Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Kalispel Tribe of Indians (KT), U. S. Fish and Wildlife Service, and Washington Department of Fish and Wildlife are engaged in conservation of bull trout (Salvelinus confluentus) in the Lake Pend Oreille (LPO) Core Area. The LPO is a complex habitat core area which falls within three states (Montana, Idaho, and Washington) and a tribal entity. As part of the conservation process, KT worked in cooperation with the U. S. Geological Survey (USGS) to complete a risk assessment for introduction of bull trout into Sullivan Lake/Harvey Creek, northeastern, Washington. The risk assessment was designed to evaluate potential risks to resident fish species, to bull trout introduced into Sullivan Lake, and to bull trout donor source populations. This risk assessment describes the potential risks associated with pathogens (introduction of pathogens and increased pathogen burden), genetics (such as risk to donor sources, straying and breeding with native bull trout, and introduction of bull-brook hybrids), and ecological interactions (such as predation and competition). Potential donor source populations were identified and evaluated using a qualitative approach based on expert opinion and a decision framework.
Literature reviews were completed for fish species composition and abundance in Sullivan Lake basin to assess potential ecological interactions and risks to these populations and to the introduced bull trout. The USGS assessed pathogen risks through two major questions: (1) whether introduced bull trout might bring pathogens into the Sullivan Lake basin that were not previously present and (2) whether the health of introduced bull trout could be adversely affected by pathogens already present in the basin. Assessment of genetic risks included demographic risks to donor source populations, potential for hybridization with native bull trout, and the risk of introducing bull-brook hybrids. Literature reviews were used in conjunction with discussions among regional biologists to identify potential donor source populations and their population attributes. A decision framework was developed by USGS in collaboration with KT biologists that identified desirable population attributes (life history behavior, abundance, population viability, feasibility of collection, and environmental match) associated with donor source populations and established ranking criteria. The population attribute information was used with the (1) decision framework, (2) established ranking criteria, and (3) expert opinion of regional biologists, to assign scores for overall ranking of donor source populations.
The LPO source population was the highest ranked and is considered a robust and stable population. The risk of introducing pathogens from LPO into Sullivan Lake via a bull trout introduction program seems low, and indirect pathogen burden risks to resident species can be mitigated using established pathogen surveillance methods. The likelihood that bull trout, introduced into Sullivan Lake, stray and spawn with native bull trout is low. Nearest-neighbor donor source populations, such as LPO, could minimize negative fitness impacts that might occur from straying and interbreeding of individuals that become entrained and help maintain natural patterns of genetic diversity in native populations. The ecological risk that a bull trout introduction presents to resident species seems to be low but with some uncertainty. Pygmy whitefish, a Washington State Sensitive species, is likely most vulnerable to extirpation with increased predation pressure with introduction of an additional piscivore into the ecosystem. The status of the pygmy whitefish in Sullivan Lake is unknown. The ecological risks most likely to reduce the viability of introduced bull trout are predation by burbot and an adequate forage base in Sullivan Lake. Prior fish surveys provided data on resident species abundance, provided an established baseline for effective monitoring, and identifying ecosystem changes post-bull trout introduction to inform future adaptive management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221032","collaboration":"Prepared in cooperation with Kalispel Tribe of Indians","usgsCitation":"Hardiman, J.M., Breyta, R.B., and Ostberg, C.O., 2022, Risk assessment for bull trout introduction into Sullivan Lake and Harvey Creek, northeastern Washington: U.S. Geological Survey Open-File Report 2022–1032, 26 p., https://doi.org/10.3133/ofr20221032.","productDescription":"Report: vii, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128540","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":400079,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1032/images"},{"id":400080,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1032/ofr20221032.XML"},{"id":400074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1032/coverthb.jpg"},{"id":400075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1032/ofr20221032.pdf","text":"Report","size":"8.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1032"},{"id":400076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCQEZ1","text":"USGS data release","description":"USGS data release","linkHelpText":"Information tables associated with a risk assessment for bull trout introduction into Sullivan Lake, northeastern, Washington including population donor sources and resident species, April 2021"}],"country":"United States","state":"Washington","otherGeospatial":"Harvey Creek, Sullivan Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.49191284179688,\n 48.67736049788919\n ],\n [\n -117.03598022460938,\n 48.67736049788919\n ],\n [\n -117.03598022460938,\n 48.89632393659644\n ],\n [\n -117.49191284179688,\n 48.89632393659644\n ],\n [\n -117.49191284179688,\n 48.67736049788919\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Informed analysis of policies related to food security, global climate change, wetland ecology, environmental nutrient flux, element cycling, groundwater weathering, continental denudation, human health, etc. depends to a large extent on quantitative estimates of solute mass fluxes into and out of all global element pools including the enigmatic global aquifer systems. Herein for the first time, we proffer the mean global solute concentration of all major and selected minor and trace solutes in the active groundwater that represents 99% of liquid fresh water on Earth. Concentrations in this significant element pool have yielded to a geospatial machine learning kNN-nearest neighbors’ algorithm with numerous geospatial predictors utilizing a large new lithology/climate/aquifer age/elevation based solute database. The predicted concentrations are consistent with traditional solute ratios, concentrations, and thermodynamic saturation indices.
We explore the response of ground motions to topography during large crustal fault earthquakes by simulating several magnitude 6.5–7.0 rupture scenarios on the Seattle fault, Washington State. Kinematic simulations are run using a 3D spectral element code and a detailed seismic velocity model for the Puget Sound region. This model includes realistic surface topography and a near‐surface low‐velocity layer; a mesh spacing of ∼30 m at the surface allows modeling of ground motions up to 3 Hz. We simulate 20 earthquake scenarios using different slip distributions and hypocenter locations on a planar fault surface. Results indicate that average ground motions in simulations with and without topography are similar. However, shaking amplification is common at topographic highs, and more than a quarter of all sites experience short‐period (≤2 s) ground‐motion amplification greater than 25%–35%, compared with models without topography. Comparisons of peak ground velocity at the top and bottom of topographic features demonstrate that amplification is sensitive to period, with the greatest amplifications typically manifesting near a topographic feature’s estimated resonance frequency and along azimuths perpendicular to its primary axis of elongation. However, interevent variability in topographic response can be significant, particularly at shorter periods (<1 s). We do not observe a clear relationship between source centroid‐to‐site azimuths and the strength of topographic amplification. Overall, our results suggest that although topographic resonance does influence the average ground motions, other processes (e.g., localized focusing and scattering) also play a significant role in determining topographic response. However, the amount of consistent, significant amplification due to topography suggests that topographic effects should likely be considered in some capacity during seismic hazard studies.
In southeastern New York, the villages of Ellenville, Wurtsboro, Woodridge, the hamlet of Mountain Dale, and surrounding communities in the Neversink River and Rondout Creek drainage basins rely on wells that pump groundwater from valley-fill glacial aquifers for public water supply. Glacial aquifers are vulnerable to contamination because they are highly permeable and have a shallow depth to water table. To protect the quality of these water resources, water managers need accurate information about the areas that contribute recharge to production wells that pump from these aquifers. The New York State Department of Environmental Conservation and the New York State Department of Health designated eight priority wells in this region for which water supply protection is of primary concern.
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 with the general objectives of (1) improving understanding of regional groundwater-flow system, (2) delineating areas contributing recharge to eight priority production wells, and (3) quantifying the uncertainty of these contributing areas in a probabilistic way that can be used to inform decision-making related to priority well source-water protection. To complete these objectives, a MODFLOW 6 groundwater model was created encompassing the eight priority wells and the surrounding flow system, which includes parts of the Neversink River and Rondout Creek Basins in Sullivan County and Ulster County, New York. The model was built using Python tools (such as flopy, modflow-setup, and sfrmaker) that facilitate transparent and repeatable model development using existing datasets. The model parameters were estimated with a stepwise approach using an iterative ensemble smoother implementation of the Parameter ESTimation software PEST++ (version 5.0.0). We evaluated initial “best guess” parameter bounds with a prior Monte Carlo analysis. Results of the first prior Monte Carlo analysis were used to make informed adjustments to model parameter bounds (typically resulting in expanded bounds), and a second prior Monte Carlo analysis was run to identify improved ranges for model parameters during history matching.
The history matching effort produced an ensemble of parameter values for the groundwater-flow model that spans the range of values within prior uncertainty bounds. The ensemble is informed by the historical observation data, within a reasonable range of uncertainty on those observations. This history-matched ensemble was used in a particle tracking Monte Carlo analysis to delineate the areas contributing recharge to priority wells. The groundwater-flow and particle tracking (MODPATH7) models were run once for each ensemble member. Deterministic contributing areas computed for each ensemble member were aggregated to produce maps showing the probability that a location contributes recharge to priority wells. Finally, the particle tracking Monte Carlo analysis was repeated for six pumping scenarios, representing a wide range of possible pumping levels, to incorporate uncertainty in future pumping rates related to population growth or other management decisions. Increasing pumping rates generally led to larger contributing recharge areas and larger areas of high probability that a location contributes recharge to priority wells. These maps show the overall uncertainty of the areas contributing recharge to priority wells in the study area and provide a tool for risk-based decision making for protection of well source water.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215112","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health","usgsCitation":"Corson-Dosch, N.T., Fienen, M.N., Finkelstein, J.S., Leaf, A.T., White, J.T., Woda, J., and Williams, J.H., 2022, Areas contributing recharge to priority wells in valley-fill aquifers in the Neversink River and Rondout Creek drainage basins, New York: U.S. Geological Survey Scientific Investigations Report 2021–5112, 50 p., https://doi.org/10.3133/sir20215112.","productDescription":"Report: ix, 50 p.; 2 Data Releases","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125165","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399109,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":399101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5112/coverthb.jpg"},{"id":399102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5112/sir20215112.pdf","text":"Report","size":"22.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5112"},{"id":399104,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5112/sir20215112.XML"},{"id":399105,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5112/images/"},{"id":399107,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://ny.water.usgs.gov/maps/neversink/","text":"Neversink-Rondout Source Water Mapper"},{"id":399982,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20215112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5112"},{"id":399106,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225024","text":"Scientific Investigations Report 2022–5024","linkHelpText":"- Data Sources and Methods for Digital Mapping of Eight Valley-Fill Aquifer Systems in Upstate New York"},{"id":399108,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HWSOHP","text":"USGS data release","linkHelpText":"Groundwater model archive and workflow for Neversink/Rondout Basin, New York, source water delineation"},{"id":502119,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112974.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Neversink River and Rondout Creek Drainage Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8663330078125,\n 41.40153558289846\n ],\n [\n -74.1961669921875,\n 41.40153558289846\n ],\n [\n -74.1961669921875,\n 41.99216023337633\n ],\n [\n -74.8663330078125,\n 41.99216023337633\n ],\n [\n -74.8663330078125,\n 41.40153558289846\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Digital hydrogeologic maps were developed in eight study areas in upstate New York by the U.S. Geological Survey in cooperation with the New York State Department of Environmental Conservation. The digital maps define the hydrogeologic framework of the valley-fill aquifers and surrounding till-covered uplands in the vicinity of the villages of Ellenville and Wurtsboro and hamlets of Woodbourne and South Fallsburg in Sullivan and Ulster Counties, town of Greene in Chenango County, city of Cortland and town of Cincinnatus in Cortland County, city of Jamestown in Chautauqua County, city of Olean and village of Ellicottville in Cattaraugus County, and villages of Fishkill and Wappinger Falls in Dutchess County. The hydrogeologic framework provided the foundation for groundwater-flow models that were used in the delineation of areas contributing groundwater flow to production wells screened in four of the eight valley-fill aquifers considered in this study. The hydrogeologic framework for the other four study areas was developed for potential future use in groundwater contributing-area studies.
Data used in the creation of all digital surfaces and thicknesses included published surficial geology; aquifer maps and hydrogeologic sections; light detection and ranging (lidar) datasets; the Soil Survey Geographic Database; and lithologic well logs from the National Water Information System, New York State Department of Environmental Conservation, New York State Department of Transportation, and Empire State Organized Geologic Information System databases. Digital maps of the surficial geology; thickness of the surficial sand and gravel aquifers; and tops of the confining lacustrine silt and clay units, confined sand and gravel aquifers, and bedrock surfaces were created by using ArcGIS (a geographic information system). All surfaces and thicknesses were generated by using one of the following ArcGIS interpolation tools: Topo to Raster, Natural Neighbors, Kriging, or Empirical Bayesian Kriging. The datasets developed in this study provide a greater understanding of the underlying hydrogeologic framework in glacial valley-fill aquifers and can be applied in the evaluation of groundwater-supply development and protection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225024","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Finkelstein, J.S., Woda, J.C., and Williams, J.H., 2022, Data sources and methods for digital mapping of eight valley-fill aquifer systems in upstate New York: U.S. Geological Survey Scientific Investigations Report 2022–5024, 21 p., https://doi.org/10.3133/sir20225024.","productDescription":"Report: v, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122133","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":398708,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":398704,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5024/coverthb2.jpg"},{"id":398705,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5024/sir20225024.pdf","text":"Report","size":"4.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5024"},{"id":502380,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112976.htm","linkFileType":{"id":5,"text":"html"}},{"id":399981,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225024/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5024"},{"id":398710,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20215112","text":"Scientific Investigations Report 2021–5112","linkHelpText":"- Areas Contributing Recharge to Priority Wells in Valley-fill Aquifers in the Neversink River and Rondout Creek Drainage Basins, New York"},{"id":398709,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20215083","text":"Scientific Investigations Report 2021–5083","linkHelpText":"- Areas Contributing Recharge to Selected Production Wells in Unconfined and Confined Glacial Valley-Fill Aquifers in Chenango River Basin, New York"},{"id":398707,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5024/images/"},{"id":398706,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5024/sir20225024.XML"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.200439453125,\n 40.59727063442024\n ],\n [\n -73.223876953125,\n 40.59727063442024\n ],\n [\n -73.223876953125,\n 43.48481212891603\n ],\n [\n -79.200439453125,\n 43.48481212891603\n ],\n [\n -79.200439453125,\n 40.59727063442024\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
In the Chenango River Basin of central New York, unconfined and confined glacial valley-fill aquifers are an important source of drinking-water supplies. The risk of contaminating water withdrawn by wells that tap these aquifers might be reduced if the areas contributing recharge to the wells are delineated and these areas protected from land uses that might affect the water quality. The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation and the New York State Department of Health, began an investigation in 2019 to improve understanding of groundwater flow and delineate areas contributing recharge to 16 production wells clustered in three study areas in the basin as part of an effort to protect the source of water to these wells. Areas contributing recharge were delineated on the basis of numerical steady-state groundwater-flow models representing long-term average hydrologic conditions.
In the Cortland study area, four water suppliers operate 10 production wells that withdraw a total average rate of 2,480 gallons per minute from an unconfined aquifer consisting of well-sorted sand and gravel deposits. Simulated areas contributing recharge to these wells at their average pumping rates covered a total area of 6.93 square miles. Simulated areas contributing recharge extend upgradient from the wells to upland till deposits and to groundwater divides. Some simulated areas contributing recharge include isolated areas remote from the wells. Short simulated groundwater traveltimes from recharging locations to discharging wells indicated that the wells are vulnerable to contamination from land-surface activities; 50 percent of the traveltimes were 10 years or less. Land cover in some of the areas contributing recharge included a substantial amount of urban and agriculture land use.
The groundwater-flow model of the Cortland study area was calibrated to available hydrologic data by inverse modeling using nonlinear regression. The parameter variance-covariance matrix from model calibration was used to create parameter sets that reflect the uncertainty of the parameter estimates and the correlation among parameters to evaluate the uncertainty associated with the single, predicted contributing areas to the wells. This analysis led to contributing areas expressed as a probability distribution. Because of the effects of parameter uncertainty, the size of the probabilistic contributing areas was larger than the size of the single, predicted contributing area for the wells. Thus, some areas not in the single, predicted contributing area might actually be in the contributing area, including additional areas of urban and agriculture land use that have the potential to contaminate groundwater. Additional areas that might be in the contributing area included recharge originating near the pumping wells that have relatively short groundwater-flow paths and traveltimes.
In each of the Greene and Cincinnatus study areas, one water supplier operates three wells that are screened near the top of the bedrock surface in a confined aquifer consisting of poorly to well-sorted sand and gravel deposits. This confined aquifer is overlain by a lacustrine confining unit of very fine sand, silt, and clay, which in turn is overlain by a thin unconfined aquifer of sand and gravel. The groundwater-flow models for these two areas were manually calibrated because of the limited hydrologic data. Simulated areas contributing recharge to the Greene study area wells covered a total area of 0.35 square mile for the average pumping rate of 170 gallons per minute. The contributing areas extended southeastward of the wells to the groundwater divide in the till uplands. The contributing areas also included remote, isolated areas on the opposite side of the Chenango River from the wells primarily in the till uplands. For the Cincinnatus study area wells, which have a low average pumping rate (34 gallons per minute), the simulated contributing areas totaled 0.06 square mile and were on the same side of the river as the wells, but they are isolated areas remote from the wells primarily in the till-covered bedrock uplands. Land cover in these contributing areas for both study areas is primarily agriculture and forested, with the contributing areas to the Greene study area wells also including some urban land uses. Because the Greene and Cincinnatus study area wells are screened relatively deep and some flow paths to the wells partly travel through the confining unit, which impedes the connection with surface sources of recharge, overall groundwater traveltimes are greater than for wells in the Cortland study area. Fifty percent of Cortland study area wells, but only 9 and 44 percent of Greene and Cincinnatus study area wells, respectively, have groundwater traveltimes of 10 years or less.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215083","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation and New York State Department of Health","usgsCitation":"Friesz, P.J., Williams, J.H., Finkelstein, J.S., and Woda, J.C., 2022, Areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York (ver. 1.1, 2026): U.S. Geological Survey Scientific Investigations Report 2021–5083, 48 p., https://doi.org/10.3133/sir20215083.","productDescription":"Report: vi, 48 p.; 2 Data Releases; Database","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126791","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":502109,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112975.htm","linkFileType":{"id":5,"text":"html"}},{"id":500551,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5083/versionHist.txt","size":"892 B","linkFileType":{"id":2,"text":"txt"}},{"id":398545,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20225024","text":"Scientific Investigations Report 2022–5024","linkHelpText":"- Data Sources and Methods for Digital Mapping of Eight Valley-Fill Aquifer Systems in Upstate New York"},{"id":398544,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96R5K5R","text":"USGS data release","linkHelpText":"Interpolated hydrogeologic framework and digitized datasets for upstate New York study areas"},{"id":398543,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":398541,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5083/images/"},{"id":398540,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5083/sir20215083.XML"},{"id":398539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5083/sir20215083.pdf","text":"Report","size":"18.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5083"},{"id":398538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5083/coverthb3.jpg"},{"id":399980,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215083/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5083"},{"id":398542,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HU2G1K","text":"USGS data release","linkHelpText":"MODFLOW -NWT groundwater-flow models used to delineate areas contributing recharge to selected production wells in unconfined and confined glacial valley-fill aquifers in Chenango River Basin, New York"}],"country":"United States","state":"New York","otherGeospatial":"Chenango River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125000001,\n 42.13896840458089\n ],\n [\n -75.16845703125,\n 42.13896840458089\n ],\n [\n -75.16845703125,\n 42.90011265525331\n ],\n [\n -76.11328125000001,\n 42.90011265525331\n ],\n [\n -76.11328125000001,\n 42.13896840458089\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: May 2022; Version 1.1: April 2026","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Bull trout (Salvelinus confluentus) in the Klamath Basin are on the southernmost border of the range of the species, where threats are most severe and where bull trout are most imperiled. In their recovery plan the U.S. Fish and Wildlife Service (2015, https://ecos.fws.gov/ecp/report/species-with-recovery-plans) suggested that Klamath Basin bull trout are at increased risk of extirpation due to habitat fragmentation, degradation of habitat complexity, and introduction of non-native trout species that often outcompete bull trout. The goals of this study were to determine if there was a lack of connectivity between habitat areas impeding migration, habitat differences, or interference by non-native species affecting bull trout distribution in the Klamath Basin. This study examined three populations of bull trout in conjunction with a concurrent native species (redband trout [Oncorhynchus mykiss gairdnerii]), and a concurrent non-native species (brown trout [Salmo trutta]) in tributaries of the upper Sprague River within the Klamath Basin. Culverts present at the beginning of the study may have impeded migration of bull trout, but culvert upgrades made during the study appeared to eliminate the impediments to migration. The presence of non-native brown trout appeared to cause bull trout to use a smaller portion of Leonard Creek, whereas the low numbers of brown trout in the studied portion of Brownsworth Creek did not appear to interfere with the local distribution of bull trout. Downstream migration of bull trout may have been impeded if there were increased numbers of brown trout or increased temperatures in the lower portions of the creeks outside of the study area. Although habitat complexity was not examined in detail during this study, there was an attempt to enhance the habitat for bull trout by introducing large woody debris into treatment sections of the creeks. We compared bull trout numbers between the treatment sections and nearby control sections prior to and after introduction of the large woody debris. The introduction of large woody debris did not appear to enhance the use of those areas by bull trout, but the large woody debris may not have been of suitable size to enhance the habitat for bull trout.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221022","usgsCitation":"Martin, B.A., Banish, N., Hewitt, D.A., Hayes, B.S., Dolan-Caret, A., Harris, A.C., and Kelsey, C., 2022, Distribution of bull trout (Salvelinus confluentus) in conjunction with habitat and trout assemblages in creeks within the Klamath Basin, Oregon 2010–16: U.S. Geological Survey Open-File Report 2022–1022, 28 p., https://doi.org/10.3133/ofr20221022.","productDescription":"vi, 28 p.","onlineOnly":"Y","ipdsId":"IP-133280","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":399925,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1022/ofr20221022.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1022"},{"id":399924,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1022/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Klamath Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.80517578125,\n 42.00848901572399\n ],\n [\n -120.838623046875,\n 42.00848901572399\n ],\n [\n -120.838623046875,\n 42.924251753870685\n ],\n [\n -122.80517578125,\n 42.924251753870685\n ],\n [\n -122.80517578125,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The U.S. Geological Survey completed gravity transects in 2015, 2018, and 2019 over four features: the Bright Angel Fault, Bright Angel Monocline, Tusayan Graben, and Redlands Ranch Fault Zone in the Coconino Plateau, Coconino County, Arizona, to determine if the existence and width of high porosity (low density) zones could be inferred from the resulting gravity contrasts, which could be used to update groundwater models of the region. Faults and other geological structures in the Coconino Plateau are commonly thought to play a role in the movement of groundwater in the area, but limited data exist to constrain their influence. Some groundwater models of the region have used zones of enhanced permeability and porosity along or near features to model their effect on groundwater flow but have not shown sensitivity to the width of the zones used. Enhanced porosity zones in the subsurface, such as those included along or near features in some groundwater models of the region, could create small mass deficiencies detectable by microgravity methods. However, 3 of the 4 gravity transects, the Bright Angel Fault, Bright Angel Monocline, and Tusayan Graben, showed no negative gravity anomaly over the features that could indicate the presence of a low-density zone. Only the Redlands Ranch Fault Zone that had nearby collapse features showed a negative gravity anomaly that was modeled as a zone of 0.017 increased porosity about 800 meters wide, corresponding to the relative dimension and enhanced porosity used in groundwater models of the area. This study was unable to verify the existence of enhanced porosity zones at the selected locations along the other features. However, faults and other features may affect groundwater flow in different ways at different locations, and this work does not preclude the existence of enhanced porosity zones at other places along these faults.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225031","usgsCitation":"Wildermuth, L.M., 2022, Gravity surveys for estimating possible width of enhanced porosity zones across structures on the Coconino Plateau, Coconino County, north-central Arizona: U.S. Geological Survey Scientific Investigations Report 2022–5031, 22 p., https://doi.org/10.3133/sir20225031.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","ipdsId":"IP-121579","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":502389,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112973.htm","linkFileType":{"id":5,"text":"html"}},{"id":399839,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZYHEBB","text":"Data from “Gravity surveys for estimating possible width of enhanced porosity zones across structures on the Coconino Plateau, Coconino County, north-central Arizona”","description":"Wildermuth, L.M., 2021, Data from “Gravity surveys for estimating possible width of enhanced porosity zones across structures on the Coconino Plateau, Coconino County, north-central Arizona”: U.S. Geological Survey data release, https://doi.org/10.5066/P9ZYHEBB."},{"id":399838,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5031/sir20225031.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":399837,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5031/covrthb.jpg"}],"country":"United States","state":"Arizona","county":"Coconino 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Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The relationships that control seed production in trees are fundamental to understanding the evolution of forest species and their capacity to recover from increasing losses to drought, fire, and harvest. A synthesis of fecundity data from 714 species worldwide allowed us to examine hypotheses that are central to quantifying reproduction, a foundation for assessing fitness in forest trees. Four major findings emerged. First, seed production is not constrained by a strict trade-off between seed size and numbers. Instead, seed numbers vary over ten orders of magnitude, with species that invest in large seeds producing more seeds than expected from the 1:1 trade-off. Second, gymnosperms have lower seed production than angiosperms, potentially due to their extra investments in protective woody cones. Third, nutrient-demanding species, indicated by high foliar phosphorus concentrations, have low seed production. Finally, sensitivity of individual species to soil fertility varies widely, limiting the response of community seed production to fertility gradients. In combination, these findings can inform models of forest response that need to incorporate reproductive potential.
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Pearse, I.S., Perez-Ramos, I., Piechnik, L., Poulsen, J., Poulton-Kamakura, R., Redmond, M., Reid, C.D., Rodman, K., Rodrigues-Sanchez, F., Sanguinetti, J., Scher, C.L., Schlesinger, W.H., Schmidt Van Marle, H., Seget, B., Sharma, S., Silman, M., Steele, M.A., Stephenson, N.L., Straub, J.N., Sun, I., Sutton, S., Swenson, J., Swift, M., Thomas, P., Uriarte, M., Vacchiano, G., Veblen, T., Whipple, A.V., Whitham, T.G., Wion, A., Wright, B., Wright, S.J., Zhu, K., Zimmermann, J., Zlotin, R., Zywiec, M., and Clark, J.S., 2022, Limits to reproduction and seed size-number trade-offs that shape forest dominance and future recovery: Nature Communications, v. 13, 2381, 12 p., https://doi.org/10.1038/s41467-022-30037-9.","productDescription":"2381, 12 p.","ipdsId":"IP-132526","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research 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Understanding how landscape composition impacts gene flow (i.e., connectivity) and interacts with scale is essential to conservation decision-making. We used a landscape genetics approach implementing a recently developed statistical model based on the generalized Wishart probability distribution to identify the primary landscape features affecting gene flow and estimate the degree to which each component influences connectivity for Gunnison sage-grouse (Centrocercus minimus). We were interested in two spatial scales: among distinct populations rangewide and among leks (i.e., breeding grounds) within the largest population, Gunnison Basin. Populations and leks are nested within a landscape fragmented by rough terrain and anthropogenic features, although requisite sagebrush habitat is more contiguous within populations. Our best fit models for each scale confirm the importance of sagebrush habitat in connectivity, although the important sagebrush characteristics differ. For Gunnison Basin, taller shrubs and higher quality nesting habitat were the primary drivers of connectivity, while more sagebrush cover and less conifer cover facilitated connectivity rangewide. Our findings support previous assumptions that Gunnison sage-grouse range contraction is largely the result of habitat loss and degradation. Importantly, we report direct estimates of resistance for landscape components that can be used to create resistance surfaces for prioritization of specific locations for conservation or management (i.e., habitat preservation, restoration, or development) or as we demonstrated, can be combined with simulation techniques to predict impacts to connectivity from potential management actions.
Effective management and restoration of salt marshes and other vegetated intertidal habitats require objective and spatially integrated metrics of geomorphic status and vulnerability. The unvegetated-vegetated marsh ratio (UVVR), a recently developed metric, can be used to establish present-day vegetative cover, identify stability thresholds, and quantify vulnerability to open-water conversion over a range of spatial scales. We developed a Landsat-based approach to quantify the within-pixel vegetated fraction and UVVR for coastal wetlands of the conterminous United States, at 30-m resolution for 2014–2018. Here we present the methodology used to generate the UVVR from spectral indices, along with calibration, validation, and spatial autocorrelation assessments. We then demonstrate multiple applications of the data across varying spatial scales: first, we aggregate the UVVR across individual states and estuaries to quantify total vegetated wetland area for the nation. On the state level, Louisiana and Florida account for over 50% of the nation’s total, while on the estuarine level, the Chesapeake Bay Estuary and selected Louisiana coastal areas each account for over 6% of the nation’s total vegetated wetland area. Second, we present cases where this dataset can be used to track wetland change (e.g., expansion due to restoration and loss due to stressors). Lastly, we propose a classification methodology that delineates areas vulnerable to open-water expansion based on the 5-year mean and standard deviation of the UVVR. Calculating the UVVR for the period-of-record back to 1985, as well as regular updating, will fill a critical gap for tracking national status of salt marshes and other vegetated habitats through time and space.
No abstract available.
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Here, we analyzed the steady swimming kinematics of a diverse group of fish species to investigate whether their undulatory movements can be represented using a series of interconnected multi-segment models, and if so, to identify the key factors driving the segment configuration of the models. Our results show that the steady swimming kinematics of fishes can be described successfully using parsimonious models, 83% of which had fewer than five segments. In these models, the anterior segments were significantly longer than the posterior segments, and there was a direct link between segment configuration and swimming kinematics, body shape, and Reynolds number. The models representing eel-like fishes with elongated bodies and fishes swimming at high Reynolds numbers had more segments and less segment length variability along the body than the models representing other fishes. These fishes recruited their anterior bodies to a greater extent, initiating the undulatory wave more anteriorly. Two shape parameters, related to axial and overall body thickness, predicted segment configuration with moderate to high success rate. We found that head morphology was a good predictor of its segment length. While there was a large variation in head segments, the length of tail segments was similar across all models. Given that fishes exhibited variable caudal fin shapes, the consistency of tail segments could be a result of an evolutionary constraint tuned for high propulsive efficiency. The bio-inspired multi-segment models presented in this study highlight the key bending points along the body and can be used to decide on the placement of actuators in fish-inspired robots, to model hydrodynamic forces in theoretical and computational studies, or for predicting muscle activation patterns during swimming.","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-3190/ac6bd6","usgsCitation":"Akanyeti, O., di Santo, V., Goerig, E., Wainwright, D.K., Liao, J., Castro-Santos, T.R., and Lauder, G., 2022, Fish-inspired segment models for undulatory swimming: Bioinspiration & Biomimetics, v. 17, 046007, 14 p., https://doi.org/10.1088/1748-3190/ac6bd6.","productDescription":"046007, 14 p.","ipdsId":"IP-136956","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447956,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-3190/ac6bd6","text":"Publisher Index Page"},{"id":409309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","noUsgsAuthors":false,"publicationDate":"2022-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Akanyeti, O.","contributorId":269927,"corporation":false,"usgs":false,"family":"Akanyeti","given":"O.","email":"","affiliations":[{"id":16758,"text":"Aberystwyth University","active":true,"usgs":false}],"preferred":false,"id":856915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"di Santo, V.","contributorId":269925,"corporation":false,"usgs":false,"family":"di Santo","given":"V.","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":856916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goerig, Elsa","contributorId":261644,"corporation":false,"usgs":false,"family":"Goerig","given":"Elsa","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":856917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wainwright, Dylan K.","contributorId":299039,"corporation":false,"usgs":false,"family":"Wainwright","given":"Dylan","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":856945,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liao, J.C.","contributorId":269929,"corporation":false,"usgs":false,"family":"Liao","given":"J.C.","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":856918,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":856919,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lauder, George 0000-0003-0731-286X","orcid":"https://orcid.org/0000-0003-0731-286X","contributorId":298066,"corporation":false,"usgs":false,"family":"Lauder","given":"George","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":856920,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232096,"text":"70232096 - 2022 - Adaptive management framework and decision support tool for invasive annual bromes in seven Northern Great Plains National Park Service units","interactions":[],"lastModifiedDate":"2022-07-18T16:11:16.468009","indexId":"70232096","displayToPublicDate":"2022-05-01T10:40:18","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NGPN/NRR-2022/2381","title":"Adaptive management framework and decision support tool for invasive annual bromes in seven Northern Great Plains National Park Service units","docAbstract":"National Park Service (NPS) units in the northern Great Plains (NGP) were established to preserve and interpret the history of the United States, protect and showcase unusual geology and paleontology, and provide a home for vanishing large wildlife. A unifying feature among these national parks, monuments, and historic sites is northern mixed-grass prairie, which not only provides background scenery and habitat but is the foundation of many park missions. As recognition of the prairie’s importance to park fundamental resources and values has grown, so too has the realization that invasive plants threaten these values by reducing native species diversity, altering food webs, and marring the visitor experience. Cheatgrass (Bromus tectorum) and Japanese brome (Bromus japonicus)—collectively referred to as “annual bromes”—are of particular concern because of their documented increase through time, and their association with lower native plant diversity, in NGP parks. A variety of grazing, herbicide-application, and prescribed-fire experiments have shown promising short-term results for controlling annual bromes in research-scale plots in the NGP, but it is unclear whether these management actions will be as effective at the larger spatial and longer temporal scales relevant to park management. When uncertainties about the effectiveness of different management actions cannot be answered with traditional research approaches in time to prevent resource degradation, yet recurrent management decisions must be made, an adaptive management approach may be appropriate. Thus, in 2017, we began to develop the ABAM—Annual Brome Adaptive Management—framework. The aim of this framework is to reduce uncertainties about methods for controlling annual bromes in seven NGP parks through a formal process of learning from the application of on-going management. A uniform framework across seven parks provides greater opportunities for reducing these uncertainties compared to a single park acting alone or to multiple parks using different adaptive management frameworks.
This technical report details the development and expected implementation of the ABAM framework. After briefly introducing the issue (Section 1) and describing the context in which the framework was developed (Section 2), the report describes how a structured decision-making process was used to frame the problem, determine concrete objectives, and decide the alternative actions for achieving those objectives that the framework would be designed around (Section 3). Then the report describes the process used to develop the ABAM decision support system (Section 4). At the core of this system is the ABAM decision support tool, a Bayesian decision network built on nearly two decades of vegetation monitoring data from NGP parks, as well as current literature and ABAMspecific experiments. This tool, referred to as the ABAM model by its intended users, is built to work with the existing vegetation monitoring, prescribed fire, and invasive plant management programs that support the seven ABAM parks. In Section 5, the report describes how output from the ABAM model is produced and used in annual vegetation management decision making. It describes the ABAM R package (Baldwin et al. 2021) and an example R script that leads a user through an annual workflow using the model and the package. This workflow updates the model with information from new monitoring events following management actions of prescribed fire, herbicide application, or a combination thereof. With the updated model, data describing the current condition of vegetation in park management units, and current data for environmental factors included in the model (soil texture, slope, weather, and grazing), the user then runs the model to predict future vegetation conditions—and managers’ happiness with the outcome—in response to each of 10 management actions for each management unit in each park. These predictions inform managers’ decisions regarding locations and types of management actions to apply in the upcoming year.
The ABAM framework is in its infancy, and the report concludes (Section 6) with a discussion of its longer-term viability. Successful adaptive management requires commitment for the long term, likely decades. Currently, the predictions of the decision support tool are not expected to be highly accurate, but they ideally will improve over time as more management actions are applied and their outcomes are captured by monitoring. We designed the ABAM decision support tool to work with the existing management and monitoring resources in ABAM parks to maximize the sustainability of the model’s use, but the ABAM framework requires more than the model. Because this application of an adaptive management framework supported by a quantitative decision support tool to guide vegetation management is unique within the NPS (to our knowledge), institutional knowledge and mechanisms for long-term implementation of the ABAM framework do not exist within the agency. Additionally, the ABAM model and the data that inform it could be improved in a variety of ways. Thus, this report concludes with a discussion of ways to both sustain and improve upon the work completed so far.
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baldwinh@usgs.gov","orcid":"https://orcid.org/0000-0003-1939-5439","contributorId":5635,"corporation":false,"usgs":true,"family":"Baldwin","given":"Heather","email":"baldwinh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Post van der Burg, Max 0000-0002-3943-4194 maxpostvanderburg@usgs.gov","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":4947,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","email":"maxpostvanderburg@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844198,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235743,"text":"70235743 - 2022 - Testing environmental DNA from wolf snow tracks for species, sex, and individual identification – An addendum","interactions":[],"lastModifiedDate":"2022-08-22T15:22:14.712971","indexId":"70235743","displayToPublicDate":"2022-05-01T10:16:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5015,"text":"Canadian Wildlife Biology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Testing environmental DNA from wolf snow tracks for species, sex, and individual identification – An addendum","docAbstract":"No abstract available.
","language":"English","publisher":"Alpha Wildlife Publications","usgsCitation":"Barber-Meyer, S., Zeller, V., and Pilgrim, K., 2022, Testing environmental DNA from wolf snow tracks for species, sex, and individual identification – An addendum: Canadian Wildlife Biology and Management, v. 11, no. 1, p. 14-16.","productDescription":"3 p.","startPage":"14","endPage":"16","ipdsId":"IP-137284","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":405391,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":405390,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cwbm.ca/testing-environmental-dna-from-wolf-snow-tracks-for-species-sex-and-individual-identification-an-addendum/"}],"country":"United States","state":"Minnesota","otherGeospatial":"Superior National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.36480712890625,\n 47.557993859037765\n ],\n [\n -90.0054931640625,\n 47.557993859037765\n ],\n [\n -90.0054931640625,\n 48.09642606004488\n ],\n [\n -92.36480712890625,\n 48.09642606004488\n ],\n [\n -92.36480712890625,\n 47.557993859037765\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barber-Meyer, Shannon 0000-0002-3048-2616","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":217939,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":849172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zeller, Victoria","contributorId":295462,"corporation":false,"usgs":false,"family":"Zeller","given":"Victoria","email":"","affiliations":[],"preferred":false,"id":849173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilgrim, Kristy","contributorId":295313,"corporation":false,"usgs":false,"family":"Pilgrim","given":"Kristy","affiliations":[{"id":63838,"text":"US Forest Service National Genomics Center, Missoula, MT","active":true,"usgs":false}],"preferred":false,"id":849174,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70233196,"text":"70233196 - 2022 - USGS Critical Minerals Review: 2021","interactions":[],"lastModifiedDate":"2022-07-22T15:15:48.502758","indexId":"70233196","displayToPublicDate":"2022-05-01T10:14:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2755,"text":"Mining Engineering","active":true,"publicationSubtype":{"id":10}},"title":"USGS Critical Minerals Review: 2021","docAbstract":"In 2021, the U.S. Geological Survey (USGS) continued to play a central role in understanding and anticipating potential supply chain disruptions by defining and quantitatively evaluating mineral criticality. In addition, the USGS continued to evaluate new sources of domestic critical minerals by conducting mineral resource assessments, mapping and surveying regions prospective for critical minerals, re-assessing mine waste, and pursuing fundamental research in support of responsible, sustainable mining.","language":"English","publisher":"Society for Mining, Metallurgy & Exploration","usgsCitation":"Fortier, S.M., Nassar, N.T., Graham, G.E., Hammarstrom, J.M., Day, W.C., Mauk, J.L., and Seal,, R., 2022, USGS Critical Minerals Review: 2021: Mining Engineering, v. 74, no. 5.","productDescription":"1 p.","startPage":"34","ipdsId":"IP-139936","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":404345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":404344,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://me.smenet.org/abstract.cfm?preview=1&articleID=10596&page=34"}],"volume":"74","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fortier, Steven M. 0000-0001-8123-5749","orcid":"https://orcid.org/0000-0001-8123-5749","contributorId":202406,"corporation":false,"usgs":true,"family":"Fortier","given":"Steven","email":"","middleInitial":"M.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":846763,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":846764,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":846765,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammarstrom, Jane M. 0000-0003-2742-3460 jhammars@usgs.gov","orcid":"https://orcid.org/0000-0003-2742-3460","contributorId":1226,"corporation":false,"usgs":true,"family":"Hammarstrom","given":"Jane","email":"jhammars@usgs.gov","middleInitial":"M.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":846766,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Day, Warren C. 0000-0002-9278-2120 wday@usgs.gov","orcid":"https://orcid.org/0000-0002-9278-2120","contributorId":1308,"corporation":false,"usgs":true,"family":"Day","given":"Warren","email":"wday@usgs.gov","middleInitial":"C.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":846767,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mauk, Jeffrey L. 0000-0002-6244-2774 jmauk@usgs.gov","orcid":"https://orcid.org/0000-0002-6244-2774","contributorId":4101,"corporation":false,"usgs":true,"family":"Mauk","given":"Jeffrey","email":"jmauk@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":846768,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":846769,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237073,"text":"70237073 - 2022 - Highly specialized recreationists contribute the most to the citizen science project eBird","interactions":[],"lastModifiedDate":"2022-09-29T15:03:37.83503","indexId":"70237073","displayToPublicDate":"2022-05-01T09:55:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Highly specialized recreationists contribute the most to the citizen science project eBird","docAbstract":"Contributory citizen science projects (hereafter “contributory projects”) are a powerful tool for avian conservation science. Large-scale projects such as eBird have produced data that have advanced science and contributed to many conservation applications. These projects also provide a means to engage the public in scientific data collection. A common challenge across contributory projects like eBird is to maintain participation, as some volunteers contribute just a few times before disengaging. To maximize contributions and manage an effective program that has broad appeal, it is useful to better understand factors that influence contribution rates. For projects capitalizing on recreation activities (e.g., birding), differences in contribution levels might be explained by the recreation specialization framework, which describes how recreationists vary in skill, behavior, and motives. We paired data from a survey of birders across the United States and Canada with data on their eBird contributions (n = 28,926) to test whether those who contributed most are more specialized birders. We assigned participants to 4 contribution groups based on eBird checklist submissions and compared groups’ specialization levels and motivations. More active contribution groups had higher specialization, yet some specialized birders were not active participants. The most distinguishing feature among groups was the behavioral dimension of specialization, with active eBird participants owning specialized equipment and taking frequent trips away from home to bird. Active participants had the strongest achievement motivations for birding (e.g., keeping a life list), whereas all groups had strong appreciation motivations (e.g., enjoying the sights and sounds of birding). Using recreation specialization to characterize eBird participants can help explain why some do not regularly contribute data. Project managers may be able to promote participation, particularly by those who are specialized but not contributing, by appealing to a broader suite of motivations that includes both appreciation and achievement motivations, and thereby increase data for conservation.","language":"English","publisher":"Oxford University Press","doi":"10.1093/ornithapp/duac008","usgsCitation":"Rosenblatt, C.J., Dayer, A., Duberstein, J., Phillips, T.B., Harshaw, H., Fulton, D.C., Cole, N.W., Raedeke, A., Rutter, J., and Wood, C.L., 2022, Highly specialized recreationists contribute the most to the citizen science project eBird: Ornithological Applications, v. 124, no. 2, 16 p., https://doi.org/10.1093/ornithapp/duac008.","productDescription":"16 p.","ipdsId":"IP-122716","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":447960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duac008","text":"Publisher Index Page"},{"id":407601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"124","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenblatt, Connor J.","contributorId":297070,"corporation":false,"usgs":false,"family":"Rosenblatt","given":"Connor","email":"","middleInitial":"J.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":853251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dayer, Ashley A.","contributorId":278637,"corporation":false,"usgs":false,"family":"Dayer","given":"Ashley A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":853252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duberstein, Jennifer N.","contributorId":278642,"corporation":false,"usgs":false,"family":"Duberstein","given":"Jennifer N.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":853253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phillips, Tina B.","contributorId":149656,"corporation":false,"usgs":false,"family":"Phillips","given":"Tina","email":"","middleInitial":"B.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":853254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harshaw, H. W.","contributorId":297072,"corporation":false,"usgs":false,"family":"Harshaw","given":"H. W.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":853257,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":853256,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cole, Nicholas W. 0000-0003-1204-971X","orcid":"https://orcid.org/0000-0003-1204-971X","contributorId":278636,"corporation":false,"usgs":true,"family":"Cole","given":"Nicholas","email":"","middleInitial":"W.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":853255,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raedeke, Andrew H.","contributorId":297073,"corporation":false,"usgs":false,"family":"Raedeke","given":"Andrew H.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":853258,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rutter, Jonathan D.","contributorId":278638,"corporation":false,"usgs":false,"family":"Rutter","given":"Jonathan D.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":853259,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wood, Christopher L.","contributorId":297074,"corporation":false,"usgs":false,"family":"Wood","given":"Christopher","email":"","middleInitial":"L.","affiliations":[{"id":36682,"text":"Cornell Lab of Ornithology","active":true,"usgs":false}],"preferred":false,"id":853260,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70239128,"text":"70239128 - 2022 - Status and trends of North American bats: Summer occupancy analysis 2010-2019","interactions":[],"lastModifiedDate":"2022-12-28T15:45:33.125558","indexId":"70239128","displayToPublicDate":"2022-05-01T09:32:59","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Status and trends of North American bats: Summer occupancy analysis 2010-2019","docAbstract":"• We developed an analytical pipeline supported by web-based infrastructure for integrating continental scale bat monitoring data (stationary acoustic, mobile acoustic, and capture records) to estimate summer (May 1–Aug 31) occupancy probabilities and changes in occupancy over time for 12 North American bat species. This serves as one of multiple lines of evidence that inform the status and trends of bat populations.
• We analyzed data from a total of 12 bat species (Table 1), 11 of which have tested positive for Pseudogymnoascus destructans (Pd), a fungal pathogen that causes white-nose syndrome (WNS)—a disease that has led to significant rates of mortality for subterranean hibernating bat species in North America. A twelfth species was also selected because of high rates of mortality at wind energy facilities. Additional species were considered but not selected due to data limitations.
• We estimated occupancy probabilities for 2010 through 2019 for three species (Myotis lucifugus, MYLU; Myotis septentrionalis, MYSE; and Perimyotis subflavus, PESU). For an additional nine species, we estimated occupancy probabilities for 2016 through 2019 (Myotis evotis, MYEV; Myotis grisescens, MYGR; Myotis leibii, MYLE; Myotis thysanodes, MYTH; Myotis volans, MYVO; Myotis yumanensis, MYYU; Eptesicus fuscus, EPFU; Lasionycteris noctivagans, LANO; and Lasiurus cinereus, LACI). • For each species, we provide range-wide occupancy probability predictions (e.g., predicted summer occupancy distribution maps) each year at a spatial resolution of 100 km2 and provide regional estimates of mean occupancy probability aggregated at larger spatial scales (state/province/territory, range-wide).
• For each species, we also provide trends over time (average annual change rate and total change rate) in mean occupancy probabilities at multiple spatial scales (state/province/territory, range-wide) and when possible, over multiple timescales (short, medium, long).
• Results suggest that over the short-term (2016-2019), two (Myotis lucifugus and Perimyotis subflavus) of 12 species have experienced declines in range-wide average occupancy probability with at least 95% certainty. Seven species showed either minor increases or decreases in range-wide average occupancy probability but with less than 95% certainty in both trend indicators. Results over the longer term (eight years and 10 years of sampling) suggest that three hibernating species known to be highly affected by white-nose syndrome (Myotis lucifugus, Myotis septentrionalis, and Perimyotis subflavus) have experienced marked declines in range-wide average occupancy probabilities, with severity varying by species and region. Finally, the results for three species (Eptesicus fuscus, Lasiurus cinereus, Lasionycteris noctivagans) were inconclusive due to 1) borderline convergence issues in the model fitting procedure which suggests potentially unreliable estimates, 2) failure to reliably distinguish between false positives and true positive detections for ambiguous detections, and 3) largely uninformative covariates for occupancy and detection.
• For Myotis lucifugus, Myotis septentrionalis, and Perimyotis subflavus we found meaningful associations in space and time between declining winter populations (likely a result of WNS) and summer occupancy distributions.
• The representativeness of sampling data for each species’ status and trend estimates (e.g., state/province/territory) were also evaluated based on the percent of grid cells sampled each year with a goal of understanding the reliability of regional estimates and improving future monitoring efforts.
• This work represents the most comprehensive effort to date to model North American bat distributions across their continental ranges. Despite current limitations highlighted in the discussion, the analytical methods and resulting status and trends estimates provide the best available science on summer bat populations across North America and will continue to improve over time as monitoring data sets and analytical methods improve.
• Moving forward, our occupancy analyses will continue to improve with submission of more 1) data from currently underrepresented areas (i.e., improved geographic representation), 2) manually-vetted acoustic recordings, 3) capture records, and 4) roost location and count data (summer and winter).
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.7944/P927I36K","usgsCitation":"Udell, B.J., Straw, B., Cheng, T.L., Enns, K., Frick, W., Gotthold, B., Irvine, K., Lausen, C., Loeb, S., Reichard, J., Rodhouse, T., Smith, D., Stratton, C., Thogmartin, W.E., and Reichert, B., 2022, Status and trends of North American bats: Summer occupancy analysis 2010-2019, xvi, 231 p., https://doi.org/10.7944/P927I36K.","productDescription":"xvi, 231 p.","ipdsId":"IP-136669","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":435864,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92JGACB","text":"USGS data release","linkHelpText":"Status and Trends of North American Bats Summer Occupancy Analysis 2010-2019 Data 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in southeastern Oregon. Surface water in the basin is used for a variety of social, economic, and ecological benefits. While some surface water uses compete with one another, others are complementary or jointly produce multiple beneficial outcomes. The objective of this study is to conduct a baseline economic assessment of surface water in the Basin as it relates to wet meadow pasture production and outdoor recreation. Given the complex interactions between surface water management on public and private land, identifying and quantifying these economic outcomes can be used to assist future decision making in the Basin.","language":"English","publisher":"Western Agricultural Economics Association","doi":"10.22004/ag.econ.320614","usgsCitation":"Huber, C., Flyr, M., and Bair, L., 2022, Economic benefits supported by surface water in eastern Oregon’s Harney Basin: Western Economics Forum, v. 20, no. 1, p. 30-42, https://doi.org/10.22004/ag.econ.320614.","productDescription":"13 p.","startPage":"30","endPage":"42","ipdsId":"IP-134798","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":400698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Harney Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.6575927734375,\n 42.70665956351041\n ],\n [\n -117.99316406249999,\n 42.70665956351041\n ],\n [\n -117.99316406249999,\n 43.92559366355069\n ],\n [\n -119.6575927734375,\n 43.92559366355069\n ],\n [\n -119.6575927734375,\n 42.70665956351041\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Huber, Christopher 0000-0001-8446-8134 chuber@usgs.gov","orcid":"https://orcid.org/0000-0001-8446-8134","contributorId":127600,"corporation":false,"usgs":true,"family":"Huber","given":"Christopher","email":"chuber@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":843157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flyr, Matthew 0000-0002-4723-3763","orcid":"https://orcid.org/0000-0002-4723-3763","contributorId":291828,"corporation":false,"usgs":false,"family":"Flyr","given":"Matthew","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":843158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bair, Lucas 0000-0002-9911-3624","orcid":"https://orcid.org/0000-0002-9911-3624","contributorId":248714,"corporation":false,"usgs":true,"family":"Bair","given":"Lucas","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843159,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236543,"text":"70236543 - 2022 - Volcanoes of the Mojave: The 2022 Desert Symposium field trip road log","interactions":[],"lastModifiedDate":"2022-09-09T14:13:52.879693","indexId":"70236543","displayToPublicDate":"2022-05-01T09:04:46","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Volcanoes of the Mojave: The 2022 Desert Symposium field trip road log","docAbstract":"Basalt lava fields, some decorated with scoria ‘cinder’ cones, are scattered around the Mojave Desert. Most basalt fields are short-lived, but the Cima volcanic field is unique in having eruptions that span ~7.5 m.y., including the youngest eruption in the Mojave Desert at ~12 ka. Xenolith-bearing basalts that include both mantle and deep crustal rocks are known in several fields. All basalt fields except Cima are restricted to the active eastern California shear zone, and many lie directly on active faults, indicating a direct relation between faults and volcanism. The field trip will visit the Pisgah, Dish Hill, Amboy, Cima, and Bicycle Lake volcanic fields, and it will enable examination of the physical volcanology in the basalt fields, including the types of eruptions (effusive and explosive) and the resulting deposits (lava flows, scoria cones, and tuff cones). 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Accurate estimates of daily streamflow and the percentage of time that a certain volume of streamflow occurs or is exceeded in a stream is crucial information for structure design and other activities conducted by federal, state, and local officials. However, many important locations are ungaged and therefore lack the in-depth data provided at streamgages. The USGS provides hydrologic information like streamflow statistics and drainage basin characteristics in the web-based tool StreamStats (https://streamstats.usgs.gov/ss/). 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California","docAbstract":"The southwestern part of the Cima volcanic field in the Mojave National Monument, California, contains many of the youngest basaltic cinder cones and lava flows in the field (Wilshire and others, 2002). In 2014 the Hyperspectral Thermal Emission Spectrometer (HyTES) collected a swath of data across this area. This summary describes the HyTES instrument, data, and images, and compares two standard images to the geologic map and aerial photographs. In aerial photographs (visible spectrum) and HyTES images (thermal infrared spectrum), there are very good correlations of features such as pahoehoe and a`a flows (smoother or rougher surfaces, respectively), channelized features such as levees and flow-induced arcuate ridges across the channels, some flows with rafted parts of the source cinder cone, and different types of sedimentary deposits. The comparison is visual and qualitative; however, it shows the potential for applying hyperspectral data in a quantitative characterization and identification of lava flows and cones, sedimentary deposits, and locally exposed bedrock gneiss.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Desert Symposium Inc.","usgsCitation":"Buesch, D.C., and Hook, S.J., 2022, Hyperspectral Thermal Emission Spectrometer (HyTES) images of basaltic and sedimentary deposits in the southwest Cima volcanic field, California, in Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings, p. 116-119.","productDescription":"4 p.","startPage":"116","endPage":"119","ipdsId":"IP-133019","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":406453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":406434,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org/History.html"}],"country":"United States","state":"California","otherGeospatial":"Cima volcanic field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.68225860595702,\n 35.22711145535215\n ],\n [\n -115.5648422241211,\n 35.22711145535215\n ],\n [\n -115.5648422241211,\n 35.31708619029299\n ],\n [\n -115.68225860595702,\n 35.31708619029299\n ],\n [\n -115.68225860595702,\n 35.22711145535215\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buesch, David C. 0000-0002-4978-5027 dbuesch@usgs.gov","orcid":"https://orcid.org/0000-0002-4978-5027","contributorId":1154,"corporation":false,"usgs":true,"family":"Buesch","given":"David","email":"dbuesch@usgs.gov","middleInitial":"C.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851356,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hook, Simon J","contributorId":296384,"corporation":false,"usgs":false,"family":"Hook","given":"Simon","email":"","middleInitial":"J","affiliations":[{"id":27923,"text":"NASA JPL","active":true,"usgs":false}],"preferred":false,"id":851357,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231874,"text":"70231874 - 2022 - What’s past and ahead for the EMD","interactions":[],"lastModifiedDate":"2022-06-01T13:56:29.790487","indexId":"70231874","displayToPublicDate":"2022-05-01T08:52:10","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":10775,"text":"AAPG Explorer","active":true,"publicationSubtype":{"id":30}},"title":"What’s past and ahead for the EMD","docAbstract":"No abstract available.
","language":"English","publisher":"American Association of Petroleum Geologists","usgsCitation":"Birdwell, J.E., 2022, What’s past and ahead for the EMD: AAPG Explorer, no. May 2022, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-140063","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":401537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401508,"type":{"id":15,"text":"Index Page"},"url":"https://explorer.aapg.org/story/articleid/63262/whats-past-and-ahead-for-the-emd?"}],"issue":"May 2022","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":844017,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70236545,"text":"70236545 - 2022 - Remote sensing and mapping Miocene paleovalleys of the Marble, Bristol, and Old Dad Mountains in the Trilobite and Bristol Mountain Wildernesses, California","interactions":[],"lastModifiedDate":"2022-09-09T13:58:49.894103","indexId":"70236545","displayToPublicDate":"2022-05-01T08:50:22","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Remote sensing and mapping Miocene paleovalleys of the Marble, Bristol, and Old Dad Mountains in the Trilobite and Bristol Mountain Wildernesses, California","docAbstract":"Wilderness areas in the Mojave Desert, California, are remote and rugged terrain, but they contain important geology for understanding faults of the eastern California shear zone (ECSZ), and remote sensing offers techniques that can optimize mapping. The Bristol–Granite Mountain fault zone (BGMFZ) is the easternmost fault of the ECSZ with the Marble, Bristol, and Old Dad mountains on either side of the fault, as are the Trilobite and Bristol Mountain Wildernesses. In the northern Marble Mountains, a west-trending Miocene paleovalley has been proposed to have a correlative in the Old Dad Mountains and provides a constraint for right-lateral separation across the BGMFZ; however, this correlation is based on the premise that there was a unique paleovalley with a well defined geometry. In the northern Marble Mountains, a paleovalley was mapped by the distribution of (1) thickness and facies within the Lost Marble gravel (LMg) and 18.8 Ma Peach Spring Tuff (PST), and (2) adjacent highlands where the PST was deposited on basalt and dacite lava flows. Whether this paleovalley is unique, or there are other paleovalleys farther south in the Marble Mountains, requires mapping of the entire 5 by 28 km area of Miocene volcanic rocks. In the south Bristol and Old Dad mountains, there is a similar 12 by 22 km area of Miocene basalt and dacite with deposits of PST and local sedimentary rocks, including the proposed offset Lost Marble paleovalley, but the entire range needs to be mapped to establish a unique correlate. The mountains are in the Mojave Trails National Monument, and the Trilobite and Bristol Mountains wilderness areas, so access is limited. Remote sensing data, including aerial photography and hyperspectral images, are important for identification and characterization of rocks. Airborne hyperspectral Mako data can distinguish the distinctive spectral characteristics of the PST as well as several more map units identified by detailed field mapping in the Bristol Mountains. Reconnaissance maps derived from high spatial resolution Mako data can guide the detailed mapping needed to identify paleovalley or paleohighland deposits and can be used to optimize field time.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Desert Symposium Inc","usgsCitation":"Buesch, D.C., and Harvey, J., 2022, Remote sensing and mapping Miocene paleovalleys of the Marble, Bristol, and Old Dad Mountains in the Trilobite and Bristol Mountain Wildernesses, California, in Volcanoes in the Mojave: 2022 Desert symposium field guide and proceedings, p. 94-102.","productDescription":"9 p.","startPage":"94","endPage":"102","ipdsId":"IP-132116","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":406452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":406435,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org/History.html"}],"country":"United States","state":"California","otherGeospatial":"Trilobite and Bristol Mountain Wildernesses","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.05270385742188,\n 34.48957975202644\n ],\n [\n -115.41824340820312,\n 34.48957975202644\n ],\n [\n -115.41824340820312,\n 35\n ],\n [\n -116.05270385742188,\n 35\n ],\n [\n -116.05270385742188,\n 34.48957975202644\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buesch, David C. 0000-0002-4978-5027 dbuesch@usgs.gov","orcid":"https://orcid.org/0000-0002-4978-5027","contributorId":1154,"corporation":false,"usgs":true,"family":"Buesch","given":"David","email":"dbuesch@usgs.gov","middleInitial":"C.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":851358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harvey, Janet","contributorId":296385,"corporation":false,"usgs":false,"family":"Harvey","given":"Janet","email":"","affiliations":[{"id":64025,"text":"Heidelberg University Institute of Earth Sciences","active":true,"usgs":false}],"preferred":false,"id":851359,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}