{"pageNumber":"140","pageRowStart":"3475","pageSize":"25","recordCount":46650,"records":[{"id":70236653,"text":"pp1869 - 2022 - Attribution of monotonic trends and change points in peak streamflow across the conterminous United States using a multiple working hypotheses framework, 1941–2015 and 1966–2015","interactions":[],"lastModifiedDate":"2026-03-31T21:12:31.471654","indexId":"pp1869","displayToPublicDate":"2022-09-28T11:58:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1869","displayTitle":"Attribution of Monotonic Trends and Change Points in Peak Streamflow Across the Conterminous United States Using a Multiple Working Hypotheses Framework, 1941–2015 and 1966–2015","title":"Attribution of monotonic trends and change points in peak streamflow across the conterminous United States using a multiple working hypotheses framework, 1941–2015 and 1966–2015","docAbstract":"

The U.S. Geological Survey has a long history of leading flood-frequency analysis studies. These studies play a critical role in the assessment of risk, protection of lives, and planning and design of flood protection infrastructure. Standard flood-frequency analysis is based on the assumption of stationarity—that is, that the distribution of floods at a given site varies around a particular mean within a particular envelope of variance (and skew) and that these parameters of the underlying statistical distribution representative of the floods do not vary over time. Gradual or abrupt changes in one or more of the distributional parameters are called nonstationarities and violate the underlying assumptions of current U.S. Federal Government guidelines for flood-frequency analysis. Uncertainty exists as to what degree of violations calls for the use of a modified method for flood-frequency analysis and what the modified method(s) should be.

When deciding whether to perform nonstationary flood-frequency analysis and choosing a method for such analysis, it is important to understand the causes of the nonstationarity. Gradual or abrupt changes in distributional properties of floods may be the result of numerous factors, such as regulation, diversion, land-use change, or climate change.

In the interest of developing a cohesive national approach for better understanding the causes of nonstationarities and incorporating potential or observed changes into flood-frequency estimates, subject-matter experts from the U.S. Geological Survey and cooperators worked together to develop a multiple working hypotheses framework for making attributions and a common vocabulary for making provisions of confidence. Seven regional teams of these experts used ancillary datasets and institutional knowledge to evaluate plausible causes for monotonic trends and change points in annual peak-streamflow data for the conterminous United States that had been identified in an earlier phase of the project.

The first chapter of this professional paper describes the development of a list of the potential attributions, presents a literature review of the potential attributions, describes the regional approach, summarizes insights obtained from the attribution process, and suggests future research. The other chapters provide the methods used for attribution in the seven regions—Pacific Northwest, Upper Plains, Midwest, Northeast, Southwest, South-Central, and Southeast—and summarize the regional patterns of nonstationarities.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1869","collaboration":"Prepared in cooperation with the U.S. Department of Transportation, Federal Highway Administration","usgsCitation":"Ryberg, K.R., ed., 2022, Attribution of monotonic trends and change points in peak streamflow across the contermi­nous United States using a multiple working hypotheses framework, 1941–2015 and 1966–2015: U.S. Geological Survey Professional Paper 1869, 8 chapters (A–H), variously paged, https://doi.org/10.3133/pp1869.","productDescription":"Report: 328 p.; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-110840","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":501885,"rank":5,"type":{"id":36,"text":"NGMDB Index 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Integrated Modeling and Prediction Division
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 415
Reston, VA 20192

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-09-28","noUsgsAuthors":false,"publicationDate":"2022-09-28","publicationStatus":"PW","contributors":{"editors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851708,"contributorType":{"id":2,"text":"Editors"},"rank":1}]}} ,{"id":70237040,"text":"sir20225083 - 2022 - A computer-aided approach for adapting stage-discharge ratings and characterizing uncertainties of streamflow data with discrete measurements","interactions":[],"lastModifiedDate":"2022-09-28T15:15:05.348545","indexId":"sir20225083","displayToPublicDate":"2022-09-28T08:30:44","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5083","displayTitle":"A Computer-Aided Approach for Adapting Stage-Discharge Ratings and Characterizing Uncertainties of Streamflow Data with Discrete Measurements","title":"A computer-aided approach for adapting stage-discharge ratings and characterizing uncertainties of streamflow data with discrete measurements","docAbstract":"

Relations between stage (water level) and discharge of streamflow through a natural channel are the result of time-varying processes, which are commonly described by time-varying stage-discharge ratings. Hydrographers with the U.S. Geological Survey successfully maintain the accuracy of streamflow data by manually applying time-tested approaches to adapt ratings to temporal changes in hydraulic conditions. The difficulty with the manual approach is that it is a subjective, time-consuming process that requires considerable skill and experience to implement. In addition, manual adjustments of ratings make quantification of resulting streamflow data uncertainties problematic. A computer-aided adaptive stage-discharge estimation approach is proposed to track sequential changes in the relation between stage and discharge at continuous-record streamgages. In this report, adaptations are based strictly on discrete measurement data that are then used to compute the magnitudes and uncertainties of streamflow. The approach entails the parameterization of a cubic regression spline (CRS) for the stage-discharge relation based on an existing rating or on a set of discrete measurements. A state-space model is then parameterized to track temporal changes in stage-discharge relations beginning with the initial CRS parameterization using discrete measurements. Finally, Kalman estimation is used with the state-space model to estimate the magnitude and uncertainty of flows. In a case study using data from streamgage U.S. Geological Survey 04122500 Marquette River at Scottville, Michigan, a five-parameter CRS model was estimated from data in an existing stage-discharge rating to provide an initial CRS parameter set for a state-space model. The initial CRS parameters were updated sequentially in a state-space model based on periodic discrete measurements of stage and discharge that spanned a 30-year period for this analysis. Additional analysis is needed to determine the timing of rapidly varying shifts more precisely in stage-discharge relations than the relatively infrequent discrete measurements currently enabled. Unit streamflow estimates based on flow in a local streamgaging network may provide a basis for adapting a stage-discharge rating at unit time intervals by augmenting discrete measurement data within the state-space model.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225083","usgsCitation":"Holtschlag, D.J., 2022, A computer-aided approach for adapting stage-discharge ratings and characterizing uncertainties of streamflow data with discrete measurements: U.S. Geological Survey Scientific Investigations Report 2022–5083, 36 p., https://doi.org/10.3133/sir20225083.","productDescription":"viii, 36 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-130821","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407483,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5083/images"},{"id":407482,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5083/sir20225083.XML"},{"id":407480,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5083/coverthb.jpg"},{"id":407481,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5083/sir20225083.pdf","text":"Report","size":"47.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5083"}],"country":"United States","state":"Michigan","otherGeospatial":"Pere Marquette River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.20147705078125,\n 43.56845179881218\n ],\n [\n -85.49011230468749,\n 43.56845179881218\n ],\n [\n -85.49011230468749,\n 44.03429525903966\n ],\n [\n -86.20147705078125,\n 44.03429525903966\n ],\n [\n -86.20147705078125,\n 43.56845179881218\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

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2022-09-28","noUsgsAuthors":false,"publicationDate":"2022-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Holtschlag, David J. 0000-0001-5185-4928 dholtschlag@usgs.gov","orcid":"https://orcid.org/0000-0001-5185-4928","contributorId":5447,"corporation":false,"usgs":true,"family":"Holtschlag","given":"David","email":"dholtschlag@usgs.gov","middleInitial":"J.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853153,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70236030,"text":"ofr20221075 - 2022 - National strategy for landslide loss reduction","interactions":[],"lastModifiedDate":"2022-09-28T15:11:42.278953","indexId":"ofr20221075","displayToPublicDate":"2022-09-28T07:55:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1075","displayTitle":"National Strategy for Landslide Loss Reduction","title":"National strategy for landslide loss reduction","docAbstract":"

Executive Summary

Landslide hazards are present in all 50 States and most U.S. territories, and they affect lives, property, infrastructure, and the environment. Landslides are the downslope move­ment of earth materials under the force of gravity. They can occur without any obvious trigger. Widespread or severe land­slide events are often driven by such hazards as hurricanes, earthquakes, volcanic eruptions, heavy rain events, flooding, and wildfires. Landslides can also cause their own cascading consequences, such as the spread of hazardous materials or the creation of devastating local tsunamis.

This strategy document describes goals and strategic actions of a comprehensive strategy to meet key challenges to reducing the Nation’s risk from landslide hazards equitably and effectively. The document follows the direction of the National Landslide Preparedness Act (Public Law 116–323) by presenting a strategy for addressing landslide hazards, including risk reduction and response. The act directs the Department of the Interior to establish a program that will work with State, Tribal, and local governments as well as with academia, the private sector, community-based groups, and nonprofit organizations to identify landslide hazards and risk and improve communication, coordination, and emergency preparedness, with the objective of reducing landslide losses. As the only Federal program dedicated to landslide hazard science, the U.S. Geological Survey’s Landslide Hazards Program will lead and coordinate many of the efforts described in this strategy document. Landslide hazard risk reduction must be undertaken collectively and collaboratively across the Federal Government. This strategy document will provide a framework for the creation of an interagency management plan that describes the programs, projects, workforce, and budgets required to carry out the national strategy.

The strategy outlined in this document presents a vision of how to equitably produce, communicate, and apply landslide data and science to support a broad range of land management, infrastructure, planning, and emergency response decisions. These decisions are made by a variety of actors, including private and nonprofit landholders; State, Tribal, territorial, city, and county planners; emergency managers; engineers; infrastructure managers; Federal agencies and their partners; and community leaders and individuals. Supporting those decisions and reducing the Nation’s vulnerability to landslides requires overcoming three main challenges: (1) gaps in basic information needed to describe and understand landslide occurrence and societal risk, (2) difficulty in accurately mapping and forecasting landslide hazards, and (3) communication and coordination among the many jurisdictions and sectors that have responsi­bility for and interest in reducing landslide losses. To address those challenges, this strategy document puts forward a series of strategic actions to achieve four goals:

These strategic actions focus on expanding the knowl­edge of societal risk posed by landslides as well as better understanding of where, when, and why they occur. They focus on applying that knowledge to support landslide risk reduction efforts and decisions, including the establishment of new advisory, coordination, and working groups focused on landslide hazard and risk. They take into account that supporting landslide loss reduction decisions also requires new guidance, tools, and training codeveloped with the entities, organizations, and individuals faced with making those decisions. Finally, they address actions needed to support and expand landslide warning information and improve the technical response to landslide emergencies.

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Landslide Hazards Program
Natural Hazards Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-09-28","noUsgsAuthors":false,"publicationDate":"2022-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":849717,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":849718,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pennaz, Alice 0000-0002-7336-2761","orcid":"https://orcid.org/0000-0002-7336-2761","contributorId":205792,"corporation":false,"usgs":true,"family":"Pennaz","given":"Alice","email":"","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":849719,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dacey, Connor M. 0000-0001-6953-4164","orcid":"https://orcid.org/0000-0001-6953-4164","contributorId":295679,"corporation":false,"usgs":true,"family":"Dacey","given":"Connor","email":"","middleInitial":"M.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":849720,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":849721,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schaefer, Lauren N. 0000-0003-3216-7983","orcid":"https://orcid.org/0000-0003-3216-7983","contributorId":241997,"corporation":false,"usgs":true,"family":"Schaefer","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":849722,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Slaughter, Stephen L. 0000-0002-4322-3330","orcid":"https://orcid.org/0000-0002-4322-3330","contributorId":224686,"corporation":false,"usgs":true,"family":"Slaughter","given":"Stephen","email":"","middleInitial":"L.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":849723,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237010,"text":"70237010 - 2022 - Modeling of fire spread in sagebrush steppe using FARSITE: An approach to improving input data and simulation accuracy","interactions":[],"lastModifiedDate":"2022-09-27T18:10:51.541061","indexId":"70237010","displayToPublicDate":"2022-09-27T12:56:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling of fire spread in sagebrush steppe using FARSITE: An approach to improving input data and simulation accuracy","docAbstract":"

Background: Model simulations of wildfire spread and assessments of their accuracy are needed for understanding and managing altered fire regimes in semiarid regions. The accuracy of wildfire spread simulations can be evaluated from post hoc comparisons of simulated and actual wildfire perimeters, but this requires information on pre-fire vegetation fuels that is typically not available. We assessed the accuracy of the Fire-Area Simulator (FARSITE) model parameterized with maps of fire behavior fuel models (FBFMs) obtained from the widely used LANDFIRE, as well as alternative means which utilized the classification of Rangeland Analysis Platform (RAP) satellite-derived vegetation cover maps to create FBFM maps. We focused on the 2015 Soda wildfire, which burned 113,000 ha of sagebrush steppe in the western USA, and then assessed the transferability of our RAP-to-FBFM selection process, which produced the most accurate reconstruction of the Soda wildfire, on the nearby 2016 Cherry Road wildfire.

Results: Parameterizing FARSITE with maps of FBFMs from LANDFIRE resulted in low levels of agreement between simulated and observed area burned, with maximum Sorensen’s coefficient (SC) and Cohen’s kappa (K) values of 0.38 and 0.36, respectively. In contrast, maps of FBFMs derived from unsupervised classification of RAP vegetation cover maps led to much greater simulated-to-observed burned area agreement (SC = 0.70, K = 0.68). The FBFM map that generated the greatest simulated-to-observed burned area agreement for the Soda wildfire was then used to crosswalk FBFMs to another nearby wildfire (2016 Cherry Road), and this FBFM selection led to high FARSITE simulated-to-observed burned area agreement (SC = 0.80, K = 0.79).

Conclusions: Using RAP to inform pre-fire FBFM selection increased the accuracy of FARSITE simulations compared to parameterization with the standard LANDFIRE FBFM maps, in sagebrush steppe. Additionally, the crosswalk method appeared to have regional generalizability. Flanking and backfires were the primary source of disagreements between simulated and observed fire spread in FARSITE, which are sources of error that may require modeling of lateral heterogeneity in fuels and fire processes at finer scales than used here.

","language":"English","publisher":"Springer","doi":"10.1186/s42408-022-00147-2","usgsCitation":"Price, S.J., and Germino, M., 2022, Modeling of fire spread in sagebrush steppe using FARSITE: An approach to improving input data and simulation accuracy: Fire Ecology, v. 18, 23, 16 p., https://doi.org/10.1186/s42408-022-00147-2.","productDescription":"23, 16 p.","ipdsId":"IP-135384","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":446305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-022-00147-2","text":"Publisher Index Page"},{"id":407453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.333984375,\n 42.85180609584705\n ],\n [\n -116.43310546875,\n 42.85180609584705\n ],\n [\n -116.43310546875,\n 43.67581809328341\n ],\n [\n -117.333984375,\n 43.67581809328341\n ],\n [\n -117.333984375,\n 42.85180609584705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2022-09-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Price, Samuel J. 0000-0003-4172-4139","orcid":"https://orcid.org/0000-0003-4172-4139","contributorId":297001,"corporation":false,"usgs":true,"family":"Price","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":853070,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":853071,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256621,"text":"70256621 - 2022 - Hydrologic and environmental thresholds in stream fish assemblage structure across flow regimes","interactions":[],"lastModifiedDate":"2024-08-27T14:56:59.249872","indexId":"70256621","displayToPublicDate":"2022-09-27T09:52:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic and environmental thresholds in stream fish assemblage structure across flow regimes","docAbstract":"

The characteristic pattern of variation in flow magnitude, frequency, duration, timing, and rate of change defines the flow regime of rivers and streams and is a key driver of ecosystem processes in fluvial ecosystems. Understanding how freshwater biotic assemblages change across gradients of hydrology and anthropogenic-source disturbance in different streamflow regimes is crucial to managing for sustainable environmental flows and watershed conservation. We compiled long-term (1916–2016) occurrence records for fishes collected in the Ouachita-Ozark Interior Highlands and West Gulf Coastal Plain streams, together with hydrologic metrics calculated from daily streamflow data measured at USGS stream gauging stations (n = 111), to examine important drivers and thresholds for fish assemblage turnover in groundwater (GW), runoff (RO), and intermittent (INT) flow regimes. We also examined the importance of spatial gradients (latitude, longitude, elevation, drainage area) and anthropogenic-source stressors (Hydrologic Disturbance Index; HDI) for fish assemblage turnover using a gradient forest modeling approach. Watershed fragmentation was of high importance for fish assemblage turnover in RO and INT streams, while changes in dam storage were more important for fishes in GW streams. Hydrologic metrics describing seasonal and stochastic properties of daily streamflow (Mag6) were most important for fish assemblage turnover in INT streams. Timing of high flow events had significantly higher importance compared to flow magnitude, duration, and frequency metrics, especially for fish assemblages in GW and INT streams. The frequency and timing of low flow events had high importance for fish assemblage turnover across all stream flow classes, while the magnitude of low flows and the magnitude and rate of change of average flows was most important for INT stream fish assemblages. In addition to benefiting multi-species conservation and management actions through identification of local and regional flow-ecology relationships generalized across different flow regimes, the results of this study provide a better understanding of complex nonlinear threshold effects, which is critical to anticipating changes in aquatic ecosystems and communities.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.109500","usgsCitation":"Fox, J.T., and Magoulick, D.D., 2022, Hydrologic and environmental thresholds in stream fish assemblage structure across flow regimes: Ecological Indicators, v. 144, 109500, 12 p., https://doi.org/10.1016/j.ecolind.2022.109500.","productDescription":"109500, 12 p.","ipdsId":"IP-141446","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":446307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.109500","text":"Publisher Index Page"},{"id":433199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.0072909145776,\n 32.996331346931214\n ],\n [\n -91.86367305364405,\n 32.95089330614236\n ],\n [\n -91.50751795508893,\n 33.152929696557536\n ],\n [\n -91.43542283894192,\n 33.983279606945345\n ],\n [\n -91.88620071539745,\n 34.66359118761602\n ],\n [\n -90.24911433667984,\n 36.41777106986588\n ],\n [\n -89.39173105840047,\n 37.127658750904004\n ],\n [\n -90.27870353794154,\n 38.050842204957206\n ],\n [\n -92.57312445398881,\n 38.27074078230794\n ],\n [\n -94.07764032833012,\n 38.04668122401986\n ],\n [\n -94.69043619691729,\n 36.83802911087706\n ],\n [\n -96.4642878616655,\n 35.22823697686589\n ],\n [\n -96.5088412530875,\n 33.78407715154462\n ],\n [\n -95.25898895253292,\n 33.910484415403275\n ],\n [\n -94.09522136802627,\n 33.61028174407495\n ],\n [\n -94.0072909145776,\n 32.996331346931214\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"144","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fox, John Tyler","contributorId":341398,"corporation":false,"usgs":false,"family":"Fox","given":"John","email":"","middleInitial":"Tyler","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908352,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236979,"text":"ofr20221077 - 2022 - Field investigation of sub-isokinetic sampling by the US D-96-type suspended-sediment sampler and its effect on suspended-sediment measurements","interactions":[],"lastModifiedDate":"2026-03-30T20:32:00.626998","indexId":"ofr20221077","displayToPublicDate":"2022-09-27T09:04:33","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1077","displayTitle":"Field Investigation of Sub-Isokinetic Sampling by the US D-96-Type Suspended-Sediment Sampler and its Effect on Suspended-Sediment Measurements","title":"Field investigation of sub-isokinetic sampling by the US D-96-type suspended-sediment sampler and its effect on suspended-sediment measurements","docAbstract":"

Collection of accurate suspended-sediment data using depth-integrating samplers requires that they operate isokinetically, that is, that they sample at the local stream velocity unaffected by the presence of the suspended-sediment sampler. Sub-isokinetic suspended-sediment sampling causes grain-size dependent positive biases in the suspended-sediment concentration measured by the suspended-sediment sampler. Collapsible bag suspended-sediment samplers like the US D-96 and the lighter US D-96-A1 depth-integrating samplers have shown a tendency to sample sub-isokinetically under low stream velocities (below ~3.5 feet per second), colder water temperatures, and longer sampling durations. Previous work concluded that the time-dependent decrease in the intake efficiency of the US D-96-type sampler could be partially overcome by increasing the venting of water from the sampler cavity by shortening the sampler tray. The standard-length sampler tray partially blocks the rear vent hole; shortening the sampler tray effectively increases the area of the sampler-cavity rear vent hole. This previous work showed that removing the partial blockage of the rear vent hole caused by the sampler tray resulted in both an increase in intake efficiency and a decrease in the positive bias in measured suspended-sand concentration.

Herein, a series of tests were conducted on the Colorado River in Arizona using different modifications to a US D-96-A1 sampler to see if physical enlargement of the rear vent hole would produce further improvements in intake efficiency. Results from these tests show that physical enlargement of the rear vent hole, beyond that already effectively achieved by shortening the sampler tray, did not result in any further improvement in intake efficiency. However, these tests also indicated that physically increasing the area of the rear vent hole did not affect the suspended-sediment data collected by the US D-96-A1 sampler. Furthermore, comparisons of suspended-sediment data collected using the US D-96-A1 sampler and the isokinetic US P-61-A1 point-integrating sampler show that the suspended-sediment data collected by the US D-96-type sampler can be accurate in certain circumstances despite the tendency of this sampler to sample sub-isokinetically over the entire depth of a sampling vertical. We surmise that this result could arise from the US D-96-A1 sampler collecting sample isokinetically when the water-sediment mixture enters the nozzle, but that the water-sediment mixture only enters the nozzle intermittently while the sampler transits a sampling vertical.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221077","usgsCitation":"Sabol, T.A., Topping, D.J., Griffiths, R.E., and Dramais, G., 2022, Field investigation of sub-isokinetic sampling by the US D-96-type suspended-sediment sampler and its effect on suspended-sediment measurements: U.S. Geological Survey Open-File Report 2022-1077, 14 p., https://doi.org/10.3133/ofr20221077.","productDescription":"v, 14 p.","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-127691","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":501827,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113586.htm","linkFileType":{"id":5,"text":"html"}},{"id":407352,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1077/ofr20221077.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1077"},{"id":407351,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1077/covrthb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.58950805664062,\n 36.830722025409784\n ],\n [\n -111.54144287109375,\n 36.830722025409784\n ],\n [\n -111.54144287109375,\n 36.88236678807325\n ],\n [\n -111.58950805664062,\n 36.88236678807325\n ],\n [\n -111.58950805664062,\n 36.830722025409784\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.38714599609375,\n 35.755149755962755\n ],\n [\n -113.33358764648438,\n 35.755149755962755\n ],\n [\n -113.33358764648438,\n 35.777435736805614\n ],\n [\n -113.38714599609375,\n 35.777435736805614\n ],\n [\n -113.38714599609375,\n 35.755149755962755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2022-09-27","noUsgsAuthors":false,"publicationDate":"2022-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Sabol, Thomas A. 0000-0002-4299-2285 tsabol@usgs.gov","orcid":"https://orcid.org/0000-0002-4299-2285","contributorId":3403,"corporation":false,"usgs":true,"family":"Sabol","given":"Thomas","email":"tsabol@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":852895,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577 dtopping@usgs.gov","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":296930,"corporation":false,"usgs":true,"family":"Topping","given":"David J.","email":"dtopping@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":852896,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Griffiths, Ronald E. 0000-0003-3620-2926 rgriffiths@usgs.gov","orcid":"https://orcid.org/0000-0003-3620-2926","contributorId":162,"corporation":false,"usgs":true,"family":"Griffiths","given":"Ronald","email":"rgriffiths@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":852897,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dramais, Guillaume 0000-0002-2703-9314","orcid":"https://orcid.org/0000-0002-2703-9314","contributorId":238955,"corporation":false,"usgs":false,"family":"Dramais","given":"Guillaume","email":"","affiliations":[{"id":47837,"text":"Ph.D. student, IRSTEA, Flagstaff, Arizona","active":true,"usgs":false}],"preferred":false,"id":852898,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236980,"text":"ofr20221065 - 2022 - Pharmaceuticals and personal care products in passive samplers at seven coastal sites off West Maui, Hawaiʻi:","interactions":[],"lastModifiedDate":"2026-03-30T20:19:27.663825","indexId":"ofr20221065","displayToPublicDate":"2022-09-27T08:56:35","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1065","displayTitle":"Pharmaceuticals and Personal Care Products in Passive Samplers at Seven Coastal Sites off West Maui, Hawai‘i","title":"Pharmaceuticals and personal care products in passive samplers at seven coastal sites off West Maui, Hawaiʻi:","docAbstract":"

Passive membrane samplers—semipermeable membrane devices and polar organic chemical integrative samplers—were deployed for 22 continuous days at 7 sites along the West Maui, Hawaiʻi, coastline in February and March 2017 to assess organic contaminants at shallow coral reef ecosystems from diverse upstream inputs. The distribution of organic compounds observed at these coastal sites showed considerable variability; high concentrations of microbially sourced organic compounds observed at all sites, with pentadecane as the predominant normal alkane, showed the relative importance of marine and microbial organic matter to the coastal carbon pool. Pharmaceuticals and personal care products, as well as flame retardants, were also detected at all sites. Of the seven sites sampled, the Kahekili Beach Park site had the highest number of unique contaminants and the Honokōwai Stream site had the highest concentrations of compounds. Two individual compounds, a flame retardant and a fragrance, were ubiquitous across the studied West Maui reefs, including at the least-developed site. A direct correlation to upstream land-use practices or legacy agricultural inputs was not readily observed since polychlorinated biphenyls, pesticides, herbicides, or insecticides were not detected. Results provide a snapshot of relative contaminant abundances as well as inputs to select nearshore environments along the West Maui coastline captured during the 2017 wet season, which was drier than expected. These data can be useful for understanding the range of stressors potentially affecting nearshore ecosystems, such as groundwater inputs and watershed runoff.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221065","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the State of Hawai‘i Department of Health","usgsCitation":"Campbell, P.L., Prouty, N.G., and Storlazzi, C.D., 2022, Pharmaceuticals and personal care products in passive samplers at seven coastal sites off West Maui, Hawaiʻi: U.S. Geological Survey Open-File Report 2022–1065, 14 p., https://doi.org/10.3133/ofr20221065.","productDescription":"Report: vii, 12 p.; Data Release","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-132890","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501819,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113585.htm","linkFileType":{"id":5,"text":"html"}},{"id":435674,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZFE0OP","text":"USGS data release","linkHelpText":"Pharmaceuticals and personal care products measured in passive samplers at seven coastal sites off West Maui during February and March 2017"},{"id":407356,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1065/ofr20221065.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1065"},{"id":407355,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1065/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.708984375,\n 20.78693059257028\n ],\n [\n -156.5826416015625,\n 20.78693059257028\n ],\n [\n -156.5826416015625,\n 21.04349121680354\n ],\n [\n -156.708984375,\n 21.04349121680354\n ],\n [\n -156.708984375,\n 20.78693059257028\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2022-09-27","noUsgsAuthors":false,"publicationDate":"2022-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Campbell, Pamela L. 0000-0001-7056-4352","orcid":"https://orcid.org/0000-0001-7056-4352","contributorId":211947,"corporation":false,"usgs":true,"family":"Campbell","given":"Pamela","email":"","middleInitial":"L.","affiliations":[],"preferred":true,"id":852899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":852900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":852901,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70246256,"text":"70246256 - 2022 - Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response in-vitro","interactions":[],"lastModifiedDate":"2023-06-28T13:47:31.187151","indexId":"70246256","displayToPublicDate":"2022-09-27T08:33:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16135,"text":"GeoHealth","active":true,"publicationSubtype":{"id":10}},"title":"Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response in-vitro","docAbstract":"

Tungurahua volcano (Ecuador) intermittently emitted ash between 1999 and 2016, enduringly affecting the surrounding rural area and its population, but its health impact remains poorly documented. We aim to assess the respiratory health hazard posed by the 16–17 August 2006 most intense eruptive phase of Tungurahua. We mapped the spatial distribution of the health-relevant ash size fractions produced by the eruption in the area impacted by ash fallout. We quantified the mineralogy, composition, surface texture, and morphology of a respirable ash sample isolated by aerodynamic separation. We then assessed the cytotoxicity and pro-inflammatory potential of this respirable ash toward lung tissues in-vitro using A549 alveolar epithelial cells, by electron microscopy and biochemical assays. The eruption produced a high amount of inhalable and respirable ash (12.0–0.04 kg/m2 of sub-10 μm and 5.3–0.02 kg/m2 of sub-4 μm ash deposited). Their abundance and proportion vary greatly across the deposit within the first 20 km from the volcano. The respirable ash is characteristic of an andesitic magma and no crystalline silica is detected. Morphological features and surface textures are complex and highly variable, with few fibers observed. In-vitro experiments show that respirable volcanic ash is internalized by A549 cells and processed in the endosomal pathway, causing little cell damage, but resulting in changes in cell morphology and membrane texture. The ash triggers a weak pro-inflammatory response. These data provide the first understanding of the respirable ash hazard near Tungurahua and the extent to which it varies spatially in a fallout deposit.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GH000680","usgsCitation":"Eychenne, J., Gurioli, L., Damby, D., Belville, C., Schiavi, F., Marceau, G., Szczepaniak, C., Blavignac, C., Laumonier, M., Gardes, E., Le Pennec, J., Nedelec, J., Blanchon, L., and Sapin, V., 2022, Spatial distribution and physicochemical properties of respirable volcanic ash from the 16-17 August 2006 Tungurahua eruption (Ecuador), and alveolar epithelium response in-vitro: GeoHealth, v. 6, no. 12, e2022GH000680, 21 p., https://doi.org/10.1029/2022GH000680.","productDescription":"e2022GH000680, 21 p.","ipdsId":"IP-142222","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446315,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gh000680","text":"Publisher Index Page"},{"id":418584,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.26112606583433,\n -1.2298836523803232\n ],\n [\n -79.4800078645985,\n -1.2298836523803232\n ],\n [\n -79.4800078645985,\n -2.1006015647003693\n ],\n [\n -78.26112606583433,\n -2.1006015647003693\n ],\n [\n -78.26112606583433,\n -1.2298836523803232\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Eychenne, Julia","contributorId":168818,"corporation":false,"usgs":false,"family":"Eychenne","given":"Julia","email":"","affiliations":[{"id":25364,"text":"Univ. 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Natural resource management is often challenged with a mismatch between the scale of decision-making and the scale of the biological, ecological, and physical processes that control a system. Bioregional approaches to adaptive management have emerged as an approach to inform natural resource management at ecologically relevant scales and across multi-level governance structures. The implementation of adaptive management requires the determination of ecological and social priorities that can inform a desired system state across multiple governing bodies. We use the Northern Gulf of Mexico, United States, as a case study for a bioregional approach to adaptive management and illustrate a method for developing objectives and management priorities across programs and jurisdictions. Through this synthesis, using qualitative coding methods to develop a shared vocabulary across the diverse dataset, we identified commonalities and differences in ecological and human community priorities across the five states which line the Northern Gulf of Mexico. Using these shared priorities, we conceptualize a network of priority-focused objectives as a starting point for further stakeholder engagement and effectively monitoring and evaluating progress across boundaries. This approach serves as a framework for cross-program adaptive management by illustrating a desired system state that reflects the shared priorities among decision-making authorities in this region and offering individual programs or projects a method to articulate their contributions to the broader set of shared priorities Gulf-wide. This method can be used by restoration managers in any region of the world to align project objectives within cross-jurisdictional boundaries and illustrate the value of a bioregional approach to restoration.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.958684","usgsCitation":"Guilbeau, K.G., Hijuelos, A.C., Romanach, S., and Steyer, G., 2022, Identifying shared priorities for a bioregional approach to restoration in the Northern Gulf of Mexico: Frontiers in Ecology and Evolution, v. 10, 958684, 13 p.; Data Release, https://doi.org/10.3389/fevo.2022.958684.","productDescription":"958684, 13 p.; Data Release","ipdsId":"IP-134472","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446320,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.958684","text":"Publisher Index Page"},{"id":407693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417825,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BOFJV6"}],"country":"United States","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.107421875,\n 24.766784522874453\n ],\n [\n -78.3984375,\n 24.766784522874453\n ],\n [\n -78.3984375,\n 32.69486597787505\n ],\n [\n -100.107421875,\n 32.69486597787505\n ],\n [\n -100.107421875,\n 24.766784522874453\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Guilbeau, Kelly G.","contributorId":297126,"corporation":false,"usgs":false,"family":"Guilbeau","given":"Kelly","email":"","middleInitial":"G.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":853392,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hijuelos, Ann C 0000-0003-0922-6754","orcid":"https://orcid.org/0000-0003-0922-6754","contributorId":297128,"corporation":false,"usgs":false,"family":"Hijuelos","given":"Ann","email":"","middleInitial":"C","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":853393,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":220761,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853394,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steyer, Gregory 0000-0001-7231-0110","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":218813,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":853395,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237299,"text":"70237299 - 2022 - Neural net detection of seismic features related to gas hydrates and free gas accumulations on the northern U.S. Atlantic margin","interactions":[],"lastModifiedDate":"2022-10-07T11:55:34.680143","indexId":"70237299","displayToPublicDate":"2022-09-27T06:52:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3906,"text":"Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Neural net detection of seismic features related to gas hydrates and free gas accumulations on the northern U.S. Atlantic margin","docAbstract":"

Bottom-simulating reflections (BSRs) that sometimes mark the base of the gas hydrate stability zone in marine sediments are often identified based on the reverse polarity reflections that cut across stratigraphic layering in seismic amplitude data. On the northern U.S. Atlantic margin (USAM) between Cape Hatteras and Hudson Canyon, legacy seismic data have revealed pronounced BSRs south of the deepwater extension of Hudson Canyon and more subtle ones from offshore Delaware south to Cape Hatteras, where the reflections sometimes follow stratigraphic layering. Using high-resolution seismic data acquired during the 2018 Mid-Atlantic Resource Imaging Experiment and a supervised neural net, we identify seismic features associated with gas hydrates and/or the top of gas between Hudson Canyon and Cape Hatteras. Using seismic attributes especially sensitive to the presence of gas, we train a neural network algorithm on seismic data from an area with strong BSRs and then apply the model to the rest of the data set. The results indicate that gas hydrate and/or shallow free gas are significantly more widespread on the northern part of the USAM than previously known. Seismic indicators of gas extend landward from the 2000 m isobath to the upper continental slope in sectors with (offshore Virginia) and, to a lesser extent, without (offshore New Jersey) pervasive upper slope methane seeps. Higher sand content and intermediate sediment thickness, factors related to the container size and gas charge in a petroleum systems framework, are associated with more robust gas indicators.

","language":"English","publisher":"Society of Exploration Geologists","doi":"10.1190/INT-2021-0248.1","usgsCitation":"Majumdar, U., Miller, N.C., and Ruppel, C.D., 2022, Neural net detection of seismic features related to gas hydrates and free gas accumulations on the northern U.S. Atlantic margin: Interpretation, v. 10, no. 4, p. T785-T806, https://doi.org/10.1190/INT-2021-0248.1.","productDescription":"22 p.","startPage":"T785","endPage":"T806","ipdsId":"IP-132770","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":408081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"U.S. Atlantic margin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.87109375,\n 34.63320791137959\n ],\n [\n -69.12597656249999,\n 34.63320791137959\n ],\n [\n -69.12597656249999,\n 41.47566020027821\n ],\n [\n -77.87109375,\n 41.47566020027821\n ],\n [\n -77.87109375,\n 34.63320791137959\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Majumdar, Urmi","contributorId":297398,"corporation":false,"usgs":false,"family":"Majumdar","given":"Urmi","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":854085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Nathaniel C. 0000-0003-3271-2929 ncmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3271-2929","contributorId":174592,"corporation":false,"usgs":true,"family":"Miller","given":"Nathaniel","email":"ncmiller@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":854086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruppel, Carolyn D. 0000-0003-2284-6632 cruppel@usgs.gov","orcid":"https://orcid.org/0000-0003-2284-6632","contributorId":195778,"corporation":false,"usgs":true,"family":"Ruppel","given":"Carolyn","email":"cruppel@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":854087,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259663,"text":"70259663 - 2022 - Drought and water management in ancient Maya society","interactions":[],"lastModifiedDate":"2024-10-21T11:04:04.422118","indexId":"70259663","displayToPublicDate":"2022-09-27T06:01:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5866,"text":"Progress in Physical Geography: Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Drought and water management in ancient Maya society","docAbstract":"
Paleoclimate research in the Maya region of Mesoamerica provides compelling evidence of drought during key periods of cultural transition in Maya society. These include the transition from the Preclassic to the Classic, and from Classic to the Postclassic. Previous research emphasized a causal relationship between drought and cultural change, or so-called “collapse” in the Maya region. Recent advances in the range and precision of climate-sensitive proxies and the development of new archives have enabled quantitative reconstructions of past hydroclimate, as well as providing evidence of high impact, short-duration events, such as tropical cyclones. Simultaneously, archaeological research has unearthed widespread evidence of technologies used by the Maya to exert control over water resources in urban, rural, and agricultural settings. Evidence suggests that many of these water features were in use for multiple generations, possibly centuries, and many were constructed during the Terminal Preclassic and Terminal Classic periods. We suggest that, given the availability of new archaeological and paleoclimate records, these data can be combined to identify the full complexity of Maya adaptation to hydroclimate variability to emphasize adaptation and resilience to both water scarcity and over-abundance (e.g., flooding). Such syntheses, which can offer lessons for present-day efforts to grapple with regional climate change, will benefit from additional studies in data-poor zones of the Maya region, as well as public archiving of paleoclimate and archaeological data.
","language":"English","publisher":"Sage","doi":"10.1177/03091333221129784","usgsCitation":"Bhattacharya, T., Krause, S., Penny, D., and Wahl, D., 2022, Drought and water management in ancient Maya society: Progress in Physical Geography: Earth and Environment, v. 47, no. 2, p. 189-204, https://doi.org/10.1177/03091333221129784.","productDescription":"16 p.","startPage":"189","endPage":"204","ipdsId":"IP-138443","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":463050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.43938375079819,\n 13.678593843803739\n ],\n [\n -84.49700093829858,\n 13.678593843803739\n ],\n [\n -84.49700093829858,\n 23.33271697523854\n ],\n [\n -95.43938375079819,\n 23.33271697523854\n ],\n [\n -95.43938375079819,\n 13.678593843803739\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-09-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Bhattacharya, Tripti","contributorId":288113,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Tripti","email":"","affiliations":[{"id":27763,"text":"Univ. of Arizona","active":true,"usgs":false}],"preferred":false,"id":916171,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krause, Samantha","contributorId":345276,"corporation":false,"usgs":false,"family":"Krause","given":"Samantha","email":"","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":916172,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Penny, Dan 0000-0002-7905-0339","orcid":"https://orcid.org/0000-0002-7905-0339","contributorId":345277,"corporation":false,"usgs":false,"family":"Penny","given":"Dan","email":"","affiliations":[{"id":16826,"text":"University of Sydney","active":true,"usgs":false}],"preferred":false,"id":916173,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wahl, David 0000-0002-0451-3554","orcid":"https://orcid.org/0000-0002-0451-3554","contributorId":206113,"corporation":false,"usgs":true,"family":"Wahl","given":"David","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916174,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236960,"text":"dr1163 - 2022 - Groundwater and surface-water data collection for the Walla Walla River Basin, Washington, 2018–22","interactions":[],"lastModifiedDate":"2026-03-18T19:34:40.771206","indexId":"dr1163","displayToPublicDate":"2022-09-26T10:33:29","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1163","displayTitle":"Groundwater and Surface-Water Data Collection for the Walla Walla River Basin, Washington, 2018–22","title":"Groundwater and surface-water data collection for the Walla Walla River Basin, Washington, 2018–22","docAbstract":"

The semi-arid Walla Walla River Basin (WWRB) spans 1777 square miles in the states of Washington and Oregon and supports a diverse agricultural region as well as cities and rural communities that are partially reliant on groundwater. Historically, surface water and groundwater data have been collected in the WWRB by several entities including federal, state, local, and tribal governments; irrigation districts; universities; and non-profits. This report describes the surface and groundwater data collection by the U.S. Geological Survey from February 2018 to April 2022 to provide the Washington State Department of Ecology and other stakeholders basic knowledge of existing water resources in the WWRB, Washington. Additionally, the data were collected to build a long-term groundwater dataset, with the intent to provide data for better understanding to assist in informed decisions about groundwater use, management, and conservation throughout the basin, and for future inclusion in a conceptual model of the groundwater-flow system (conceptual model). Data were collected and compiled for 237 sites—191 wells and 46 surface-water discharge sites. A small annual network of deep basalt wells was established in February 2018 to commence the data collection. In March 2020 and April 2021, additional field inventories were performed by locating and measuring groundwater wells in the WWRB. A subset of the inventoried wells were selected for an annual or a quarterly water level network to be measured until July 2024. In August 2020, field reconnaissance identified 46 surface-water sites to be measured for discharge, estimating gaining and losing reaches in streams for groundwater influences.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1163","collaboration":"Prepared in cooperation with Washington Department of Ecology","usgsCitation":"Fasser, E.T., and Dunn, S.B., 2022, Groundwater and surface-water data collection for the Walla Walla River Basin, Washington, 2018–22: Data Report 1163, 8 p., https://doi.org/10.3133/dr1163.","productDescription":"Report: vi, 8 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-140692","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":407252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1163/coverthb.jpg"},{"id":407253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1163/dr1163.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1163"},{"id":407254,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1163/images"},{"id":407255,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1163/dr1163.XML"},{"id":407256,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D2C2DK","text":"USGS data release","description":"USGS data release","linkHelpText":"Dataset of groundwater and surface water data collection for the Walla Walla Basin in Washington, 2018–2022"},{"id":407257,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1163/full","text":"Report"},{"id":501271,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113584.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Walla Walla River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.21264648437499,\n 45.62172169252446\n ],\n [\n -117.191162109375,\n 45.62172169252446\n ],\n [\n -117.191162109375,\n 47.092565552235705\n ],\n [\n -119.21264648437499,\n 47.092565552235705\n ],\n [\n -119.21264648437499,\n 45.62172169252446\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402

","tableOfContents":"","publishedDate":"2022-09-26","noUsgsAuthors":false,"publicationDate":"2022-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Fasser, Elisabeth T. 0000-0002-3945-6633 efasser@usgs.gov","orcid":"https://orcid.org/0000-0002-3945-6633","contributorId":3973,"corporation":false,"usgs":true,"family":"Fasser","given":"Elisabeth","email":"efasser@usgs.gov","middleInitial":"T.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852817,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunn, Sarah B. 0000-0003-4463-0074","orcid":"https://orcid.org/0000-0003-4463-0074","contributorId":291768,"corporation":false,"usgs":false,"family":"Dunn","given":"Sarah B.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":852818,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237772,"text":"70237772 - 2022 - Tapwater exposures, effects potential, and residential risk management in Northern Plains Nations","interactions":[],"lastModifiedDate":"2022-10-24T15:20:21.435018","indexId":"70237772","displayToPublicDate":"2022-09-26T10:08:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10071,"text":"Environmental Science and Technology Water","active":true,"publicationSubtype":{"id":10}},"title":"Tapwater exposures, effects potential, and residential risk management in Northern Plains Nations","docAbstract":"

In the United States (US), private-supply tapwater (TW) is rarely monitored. This data gap undermines individual/community risk-management decision-making, leading to an increased probability of unrecognized contaminant exposures in rural and remote locations that rely on private wells. We assessed point-of-use (POU) TW in three northern plains Tribal Nations, where ongoing TW arsenic (As) interventions include expansion of small community water systems and POU adsorptive-media treatment for Strong Heart Water Study participants. Samples from 34 private-well and 22 public-supply sites were analyzed for 476 organics, 34 inorganics, and 3 in vitro bioactivities. 63 organics and 30 inorganics were detected. Arsenic, uranium (U), and lead (Pb) were detected in 54%, 43%, and 20% of samples, respectively. Concentrations equivalent to public-supply maximum contaminant level(s) (MCL) were exceeded only in untreated private-well samples (As 47%, U 3%). Precautionary health-based screening levels were exceeded frequently, due to inorganics in private supplies and chlorine-based disinfection byproducts in public supplies. The results indicate that simultaneous exposures to co-occurring TW contaminants are common, warranting consideration of expanded source, point-of-entry, or POU treatment(s). This study illustrates the importance of increased monitoring of private-well TW, employing a broad, environmentally informative analytical scope, to reduce the risks of unrecognized contaminant exposures.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.2c00293","usgsCitation":"Bradley, P., Romanok, K., Smalling, K., Focazio, M.J., Charboneau, R., George, C.M., Navas-Acien, A., O’Leary, M., Red Cloud, R., Zacher, T., Breitmeyer, S.E., Cardon, M.C., Cuny, C.K., Ducheneaux, G., Enright, K., Evans, N., Gray, J., Harvey, D.E., Hladik, M.L., Kanagy, L.K., Loftin, K.A., McCleskey, R., Medlock-Kakaley, E., Meppelink, S.M., Valder, J., and Weis, C.P., 2022, Tapwater exposures, effects potential, and residential risk management in Northern Plains Nations: Environmental Science and Technology Water, v. 2, no. 10, p. 1772-1788, https://doi.org/10.1021/acsestwater.2c00293.","productDescription":"17 p.","startPage":"1772","endPage":"1788","ipdsId":"IP-117867","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":446328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Prior studies have repeatedly shown that probabilistic seismic hazard maps from several different countries predict higher shaking than that observed. Previous map assessments have not, however, considered the influence of site response on hazard. Seismologists have long acknowledged the influence of near-surface geology, in particular low-impedance sediment layers, on earthquake ground-motion at frequencies of engineering concern. Although the overall effects of site response are complex, modern ground-motion models (GMMs) account for site effects using terms based on VS30, the time-averaged shear-wave velocity in the upper 30 m of the Earth’s surface. In this study, we consider general implications of incorporating site terms from modern GMMs using site-specific VS30 as a proxy in probabilistic seismic hazard maps for California. At the long periods (1–5 s) that affect tall buildings, site terms amplify the mapped hazard by factors of 1–3 at many sites relative to maps calculated for the standard reference soft-rock site condition, VS30 = 760 m/s. However, at the short periods of ground-motion that are the main contributors to peak ground acceleration (PGA) and thus affect smaller structures, only negligible effects occur due to nonlinear deamplification of strong ground-motion at high frequencies. Nonlinear deamplification increases as the shaking level increases. For very strong shaking, deamplification can overcome the linear amplification, yielding net deamplification. We explore the implications of these results for the evaluation of hazard maps. Because site effects do not change the maps appreciably at short periods, we can exclude site response as an explanation for why the maps overpredict historically observed shaking as captured by the California Historical Intensity Mapping Project (CHIMP) dataset. The results are expected to be generalizable to regions that are comparable to California in terms of structure and seismicity rates. In low-to-moderate-seismicity regions where the hazard reflects weaker shaking, nonlinear site response is expected to be less important for the hazard.

","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2022.931340","usgsCitation":"Gallahue, M.M., Salditch, L.M., Lucas, M.C., Neely, J.S., Stein, S., Abrahamson, N.A., Williams, T., and Hough, S.E., 2022, A study on the effect of site response on California seismic hazard map assessment: Frontiers in Earth Science: Geohazards and Georisks, v. 10, 931340, 11 p., https://doi.org/10.3389/feart.2022.931340.","productDescription":"931340, 11 p.","ipdsId":"IP-135357","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":446342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.931340","text":"Publisher Index Page"},{"id":407779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallahue, Molly M.","contributorId":263448,"corporation":false,"usgs":false,"family":"Gallahue","given":"Molly","email":"","middleInitial":"M.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":853517,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Salditch, Leah Marschall 0000-0002-4478-1836","orcid":"https://orcid.org/0000-0002-4478-1836","contributorId":297144,"corporation":false,"usgs":true,"family":"Salditch","given":"Leah","email":"","middleInitial":"Marschall","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":853518,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lucas, Madeleine C.","contributorId":263451,"corporation":false,"usgs":false,"family":"Lucas","given":"Madeleine","email":"","middleInitial":"C.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":853519,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Neely, James S.","contributorId":263454,"corporation":false,"usgs":false,"family":"Neely","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":853520,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stein, Seth","contributorId":263457,"corporation":false,"usgs":false,"family":"Stein","given":"Seth","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":853521,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abrahamson, Norman A.","contributorId":115451,"corporation":false,"usgs":false,"family":"Abrahamson","given":"Norman","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":853522,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, Tessa","contributorId":297145,"corporation":false,"usgs":false,"family":"Williams","given":"Tessa","email":"","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":853523,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":853524,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70239833,"text":"70239833 - 2022 - Wind turbine wakes can impact down-wind vegetation greenness","interactions":[],"lastModifiedDate":"2023-01-23T12:18:53.782104","indexId":"70239833","displayToPublicDate":"2022-09-26T06:16:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Wind turbine wakes can impact down-wind vegetation greenness","docAbstract":"

Global wind energy has expanded 5-fold since 2010 and is predicted to expand another 8–10-fold over the next 30 years. Wakes generated by wind turbines can alter downwind microclimates and potentially downwind vegetation. However, the design of past studies has made it difficult to isolate the impact of wake effects on vegetation from land cover change. We used hourly wind data to model wake and non-wake zones around 17 wind facilities across the U.S. and compared remotely-sensed vegetation greenness in wake and non-wake zones before and after construction. We located sampling sites only in the dominant vegetation type and in areas that were not disturbed before or after construction. We found evidence for wake effects on vegetation greenness at 10 of 17 facilities for portions of, or the entire growing season. Evidence included statistical significance in Before After Control Impact statistical models, differences >3% between expected and observed values of vegetation greenness, and consistent spatial patterns of anomalies in vegetation greenness relative to turbine locations and wind direction. Wakes induced both increases and decreases in vegetation greenness, which may be difficult to predict prior to construction. The magnitude of wake effects depended primarily on precipitation and to a lesser degree aridity. Wake effects did not show trends over time following construction, suggesting the changes impact vegetation greenness within a growing season, but do not accrue over years. Even small changes in vegetation greenness, similar to those found in this study, have been seen to affect higher trophic levels. Given the rapid global growth of wind energy, and the importance of vegetation condition for agriculture, grazing, wildlife, and carbon storage, understanding how wakes from wind turbines impact vegetation is essential to exploit or ameliorate these effects.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ac8da9","usgsCitation":"Diffendorfer, J., Vanderhoof, M.K., and Ancona, Z.H., 2022, Wind turbine wakes can impact down-wind vegetation greenness: Environmental Research Letters, v. 17, 104025, 14 p., https://doi.org/10.1088/1748-9326/ac8da9.","productDescription":"104025, 14 p.","ipdsId":"IP-136847","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":446345,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac8da9","text":"Publisher Index Page"},{"id":435679,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P3J7GR","text":"USGS data release","linkHelpText":"Wind turbine wakes can impact down-wind vegetation greenness"},{"id":412204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","noUsgsAuthors":false,"publicationDate":"2022-09-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":862081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":862082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":862083,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240259,"text":"70240259 - 2022 - Insights on multistage rock avalanche behavior from runout modeling constrained by seismic inversions","interactions":[],"lastModifiedDate":"2023-02-02T13:02:32.684492","indexId":"70240259","displayToPublicDate":"2022-09-25T07:01:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7167,"text":"Journal of Geophysical Research: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Insights on multistage rock avalanche behavior from runout modeling constrained by seismic inversions","docAbstract":"

Inversion of low-frequency regional seismic records to solve for a time series of bulk forces exerted on the earth by a landslide (a force-time function) is increasingly being used to infer volumes and dynamics of large, highly energetic landslides, such as rock avalanches and flowslides, and to provide calibration information on event dynamics and volumes for numerical landslide runout models. Much of the work to date using landslide runout modeling constrained by seismic data has focused on using single-phase models with frictional or velocity-weakening rheologies. Awareness of multistage landslide initiations is increasing, with discrete failures separated in time contributing to the final impact of an event. Our work utilizes a method for incorporating seismic data as a calibration constraint for landslide runout models, considering variable rheologies and different initiation conditions. This study presents a systematic examination of multiple rheologies and initiation conditions, and shows how these factors affect the force-time function derived from the landslide runout model. Our work confirms that, while rheology and fragmenting or initially coherent initiations affect the force-time function, multiple collapses separated by tens of seconds have the greatest impact on the shape and amplitude. We apply this method to the analysis of three real rock avalanches to better constrain plausible initiation conditions and rheology parameters using both seismic and field data. This study provides insights on how assumptions about the initiation dynamics of the source zone and the runout model definition can aid in the interpretation of seismic inversions for multistage rock avalanches.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB023444","usgsCitation":"Mitchell, A., Allstadt, K.E., George, D.L., Aaron, J., McDougall, S., Moore, J.R., and Menounous, B., 2022, Insights on multistage rock avalanche behavior from runout modeling constrained by seismic inversions: Journal of Geophysical Research: Solid Earth, v. 127, no. 10, e2021JB023444, 29 p., https://doi.org/10.1029/2021JB023444.","productDescription":"e2021JB023444, 29 p.","ipdsId":"IP-134671","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446348,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021jb023444","text":"External Repository"},{"id":412609,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Mitchell, Andrew","contributorId":225513,"corporation":false,"usgs":false,"family":"Mitchell","given":"Andrew","email":"","affiliations":[{"id":41153,"text":"Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":863122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":863123,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":863124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aaron, Jordan","contributorId":194904,"corporation":false,"usgs":false,"family":"Aaron","given":"Jordan","email":"","affiliations":[],"preferred":false,"id":863125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDougall, Scott","contributorId":194908,"corporation":false,"usgs":false,"family":"McDougall","given":"Scott","email":"","affiliations":[],"preferred":false,"id":863126,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moore, Jeffrey R.","contributorId":194909,"corporation":false,"usgs":false,"family":"Moore","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":863127,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Menounous, Brian","contributorId":301940,"corporation":false,"usgs":false,"family":"Menounous","given":"Brian","email":"","affiliations":[{"id":65373,"text":"University of Northern British Columbia, Geography Program and Natural Resources and Environmental Studies Institute","active":true,"usgs":false}],"preferred":false,"id":863128,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237580,"text":"70237580 - 2022 - Evaluating acid-aluminum stress in streams of the Northeastern U.S. at watershed, fish community and physiological scales","interactions":[],"lastModifiedDate":"2022-10-14T13:14:07.821412","indexId":"70237580","displayToPublicDate":"2022-09-23T14:39:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating acid-aluminum stress in streams of the Northeastern U.S. at watershed, fish community and physiological scales","docAbstract":"

In spite of overall improvements in air and water quality, biological stress from low pH and high concentrations of inorganic aluminum continue to impact fish and fish habitat in northeastern North America, with independent and interactive effects on individuals, populations and communities. Integrative indicators can therefore be useful in monitoring both impact and recovery across multiple scales. Using coupled water chemistry (pH, conductivity, and base cation and inorganic aluminum concentration), geographic (site elevation and watershed area) and biological (fish diversity, fish abundance, gill aluminum concentration and gill physiology) data, we developed an integrated indicator of acid aluminum stress across the White and Green mountains in central New England, USA. As has been established in a number of previous studies, preliminary analysis clearly indicated that across all sites, inorganic aluminum concentration was consistently greatest during the spring season. Structural Equation modelling (SEM) revealed that toxic conditions (concurrent low pH and high concentrations of inorganic aluminum) were well summarized with an integrated toxicity score, related to both base cation concentrations and elevation, with sites at higher elevations more likely to experience toxic conditions as well as low base cation concentrations. As hypothesized, fish diversity and abundance were negatively related to toxicity score. In spite of considerable variation among individuals, gill aluminum was positively related to toxicity score for both Atlantic salmon and brook trout. Observed elevated gill aluminum levels associated with reduced gill metabolic activity in Atlantic salmon smolts from impacted systems likely result in impaired osmoregulatory function and seawater tolerance. Overall, our results suggest that the integrated toxicity score metric is strongly associated with a syndrome of physiological stress, reduced abundance, and low species diversity for stream fishes in New England and can likely serve as a reliable indicator of continued impairment or recovery of acid-aluminum vulnerable systems in this ecoregion.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.109480","usgsCitation":"Zdasiuk, B.J., Chen, C.Y., McCormick, S.D., Nislow, K., Singley, J.G., and Kelly, J.T., 2022, Evaluating acid-aluminum stress in streams of the Northeastern U.S. at watershed, fish community and physiological scales: Ecological Indicators, v. 144, 109480, 12 p., https://doi.org/10.1016/j.ecolind.2022.109480.","productDescription":"109480, 12 p.","ipdsId":"IP-138154","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446353,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.109480","text":"Publisher Index Page"},{"id":408282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire, Vermont","otherGeospatial":"Ammonoosuc basin, Merrimack basin, Saco River basin, West River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.80502319335938,\n 42.837709559849614\n ],\n [\n -72.54684448242188,\n 42.837709559849614\n ],\n [\n -72.54684448242188,\n 43.03577208929465\n ],\n [\n -72.80502319335938,\n 43.03577208929465\n ],\n [\n -72.80502319335938,\n 42.837709559849614\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.9879150390625,\n 43.757208878849376\n ],\n [\n -71.08978271484375,\n 43.757208878849376\n ],\n [\n -71.08978271484375,\n 44.5063000997406\n ],\n [\n -71.9879150390625,\n 44.5063000997406\n ],\n [\n -71.9879150390625,\n 43.757208878849376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"144","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zdasiuk, Benjamin J","contributorId":297871,"corporation":false,"usgs":false,"family":"Zdasiuk","given":"Benjamin","email":"","middleInitial":"J","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":854525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Celia Y.","contributorId":145630,"corporation":false,"usgs":false,"family":"Chen","given":"Celia","email":"","middleInitial":"Y.","affiliations":[{"id":16179,"text":"Dartmouth College, Hanover NH","active":true,"usgs":false}],"preferred":false,"id":854526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nislow, Keith H.","contributorId":276357,"corporation":false,"usgs":false,"family":"Nislow","given":"Keith H.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":854528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singley, Joel G","contributorId":297873,"corporation":false,"usgs":false,"family":"Singley","given":"Joel","email":"","middleInitial":"G","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":854529,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, John T.","contributorId":212827,"corporation":false,"usgs":false,"family":"Kelly","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":38688,"text":"Department of Biology & Environmental Science, University of New Haven","active":true,"usgs":false}],"preferred":false,"id":854530,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236126,"text":"ofr20211034 - 2022 - Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012:","interactions":[],"lastModifiedDate":"2022-09-26T15:57:24.085486","indexId":"ofr20211034","displayToPublicDate":"2022-09-23T13:33:51","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1034","displayTitle":"Inventory of Eelgrass (Zostera marina) and Seaweeds at the End of the Alaska Peninsula, August–September 2012","title":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012:","docAbstract":"

Coastal communities in Alaska are undergoing rapid environmental change from increasing temperatures and baseline data are needed to monitor potential impacts. We conducted the first surveys of the abundance and distribution of eelgrass (Zostera marina) and seaweeds in the western part of Izembek National Wildlife Refuge at the end of the Alaska Peninsula. Six embayments and two offshore islands were surveyed in August–September of 2012. Biotic (percent cover of eelgrass/seaweeds, presence/absences of five sessile invertebrates), and abiotic (water temperature, salinity, and depth) data were recorded at 257 survey points (range =9–74 points per site) across all sites. Twenty-two genera/species of seaweeds were identified at the six embayments. New seaweed species for the offshore islands of Sanak and Caton were added to an existing seaweed collection accessioned at the University of British Columbia Herbarium. We also collected samples of eelgrass to be accessioned at U.S. Geological Survey, Alaska Science Center-Molecular Ecology Laboratory, for future genetic analyses. Fifty-three species of birds and 13 species of mammals were observed and recorded during the survey period.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211034","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly, T.F., Dau, N.C., Lind, O., Payne, K.J., and Lindstrom, S.C., 2022, Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012: U.S. Geological Survey Open-File Report 2021–1034, 14 p., https://doi.org/10.3133/ofr20211034.","productDescription":"Report: iv, 14 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-118597","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":405872,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K1ZOMY","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data from eelgrass (Zostera marina), seaweeds and selected invertebrates at six embayments and two islands at the end of the Alaska Peninsula"},{"id":405873,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405874,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201080","text":"OFR 2020-1080 —","description":"OFR 2020-1080","linkHelpText":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska"},{"id":405870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1034/coverthb.jpg"},{"id":405871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1034/ofr20211034.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1034"},{"id":405875,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":405876,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":405877,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -165.10253906249997,\n 53.98193516209167\n ],\n [\n -161.0595703125,\n 53.98193516209167\n ],\n [\n -161.0595703125,\n 56.19448087726972\n ],\n [\n -165.10253906249997,\n 56.19448087726972\n ],\n [\n -165.10253906249997,\n 53.98193516209167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508

","tableOfContents":"","publishedDate":"2022-09-23","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":850169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogrefe, Kyle R. khogrefe@usgs.gov","contributorId":4264,"corporation":false,"usgs":true,"family":"Hogrefe","given":"Kyle","email":"khogrefe@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":850170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Tyronne F.","contributorId":242965,"corporation":false,"usgs":false,"family":"Donnelly","given":"Tyronne","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":850171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dau, Neils C.","contributorId":295925,"corporation":false,"usgs":false,"family":"Dau","given":"Neils","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":850172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lind, Orville","contributorId":295926,"corporation":false,"usgs":false,"family":"Lind","given":"Orville","email":"","affiliations":[],"preferred":false,"id":850173,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Payne, Kevin J.","contributorId":295927,"corporation":false,"usgs":false,"family":"Payne","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":850174,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lindstrom, Sandra C.","contributorId":242967,"corporation":false,"usgs":false,"family":"Lindstrom","given":"Sandra","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":850175,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219039,"text":"ofr20201080 - 2022 - Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska","interactions":[],"lastModifiedDate":"2022-09-26T15:40:46.18041","indexId":"ofr20201080","displayToPublicDate":"2022-09-23T12:19:22","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1080","displayTitle":"Distribution of Eelgrass (Zostera marina) in Coastal Waters Adjacent to Togiak National Wildlife Refuge, Alaska","title":"Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska","docAbstract":"

Declines in the distribution and abundance of seagrasses worldwide have prompted a need for baseline distribution maps of eelgrass (Zostera marina) in Alaska. We used high-resolution digital-color aerial photography and multi-spectral satellite imagery to map the distribution and spatial extent of eelgrass at 21 sites in coastal waters adjacent to Togiak National Wildlife Refuge (TNWR) in northwestern Bristol Bay and southern Kuskokwim Bay. The total spatial extent of eelgrass meadows was estimated to be 6,489 hectare (ha) almost equally divided between Bristol Bay (3,001 ha) and Kuskokwim Bay (3,488 ha). The four largest eelgrass beds occurred in Chagvan Bay (1,933 ha), the north side of Hagemeister Island (1,168 ha), Goodnews Bay (874 ha), and Nanvak Bay (599 ha). This report provides key baseline data useful for establishing a monitoring plan to assess trends in eelgrass along the coast of TNWR.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ward, D.H., Hogrefe, K.R., Donnelly, T.F., and Swaim, M.A., 2022, Distribution of eelgrass (Zostera marina) in coastal waters adjacent to Togiak National Wildlife Refuge, Alaska: U.S. Geological Survey Open-File Report 2020–1080, 21 p., https://doi.org/10.3133/ofr20201080.","productDescription":"Report: v, 21 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-114072","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":384513,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92BMFTH","text":"USGS data release","description":"USGS data release","linkHelpText":"Point sampling data for eelgrass (Zostera marina) abundance adjacent to the Togiak National Wildlife Refuge, Alaska"},{"id":384512,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1080/ofr20201080.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1080"},{"id":384511,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1080/coverthb1.jpg"},{"id":405745,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201035","text":"OFR 2020-1035 —","description":"OFR 2020-1035","linkHelpText":"Abundance and distribution of eelgrass (Zostera marina) and seaweeds at Izembek National Wildlife Refuge, Alaska, 2007–10"},{"id":405747,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201114","text":"OFR 2020-1114 —","description":"OFR 2020-1114","linkHelpText":"Eelgrass (Zostera marina) and Seaweed Abundance along the Coast of Togiak National Wildlife Refuge, Alaska, 2008–10"},{"id":405748,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201143","text":"OFR 2020-1143 —","description":"OFR 2020-1143","linkHelpText":"Eelgrass (Zostera marina) and seaweed abundance along the coast of Nunivak Island, Yukon Delta National Wildlife Refuge, Alaska, 2010"},{"id":405746,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201144","text":"OFR 2020-1144 —","description":"OFR 2020-1144","linkHelpText":"Eelgrass (Zostera marina) and seaweed assessment Alaska Peninsula-Becharof National Wildlife Refuges, 2010"},{"id":384514,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEK4JI","text":"USGS data release","description":"USGS data release","linkHelpText":"Imagery and mapping data of eelgrass (Zostera marina) distribution, Alaska and Baja California, Mexico"},{"id":405749,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211034","text":"OFR 2021-1034 —","description":"OFR 2021-1034","linkHelpText":"Inventory of eelgrass (Zostera marina) and seaweeds at the end of the Alaska Peninsula, August–September 2012"}],"country":"United States","state":"Alaska","otherGeospatial":"Togiak National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.25,\n 58.5\n ],\n [\n -159.75,\n 58.5\n ],\n [\n -159.75,\n 59.25\n ],\n [\n -162.25,\n 59.25\n ],\n [\n -162.25,\n 58.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508

","tableOfContents":"","publishedDate":"2022-09-23","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":812527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogrefe, Kyle R. khogrefe@usgs.gov","contributorId":4264,"corporation":false,"usgs":true,"family":"Hogrefe","given":"Kyle","email":"khogrefe@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":812528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Tyronne F.","contributorId":242965,"corporation":false,"usgs":false,"family":"Donnelly","given":"Tyronne","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":812529,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swaim, Michael A.","contributorId":255543,"corporation":false,"usgs":false,"family":"Swaim","given":"Michael","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":812530,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236995,"text":"70236995 - 2022 - A century of drought in Hawai‘i: Geospatial analysis and synthesis across hydrological, ecological, and socioeconomic scales","interactions":[],"lastModifiedDate":"2022-09-27T12:16:25.356527","indexId":"70236995","displayToPublicDate":"2022-09-23T07:13:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3504,"text":"Sustainability","active":true,"publicationSubtype":{"id":10}},"title":"A century of drought in Hawai‘i: Geospatial analysis and synthesis across hydrological, ecological, and socioeconomic scales","docAbstract":"
Drought is a prominent feature of Hawaiʻi’s climate. However, it has been over 30 years since the last comprehensive meteorological drought analysis, and recent drying trends have emphasized the need to better understand drought dynamics and multi-sector effects in Hawaiʻi. Here, we provide a comprehensive synthesis of past drought effects in Hawaiʻi that we integrate with geospatial analysis of drought characteristics using a newly developed 100-year (1920–2019) gridded Standardized Precipitation Index (SPI) dataset. The synthesis examines past droughts classified into five categories: Meteorological, agricultural, hydrological, ecological, and socioeconomic drought. Results show that drought duration and magnitude have increased significantly, consistent with trends found in other Pacific Islands. We found that most droughts were associated with El Niño events, and the two worst droughts of the past century were multi-year events occurring in 1998–2002 and 2007–2014. The former event was most severe on the islands of O’ahu and Kaua’i while the latter event was most severe on Hawaiʻi Island. Within islands, we found different spatial patterns depending on leeward versus windward contrasts. Droughts have resulted in over $80 million in agricultural relief since 1996 and have increased wildfire risk, especially during El Niño years. In addition to providing the historical context needed to better understand future drought projections and to develop effective policies and management strategies to protect natural, cultural, hydrological, and agricultural resources, this work provides a framework for conducting drought analyses in other tropical island systems, especially those with a complex topography and strong climatic gradients.
","language":"English","publisher":"MDPI","doi":"10.3390/su141912023","usgsCitation":"Frazier, A.G., Giardina, C.P., Giambelluca, T.W., Brewington, L., Chen, Y., Chu, P., Fortini, L., Helweg, D., Keener, V.W., Longman, R., Lucas, M.P., Mair, A., Oki, D.S., Reyes, J., Yelenik, S.G., and Trauernicht, C., 2022, A century of drought in Hawai‘i: Geospatial analysis and synthesis across hydrological, ecological, and socioeconomic scales: Sustainability, v. 14, no. 19, e12023, 25 p., https://doi.org/10.3390/su141912023.","productDescription":"e12023, 25 p.","ipdsId":"IP-121622","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":446356,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/su141912023","text":"Publisher Index Page"},{"id":407392,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.48828125,\n 18.47960905583197\n ],\n [\n -154.248046875,\n 18.47960905583197\n ],\n [\n -154.248046875,\n 22.755920681486405\n ],\n [\n -160.48828125,\n 22.755920681486405\n ],\n [\n -160.48828125,\n 18.47960905583197\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"19","noUsgsAuthors":false,"publicationDate":"2022-09-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Frazier, Abby G.","contributorId":221112,"corporation":false,"usgs":false,"family":"Frazier","given":"Abby","email":"","middleInitial":"G.","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":852968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Giardina, Christian P. 0000-0002-3431-5073","orcid":"https://orcid.org/0000-0002-3431-5073","contributorId":182695,"corporation":false,"usgs":false,"family":"Giardina","given":"Christian","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":852969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giambelluca, Thomas W","contributorId":296956,"corporation":false,"usgs":false,"family":"Giambelluca","given":"Thomas","email":"","middleInitial":"W","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":852970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brewington, Laura","contributorId":239493,"corporation":false,"usgs":false,"family":"Brewington","given":"Laura","email":"","affiliations":[{"id":13398,"text":"East-West Center","active":true,"usgs":false}],"preferred":false,"id":852971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chen, Yi-Leng","contributorId":173747,"corporation":false,"usgs":false,"family":"Chen","given":"Yi-Leng","email":"","affiliations":[{"id":27289,"text":"Department of Meteorology, University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":852972,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chu, Pao-Shin","contributorId":296957,"corporation":false,"usgs":false,"family":"Chu","given":"Pao-Shin","email":"","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":852973,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research 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Center","active":true,"usgs":false}],"preferred":false,"id":852977,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lucas, Matthew P","contributorId":296959,"corporation":false,"usgs":false,"family":"Lucas","given":"Matthew","email":"","middleInitial":"P","affiliations":[{"id":64253,"text":"University of Hawaiʻi at Mānoa","active":true,"usgs":false}],"preferred":false,"id":852978,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mair, Alan 0000-0003-0302-6647 dmair@usgs.gov","orcid":"https://orcid.org/0000-0003-0302-6647","contributorId":4975,"corporation":false,"usgs":true,"family":"Mair","given":"Alan","email":"dmair@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852979,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Oki, Delwyn S. 0000-0002-6913-8804","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":221122,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn","email":"","middleInitial":"S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852980,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Reyes, Julian","contributorId":296960,"corporation":false,"usgs":false,"family":"Reyes","given":"Julian","affiliations":[{"id":64254,"text":"USDA Climate Hubs","active":true,"usgs":false}],"preferred":false,"id":852981,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":256836,"corporation":false,"usgs":false,"family":"Yelenik","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":51875,"text":"formerly U.S. Geological Survey; currently Rocky Mountain Research Station, U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":852982,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Trauernicht, Clay","contributorId":221125,"corporation":false,"usgs":false,"family":"Trauernicht","given":"Clay","email":"","affiliations":[{"id":40329,"text":"University of Hawai‘i at Mānoa, Department of Natural Resources and Environmental Management","active":true,"usgs":false}],"preferred":false,"id":852983,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70236829,"text":"ofr20221081 - 2022 - A sagebrush conservation design to proactively restore America’s sagebrush biome","interactions":[],"lastModifiedDate":"2022-09-22T16:12:53.285869","indexId":"ofr20221081","displayToPublicDate":"2022-09-22T10:55:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1081","displayTitle":"A Sagebrush Conservation Design to Proactively Restore America’s Sagebrush Biome","title":"A sagebrush conservation design to proactively restore America’s sagebrush biome","docAbstract":"

A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time.

First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term.

A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221081","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the U.S. Fish and Wildlife Service","usgsCitation":"Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2022, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081.","productDescription":"Report: viii, 38 p.; Data Release; 3 Figures: 7.99 × 6.10 inches or smaller","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138940","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":407103,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig10.pdf","text":"Figure 10, full size","size":"5.46 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Conifer 2020"},{"id":407104,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig11.pdf","text":"Figure 11, full size","size":"9.45 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Human Modification 2020"},{"id":407025,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig09.pdf","text":"Figure 9, full size","size":"5.57 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Invasive Annual Grass 2020"},{"id":407023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081.pdf","text":"Report","size":"32.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":407022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1081/coverthb.jpg"},{"id":407024,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Y5CDV","text":"USGS data release","linkHelpText":"Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6845703125,\n 33\n ],\n [\n -101.25,\n 33\n ],\n [\n -101.25,\n 49\n ],\n [\n -121.6845703125,\n 49\n ],\n [\n -121.6845703125,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Sagebrush Ecosystem Specialist
Land Management Research Program
Ecosystems Mission Area
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-09-22","noUsgsAuthors":false,"publicationDate":"2022-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Doherty, Kevin 0000-0003-3635-7346","orcid":"https://orcid.org/0000-0003-3635-7346","contributorId":176149,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin","email":"","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":852637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Theobald, David M. 0000-0002-1271-9368","orcid":"https://orcid.org/0000-0002-1271-9368","contributorId":10271,"corporation":false,"usgs":false,"family":"Theobald","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13470,"text":"Conservation Science 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Animals that regularly traverse habitat extremes between the subtropics and subarctic are expected to exhibit foraging behaviors that respond to changes in dynamic ocean habitats, and these behaviors may facilitate adaptations to novel and changing climates. During the chick-provisioning stage, Laysan albatross Phoebastria immutabilis parents regularly undertake short- and long-distance foraging trips throughout the vast central North Pacific Ocean. We examined GPS tracking data among chick-provisioning albatrosses in Hawai‘i to characterize habitats during short- and long-distance trips. The study period encompassed a marine heatwave (2014) and the cooling period after an extreme El Niño event (2016), enabling us to examine foraging habitats under novel and changing climates. First passage time and generalized additive mixed models indicated that during 183 short and 110 long trips (n = 32 birds), wind-assisted flight efficiency, proximity to productive areas, and moonlit-searching were important in both subtropical and subarctic habitats. Laysan albatross took foraging trips that had similar lengths and durations in 2014 and 2016 and visited similar areas, indicating that their foraging range did not expand in response to climatic variability. A strategy that uses similar foraging areas across years combined with reliance on environmental processes that enhance flight efficiency (wind) and that enable searching behaviors (moonlight) indicate that Laysan albatross exhibit complex behavioral plasticity that allows them to utilize subtropical and subarctic habitats affected by dynamic climate variability. This strategy may benefit their ability to respond to oceanographic and climatic change, including expanding warm water regions and changing atmospheric conditions influenced by global warming.

","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps14148","usgsCitation":"Gilmour, M.E., Felis, J.J., Hester, M.M., Young, L.C., and Adams, J., 2022, Laysan albatross exhibit complex behavioral plasticity in the subtropical and subarctic North Pacific Ocean: Marine Ecology Progress Series, v. 697, p. 125-147, https://doi.org/10.3354/meps14148.","productDescription":"23 p.","startPage":"125","endPage":"147","ipdsId":"IP-137932","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14148","text":"Publisher Index Page"},{"id":409685,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Hawaii","otherGeospatial":"Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -179.9,\n 35.095301697524135\n ],\n [\n -177.86550682042343,\n 28.860574564487507\n ],\n [\n -155.53062317940885,\n 20.809565675106214\n ],\n [\n -147.22958348721264,\n 19.95935891124141\n ],\n [\n -134.00475959018274,\n 35.360858467785576\n ],\n [\n -136.90695297728425,\n 50.99805127330757\n ],\n [\n -142.66136789996617,\n 59.0913410074362\n ],\n [\n -147.22441346291703,\n 60.36962329343831\n ],\n [\n -154.7675208704121,\n 56.641133852727194\n ],\n [\n -176.45903611138576,\n 54.11395665374428\n ],\n [\n -178,\n 52.825004330008824\n ],\n [\n -179.9,\n 35.095301697524135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"697","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilmour, Morgan Elizabeth 0000-0002-2618-1095","orcid":"https://orcid.org/0000-0002-2618-1095","contributorId":289509,"corporation":false,"usgs":true,"family":"Gilmour","given":"Morgan","email":"","middleInitial":"Elizabeth","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857662,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felis, Jonathan J. 0000-0002-0608-8950 jfelis@usgs.gov","orcid":"https://orcid.org/0000-0002-0608-8950","contributorId":4825,"corporation":false,"usgs":true,"family":"Felis","given":"Jonathan","email":"jfelis@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hester, Michelle M. 0000-0002-0769-5904","orcid":"https://orcid.org/0000-0002-0769-5904","contributorId":197785,"corporation":false,"usgs":false,"family":"Hester","given":"Michelle","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":857664,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Lindsay C.","contributorId":149044,"corporation":false,"usgs":false,"family":"Young","given":"Lindsay","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":857665,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857666,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259620,"text":"70259620 - 2022 - The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil","interactions":[],"lastModifiedDate":"2024-10-17T12:03:37.308351","indexId":"70259620","displayToPublicDate":"2022-09-22T06:55:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil","docAbstract":"Fungi and bacteria are the two dominant groups of soil microbial communities worldwide. By controlling the turnover of soil organic matter, these organisms directly regulate the exchange of carbon between the soil and the atmosphere. Fundamental differences in the physiology and life history of bacteria and fungi suggest that variation in the biogeography of soil fungal and bacterial relative abundance could drive striking differences in carbon decomposition and soil organic matter formation across different biomes. However, a lack of global and predictive information on the distribution of these organisms in terrestrial 45 ecosystems has prevented the inclusion of soil fungal and bacterial relative abundance and the associated processes into global biogeochemical models. Here, we used a global scale dataset in the top soil surface (>3000 distinct observations of soil fungal and bacterial abundance) to generate the first quantitative and spatially high resolution (1km) explicit map of soil fungal proportion, defined as fungi/fungi + bacteria, across terrestrial ecosystems. We reveal striking latitudinal trends where fungal dominance increases in cold and high latitude environments with large soil carbon stocks. There was strong non-linear response of fungal 50 dominance to environmental gradient, i.e., mean annual temperature (MAT) and net primary productivity (NPP). Fungi and bacteria dominated in regions with low and high MAT and NPP, respectively, thus representing slow vs. fast soil energy channels, a concept with a long history in soil ecology. These high-resolution models provide the first steps towards representing the major soil microbial groups and their functional differences in global biogeochemical models to improve predictions of soil organic matter turnover under current and future climate scenarios","language":"English","publisher":"Earth System Science Data","doi":"10.5194/essd-14-4339-2022","usgsCitation":"Yu, K., Hoogen, J.V., Wang, Z., Averill, C., Routh, D., Smith, G.R., Drenovsky, R.E., Scow, K., Mo, F., Waldrop, M., Yang, Y., Tang, W., De Vries, F., Bardgett, R., Manning, P., Bastida, F., Baer, S.G., Bach, E., Garcia, C.J., Wang, Q., Ma, L., Chen, B., He, X., Teurlinex, S., Heijboer, A., Bradley, J.A., and Crowther, T.W., 2022, The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil: Earth System Science Data, v. 14, p. 4339-4350, https://doi.org/10.5194/essd-14-4339-2022.","productDescription":"12 p,","startPage":"4339","endPage":"4350","ipdsId":"IP-116714","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467161,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-14-4339-2022","text":"Publisher Index Page"},{"id":462936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2022-09-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Yu, Kailiang","contributorId":221398,"corporation":false,"usgs":false,"family":"Yu","given":"Kailiang","email":"","affiliations":[{"id":40362,"text":"Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22904, USA","active":true,"usgs":false}],"preferred":false,"id":915995,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoogen, Johan van den","contributorId":345210,"corporation":false,"usgs":false,"family":"Hoogen","given":"Johan","email":"","middleInitial":"van den","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":915996,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Zhiqiang","contributorId":345211,"corporation":false,"usgs":false,"family":"Wang","given":"Zhiqiang","email":"","affiliations":[{"id":82525,"text":"Chengdu University","active":true,"usgs":false}],"preferred":false,"id":915997,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Averill, Colin","contributorId":245299,"corporation":false,"usgs":false,"family":"Averill","given":"Colin","email":"","affiliations":[],"preferred":false,"id":915998,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Routh, Devin","contributorId":345212,"corporation":false,"usgs":false,"family":"Routh","given":"Devin","email":"","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":915999,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Gabriel Reuben","contributorId":345213,"corporation":false,"usgs":false,"family":"Smith","given":"Gabriel","email":"","middleInitial":"Reuben","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":916000,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Drenovsky, Rebecca E.","contributorId":345214,"corporation":false,"usgs":false,"family":"Drenovsky","given":"Rebecca","email":"","middleInitial":"E.","affiliations":[{"id":27555,"text":"John Carroll University","active":true,"usgs":false}],"preferred":false,"id":916001,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Scow, Kate M.","contributorId":345215,"corporation":false,"usgs":false,"family":"Scow","given":"Kate M.","affiliations":[{"id":82527,"text":"U. 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W.","contributorId":177398,"corporation":false,"usgs":false,"family":"Crowther","given":"Thomas","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":916021,"contributorType":{"id":1,"text":"Authors"},"rank":27}]}} ,{"id":70236867,"text":"fs20223074 - 2022 - Loss of street tree canopy increases stormwater runoff","interactions":[],"lastModifiedDate":"2026-03-25T16:41:23.615596","indexId":"fs20223074","displayToPublicDate":"2022-09-21T14:01:11","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3074","displayTitle":"Loss of Street Tree Canopy Increases Stormwater Runoff","title":"Loss of street tree canopy increases stormwater runoff","docAbstract":"

Urban forests have largely been overlooked for the role they play in reducing stormwater runoff volume by using hydrologic processes such as interception (rainfall intercepted by tree canopy), evapotranspiration (the transfer of water from vegetation into the atmosphere) and infiltration (percolation of rainwater into the Earth’s soil). Early research into the effects of trees on urban stormwater runoff used simple estimates based on assumptions of canopy coverage and design storm criteria. In a review of available literature on how capable urban trees are at reducing runoff, the Center for Watershed Protection (2017) found only six studies; three of them used measured data from a single plot, and the other three used models. When identifying gaps in research on the role of trees in stormwater management, Kuehler and others (2017) highlighted the need for studies that scale the local effects of urban trees to the larger sewershed catchment area, allowing a more holistic understanding of the urban tree canopy effects on hydrology.

For these reasons, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, U.S. Forest Service, and the University of Wisconsin, quantified the effect of removing urban street trees and their canopy on stormwater generation in a medium-density residential area. Using a paired-catchment experimental design, rainfall-runoff relations were characterized in two medium-density residential catchments in Fond du Lac, Wisconsin, during May through September in 2018–20. Results of the study are detailed in Selbig and others (2022).

During the calibration phase, hydrograph metrics from paired runoff events were used to develop the relation between the control and test catchments with street trees in place. The ability to measure changes to the rainfall-runoff response after removal of tree canopy was made possible by an aggressive tree removal program by the city as a response to rapid infestation from the Agrilus planipennis (emerald ash borer). In March 2020, a total of 31 street trees were removed at the onset of the treatment period, resulting in a loss of 2,990 square meters of canopy over streets, driveways, sidewalks, and grassed areas.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223074","usgsCitation":"Selbig, W.R., Loheide, S.P., II, Shuster, W., Scharenbroch, B.C., Coville, R.C., Kruegler, J., Avery, W., Haefner, R., and Nowak, D., 2022, Loss of street tree canopy increases stormwater runoff: U.S. Geological Survey Fact Sheet 2022–3074, 4 p., https://doi.org/10.3133/fs20223074.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141242","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407081,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3074/fs20223074.XML"},{"id":407082,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3074/images"},{"id":407157,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20223074/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":407079,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3074/coverthb.jpg"},{"id":407080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3074/fs20223074.pdf","text":"Report","size":"1.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022–3074"},{"id":501510,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113526.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","city":"Fond du Lac","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.494873046875,\n 43.72148995228582\n ],\n [\n -88.37127685546875,\n 43.72148995228582\n ],\n [\n -88.37127685546875,\n 43.82065657651688\n ],\n [\n -88.494873046875,\n 43.82065657651688\n ],\n [\n -88.494873046875,\n 43.72148995228582\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

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2022-09-21","noUsgsAuthors":false,"publicationDate":"2022-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852407,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loheide, Steven P. II","contributorId":62377,"corporation":false,"usgs":false,"family":"Loheide","given":"Steven","suffix":"II","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":852408,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shuster, William","contributorId":147261,"corporation":false,"usgs":false,"family":"Shuster","given":"William","affiliations":[{"id":16813,"text":"Sustainable Environments Branch, National Risk Management Research Laboratory, Office of Research and Development, EPA","active":true,"usgs":false}],"preferred":false,"id":852409,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scharenbroch, Bryant C. 0000-0002-9342-7550","orcid":"https://orcid.org/0000-0002-9342-7550","contributorId":269849,"corporation":false,"usgs":false,"family":"Scharenbroch","given":"Bryant","email":"","middleInitial":"C.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":852410,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coville, Robert C. 0000-0002-6895-2564","orcid":"https://orcid.org/0000-0002-6895-2564","contributorId":269851,"corporation":false,"usgs":false,"family":"Coville","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":852411,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kruegler, James 0000-0002-2671-0807","orcid":"https://orcid.org/0000-0002-2671-0807","contributorId":269853,"corporation":false,"usgs":false,"family":"Kruegler","given":"James","email":"","affiliations":[{"id":40823,"text":"Davey Institute","active":true,"usgs":false}],"preferred":false,"id":852412,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Avery, William 0000-0002-2651-9906","orcid":"https://orcid.org/0000-0002-2651-9906","contributorId":269858,"corporation":false,"usgs":false,"family":"Avery","given":"William","email":"","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":852413,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haefner, Ralph J. 0000-0002-4363-9010 rhaefner@usgs.gov","orcid":"https://orcid.org/0000-0002-4363-9010","contributorId":1793,"corporation":false,"usgs":true,"family":"Haefner","given":"Ralph","email":"rhaefner@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852414,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nowak, David 0000-0002-2043-0062","orcid":"https://orcid.org/0000-0002-2043-0062","contributorId":269856,"corporation":false,"usgs":false,"family":"Nowak","given":"David","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":852415,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70237705,"text":"70237705 - 2022 - Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development","interactions":[],"lastModifiedDate":"2022-10-31T14:56:21.921244","indexId":"70237705","displayToPublicDate":"2022-09-21T08:23:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development","docAbstract":"

Context

Wildlife avoid human disturbances, including roads and development. Avoidance and displacement of wildlife into less suitable habitat due to human development can affect their energy expenditures and fitness. The heart rate and oxygen uptake of large mammals varies with both natural aspects of their habitat (terrain, climate, predators, etc.) and anthropogenic influence (noise, light, fragmentation, etc.). Although incorporating physiological analyses of energetics can inform the impacts of both development and conservation, management decisions rarely incorporate individuals’ energetic requirements when deciding on locations for potential development.

Objectives

We aimed to estimate the change in expected energy expenditure, numerically and spatially, for mule deer to traverse a landscape with varying levels of oil and gas development through time.

Methods

Using calculations of energy expenditure of mule deer (Odocoileus hemionus) by weight, in relation to physical terrain components, plus avoidance factors for anthropogenic disturbance, we developed a spatiotemporal model of the minimum energy required for mule deer to traverse a landscape. We compared expected energy expenditure across 12 study sites with increasing levels of oil and gas development and over time in our study area, on the northern Colorado Plateau of Utah.

Results

We found that energy expenditure can be increased by development, regardless of terrain, through increased travel distance associated with avoidance behavior. Maximum median energy expenditure to traverse a 1400 ha sample area rose from 1135 to 1935 kilocalories, a 70% increase in energy required of a mule deer. There was a significant relationship between energy expenditure and the size of oil and gas development (p < 0.001), its compactness (p < 0.05), and its ‘thinness’ (p < 0.001), but not terrain ruggedness (p = 0.25).

Conclusion

As the energy costs of movement correlate across multiple species of large mammals, our analysis of the energetic cost, for mule deer, associated with development can serve as a quantitative representative of the impacts of oil and gas development for multiple mammals—including threatened or endangered species. Our bioenergetic cost-distance model provides a means of delineating impediments to efficient movement and can be used to quantify the expected energetic costs of proposed future developments. As wildlife are exposed to increasing anthropogenic stressors which reduce fitness, it is important to make strategic siting decisions to reduce energetic costs imposed by human activities.

","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01521-w","usgsCitation":"Chambers, S.N., Villarreal, M.L., Duane, O.J., Munson, S.M., Stuber, E.F., Tyree, G., Waller, E.K., and Duniway, M.C., 2022, Conflict of energies: Spatially modeling mule deer caloric expenditure in response to oil and gas development: Landscape Ecology, v. 37, p. 2947-2961, https://doi.org/10.1007/s10980-022-01521-w.","productDescription":"15 p.","startPage":"2947","endPage":"2961","ipdsId":"IP-138879","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":435685,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99JGAYG","text":"USGS data release","linkHelpText":"Maps of mule deer avoidance areas based on density of oil and gas developments, Book Cliffs, Utah"},{"id":408538,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"northern Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.753173828125,\n 38.77978137804918\n ],\n [\n -109.072265625,\n 38.77978137804918\n ],\n [\n -109.072265625,\n 40.49709237269567\n ],\n [\n -110.753173828125,\n 40.49709237269567\n ],\n [\n -110.753173828125,\n 38.77978137804918\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambers, Samuel Norton 0000-0002-9840-7989","orcid":"https://orcid.org/0000-0002-9840-7989","contributorId":297110,"corporation":false,"usgs":true,"family":"Chambers","given":"Samuel","email":"","middleInitial":"Norton","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":855075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":855076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duane, Olivia Jane Marie","contributorId":298083,"corporation":false,"usgs":true,"family":"Duane","given":"Olivia","email":"","middleInitial":"Jane Marie","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":855077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":855078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stuber, Erica Francis 0000-0002-2687-6874","orcid":"https://orcid.org/0000-0002-2687-6874","contributorId":298084,"corporation":false,"usgs":true,"family":"Stuber","given":"Erica","email":"","middleInitial":"Francis","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":855079,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tyree, Gayle L","contributorId":298085,"corporation":false,"usgs":false,"family":"Tyree","given":"Gayle L","affiliations":[{"id":64492,"text":"Plant and Environmental Sciences Department, New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":855080,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Waller, Eric K","contributorId":298087,"corporation":false,"usgs":false,"family":"Waller","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":64493,"text":"Independent USGS contractor","active":true,"usgs":false}],"preferred":false,"id":855081,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":855082,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}