The 2024 Crustal Deformation Modeling Workshop was held June 10–14 at the Colorado School of Mines. The workshop included two days of tutorials on using PyLith for crustal deformation modeling, followed by three days of science talks and discussions. The workshop focused on four primary themes:
● Constraining long-term fault slip rates and their uncertainties using geodetic and geologic data;
● Earthquake cycle modeling with a focus on constraining models using seismic and geodetic data;
● Interaction of fluids and faulting; and
● Separating contributions of surface loading and tectonic loading in crustal deformation.
The complete agenda is available on the CIG website.
","conferenceTitle":"2024 Crustal Deformation Modeling Workshop","conferenceDate":"June 10-14, 2024","conferenceLocation":"Golden, CO","language":"English","publisher":"Computational Infrastructure for Geodynamics","usgsCitation":"Aagaard, B.T., Knepley, M., Lindsey, E., Materna, K.Z., Martens, H.R., and Williams, C., 2024, 2024 Crustal Deformation Modeling Workshop report, 2024 Crustal Deformation Modeling Workshop, Golden, CO, June 10-14, 2024, 4 p.","productDescription":"4 p.","ipdsId":"IP-172275","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":464748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":464724,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://geodynamics.org/resources/2113/supportingdocs"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":920126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knepley, Matthew","contributorId":304241,"corporation":false,"usgs":false,"family":"Knepley","given":"Matthew","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":920127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindsey, Eric","contributorId":261913,"corporation":false,"usgs":false,"family":"Lindsey","given":"Eric","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":920128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Materna, Kathryn Z. 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":209697,"corporation":false,"usgs":false,"family":"Materna","given":"Kathryn","middleInitial":"Z.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":920129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martens, Hilary R","contributorId":215383,"corporation":false,"usgs":false,"family":"Martens","given":"Hilary","email":"","middleInitial":"R","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":920130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Charles 0000-0001-7435-9196","orcid":"https://orcid.org/0000-0001-7435-9196","contributorId":243027,"corporation":false,"usgs":false,"family":"Williams","given":"Charles","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":920131,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70272709,"text":"70272709 - 2024 - Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape","interactions":[],"lastModifiedDate":"2025-12-08T14:19:02.218112","indexId":"70272709","displayToPublicDate":"2024-07-01T08:57:37","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape","docAbstract":"Permafrost holds more than twice the amount of carbon currently in the atmosphere, but this large carbon reservoir is vulnerable to thaw and erosion under a rapidly changing Arctic climate. Convective storms are becoming increasingly common during Arctic summers and can amplify runoff and erosion. These extreme events, in concert with active layer deepening, may accelerate carbon loss from the Arctic landscape. However, we lack measurements of carbon fluxes during these events.\nRivers are sensitive to physical, chemical, and hydrological perturbations, and thus are excellent systems for studying landscape responses to thunderstorms. We present observations from the Canning River, Alaska, which drains the northern Brooks Range and flows across a continuous permafrost landscape to the Beaufort Sea. During summer 2022 and 2023 field campaigns, we opportunistically monitored river discharge, sediment, and organic carbon fluxes during several thunderstorms. During one notable storm, river discharge nearly doubled from ~130 m3/s to ~240 m3/s, suspended sediment flux increased 70-fold, and the particulate organic carbon (POC) flux increased 90-fold relative to non-storm conditions. Taken together, the river exported ~16 metric tons of POC over one hour of this sustained event, not including the additional flux of woody debris. Furthermore, the dissolved organic carbon (DOC) flux nearly doubled. Although these thunderstorm-driven fluxes are short-lived (hours to days), they play an outsized role in exporting organic carbon from Arctic rivers. Understanding how these extreme events impact river water, sediment, and carbon dynamics will help predict how Arctic climate change will modify the global carbon cycle.","conferenceTitle":"12th International Conference on Permafrost","conferenceDate":"June 16-20, 2024","conferenceLocation":"Whitehorse, Yukon","language":"English","publisher":"International Conference on Permafrost","doi":"10.52381/ICOP2024.104.1","usgsCitation":"Repasch, M., Arcuri, J., Overeem, I., Anderson, S.P., Anderson, R.G., and Koch, J.C., 2024, Impacts of convective storms on runoff, erosion, and carbon export in a continuous permafrost landscape, 12th International Conference on Permafrost, Whitehorse, Yukon, June 16-20, 2024, p. 341-348, https://doi.org/10.52381/ICOP2024.104.1.","productDescription":"8 p.","startPage":"341","endPage":"348","ipdsId":"IP-157679","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":497137,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Canning River, North Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -144.75,\n 70.25\n ],\n [\n -146.5,\n 70.25\n ],\n [\n -146.5,\n 68.5\n ],\n [\n -144.75,\n 68.5\n ],\n [\n -144.75,\n 70.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-06-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Repasch, Marisa 0000-0003-2636-9896","orcid":"https://orcid.org/0000-0003-2636-9896","contributorId":334190,"corporation":false,"usgs":false,"family":"Repasch","given":"Marisa","email":"","affiliations":[],"preferred":false,"id":951399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arcuri, Josie","contributorId":363269,"corporation":false,"usgs":false,"family":"Arcuri","given":"Josie","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":951400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overeem, Irina","contributorId":197487,"corporation":false,"usgs":false,"family":"Overeem","given":"Irina","email":"","affiliations":[],"preferred":false,"id":951401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Suzanne P. 0000-0002-6796-6649","orcid":"https://orcid.org/0000-0002-6796-6649","contributorId":172732,"corporation":false,"usgs":false,"family":"Anderson","given":"Suzanne","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":951402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Robert G.","contributorId":197569,"corporation":false,"usgs":false,"family":"Anderson","given":"Robert","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":951403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":951404,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259712,"text":"70259712 - 2024 - New 10Be-26Al isochron burial dating informs the Pliocene and Pleistocene evolution of the lower Colorado River, southwestern United States","interactions":[],"lastModifiedDate":"2024-10-19T13:59:50.376643","indexId":"70259712","displayToPublicDate":"2024-07-01T08:55:25","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1582,"text":"Episodes","active":true,"publicationSubtype":{"id":10}},"title":"New 10Be-26Al isochron burial dating informs the Pliocene and Pleistocene evolution of the lower Colorado River, southwestern United States","docAbstract":"Four new 10Be-26Al isochron burial ages ranging from 4.4 to 2 Ma on ancestral Colorado River deposits in the lower Colorado River corridor (LCRC) help constrain the river’s evolution during the Pliocene and early Pleistocene. They help fill a gap between previous work that focused on older and younger deposits: Older dated deposits include the 5 Ma Bouse Formation, which records the integration of the Colorado River through a series of preexisting basins to the Gulf of California and the ca. 4.5 - 3.5 Ma Bullhead Alluvium, a 200 to 300 m thick aggregational package that immediately followed integration. The much younger, 100 - 70 ka, Chemehuevi Formation is another major aggradation package mapped throughout the LCRC. The new burial ages on the facies of Santa Fe Railway (4.37 ± 0.71 Ma), boulder conglomerate of Bat Cave Wash (2.12 ± 0.26 and 2.05 ± 0.31 Ma), and the Palo Verde alluvium (3.03 ± 0.26 Ma) partially fill in a 3.5 M.y. gap between the deposition of the Bullhead Alluvium and the Chemeheuvi Formation and document the timescales over which the Colorado River was able to remove the Bullhead aggradational package and initiate newer and smaller aggradational pulses.
As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use.
Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.
","language":"English","publisher":"NOAA","doi":"10.25923/4jqw-mw29","usgsCitation":"Nisonson, H., Kiser, A.H., Gressler, B.P., Leight, A., and Young, J.A., 2024, Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin: NOAA Technical Memorandum NOS NCCOS 332, vi, 41 p., https://doi.org/10.25923/4jqw-mw29.","productDescription":"vi, 41 p.","ipdsId":"IP-163665","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":432484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patuxent River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.5870885061828,\n 38.29653642168418\n ],\n [\n -76.39765972760698,\n 38.2523876845089\n ],\n [\n -76.39469990294168,\n 38.39635283845547\n ],\n [\n -76.5219723635473,\n 38.51224538633858\n ],\n [\n -76.5930081555134,\n 38.75962947245472\n ],\n [\n -76.57524920752218,\n 38.93252018914461\n ],\n [\n -76.82683430406816,\n 39.192214789667304\n ],\n [\n -77.06635333905636,\n 39.45429197245687\n ],\n [\n -77.25054644375115,\n 39.48452671490274\n ],\n [\n -77.41561427713579,\n 39.40769859848646\n ],\n [\n -77.04882238288116,\n 39.139433495010024\n ],\n [\n -76.96002764292388,\n 39.04065023841653\n ],\n [\n -76.82979535765293,\n 38.90718862957951\n ],\n [\n -76.82091588365701,\n 38.66493779010759\n ],\n [\n -76.74100152907371,\n 38.412588059675414\n ],\n [\n -76.5870885061828,\n 38.29653642168418\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nisonson, H","contributorId":342011,"corporation":false,"usgs":false,"family":"Nisonson","given":"H","affiliations":[{"id":81821,"text":"Cooperative Oxford Lab","active":true,"usgs":false}],"preferred":false,"id":909477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiser, Alexander H. 0000-0002-2871-0640","orcid":"https://orcid.org/0000-0002-2871-0640","contributorId":342012,"corporation":false,"usgs":true,"family":"Kiser","given":"Alexander","middleInitial":"H.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":909478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gressler, Benjamin P. 0000-0001-6639-8558","orcid":"https://orcid.org/0000-0001-6639-8558","contributorId":270167,"corporation":false,"usgs":true,"family":"Gressler","given":"Benjamin","middleInitial":"P.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":909479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leight, A","contributorId":342013,"corporation":false,"usgs":false,"family":"Leight","given":"A","email":"","affiliations":[{"id":81821,"text":"Cooperative Oxford Lab","active":true,"usgs":false}],"preferred":false,"id":909480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":909481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259933,"text":"70259933 - 2024 - Oil and gas development influences potential for dust emission from the Upper Colorado River Basin, USA","interactions":[],"lastModifiedDate":"2024-10-30T21:48:35.428948","indexId":"70259933","displayToPublicDate":"2024-07-01T07:09:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Oil and gas development influences potential for dust emission from the Upper Colorado River Basin, USA","docAbstract":"Wind erosion and dust emission from drylands have large consequences for ecosystem function and human health. Wind erosion is naturally reduced by soil crusting and sheltering by non-erodible roughness elements such as plants. Land uses that reduce surface roughness and disturb the soil surface can dramatically increase dust emission. Extraction of oil and gas is a common and growing land use in the western United States (US) that removes vegetation and other roughness elements for construction of well pads and unpaved access roads, resulting in thousands of small (1–4 ha), discrete patches of unprotected soil. Here, we use a satellite albedo-based model to assess the effect of oil/gas activity on surface roughness in the Uinta-Piceance Basin, an area of the Upper Colorado River Basin (UCRB) with dense oil and natural gas development and modelled how the change in surface roughness could impact aeolian sediment flux and dust emission. We also investigated how regional drought influences the response of surface roughness to well pads and access roads. Oil/gas activity reduced surface roughness and increased modelled aeolian sediment flux at the landscape scale across much of the study region, resulting in a modest increase of 10 139 kg of dust per year, which is small relative to dust loads from a single regional dust event observed in the region, but downwind impact could be significant. The magnitude of surface roughness reductions by oil/gas activity was generally consistent among land cover types. However, in parts of the basin that had high cover of annual forbs and grasses, oil/gas activity was associated with larger surface roughness and smaller potential dust emission. Drought decreased surface roughness across disturbed and undisturbed sites, but there was no interactive effect of oil/gas activity and drought on surface roughness. These results suggest that oil/gas activity may increase sediment fluxes and likely contributes to dust emission from landscapes in the UCRB. Understanding how drought and land use change contribute to dust emissions will benefit mitigation of undesirable impacts of wind erosion and dust transport.
","language":"English","publisher":"British Society for Geomorphology","doi":"10.1002/esp.5887","usgsCitation":"Tyree, G.L., Chappell, A., Villarreal, M.L., Dhital, S., Duniway, M.C., Edwards, B., Faist, A., Nauman, T., and Webb, N., 2024, Oil and gas development influences potential for dust emission from the Upper Colorado River Basin, USA: Earth Surface Processes and Landforms, v. 49, no. 11, p. 3292-3307, https://doi.org/10.1002/esp.5887.","productDescription":"16 p.","startPage":"3292","endPage":"3307","ipdsId":"IP-158320","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":466989,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5887","text":"Publisher Index Page"},{"id":463243,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.52852783555946,\n 41.270866482469955\n ],\n [\n -112.52852783555946,\n 37.65911929948223\n ],\n [\n -106.31026611680981,\n 37.65911929948223\n ],\n [\n -106.31026611680981,\n 41.270866482469955\n ],\n [\n -112.52852783555946,\n 41.270866482469955\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tyree, Gayle Loren 0000-0002-9949-6426","orcid":"https://orcid.org/0000-0002-9949-6426","contributorId":257744,"corporation":false,"usgs":true,"family":"Tyree","given":"Gayle","email":"","middleInitial":"Loren","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":916865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chappell, A.","contributorId":345530,"corporation":false,"usgs":false,"family":"Chappell","given":"A.","email":"","affiliations":[{"id":82618,"text":"Cardiff University, School of Earth and Ocean Sciences, Cardiff, Wales, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":916866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":916867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dhital, S.","contributorId":345531,"corporation":false,"usgs":false,"family":"Dhital","given":"S.","email":"","affiliations":[{"id":80080,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":916868,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":916869,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, B.L.","contributorId":345532,"corporation":false,"usgs":false,"family":"Edwards","given":"B.L.","email":"","affiliations":[{"id":80080,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":916870,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Faist, A.M.","contributorId":345533,"corporation":false,"usgs":false,"family":"Faist","given":"A.M.","email":"","affiliations":[{"id":82619,"text":"University of Montana, Department of Ecosystem and Conservation Sciences, Missoula, MT, USA","active":true,"usgs":false}],"preferred":false,"id":916871,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nauman, T.W.","contributorId":345534,"corporation":false,"usgs":false,"family":"Nauman","given":"T.W.","email":"","affiliations":[{"id":82620,"text":"USDA-NRCS National Soil Survey Center, Lincoln, NE, USA","active":true,"usgs":false}],"preferred":false,"id":916872,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Webb, N.P.","contributorId":345535,"corporation":false,"usgs":false,"family":"Webb","given":"N.P.","email":"","affiliations":[{"id":80080,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":916873,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255704,"text":"70255704 - 2024 - Reduction of large vessel traffic improves water quality and alters fish habitat-use throughout a large river","interactions":[],"lastModifiedDate":"2024-07-02T11:59:32.798901","indexId":"70255704","displayToPublicDate":"2024-07-01T06:57:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Reduction of large vessel traffic improves water quality and alters fish habitat-use throughout a large river","docAbstract":"Rivers are increasingly used as superhighways for the continental-scale transportation of freight goods, but the ecological impact of large vessel traffic on river ecosystems is difficult to study. Recently, the temporary maintenance closure of lock and dam systems on the Illinois Waterway (USA) brought commercial vessel traffic to a halt along the river's length, offering a rare opportunity to study the response of the ecosystem before, during, and after an extended pause of this persistent anthropogenic disturbance. We observed improvements in main- and side-channel water quality and a redistribution of fish habitat-use during a months-long, near-complete reduction of large vessel traffic. Over 3600 water quality and 1300 fish community samples indicate that large vessel traffic reduction coincided with a 33 % reduction in turbidity as well as increased use of sampling strata near vessel navigation corridors by sound-sensitive and rheophilic fishes. Gizzard shad (Dorosoma cepedianum), the most abundant species in the system, also expanded their use of these ‘impact’ areas. Though inland waterway transport is an economically- and climate-friendly alternative to trucking and rail for the shipment of freight, our data suggest that intense vessel traffic may have profound physical and biological impacts across a large river. Monitoring and mitigation of ecological impacts of the ongoing expansion of inland waterway transport around the world will be critical to balancing large rivers as both useful navigation corridors and functional ecosystems.
Side-scan sonar (SSS) is a powerful tool that can be used to address many key questions in fisheries science. In principle, SSS uses dual transducers to transmit a narrow-beam, wide-angle acoustic signal as the survey vessel transits an area. The intensity of reflected sound is recorded to generate an image mosaic comprised of benthic substrates and targets in the water column, including organisms such as fish. Although SSS has been around for decades, recent advancements have opened new opportunities to leverage this technology to directly measure fish populations. In this paper, we review the current state of the science and identify opportunities to further refine SSS for fisheries applications.
We developed Vp, Vs, Vp/Vs ratio, and Poisson’s ratio models of the uppermost crust (<4 km depth) from the eastern San Francisco (SF) Bay (California, USA) to near the Calaveras fault along a 15-km-long, linear profile. Upper crustal velocities are highly variable beneath, west, and well east of the Hayward fault. We observe eight notable features, from west to east: (1) Near San Francisco Bay, there is an ~2-km-wide structure with high Vp/Vs ratios (up to 5) and Poisson’s ratios (up to 0.48) extending from the surface to the base of our model, which we suggest the structure is a near-vertical fault that lies along a straight-line projection between the Silver Creek fault to the south and the Point Richmond fault to the north. The structure may be part of an ~90-km-long fault along the eastern SF Bay. (2) The western East Bay Plain, the lower lying area between the bay and the hills, includes up to 800 m of low-velocity sediments (Vp ~1600–3000 m/s, Vs ~500 m/s to ~1000 m/s), underlain by higher velocity basement rocks (Vp ~3000–5800 m/s; Vs ~1000–1500 m/s). (3) Between ~1 km and 3 km east of the Bay shoreline, sediments thin in a series of steps (likely faults) toward the Hayward fault. (4) Between ~3 km west and ~1 km east of the Hayward fault (at the East Chabot fault) at depths greater than 1 km, basement Vp (up to 6000 m/s) and Vs (up to 2800 m/s) are high, and Vp/Vs ratios (<2) and Poisson’s ratios (<0.3) are low, suggesting crystalline rocks. Furthermore, a near-vertical zone of low Vp/Vs ratios and Poisson’s ratios is between near-surface traces of the Hayward and East Chabot faults, likely corresponding to the San Leandro Gabbro of Ponce et al. (2003). (5) Eastward of the East Chabot fault in the upper 1.5 km, basement Vp (~3000 m/s to ~4200 m/s) and Vs (~1200–2000 m/s) are lower than those west of the fault. (6) In the eastern Hayward/Oakland Hills, there are zones of laterally varying, high- and low-velocity (Vp ~2500–3000 m/s) Jurassic–Cretaceous and Tertiary sediments in the shallow subsurface that likely extend much deeper than imaged. (7) Seismic energy that propagates westward from sources east of the Hayward fault (HF) appear weaker than energy that propagates eastward from sources west of the HF, suggesting that the HF acts as a partial barrier to shallow seismic energy propagation into the more populated eastern SF Bay area. (8) Unlike many fault zones, it appears that the active trace of the Hayward fault (in our study area) is not cored by a prominent, low-velocity zone relative to rocks to the east and west of the active trace. However, the active trace does mark a prominent change from relatively higher velocities to the west and lower velocities to the east.
","language":"English","publisher":"GeoScienceWorld","doi":"10.1130/B36919.1","usgsCitation":"Catchings, R.D., Strayer, L.M., Chan, J.H., Goldman, M., McEvilly, A., and Suppe, J., 2024, Upper crustal seismic velocity structure of the Hayward fault zone, San Francisco Bay, California, USA: Results from the 2016 East Bay Seismic Experiment (EBSI-16): GSA Bulletin, v. 136, no. 7-8, p. 3261-3276, https://doi.org/10.1130/B36919.1.","productDescription":"16 p.","startPage":"3261","endPage":"3276","ipdsId":"IP-124842","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.49518270211846,\n 37.855388424415295\n ],\n [\n -122.49518270211846,\n 37.389789695119845\n ],\n [\n -121.96137507277817,\n 37.389789695119845\n ],\n [\n -121.96137507277817,\n 37.855388424415295\n ],\n [\n -122.49518270211846,\n 37.855388424415295\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"136","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2024-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":928559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strayer, Luther M.","contributorId":139930,"corporation":false,"usgs":false,"family":"Strayer","given":"Luther","email":"","middleInitial":"M.","affiliations":[{"id":13318,"text":"California State University East Bay","active":true,"usgs":false}],"preferred":false,"id":928585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chan, Joanne H. 0000-0002-2065-2423 jchan@usgs.gov","orcid":"https://orcid.org/0000-0002-2065-2423","contributorId":178625,"corporation":false,"usgs":true,"family":"Chan","given":"Joanne","email":"jchan@usgs.gov","middleInitial":"H.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":928587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McEvilly, Andrian T.","contributorId":351006,"corporation":false,"usgs":false,"family":"McEvilly","given":"Andrian T.","affiliations":[],"preferred":false,"id":928588,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Suppe, J.","contributorId":61178,"corporation":false,"usgs":true,"family":"Suppe","given":"J.","email":"","affiliations":[{"id":68365,"text":"Department of Earth and Atmospheric Sciences, University of Houston","active":true,"usgs":false}],"preferred":false,"id":928589,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257474,"text":"70257474 - 2024 - Predicting the response of a long-distance migrant to changing environmental conditions in winter","interactions":[],"lastModifiedDate":"2024-08-16T15:14:57.180412","indexId":"70257474","displayToPublicDate":"2024-06-29T10:10:26","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Predicting the response of a long-distance migrant to changing environmental conditions in winter","docAbstract":"Access to high-quality food is critical for long-distance migrants to provide energy for migration and arrival at breeding grounds in good condition. We studied effects of changing abundance and availability of a marine food, common eelgrass (Zostera marina L.), on an arctic-breeding, migratory goose, black brant (Brant bernicla nigricans Lawrence 1846), at a key non-breeding site, Bahía San Quintín, Mexico. Eelgrass, the primary food of brant, is consumed when exposed by the tide or within reach from the water's surface. Using an individual-based model, we predicted effects of observed changes (1991–2013) in parameters influencing food abundance and availability: eelgrass biomass (abundance), eelgrass shoot length (availability, as longer shoots more within reach), brant population size (availability, as competition greater with more birds), and sea level (availability, as less food within reach when sea level higher). The model predicted that the ability to gain enough energy to migrate was most strongly influenced by eelgrass biomass (threshold January biomass for migration = 60 g m−2 dry mass). Conversely, annual variation in population size (except for 1998), was relatively low, and variation in eelgrass shoot length and sea level were not strongly related to ability to migrate. We used observed data on brant body mass at Bahía San Quintín and annual survival to test for effects of eelgrass biomass in the real system. The lowest observed values of body mass and survival were in years when biomass was below 60 g m−2, although in some years of low biomass body mass and/or survival was higher. This suggests that the real birds may have some capacity to compensate to meet their energy demands when eelgrass biomass is low. We discuss consequences for brant population trends and conservation.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11619","usgsCitation":"Stillman, R.A., Rivers, E., Gilkerson, W., Wood, K.A., Clausen, P., Deane, C., and Ward, D.H., 2024, Predicting the response of a long-distance migrant to changing environmental conditions in winter: Ecology and Evolution, v. 14, no. 7, e11619, 15 p., https://doi.org/10.1002/ece3.11619.","productDescription":"e11619, 15 p.","ipdsId":"IP-160623","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":439315,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11619","text":"Publisher Index Page"},{"id":434933,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T43R88","text":"USGS data release","linkHelpText":"Data from Black Brant (Branta bernicla nigricans) Overwintering in Three Lagoons Along the Baja California Peninsula, Mexico"},{"id":432860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Baja California","otherGeospatial":"Bahía San Quintín","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.06430118019601,\n 30.523772316473426\n ],\n [\n -116.06430118019601,\n 30.37195862378512\n ],\n [\n -115.92021973295668,\n 30.37195862378512\n ],\n [\n -115.92021973295668,\n 30.523772316473426\n ],\n [\n -116.06430118019601,\n 30.523772316473426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Stillman, Richard A.","contributorId":151661,"corporation":false,"usgs":false,"family":"Stillman","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":910500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivers, E.M.","contributorId":245657,"corporation":false,"usgs":false,"family":"Rivers","given":"E.M.","email":"","affiliations":[{"id":49249,"text":"Merkel & Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":910501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilkerson, W.","contributorId":245658,"corporation":false,"usgs":false,"family":"Gilkerson","given":"W.","affiliations":[{"id":49250,"text":"Wildfowl & Wetlands Trust","active":true,"usgs":false}],"preferred":false,"id":910502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wood, K. A.","contributorId":167726,"corporation":false,"usgs":false,"family":"Wood","given":"K.","email":"","middleInitial":"A.","affiliations":[{"id":24818,"text":"Department of Life and Environmental Sciences, Bournemouth University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":910503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clausen, P.","contributorId":245661,"corporation":false,"usgs":false,"family":"Clausen","given":"P.","email":"","affiliations":[{"id":49252,"text":"Department of Bioscience – Wildlife Ecology, Aarhus University","active":true,"usgs":false}],"preferred":false,"id":910505,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deane, C.","contributorId":342932,"corporation":false,"usgs":false,"family":"Deane","given":"C.","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":910506,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":910507,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255892,"text":"70255892 - 2024 - Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins","interactions":[],"lastModifiedDate":"2024-07-10T14:33:11.692633","indexId":"70255892","displayToPublicDate":"2024-06-29T09:21:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5836,"text":"Journal of Hydrology X","onlineIssn":"2589-9155","active":true,"publicationSubtype":{"id":10}},"title":"Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins","docAbstract":"The magnitude and timing of extreme events such as debris and floodflows (collectively referred to as floodflows) in post-wildfire basins are difficult to measure and are even more difficult to predict. To address this challenge, a sensor ensemble consisting of noncontact, ground-based (near-field), Doppler velocity (velocity) and pulsed (stage or gage height) radars, rain gages, and a redundant radio communication network was leveraged to monitor flood wave velocities, to validate travel times, and to compliment observations from NEXRAD weather radar. The sensor ensemble (DEbris and Floodflow Early warNing System, DEFENS) was deployed in Waldo Canyon, Pike National Forest, Colorado, USA, which was burned entirely (100 percent burned) by the Waldo Canyon fire during the summer of 2012 (MTBS, 2020).
Surface velocity, stage, and precipitation time series collected during the DEFENS deployment on 10 August 2015 were used to monitor and predict flood wave velocities and travel times as a function of stream discharge (discharge; streamflow). The 10 August 2015 event exhibited spatial and temporal variations in rainfall intensity and duration that resulted in a discharge equal to 5.01 cubic meters per second (m3/s). Discharge was estimated post-event using a slope-conveyance indirect discharge method and was verified using velocity radars and the probability concept algorithm. Mean flood wave velocities – represented by the kinematic celerity (
Measured flood wave velocities and travel times agreed well with predicted values. Absolute percent differences between predicted and measured flood wave velocities ranged from 1.6 percent to 49 percent and varied with water slope, hydraulic radius, and depth. The kinematic celerity was a better predictor for steep slopes and wide flood plains associated with the Upper Waldo and Middle Waldo radar streamgages; whereas, the dynamic celerity was a better surrogate for shallow slopes and incised channels such as the Lower Waldo radar streamgage.
The method demonstrates the potential extensibility of a post-wildfire warning system by (1) leveraging multiple systems (i.e., weather radar, near-field velocity and stage radars, and rain gages) for accurate and timely warnings of debris and floodflows, (2) establishing an order of operations to site, install, and operate near-field radars and conventional rain gages to record floodflows, forecast travel times, and document geomorphic change in this basin and hydrologically similar basins that lack data, and (3) communicating data operationally with the Colorado Department of Transportation engineering staff, National Weather Service forecasters, and emergency managers.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2024.100180","usgsCitation":"Fulton, J.W., Hall, N.G., Hempel, L.A., Gourley, J., Henneberg, M.F., Kohn, M.S., Farmer, W., Asquith, W.H., Wasielewski, D., Stecklein, A.S., Mommandi, A., and Khan, A., 2024, Use of Doppler velocity radars to monitor and predict debris and flood wave velocities and travel times in post-wildfire basins: Journal of Hydrology X, v. 24, 100180, 17 p., https://doi.org/10.1016/j.hydroa.2024.100180.","productDescription":"100180, 17 p.","ipdsId":"IP-112029","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":439317,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2024.100180","text":"Publisher Index Page"},{"id":430892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Waldo Canyon basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105,\n 39\n ],\n [\n -105,\n 38.8333\n ],\n [\n -104.8167,\n 38.8333\n ],\n [\n -104.8167,\n 39\n ],\n [\n -105,\n 39\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fulton, John W, 0000-0002-5335-0720","orcid":"https://orcid.org/0000-0002-5335-0720","contributorId":213630,"corporation":false,"usgs":true,"family":"Fulton","given":"John","middleInitial":"W,","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905913,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Nicholas Graff 0000-0002-7331-8947","orcid":"https://orcid.org/0000-0002-7331-8947","contributorId":315497,"corporation":false,"usgs":true,"family":"Hall","given":"Nicholas","email":"","middleInitial":"Graff","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905914,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hempel, Laura A. 0000-0001-5020-6056","orcid":"https://orcid.org/0000-0001-5020-6056","contributorId":224286,"corporation":false,"usgs":true,"family":"Hempel","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gourley, J.J.","contributorId":340018,"corporation":false,"usgs":false,"family":"Gourley","given":"J.J.","email":"","affiliations":[{"id":41181,"text":"NOAA National Severe Storms Laboratory","active":true,"usgs":false}],"preferred":false,"id":905916,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henneberg, Mark F. 0000-0002-6991-1211 mfhenneb@usgs.gov","orcid":"https://orcid.org/0000-0002-6991-1211","contributorId":187481,"corporation":false,"usgs":true,"family":"Henneberg","given":"Mark","email":"mfhenneb@usgs.gov","middleInitial":"F.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905917,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kohn, Michael S. 0000-0002-5989-7700 mkohn@usgs.gov","orcid":"https://orcid.org/0000-0002-5989-7700","contributorId":4549,"corporation":false,"usgs":true,"family":"Kohn","given":"Michael","email":"mkohn@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905918,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":905919,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Asquith, William H. 0000-0002-7400-1861 wasquith@usgs.gov","orcid":"https://orcid.org/0000-0002-7400-1861","contributorId":1007,"corporation":false,"usgs":true,"family":"Asquith","given":"William","email":"wasquith@usgs.gov","middleInitial":"H.","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905920,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wasielewski, Daniel","contributorId":340019,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Daniel","email":"","affiliations":[{"id":41181,"text":"NOAA National Severe Storms Laboratory","active":true,"usgs":false}],"preferred":false,"id":905921,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stecklein, Andrew S.","contributorId":340020,"corporation":false,"usgs":false,"family":"Stecklein","given":"Andrew","email":"","middleInitial":"S.","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905922,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mommandi, Amanullah","contributorId":340021,"corporation":false,"usgs":false,"family":"Mommandi","given":"Amanullah","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905923,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Khan, Aziz","contributorId":340022,"corporation":false,"usgs":false,"family":"Khan","given":"Aziz","affiliations":[{"id":78854,"text":"Colorado Department of Transportation","active":true,"usgs":false}],"preferred":false,"id":905924,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70255757,"text":"70255757 - 2024 - Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: A current view and future perspectives","interactions":[],"lastModifiedDate":"2024-07-03T12:02:59.820318","indexId":"70255757","displayToPublicDate":"2024-06-29T07:01:50","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Characterising, quantifying, and accessing eruption source parameters of explosive volcanic eruptions for operational simulation of tephra dispersion: A current view and future perspectives","docAbstract":"Eruption source parameters (ESPs) are crucial for characterising volcanic eruptions and are essential inputs to numerical models used for hazard assessment. Key ESPs of explosive volcanic eruptions include plume height, mass eruption rate, eruption duration, and grain-size distribution. Some of these ESPs can be directly observed during an eruption, but others are difficult to measure in real-time, or indeed, accurately and precisely quantify afterwards. Estimates of ESPs for eruptions that cannot be observed, for example, due to the remote location of a volcano or poor weather conditions, are often defined using expert judgement and data from past eruptions, both from the volcano of interest and analogue volcanoes farther afield. Analysis of such information is time intensive and difficult, particularly during eruption response. These difficulties have resulted in the production of datasets to aid quick identification of ESPs prior to or during an eruption for use in operational response settings such as those at volcano observatories and Volcanic Ash Advisory Centres. These resources include the Mastin et al. (2009a) ESP dataset and the Catalogue of Icelandic Volcanoes and European Catalogue of Volcanoes aviation tables. Here, we review and compare these resources, which take different approaches to assigning ESPs. We identify future areas for development of these resources, highlighting the need for frequent updates as more knowledge of volcanic activity is gained and as modelling capabilities and requirements change.
An innovative payload containing a sensitive mid-wave infrared camera was flown on an uncrewed aircraft system (UAS) to acquire thermal imagery along a reach of the Sacramento River, California, USA. The imagery was used as input for an ensemble particle image velocimetry (PIV) algorithm to produce near-continuous maps of surface flow velocity along a reach approximately 1 km in length. To assess the accuracy of PIV velocity estimates, in situ measurements of flow velocity were obtained with an acoustic Doppler current profiler (ADCP). ADCP measurements were collected along pre-planned cross-section lines within the area covered by the imagery. The PIV velocities showed good agreement with the depth-averaged velocity measured by the ADCP, with
The management and conservation of faunas at regional scales depend on current data on species diversity and distribution. Relative to the ichthyofauna of West Virginia, few studies have recently documented the richness and distribution of fishes in the state, with even the most comprehensive publications rarely discussing either the conservation status of individual fishes or historical sportfish stockings. In this paper, we review the state's historic sportfish stockings. We also clarify inconsistent published data and present new information for several species, delete 3 species from the West Virginia ichthyofaunal list, and add 35 species representing extant or extirpated populations. Lastly, we provide a checklist of 195 species by major drainage of West Virginia's fishes, including their origin (i.e., native or introduced).
The US faces multiple challenges in facilitating the safe, effective, and proactive use of fire as a landscape management tool. This intentional fire use exposes deeply ingrained communication challenges and distinct but overlapping strategies of prescribed fire, cultural burning, and managed wildfire. We argue for a new conceptual model that is organized around ecological conditions, capacity to act, and motivation to use fire and can integrate and expand intentional fire use as a tool. This result emerges from more considered collaboration and communication of values and needs to address the negative consequences of contemporary fire use. When applied as a communication and translation tool, there is potential to lower barriers to faster and more successful collaboration among stakeholders. Such improvements are a vital part of strategies to address climate adaptation, wildfire mitigation, and the well-being of ecosystems.
","language":"English","publisher":"Cell Press","doi":"10.1016/j.crsus.2024.100125","usgsCitation":"Russell, A., Fontana, N., Hoecker, T., Kamanu, A., Majumder, R., Stephens, J., Young, A., Cravens, A.E., Giardina, C., Hiers, K., Littell, J., and Terando, A., 2024, A fire-use decision model to improve the United States’ wildfire management and support climate change adaptation: Cell Reports Sustainability, v. 1, no. 6, 100125, 14 p., https://doi.org/10.1016/j.crsus.2024.100125.","productDescription":"100125, 14 p.","ipdsId":"IP-165285","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":487960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":938281,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":938282,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70263882,"text":"70263882 - 2024 - Seismically detected cratering on Mars: Enhanced recent impact flux?","interactions":[],"lastModifiedDate":"2025-02-27T15:56:41.687204","indexId":"70263882","displayToPublicDate":"2024-06-28T08:52:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Seismically detected cratering on Mars: Enhanced recent impact flux?","docAbstract":"Seismic observations of impacts on Mars indicate a higher impact flux than previously measured. Using six confirmed seismic impact detections near the NASA InSight lander and two distant large impacts, we calculate appropriate scalings to compare these rates with lunar-based chronology models. We also update the impact rate from orbital observations using the most recent catalog of new craters on Mars. The snapshot of the current impact rate at Mars recorded seismically is higher than that found using orbital detections alone. The measured rates differ between a factor of 2 and 10, depending on the diameter, although the sample size of seismically detected impacts is small. The close timing of the two largest new impacts found on Mars in the past few decades indicates either a heightened impact rate or a low-probability temporal coincidence, perhaps representing recent fragmentation of a parent body. We conclude that seismic methods of detecting current impacts offer a more complete dataset than orbital imaging.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.adk7615","usgsCitation":"Daubar, I.J., Garcia, R., Stott, A.E., Fernando, B., Collins, G.S., Dundas, C., Wojcicka, N., Zenhausern, G., McEwen, A.S., Stahler, S., Golombek, M.P., Charalambous, C., Giardini, D., Lognonne, P., and Banerdt, W., 2024, Seismically detected cratering on Mars: Enhanced recent impact flux?: Science Advances, v. 10, no. 26, eadk7615, 9 p., https://doi.org/10.1126/sciadv.adk7615.","productDescription":"eadk7615, 9 p.","ipdsId":"IP-152454","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":487204,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.adk7615","text":"Publisher Index 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E.","contributorId":236698,"corporation":false,"usgs":false,"family":"Stott","given":"Alexander","email":"","middleInitial":"E.","affiliations":[{"id":47531,"text":"Department of Electrical and Electronic Engineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":928855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernando, Benjamin","contributorId":351539,"corporation":false,"usgs":false,"family":"Fernando","given":"Benjamin","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":928856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Gareth S.","contributorId":328863,"corporation":false,"usgs":false,"family":"Collins","given":"Gareth","email":"","middleInitial":"S.","affiliations":[{"id":24608,"text":"Imperial College 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Zurich","active":true,"usgs":false}],"preferred":false,"id":928860,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McEwen, Alfred S.","contributorId":61657,"corporation":false,"usgs":false,"family":"McEwen","given":"Alfred","email":"","middleInitial":"S.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":928861,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Stahler, Simon C.","contributorId":351542,"corporation":false,"usgs":false,"family":"Stahler","given":"Simon C.","affiliations":[{"id":12483,"text":"ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":928862,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Golombek, Matthew P.","contributorId":175450,"corporation":false,"usgs":false,"family":"Golombek","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":7023,"text":"Jet Propulsion Laboratory, California Institute of 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Cite","active":true,"usgs":false}],"preferred":false,"id":928866,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Banerdt, W. Bruce","contributorId":351546,"corporation":false,"usgs":false,"family":"Banerdt","given":"W. Bruce","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":928867,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70255863,"text":"70255863 - 2024 - The National Ocean Biodiversity Strategy","interactions":[],"lastModifiedDate":"2025-01-24T20:43:15.098019","indexId":"70255863","displayToPublicDate":"2024-06-28T07:02:21","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"The National Ocean Biodiversity Strategy","docAbstract":"President Biden has been clear that the ocean is central to life on Earth. As he has proclaimed, “the ocean powers millions of jobs; feeds and sustains us; and is a rejuvenating source of inspiration, exploration, and recreation.” The Biden-Harris Administration has worked hard to fulfill the President’s goal to protect and conserve at least 30% of U.S. waters by 2030. The ocean faces increased threats from warming, overfishing, increased acidity, and loss of biodiversity. It is now more important than ever to sustain the many benefits that the ocean, coasts, and Great Lakes provide, including food, a favorable climate, recreation, physical and mental health, and for many, a sense of cultural identity. Ocean life represents an irreplaceable heritage, the foundation of a habitable planet, and a vast trove of resources. Keeping our ocean healthy requires reliable information on the changing status of these living organisms, the drivers of biodiversity change, and options for effectively addressing those drivers. Over 2 million species are estimated to live in the ocean, yet only about 240,000 species have been described by scientists. Most of the ocean’s benefits result from those diverse species interacting with one another and the environment they create. To protect and conserve the ocean, we as a nation need to make better use of existing knowledge and prioritize acquiring new biodiversity knowledge to enable better policy and management decisions. The ability to monitor ocean species and habitats has expanded dramatically over the past decade, with innovations in technology, genomics, taxonomy, big data management and sharing, artificial intelligence, and machine learning. Yet large fractions of the U.S. ocean remain almost unknown. The National Ocean Biodiversity Strategy (strategy) reflects the urgent need to leverage these advances. The goals of this strategy must be guided by the nation’s diverse voices and ways of knowing, in order to maximize effective and equitable stewardship of the ocean’s diverse life and its benefits to people. The strategy is intended as a guiding document for government to advance three overarching goals:
● Goal 1: Drive delivery of ocean biodiversity knowledge at the national scale. Objectives include developing an Implementation Plan for achieving the strategy’s three goals; establishing a coordination mechanism to manage the implementation; and documenting gaps in biodiversity knowledge and the benefits of ocean biodiversity to people and economies.
● Goal 2: Strengthen tools and institutions to deliver ocean biodiversity knowledge. Objectives include establishing a robust information pipeline to support indicators and dynamic maps of ocean biodiversity, from the coasts to the deep sea. This pipeline should include expanded observing systems and comprehensive data management; science and technology solutions to accelerate the availability of biodiversity information; and plans to leverage previous investments to rebuild and expand the nation’s human capital and infrastructure to sustain foundational taxonomy and biodiversity science.
● Goal 3: Protect, conserve, restore, and sustainably use ocean biodiversity. Objectives include expanding the collection, delivery, and use of biodiversity knowledge to inform actions that advance ocean protection, conservation, restoration, and sustainable development. Government should lead in establishing and incentivizing diverse partnerships across scales and sectors to implement those actions and should educate and involve the public to discover and value the nation’s diverse ocean life. Achieving these goals will require commitments across society: new federal and private investments, coordination across sectors to address climate and equity challenges, and engagement of Indigenous Knowledge holders and frontline communities as full partners throughout planning and implementation. The Subcommittee on Ocean Science and Technology (SOST) IWG-Biodiversity will begin developing an Implementation Plan to describe and direct specific actions to implement the strategy. Successful implementation of the strategy will harmonize and expand collection and delivery of timely knowledge on ocean life to all of society. The strategy will also enable evidence-based management and protection of the ocean. Advancing the strategy will build human and institutional capital and partnerships that support both existing mandates and new needs to rebuild and sustain biodiversity, achieve healthy ocean ecosystems, and manage living resources. Implementing the strategy will deliver knowledge for monitoring, modeling, forecasting, and assessments that support food security, public health, and cultural values, and that more effectively protect, conserve, and restore nature.
","language":"English","publisher":"White House Office of Science, Technology, and Policy (OSTP)","collaboration":"National Oceanic and Atmospheric Administration, Smithsonian, Bureau of Ocean Energy Management, University Corporation for Atmospheric Research, Environmental Protection Agency, Office of Naval Research, National Science Foundation, National Aeronautics and Space Administration","usgsCitation":"Canonico, G., Duffy, J., Edmonson, M., Fillingham, K., Benson, A., Bisson, K., Demopoulos, A., Hinchey, B., Matsumoto, K., Meyer, C., Price, J., Shen, E., Turner, W., Weise, M., Vander Woude, A., and Wenzel, L., 2024, The National Ocean Biodiversity Strategy, vi, 18 p.","productDescription":"vi, 18 p.","ipdsId":"IP-166759","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":430840,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430823,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://bidenwhitehouse.archives.gov/wp-content/uploads/2024/06/NSTC_National-Ocean-Biodiversity-Strategy.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Canonico, Gabrielle","contributorId":217563,"corporation":false,"usgs":false,"family":"Canonico","given":"Gabrielle","email":"","affiliations":[{"id":39659,"text":"National Oceanographic and Atmospheric Administration, US Integrated Ocean Observing System, Silver Spring, MD, USA","active":true,"usgs":false}],"preferred":false,"id":905819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duffy, J. Emmett","contributorId":270555,"corporation":false,"usgs":false,"family":"Duffy","given":"J. Emmett","affiliations":[],"preferred":false,"id":905820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edmonson, Masha","contributorId":339977,"corporation":false,"usgs":false,"family":"Edmonson","given":"Masha","email":"","affiliations":[{"id":36726,"text":"University Corporation for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":905821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fillingham, Katie","contributorId":339534,"corporation":false,"usgs":false,"family":"Fillingham","given":"Katie","email":"","affiliations":[{"id":36726,"text":"University Corporation for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":905822,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Benson, Abigail 0000-0002-4391-107X","orcid":"https://orcid.org/0000-0002-4391-107X","contributorId":202078,"corporation":false,"usgs":true,"family":"Benson","given":"Abigail","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":905823,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bisson, Kelsey","contributorId":339979,"corporation":false,"usgs":false,"family":"Bisson","given":"Kelsey","email":"","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":905824,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":210316,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":905825,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hinchey, Beth","contributorId":339981,"corporation":false,"usgs":false,"family":"Hinchey","given":"Beth","email":"","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":905826,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Matsumoto, Katsumi","contributorId":224012,"corporation":false,"usgs":false,"family":"Matsumoto","given":"Katsumi","email":"","affiliations":[],"preferred":false,"id":905827,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meyer, Chris","contributorId":339540,"corporation":false,"usgs":false,"family":"Meyer","given":"Chris","email":"","affiliations":[],"preferred":false,"id":905828,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Price, James","contributorId":156327,"corporation":false,"usgs":false,"family":"Price","given":"James","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":false,"id":905829,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shen, Elaine","contributorId":339535,"corporation":false,"usgs":false,"family":"Shen","given":"Elaine","email":"","affiliations":[{"id":12642,"text":"National Science Foundation","active":true,"usgs":false}],"preferred":false,"id":905830,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Turner, Woody","contributorId":149221,"corporation":false,"usgs":false,"family":"Turner","given":"Woody","email":"","affiliations":[{"id":17679,"text":"Earth Science Division, NASA Headquarters, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":905831,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Weise, Mike","contributorId":339528,"corporation":false,"usgs":false,"family":"Weise","given":"Mike","email":"","affiliations":[{"id":63888,"text":"Office of Naval Research","active":true,"usgs":false}],"preferred":false,"id":905832,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Vander Woude, Andrea","contributorId":339983,"corporation":false,"usgs":false,"family":"Vander Woude","given":"Andrea","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":905833,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wenzel, Lauren","contributorId":339985,"corporation":false,"usgs":false,"family":"Wenzel","given":"Lauren","email":"","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":905834,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70255993,"text":"70255993 - 2024 - Coyote use of prairie dog colonies is most frequent in areas used by American badgers","interactions":[],"lastModifiedDate":"2024-07-12T11:54:50.180369","indexId":"70255993","displayToPublicDate":"2024-06-28T06:52:29","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Coyote use of prairie dog colonies is most frequent in areas used by American badgers","docAbstract":"The consequences of intraguild predation on vulnerable subordinate species are an important consideration in the recovery of endangered species. In prairie ecosystems, coyotes (Canis latrans) are the primary predator of endangered black-footed ferrets (Mustela nigripes; hereafter, ferrets) and presumably compete for prairie dog (Cynomys spp.) prey. Coyote predation of ferrets is thought to occur at night when ferrets are active aboveground; however, the apparent source of competition, diurnal prairie dogs, are belowground and inaccessible to coyotes at this time, presenting a perplexing temporal mismatch between actual and expected times that coyotes and ferrets come into conflict. Our study used remote wildlife cameras, occupancy models, and overlap of circadian activity patterns to investigate how landscape features, prairie dog colony attributes, and attraction to sympatric species, i.e., American badgers (Taxidea taxus; hereafter, badgers) and lagomorphs (cottontail rabbits and jackrabbits) influence Coyote use of prairie dog colonies and potential Coyote–ferret interactions. We first evaluated Coyote use (i.e., occupancy) between prairie dog colonies and surrounding available grasslands, finding that coyotes whose home ranges include prairie dog colonies used colonies nearly twice as much as surrounding grasslands. Next, we investigated biotic and abiotic factors that may influence Coyote use and frequency of use (i.e., detection probability) on prairie dog colonies. We found high Coyote use across all areas on prairie dog colonies; however, their frequency of use increased in areas that were also used by badgers. High overlap between Coyote and badger activity patterns (81%) further supports the spatial use patterns revealed by our occupancy analysis, and badgers and coyotes are known to form hunting associations. Interspecific competition and overlapping patterns of resource use between badgers and ferrets have been documented in previous studies; our study supports these findings and suggests that Coyote attraction to badger activity may influence Coyote–ferret interactions.
","language":"English","publisher":"American Society of Mammalogists","doi":"10.1093/jmammal/gyae066","usgsCitation":"Windell, R., Bailey, L., Livieri, T., Eads, D.A., Biggins, D.E., and Breck, S., 2024, Coyote use of prairie dog colonies is most frequent in areas used by American badgers: Journal of Mammalogy, gyae066, 13 p., https://doi.org/10.1093/jmammal/gyae066.","productDescription":"gyae066, 13 p.","ipdsId":"IP-143126","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439326,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyae066","text":"Publisher Index Page"},{"id":430999,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Badlands National Park and Buffalo Gap National Grasslands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.32502836704836,\n 44.214125770187906\n ],\n [\n -103.32502836704836,\n 43.29882491564226\n ],\n [\n -101.26878611620063,\n 43.29882491564226\n ],\n [\n -101.26878611620063,\n 44.214125770187906\n ],\n [\n -103.32502836704836,\n 44.214125770187906\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2024-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Windell, Rebecca","contributorId":279885,"corporation":false,"usgs":false,"family":"Windell","given":"Rebecca","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":906282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Larissa L.","contributorId":229353,"corporation":false,"usgs":false,"family":"Bailey","given":"Larissa L.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":906283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Livieri, Travis","contributorId":279912,"corporation":false,"usgs":false,"family":"Livieri","given":"Travis","affiliations":[{"id":6753,"text":"Prairie Wildlife Research","active":true,"usgs":false}],"preferred":false,"id":906284,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":906285,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":906286,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Breck, Stewart","contributorId":199403,"corporation":false,"usgs":false,"family":"Breck","given":"Stewart","affiliations":[],"preferred":false,"id":906287,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257140,"text":"70257140 - 2024 - Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA","interactions":[],"lastModifiedDate":"2024-08-12T11:42:08.195199","indexId":"70257140","displayToPublicDate":"2024-06-28T06:30:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13453,"text":"NHESS","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating post-wildfire debris-flow rainfall thresholds and volume models at the 2020 Grizzly Creek Fire in Glenwood Canyon, Colorado, USA","docAbstract":"As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1) rainfall intensity thresholds for postfire debris-flow initiation and (2) debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well these models performed over a 2-year period in the 2020 Grizzly Creek Fire burn perimeter in Glenwood Canyon, Colorado, USA, through the development of a debris-flow response inventory. The study area had the advantage of a network of 11 rain gauges for rainfall intensity measurements and repeat lidar data for volume estimates. Our observations showed that 89 % of observed debris flows in the first year postfire were triggered by rainfall rates higher than the fire-wide rainfall threshold produced by the current USGS operational model (M1). No debris flows were observed in the second year postfire, despite eight rainstorms with intensities higher than the modeled rainfall threshold. We found that the operational model for debris-flow initiation rainfall thresholds works well in this region during the first year but may be too conservative in year 2 due to vegetation recovery and sediment depletion. However, rainfall thresholds in the second year can be improved by using updated remote sensing imagery to recalculate the debris-flow initiation likelihood with the M1 model. The current volume model overestimates for this region by a median value of 4.4 times. However, the offset between the model estimates and observations is approximately linear, and the volumes from the Grizzly Creek debris flows had a similar magnitude to historic postfire debris flows in the region. Consequently, the current volume model could be adjusted with a regional correction factor.
The Plains Spotted Skunk (Spilogale interrupta) is a small carnivore native to central North America that has experienced significant population reductions, and there is a lack of information about the species that could inform conservation. Our study aimed to address knowledge gaps about the distribution and habitat associations of the species in South Dakota using species distribution modeling. We used species location data collected from state natural resource managers, trappers, and members of online social media groups dedicated to hunting and wildlife conservation; environmental predictors; and 6 predictive modeling algorithms (i.e., artificial neural networks, artificial classification tree analysis, generalized boosting models, maximum entropy, multivariate adaptive regression splines, and random forests) to develop climate and landcover ensemble distribution models. The most important climate and landcover predictors were mean temperature diurnal range (i.e., average monthly differences between daily high and low temperatures) and proportion of area classified as pasture. Ensemble model concordance identified approximately 31,300 km2 of potential Plains Spotted Skunk habitat primarily in eastern South Dakota and between the watersheds of the Missouri and James rivers. Our results offer insights that can guide conservation and inform effective management strategies for conserving Plains Spotted Skunk populations in the northern Great Plains. The promotion of low-intensity agricultural practices such as maintaining pastures, farm buildings, fences rows, and the management of woodland encroachment may improve habitat suitability and facilitate the recovery of plains spotted skunks in the region.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyae063","usgsCitation":"White, K.M., Cheeseman, A.E., Stafford, J.D., and Lonsinger, R.C., 2024, Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution: Journal of Mammalogy, https://doi.org/10.1093/jmammal/gyae063.","ipdsId":"IP-154327","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":462702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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Dakota\",\"nation\":\"USA \"}}]}","edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Kara M.","contributorId":341115,"corporation":false,"usgs":false,"family":"White","given":"Kara","email":"","middleInitial":"M.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheeseman, Amanda E.","contributorId":341116,"corporation":false,"usgs":false,"family":"Cheeseman","given":"Amanda","email":"","middleInitial":"E.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stafford, Joshua D. 0000-0001-7590-8708 jstafford@usgs.gov","orcid":"https://orcid.org/0000-0001-7590-8708","contributorId":267260,"corporation":false,"usgs":true,"family":"Stafford","given":"Joshua","email":"jstafford@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907965,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257547,"text":"70257547 - 2024 - Climate, food and humans predict communities of mammals in the United States","interactions":[],"lastModifiedDate":"2024-09-10T15:02:17.761832","indexId":"70257547","displayToPublicDate":"2024-06-27T10:18:40","publicationYear":"2024","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":"Climate, food and humans predict communities of mammals in the United States","docAbstract":"The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities.
Conterminous United States.
2010–2021.
Twenty-five species of mammals.
We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities.
Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west.
Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.
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(USGS site ID 11502500) Rivers using high temporal resolution turbidity and streamflow data to develop surrogate regression models. Regression models were updated and validated for TP at the Williamson River site, and additional data improved a prior published TP model, increasing the coefficient of determination (R2) from 0.73 to 0.88. A new TP regression model was developed for the Sprague River site using 2 years of data and showed promising results with an R2 of 0.93. Suspended-sediment concentration (SSC) surrogate models were also updated at these sites using a longer period of record than the TP models and improved characterization of sediment transport conditions at these monitoring sites.
Computations of TP loads were compared to the annual loading capacity dictated by the total maximum daily load (TMDL) for Upper Klamath Lake and showed that the combined TP load of the Williamson and Sprague Rivers approaches the annual loading capacity in water years with high annual streamflow. TP loads were also compared to loads computed by the Klamath Tribes using a long-term dataset and a regression and interpolation algorithm (RIA). The comparison showed that the two methods report similar annual loads, with the surrogate regression method generally reporting lower loads than the RIA, and the RIA annual loads falling within the range of uncertainty of the surrogate regression model results. Determining the effect of habitat and stream restoration on basin-scale TP and suspended-sediment loading is challenging using the surrogate regression method at these sites given the short period of record that TP and suspended-sediment load (SSL) data are available. However, long-term analysis by the Klamath Tribes in their larger monitoring network could provide insight into the impact of restoration at smaller spatial scales compared to the basin-wide assessment produced in this study.
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