"Jean-Pierre","email":"","middleInitial":"L.","affiliations":[{"id":6962,"text":"Science and Technology Branch, Environment Canada","active":true,"usgs":false}],"preferred":false,"id":797142,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Savoy, Lucas","contributorId":171896,"corporation":false,"usgs":false,"family":"Savoy","given":"Lucas","affiliations":[{"id":6928,"text":"BioDiversity Research Institute, Gorham, ME 04038","active":true,"usgs":false}],"preferred":false,"id":797143,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Schummer, Michael L.","contributorId":176347,"corporation":false,"usgs":false,"family":"Schummer","given":"Michael","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":797144,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Spiegel, Caleb S.","contributorId":216938,"corporation":false,"usgs":false,"family":"Spiegel","given":"Caleb","email":"","middleInitial":"S.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":797145,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"McWilliams, Scott R.","contributorId":172328,"corporation":false,"usgs":false,"family":"McWilliams","given":"Scott","email":"","middleInitial":"R.","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":797146,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70212606,"text":"70212606 - 2020 - Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates","interactions":[],"lastModifiedDate":"2020-08-24T13:27:00.53768","indexId":"70212606","displayToPublicDate":"2020-08-18T08:21:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Bioclimatic modeling of potential vegetation types as an alternative to species distribution models for projecting plant species shifts under changing climates","docAbstract":"Land managers need new tools for planning novel futures due to climate change. Species distribution modeling (SDM) has been used extensively to predict future distributions of species under different climates, but their map products are often too coarse for fine-scale operational use. In this study we developed a flexible, efficient, and robust method for mapping current and future distributions and abundances of vegetation species and communities at the fine spatial resolutions that are germane to land management. First, we mapped Potential Vegetation Types (PVTs) using conventional statistical modeling techniques (Random Forests) that used bioclimatic ecosystem process and climate variables as predictors. We obtained over 50% accuracy across 13 mapped PVTs for our study area. We then applied future climate projections as climate input to the Random Forest model to generate future PVT maps, and used field data describing the occurrence of tree and non-tree species in each PVT category to model and map species distribution for current and future climate. These maps were then compared to two previous SDM mapping efforts with over 80% agreement and equivalent accuracy. Because PVTs represent the biophysical potential of the landscape to support vegetation communities as opposed to the vegetation that currently exists, they can be readily linked to climate forecasts and correlated with other, climate-sensitive ecological processes significant in land management, such as fire regimes and site productivity.
The devastating impacts of the widespread flooding and landsliding in Puerto Rico following the September 2017 landfall of Hurricane Maria highlight the increasingly extreme atmospheric disturbances and enhanced hazard potential in mountainous humid‐tropical climate zones. Long‐standing conceptual models for hydrologically driven hazards in Puerto Rico posit that hillslope soils remain wet throughout the year, and therefore, that antecedent soil wetness imposes a negligible effect on hazard potential. Our post‐Maria in situ hillslope hydrologic observations, however, indicate that while some slopes remain wet throughout the year, others exhibit appreciable seasonal and intra‐storm subsurface drainage. Therefore, we evaluated the performance of hydro‐meteorological (soil wetness and rainfall) versus intensity‐duration (rainfall only) hillslope hydrologic response thresholds that identify the onset of positive pore‐water pressure, a predisposing factor for widespread slope instability in this region. Our analyses also consider the role of soil‐water storage and infiltration rates on runoff generation, which are relevant factors for flooding hazards. We found that the hydro‐meteorological thresholds outperformed intensity‐duration thresholds for a seasonally wet, coarse‐grained soil, although they did not outperform intensity‐duration thresholds for a perennially wet, fine‐grained soil. These end‐member soils types may also produce radically different stormflow responses, with subsurface flow being more common for the coarse‐grained soils underlain by intrusive rocks versus infiltration excess and/or saturation excess for the fine‐grained soils underlain by volcaniclastic rocks. We conclude that variability in soil‐hydraulic properties, as opposed to climate zone, is the dominant factor that controls runoff generation mechanisms and modulates the relative importance of antecedent soil wetness for our hillslope hydrologic response thresholds.
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Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":800661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Joel B. 0000-0001-7219-7875","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":242670,"corporation":false,"usgs":false,"family":"Smith","given":"Joel B.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":800662,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212542,"text":"70212542 - 2020 - Increasing threat of coastal groundwater hazards from sea-level rise in California","interactions":[],"lastModifiedDate":"2023-03-27T17:14:24.747405","indexId":"70212542","displayToPublicDate":"2020-08-17T10:18:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2841,"text":"Nature Climate Change","onlineIssn":"1758-6798","printIssn":"1758-678X","active":true,"publicationSubtype":{"id":10}},"title":"Increasing threat of coastal groundwater hazards from sea-level rise in California","docAbstract":"Projected sea-level rise will raise coastal water tables, resulting in groundwater hazards that threaten shallow infrastructure and coastal ecosystem resilience. Here we model a range of sea-level rise scenarios to assess the responses of water tables across the diverse topography and climates of the California coast. With 1 m of sea-level rise, areas flooded from below are predicted to expand ~50–130 m inland, and low-lying coastal communities such as those around San Francisco Bay are most at risk. Coastal topography is a controlling factor; long-term rising water tables will intercept low-elevation drainage features, allowing for groundwater discharge that damps the extent of shoaling in ~70% (68.9–82.2%) of California’s coastal water tables. Ignoring these topography-limited responses increases flooded-area forecasts by ~20% and substantially underestimates saltwater intrusion. All scenarios estimate that areas with shallow coastal water tables will shrink as they are inundated by overland flooding or are topographically limited from rising inland.
Two key knowledge gaps currently limit the development of more predictive and general models of pathogen transmission: (1) the physiological basis of heterogeneity in host contribution to pathogen transmission (reservoir potential) remains poorly understood, and (2) a general means of integrating the ecological dynamics of host communities has yet to emerge. If the traits responsible for differences in reservoir potential also modulate host community dynamics, these traits could be used to predict pathogen transmission as host communities change. In two greenhouse experiments, across 23 host species and two levels of resource supply, the reservoir potential of plant hosts increased significantly along the Leaf Economic Spectrum, a global axis of plant physiological trait covariation that features prominently in models of plant community ecology. This indicates that the traits of the Leaf Economic Spectrum underlie broad differences in reservoir potential across host species and resource supplies. Therefore, host traits could be used to integrate epidemiological models of pathogen transmission with ecological models of host community change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3164","usgsCitation":"Welsh, M.E., Cronin, J.P., and Mitchell, C.E., 2020, Trait‐based variation in host contribution to pathogen transmission across species and resource supplies: Ecology, v. 101, no. 11, e03164, 12 p., https://doi.org/10.1002/ecy.3164.","productDescription":"e03164, 12 p.","ipdsId":"IP-111122","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":377724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"101","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Welsh, Miranda E","contributorId":172466,"corporation":false,"usgs":false,"family":"Welsh","given":"Miranda","email":"","middleInitial":"E","affiliations":[{"id":27051,"text":"University of North Carolina at Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":796889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cronin, James P. 0000-0001-6791-5828 jcronin@usgs.gov","orcid":"https://orcid.org/0000-0001-6791-5828","contributorId":5834,"corporation":false,"usgs":true,"family":"Cronin","given":"James","email":"jcronin@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":796890,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Charles E.","contributorId":197317,"corporation":false,"usgs":false,"family":"Mitchell","given":"Charles","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":796891,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212589,"text":"70212589 - 2020 - A multi-state occupancy modelling framework for robust estimation of disease prevalence in multi-tissue disease systems","interactions":[],"lastModifiedDate":"2020-12-14T16:00:22.571788","indexId":"70212589","displayToPublicDate":"2020-08-16T09:01:14","publicationYear":"2020","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":"A multi-state occupancy modelling framework for robust estimation of disease prevalence in multi-tissue disease systems","docAbstract":"Watershed assessments have become common for prioritizing restoration in river networks. These assessments primarily focus on geomorphic conditions of rivers but less frequently incorporate non-geomorphic abiotic factors such as water chemistry and temperature, and biotic factors such as the structure of food webs. Using a dynamic food web model that integrates physical and ecological environmental conditions of rivers, we simulated how juvenile salmon (Oncorhynchus spp.) biomass responded to restoration at twelve sites distributed across the Methow River (Washington, USA), ranging from headwater tributaries to mainstem reaches. We explored responses to three common river restoration strategies: (1) physical habitat modification, (2) nutrient supplementation, and (3) increased riparian vegetation cover. We also simulated how different food web configurations that exist in salmon-bearing streams, such as the presence of ‘non-target’ fishes and ‘armored’ predation resistant invertebrates, could mediate restoration outcomes. Some locations in the river network experienced relatively large increases in modeled fish biomass with restoration, whereas other locations were almost entirely unresponsive. Spatial variation in restoration outcomes was primarily controlled by non-geomorphic environmental conditions, such as nutrient availability, water temperature, and stream canopy cover. Restoration responses also varied significantly with different food web configurations, suggesting that as the structure of food webs varies across river networks, so too could the outcome of restoration. These findings illustrate that ecological responses to restoration may exhibit substantial spatial variation within river networks, resulting from heterogeneity in environmental conditions that are commonly overlooked—but which can and should be considered—in restoration planning and prioritization.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fooweb.2020.e00160","usgsCitation":"Whitney, E.J., Bellmore, J.R., Benjamin, J.R., Jordan, C.E., Dunham, J.B., Newsom, M., and Nahorniak, M., 2020, Beyond sticks and stones: Integrating physical and ecological conditions into watershed restoration assessments using a food web modeling approach: Food Webs, v. 25, e00160, 16 p., https://doi.org/10.1016/j.fooweb.2020.e00160.","productDescription":"e00160, 16 p.","ipdsId":"IP-117798","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":455628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.fooweb.2020.e00160","text":"Publisher Index Page"},{"id":385077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Methow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.6685791015625,\n 48.59023420704331\n ],\n [\n -119.10415649414061,\n 48.59023420704331\n ],\n [\n -119.10415649414061,\n 48.99824008113872\n ],\n [\n -119.6685791015625,\n 48.99824008113872\n ],\n [\n -119.6685791015625,\n 48.59023420704331\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Whitney, Emily J","contributorId":257423,"corporation":false,"usgs":false,"family":"Whitney","given":"Emily","email":"","middleInitial":"J","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":814213,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellmore, James R 0000-0002-5140-6460","orcid":"https://orcid.org/0000-0002-5140-6460","contributorId":195609,"corporation":false,"usgs":false,"family":"Bellmore","given":"James","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":814214,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":814215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jordan, Chris E","contributorId":217592,"corporation":false,"usgs":false,"family":"Jordan","given":"Chris","email":"","middleInitial":"E","affiliations":[{"id":39677,"text":"National Marine Fisheries Service, National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":814216,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":814217,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newsom, Michael","contributorId":178562,"corporation":false,"usgs":false,"family":"Newsom","given":"Michael","affiliations":[],"preferred":false,"id":814218,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nahorniak, Matt","contributorId":257424,"corporation":false,"usgs":false,"family":"Nahorniak","given":"Matt","email":"","affiliations":[{"id":52015,"text":"South Fork Research","active":true,"usgs":false}],"preferred":false,"id":814219,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70248734,"text":"70248734 - 2020 - What to do when invaders are out of control?","interactions":[],"lastModifiedDate":"2023-09-19T11:47:44.296207","indexId":"70248734","displayToPublicDate":"2020-08-15T06:44:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5067,"text":"WIREs Water","active":true,"publicationSubtype":{"id":10}},"title":"What to do when invaders are out of control?","docAbstract":"Biological invasions threaten species and ecosystems worldwide. Impacts from invasions are especially prevalent in freshwaters, where managers have struggled to contain the problem. Conventional approaches to managing invaders focus on prevention and control. In practice, these measures have proven to be variably effective. Control or eradication of established invaders is particularly difficult and, even if ecologically feasible, it may not be socially desirable. Here we propose a new alternative to managing invasive species: managing impact modifiers (MIM). The MIM approach focuses on managing impacts, rather than controlling the invader directly. We reviewed the literature for the world's worst invasive fishes in freshwaters to show there is strong evidence to support the potential for MIM as an effective means of managing impacts of invasions. This included evidence pointing to characteristics of the environment or species themselves that modify impacts of invasions. Detail of three case studies reinforces the potential for MIM as a viable option. Although MIM appears promising, effective application could involve significant investment in an information gathering phase to identify impact modifiers and the means to manage them. Accordingly, MIM is best incorporated into management plans that include a strong learning or adaptive component. Ultimately, MIM may be one of the only viable alternatives for managing invasive species that are truly out of control.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1476","usgsCitation":"Dunham, J., Arismendi, I., Murphy, C., Koeberle, A., Olivos, J.A., Pearson, J.B., Pickens, F., Roon, D., and Stevenson, J.R., 2020, What to do when invaders are out of control?: WIREs Water, v. 7, no. 5, e1476, 13 p., https://doi.org/10.1002/wat2.1476.","productDescription":"e1476, 13 p.","ipdsId":"IP-115614","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":420940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-08-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":883365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arismendi, Ivan 0000-0002-8774-9350","orcid":"https://orcid.org/0000-0002-8774-9350","contributorId":202207,"corporation":false,"usgs":false,"family":"Arismendi","given":"Ivan","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":883366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Christina","contributorId":329814,"corporation":false,"usgs":false,"family":"Murphy","given":"Christina","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":883367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koeberle, Alex","contributorId":329815,"corporation":false,"usgs":false,"family":"Koeberle","given":"Alex","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":883368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olivos, J Andres","contributorId":329816,"corporation":false,"usgs":false,"family":"Olivos","given":"J","email":"","middleInitial":"Andres","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":883369,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearson, James B","contributorId":221480,"corporation":false,"usgs":false,"family":"Pearson","given":"James","email":"","middleInitial":"B","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":883370,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pickens, Francisco","contributorId":329817,"corporation":false,"usgs":false,"family":"Pickens","given":"Francisco","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":883371,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roon, David","contributorId":257063,"corporation":false,"usgs":false,"family":"Roon","given":"David","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":883372,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stevenson, John R.","contributorId":147936,"corporation":false,"usgs":false,"family":"Stevenson","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":883373,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70216784,"text":"70216784 - 2020 - Slender salamanders (genus Batrachoseps) reveal Southern California to be a center for the diversification, persistence, and introduction of salamander lineages","interactions":[],"lastModifiedDate":"2020-12-07T16:47:28.057194","indexId":"70216784","displayToPublicDate":"2020-08-14T10:37:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Slender salamanders (genus Batrachoseps) reveal Southern California to be a center for the diversification, persistence, and introduction of salamander lineages","title":"Slender salamanders (genus Batrachoseps) reveal Southern California to be a center for the diversification, persistence, and introduction of salamander lineages","docAbstract":"The southern California biodiversity hotspot has had a complex geological history, with both plate tectonic forces and sea level changes repeatedly reconfiguring the region, and likely driving both lineage splittings and extinctions. Here we investigate patterns of genetic divergence in two species of slender salamanders (Plethodontidae: Batrachoseps) in this region. The complex geological history in combination with several organismal traits led us to predict that these species harbor multiple ancient mitochondrial lineages endemic to southern California. These species belong to a clade characterized by fine-scale mitochondrial structure, which has been shown to track ancient splits. Both focal species, Batrachoseps major and B. nigriventris, are relatively widely distributed in southern California, and estimated to have persisted there across millions of years. Recently several extralimital populations of Batrachoseps were found in the San Joaquin Valley of California, a former desert area that has been extensively modified for agriculture. The origins of these populations are unknown, but based on morphology, they are hypothesized to result from human-mediated introductions of B. major.
We sequenced the mitochondrial gene cytochrome b from a geographically comprehensive sampling of the mitochondrial lineages of B. major and B. nigriventris that are endemic to southern California. We used phylogenetic analyses to characterize phylogeographic structure and identify mitochondrial contact zones. We also included the San Joaquin Valley samples to test whether they resulted from introductions. We used a bootstrap resampling approach to compare the strength of isolation-by-distance in both Batrachoseps species and four other salamander species with which they co-occur in southern California.
The northern lineage of B. major harbors at least eight deeply differentiated, geographically cohesive mitochondrial subclades. We identify geographic contact between many of these mtDNA lineages and some biogeographic features that are concordant with lineage boundaries. Batrachoseps nigriventris also has multiple deeply differentiated clades within the region. Comparative analyses highlight the smaller spatial scales over which mitochondrial divergence accumulates in Batrachoseps relative to most other salamander species in southern California. The extralimital populations of Batrachoseps from the San Joaquin Valley are assigned to B. major and are shown to result from at least two independent introductions from different source populations. We also suggest that B. major on Catalina Island, where it is considered native, may be the result of an introduction. Some of the same traits that facilitate the build-up of deep phylogeographic structure in Batrachoseps likely also contribute to its propensity for introductions, and we anticipate that additional introduced populations will be discovered.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.9599","usgsCitation":"Jockusch, E.L., Hansen, R.W., Fisher, R.N., and Wake, D., 2020, Slender salamanders (genus Batrachoseps) reveal Southern California to be a center for the diversification, persistence, and introduction of salamander lineages: PeerJ, v. 8, e9599, 37 p., https://doi.org/10.7717/peerj.9599.","productDescription":"e9599, 37 p.","ipdsId":"IP-119579","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455632,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.9599","text":"Publisher Index Page"},{"id":381041,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.09228515624999,\n 32.54681317351514\n ],\n [\n -115.411376953125,\n 32.731840896865684\n ],\n [\n -116.34521484375001,\n 34.23451236236987\n ],\n [\n -116.378173828125,\n 35.42486791930558\n ],\n [\n -118.377685546875,\n 36.94989178681327\n ],\n [\n -119.86083984375,\n 37.76202988573211\n ],\n [\n -121.08032226562499,\n 38.556757147352215\n ],\n [\n -122.618408203125,\n 39.436192999314095\n ],\n [\n -123.804931640625,\n 42.00848901572399\n ],\n [\n -124.23339843749999,\n 41.95949009892467\n ],\n [\n -124.112548828125,\n 41.40153558289846\n ],\n [\n -124.45312499999999,\n 40.38839687388361\n ],\n [\n -124.12353515624999,\n 40.002371935876475\n ],\n [\n -123.804931640625,\n 39.49556336059472\n ],\n [\n -123.958740234375,\n 39.30029918615029\n ],\n [\n -123.760986328125,\n 38.95940879245423\n ],\n [\n -122.80517578125,\n 37.91820111976663\n ],\n [\n -122.15698242187499,\n 36.96744946416934\n ],\n [\n -121.83837890625,\n 36.32397712011264\n ],\n [\n -121.17919921875001,\n 35.523285179107816\n ],\n [\n -120.7177734375,\n 35.0120020431607\n ],\n [\n -120.684814453125,\n 34.58799745550482\n ],\n [\n -120.4541015625,\n 33.815666308702774\n ],\n [\n -118.57543945312501,\n 32.54681317351514\n ],\n [\n -117.09228515624999,\n 32.54681317351514\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Jockusch, Elizabeth L","contributorId":245467,"corporation":false,"usgs":false,"family":"Jockusch","given":"Elizabeth","email":"","middleInitial":"L","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":806243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Robert W","contributorId":245468,"corporation":false,"usgs":false,"family":"Hansen","given":"Robert","email":"","middleInitial":"W","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":806244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":806245,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wake, David B","contributorId":245469,"corporation":false,"usgs":false,"family":"Wake","given":"David B","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":806246,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212504,"text":"70212504 - 2020 - Capturing spatiotemporal patterns in presence-absence data to inform monitoring and sampling designs for the threatened Dakota skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States","interactions":[],"lastModifiedDate":"2020-10-28T15:47:42.467946","indexId":"70212504","displayToPublicDate":"2020-08-14T08:56:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1536,"text":"Environmental Entomology","active":true,"publicationSubtype":{"id":10}},"title":"Capturing spatiotemporal patterns in presence-absence data to inform monitoring and sampling designs for the threatened Dakota skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States","docAbstract":"Declines among species of insect pollinators, especially butterflies, has garnered attention from scientists and managers. Often these declines have spurred governments to declare some species as threatened or endangered. We used existing presence–absence data from surveys for the threatened Dakota skipper Hesperia dacotae (Skinner) to build statistical maps of species presence that could be used to inform future monitoring designs. We developed a hierarchical Bayesian modeling approach to estimate the spatial distribution and temporal trend in Dakota skipper probability of presence. Our model included a spatial random effect and fixed effects for the proportion of two grassland habitat types: those on well-drained soils and those on poorly drained soils; as well as the topographic slope. The results from this model were then used to assess sampling strategies with two different monitoring objectives: locating new Dakota skipper colonies or monitoring the proportion of historically (pre-2000) extant colonies. Our modeling results suggested that the distribution of Dakota skippers followed the distribution of remnant grasslands and that probabilities of presence tended to be higher in topographically diverse grasslands with well-drained soils. Our analysis also showed that the probability of presence declined throughout the northern Great Plains range. Our simulations of the different sampling designs suggested that new detections were expected when sampling where Dakota skippers likely occurred historically, but this may lead to a tradeoff with monitoring existing sites. Prior information about the extant sites may help to ameliorate this tradeoff.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ee/nvaa081","usgsCitation":"Post van der Burg, M., Austin, J.E., Wiltermuth, M.T., Newton, W.E., and MacDonald, G.J., 2020, Capturing spatiotemporal patterns in presence-absence data to inform monitoring and sampling designs for the threatened Dakota skipper (Lepidoptera: Hesperiidae) in the Great Plains of the United States: Environmental Entomology, v. 49, no. 5, p. 1252-1261, https://doi.org/10.1093/ee/nvaa081.","productDescription":"10 p.","startPage":"1252","endPage":"1261","ipdsId":"IP-113665","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455635,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ee/nvaa081","text":"Publisher Index Page"},{"id":377598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.72412109375,\n 42.68243539838623\n ],\n [\n -91.91162109375,\n 42.89206418807337\n ],\n [\n -91.318359375,\n 43.30919109985686\n ],\n [\n -91.51611328125,\n 43.83452678223682\n ],\n [\n -92.900390625,\n 44.85586880735725\n ],\n [\n -93.0322265625,\n 45.78284835197676\n ],\n [\n -94.68017578125,\n 48.647427805533546\n ],\n [\n -95.77880859375,\n 48.951366470947725\n ],\n [\n -103.90869140625,\n 48.951366470947725\n ],\n [\n -100.39306640625,\n 43.35713822211053\n ],\n [\n -94.72412109375,\n 42.68243539838623\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Post van der Burg, Max 0000-0002-3943-4194 maxpostvanderburg@usgs.gov","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":4947,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","email":"maxpostvanderburg@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Austin, Jane E. 0000-0001-8775-2210 jaustin@usgs.gov","orcid":"https://orcid.org/0000-0001-8775-2210","contributorId":146411,"corporation":false,"usgs":true,"family":"Austin","given":"Jane","email":"jaustin@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Newton, Wesley E. 0000-0002-1377-043X wnewton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-043X","contributorId":3661,"corporation":false,"usgs":true,"family":"Newton","given":"Wesley","email":"wnewton@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"MacDonald, Garrett J. 0000-0002-9487-7721","orcid":"https://orcid.org/0000-0002-9487-7721","contributorId":238820,"corporation":false,"usgs":true,"family":"MacDonald","given":"Garrett","email":"","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":796627,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212556,"text":"70212556 - 2020 - Quantifying ecospace utilization and ecosystemengineering during the early Phanerozoic—The role of bioturbation and bioerosion","interactions":[],"lastModifiedDate":"2020-08-21T12:36:32.034129","indexId":"70212556","displayToPublicDate":"2020-08-14T08:33:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying ecospace utilization and ecosystemengineering during the early Phanerozoic—The role of bioturbation and bioerosion","docAbstract":"The Cambrian explosion (CE) and the great Ordovician biodiversification event (GOBE) are the two most important radiations in Paleozoic oceans. We quantify the role of bioturbation and bioerosion in ecospace utilization and ecosystem engineering using information from 1367 stratigraphic units. An increase in all diversity metrics is demonstrated for the Ediacaran-Cambrian transition, followed by a decrease in most values during the middle to late Cambrian, and by a more modest increase during the Ordovician. A marked increase in ichnodiversity and ichnodisparity of bioturbation is shown during the CE and of bioerosion during the GOBE. Innovations took place first in offshore settings and later expanded into marginal-marine, nearshore, deep-water, and carbonate environments. This study highlights the importance of the CE, despite its Ediacaran roots. Differences in infaunalization in offshore and shelf paleoenvironments favor the hypothesis of early Cambrian wedge-shaped oxygen minimum zones instead of a horizontally stratified ocean.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abb0618","usgsCitation":"Buatois, L.A., Mangano, M.G., Minter, N.J., Zhou, K., Wisshak, M., Wilson, M.A., and Olea, R., 2020, Quantifying ecospace utilization and ecosystemengineering during the early Phanerozoic—The role of bioturbation and bioerosion: Science Advances, v. 6, no. 33, eabb0618, 12 p., https://doi.org/10.1126/sciadv.abb0618.","productDescription":"eabb0618, 12 p.","onlineOnly":"Y","ipdsId":"IP-117225","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":455638,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.abb0618","text":"Publisher Index Page"},{"id":377682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"33","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buatois, Luis A. 0000-0001-9523-750X","orcid":"https://orcid.org/0000-0001-9523-750X","contributorId":195823,"corporation":false,"usgs":false,"family":"Buatois","given":"Luis","email":"","middleInitial":"A.","affiliations":[{"id":35641,"text":"Kansas Geological Survey","active":true,"usgs":false}],"preferred":false,"id":796849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mangano, M. Gabriela 0000-0001-8747-6033","orcid":"https://orcid.org/0000-0001-8747-6033","contributorId":238882,"corporation":false,"usgs":false,"family":"Mangano","given":"M.","email":"","middleInitial":"Gabriela","affiliations":[{"id":13248,"text":"University of Saskatchewan","active":true,"usgs":false}],"preferred":false,"id":796850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minter, Nicholas J 0000-0002-4246-8539","orcid":"https://orcid.org/0000-0002-4246-8539","contributorId":238883,"corporation":false,"usgs":false,"family":"Minter","given":"Nicholas","email":"","middleInitial":"J","affiliations":[{"id":38839,"text":"University of Portsmouth","active":true,"usgs":false}],"preferred":false,"id":796851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhou, Kai","contributorId":238884,"corporation":false,"usgs":false,"family":"Zhou","given":"Kai","email":"","affiliations":[{"id":13248,"text":"University of Saskatchewan","active":true,"usgs":false}],"preferred":false,"id":796852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wisshak, Max 0000-0001-7531-3317","orcid":"https://orcid.org/0000-0001-7531-3317","contributorId":238885,"corporation":false,"usgs":false,"family":"Wisshak","given":"Max","email":"","affiliations":[{"id":47815,"text":"Senckenberg am Meer: Wilhelmshaven, Niedersachsen, DE","active":true,"usgs":false}],"preferred":false,"id":796853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wilson, Mark A. 0000-0002-4651-0589","orcid":"https://orcid.org/0000-0002-4651-0589","contributorId":208038,"corporation":false,"usgs":false,"family":"Wilson","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":37683,"text":"College of Wooster, OH","active":true,"usgs":false}],"preferred":false,"id":796854,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Olea, Ricardo A. 0000-0003-4308-0808","orcid":"https://orcid.org/0000-0003-4308-0808","contributorId":224285,"corporation":false,"usgs":true,"family":"Olea","given":"Ricardo A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":796855,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70214473,"text":"70214473 - 2020 - Biological effects of hydrocarbon degradation intermediates: Is the total petroleum hydrocarbon analytical method adequate for risk assessment?","interactions":[],"lastModifiedDate":"2020-09-28T12:57:15.308453","indexId":"70214473","displayToPublicDate":"2020-08-13T07:42:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Biological effects of hydrocarbon degradation intermediates: Is the total petroleum hydrocarbon analytical method adequate for risk assessment?","docAbstract":"In crude oil contaminant plumes, the dissolved organic carbon (DOC) is mainly hydrocarbon degradation intermediates only partly quantified by the diesel range total petroleum hydrocarbon (TPHd) method. To understand potential biological effects of degradation intermediates, we tested three fractions of DOC: (1) solid-phase extract (HLB); (2) dichloromethane (DCM-total) extract used in TPHd; and (3) DCM extract with hydrocarbons isolated by silica gel cleanup (DCM-SGC). Bioactivity of extracts from five wells spanning a range of DOC was tested using an in vitro multiplex reporter system that evaluates modulation of the activity of 46 transcription factors; extracts were evaluated at concentrations equivalent to the well water samples. The aryl hydrocarbon receptor (AhR) and pregnane X receptor (PXR) transcription factors showed the greatest upregulation, with HLB exceeding DCM-total, and no upregulation in the hydrocarbon fraction (DCM-SGC). The HLB extracts were further studied with HepG2 chemically activated luciferase expression (CALUX) in vitro assays at nine concentrations ranging from 40 to 0.01 times the well water concentrations. Responses decreased with distance from the source but were still present at two wells without detectable hydrocarbons. Thus, our in vitro assay results indicate that risks associated with degradation intermediates of hydrocarbons in groundwater will be underestimated when protocols that remove these chemicals are employed.
Constraining the subsurface structural geometry of the central Himalaya continues to prove difficult, even after the 2015 Gorkha earthquake and the resulting insights into the trajectory of the Main Himalayan thrust (MHT). To this end, we apply a thermokinematic model to evaluate four possible balanced cross section geometries based on three estimates of the MHT in central Nepal. We compare the effect of different décollement and duplex geometries on predicted cooling ages and compare these to new and published ages. We find that the best‐fit geometry able to reproduce the cooling ages at the surface is a hinterland‐dipping duplex, which has been translated over a mid‐crustal ramp located ~110 km north of the Main Frontal thrust. We find that the temporal evolution of the duplex and MHT mid‐crustal ramp both play an integral role in producing the observed cooling ages, implying that the common assumption that the active décollement and ramp geometry solely control the distribution of cooling ages is incorrect. Furthermore, results indicate that the Ramgarh‐Munsiari thrust was emplaced between 17 and ~10 Ma, followed by the Trishuli thrust. Duplex growth occurs between 6.5 Ma and 0.75 Ma, with its constituent thrust sheets moving at variable rates between 10 – 42 mm/yr. Young out‐of‐sequence thrusting (5 km of displacement) in the hinterland produces a slightly improved fit to the cooling ages. Finally, the resulting thermal field modeled from our best‐fit geometry suggests a possible basis for the nucleation and rupture characteristics of the Gorkha earthquake.
","language":"English","publisher":"Wiley","doi":"10.1029/2020TC006399","usgsCitation":"Ghoshal, S., McQuarrie, N., Robinson, D., Adhikari, D., Morgan, L.E., and Ehlers, T.A., 2020, Constraining central Himalayan (Nepal) fault geometry through integrated thermochronology and thermokinematic modeling: Tectonics, v. 39, no. 9, e2020TC006399, 33 p., https://doi.org/10.1029/2020TC006399.","productDescription":"e2020TC006399, 33 p.","ipdsId":"IP-106082","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":436820,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FA0HI0","text":"USGS data release","linkHelpText":"Argon data for Nepal"},{"id":378320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","otherGeospatial":"Himalayan Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 88.08837890625,\n 27.839076094777816\n ],\n [\n 86.627197265625,\n 28.033197847676377\n ],\n [\n 84.737548828125,\n 28.69058765425071\n ],\n [\n 81.815185546875,\n 30.477082932837682\n ],\n [\n 81.353759765625,\n 30.306503259848835\n ],\n [\n 81.353759765625,\n 28.613459424004414\n ],\n [\n 83.902587890625,\n 27.010196431931526\n ],\n [\n 87.1875,\n 26.10612083235552\n ],\n [\n 89.033203125,\n 26.204734267107604\n ],\n [\n 88.79150390625,\n 27.303451991034542\n ],\n [\n 88.736572265625,\n 27.994401411046148\n ],\n [\n 88.08837890625,\n 27.839076094777816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Ghoshal, Surydoy","contributorId":237864,"corporation":false,"usgs":false,"family":"Ghoshal","given":"Surydoy","email":"","affiliations":[{"id":47628,"text":"Department of Geology and Environmental Sciences, University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":795489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McQuarrie, Nadine","contributorId":193432,"corporation":false,"usgs":false,"family":"McQuarrie","given":"Nadine","email":"","affiliations":[],"preferred":false,"id":795490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robinson, Delores","contributorId":237866,"corporation":false,"usgs":false,"family":"Robinson","given":"Delores","affiliations":[{"id":47629,"text":"University of Alabama, Tuscaloosa","active":true,"usgs":false}],"preferred":false,"id":795491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Adhikari, D.P.","contributorId":237868,"corporation":false,"usgs":false,"family":"Adhikari","given":"D.P.","email":"","affiliations":[{"id":16728,"text":"Tribhuvan University","active":true,"usgs":false}],"preferred":false,"id":795492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morgan, Leah E. 0000-0001-9930-524X lemorgan@usgs.gov","orcid":"https://orcid.org/0000-0001-9930-524X","contributorId":176174,"corporation":false,"usgs":true,"family":"Morgan","given":"Leah","email":"lemorgan@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":795493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ehlers, Todd A.","contributorId":206718,"corporation":false,"usgs":false,"family":"Ehlers","given":"Todd","email":"","middleInitial":"A.","affiliations":[{"id":37382,"text":"University of Tübingen","active":true,"usgs":false}],"preferred":false,"id":795494,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211862,"text":"sir20205060 - 2020 - Flood-inundation maps for Dardenne Creek in St. Charles County, Missouri, 2019","interactions":[],"lastModifiedDate":"2020-08-12T23:31:17.152064","indexId":"sir20205060","displayToPublicDate":"2020-08-12T14:12:35","publicationYear":"2020","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":"2020-5060","displayTitle":"Flood-Inundation Maps for Dardenne Creek in St. Charles County, Missouri, 2019","title":"Flood-inundation maps for Dardenne Creek in St. Charles County, Missouri, 2019","docAbstract":"Digital flood-inundation maps for a 9.9-mile reach of Dardenne Creek, St. Charles County, Missouri, were created by the U.S. Geological Survey (USGS), in cooperation with the Missouri Department of Transportation, St. Charles County, and the Cities of O’Fallon and St. Peters, Mo. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages 05514860 Dardenne Creek at Old Town St. Peters, Mo., and 05587450 Mississippi River at Grafton, Illinois. Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System at https://doi.org/10.5066/F7P55KJN or the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ ahps2/ hydrograph.php? wfo= lsx&gage= drcm7 and https://water.weather.gov/ ahps2/ hydrograph.php? wfo= lsx&gage= grfi2, which also forecasts flood hydrographs at these sites (sites DRCM7 and GRFI2).
Flood profiles were computed for the Dardenne Creek stream reach by means of a one-dimensional model for simulating water-surface profiles with steady-state flow computations. The model was calibrated by using the current stage-streamflow relation at the USGS streamgages 05514840 Dardenne Creek at O’Fallon, Mo., and 05514860 Dardenne Creek at Old Town St. Peters, Mo., and the documented high-water marks from the flood of December 2015.
The hydraulic model was then used to compute 17 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from 16 ft, or near bankfull, to 32 ft at the reference streamgage 05514860. Stages in the lower Dardenne Creek can be affected by backwater from the Mississippi River; therefore, several sets of water-surface profiles were developed representing the extent of varying levels of backwater as referenced to the USGS streamgage 05587450 on the Mississippi River at Grafton, Ill. The upper stage for each map library exceeds the stage corresponding to the estimated 0.2-percent annual exceedance probability flood (500-year recurrence interval flood) at the streamgage location. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging data having a 0.26-ft vertical accuracy and 0.71-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with real-time information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood mitigation, preparedness and planning, flood-response activities such as evacuations and road closures, and postflood recovery efforts.
Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
While large-scale oil spills can cause acute mortality events in birds, there is increasing evidence that sublethal oil exposure can trigger physiological changes that have implications for individual performance and survival. Therefore, improved methods for identifying small amounts of oil on birds are needed. Because ultraviolet (UV) light can be used to identify thin crude oil films in water and on substrate that are not visually apparent under normal lighting conditions, we hypothesized that UV light could be useful for detecting small amounts of oil present on the plumage of birds. We evaluated black skimmers (Rynchops niger), brown pelicans (Pelecanus occidentalis), clapper rails (Rallus crepitans), great egrets (Ardea alba), and seaside sparrows (Ammodramus maritimus) exposed to areas affected by the Deepwater Horizon oil spill in the Gulf of Mexico as well as from reference areas from 20 June, 2010 to 23 February, 2011. When visually assessed without UV light, 19.6% of birds evaluated from areas affected by the spill were determined to be oiled (previously published data), whereas when examined under UV light, 56.3% of the same birds were determined to have oil exposure. Of 705 individuals examined in areas potentially impacted by the spill, we found that fluorescence under UV light assessment identified 259 oiled birds that appeared to be oil-free on visual exam, supporting its utility as a simple tool for improving detection of modestly oiled birds in the field. Further, UV assessment revealed an increase in qualitative severity of oiling (approximate % of body surface oiled) in 40% of birds compared to what was determined on visual exam. Additionally, black skimmers, brown pelicans, and great egrets exposed to oil as determined using UV light experienced oxidative injury to erythrocytes, had decreased numbers of circulating erythrocytes, and showed evidence of a regenerative hematological response in the form of increased reticulocytes. This evidence of adverse effects was similar to changes identified in birds with oil exposure as determined by visual examination without UV light, and is consistent with hemolytic anemia likely caused by oil exposure. Thus, UV assessment proved useful for enhancing detection of birds exposed to oil, but did not increase detection of birds experiencing clinical signs of anemia compared to standard visual oiling assessment. We conclude that UV light evaluation can help identify oil exposure in many birds that would otherwise be identified visually as unexposed during oil spill events.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-020-02255-8","usgsCitation":"Fallon, J.A., Smith, E.P., Shoch, N., Paruk, J., Adams, E., Evers, D., Jodice, P.G., Perkins, M., Meatty, D.E., and Hopkins, W., 2020, Ultraviolet-assisted oiling assessment improves detection of oiled birds experiencing clinical signs of hemolytic anemia after exposure to the Deepwater Horizon oil spill: Ecotoxicology, v. 29, p. 1399-1408, https://doi.org/10.1007/s10646-020-02255-8.","productDescription":"10 p.","startPage":"1399","endPage":"1408","ipdsId":"IP-107889","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467280,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s10646-020-02255-8","text":"External Repository"},{"id":393957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.2509765625,\n 26.15543796871355\n ],\n [\n -82.705078125,\n 26.15543796871355\n ],\n [\n -82.705078125,\n 30.600093873550072\n ],\n [\n -97.2509765625,\n 30.600093873550072\n ],\n [\n -97.2509765625,\n 26.15543796871355\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2020-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Fallon, J. A.","contributorId":270956,"corporation":false,"usgs":false,"family":"Fallon","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, E. P.","contributorId":270957,"corporation":false,"usgs":false,"family":"Smith","given":"E.","email":"","middleInitial":"P.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830210,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shoch, N.","contributorId":270958,"corporation":false,"usgs":false,"family":"Shoch","given":"N.","email":"","affiliations":[{"id":56232,"text":"Adirondack Center for Loon Conservation","active":true,"usgs":false}],"preferred":false,"id":830211,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paruk, J. D.","contributorId":270959,"corporation":false,"usgs":false,"family":"Paruk","given":"J. D.","affiliations":[{"id":56233,"text":"Saint Joseph's College of Maine","active":true,"usgs":false}],"preferred":false,"id":830212,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, E. A.","contributorId":270960,"corporation":false,"usgs":false,"family":"Adams","given":"E. A.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830213,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Evers, D. C.","contributorId":270961,"corporation":false,"usgs":false,"family":"Evers","given":"D. C.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830214,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":830215,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Perkins, M.","contributorId":270962,"corporation":false,"usgs":false,"family":"Perkins","given":"M.","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":830216,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Meatty, D. E.","contributorId":270963,"corporation":false,"usgs":false,"family":"Meatty","given":"D.","email":"","middleInitial":"E.","affiliations":[{"id":37436,"text":"Biodiversity Research Institute","active":true,"usgs":false}],"preferred":false,"id":830217,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hopkins, W. A.","contributorId":270964,"corporation":false,"usgs":false,"family":"Hopkins","given":"W. A.","affiliations":[{"id":56231,"text":"Virginia Polytechnic University","active":true,"usgs":false}],"preferred":false,"id":830218,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70215193,"text":"70215193 - 2020 - Assessing the potential for spectrally based remote sensing of salmon spawning locations","interactions":[],"lastModifiedDate":"2020-10-10T13:13:47.748404","indexId":"70215193","displayToPublicDate":"2020-08-12T08:10:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the potential for spectrally based remote sensing of salmon spawning locations","docAbstract":"Remote sensing tools are increasingly used for quantitative mapping of fluvial habitats, yet few techniques exist for continuous sampling of aquatic organisms, such as spawning salmonids. This study assessed the potential for spectrally based remote sensing of salmon spawning locations (i.e., redds) using data acquired from unmanned aircraft systems (UAS) along a large, gravel‐bed river. We developed a novel, semi‐automated approach for detecting salmon redds by applying machine learning classification and object detection techniques to UAS‐based imagery. We found that both true colour (RGB) and hyperspectral imagery could be used to identify salmon redds, though with varying degrees of accuracy. Redds were mapped with accuracies of ~0.75 from RGB imagery using logistic regression and support vector machines (SVM) classification algorithms, but this type of data could not be used to identify redds using Object‐based Image Analysis (OBIA). The hyperspectral imagery was more useful for mapping salmon redds, with accuracies greater than 0.9 for both logistic regression and SVM classifiers; OBIA of the hyperspectral data resulted in redd detection accuracies up to 0.86. The hyperspectral imagery also yielded complementary physical habitat information including water depth and substrate composition, which we quantified on the basis of a spectrally based chlorophyll absorption ratio. Overall, the hyperspectral imagery more effectively identified salmon spawning locations than RGB images and was more conducive to the classification approaches we evaluated. Each type of remotely sensed data had advantages and limitations, which are important for potential users to understand when incorporating UAS‐based data collection into river ecosystem studies.
We describe the baseline coupled model configuration and simulation characteristics of GFDL's Earth System Model Version 4.1 (ESM4.1), which builds on component and coupled model developments at GFDL over 2013–2018 for coupled carbon-chemistry-climate simulation contributing to the sixth phase of the Coupled Model Intercomparison Project. In contrast with GFDL's CM4.0 development effort that focuses on ocean resolution for physical climate, ESM4.1 focuses on comprehensiveness of Earth system interactions. ESM4.1 features doubled horizontal resolution of both atmosphere (2° to 1°) and ocean (1° to 0.5°) relative to GFDL's previous-generation coupled ESM2-carbon and CM3-chemistry models. ESM4.1 brings together key representational advances in CM4.0 dynamics and physics along with those in aerosols and their precursor emissions, land ecosystem vegetation and canopy competition, and multiday fire; ocean ecological and biogeochemical interactions, comprehensive land-atmosphere-ocean cycling of CO2, dust and iron, and interactive ocean-atmosphere nitrogen cycling are described in detail across this volume of JAMES and presented here in terms of the overall coupling and resulting fidelity. ESM4.1 provides much improved fidelity in CO2 and chemistry over ESM2 and CM3, captures most of CM4.0's baseline simulations characteristics, and notably improves on CM4.0 in (1) Southern Ocean mode and intermediate water ventilation, (2) Southern Ocean aerosols, and (3) reduced spurious ocean heat uptake. ESM4.1 has reduced transient and equilibrium climate sensitivity compared to CM4.0. Fidelity concerns include (1) moderate degradation in sea surface temperature biases, (2) degradation in aerosols in some regions, and (3) strong centennial scale climate modulation by Southern Ocean convection.