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Recently, increased frequency of costly floods has prompted consideration of whether offsetting benefits might accrue from management of floodplains for ecosystem services. We employed a simple inundation model for 800 km of the Lower Missouri River, USA, to evaluate spatial and temporal distributions of ecological floodplain inundation metrics and how those distributions might vary with levee removal and climatic change. The model evaluates inundation at 30 × 30 m resolution on a daily basis over 82 years of record. We quantified provisioning of waterfowl habitat and potential denitrification. Spatial variability is affected by ongoing geomorphic adjustments that affect floodplain connectivity. Statistical models indicate that available floodplain area and recent aggradation are predictive of most inundation metrics. Connectivity is sensitive to climate-change scenarios that predict increased floodplain inundation during spring waterfowl migrations; the greatest sensitivity to future climate exists where channel-floodplain geomorphology presently enhances floodplain connectivity. Evaluation of floodplain denitrification indicates that on average, the nonleveed part of the floodplain could denitrify 0.05%–1.7% of the mean annual nitrogen load of the river. Levee removal could increase this rate to only 3.6% of the nitrogen load. The capacity of floodplain connectivity to influence certain ecosystem services is highly variable in space along the Lower Missouri River and may be appreciably influenced by climate change. 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Louisiana holds a unique historical, economic, and cultural position in the national consciousness. Its off-shore oil operations help fuel the U.S. economy. The Port of South Louisiana is the busiest in the United States by cargo volume; the nearby Port of New Orleans is the sixth busiest. The former French and Spanish colony served as a key connection to the Caribbean long before U.S. independence, and Louisiana’s multinational effects soon melded into a Creole culture that had an outsized effect on America.

That heritage remains a powerful draw for the tourism industry in Louisiana. Gulf Coast breezes carry the aromas of tropical flowers, sweet beignets, and savory crawfish through the 13 colorful blocks of New Orleans’ French Quarter. Interwoven with the sounds of jazz, rock, country music, and zydeco, the city’s charms delight more than 18 million visitors each year.

The proximity of the Gulf Coast and the city’s elevation, however—just 6.5 feet above sea level—also offer an ominous warning of the ever-present threat of climate change and natural disaster. Hurricane Katrina battered New Orleans in 2005, an incident tied to more than 1,800 deaths that marks one of the most notorious U.S. weather-related tragedies in the 21st century. Environmental changes have amplified threats from tropical storms. Through more frequent and powerful storms, sea level rise threatens low-lying areas such as Lake Charles and creates unpredictable weather patterns that threaten the cities and agricultural operations to the north.

Landsat data offer rich information that can aid in early warning, disaster response, and the monitoring of recovery from natural disasters. Its historic, unparalleled 50-year archive of repeat Earth observations also serves to guide resiliency plans and feeds modeling that can help States like Louisiana prepare for coming coastal and inland change. Here are just a few examples of how Landsat has been used to study and understand Louisiana.

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\"}}]}","edition":"Version 1.0: July 20, 2022; Version 1.1: March 19, 2025","contact":"

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U.S. Geological Survey
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Reston, VA 20192

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","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-20","revisedDate":"2025-03-19","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":152492,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":847106,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70233439,"text":"fs20223056 - 2022 - Nebraska and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:00:52.83931","indexId":"fs20223056","displayToPublicDate":"2022-07-20T14:47:44","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":"2022-3056","displayTitle":"Nebraska and Landsat","title":"Nebraska and Landsat","docAbstract":"

The rolling plains of Nebraska occupy a storied place in the American psyche. For those living outside the Midwest, the Cornhusker State may be seen as a symbol of the Nation’s heartland, cropped border to border, with country churches and barely standing barns to be found around every turn of its gravel roads.

Although the pioneer history and agricultural heritage of the 37th State lend credence to this idyllic view, Nebraska’s varied landscapes and modern economy make the reality of life in the State more complex than its rural image would suggest.

Agriculture remains Nebraska’s top industry, but manufacturing now represents 12 percent of the State’s gross domestic product. The financial services and insurance industries account for 8 percent of the gross domestic product in Nebraska, which is the headquarters of Berkshire Hathaway and Mutual of Omaha, the latter of which is named after Nebraska’s largest city, which grew nearly 12 percent between 2010 and 2020. Cropland is indeed a prominent feature in Nebraska, but the State also is home to 8 State parks, 5 national parks, 2 national forests, and 3 national grasslands. The grasses and dunes of the Nebraska Sand Hills that stretch across the north-central quarter of the State are a National Natural Landmark.

Data from the Landsat satellite program contribute to the study and management of Nebraska’s land in myriad ways, from monitoring crop productivity and aiding in rangeland management to tracking damage from floods, droughts, or hurricanes. Land cover maps produced using data pulled from the 50-year Landsat archive can offer important insights into urban growth, land use trends, and land change patterns. Here are a few examples of how Landsat has been used in Nebraska.

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Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192

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","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-07-20","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":128240,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":847103,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70260160,"text":"70260160 - 2022 - Revisiting the depth distribution of seismicity before and after the 2004–2008 eruption of Mount St. Helens","interactions":[],"lastModifiedDate":"2024-10-29T14:47:05.488749","indexId":"70260160","displayToPublicDate":"2022-07-20T09:42:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"Revisiting the depth distribution of seismicity before and after the 2004–2008 eruption of Mount St. Helens","docAbstract":"

Hypocenter estimation at active volcanoes improves our understanding of their magmatic systems and indicates changing conditions at depth for continuously monitored volcanoes. The most active volcano in the Cascades Range, Mount St. Helens, has a multi-decadal earthquake catalog and it shows an apparent change in the depth distribution of seismicity before and after the 2004–2008 dome-building eruption and unrest sequence. We use two new resources to evaluate the accuracy of hypocenters and consequently the change in depth distribution of seismicity before and after the 2004–2008 eruption. First, we deployed a dense array of 136 three-component nodal seismographs for one month in 2017, including sub-arrays on the newly extruded dome and crater floor. Second, for events recorded during this month, we located their hypocenters using a three-dimensional (3D) wavefront-tracking location solver and a recently developed tomography model derived from active and passive source data. The relocated hypocenters are generally shallower and more concentrated beneath the crater compared to their catalog locations. The mean hypocenter movement from catalog locations is 2.86 km, with an averaged depth shift of 2.53 km upward. Comparison between 2017 hypocenters located using all of the available phase picks and those located using only the catalog picks from the permanent network suggests the improved hypocenters mostly resulted from the location solver and the 3D velocity model, with smaller changes due to the dense three-component array. Applying the same hypocenter estimation method to phase picks for all events from 1997 to 2004 and 2008–2021, we found a concentration of seismicity between sea level and ~ 1 km above it before and after the 2004–2008 eruption. The new results suggest that the seismogenic structure in the shallow magmatic system quickly re-equilibrated to its earlier state after the dome-building eruption.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2022.107629","usgsCitation":"Zhang, H., Glasgow, M., Schmandt, B., Thelen, W., Moran, S.C., and Thomas, A., 2022, Revisiting the depth distribution of seismicity before and after the 2004–2008 eruption of Mount St. Helens: Journal of Volcanology and Geothermal Research, v. 430, 107629, 10 p., https://doi.org/10.1016/j.jvolgeores.2022.107629.","productDescription":"107629, 10 p.","ipdsId":"IP-142680","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467174,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2022.107629","text":"Publisher Index Page"},{"id":463341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Untied States","state":"Washington","otherGeospatial":"Mount St. Helens","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.34522424954554,\n 46.31416654047442\n ],\n [\n -122.34522424954554,\n 46.084925777087534\n ],\n [\n -122.05723487869246,\n 46.084925777087534\n ],\n [\n -122.05723487869246,\n 46.31416654047442\n ],\n [\n -122.34522424954554,\n 46.31416654047442\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"430","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Han","contributorId":345700,"corporation":false,"usgs":false,"family":"Zhang","given":"Han","email":"","affiliations":[],"preferred":false,"id":917263,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glasgow, Margaret 0000-0001-5637-5918","orcid":"https://orcid.org/0000-0001-5637-5918","contributorId":345691,"corporation":false,"usgs":false,"family":"Glasgow","given":"Margaret","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":917264,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmandt, Brandon","contributorId":202750,"corporation":false,"usgs":false,"family":"Schmandt","given":"Brandon","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":917265,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917266,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":224629,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917267,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thomas, Amanda","contributorId":195086,"corporation":false,"usgs":false,"family":"Thomas","given":"Amanda","affiliations":[],"preferred":false,"id":917268,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233537,"text":"70233537 - 2022 - Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models","interactions":[],"lastModifiedDate":"2022-09-15T14:17:56.583793","indexId":"70233537","displayToPublicDate":"2022-07-20T06:53:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models","docAbstract":"

Several species of fish in large lakes and marine environments have a pelagic larval stage, and are subject to variable transport that can ultimately regulate survival and recruitment success. Alewife, Alosa pseudoharengus, are subject to transport by complex coastal currents during their pelagic larval stage (~ 30 d). We assessed backward-trajectory simulations, consisting of a Lagrangian particle dispersion model linked to the Finite Volume Community Ocean Model, to estimate likely hatch locations of aged larval alewife collected from locations on both the eastern and western sides of Lake Michigan during July 2015. We used four deployments of three satellite-tracked drifter buoys in coastal waters to assess model skill in estimating the origin of a drifter from its final location. We found that the trajectories of drifters varied greatly, depending on wind events and associated coastal transport processes, including upwelling/downwelling and coastal jet currents. In 2 of 12 cases, the backward trajectory simulations failed to predict the drifter origin, associated with transport of 170 km in a narrow coastal jet current. In the remaining 10 cases, the known drifter origin was within 3.5 km of the spatial patch of predicted possible origins for a scenario of horizontal diffusivity (188 m2 s−1) consistent with the offshore model grid resolution. Modeled backward trajectories estimated that alewife originated from the same side of the lake where they were collected, within ~ 100 km of the collection site. Our paper demonstrates the utility of hydrodynamic models to estimate a region of origin for aged larval fish.

","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lno.12186","usgsCitation":"Rowe, M.D., Prendergast, S.E., Alofs, K., Bunnell, D.B., Rutherford, E.S., and Anderson, E.J., 2022, Predicting larval alewife transport in Lake Michigan using hydrodynamic and Lagrangian particle dispersion models: Limnology and Oceanography, v. 67, no. 9, p. 2042-2058, https://doi.org/10.1002/lno.12186.","productDescription":"17 p.","startPage":"2042","endPage":"2058","ipdsId":"IP-135521","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":447056,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/lno.12186","text":"External Repository"},{"id":404412,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.79248046875,\n 45.61403741135093\n ],\n [\n -84.715576171875,\n 45.75219336063106\n ],\n [\n -84.814453125,\n 46.057985244793024\n ],\n [\n -85.14404296875,\n 46.29381556233369\n ],\n [\n -86.077880859375,\n 46.32417161725691\n ],\n [\n -87.51708984375,\n 45.90529985724799\n ],\n [\n -88.13232421875,\n 45.120052841530544\n ],\n [\n -88.341064453125,\n 44.5435052132082\n ],\n [\n -88.22021484375,\n 44.36313311380771\n ],\n [\n -88.209228515625,\n 43.636075155965784\n ],\n [\n -88.43994140625,\n 42.47209690919285\n ],\n [\n -87.747802734375,\n 41.47566020027821\n ],\n [\n -86.63818359375,\n 41.41801503608024\n ],\n [\n -85.858154296875,\n 42.147114459220994\n ],\n [\n -85.97900390625,\n 43.197167282501276\n ],\n [\n -86.099853515625,\n 43.97700467496408\n ],\n [\n -85.0341796875,\n 44.72332018895825\n ],\n [\n -84.79248046875,\n 45.61403741135093\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"67","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Rowe, Mark D","contributorId":293584,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark","email":"","middleInitial":"D","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":847363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prendergast, Sara E","contributorId":293586,"corporation":false,"usgs":false,"family":"Prendergast","given":"Sara","email":"","middleInitial":"E","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":847364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alofs, Karen M","contributorId":293588,"corporation":false,"usgs":false,"family":"Alofs","given":"Karen M","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":847365,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bunnell, David B. 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":216540,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":847366,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rutherford, Edward S.","contributorId":175426,"corporation":false,"usgs":false,"family":"Rutherford","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12789,"text":"NOAA Great Lakes Environmental Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":847367,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anderson, Eric J.","contributorId":140817,"corporation":false,"usgs":false,"family":"Anderson","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":12789,"text":"NOAA Great Lakes Environmental Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":847368,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70234303,"text":"70234303 - 2022 - Remote sensing application for landslide detection, monitoring along eastern Lake Michigan (Miami Park, MI)","interactions":[],"lastModifiedDate":"2022-08-08T11:59:59.370657","indexId":"70234303","displayToPublicDate":"2022-07-20T06:51:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing application for landslide detection, monitoring along eastern Lake Michigan (Miami Park, MI)","docAbstract":"
We assessed the nature and spatial and temporal patterns of deformation over the Miami Park bluffs on the eastern margin of Lake Michigan and investigated the factors controlling its observed deformation. Our approach involved the following steps: (1) extracting bluff deformation rates (velocities along the line of sight of the satellite) using a stack of Sentinel-1A radar imagery in ascending acquisition geometry acquired between 2017 and 2021 and applying the Intermittent Small Baseline Subset (ISBAS) InSAR time series analysis method; (2) generating high-resolution (5 cm) elevation models and orthophotos from temporal unmanned aerial vehicle (UAV) surveys acquired in 2017, 2019, and 2021; and (3) comparing the temporal variations in mass wasting events to other relevant datasets including the ISBAS-based bluff deformation time series, lake level (LL) variations, and local glacial stratigraphy. We identified areas witnessing high line-of-sight (LOS) deformation rates (up to −21 mm/year) along the bluff from the ISBAS analysis and seasonal deformation patterns associated with freeze-thaw cycles, suggesting a causal effect. The acceleration of slope failures detected from field and UAV acquisitions correlated with high LLs and intensified onshore wave energy in 2020. The adopted methodology successfully predicts landslides caused by freezes and thaws of the slope face by identifying prolonged slow deformation preceding slope failures, but it does not predict the catastrophic landslides preceded by short-lived LOS deformation related to LL rise.
","language":"English","publisher":"MDPI","doi":"10.3390/rs14143474","usgsCitation":"Sataer, G., Sultan, M., Emil, M.K., Yellich, J.A., Palaseanu-Lovejoy, M., Becker, R., Gebremichael, E., and Abdelmohsen, K., 2022, Remote sensing application for landslide detection, monitoring along eastern Lake Michigan (Miami Park, MI): Remote Sensing, v. 14, no. 14, 3474, 23 p., https://doi.org/10.3390/rs14143474.","productDescription":"3474, 23 p.","ipdsId":"IP-142840","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":447058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs14143474","text":"Publisher Index Page"},{"id":404913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","city":"Miami Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.3089370727539,\n 42.39126217354059\n ],\n [\n -86.17298126220703,\n 42.39126217354059\n ],\n [\n -86.17298126220703,\n 42.52272381854161\n ],\n [\n -86.3089370727539,\n 42.52272381854161\n ],\n [\n -86.3089370727539,\n 42.39126217354059\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"14","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Sataer, Guzalay 0000-0002-4775-813X","orcid":"https://orcid.org/0000-0002-4775-813X","contributorId":294656,"corporation":false,"usgs":false,"family":"Sataer","given":"Guzalay","email":"","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":848510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sultan, Mohamed 0000-0002-3841-4802","orcid":"https://orcid.org/0000-0002-3841-4802","contributorId":294658,"corporation":false,"usgs":false,"family":"Sultan","given":"Mohamed","email":"","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":848511,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Emil, Mustafa Kemal 0000-0001-5579-0386","orcid":"https://orcid.org/0000-0001-5579-0386","contributorId":294661,"corporation":false,"usgs":false,"family":"Emil","given":"Mustafa","email":"","middleInitial":"Kemal","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":848512,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yellich, John A.","contributorId":243236,"corporation":false,"usgs":false,"family":"Yellich","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":33641,"text":"Michigan Geological Survey","active":true,"usgs":false}],"preferred":false,"id":848513,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":848514,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Becker, Richard 0000-0003-2514-2040","orcid":"https://orcid.org/0000-0003-2514-2040","contributorId":243234,"corporation":false,"usgs":false,"family":"Becker","given":"Richard","email":"","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":848515,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gebremichael, Esayas 0000-0002-9376-9884","orcid":"https://orcid.org/0000-0002-9376-9884","contributorId":294665,"corporation":false,"usgs":false,"family":"Gebremichael","given":"Esayas","email":"","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":848516,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Abdelmohsen, Karem 0000-0001-7572-7069","orcid":"https://orcid.org/0000-0001-7572-7069","contributorId":294666,"corporation":false,"usgs":false,"family":"Abdelmohsen","given":"Karem","email":"","affiliations":[{"id":15306,"text":"Western Michigan University","active":true,"usgs":false}],"preferred":false,"id":848517,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70234216,"text":"70234216 - 2022 - The evolution of rock friction is more sensitive to slip than elapsed time, even at near-zero slip rates","interactions":[],"lastModifiedDate":"2022-08-03T11:48:10.929511","indexId":"70234216","displayToPublicDate":"2022-07-20T06:45:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"The evolution of rock friction is more sensitive to slip than elapsed time, even at near-zero slip rates","docAbstract":"
For many decades, frictional strength increase at low slip rates has been ascribed to time-dependent contact-area growth across the sliding interface. As a result, phenomenological models that correctly predict contact-area growth, as observed in laboratory experiments, have also been widely assumed to be appropriate descriptors of frictional strength evolution. We present experiments that impose more than 5-orders-of-magnitude slip-rate reductions on granite to show that frictional strength evolution in these rocks unequivocally refutes such models. Instead, the data suggest that, even at subnanometric slip rates, frictional strength dominantly evolves with accrued slip. This remarkable slip-sensitivity of friction requires changes of intrinsic strength of the interface with slip that are absent from popular conceptual models of friction at the microscopic contact scale.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2119462119","usgsCitation":"Bhattacharyaa, P., Rubin, A., Tullis, T., Beeler, N.M., and Okazaki, K., 2022, The evolution of rock friction is more sensitive to slip than elapsed time, even at near-zero slip rates: Proceedings of the National Academy of Sciences, v. 119, no. 30, e2119462119, 11 p., https://doi.org/10.1073/pnas.2119462119.","productDescription":"e2119462119, 11 p.","ipdsId":"IP-117749","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":447061,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9335215","text":"Publisher Index Page"},{"id":404743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"119","issue":"30","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Bhattacharyaa, Pathikrit","contributorId":294517,"corporation":false,"usgs":false,"family":"Bhattacharyaa","given":"Pathikrit","email":"","affiliations":[{"id":63586,"text":"National Institute of Science Education and Research, Bhubaneswar, India","active":true,"usgs":false}],"preferred":false,"id":848200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rubin, Allan","contributorId":294518,"corporation":false,"usgs":false,"family":"Rubin","given":"Allan","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":848201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tullis, Terry","contributorId":294519,"corporation":false,"usgs":false,"family":"Tullis","given":"Terry","affiliations":[{"id":16929,"text":"Brown University","active":true,"usgs":false}],"preferred":false,"id":848202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beeler, Nicholas M. 0000-0002-3397-8481 nbeeler@usgs.gov","orcid":"https://orcid.org/0000-0002-3397-8481","contributorId":2682,"corporation":false,"usgs":true,"family":"Beeler","given":"Nicholas","email":"nbeeler@usgs.gov","middleInitial":"M.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":848203,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Okazaki, Keishi","contributorId":294520,"corporation":false,"usgs":false,"family":"Okazaki","given":"Keishi","email":"","affiliations":[{"id":63589,"text":"JAMSTEC","active":true,"usgs":false}],"preferred":false,"id":848204,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70233593,"text":"70233593 - 2022 - Geologic framework, anthropogenic impacts, and hydrodynamics contribute to variable sediment availability and shoreface morphology at the Rockaway Peninsula, NY","interactions":[],"lastModifiedDate":"2022-07-27T11:42:06.208617","indexId":"70233593","displayToPublicDate":"2022-07-20T06:39:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Geologic framework, anthropogenic impacts, and hydrodynamics contribute to variable sediment availability and shoreface morphology at the Rockaway Peninsula, NY","docAbstract":"
Recent field and modeling studies have shown that barrier island resiliency is sensitive to sediment fluxes from the shoreface, making it important to evaluate how shoreface sediment availability varies in coastal systems. To do this, we assessed shoreface geology and morphology along the Rockaway Peninsula, NY, USA. We find that spatial variability in shoreface volume is influenced by sediment accommodation above the Holocene-Pleistocene (H-P) contact, historical barrier island evolution, and natural and engineered morphologic features, suggesting that simply identifying the H-P boundary may not be adequate for defining the shoreface reservoir. Further, sediment flux from the lower shoreface to the beach may be reduced by geologically limited cross-shore sediment distribution and shoreface steepening mediated by human modifications to the shoreline. Finally, the geologic limit of the shoreface is often shallower than a wave-based estimate of shoreface extent, implying that the geologic shoreface extent at our study site can be mobilized over short time scales (years-decades) and that the wave-based shoreface extent may be inaccurate when estimating shoreline response to sea-level rise. Our results demonstrate that the combination of hydrodynamics, humans, and geology on shoreface sediment fluxes impact how barrier islands respond to future changes in sediment supply and climate.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse10070989","usgsCitation":"Wei, E.A., and Miselis, J.L., 2022, Geologic framework, anthropogenic impacts, and hydrodynamics contribute to variable sediment availability and shoreface morphology at the Rockaway Peninsula, NY: Journal of Marine Science and Engineering, v. 10, no. 7, 989, 26 p., https://doi.org/10.3390/jmse10070989.","productDescription":"989, 26 p.","ipdsId":"IP-131353","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":447065,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse10070989","text":"Publisher Index Page"},{"id":435763,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FH8ZJW","text":"USGS data release","linkHelpText":"Grain-Size Data From Sediment Samples at Seven Mile Island, New Jersey and Rockaway Peninsula, New York"},{"id":404478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Rockaway Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.2236328125,\n 40.53676418550201\n ],\n [\n -73.49029541015625,\n 40.53676418550201\n ],\n [\n -73.49029541015625,\n 40.95501133048621\n ],\n [\n -74.2236328125,\n 40.95501133048621\n ],\n [\n -74.2236328125,\n 40.53676418550201\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Wei, Emily A. 0000-0003-4008-0933","orcid":"https://orcid.org/0000-0003-4008-0933","contributorId":223488,"corporation":false,"usgs":true,"family":"Wei","given":"Emily","email":"","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miselis, Jennifer L. 0000-0002-4925-3979 jmiselis@usgs.gov","orcid":"https://orcid.org/0000-0002-4925-3979","contributorId":3914,"corporation":false,"usgs":true,"family":"Miselis","given":"Jennifer","email":"jmiselis@usgs.gov","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":847508,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237620,"text":"70237620 - 2022 - Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations","interactions":[],"lastModifiedDate":"2022-10-14T14:46:49.261277","indexId":"70237620","displayToPublicDate":"2022-07-19T09:45:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3283,"text":"Reviews of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations","docAbstract":"

Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near-real time recording of ground motion with state-of-art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi-instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021RG000749","usgsCitation":"Ringler, A.T., Anthony, R.E., Aster, R., Ammon, C., Arrowsmith, S., Benz, H.M., Ebeling, C., Frassetto, A., Kim, W.Y., Koelemeijer, P., Lau, H.C., Lekic, V., Montagner, J.P., Richards, P., Schaff, D., Vallee, M., and Yeck, W.L., 2022, Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations: Reviews of Geophysics, v. 60, no. 3, e2021RG000749, 98 p., https://doi.org/10.1029/2021RG000749.","productDescription":"e2021RG000749, 98 p.","ipdsId":"IP-132866","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":447073,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021rg000749","text":"External Repository"},{"id":408320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aster, R. C.","contributorId":215408,"corporation":false,"usgs":false,"family":"Aster","given":"R. C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":854670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ammon, C. J.","contributorId":297931,"corporation":false,"usgs":false,"family":"Ammon","given":"C. J.","affiliations":[{"id":64457,"text":"The Pennsylvania State University, University Park","active":true,"usgs":false}],"preferred":false,"id":854671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arrowsmith, S.","contributorId":297932,"corporation":false,"usgs":false,"family":"Arrowsmith","given":"S.","email":"","affiliations":[{"id":20300,"text":"Southern Methodist University","active":true,"usgs":false}],"preferred":false,"id":854672,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854673,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebeling, C.","contributorId":297933,"corporation":false,"usgs":false,"family":"Ebeling","given":"C.","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":854674,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frassetto, A.","contributorId":297942,"corporation":false,"usgs":false,"family":"Frassetto","given":"A.","email":"","affiliations":[],"preferred":false,"id":854695,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kim, W. Y.","contributorId":297934,"corporation":false,"usgs":false,"family":"Kim","given":"W.","email":"","middleInitial":"Y.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":854675,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koelemeijer, Paula","contributorId":236644,"corporation":false,"usgs":false,"family":"Koelemeijer","given":"Paula","email":"","affiliations":[{"id":47486,"text":"Department of Earth Sciences, Royal Holloway University of London, Egham, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":854696,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lau, H. C. P.","contributorId":297935,"corporation":false,"usgs":false,"family":"Lau","given":"H.","email":"","middleInitial":"C. P.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":854676,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lekic, V.","contributorId":251944,"corporation":false,"usgs":false,"family":"Lekic","given":"V.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":854677,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Montagner, J. P.","contributorId":297943,"corporation":false,"usgs":false,"family":"Montagner","given":"J.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":854697,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Richards, P. G.","contributorId":297937,"corporation":false,"usgs":false,"family":"Richards","given":"P. G.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":854679,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schaff, D. P.","contributorId":297936,"corporation":false,"usgs":false,"family":"Schaff","given":"D. P.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":854678,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Vallee, M.","contributorId":297938,"corporation":false,"usgs":false,"family":"Vallee","given":"M.","affiliations":[{"id":64458,"text":"Universite de Paris, CNRS","active":true,"usgs":false}],"preferred":false,"id":854680,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":854681,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70233213,"text":"70233213 - 2022 - Gull plumages are, and are not, what they appear to human vision","interactions":[],"lastModifiedDate":"2022-07-19T14:16:53.782233","indexId":"70233213","displayToPublicDate":"2022-07-19T09:13:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":783,"text":"Annales Zoologici Fennici","active":true,"publicationSubtype":{"id":10}},"title":"Gull plumages are, and are not, what they appear to human vision","docAbstract":"

Clear correlations between human and bird visual assessments of color have been documented, and are often assumed, despite fundamental differences in human and avian visual physiology and morphology. Analyses of plumage colors with avian perceptual models have shown widespread hidden inter-sexual and inter-specific color variation among passerines perceived as monochromatic to humans, highlighting the uncertainty of human vision to predict potentially relevant variation in color. Herein, we use reflectance data from 13 Larus gull species as an exemplar data set to study concordance between human vision and avian visual modeling of feather colors near, or below, the human threshold for discrimination. We found little evidence among gulls for sexual dichromatism hidden from human vision, but did find inter-specific color variation among gulls that is not seen by humans. Neither of these results were predictable a priori, and we reassert that reflectance measurements of actual feather colors, analyzed with avian relevant visual models, represent best practice when studying bird coloration.

","language":"English","publisher":"Finnish Zoological and Botanical Publishing Board","doi":"10.5735/086.059.0116","usgsCitation":"Eaton, M.D., Benites, P., Campillo, L., Wilson, R.E., and Sonsthagen, S.A., 2022, Gull plumages are, and are not, what they appear to human vision: Annales Zoologici Fennici, v. 59, no. 1, p. 187-203, https://doi.org/10.5735/086.059.0116.","productDescription":"7 p.","startPage":"187","endPage":"203","ipdsId":"IP-118461","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":404018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eaton, Muir D","contributorId":293231,"corporation":false,"usgs":false,"family":"Eaton","given":"Muir","email":"","middleInitial":"D","affiliations":[{"id":63252,"text":"Drake University","active":true,"usgs":false}],"preferred":false,"id":846813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benites, Pilar","contributorId":293232,"corporation":false,"usgs":false,"family":"Benites","given":"Pilar","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":846814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campillo, Luke","contributorId":293233,"corporation":false,"usgs":false,"family":"Campillo","given":"Luke","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":846815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Robert E.","contributorId":293234,"corporation":false,"usgs":false,"family":"Wilson","given":"Robert","email":"","middleInitial":"E.","affiliations":[{"id":63255,"text":"Nebraska State Museum","active":true,"usgs":false}],"preferred":false,"id":846816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","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":846817,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70233201,"text":"70233201 - 2022 - Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability","interactions":[],"lastModifiedDate":"2022-07-19T14:12:33.66365","indexId":"70233201","displayToPublicDate":"2022-07-19T09:08:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability","docAbstract":"

Quantifying the petrophysical and geomechanical properties of gas hydrate reservoirs is essential for understanding the natural hydrate system and predicting gas production behavior for future resource development. Pressure-core analysis tools were used to characterize methane hydrate–bearing sediments recovered from the Gulf of Mexico Green Canyon Block 955, under an international collaboration with The University of Texas and the National Institute of Advanced Industrial Science and Technology. Pressure-core samples were successfully transferred from Austin, Texas to Sapporo, Japan. Index property measurements (grain size, grain density, hydration number, gas composition, thermal conductivity), along with triaxial compression, consolidation, and permeability tests with a nuclear magnetic resonance (NMR) analyzer were conducted. Compression tests at different strain rates confirmed a strain rate dependence for hydrate-bearing sediment, and an equation for predicting strength as a function of hydrate saturation and strain rate is proposed. Compression and swelling indices were obtained from high-effective stress consolidation tests. Furthermore, secondary compression coefficients for hydrate-bearing sediments were obtained, suggesting that hydrate exhibits creeping behavior on timescales of minutes to hours. A relatively high initial permeability of a few millidarcys was confirmed. In addition, the first NMR signal measurement was performed on a hydrate-bearing pressure core to acquire the NMR transverse or spin-spin (T2) distribution. Results confirm that the Schlumberger Doll Research model and Timur-Coates model predictions underestimate permeability measured directly via fluid flow. Permeability estimated using specific surface values derived from NMR T2 distributions is in good agreement with flow test results. Finally, an extended Timur-Coates model was proposed and predicts intrinsic permeability with high accuracy.

","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/04272120204","usgsCitation":"Yoneda, J., Jin, Y., Muraoka, M., Oshima, M., Suzuki, K., Waite, W., and Flemings, P., 2022, Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability: AAPG Bulletin, v. 106, no. 5, p. 1143-1177, https://doi.org/10.1306/04272120204.","productDescription":"35 p.","startPage":"1143","endPage":"1177","ipdsId":"IP-124941","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":404017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Green Canyon, Green Canyon Block 955, 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 -90.76904296874999,\n 26.194876675795218\n ],\n [\n -89.23095703125,\n 26.194876675795218\n ],\n [\n -89.23095703125,\n 27.6251403350933\n ],\n [\n -90.76904296874999,\n 27.6251403350933\n ],\n [\n -90.76904296874999,\n 26.194876675795218\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yoneda, Jun","contributorId":240073,"corporation":false,"usgs":false,"family":"Yoneda","given":"Jun","affiliations":[{"id":40273,"text":"National Institute of Advanced Industrial Science and Technology","active":true,"usgs":false}],"preferred":false,"id":846772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Yusuke","contributorId":220832,"corporation":false,"usgs":false,"family":"Jin","given":"Yusuke","email":"","affiliations":[],"preferred":false,"id":846773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muraoka, Michihiro","contributorId":248423,"corporation":false,"usgs":false,"family":"Muraoka","given":"Michihiro","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oshima, Motoi","contributorId":248424,"corporation":false,"usgs":false,"family":"Oshima","given":"Motoi","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suzuki, Kiyofumi","contributorId":248425,"corporation":false,"usgs":false,"family":"Suzuki","given":"Kiyofumi","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":846777,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flemings, Peter","contributorId":198205,"corporation":false,"usgs":false,"family":"Flemings","given":"Peter","affiliations":[{"id":13127,"text":"Jackson School of Geosciences, University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":846778,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70233462,"text":"70233462 - 2022 - Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems","interactions":[],"lastModifiedDate":"2022-07-21T13:59:42.32542","indexId":"70233462","displayToPublicDate":"2022-07-19T08:55:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems","docAbstract":"

Sagebrush ecosystems of western North America are threatened by invasive annual grasses and wildfires that can remove fire-intolerant shrubs for decades. Fuel reduction treatments are used ostensibly to aid in fire suppression, conserve wildlife habitat, and restore historical fire regimes, but long-term ecological impacts of these treatments are not clear. In 2006, we initiated fuel reduction treatments (prescribed fire, mowing, and herbicide applications [tebuthiuron and imazapic]) in six Artemisia tridentata ssp. wyomingensis communities. We evaluated long-term effects of these fuel treatments on: (1) magnitude and longevity of fuel reduction; (2) Greater Sage-grouse habitat characteristics; and (3) ecological resilience and resistance to invasive annual grasses. Responses were analyzed using repeated-measures linear mixed models. Response variables included plant biomass, cover, density and height, distances between perennial plants, and exposed soil cover. Prescribed fire produced the greatest reduction in woody fuel over time. Mowing initially reduced woody biomass, which recovered by year 10. Tebuthiuron did not significantly reduce woody biomass compared to controls. All woody fuel treatments reduced sagebrush cover to below 15% (recommended minimum for Greater Sage-grouse habitat), but only prescribed fire reduced cover to below controls. Median mowed sagebrush height remained above the recommended 30 cm. Cheatgrass (Bromus tectorum) cover increased to above the recommended maximum of 10% across all treatments and controls. Ecological resilience to woody fuel treatments was lowest with fire and greatest with mowing. Low resilience over the 10 posttreatment years was identified by: (1) poor perennial plant recovery posttreatment with sustained reductions in cover and density of some perennial plant species; (2) sustained reductions in lichen and moss cover; and (3) increases in cheatgrass cover. Although 10 years is insufficient to conclusively describe final ecological responses to fuel treatments, mowing woody fuels has the greatest potential to reduce woody fuel, minimize shrub mortality and soil disturbance, maintain lichens and mosses, and minimize long-term negative impacts on Greater Sage-grouse habitat. However, maintaining ecological resilience and resistance to invasion may be threatened by increases in cheatgrass cover, which are occurring regionally.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4176","usgsCitation":"Pyke, D.A., Shaff, S.E., Chambers, J., Schupp, E.W., Newingham, B.A., Gray, M.L., and Ellsworth, L.M., 2022, Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems: Ecosphere, v. 13, no. 7, e4176, 21 p., https://doi.org/10.1002/ecs2.4176.","productDescription":"e4176, 21 p.","ipdsId":"IP-128463","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4176","text":"Publisher Index Page"},{"id":404212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Nevada, Oregon, Utah, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.4541015625,\n 38.37611542403604\n ],\n [\n -110.8740234375,\n 38.37611542403604\n ],\n [\n -110.8740234375,\n 48.1367666796927\n ],\n [\n -120.4541015625,\n 48.1367666796927\n ],\n [\n -120.4541015625,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":847154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaff, Scott E. 0000-0001-8978-9260","orcid":"https://orcid.org/0000-0001-8978-9260","contributorId":219813,"corporation":false,"usgs":true,"family":"Shaff","given":"Scott","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":847155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chambers, Jeanne C.","contributorId":75889,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[],"preferred":false,"id":847156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schupp, Eugene W.","contributorId":178262,"corporation":false,"usgs":false,"family":"Schupp","given":"Eugene","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":847157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newingham, Beth A.","contributorId":195932,"corporation":false,"usgs":false,"family":"Newingham","given":"Beth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":847158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gray, Margaret L 0000-0002-4810-8876","orcid":"https://orcid.org/0000-0002-4810-8876","contributorId":221166,"corporation":false,"usgs":false,"family":"Gray","given":"Margaret","email":"","middleInitial":"L","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":847159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ellsworth, Lisa M.","contributorId":255109,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":51436,"text":"Fisheries and Wildlife Department, Oregon State University, Corvallis, Oregon 97331 USA","active":true,"usgs":false}],"preferred":false,"id":847160,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70233220,"text":"70233220 - 2022 - Fibropapillomatosis dynamics in green sea turtles Chelonia mydas over 15 years of monitoring in Akumal Bay, Quintana Roo, Mexico","interactions":[],"lastModifiedDate":"2022-07-19T13:56:10.749169","indexId":"70233220","displayToPublicDate":"2022-07-19T08:49:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fibropapillomatosis dynamics in green sea turtles Chelonia mydas over 15 years of monitoring in Akumal Bay, Quintana Roo, Mexico","title":"Fibropapillomatosis dynamics in green sea turtles Chelonia mydas over 15 years of monitoring in Akumal Bay, Quintana Roo, Mexico","docAbstract":"

ABSTRACT: Fibropapillomatosis (FP) is a tumor disease that affects all sea turtle species but is mainly seen in green turtles Chelonia mydas. The pathology of FP has been described extensively, but its dynamics in populations over time have been less studied. We analyzed the dynamics of FP in a population of green turtles in Akumal Bay on the central coast of the Mexican Caribbean. A total of 475 green turtles were captured over 15 yr (2004-2018). The highest prevalence of FP was found in the largest turtles, and there was a positive relationship between FP prevalence and size of turtles. FP was first detected in 2008 at a prevalence of 1.6%, and annual prevalence increased markedly from 17.9% in 2015 to 54% by 2018. Likewise, severity of FP increased over time, with most turtles falling into moderately to severely diseased categories (tumor score 2). The average size of turtles with FP was significantly larger than the size of individuals without FP. Regression of tumors was seen in 21% of turtles, tumor score was higher in smaller individuals, and only tumor score 2 was present in the largest sea turtles. An increase in the prevalence and tumor score of FP coincided with the massive arrival of Sargassum in 2015, suggesting that altered environmental conditions may have played a role. The increased prevalence of FP in Akumal Bay prompts the need to explain what might be driving this phenomenon and how widespread it is in the Caribbean.

","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03669","usgsCitation":"Munoz Teneria, F.A., Labrada-Martagon, V., Herrera-Pavon, R., Work, T.M., Gonzalez Ballesteros, E., Negrete-Philippe, A., and Maldonado-Saldana, G., 2022, Fibropapillomatosis dynamics in green sea turtles Chelonia mydas over 15 years of monitoring in Akumal Bay, Quintana Roo, Mexico: Diseases of Aquatic Organisms, v. 149, p. 133-143, https://doi.org/10.3354/dao03669.","productDescription":"11 p.","startPage":"133","endPage":"143","ipdsId":"IP-137508","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":447087,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/dao03669","text":"Publisher Index Page"},{"id":404014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Quintana Roo","otherGeospatial":"Akumal Bay, Marine Life Refuge of Akumal Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.32285499572754,\n 20.376411864521312\n ],\n [\n -87.32173919677734,\n 20.37496358008667\n ],\n [\n -87.30628967285156,\n 20.399341222168914\n ],\n [\n -87.31521606445311,\n 20.406661807347298\n ],\n [\n -87.3288631439209,\n 20.38292897619788\n ],\n [\n -87.32714653015137,\n 20.381239381095543\n ],\n [\n -87.32285499572754,\n 20.376411864521312\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Munoz Teneria, Fernando A.","contributorId":191521,"corporation":false,"usgs":false,"family":"Munoz Teneria","given":"Fernando","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":846832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Labrada-Martagon, Vanessa","contributorId":191523,"corporation":false,"usgs":false,"family":"Labrada-Martagon","given":"Vanessa","email":"","affiliations":[],"preferred":false,"id":846833,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrera-Pavon, Roberto","contributorId":191522,"corporation":false,"usgs":false,"family":"Herrera-Pavon","given":"Roberto","email":"","affiliations":[],"preferred":false,"id":846834,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":846835,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gonzalez Ballesteros, Erik","contributorId":293241,"corporation":false,"usgs":false,"family":"Gonzalez Ballesteros","given":"Erik","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":846836,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Negrete-Philippe, Ana","contributorId":191526,"corporation":false,"usgs":false,"family":"Negrete-Philippe","given":"Ana","email":"","affiliations":[],"preferred":false,"id":846837,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maldonado-Saldana, Gisela","contributorId":293243,"corporation":false,"usgs":false,"family":"Maldonado-Saldana","given":"Gisela","email":"","affiliations":[{"id":63261,"text":"Kanantik Servicios y Soluciones Ambientales","active":true,"usgs":false}],"preferred":false,"id":846838,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236308,"text":"70236308 - 2022 - Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not","interactions":[],"lastModifiedDate":"2022-09-01T12:08:58.021882","indexId":"70236308","displayToPublicDate":"2022-07-19T07:06:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not","docAbstract":"

The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.

","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-15-5481-2022","usgsCitation":"Hodson, T.O., 2022, Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not: Geoscientific Model Development, v. 15, p. 5481-5487, https://doi.org/10.5194/gmd-15-5481-2022.","productDescription":"7 p.","startPage":"5481","endPage":"5487","ipdsId":"IP-136463","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-15-5481-2022","text":"Publisher Index Page"},{"id":406059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodson, Timothy O. 0000-0003-0962-5130","orcid":"https://orcid.org/0000-0003-0962-5130","contributorId":78634,"corporation":false,"usgs":true,"family":"Hodson","given":"Timothy","email":"","middleInitial":"O.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850544,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239310,"text":"70239310 - 2022 - Thermophysical and compositional properties of paleobedforms on Mars","interactions":[],"lastModifiedDate":"2023-01-09T13:00:41.226167","indexId":"70239310","displayToPublicDate":"2022-07-19T06:59:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7353,"text":"Journal of Geophysical Research - Planets","active":true,"publicationSubtype":{"id":10}},"title":"Thermophysical and compositional properties of paleobedforms on Mars","docAbstract":"

Bedforms on Earth and Mars are often preserved in the rock record in the form of sedimentary rock with distinct cross-bedding. On rare occasions, the full-surface geometry of a bedform can be preserved through burial and lithification. These features, known as paleobedforms, are found in a variety of geographic locations on Mars. Evidence in the morphology of paleobedforms, such as the retention of impact craters and steep erosional scarps, suggests that these features are well-lithified and capable of withstanding prolonged weathering and erosion. Here, we present results from thermophysical and compositional analyses on a subset of the best preserved paleobedform candidate fields on Mars. Thermophysical modeling elucidates the changes these bedforms underwent from their unconsolidated, particulate nature to their currently observed properties. Certain paleobedforms have elevated thermal inertias (e.g., ∼300–500 J·m−2·s−1/2·K−1) when compared with modern bedforms (∼250 J·m−2·s−1/2·K−1), and modeling indicates that they have cement volumes of 0.8%–1.5% even as high as 30%. However, most paleobedform candidates have unexpectedly low thermal inertia when compared with modern dunes. Additionally, compositional analyses reveal a range of spectral characteristics within paleobedforms (e.g., primary and secondary alteration products). These features add to the already existing class of Martian surfaces in which thermal inertia does not seem to correspond to erodibility, cohesion, or mechanical strength. Studying paleobedforms with both raised and nonraised thermal inertia has provided new insights into lithification on Mars and constrained the environmental conditions leading to the formation of these enigmatic features.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JE007345","usgsCitation":"Weintraub, A.R., Edwards, C., Chojnacki, M., Edgar, L.A., Fenton, L.K., Piqueux, S., and Gullikson, A.L., 2022, Thermophysical and compositional properties of paleobedforms on Mars: Journal of Geophysical Research - Planets, v. 127, no. 8, e2022JE007345, 25 p., https://doi.org/10.1029/2022JE007345.","productDescription":"e2022JE007345, 25 p.","ipdsId":"IP-141025","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":411559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Weintraub, Aaron R.","contributorId":300676,"corporation":false,"usgs":false,"family":"Weintraub","given":"Aaron","email":"","middleInitial":"R.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":861103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edwards, Christopher S.","contributorId":206168,"corporation":false,"usgs":false,"family":"Edwards","given":"Christopher S.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":861104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chojnacki, Matthew 0000-0001-8497-8994","orcid":"https://orcid.org/0000-0001-8497-8994","contributorId":296931,"corporation":false,"usgs":false,"family":"Chojnacki","given":"Matthew","email":"","affiliations":[{"id":64240,"text":"Planetary Science Institute, Lakewood, CO, USA","active":true,"usgs":false}],"preferred":false,"id":861105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":861106,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fenton, Lori K.","contributorId":208682,"corporation":false,"usgs":false,"family":"Fenton","given":"Lori","email":"","middleInitial":"K.","affiliations":[{"id":37319,"text":"SETI Institute","active":true,"usgs":false}],"preferred":false,"id":861107,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Piqueux, Sylvain","contributorId":56986,"corporation":false,"usgs":false,"family":"Piqueux","given":"Sylvain","email":"","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":861108,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gullikson, Amber L. 0000-0002-1505-3151","orcid":"https://orcid.org/0000-0002-1505-3151","contributorId":208679,"corporation":false,"usgs":true,"family":"Gullikson","given":"Amber","email":"","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":861109,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70262047,"text":"70262047 - 2022 - Using piecewise regression to identify biological phenomena in biotelemetry datasets","interactions":[],"lastModifiedDate":"2025-01-10T17:13:33.962607","indexId":"70262047","displayToPublicDate":"2022-07-19T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using piecewise regression to identify biological phenomena in biotelemetry datasets","docAbstract":"

1. Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal’s location, movement characteristics, external environmental conditions, and internal physiological status.

2. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval.

3. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short-term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces; a physiological response (spike in heart-rate) in a black bear Ursus americanus to a stressful stimulus(presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood-rearing (i.e. hatching) for a breeding trumpeter swan.

4. We implement analyses using the MCP package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross-validation measures. 

","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.13779","usgsCitation":"Wolfson, D., Andersen, D.E., and Fieberg, J., 2022, Using piecewise regression to identify biological phenomena in biotelemetry datasets: Journal of Animal Ecology, v. 91, no. 9, p. 1755-1769, https://doi.org/10.1111/1365-2656.13779.","productDescription":"15 p.","startPage":"1755","endPage":"1769","ipdsId":"IP-134564","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467175,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.13779","text":"Publisher Index Page"},{"id":466005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfson, David W.","contributorId":348002,"corporation":false,"usgs":false,"family":"Wolfson","given":"David W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":922812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fieberg, John R.","contributorId":348003,"corporation":false,"usgs":false,"family":"Fieberg","given":"John R.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":922813,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236309,"text":"70236309 - 2022 - Tephrochronology of the Miocene Monterey and Modelo Formations, California","interactions":[],"lastModifiedDate":"2022-09-01T12:17:20.986718","indexId":"70236309","displayToPublicDate":"2022-07-17T07:15:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1727,"text":"GSA Special Papers","active":true,"publicationSubtype":{"id":10}},"title":"Tephrochronology of the Miocene Monterey and Modelo Formations, California","docAbstract":"Tuff beds have been known in the Miocene Monterey and Modelo Formations since the initial descriptions; however, age control and correlation is predominantly biostratigraphy. Here we combine tephrochronology and biostratigraphy in order to provide numerical age control for eight sedimentary sequences of the Monterey and Modelo Formations from Monterey, California to Orange County, California. We correlate 38 tuffs and tephra beds in the Monterey and Modelo Formations to 26 different dated tuffs found mainly in non-marine sequences in Nevada, Idaho and New Mexico. We also include geochemical data for an additional 19 tuffs in the Monterey and Modelo Formations for which there are no known correlative tuffs and geochemical data for 11 additional tuffs in other units that will add to the Miocene tephrostratigraphy. The identified tuffs range in age from 16 to 7 Ma with 31 tuffs erupted from volcanic centers of the Snake River Plain of northern Nevada to eastern Idaho. Twelve other tuffs erupted from the Southern Nevada Volcanic Field, one from the Sonoma Volcanic Field, north of San Francisco, and the eruptive source of 12 other tuffs is uncertain. These tuffs provide useful correlations of marine sequences deposited at varying depths along offshore Miocene California and possible insight into the distribution of air-fall tephra from so-called super eruptions","language":"English","publisher":"Geological Society of America","doi":"10.1130/2022.2556(08)","usgsCitation":"Knott, J.R., Sarna-Wojcicki, A., Barron, J.A., Wan, E., Heizler, N., and Martinez, P., 2022, Tephrochronology of the Miocene Monterey and Modelo Formations, California: GSA Special Papers, https://doi.org/10.1130/2022.2556(08).","ipdsId":"IP-122368","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2022.2556(08)","text":"Publisher Index Page"},{"id":406061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knott, Jeffrey R. 0000-0002-4600-5961","orcid":"https://orcid.org/0000-0002-4600-5961","contributorId":218427,"corporation":false,"usgs":false,"family":"Knott","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":39844,"text":"CSU Fullerton, Department of Geological Sciences","active":true,"usgs":false}],"preferred":false,"id":850545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sarna-Wojcicki, Andrei M. 0000-0002-0244-9149","orcid":"https://orcid.org/0000-0002-0244-9149","contributorId":296073,"corporation":false,"usgs":true,"family":"Sarna-Wojcicki","given":"Andrei M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":850547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wan, Elmira 0000-0002-9255-112X ewan@usgs.gov","orcid":"https://orcid.org/0000-0002-9255-112X","contributorId":296074,"corporation":false,"usgs":true,"family":"Wan","given":"Elmira","email":"ewan@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":850548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heizler, Nancy","contributorId":296075,"corporation":false,"usgs":false,"family":"Heizler","given":"Nancy","email":"","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":850549,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martinez, Priscilla","contributorId":296076,"corporation":false,"usgs":false,"family":"Martinez","given":"Priscilla","email":"","affiliations":[{"id":63349,"text":"California State University Fullerton","active":true,"usgs":false}],"preferred":false,"id":850550,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233571,"text":"70233571 - 2022 - Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","interactions":[],"lastModifiedDate":"2022-07-26T12:04:54.409941","indexId":"70233571","displayToPublicDate":"2022-07-16T06:59:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","docAbstract":"

Fully distributed hydrological models are widely used in groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertain, and thus it is important to assess the predictive model performance to ranges outside the training data, i.e., model transferability. We present a novel methodology for evaluating model transferability to areas not contained in the training data set, based on various metrics that quantify the differences in covariate distributions between training and testing data. The transferability method can be employed as a screening tool to assess the suitability of a metamodel for spatial prediction beyond its training domain. We evaluated this transferability approach on a Random Forest metamodel of a 1000 km2 fully distributed coupled groundwater model for predicting drainage fraction, the partitioning of infiltrating water between drains and groundwater. We conducted spatial cross-validation on 9 holdout sub-basins to assess metamodel transferability beyond sampling locations and compared this estimate with a random split-sample validation test. Using mappable covariates only, the metamodel showed high performance (R2 = 0.79) tested on a 20% randomly sampled holdout. Conversely, metamodel performance significantly decreased for the 9 spatial holdouts (R2 ranging from 0.13 to 0.61). We document that the proposed transferability metric correlates with metamodel predictive performance, and demonstrate its use to assess model transferability to datasets outside the training data spatial domain.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2022.128177","usgsCitation":"Bjerre, E., Fienen, M., Schneider, R., Koch, J., and Højberg, A., 2022, Assessing spatial transferability of a random forest metamodel for predicting drainage fraction: Journal of Hydrology, v. 612, no. Part B, 128177, 11 p., https://doi.org/10.1016/j.jhydrol.2022.128177.","productDescription":"128177, 11 p.","ipdsId":"IP-141041","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447100,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2022.128177","text":"Publisher Index Page"},{"id":404448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Denmark","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 7.965087890625,\n 55.78892895389262\n ],\n [\n 9.5361328125,\n 55.78892895389262\n ],\n [\n 9.5361328125,\n 56.71053615360101\n ],\n [\n 7.965087890625,\n 56.71053615360101\n ],\n [\n 7.965087890625,\n 55.78892895389262\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"612","issue":"Part B","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bjerre, Elisa","contributorId":293621,"corporation":false,"usgs":false,"family":"Bjerre","given":"Elisa","affiliations":[{"id":63347,"text":"Univeristy of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":847440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":847441,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schneider, Raphael","contributorId":293622,"corporation":false,"usgs":false,"family":"Schneider","given":"Raphael","email":"","affiliations":[{"id":63347,"text":"Univeristy of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":847442,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koch, Julian","contributorId":293623,"corporation":false,"usgs":false,"family":"Koch","given":"Julian","email":"","affiliations":[{"id":63347,"text":"Univeristy of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":847443,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Højberg, Anker L.","contributorId":187776,"corporation":false,"usgs":false,"family":"Højberg","given":"Anker L.","affiliations":[],"preferred":false,"id":847444,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70233609,"text":"70233609 - 2022 - Riparian buffers provide refugia during secondary forest succession","interactions":[],"lastModifiedDate":"2022-09-01T14:52:41.565917","indexId":"70233609","displayToPublicDate":"2022-07-16T06:37:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Riparian buffers provide refugia during secondary forest succession","docAbstract":"

Aim

Secondary forests regenerating from human disturbance are increasingly becoming a predominant forest type in many regions, and they play a significant role in forest community dynamics. Understanding the factors that underlie the variation in species responses during secondary succession is important for understanding community assembly and biodiversity monitoring and management. Because species vary in ecology and behaviour, responses to ecosystem change should vary among species. Here, we show that habitat type (riparian, upland), phylogeny, and species traits mediate anuran and lizard probability of occurrence and species richness in pasture and secondary forest.

Location

Sarapiquí and Osa Peninsula, Costa Rica.

Methods

We used phylogenetic occupancy models to estimate assemblage-level and species-specific responses to forest succession in 30 chronosequence sites that include pasture, secondary forest regenerating from pasture, and mature forest sites.

Results

For the majority of species, we found increasing probability of occurrence in upland habitats as forest regenerated from pasture to secondary forest and similar probability of occurrence in riparian habitats across pasture, secondary forest, and mature forest sites. Species' responses to forest stage were phylogenetically correlated, and the trend was especially strong for anuran response to pasture sites. Anurans with lentic larval habitat had a positive occupancy response to pasture upland habitat, and anurans with lotic larval habitat had a variable response to different forest stages compared to mature forest.

Main Conclusions

Our study, which focuses on sites that are minimally isolated from mature forest reference sites, indicated that anuran and lizard occupancy rapidly recovered to a level similar to mature forest in a relatively short time span (approximately 20 years). Riparian habitats are key ecosystem features in our system and provide refugia for organisms in early successional stages. Maintenance of vegetation along streams shows that we can mitigate forest conversion by maintaining riparian buffers.

","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13601","usgsCitation":"Thompson, M.E., Halstead, B., and Donnelly, M., 2022, Riparian buffers provide refugia during secondary forest succession: Diversity and Distributions, v. 28, no. 9, p. 2008-2019, https://doi.org/10.1111/ddi.13601.","productDescription":"12 p.","startPage":"2008","endPage":"2019","ipdsId":"IP-132963","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447102,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13601","text":"Publisher Index Page"},{"id":404477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"Osa Peninsula, Sarapiquí","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.9,\n 10.3\n ],\n [\n -83.9,\n 10.5\n ],\n [\n -84.1,\n 10.5\n ],\n [\n -84.1,\n 10.3\n ],\n [\n -83.9,\n 10.3\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.4,\n 8.32\n ],\n [\n -83.3,\n 8.32\n ],\n [\n -83.3,\n 8.52\n ],\n [\n -83.4,\n 8.52\n ],\n [\n -83.4,\n 8.32\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Michelle E.","contributorId":210341,"corporation":false,"usgs":false,"family":"Thompson","given":"Michelle","email":"","middleInitial":"E.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":847546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":847547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Maureen A.","contributorId":293649,"corporation":false,"usgs":false,"family":"Donnelly","given":"Maureen A.","affiliations":[{"id":63353,"text":"Department of Biological Sciences, Florida International University, Miami, Florida, USA","active":true,"usgs":false}],"preferred":false,"id":847548,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232990,"text":"70232990 - 2022 - The formation mechanisms for mid-latitude ice scarps on Mars","interactions":[],"lastModifiedDate":"2022-07-15T13:30:06.924916","indexId":"70232990","displayToPublicDate":"2022-07-15T08:25:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"The formation mechanisms for mid-latitude ice scarps on Mars","docAbstract":"Mid-latitude exposed ice scarps have recently been identified on Mars (Dundas et al., 2018; 2021). The presence of such surface ice exposures at relatively low latitudes was itself a mystery, and the formation dynamics of such scarps have also not been explained. In this work we model the ice ablation rates of several identified mid-latitude scarps. We find that, given certain characteristics of their geographic setting, the orientation and growth of the scarps can be explained by energy balance models.","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2022.115174","usgsCitation":"Williams, K.E., Dundas, C., and Kahre, M.A., 2022, The formation mechanisms for mid-latitude ice scarps on Mars: Icarus, v. 386, 115174, 12 p., https://doi.org/10.1016/j.icarus.2022.115174.","productDescription":"115174, 12 p.","ipdsId":"IP-138177","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":447106,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.icarus.2022.115174","text":"Publisher Index Page"},{"id":403785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"386","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Kaj E. 0000-0003-1755-1872 kewilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-1755-1872","contributorId":196988,"corporation":false,"usgs":true,"family":"Williams","given":"Kaj","email":"kewilliams@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":846629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":846630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kahre, Melinda A.","contributorId":61942,"corporation":false,"usgs":true,"family":"Kahre","given":"Melinda","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":846631,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232921,"text":"ofr20221046 - 2022 - Results of automated scanning electron microscope (SEM) analyses of rock and stream sediment samples from the Taurus porphyry copper deposit area, Tanacross quadrangle, eastern Alaska","interactions":[],"lastModifiedDate":"2026-03-30T13:29:35.729891","indexId":"ofr20221046","displayToPublicDate":"2022-07-14T16:15:00","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":"2022-1046","displayTitle":"Results of Automated Scanning Electron Microscope (SEM) Analyses of Rock and Stream Sediment Samples from the Taurus Porphyry Copper Deposit Area, Tanacross Quadrangle, Eastern Alaska","title":"Results of automated scanning electron microscope (SEM) analyses of rock and stream sediment samples from the Taurus porphyry copper deposit area, Tanacross quadrangle, eastern Alaska","docAbstract":"

Numerous porphyry copper-molybdenum-gold and epithermal deposits define a belt that extends from Eastern Alaska to western Yukon, Canada. An orientation study conducted near the Taurus porphyry deposit was designed to test methods that require minimal sample collection, preparation, and analytical time to determine the viability of indicator mineral studies as a reconnaissance exploration method. Bulk stream sediments and altered and mineralized rocks were sieved to the 0.105−0.25 millimeter fraction (+140, −60 mesh) and passed over a shaking table to create a moderate to heavy mineral separate that was mounted in epoxy and subsequently analyzed using automated scanning electron microscope (SEM) techniques. Seven polished thin sections of core were also analyzed. Among the advantages of automated SEM techniques compared to visual mineral identification are that thousands of grains can be rapidly identified in each sample (about 1 hour per sample) and small quantities of indicator minerals that may be missed during traditional visual analyses can be detected. Automated SEM analyses of stream sediment and rock samples show that specific minerals (chalcopyrite, bornite, and jarosite) are indicators of potential mineralized areas. Svanbergite, an aluminum sulfate phosphate mineral, was identified in mineralized rocks and in nearly all stream sediment samples (up to 9 kilometers) downstream from the Taurus and other porphyry occurrences but not epithermal occurrences. It was not identified in areas with no known mineralization and thus it is possibly one of the best indicator minerals for porphyry copper (+/- molybdenum, gold) occurrences.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221046","usgsCitation":"Kelley, K.D., Pfaff, K., and Graham, G.E., 2022, Results of automated scanning electron microscope (SEM) analyses of rock and stream sediment samples from the Taurus porphyry copper deposit area, Tanacross quadrangle, eastern Alaska: U.S. Geological Survey Open-File Report 2022–1046, 12 p., https://doi.org/10.3133/ofr20221046.","productDescription":"Report: vi, 12 p.; Table; Data Release","onlineOnly":"Y","ipdsId":"IP-132987","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":403682,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2022/1046/table1_1.csv","text":"Table 1.1","size":"12.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2022-1046 Table 1.1"},{"id":403617,"rank":3,"type":{"id":30,"text":"Data 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Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 973
Denver, CO 80225

","tableOfContents":"","publishedDate":"2022-07-14","noUsgsAuthors":false,"publicationDate":"2022-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Kelley, Karen D. 0000-0002-3232-5809 kdkelley@usgs.gov","orcid":"https://orcid.org/0000-0002-3232-5809","contributorId":179012,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":846508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pfaff, Katharina","contributorId":293154,"corporation":false,"usgs":false,"family":"Pfaff","given":"Katharina","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":846509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":846510,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255112,"text":"70255112 - 2022 - Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems","interactions":[],"lastModifiedDate":"2024-06-12T16:31:54.652658","indexId":"70255112","displayToPublicDate":"2022-07-14T11:23:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems","docAbstract":"

Eastern black rails (Laterallus jamaicensis jamaicensis) are among the rarest and least-studied birds in North America and were recently listed as threatened under the U.S. Endangered Species Act. Spatial models that predict habitat quality across the subspecies range are therefore needed to inform conservation, recovery, and monitoring efforts for this rare bird. We used data from 47,585 call-broadcast surveys collected at 7906 sites over a 3-decade period (1990s, 2000s, 2010s; 23 total years) to build species distribution models for eastern black rails. We used hierarchical Bayesian occupancy models and predictive model selection to develop multi-scale models that optimally predict habitat suitability for eastern black rails within tidal wetlands while also accounting for imperfect detection of these cryptic birds during field surveys. We also used raster regression techniques to translate model predictions into 30-m resolution maps of habitat suitability for eastern black rails within tidal wetlands along the eastern seaboard of the United States. The model predicted suitability of breeding habitat as a function of wetland attributes (e.g., cover of high marsh and terrestrial border), hydrologic modification, and disturbance from human development measured over multiple spatial scales. We also found differences in habitat relationships for eastern black rails when compared to models that included both North American subspecies of black rail. Important results included negative effects of shrub-scrub wetlands, and strong positive effects of high marsh, terrestrial border, and impoundments on breeding season occupancy. Our study provides an example of integrating detection-non-detection data and modern statistical methods to build predictive distribution models for an extremely rare species, while also providing rigorous predictions of breeding habitat quality for the eastern black rail within tidal wetlands. These models will facilitate optimal monitoring, habitat conservation, and recovery planning efforts for eastern black rails and provide a foundation for future research and conservation of this imperiled bird.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02222","usgsCitation":"Stevens, B., Conway, C.J., Luke, K., Weldon, A., Hand, C., Schwarzer, A., Smith, F., Watson, C., and Watts, B.D., 2022, Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems: Global Ecology and Conservation, v. 38, e02222, 12 p., https://doi.org/10.1016/j.gecco.2022.e02222.","productDescription":"e02222, 12 p.","ipdsId":"IP-136723","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02222","text":"Publisher Index Page"},{"id":430022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":903426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luke, Kirsten","contributorId":338653,"corporation":false,"usgs":false,"family":"Luke","given":"Kirsten","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weldon, Aimee","contributorId":338654,"corporation":false,"usgs":false,"family":"Weldon","given":"Aimee","email":"","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hand, Christy","contributorId":338655,"corporation":false,"usgs":false,"family":"Hand","given":"Christy","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarzer, Amy","contributorId":338656,"corporation":false,"usgs":false,"family":"Schwarzer","given":"Amy","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":903431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Fletcher","contributorId":338657,"corporation":false,"usgs":false,"family":"Smith","given":"Fletcher","email":"","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903432,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Watson, Craig","contributorId":338659,"corporation":false,"usgs":false,"family":"Watson","given":"Craig","email":"","affiliations":[{"id":81184,"text":"Atlanti Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903433,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Watts, Bryan D.","contributorId":338660,"corporation":false,"usgs":false,"family":"Watts","given":"Bryan","email":"","middleInitial":"D.","affiliations":[{"id":37406,"text":"College of William & Mary","active":true,"usgs":false}],"preferred":false,"id":903434,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70232270,"text":"ofr20191023D - 2022 - Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico — Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium","interactions":[],"lastModifiedDate":"2026-03-25T16:57:34.886331","indexId":"ofr20191023D","displayToPublicDate":"2022-07-14T10:33:00","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":"2019-1023","chapter":"D","displayTitle":"Focus Areas for Data Acquisition for Potential Domestic Resources of 13 Critical Minerals in the Conterminous United States and Puerto Rico—Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Helium, Magnesium, Manganese, Potash, Uranium, Vanadium, and Zirconium","title":"Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico — Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium","docAbstract":"

The Earth Mapping Resources Initiative (Earth MRI) is conducted in phases to identify areas for acquiring new geologic framework data to identify potential domestic resources of the 35 mineral materials designated as critical minerals for the United States. This report describes the data sources and summary results for 13 critical minerals evaluated in the conterminous United States and Puerto Rico during phase 3 of the study (antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium). Phases 1 and 2 of the Earth MRI addressed aluminum, cobalt, graphite, lithium, niobium, platinum-group elements (PGEs), rare earth elements (REEs), tantalum, tin, titanium, and tungsten. Critical minerals in Alaska are covered in a separate report. No focus areas for phase 3 critical minerals are delineated for Hawaii.

The geologic, geochemical, topographic, and geophysical mapping provided by the Earth MRI documents geologic features that reflect the extent of individual mineral systems and provides information about critical mineral deposits that may not have been previously considered. The mineral-systems approach links critical mineral commodities to deposit types that represent the manifestations of large mineral systems.

Each of the 13 critical mineral commodities for phase 3 of the Earth MRI is discussed in terms of its importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types, and is accompanied by maps and tables listing examples of focus areas in the conterminous United States and Puerto Rico. Examples of important mineral systems for this group of 13 critical minerals include basin brine path systems for barite and fluorspar, Carlin-type systems and Coeur d’Alene systems for antimony, chemical weathering and volcanogenic seafloor systems for manganese, Climax-type systems for beryllium, mafic magmatic systems for chromium, marine evaporite systems for potash and magnesium, meteoric recharge systems for uranium, petroleum systems for helium, and placer systems for zirconium and hafnium.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023D","collaboration":"Prepared in cooperation with the Association of American State Geologists","usgsCitation":"Hammarstrom, J.M., Dicken, C.L., Woodruff, L.G., Andersen, A.K., Brennan, S., Day, W.C., Drenth, B.J., Foley, N.K., Hall, S., Hofstra, A.H., McCafferty, A.E., Shah, A.K., and Ponce, D.A., 2022, Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico—Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium, chap. D of U.S. Geological Survey, Focus areas for data acquisition for potential domestic sources of critical minerals: U.S. Geological Survey Open-File Report 2019–1023, 65 p., https://doi.org/10.3133/ofr20191023D.","productDescription":"Report: xv, 66 p.; Data Release","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130167","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":435771,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DIZ9N8","text":"USGS data release","linkHelpText":"GIS, supplemental data table, and references for focus areas of potential domestic resources of critical minerals and related commodities in the United States and Puerto Rico (ver. 2.0, April 2024)"},{"id":435770,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95CHIL0","text":"USGS data release","linkHelpText":"GIS and Data Tables for Focus Areas for 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Tungsten"},{"id":402397,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023C","text":"Open-File Report 2019-1023-C","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in Alaska—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"},{"id":402684,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P95CHIL0","text":"USGS data release","linkHelpText":"- GIS and data tables for focus areas for potential domestic nonfuel sources of rare earth elements"},{"id":402685,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9U6SODG","text":"USGS data release","linkHelpText":"- GIS for focus areas of potential domestic resources of 11 critical minerals—aluminum, cobalt, graphite, lithium, niobium, platinum group elements, rare earth elements, tantalum, tin, titanium, and tungsten 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Mineral Resources Program Coordinator
Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2022-07-14","noUsgsAuthors":false,"publicationDate":"2022-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Hammarstrom, Jane M. 0000-0003-2742-3460 jhammars@usgs.gov","orcid":"https://orcid.org/0000-0003-2742-3460","contributorId":1226,"corporation":false,"usgs":true,"family":"Hammarstrom","given":"Jane","email":"jhammars@usgs.gov","middleInitial":"M.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":844934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dicken, Connie L. 0000-0002-1617-8132 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0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":844945,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ponce, David A. 0000-0003-4785-7354 ponce@usgs.gov","orcid":"https://orcid.org/0000-0003-4785-7354","contributorId":1049,"corporation":false,"usgs":true,"family":"Ponce","given":"David","email":"ponce@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":844946,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70262402,"text":"70262402 - 2022 - Africa’s drylands in a changing world: Challenges for wildlife conservation under climate and land-use changes in the Greater Etosha Landscape","interactions":[],"lastModifiedDate":"2025-01-24T14:19:15.490332","indexId":"70262402","displayToPublicDate":"2022-07-14T10:25:35","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Africa’s drylands in a changing world: Challenges for wildlife conservation under climate and land-use changes in the Greater Etosha Landscape","docAbstract":"
Proclaimed in 1907, Etosha National Park in northern Namibia is an iconic dryland system with a rich history of wildlife conservation and research. A recent research symposium on wildlife conservation in the Greater Etosha Landscape (GEL) highlighted increased concern of how intensification of global change will affect wildlife conservation based on participant responses to a questionnaire. The GEL includes Etosha and surrounding areas, the latter divided by a veterinary fence into large, private farms to the south and communal areas of residential and farming land to the north. Here, we leverage our knowledge of this ecosystem to provide insight into the broader challenges facing wildlife conservation in this vulnerable dryland environment. We first look backward, summarizing the history of wildlife conservation and research trends in the GEL based on a literature review, providing a broad-scale understanding of the socioecological processes that drive dryland system dynamics. We then look forward, focusing on eight key areas of challenge and opportunity for this ecosystem: climate change, water availability and quality, vegetation and fire management, adaptability of wildlife populations, disease risk, human-wildlife conflict, wildlife crime, and human dimensions of wildlife conservation. Using this model system, we summarize key lessons and identify critical threats highlighting future research needs to support wildlife management. Research in the GEL has followed a trajectory seen elsewhere reflecting an increase in complexity and integration across biological scales over time. Yet, despite these trends, a gap exists between the scope of recent research efforts and the needs of wildlife conservation to adapt to climate and land-use changes. Given the complex nature of climate change, in addition to locally existing system stressors, a framework of forward-thinking adaptive management to address these challenges, supported by integrative and multidisciplinary research could be beneficial. One critical area for growth is to better integrate research and wildlife management across land-use types. Such efforts have the potential to support wildlife conservation efforts and human development goals, while building resilience against the impacts of climate change. While our conclusions reflect the specifics of the GEL ecosystem, they have direct relevance for other African dryland systems impacted by global change.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02221","usgsCitation":"Turner, W.C., Périquet, S., Goelst, C., Vera, K., Cameron, E., Alexander, K., Belant, J., Cloete, C., du Preez, P., Getz, W., Hetem, R., Kamath, P., Kasaona, M., Mackenzie, M., Mendelsohn, J., Mfune, J.K., Muntifering, J., Portas, R., Scott, H., Strauss, W., Versfeld, W., Wachter, B., Wittemyer, G., and Kilian, J.W., 2022, Africa’s drylands in a changing world: Challenges for wildlife conservation under climate and land-use changes in the Greater Etosha Landscape: Global Ecology and Conservation, v. 38, e02221, 24 p., https://doi.org/10.1016/j.gecco.2022.e02221.","productDescription":"e02221, 24 p.","ipdsId":"IP-137792","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481080,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02221","text":"Publisher Index 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Those deposits have widely ranging geomorphic and sedimentologic characteristics; they can mantle, modify, or create new topography, and their emplacement and subsequent reworking can have an outsized impact on the geomorphic and sedimentologic responses of watersheds surrounding, and channels draining, volcanoes. Volcaniclastic deposits provide a wealth of information about eruptive histories, volcanic processes, and landscape responses to eruptions. The volcanic processes that produce these deposits, and consequently the character and sedimentary structures of the deposits themselves, are influenced by initiation mechanism. Deposit preservation is affected by deposit magnitude, texture, and composition, depositional environment, and climate regime. Innovative analyses of deposits from several modern eruptions and advancements in physical and numerical modelling have vastly improved our understanding of volcanic processes, interpretations of eruptive histories, and recognition of the hazards posed by volcanic eruptions. This contribution highlights and summarizes major advances that have occurred in the past few\ndecades in understanding of volcaniclastic deposits and linkages with volcanic processes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Volcanic processes in the sedimentary record: When volcanoes meet the environment","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of London","doi":"10.1144/SP520-2021-142","usgsCitation":"Major, J.J., 2022, Subaerial volcaniclastic deposits — Influences of initiation mechanisms and transport behaviour on characteristics and distributions, chap. of Volcanic processes in the sedimentary record: When volcanoes meet the environment: Special Papers of the Geological Society of London, v. 520, 72 p., https://doi.org/10.1144/SP520-2021-142.","productDescription":"72 p.","ipdsId":"IP-138407","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1144/sp520-2021-142","text":"Publisher Index Page"},{"id":403724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"520","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Major, Jon J. 0000-0003-2449-4466 jjmajor@usgs.gov","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":439,"corporation":false,"usgs":true,"family":"Major","given":"Jon","email":"jjmajor@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":846570,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}