Rangelands have immense inherent spatial and temporal variability, yet land condition and trends are often assessed at a limited number of spatially “representative” points. Spatially comprehensive, and quantitative, Ecological Potential (EP) data provide a baseline for comparison to current rangeland vegetation conditions and trends. Here, we define EP as potential fractional cover (bare ground, herbaceous, litter, shrub, and sagebrush) represented in the least disturbed areas and most productive years of the Landsat satellite archive (1985-present) for each 30-m pixel. We produce EP maps across rangelands in the western United States by training regression tree models using Rangeland Condition Monitoring Assessment and Projection (RCMAP) time-series fractional cover maps in ecologically intact sites (with limited annual herbaceous cover, no recent disturbance or vegetation treatment, and less bare ground cover than expected). As independent predictor variables in these models, we use digital soils and topography data and six bimonthly composites of the 90th percentile of Normalized Difference Vegetation Index (NDVI) and associated spectral bands from the 1985–2020 Landsat archive. EP predictions were successful in capturing biophysical gradients present in the independent variables and depicting potential cover in the absence of disturbance; we found no influence of fires or land treatments in the data. Next, we compared EP to contemporary (2018) cover, to create departure maps that can be used as a screening tool indicating degradation and providing an early warning of vegetation state change. Finally, we used a dichotomous key to convert the 1985 and 2018 RCMAP cover and EP cover into vegetation states important to land management decisions (invaded sagebrush steppe, annual grasslands, etc.). We found that in 1985, 21.2% of the study area had a different vegetation state than EP, and this percentage increased to 24.2% by 2018. More than 50% of the EP native sagebrush steppe was converted to an annual grassland, perennial grassland, or non-sagebrush shrub by 2018, and an additional 7% was classified as invaded sagebrush steppe, at risk of transition to another state. EP products provide a spatio-temporal reference of vegetation conditions from the last three decades across rangelands in the western United States. Use of the EP reference can improve adaptive management practice by providing monitoring and control data, which are often lacking, and assist in differentiating treatment effect from confounding factors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108447","usgsCitation":"Rigge, M.B., Meyer, D., and Bunde, B., 2021, Ecological potential fractional component cover based on Long-Term satellite observations across the western United States: Ecological Indicators, v. 133, 108447, 14 p., https://doi.org/10.1016/j.ecolind.2021.108447.","productDescription":"108447, 14 p.","ipdsId":"IP-129599","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":450064,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108447","text":"Publisher Index Page"},{"id":396994,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.755859375,\n 27.839076094777816\n ],\n [\n -103.271484375,\n 33.65120829920497\n ],\n [\n -98.96484375,\n 36.87962060502676\n ],\n [\n -104.853515625,\n 40.17887331434696\n ],\n [\n -105.1171875,\n 41.902277040963696\n ],\n [\n -102.216796875,\n 43.70759350405294\n ],\n [\n -102.3046875,\n 49.03786794532644\n ],\n [\n -122.6953125,\n 49.210420445650286\n ],\n [\n -123.57421875,\n 48.16608541901253\n ],\n [\n -125.595703125,\n 48.22467264956519\n ],\n [\n -124.365234375,\n 44.213709909702054\n ],\n [\n -124.8046875,\n 39.90973623453719\n ],\n [\n -120.58593749999999,\n 34.016241889667015\n ],\n [\n -118.21289062499999,\n 32.84267363195431\n ],\n [\n -116.71874999999999,\n 32.84267363195431\n ],\n [\n -115.09277343749999,\n 32.713355353177555\n ],\n [\n -114.67529296874999,\n 32.76880048488168\n ],\n [\n -114.85107421875,\n 32.52828936482526\n ],\n [\n -111.02783203125,\n 31.297327991404266\n ],\n [\n -108.17138671875,\n 31.353636941500987\n ],\n [\n -108.17138671875,\n 31.765537409484374\n ],\n [\n -106.3916015625,\n 31.74685416292141\n ],\n [\n -104.67773437499999,\n 30.183121842195515\n ],\n [\n -103.11767578124999,\n 28.94086176940557\n ],\n [\n -102.45849609375,\n 29.76437737516313\n ],\n [\n -101.53564453124999,\n 29.76437737516313\n ],\n [\n -99.73388671874999,\n 27.72243591897343\n ],\n [\n -99.755859375,\n 27.839076094777816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":837781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyer, Deb 0000-0002-8841-697X","orcid":"https://orcid.org/0000-0002-8841-697X","contributorId":288363,"corporation":false,"usgs":false,"family":"Meyer","given":"Deb","affiliations":[{"id":61730,"text":"Retired, KBR","active":true,"usgs":false}],"preferred":false,"id":837782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunde, Brett 0000-0003-0228-779X","orcid":"https://orcid.org/0000-0003-0228-779X","contributorId":288364,"corporation":false,"usgs":false,"family":"Bunde","given":"Brett","affiliations":[{"id":61731,"text":"KBR","active":true,"usgs":false}],"preferred":false,"id":837783,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231634,"text":"70231634 - 2021 - Changing impacts of Alaska-Aleutian subduction zone tsunamis in California under future sea-level rise","interactions":[],"lastModifiedDate":"2022-05-17T12:12:16.00765","indexId":"70231634","displayToPublicDate":"2021-12-08T07:05:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2840,"text":"Nature","active":true,"publicationSubtype":{"id":10}},"title":"Changing impacts of Alaska-Aleutian subduction zone tsunamis in California under future sea-level rise","docAbstract":"The amplification of coastal hazards such as distant-source tsunamis under future relative sea-level rise (RSLR) is poorly constrained. In southern California, the Alaska-Aleutian subduction zone has been identified as an earthquake source region of particular concern for a worst-case scenario distant-source tsunami. Here, we explore how RSLR over the next century will influence future maximum nearshore tsunami heights (MNTH) at the Ports of Los Angeles and Long Beach. Earthquake and tsunami modeling combined with local probabilistic RSLR projections show the increased potential for more frequent, relatively low magnitude earthquakes to produce distant-source tsunamis that exceed historically observed MNTH. By 2100, under RSLR projections for a high-emissions representative concentration pathway (RCP8.5), the earthquake magnitude required to produce >1 m MNTH falls from ~Mw9.1 (required today) to Mw8.0, a magnitude that is ~6.7 times more frequent along the Alaska-Aleutian subduction zone.
A targeted commercial fishery for Atlantic Sturgeon Acipenser oxyrinchus oxyrinchus once operated in the New York Bight, where it was assumed that most harvested Atlantic Sturgeon were natal to the Hudson River population. However, more recent evidence suggests that the fishery may have been targeting a mixed-stock aggregation, in which case harvested Atlantic Sturgeon could have been comprised of individuals from multiple populations throughout the species’ range. Although there is now a moratorium on Atlantic Sturgeon harvest in the New York Bight, modern molecular approaches provide an opportunity to use archived tissues to perform a retrospective mixed-stock analysis on the fishery. Genomic DNA extracted from archived fin spines from 80 Atlantic Sturgeon collected nearly 30 years ago suggests that the fishery primarily harvested individuals from the Hudson River population. However, based on individual-based assignment tests, our results indicate that the fishery also harvested individuals from at least eight other populations located throughout the species’ range. This study highlights how archival hard parts that were previously used for age and growth analyses can be employed for retrospective genetic analyses. Further, because the New York Bight harbors relatively high concentrations of Atlantic Sturgeon, the study shows how localized management decisions can influence Atlantic Sturgeon conservation at rangewide scales. When integrated with more recent knowledge of species ecology, these analyses can be used to evaluate the efficacy of previous management strategies and understand the effects of historical processes on contemporary demography.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10187","usgsCitation":"White, S.L., Johnson, R.L., Lubinski, B.A., Eackles, M.S., Secor, D.H., and Kazyak, D., 2021, Stock composition of the historical New York Bight Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) intercept fishery revealed through microsatellite analysis of archived spines: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 13, no. 6, p. 720-727, https://doi.org/10.1002/mcf2.10187.","productDescription":"8 p.","startPage":"720","endPage":"727","ipdsId":"IP-126143","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10187","text":"Publisher Index Page"},{"id":393866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"New York Bight","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.89978027343749,\n 40.63896734381723\n ],\n [\n -73.9324951171875,\n 40.41976938144622\n ],\n [\n -74.168701171875,\n 39.223742741391305\n ],\n [\n -72.89978027343749,\n 40.63896734381723\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-12-07","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829936,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829937,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Secor, David H.","contributorId":179379,"corporation":false,"usgs":false,"family":"Secor","given":"David","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":829938,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":829939,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226746,"text":"70226746 - 2021 - Convergence of undulatory swimming kinematics across a diversity of fishes","interactions":[],"lastModifiedDate":"2021-12-09T12:43:00.474349","indexId":"70226746","displayToPublicDate":"2021-12-07T06:41:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Convergence of undulatory swimming kinematics across a diversity of fishes","docAbstract":"Fishes exhibit an astounding diversity of locomotor behaviors from classic swimming with their body and fins to jumping, flying, walking, and burrowing. Fishes that use their body and caudal fin (BCF) during undulatory swimming have been traditionally divided into modes based on the length of the propulsive body wave and the ratio of head:tail oscillation amplitude: anguilliform, subcarangiform, carangiform, and thunniform. This classification was first proposed based on key morphological traits, such as body stiffness and elongation, to group fishes based on their expected swimming mechanics. Here, we present a comparative study of 44 diverse species quantifying the kinematics and morphology of BCF-swimming fishes. Our results reveal that most species we studied share similar oscillation amplitude during steady locomotion that can be modeled using a second-degree order polynomial. The length of the propulsive body wave was shorter for species classified as anguilliform and longer for those classified as thunniform, although substantial variability existed both within and among species. Moreover, there was no decrease in head:tail amplitude from the anguilliform to thunniform mode of locomotion as we expected from the traditional classification. While the expected swimming modes correlated with morphological traits, they did not accurately represent the kinematics of BCF locomotion. These results indicate that even fish species differing as substantially in morphology as tuna and eel exhibit statistically similar two-dimensional midline kinematics and point toward unifying locomotor hydrodynamic mechanisms that can serve as the basis for understanding aquatic locomotion and controlling biomimetic aquatic robots.
Variation in temperature is known to influence mortality patterns in ectotherms. Even though a few experimental studies on model organisms have reported a positive relationship between temperature and actuarial senescence (i.e., the increase in mortality risk with age), how variation in climate influences the senescence rate across the range of a species is still poorly understood in free-ranging animals. We filled this knowledge gap by investigating the relationships linking senescence rate, adult lifespan, and climatic conditions using long-term capture–recapture data from multiple amphibian populations. We considered two pairs of related anuran species from the Ranidae (Rana luteiventris and Rana temporaria) and Bufonidae (Anaxyrus boreas and Bufo bufo) families, which diverged more than 100 Mya and are broadly distributed in North America and Europe. Senescence rates were positively associated with mean annual temperature in all species. In addition, lifespan was negatively correlated with mean annual temperature in all species except A. boreas. In both R. luteiventris and A. boreas, mean annual precipitation and human environmental footprint both had negligible effects on senescence rates or lifespans. Overall, our findings demonstrate the critical influence of thermal conditions on mortality patterns across anuran species from temperate regions. In the current context of further global temperature increases predicted by Intergovernmental Panel on Climate Change scenarios, a widespread acceleration of aging in amphibians is expected to occur in the decades to come, which might threaten even more seriously the viability of populations and exacerbate global decline.
Estuaries, at the nexus of rivers and the ocean, are depositional areas that respond to changes in streamflow, tides, sea level, and inputs of sediment from marine and watershed sources. Understanding changes in bed elevations, deposited and eroded sediment, and water depth throughout estuaries is relevant for understanding their present-day status and long-term evolution, identifying potential hazards to human communities, and informing estuarine conservation. In response to observations of sedimentation in the Nehalem Bay, northwestern Oregon, by the Port of Nehalem, the magnitudes and patterns of bathymetric change in the Bay were documented and described by two approaches. The first approach compared changes in bed elevation with estimated volumes of erosion and deposition from overlapping survey data acquired in 1957 and 2019 for the area of the Nehalem Bay from upstream of the Highway 101 bridge to downstream of Fishery Point. The second approach examined changes in water depth for seven zones from the confluence of the North Fork and Nehalem Rivers to the mouth of the Nehalem River using nautical charts (1891, 1947, 1970, 1990, and 2004). These two approaches were used because the bathymetric surveys from 1957 and 2019 could be tied to a common vertical datum, allowing for a direct comparison of changes in bed elevations, whereas the nautical charts could not be tied to a common vertical datum, which limited the analyses to a comparison of changes in water depths over a broader time frame.
Bed elevation changes from 1957 to 2019 were assessed from upstream of the Highway 101 bridge to downstream of Fishery Point where the two surveys overlapped (2 square kilometers) using thalweg longitudinal profiles, channel cross sections, and digital elevation models (DEMs) showing the elevation differences between the two surveys (or DEMs of difference). The most prominent change between 1957 and 2019 was the migration of the thalweg (or deepest part of the channel) between the downstream end of Lazarus Island and downstream of Fishery Point; this migration resulted in sediment deposition in the former thalweg and sediment erosion in formerly shallow areas to form the new thalweg. Bed elevation changes in the thalweg also varied longitudinally between 1957 and 2019. The bed elevation of the thalweg in both surveys, however, was generally less than 1 meter (m). The thalweg in the area of overlapping surveys shortened from about 7.0 to 6.7 kilometers in length over that same period. The bed elevation changes between the DEMs showed that maximum erosion and deposition was 4.3 and 4.5 m, respectively. In this same time period, the net change in sediment volume was 230,000 cubic meters (m3), indicating net deposition. However, the error estimated for the 95 percent confidence interval analyses is ±315,000 m3, and therefore does not preclude the possibility that net erosion may have occurred.
Historical changes in water depth from soundings depicted on nautical charts from 1891, 1947, 1970, 1990, and 2004 were evaluated by assessing spatial and temporal changes for seven zones of the Nehalem Bay. Across all years and zones, water depths ranged from about 0.2 to 9.4 m, whereas median water depths ranged from 0.3 to 6.4 m. Median depths and the range of water depths did not systematically increase or decrease throughout all zones during the same periods. In all nautical charts, the zone at the mouth of the Nehalem River consistently had the deepest soundings (7.9 to 9.4 m) and the greatest range of water depths (7.3 to 8.8 m). Qualitative evaluation of the nautical charts showed minimal changes in the overall shape of the Nehalem Bay. The exception to this observation was at the mouth of the Bay, where two historical outlets to the Pacific Ocean depicted in the 1891 nautical chart were reduced to one outlet following the construction of jetties (1916 and 1918).
The results of this study emphasize that bed elevations and water depths within the Nehalem Bay have varied between 1891 and 2019, as illustrated by the lateral and vertical changes in the thalweg and changes in water depths over time. Changes in thalweg position and related patterns of sediment erosion and deposition are expected in the future as the Nehalem Bay continues to respond to changes in tides, sea level, streamflow, and sediment inputs from watershed and marine sources. The results of this study and the surveys from 1957 and 2019 provide a foundation for documenting and evaluating future changes in the Nehalem Bay and prioritizing actions to manage and protect natural resources and recreational access to the Nehalem Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215108","collaboration":"Prepared in cooperation with the Port of Nehalem","usgsCitation":"Keith, M.K., Jones, K.L., and Gordon, G.W., 2021, Historical changes in bed elevation and water depth within the Nehalem Bay, Oregon, 1891–2019: U.S. Geological Survey Scientific Investigations Report 2021–5108, 48 p., https://doi.org/10.3133/sir20215108.","productDescription":"Report: x, ; Data Release","onlineOnly":"Y","ipdsId":"IP-115603","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":392536,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VJOGM1","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Digital elevation model of the Nehalem Bay near Wheeler, Oregon 2019"},{"id":392535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5108/sir20215108.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5108"},{"id":392534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5108/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Nehalem Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.95324707031249,\n 45.62172169252446\n ],\n [\n -123.77746582031249,\n 45.62172169252446\n ],\n [\n -123.77746582031249,\n 45.761774855141226\n ],\n [\n -123.95324707031249,\n 45.761774855141226\n ],\n [\n -123.95324707031249,\n 45.62172169252446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Relatively little research has been conducted to systematically quantify the nationwide earthquake risk of gas pipelines in the US; simultaneously, national guidance is limited for operators across the country to consistently evaluate the earthquake risk of their assets. Furthermore, many challenges and uncertainties exist in a comprehensive seismic risk assessment of gas pipelines. As a first stage in a systematic nationwide assessment, we quantify the earthquake risk of gas transmission pipelines in the conterminous US due to strong ground shaking, including the associated uncertainties. Specifically, we integrate the US Geological Survey 2018 National Seismic Hazard Model, a logic tree–based exposure model, three different vulnerability models, and a consequence model. The results enable comparison against other risk assessment efforts, encourage more transparent deliberation regarding alternative approaches, and facilitate decisions on potentially assessing localized risks due to ground failures that require site-specific data. Based on the uncertainties approximated herein, the resulting sensitivity analyses suggest that the vulnerability model is the most influential source of uncertainty. Finally, we highlight research needs such as (1) developing more vulnerability models for regional seismic risk assessment of gas pipelines; (2) identifying, prioritizing, and measuring input pipeline attributes that are important for estimating seismic damage; and (3) better quantifying seismic hazards with their uncertainties at the national scale, for both ground failures and ground shaking.
Rabies is an ancient disease that is responsible for approximately 59,000 human deaths annually. Bats (Order Chiroptera) are thought to be the original hosts of rabies virus (RABV) and currently account for most rabies cases in wildlife in the Americas. Vaccination is being used to manage rabies in other wildlife reservoirs like fox and raccoon, but no rabies vaccine is available for bats. We previously developed a recombinant raccoonpox virus (RCN) vaccine candidate expressing a mosaic glycoprotein (MoG) gene that protected mice and big brown bats when challenged with RABV. In this study, we developed two new recombinant RCN candidates expressing MoG (RCN-tPA-MoG and RCN-SS-TD-MoG) with the aim of improving RCN-MoG. We assessed and compared in vitro expression, in vivo immunogenicity, and protective efficacy in vaccinated mice challenged intracerebrally with RABV. All three candidates induced significant humoral immune responses, and inoculation with RCN-tPA-MoG or RCN-MoG significantly increased survival after RABV challenge. These results demonstrate the importance of considering molecular elements in the design of vaccines, and that vaccination with either RCN-tPA-MoG or RCN-MoG confers adequate protection from rabies infection, and either may be a sufficient vaccine candidate for bats in future work.
","language":"English","publisher":"MDPI","doi":"10.3390/vaccines9121436","usgsCitation":"Malave, C.M., Lopera-Madrid, J., Medina-Magues, L.G., Rocke, T.E., and Osorio, J., 2021, Impact of molecular modifications on the Immunogenicity and efficacy of recombinant raccoon poxvirus-vectored rabies vaccine candidates in mice: Vaccines, v. 9, no. 12, 1436, 12 p., https://doi.org/10.3390/vaccines9121436.","productDescription":"1436, 12 p.","ipdsId":"IP-134515","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":450089,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/vaccines9121436","text":"Publisher Index Page"},{"id":436104,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IERY9D","text":"USGS data release","linkHelpText":"In vitro expression, immunogenicity, and efficacy data from recombinant raccoon poxvirus-vectored rabies vaccine candidates tested in mice"},{"id":392570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Malave, Carly Marie 0000-0001-6673-737X","orcid":"https://orcid.org/0000-0001-6673-737X","contributorId":269786,"corporation":false,"usgs":true,"family":"Malave","given":"Carly","email":"","middleInitial":"Marie","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":827916,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lopera-Madrid, Jaime","contributorId":215116,"corporation":false,"usgs":false,"family":"Lopera-Madrid","given":"Jaime","email":"","affiliations":[],"preferred":false,"id":827917,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Medina-Magues, Lex Guillermo","contributorId":269787,"corporation":false,"usgs":false,"family":"Medina-Magues","given":"Lex","email":"","middleInitial":"Guillermo","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":827918,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":827919,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Osorio, Jorge E.","contributorId":50392,"corporation":false,"usgs":false,"family":"Osorio","given":"Jorge E.","affiliations":[{"id":13052,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":827920,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227256,"text":"70227256 - 2021 - Diet-driven mercury contamination is associated with polar bear gut microbiota","interactions":[],"lastModifiedDate":"2022-01-05T13:26:37.237021","indexId":"70227256","displayToPublicDate":"2021-12-03T07:25:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Diet-driven mercury contamination is associated with polar bear gut microbiota","docAbstract":"The gut microbiota may modulate the disposition and toxicity of environmental contaminants within a host but, conversely, contaminants may also impact gut bacteria. Such contaminant-gut microbial connections, which could lead to alteration of host health, remain poorly known and are rarely studied in free-ranging wildlife. The polar bear (Ursus maritimus) is a long-lived, wide-ranging apex predator that feeds on a variety of high trophic position seal and cetacean species and, as such, is exposed to among the highest levels of biomagnifying contaminants of all Arctic species. Here, we investigate associations between mercury (THg; a key Arctic contaminant), diet, and the diversity and composition of the gut microbiota of polar bears inhabiting the southern Beaufort Sea, while accounting for host sex, age class and body condition. Bacterial diversity was negatively associated with seal consumption and mercury, a pattern seen for both Shannon and Inverse Simpson alpha diversity indices (adjusted R2 = 0.35, F1,18 = 8.00, P = 0.013 and adjusted R2 = 0.26, F1,18 = 6.04, P = 0.027, respectively). No association was found with sex, age class or body condition of polar bears. Bacteria known to either be involved in THg methylation or considered to be highly contaminant resistant, including Lactobacillales, Bacillales and Aeromonadales, were significantly more abundant in individuals that had higher THg concentrations. Conversely, individuals with higher THg concentrations showed a significantly lower abundance of Bacteroidales, a bacterial order that typically plays an important role in supporting host immune function by stimulating intraepithelial lymphocytes within the epithelial barrier. These associations between diet-acquired mercury and microbiota illustrate a potentially overlooked outcome of mercury accumulation in polar bears.
This article reviews the status of knowledge gaps and co-production process challenges that impede coastal flood hazard resilience planning in communities of northwestern Alaska, where threat levels are high. Discussion focuses on the state of knowledge arising after preparation of the 2019 IPCC Special Report on the Ocean and Cryosphere in a Changing Climate and highlights prospects to address urgent needs. The intent is to identify some key steps necessary to advance the integration of relevant multidisciplinary observations with flood modeling and infrastructure mapping to co-produce new online hazard and risk assessment tools that inform local community planning and improve science collaboration among Federal, state, and regional partners for enhanced pre-storm preparations and post-storm recovery, including partial or complete relocation. By focusing coastal data integration for delivery of priority geospatial hazard map products through a consistent yet customized approach to adaptation planning, the broad collaborative effort in Alaska may yield a path of stakeholder service delivery that can be applied to many Arctic communities and other vulnerable regions of the world.
Responsible stewardship of native fish populations and riparian plants in the Meduxnekeag River watershed in northeastern Maine is a high priority for the Houlton Band of Maliseet Indians. Understanding the potential changes in hydrology and water temperature as a result of climate change is important to this priority for evaluating future habitat conditions in the watershed. This report, prepared in cooperation with the Houlton Band of Maliseet Indians, documents and presents the results of a model using the Precipitation-Runoff Modeling System (PRMS), a hydrologic model designed to provide streamflow and water temperature simulations under predicted changes in precipitation and air temperature during the next century.
To estimate streamflows and water temperature in the Meduxnekeag River watershed, a PRMS model was developed and calibrated. By using the calibrated PRMS model, simulations were made for projected scenarios of 0, 5, 10, and 15 percent increases in precipitation and for increases in air temperature of 0.0, 3.6, 7.0, and 10.4 degrees Fahrenheit (°F). The increases in precipitation and temperature were applied to all the daily input values uniformly. These scenarios were based upon the results from 30 climate change models summarized in the National Climate Change Viewer. Streamflows and water temperatures modeled for different climate scenarios were compared with streamflows and water temperatures modeled with unadjusted climate inputs.
Overall, streamflow increased with increasing precipitation and decreased with increasing air temperature. Water temperature increased with increasing air temperature. At the outlet of the studied Meduxnekeag River watershed, with both a 15 percent increase in precipitation and a 10.4 °F increase in air temperature, the mean annual streamflow increased by 17 percent from 489 cubic feet per second (ft3/s) to 572 ft3/s, and the mean annual maximum streamflow decreased by 8.3 percent from 3,870 ft3/s to 3,550 ft3/s. At the same location and under the same scenario, the mean annual water temperature increased by 17.5 percent from 47.4 °F to 55.7 °F.
Significant changes in mean monthly streamflows were found with increasing air temperature. The PRMS model results showed that when air temperature was increased, there was an increase in mean monthly streamflow during the winter months and a decrease in mean monthly streamflow during the spring months. In addition, with a 10.4 °F increase in the air temperature, the month with the greatest monthly streamflow changed from April to December. In addition, the PRMS model estimated that the mean annual maximum snowpack in snow water equivalent for the watershed would decrease from 7.67 inches to 1.26 inches, and the mean annual date of the maximum snowpack would change from March 21 to January 28 with a 15 percent increase in precipitation and a 10.4 °F increase in air temperature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215104","collaboration":"Prepared in cooperation with the Houlton Band of Maliseet Indians","usgsCitation":"Bjerklie, D.M., and Olson, S.A., 2021, Simulating the effects of climate-related changes to air temperature and precipitation on streamflow and water temperature in the Meduxnekeag River watershed, Maine: U.S. Geological Survey Scientific Investigations Report 2021–5104, 35 p., https://doi.org/10.3133/sir20215104.","productDescription":"Report: vi, 35 p.; Data Release","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123224","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":392380,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215104/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":392032,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5104/sir20215104.XML"},{"id":392030,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EB4H6H","text":"USGS data release","linkHelpText":"Data for simulating the effects of air temperature and precipitation changes on streamflow and water temperature in the Meduxnekeag River watershed, Maine"},{"id":392029,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5104/sir20215104.pdf","text":"Report","size":"20.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5104"},{"id":392031,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5104/images/"},{"id":392028,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5104/coverthb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Meduxnekeag River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.302001953125,\n 45.92154267288144\n ],\n [\n -67.78289794921875,\n 45.92154267288144\n ],\n [\n -67.78289794921875,\n 46.26913887119721\n ],\n [\n -68.302001953125,\n 46.26913887119721\n ],\n [\n -68.302001953125,\n 45.92154267288144\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
This study addresses the characteristics, potential hazards, and both eruptive and non-eruptive role of water at selected volcanic crater lakes in Alaska. Crater lakes are an important feature of some stratovolcanoes in Alaska. Of the volcanoes in the state with known Holocene eruptive activity, about one third have summit crater lakes. Also included are two volcanoes with small caldera lakes (Katmai, Kaguyak). The lakes play an important but not well studied role in influencing eruptive behavior and pose some significant hydrologic hazards. Floods from crater lakes in Alaska are evaluated by estimating maximum potential crater lake water volumes and peak outflow discharge with a dam-break model. Some recent eruptions and hydrologic events that involved crater lakes also are reviewed. The large volumes of water potentially hosted by crater lakes in Alaska indicate that significant flowage hazards resulting from catastrophic breaching of crater rims are possible. Estimates of maximum peak flood discharge associated with breaching of lake-filled craters derived from dam-break modeling indicate that flood magnitudes could be as large as 103–106 m3/s if summit crater lakes drain rapidly when at maximum volume. Many of the Alaska crater lakes discussed are situated in hydrothermally altered craters characterized by complex assemblages of stratified unconsolidated volcaniclastic deposits, in a region known for large magnitude (>M7) earthquakes. Although there are only a few historical examples of eruptions involving crater lakes in Alaska, these provide noteworthy examples of the role of external water in cooling pyroclastic deposits, acidic crater-lake drainage, and water-related hazards such as lahars and base surge.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2021.751216","usgsCitation":"Waythomas, C.F., 2021, Selected crater and small caldera lakes in Alaska: Characteristics and hazards: Frontiers in Earth Science, v. 9, 751216, 23 p., https://doi.org/10.3389/feart.2021.751216.","productDescription":"751216, 23 p.","ipdsId":"IP-132664","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467219,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2021.751216","text":"Publisher Index Page"},{"id":463360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -142.4292614840023,\n 61.7867815706897\n ],\n [\n -179,\n 61.7867815706897\n ],\n [\n -179,\n 49.606118935666444\n ],\n [\n -144.99994877731635,\n 56.83072738947416\n ],\n [\n -142.4292614840023,\n 61.7867815706897\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2022-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Waythomas, Christopher F. 0000-0002-3898-272X cwaythomas@usgs.gov","orcid":"https://orcid.org/0000-0002-3898-272X","contributorId":640,"corporation":false,"usgs":true,"family":"Waythomas","given":"Christopher","email":"cwaythomas@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917093,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70226845,"text":"70226845 - 2021 - A characterization of deep-sea coral and sponge communities along the California and Oregon coast using a remotely operated vehicle on the EXPRESS 2018 expedition","interactions":[],"lastModifiedDate":"2022-01-20T17:47:27.706118","indexId":"70226845","displayToPublicDate":"2021-12-01T11:47:06","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5134,"text":"NOAA Technical Memorandum","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NMFS-SWFSC 657","title":"A characterization of deep-sea coral and sponge communities along the California and Oregon coast using a remotely operated vehicle on the EXPRESS 2018 expedition","docAbstract":"Deep-sea coral and sponge (DSCS) communities serve as essential fish habitats (EFH) by providing shelter and nursery habitat, increasing diversity, and increasing prey availability (Freese and Wing, 2003; Bright, 2007; Baillon et al., 2012; Henderson et al., 2020). Threats to these long-lived, fragile organisms from bottom contact fishing gear, potential offshore renewable energy development, and ocean warming and acidification have increased the need for DSCS research along the U.S. West Coast (Gomez et al., 2018; Salgado et al., 2018; Yoklavich, et al., 2018; Gugliotti et al., 2019). The focus of these studies has varied from species distribution and abundance (Yoklavich and Love, 2005; Tissot et al., 2006) to developing and validating predictive distribution models (Huff et al., 2013; Rooper et al., 2017; Kreidler, 2020) to finding medicinal uses for corals and sponges (Essack et al., 2011; Shrestha et al., 2018). Due to the vast area of unexplored seafloor within the U.S. exclusive economic zone (EEZ; 200 nautical miles off the coast) and the technological requirements and expanse of deep-sea research, there is still much to learn about the distributions and biology of DSCS. This information is critical to resource managers for effective conservation and management of DSCS habitats. Protections are provided by the Pacific Fishery Management Council (PFMC) designation of groundfish EFH conservation areas (EFHCA) and the National Marine Sanctuaries Act (NMSA). Areas designated as EFHCA are closed to bottom trawl fishing to protect and preserve seafloor habitats. Recently the PFMC adopted Amendment 28 to the Groundfish Fishery Management Plan (GFMP; Pacific Fishery Management Council, 2019) which modified EFHCAs by closing new areas identified as vulnerable and reopening areas deemed not vulnerable. The NMSA prohibits bottom disturbance from certain activities within areas designated as national marine sanctuaries, such as oil and gas exploration or extraction, cable laying, and other forms of seabed alteration or construction that disturb benthic communities. \n\nNOAA’s Deep-Sea Coral and Research Technology Program (DSCRTP) began a 4-yr funding initiative for the U.S. West Coast in 2017. The goals of the West Coast Deep-Sea Coral Initiative (WCDSCI) were to: 1) gather baseline information on areas subject to fishing regulation changes prior to the implementation of Amendment 28; 2) improve our understanding of known DSCS bycatch “hot spots”; and 3) explore and assess DSCS resources within NOAA National Marine Sanctuaries with emphasis on areas of sanctuary resource protection and management concerns. During the first year of the program, a research cruise was developed to survey the West Coast from Oregon to California studying the DSCS ecosystems in priority areas. The 31-day expedition (9 Oct – 8 Nov, 2018) was launched from the NOAA Ship Bell M. Shimada, beginning in Newport, OR and ending in San Diego, CA. \n\nThe science team assembled for this cruise were members of the EXpanding Pacific Research and Exploration of Submerged Systems (EXPRESS) campaign, which brings together researchers from federal and nonfederal institutions to collaborate on scientific expeditions targeting the deepwater areas off California, Oregon, and Washington. EXPRESS supports researchers leveraging funding, resources, personnel, and expertise to accomplish more science than would have been possible by a single entity alone. The 2018 coastwide expedition included research partners from National Marine Fisheries Service (NMFS) Southwest Fisheries Science Center (SWFSC) and Northwest Fisheries Science Center (NWFSC), National Ocean Service (Channel Islands, Cordell Bank, Greater Farallones, and Monterey Bay National Marine Sanctuaries), Bureau of Ocean Energy Management (BOEM), U.S. Geological Survey (USGS), and Monterey Bay Aquarium Research Institute (MBARI). \n\nResearch objectives for the cruise were to:\n\n1) Collect DSCS baseline information at 10 of the EFHCA sites undergoing protection modifications by the Pacific Fishery Management Council.\n\n2) Collect DSCS and fish data at previously unexplored sites within West Coast National Marine Sanctuaries.\n\n3) Revisit a subset of previously surveyed sites to document if changes in DCSC have occurred over time.\n\n4) Collect information to validate BOEM supported cross-shelf habitat suitability models for DSCS.\n\n5) Collect samples to help in identifying (and understanding) West Coast DSCS and expand use of new technologies (ROV, AUV, and environmental DNA [eDNA]).\n\n6) Collect water samples for coastwide eDNA, nutrient, and carbon chemistry studies.","language":"English","publisher":"NOAA","doi":"10.25923/sd6f-j739","usgsCitation":"Laidig, T., Watters, D., Prouty, N.G., Everett, M., Duncan, L., Clarke, L., Caldow, C., and Demopoulos, A., 2021, A characterization of deep-sea coral and sponge communities along the California and Oregon coast using a remotely operated vehicle on the EXPRESS 2018 expedition: NOAA Technical Memorandum NMFS-SWFSC 657, 122 p., https://doi.org/10.25923/sd6f-j739.","productDescription":"122 p.","ipdsId":"IP-134460","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":394597,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.01318359375001,\n 34.288991865037524\n ],\n [\n -120.498046875,\n 34.288991865037524\n ],\n [\n -120.498046875,\n 46.08847179577592\n ],\n [\n -126.01318359375001,\n 46.08847179577592\n ],\n [\n -126.01318359375001,\n 34.288991865037524\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Laidig, Tom","contributorId":270131,"corporation":false,"usgs":false,"family":"Laidig","given":"Tom","email":"","affiliations":[{"id":56090,"text":"NOAA Fisheries, SWFSC, Fisheries Ecology Division, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":828462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watters, Diana","contributorId":270132,"corporation":false,"usgs":false,"family":"Watters","given":"Diana","email":"","affiliations":[{"id":56090,"text":"NOAA Fisheries, SWFSC, Fisheries Ecology Division, Santa Cruz, CA","active":true,"usgs":false}],"preferred":false,"id":828463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":828464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Everett, Meredith","contributorId":270133,"corporation":false,"usgs":false,"family":"Everett","given":"Meredith","email":"","affiliations":[{"id":56092,"text":"NOAA Fisheries, NWFSC, Seattle WA","active":true,"usgs":false}],"preferred":false,"id":828465,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duncan, Lizzie","contributorId":270134,"corporation":false,"usgs":false,"family":"Duncan","given":"Lizzie","email":"","affiliations":[{"id":56094,"text":"NOAA, NOS, Channel Islands National Marine Sanctuary, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":828466,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clarke, Liz","contributorId":270135,"corporation":false,"usgs":false,"family":"Clarke","given":"Liz","email":"","affiliations":[{"id":56092,"text":"NOAA Fisheries, NWFSC, Seattle WA","active":true,"usgs":false}],"preferred":false,"id":828467,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Caldow, Chris","contributorId":270136,"corporation":false,"usgs":false,"family":"Caldow","given":"Chris","affiliations":[{"id":56094,"text":"NOAA, NOS, Channel Islands National Marine Sanctuary, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":828468,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":222185,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":828469,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225624,"text":"70225624 - 2021 - Invasive carp population modeling to support an adaptive management framework","interactions":[],"lastModifiedDate":"2024-03-21T16:31:40.616244","indexId":"70225624","displayToPublicDate":"2021-12-01T11:29:33","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":9543,"text":"Interim Summary Report","active":true,"publicationSubtype":{"id":3}},"title":"Invasive carp population modeling to support an adaptive management framework","docAbstract":"No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Interim summary report: Invasive carp monitoring and response plan 2021","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Asian Carp Regional Coordinating Committee","usgsCitation":"Erickson, R.A., 2021, Invasive carp population modeling to support an adaptive management framework: Interim Summary Report, 4 p.","productDescription":"4 p.","startPage":"132","endPage":"135","ipdsId":"IP-129312","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":426839,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391072,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":825981,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70226584,"text":"sir20215120 - 2021 - Continuous turbidity data used to compute constituent concentrations in the South Loup River, Nebraska, 2017–18","interactions":[],"lastModifiedDate":"2021-12-02T17:09:50.249472","indexId":"sir20215120","displayToPublicDate":"2021-12-01T11:08:31","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5120","displayTitle":"Continuous Turbidity Data Used to Compute Constituent Concentrations in the South Loup River, Nebraska, 2017–18","title":"Continuous turbidity data used to compute constituent concentrations in the South Loup River, Nebraska, 2017–18","docAbstract":"The South Loup River in central Nebraska has been impaired by bacteria since at least 2004, which has resulted in the river not meeting its intended use as a recreational waterway. As part of a strategy for reducing the bacterial load in the river, the U.S. Geological Survey, in cooperation with the Lower Loup Natural Resources District, made continuous estimates of Escherichia coli (E. coli) and nutrient concentrations during seasonal monitoring at the South Loup River at Saint Michael, Nebraska, during 2017–18. Continuous turbidity data were collected from mid-April through October in 2017 and 2018 and were paired with 35 co-occurring discrete water samples that were analyzed for E. coli, nutrients, and suspended solids. Surrogate models relating the discrete concentrations to the continuous turbidity data were developed using ordinary-least-squares regression and were evaluated for model performance and uncertainty. Although the model assumptions were met for E. coli, the imprecision of the E. coli model was considerably higher than the other constituents, probably because of measurement imprecision and greater sensitivity to environmental factors. Once the models were developed, the turbidity data were used to predict continuous constituent concentrations and corresponding prediction intervals, which were made available online as part of the U.S. Geological Survey National Water Information System database. It is expected that results from these models will provide stakeholders with an understanding of constituent concentrations during the 2017–18 monitoring period and the results will also provide a good reference point for any future comparisons.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215120","collaboration":"Prepared in cooperation with the Lower Loup Natural Resources District","usgsCitation":"Rus, D.L., and Densmore, B.K., 2021, Continuous turbidity data used to compute constituent concentrations in the South Loup River, Nebraska, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5120, 10 p., https://doi.org/10.3133/sir20215120.","productDescription":"Report: vi, 10 p.; 2 Datasets","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-127801","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":392236,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5120/coverthb.jpg"},{"id":392237,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5120/sir20215120.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5120"},{"id":392238,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://www.waterqualitydata.us/","text":"National Water Quality Monitoring Council website and digital data","linkHelpText":"— Water quality portal"},{"id":392239,"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"}],"country":"United States","state":"Nebraska","otherGeospatial":"South Loup River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.667724609375,\n 40.93841495689795\n ],\n [\n -98.2177734375,\n 40.93841495689795\n ],\n [\n -98.2177734375,\n 42.02481360781777\n ],\n [\n -100.667724609375,\n 42.02481360781777\n ],\n [\n -100.667724609375,\n 40.93841495689795\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
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5231 South 19th Street
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The U.S. Geological Survey (USGS) assesses potential for undiscovered, technically recoverable oil and gas resources in priority geologic provinces and quantifies resource volume estimates within subdivisions called assessment units (AUs). AU boundaries are defined by USGS geologists using quantitative and qualitative geologic information. Variables contained in IHS Markit’s well and production databases can quantify and/or function as proxies for many of the qualitative, boundary-defining variables. This research explores a new approach to determine AU boundaries and the potential to automate their definition, using data analytics and machine learning algorithms on key, qualitative variables within the IHS Markit databases. Well and production data from the U.S. onshore Gulf Coast region for the Upper Cretaceous Eagle Ford Group and Austin Chalk are used in this analysis because each is relatively geologically uniform in Texas and both have recently been assessed by the USGS. The Eagle Ford is an example of an in situ continuous oil and gas accumulation, and the overlying Austin Chalk is an example of a combined conventional and continuous resource, sourced from the underlying Eagle Ford. Wellspecific values were extracted or calculated from data in IHS Markit’s well and production databases for depth to top and base of the formations, formation thickness, bottom-hole temperature, temperature gradient, temperature at base of formation, cumulative oil and gas production values, barrels of oil equivalent, oil and gas gravities, mud weights from initial well test, depth pressure ratio, and excess pressure. A raster for each variable was interpolated using the natural neighbor technique from the spatial analyst toolbox in ArcGIS. Rasters were then transformed using minimum-maximum scaling, which rescales the distribution to the range of 0–1. Clustering was completed using the iso cluster unsupervised classification tool on the normalized rasters. Raster cell groupings from two to ten were explored, with initial results demonstrating that four to six classes return the most differentiable groups, with depth to formation, oil gravity, pressure, and temperature variables containing the greatest between-group differences. Modeled clusters have spatial similarities to the geologically defined AUs, with indication that temperature and pressure are the most fundamental to AU definition. Input from geologists will remain crucial for further dividing clusters and defining final AUs, since AUs are defined by both qualitative and quantitative information; however, this research documents promising cluster modeling results for the automation of initial AU definitions.
","conferenceTitle":"SEG-AAPG International Meeting for Applied Geoscience & Energy (IMAGE) 2021","conferenceDate":"September 26-October 1, 2021","conferenceLocation":"Denver, CO","language":"English","publisher":"Society of Exploration Geophysicists and the American Association of Petroleum Geologists","usgsCitation":"Shorten, C., Kinney, S.A., and Whidden, K.J., 2021, Exploring basin-scale relations and unsupervised classification to quantify and automate the definition of assessment units in USGS continuous oil and gas resource assessments, SEG-AAPG International Meeting for Applied Geoscience & Energy (IMAGE) 2021, Denver, CO, September 26-October 1, 2021, 10 p.","productDescription":"10 p.","ipdsId":"IP-131669","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":490854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":490853,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://archives.datapages.com/data/international-meeting-for-applied-geoscience-and-energy/data/2021/7321.htm?q=%2BauthorStrip%3Ashorten","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana, Mississippi, Texas","otherGeospatial":"Upper Cretaceous Eagle Ford Group and Austin Chalk","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -101.46300363364126,\n 29.853524579639256\n ],\n [\n -98.79174292446497,\n 26.39154767945162\n ],\n [\n -97.15568801261291,\n 25.692988402953162\n ],\n [\n -96.30476798511874,\n 27.907758438495677\n ],\n [\n -93.26076686296953,\n 29.3771372231364\n ],\n [\n -88.088627576136,\n 28.580800903730534\n ],\n [\n -88.5714936485433,\n 32.46814742644388\n ],\n [\n -94.5985927100297,\n 32.52158667154865\n ],\n [\n -101.46300363364126,\n 29.853524579639256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shorten, Chilisa Marie 0000-0003-1828-2002","orcid":"https://orcid.org/0000-0003-1828-2002","contributorId":267256,"corporation":false,"usgs":true,"family":"Shorten","given":"Chilisa Marie","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824843,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinney, Scott A. 0000-0001-5008-5813 skinney@usgs.gov","orcid":"https://orcid.org/0000-0001-5008-5813","contributorId":1395,"corporation":false,"usgs":true,"family":"Kinney","given":"Scott","email":"skinney@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824844,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whidden, Katherine J. 0000-0002-7841-2553 kwhidden@usgs.gov","orcid":"https://orcid.org/0000-0002-7841-2553","contributorId":3960,"corporation":false,"usgs":true,"family":"Whidden","given":"Katherine","email":"kwhidden@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":824845,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229714,"text":"70229714 - 2021 - Effects of sample gear on estuarine nekton assemblage assessments and food web model simulations","interactions":[],"lastModifiedDate":"2022-03-17T13:23:05.511118","indexId":"70229714","displayToPublicDate":"2021-12-01T10:32:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Effects of sample gear on estuarine nekton assemblage assessments and food web model simulations","docAbstract":"Long-term fisheries-independent sampling data inform population status and trends of species-specific biomass and are often used to drive biomass-based food web models such as the Comprehensive Aquatic Systems Model (CASM). Indicators such as total biomass and mean trophic level derived from these data and from CASM outputs inform management and facilitate assessments of on-going and predicted coastal change and restoration activities on fisheries, but rely on consistent sampling to enable comparisons across space and time. Changes in coastal estuarine gradients, combined with the availability of new sampling technologies, highlight a need to assess the potential consequences of changing sampling technologies on fisheries data and the cascading impact on model outputs. In Louisiana, USA, CASM models are used to inform coastal restoration projects, relying on 40 years of fisheries-independent data derived from 50′ seine sampling. However, alternative use of electrofishers as a sampling method has been proposed to replace the seine sampling. In this study, we examine data from concurrent seine and electrofisher sampling in Barataria Basin, Louisiana, and compare biomass, assemblage data and CASM outputs related to species biomass, food web structure and energy cycling. In a paired comparison of data in 2018–2019, the electrofisher captured higher total catch and diversity compared to the seine. The electrofisher samples were dominated by shrimp (grass, white, brown) and larger bodied fish, while seine samples were dominated by smaller-bodied fish (i.e., anchovy, menhaden). Ecosystem indicators derived from running the CASM using biomass data from seine and electrofisher sampling separately in two different simulation exercises provide contrasting results. In Simulation Exercise 1, the use of different datasets (long-term CASM calibration, 2018–2019 seine, 2018–2019 electrofisher) to initialize the CASM biomasses did not result in large or long-running changes in the simulated biomasses over time. In contrast, in Simulation Exercise 2, CASM model outputs using adjusted gear ratios indicated changes in biomass structure when using electrofisher data, with a doubling of total food web biomass due to the increased shrimp count, and a 13% increase in total energy flow through the food web. Conversions based on area and gear efficiency for overall catch may be useful in maintaining the continuity of historical data. However, differences in species-specific catch due to gear selectivity could have large consequences for constructing and calibrating fish and ecosystem models and remain difficult to reconcile. These differences in assemblages, and estimated biomasses for key food web species, suggest careful consideration in changing gears.
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M.","contributorId":220867,"corporation":false,"usgs":false,"family":"Taylor","given":"C.","email":"","middleInitial":"M.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":838076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Watkins, Katherine S.","contributorId":288553,"corporation":false,"usgs":false,"family":"Watkins","given":"Katherine","email":"","middleInitial":"S.","affiliations":[{"id":52096,"text":"Dynamic Solutions, LLC","active":true,"usgs":false}],"preferred":false,"id":838077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kiskaddon, E.","contributorId":276292,"corporation":false,"usgs":false,"family":"Kiskaddon","given":"E.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":838079,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baustian, M.","contributorId":288555,"corporation":false,"usgs":false,"family":"Baustian","given":"M.","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":838080,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70242771,"text":"70242771 - 2021 - Supplemental vegetation monitoring plots at Little Bighorn Battlefield National Monument to accelerate learning of the Annual Brome Adaptive Management (ABAM) model","interactions":[],"lastModifiedDate":"2024-03-05T16:44:23.727968","indexId":"70242771","displayToPublicDate":"2021-12-01T10:23:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":7577,"text":"Annual Report","active":true,"publicationSubtype":{"id":4}},"title":"Supplemental vegetation monitoring plots at Little Bighorn Battlefield National Monument to accelerate learning of the Annual Brome Adaptive Management (ABAM) model","docAbstract":"The Annual Brome Adaptive Management (ABAM) project is a consortium of seven parks in the Northern Great Plains (NGP) working together to better understand how to control invasive annual grasses (including Bromus species) through an adaptive management approach. This approach is supported by a quantitative model that uses current data from standardized vegetation monitoring plots in all seven parks to annually update the model’s parameters and predictions regarding the effects of different management actions on invasive annual grasses and other components of the mixed-grass prairie plant community. This updating of the model is called “learning.”
The original ABAM model has little information about the effects of the herbicide indaziflam on target invasive annual grasses and other components of the vegetation in conditions like those that frequently occur in ABAM parks (i.e., ungrazed). The purpose of this study is to provide some of that information and therefore accelerate the rate of learning accomplished in the adaptive management cycle.
","language":"English","publisher":"National Park Service","usgsCitation":"Symstad, A., Richardson, T., and Swanson, D., 2021, Supplemental vegetation monitoring plots at Little Bighorn Battlefield National Monument to accelerate learning of the Annual Brome Adaptive Management (ABAM) model: Annual Report, 5 p.","productDescription":"5 p.","ipdsId":"IP-152073","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":415838,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/RPRS/IAR/Profile/573320","linkFileType":{"id":5,"text":"html"}},{"id":426325,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Little Bighorn Battlefield National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.42780937203189,\n 45.55613907753829\n ],\n [\n -107.41424157476726,\n 45.56297486244455\n ],\n [\n -107.42644541357662,\n 45.57478473659421\n ],\n [\n -107.44274112775177,\n 45.56674424099478\n ],\n [\n -107.44575619381031,\n 45.566543213856875\n ],\n [\n -107.44259755317754,\n 45.56327642203598\n ],\n [\n -107.43943891254428,\n 45.56151730159996\n ],\n [\n -107.4385056778117,\n 45.560763375980855\n ],\n [\n -107.44123359472226,\n 45.55814968884059\n ],\n [\n -107.43814674137612,\n 45.55679253410301\n ],\n [\n -107.43584954818878,\n 45.55789836636245\n ],\n [\n -107.43412665329791,\n 45.56016022820128\n ],\n [\n -107.43218839654568,\n 45.55855180246806\n ],\n [\n -107.43419844058504,\n 45.55593801245129\n ],\n [\n -107.43075265080329,\n 45.555586146818285\n ],\n [\n -107.42780937203189,\n 45.55613907753829\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Symstad, Amy 0000-0003-4231-2873 asymstad@usgs.gov","orcid":"https://orcid.org/0000-0003-4231-2873","contributorId":201095,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy","email":"asymstad@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":869743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richardson, Timm","contributorId":334581,"corporation":false,"usgs":false,"family":"Richardson","given":"Timm","email":"","affiliations":[],"preferred":false,"id":895969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swanson, Dan","contributorId":334582,"corporation":false,"usgs":false,"family":"Swanson","given":"Dan","email":"","affiliations":[],"preferred":false,"id":895970,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241051,"text":"70241051 - 2021 - Inter- and intra-annual effects of lethal removal on common raven abundance in Nevada and California, USA","interactions":[],"lastModifiedDate":"2023-03-08T15:17:43.127372","indexId":"70241051","displayToPublicDate":"2021-12-01T09:10:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Inter- and intra-annual effects of lethal removal on common raven abundance in Nevada and California, USA","docAbstract":"Populations of common ravens (Corvus corax; ravens) have increased rapidly within sagebrush (Artemisia spp.) ecosystems between 1960 and 2020. Although ravens are native to North America, their population densities have expanded to levels that negatively influence the population dynamics of other wildlife species of conservation concern, such as greater sage-grouse (Centrocercus urophasianus) and desert tortoises (Gopherus agassizii). For this reason, lethal removal, such as the application of the avicide DRC-1339, has been used to manage raven numbers at local scales and under certain circumstances. Because the relative effectiveness of DRC-1339 in reducing raven populations densities is not thoroughly understood, we completed 2 case studies using a before-after-control-impact experimental design of density estimates generated from point count data within a Bayesian hierarchical distance sampling framework. Specifically, we analyzed >16,000 point count surveys collected during 2009–2019 and split into 2 study designs covering multiple field sites within the Great Basin region. The first experiment evaluated intra-annual changes in density by comparing before and after treatment time periods within a single breeding season for multiple treatment regions compared to 2 control regions. The other experiment focused on inter-annual differences by comparing time periods across years before and after the onset of annual avicide application for a single treatment region compared to multiple control regions. Our models estimated a 100% probability of decline in density relative to control sites for both the intra- and inter-annual model designs. At treatment sites, expected densities of ravens varied but were reduced by 43% (95% CRI: 33–49%) and 54% (95% CRI: 24–71%) according to intra- and inter-annual analyses, respectively, whereas densities increased by 42% (95% CRI: 27–60%) and 15% (95% CRI: -17 to 58%) at control sites. Although population densities were reduced with treatments, trends indicated that sustained effort would likely be needed to maintain densities at acceptable levels within regions of interest. Effectively reducing the adverse effects of raven populations on other native species likely will depend on a variety of targeted management actions such as improving habitat quality for prey species, possibly reducing ravens’ population density, and treating the cause of increased raven abundance to reduce future carrying capacity and prevent rebounds.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/p79d-en84","usgsCitation":"O’Neil, S.T., Coates, P.S., Brockman, J.C., Jackson, P.J., Spencer, J.O., and Williams, P.J., 2021, Inter- and intra-annual effects of lethal removal on common raven abundance in Nevada and California, USA: Human–Wildlife Interactions, v. 15, no. 3, 20, 16 p., https://doi.org/10.26077/p79d-en84.","productDescription":"20, 16 p.","ipdsId":"IP-130888","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":413856,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.06172324180406,\n 41.791122396069284\n ],\n [\n -120.49545088606604,\n 41.791122396069284\n ],\n [\n -120.49545088606604,\n 37.428574642347996\n ],\n [\n -114.06172324180406,\n 37.428574642347996\n ],\n [\n -114.06172324180406,\n 41.791122396069284\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brockman, Julia C.","contributorId":302928,"corporation":false,"usgs":false,"family":"Brockman","given":"Julia","email":"","middleInitial":"C.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":865867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":865868,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Spencer, Jack O. Jr.","contributorId":196229,"corporation":false,"usgs":false,"family":"Spencer","given":"Jack","suffix":"Jr.","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":865869,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Perry J.","contributorId":169058,"corporation":false,"usgs":false,"family":"Williams","given":"Perry","email":"","middleInitial":"J.","affiliations":[{"id":25400,"text":"U.S. Fish and Wildlife Service, Big Oaks National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":865870,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70241052,"text":"70241052 - 2021 - Spatial modeling of common raven density and occurrence helps guide landscape management within Great Basin sagebrush ecosystems","interactions":[],"lastModifiedDate":"2023-03-08T15:03:20.772821","indexId":"70241052","displayToPublicDate":"2021-12-01T08:55:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Spatial modeling of common raven density and occurrence helps guide landscape management within Great Basin sagebrush ecosystems","docAbstract":"Common ravens (Corvus corax; ravens) are a behaviorally flexible nest predator of several avian species, including species of conservation concern. Movement patterns based on life history phases, particularly territoriality of breeding birds and transiency of nonbreeding birds, are thought to influence the frequency and efficacy of nest predation. As such, predicting where on the landscape territorial resident and non-territorial transient birds may be found in relation to the distribution of sensitive prey is of increasing importance to managers and conservationists. From 2007 to 2019, we conducted raven point count surveys between mid-March and mid-September across 43 different field sites representing typical sagebrush (Artemisia spp.) ecosystems of the Great Basin, USA. The surveys conducted during 2007–2016 were used in previously published maps of raven occurrence and density. Here, we examined the relationship between occurrence and density of ravens using spatially explicit predictions from 2 previously published studies and differentiate areas occupied by higher concentrations of resident ravens as opposed to transients. Surveys conducted during 2017–2019 were subsequently used to evaluate the predicted trends from our analytical approach. Specifically, we used residuals from a generalized linear regression to establish the relationship between occurrence and density, which ultimately resulted in a spatially explicit categorical map that identifies areas of resident versus transient ravens. We evaluated mapped categories using independently collected observed raven group sizes from the 2017–2019 survey data, as well as an independent dataset of global positioning system locations of resident and transient individuals monitored during 2019–2020. We observed moderate agreement between the mapped categories and independent datasets for both evaluation approaches. Our map provides broad inference about spatial variation in potential predation risk from ravens for species such as greater sage-grouse (Centrocercus urophasianus) and can be used as a valuable spatial layer for decision support tools aimed at guiding raven management decisions and, ultimately, improving survival and reproduction of sensitive prey within the Great Basin.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/djza-3976","usgsCitation":"Webster, S.C., O’Neil, S.T., Brussee, B.E., Coates, P.S., Jackson, P.J., Tull, J.C., and Delehanty, D.J., 2021, Spatial modeling of common raven density and occurrence helps guide landscape management within Great Basin sagebrush ecosystems: Human–Wildlife Interactions, v. 15, no. 3, 10, 19 p., https://doi.org/10.26077/djza-3976.","productDescription":"10, 19 p.","ipdsId":"IP-130899","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436107,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P900CRGI","text":"USGS data release","linkHelpText":"Raven Occurrence and Density in the Great Basin Region of the Western United States (2007-2019)"},{"id":413854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Idaho, Montana, Nevada, Oregon, Utah, Wyoming","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.34468329014176,\n 34.70098212463705\n ],\n [\n -114.04075025505519,\n 35.8909084565023\n ],\n [\n -113.16542551306084,\n 37.230073866537396\n ],\n [\n -112.47946810222676,\n 37.386067115551796\n ],\n [\n -111.1464418743502,\n 39.05064247472356\n ],\n [\n -110.68528003854762,\n 41.12528608167821\n ],\n [\n -111.00237117235294,\n 42.26628304547768\n ],\n [\n -111.68989801144276,\n 42.8686045283136\n ],\n [\n -111.47814016329308,\n 43.59652326820736\n ],\n [\n -111.75325040068994,\n 44.7964787747967\n ],\n [\n -112.71726362386148,\n 44.73453532393705\n ],\n [\n -113.65013416063762,\n 45.57183439950148\n ],\n [\n -115.06168907025786,\n 44.889399700106594\n ],\n [\n -115.29104427203676,\n 43.254541948308145\n ],\n [\n 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0000-0003-4981-2010","orcid":"https://orcid.org/0000-0003-4981-2010","contributorId":302117,"corporation":false,"usgs":true,"family":"Webster","given":"Sarah","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 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Previous model-derived and geological reconstruction estimates of the sea-level contribution of the Greenland Ice Sheet during the Last Interglacial vary widely. Here, we conduct a transient climate simulation from 127 to 119 ka using the Community Earth System Model (CESM2), which includes a dynamic ice sheet component (the Community Ice Sheet Model, CISM2) that is interactively coupled to the atmosphere, land, ocean, and sea ice components. Vegetation distribution is updated every 500 years based on biomes simulated using a monthly climatology to force the BIOME4 equilibrium vegetation model. Results show a substantial retreat of the Greenland Ice Sheet, reaching a minimum extent at 121.9 ka, equivalent to a 3.0 m rise in sea level relative to the present day, followed by gradual regrowth. In contrast, a companion simulation employing static vegetation based on pre-industrial conditions shows a much smaller ice-sheet retreat, highlighting the importance of the changes in high-latitude vegetation distribution for amplifying the ice-sheet response.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021PA004272","usgsCitation":"Sommers, A., Otto-Bliesner, B., Lipscomb, W., Lofverstrom, M., Shafer, S., Bartlein, P.J., Brady, E.C., Kluzek, E., Leguy, G., Thayer-Calder, K., and Tomas, R., 2021, Retreat and regrowth of the Greenland Ice Sheet during the Last Interglacial as simulated by the CESM2-CISM2 coupled climate–ice sheet model: Paleoceanography and Paleoclimatology, v. 36, no. 12, e2021PA004272, 19 p., https://doi.org/10.1029/2021PA004272.","productDescription":"e2021PA004272, 19 p.","ipdsId":"IP-117386","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science 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Robert","contributorId":289179,"corporation":false,"usgs":false,"family":"Tomas","given":"Robert","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":838648,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70236081,"text":"70236081 - 2021 - Changes in liquefaction severity in the San Francisco Bay Area with sea-level rise","interactions":[],"lastModifiedDate":"2022-08-29T12:15:11.769298","indexId":"70236081","displayToPublicDate":"2021-12-01T07:11:23","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Changes in liquefaction severity in the San Francisco Bay Area with sea-level rise","docAbstract":"This paper studies the impacts of sea-level rise on liquefaction triggering and severity around the San Francisco Bay Area, California, for the M 7.0 “HayWired” earthquake scenario along the Hayward fault. This work emerged from stakeholder engagement for the US Geological Survey releases of the HayWired earthquake scenario and the Coastal Storm Modeling System projects, in which local planners and engineers asked where, why, and by how much liquefaction hazards may change due to sea-level rise in the future. We assess the impacts of sea-level rise on liquefaction by computing changes in liquefaction potential index (LPI) for over 400 cone penetration test (CPT) soundings around the San Francisco Bay for groundwater table models developed for current and increased sea levels of up to 5 m. For the M 7.0 HayWired earthquake scenario, we find that while the majority of sites are insensitive to sea-level changes of less than 1 m, some sites are highly sensitive to small changes in water levels. We then repeat these analyses for a uniform shaking scenario to isolate the effects of sea-level rise and we find similar patterns of change. For both earthquake scenarios, modest changes in overall LPI are expected for increases in sea level, but individual sites may see significant increases in liquefaction likelihood and severity.
Aquatic systems in and around the Long Island Sound (LIS) provide a variety of ecological and economic benefits, but in some areas of the LIS, aquatic ecosystems have become degraded by excess nitrogen. A substantial fraction of the nitrogen inputs to the LIS are transported through the groundwater-flow system. Because groundwater travel times in surficial aquifers can exceed 100 years, multiyear lags are introduced between inputs at the water table in recharge areas and discharge to inland or coastal receiving waters. The U.S. Geological Survey, in cooperation with the Connecticut Department of Energy and Environmental Protection and the U.S. Environmental Protection Agency’s Long Island Sound Study, developed a steady-state groundwater model of the watersheds draining from the northern shore of the LIS for the purpose of calculating groundwater budgets and travel times to coastal waters.
The model was developed by using the MODFLOW–NWT software and existing spatial data on aquifers, river networks, land-surface altitudes, land cover, groundwater recharge, and water use. Coastal waters were delineated on the basis of the National Wetland Inventory; all non-coastal waters were collectively termed “inland waters.” A coarse-resolution model was calibrated by using the PEST++ software, long-term records of water levels in 65 wells, stream altitudes from 477 streams, base-flow records for 14 streamgages that are relatively unaffected by withdrawals, and error metrics based on incorrectly simulated flooding and incorrectly simulated dry streams. The calibrated values were used in a fine-resolution model in which the mean absolute residuals were 4.5 meters for groundwater levels, 1.3 meters for stream altitudes, and 7,200 cubic meters per day (2.9 cubic feet per second) for base flow. About 89 percent of the terrestrial cells were correctly simulated with the water table below land surface, and nearly 90 percent of the cells representing streams were correctly simulated as having the water table above the stream bottom. Together, these metrics suggest that this model is robust for simulating regional-scale groundwater patterns.
Simulated groundwater budgets were compiled for the entire study area, for each HUC12 (Hydrologic Unit Code no. 12) watershed and its adjacent coastal waters, if applicable, within the study area, and for 14 coastal-embayment watersheds. Most groundwater (90.6 percent of inflows) discharged to inland waters, with smaller fractions to coastal waters (7.0 percent) and well withdrawals (2.4 percent). When computed for HUC12 watersheds with coastal discharge, the portions of groundwater discharging to coastal waters ranged from 0.02 to 66 percent of groundwater outflows, with a median of 13 percent. Within priority-embayment watersheds, the portions of groundwater discharging to coastal waters ranged from 2 to 56 percent, with a median of 15 percent.
Groundwater travel times also were simulated for the entire study area, for each HUC12 watershed and its adjacent coastal waters, if applicable, within the study area and for 14 priority coastal embayments. Within the entire study area, the median groundwater travel time was 1.9 years, with an interquartile range of 0.1 to 5.9 years. Sensitivity analysis of groundwater travel times within a subbasin in the study area indicates that the travel times are a function of the grid resolution, with coarser grids resulting in shorter median travel times. Travel times for groundwater discharging to coastal waters were similar to travel times for groundwater discharging to inland waters, with a median of 1.9 years. Median travel times for the HUC12 watersheds ranged from 0.9 to 53.5 years, with a median of 1.8 years. Among HUC12 watersheds that include coastal areas, travel times for groundwater discharging to coastal waters ranged from less than 1 to 61.6 years, with a median of 2.8 years. The HUC12 watersheds with the longest simulated travel times were in the urban area near New York City where the model performance is less accurate. Median travel times for groundwater discharging to coastal waters within the priority-embayment watersheds ranged from less than 1 to 18.6 years, with a median of 2.3 years.
A more focused analysis was conducted for the Niantic River watershed to demonstrate the applicability of the regional model to local-scale nitrogen-transport analyses by using nitrogen-input and -attenuation rates from literature sources. Nitrogen inputs were estimated by using land-cover-based loading factors, and attenuation was estimated by using attenuation factors based on geologic zones and soil properties. Based on this analysis, groundwater transports an estimated 22,000 kilograms of nitrogen per year (2.9 kilograms of nitrogen per hectare per year) to streams, rivers, and coastal waters within the Niantic River watershed. Approximately 36 percent of discharging nitrogen is from atmospheric-deposition sources, 38 percent is from fertilizers, and 26 percent is from septic systems. Most of the groundwater-transported nitrogen (88 percent) discharges first to streams and rivers, with only 12 percent discharging directly to coastal waters. Travel times for groundwater-transported nitrogen ranged from less than 1 day to more than 100 years, with a median of 1.6 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215116","collaboration":"Prepared in cooperation with the United States Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2021, Simulation of groundwater budgets and travel times for watersheds on the north shore of Long Island Sound, with implications for nitrogen-transport studies: U.S. Geological Survey Scientific Investigations Report 2021–5116, 84 p., https://doi.org/10.3133/sir20215116.","productDescription":"Report: x, 84 p.; 2 Data Releases","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-117840","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":391933,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91TQ895","text":"USGS data release","linkHelpText":"Summary data on groundwater budgets and travel times for watersheds on the north shore of Long Island Sound"},{"id":391932,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BLHPIT","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH groundwater flow models of steady-state conditions in coastal Connecticut and adjacent areas of New York and Rhode Island, as well as a nitrogen transport model of the Niantic River watershed"},{"id":391931,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5116/sir20215116.pdf","text":"Report","size":"30.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5116"},{"id":391930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5116/coverthb.jpg"}],"country":"United States","state":"Connecticut, New York, Rhode Island","otherGeospatial":"Long island Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.9324951171875,\n 40.826280356677124\n ],\n [\n -71.45782470703125,\n 40.826280356677124\n ],\n [\n -71.45782470703125,\n 41.50857729743935\n ],\n [\n -73.9324951171875,\n 41.50857729743935\n ],\n [\n -73.9324951171875,\n 40.826280356677124\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532