Surveys are important tools in business resilience and recovery research because of their ability to capture disaggregated economic information; however, they can be difficult and costly due to business operational dynamics and the larger challenges of disaster research. The COVID-19 pandemic serves as a recent example where demand for business data was high across both research and practice. Yet, the methods and modes for collecting data were limited due to safety, health, and ethical concerns. This research seeks to address the lack of tailored guidance for conducting business resilience and recovery surveys by collecting and synthesizing instruments and best practices from previous survey efforts. These previous surveys were undertaken by a diverse group of organizations with varied research questions, objectives, and hazard events of interest. This paper discusses six broad lessons: clearly define purpose, objectives, and concepts; recognize that response rates will be low, consider disaster dynamics in the research design, address bias that can be exacerbated by disasters, take care to acknowledge the unique ethical considerations of disaster resilience surveys in the business and economic context, and verify and validate data at all stages of the survey process. These lessons, in addition to the published instruments themselves, support researchers or practitioners who wish to conduct their own business resilience and recovery surveys in the future.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijdrr.2023.103743","usgsCitation":"Watson, M., Brown, C., Handmer, J., Kroll, C., Wein, A., Helgeson, J., Rose, A., Dormady, N., and Kim, J., 2023, Methods and lessons for business resilience and recovery surveys: International Journal of Disaster Risk Reduction, v. 93, 103743, 14 p., https://doi.org/10.1016/j.ijdrr.2023.103743.","productDescription":"103743, 14 p.","ipdsId":"IP-143482","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":443384,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ijdrr.2023.103743","text":"Publisher Index Page"},{"id":417437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"93","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Watson, Maria","contributorId":305734,"corporation":false,"usgs":false,"family":"Watson","given":"Maria","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":873757,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Charlotte","contributorId":305735,"corporation":false,"usgs":false,"family":"Brown","given":"Charlotte","email":"","affiliations":[{"id":66278,"text":"Resilient Organisations Ltd","active":true,"usgs":false}],"preferred":false,"id":873758,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Handmer, John","contributorId":305736,"corporation":false,"usgs":false,"family":"Handmer","given":"John","email":"","affiliations":[{"id":66279,"text":"International Institute for Applied Systems Analysis (IIASA)","active":true,"usgs":false}],"preferred":false,"id":873759,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kroll, Cynthia","contributorId":305737,"corporation":false,"usgs":false,"family":"Kroll","given":"Cynthia","affiliations":[{"id":66280,"text":"Cynthia Kroll Consulting","active":true,"usgs":false}],"preferred":false,"id":873760,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873761,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Helgeson, Jennifer","contributorId":305738,"corporation":false,"usgs":false,"family":"Helgeson","given":"Jennifer","affiliations":[{"id":66281,"text":"National Institute of Standards","active":true,"usgs":false}],"preferred":false,"id":873762,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rose, Adam","contributorId":305740,"corporation":false,"usgs":false,"family":"Rose","given":"Adam","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":873763,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dormady, Noah","contributorId":305741,"corporation":false,"usgs":false,"family":"Dormady","given":"Noah","email":"","affiliations":[{"id":49186,"text":"University of Ohio","active":true,"usgs":false}],"preferred":false,"id":873764,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kim, Juri","contributorId":305742,"corporation":false,"usgs":false,"family":"Kim","given":"Juri","email":"","affiliations":[{"id":36972,"text":"University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":873765,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243829,"text":"sir20235011 - 2023 - The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21","interactions":[],"lastModifiedDate":"2026-03-02T19:03:04.474237","indexId":"sir20235011","displayToPublicDate":"2023-05-23T11:25:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5011","displayTitle":"The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21","title":"The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21","docAbstract":"The U.S. Geological Survey Science Application for Risk Reduction (SAFRR) Project has created four major hazard scenarios—ShakeOut, ARkStorm, Tsunami Scenario, and HayWired—with multidisciplinary teams of scientists, academics, and practitioners. By presenting a clear and highly detailed narrative of potential damage from earthquakes, tsunamis, and winter storms, the scenarios are intended to foster science-based preparedness strategies and disaster risk reduction innovations.
This evaluation explores the presence of these scenarios in cultures of preparedness and their role in disaster risk reduction, and reports barriers and enablers to creating and using these scenarios. To do this, the evaluation team developed a mixed-methods study that includes background research for each scenario, qualitative interviews, data collection of media and academic engagement, and examples of SAFRR scenario use in hazard planning. The data collection led to the development of a hazard scenario evaluation tool that combines theories from multiple disciplines to create a best practice set of categories that aid in scenario use and efficacy. The evaluation tool categories—actionable, longitudinal, educational, relevant, and thorough—are organized as a series of checklists that are used to determine how the scenario planners prioritized different aspects of the scenarios to achieve their goals. The tool could also be used to aid scenario planning for other regional disaster risk reduction scenarios of a similar scope.
Findings from this evaluation include detailed narratives of scenario use over time, demonstrating that the scenarios have continued to be useful in hazard planning and preparedness across the globe. Examples of use include using the scenarios to advocate for resilient building and development policy, to promote hazard response exercises, and as source data for the development of new hazard models and science. The scenarios themselves are innovative, both in the hazard science created for scenario development and in their branding and public engagement as U.S. Geological Survey products. This SAFFR retrospective is a descriptive evaluation and does not formally address the effects of the scenarios. Nevertheless, this report does include evidence of scenario affects as discovered through qualitative interviews and research, which is presented to explore how the SAFRR scenarios have been received by cultures of preparedness.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235011","usgsCitation":"Smithhisler, N., and Burkardt, N., 2023, The Science Application for Risk Reduction (SAFRR) Scenario Retrospective 2006–21: U.S. Geological Survey Scientific Investigations Report 2023–5011, 116 p., https://doi.org/10.3133/sir20235011.","productDescription":"Report: viii, 116 p.; Program Agenda","onlineOnly":"Y","ipdsId":"IP-129947","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":417356,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5011/sir20235011.xml"},{"id":417355,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5011/images"},{"id":417314,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sir/2023/5011/agenda.pdf","text":"ARkSTORM Summit Program","size":"3.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"ARkSTORM Summit Program"},{"id":417313,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5011/sir20235011.pdf","text":"Report","size":"15.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5011"},{"id":417312,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5011/coverthb.jpg"},{"id":500692,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114738.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Individual variation in life-history traits can have important implications for the ability of populations to respond to environmental variability and change. In migratory animals, flexibility in the timing of life-history events, such as juvenile emigration from natal areas, can influence the effects of population density and environmental conditions on habitat use and population dynamics. We evaluated the functional relationships between population density and environmental covariates and the abundance of juveniles expressing different life-history pathways in a migratory fish, Chinook salmon (Oncorhynchus tshawytscha), in the Wenatchee River basin in Washington State, USA. We found that the abundance of younger emigrants from natal streams was best described by an accelerating or near-linear function of spawners, whereas the abundance of older emigrants was best described by a decelerating function of spawners. This supports the hypothesis that emigration timing varies in response to density in natal areas, with younger-emigrating life-history pathways comprising a larger proportion of emigrants when densities of conspecifics are high. We also observed positive relationships between winter stream discharge and abundance of younger emigrants, supporting the hypothesis that habitat conditions can also influence the prevalence of different life-history pathways. Our results suggest that early emigration, and a resultant increase in the use of downstream rearing habitats, may increase at higher population densities and with greater winter precipitation. Winter precipitation is projected to increase in this system due to climate warming. Characterizing relationships between life-history prevalence and environmental conditions may improve our understanding of species habitat requirements and is a first step in understanding the dynamics of species with diverse life-history strategies. As environmental conditions change—due to climate change, management, or other factors—resultant life-history changes are likely to have important demographic implications that will be challenging to predict when life-history diversity is not accounted for in population models.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10087","usgsCitation":"Sorel, M.H., Murdoch, A.R., Zabel, R.W., Kamphaus, C.M., Buhle, E.R., Scheuerell, M.D., and Converse, S.J., 2023, Effects of population density and environmental conditions on life-history prevalence in a migratory fish: Ecology and Evolution, v. 13, no. 5, e10087, 13 p., https://doi.org/10.1002/ece3.10087.","productDescription":"e10087, 13 p.","ipdsId":"IP-141269","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443411,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10087","text":"Publisher Index Page"},{"id":430137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Wenatchee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.2,\n 48.2\n ],\n [\n -121.2,\n 47.4\n ],\n [\n -120.2,\n 47.4\n ],\n [\n -120.2,\n 48.2\n ],\n [\n -121.2,\n 48.2\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Sorel, Mark H.","contributorId":171739,"corporation":false,"usgs":false,"family":"Sorel","given":"Mark","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":903776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Murdoch, Andrew R.","contributorId":339213,"corporation":false,"usgs":false,"family":"Murdoch","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":903777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zabel, Richard W.","contributorId":272049,"corporation":false,"usgs":false,"family":"Zabel","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":903778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kamphaus, Cory M.","contributorId":339215,"corporation":false,"usgs":false,"family":"Kamphaus","given":"Cory","email":"","middleInitial":"M.","affiliations":[{"id":39287,"text":"Yakama Nation Fisheries","active":true,"usgs":false}],"preferred":false,"id":903779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buhle, Eric R.","contributorId":339062,"corporation":false,"usgs":false,"family":"Buhle","given":"Eric","email":"","middleInitial":"R.","affiliations":[{"id":81244,"text":"Biomark Applied Biological Services","active":true,"usgs":false}],"preferred":false,"id":903780,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903781,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":903782,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243980,"text":"70243980 - 2023 - Influence of increased freshwater inflow on nitrogen and phosphorus budgets in a dynamic subtropical estuary, Barataria Basin, Louisiana","interactions":[],"lastModifiedDate":"2023-05-30T14:19:45.884972","indexId":"70243980","displayToPublicDate":"2023-05-23T08:48:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Influence of increased freshwater inflow on nitrogen and phosphorus budgets in a dynamic subtropical estuary, Barataria Basin, Louisiana","docAbstract":"Coastal Louisiana is currently experiencing high rates of wetland loss and large-scale ecosystem restoration is being implemented. One of the largest and most novel restoration projects is a controlled sediment diversion, proposed to rebuild and sustain wetlands by diverting sediment- and nutrient-rich water from the Mississippi River. However, the impact of this proposed sediment diversion on the nutrient budget of the receiving basin is largely unknown. A water quality model was developed to investigate the impact of the planned Mid-Barataria Sediment Diversion on the nutrient budget of the Barataria Basin (herein referred to as ‘the Basin’). The model results indicate that the planned diversion will increase TN and TP pools by about 38% and 17%, respectively, even with TN and TP loadings that increase by >300%. Water quality model results suggest that the increase of nutrients in the basin will be mitigated by increased advection transport (i.e., decreased residence time from ~170 days to ~40 days, leading to greater flushing) and increased removal via assimilation, denitrification, and settling within the Basin. Advection transport resulted in higher TN removal in the Basin than other processes, such as uptake or denitrification. Approximately 25% of the additional TN loading and 30% of the additional TP loading were processed within the Basin through the assimilation of phytoplankton and wetland vegetation, denitrification, and burial in the sediment/soils. These nutrient budgets help to better understand how the planned large-scale sediment diversion project may change the future ecological conditions within the estuaries of coastal Louisiana and near-shore northern Gulf of Mexico.
","language":"English","publisher":"MDPI","doi":"10.3390/w15111974","usgsCitation":"Jung, H., Nuttle, W.K., Baustian, M.M., and Carruthers, T., 2023, Influence of increased freshwater inflow on nitrogen and phosphorus budgets in a dynamic subtropical estuary, Barataria Basin, Louisiana: Water, v. 15, no. 11, 1974, 26 p., https://doi.org/10.3390/w15111974.","productDescription":"1974, 26 p.","ipdsId":"IP-147358","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":443414,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w15111974","text":"Publisher Index Page"},{"id":417528,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.95930658584572,\n 28.576529058181706\n ],\n [\n -89.35817902592156,\n 29.01036697967102\n ],\n [\n -89.24031087691701,\n 29.18888336467873\n ],\n [\n -89.67249408993412,\n 29.421859677181203\n ],\n [\n -90.17539819235454,\n 29.835100651604293\n ],\n [\n -90.3954187371632,\n 29.739625500016345\n ],\n [\n -90.2186165136559,\n 29.182023077646647\n ],\n [\n -89.95930658584572,\n 28.576529058181706\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Jung, Hoonshin","contributorId":305843,"corporation":false,"usgs":false,"family":"Jung","given":"Hoonshin","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":873997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nuttle, William K.","contributorId":189603,"corporation":false,"usgs":false,"family":"Nuttle","given":"William","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":873998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":873999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carruthers, Tim J. B.","contributorId":140566,"corporation":false,"usgs":false,"family":"Carruthers","given":"Tim J. B.","affiliations":[],"preferred":false,"id":874000,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243839,"text":"70243839 - 2023 - Spatiotemporal patterns and environmental drivers of eastern redcedar (Juniperus virginiana) abundance along the Missouri River, USA","interactions":[],"lastModifiedDate":"2023-06-09T15:26:11.659239","indexId":"70243839","displayToPublicDate":"2023-05-23T08:14:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Spatiotemporal patterns and environmental drivers of eastern redcedar (Juniperus virginiana) abundance along the Missouri River, USA","title":"Spatiotemporal patterns and environmental drivers of eastern redcedar (Juniperus virginiana) abundance along the Missouri River, USA","docAbstract":"Context: Changes in disturbance regimes, including reductions in flooding and geomorphic dynamism from dam construction and flow regulation, have facilitated invasion by eastern redcedar (Juniperus virginiana L.), an upland tree species, in the understory of floodplain forests along the Missouri National Recreational River (MNRR).
Objectives: Our aim was to determine the spatiotemporal patterns and environmental drivers of redcedar invasion along the MNRR.
Methods: We used the Normalized Difference Vegetation Index (NDVI) calculated from winter Landsat imagery to construct a time series of maps showing spatial changes in redcedar abundance and distribution from 1982 to 2017 in both riparian and upland habitats along the MNRR. We determined how environmental factors (e.g., soil drainage ability, flood recurrence interval, 1980s LULC, lateral distance to the river, and channel incision) have influenced current (2017) redcedar occurrence and abundance in riparian habitats using random forest models (RFM).
Results: Time-series maps indicated that detectable redcedar cover occurred over less than 5% of the study area before 1985, increased steadily from 1985 to 2000, and more than tripled from 2000 to 2010. After 2010, redcedar abundance continued to increase in upland areas but declined following the 2011 Missouri River flood in the floodplain. RFMs indicated that river incision, distance to the river, soil drainage, 1984 LULC, and flood recurrence interval were important features influencing redcedar occurrence and abundance in the floodplain.
Conclusion: Unless preventive measures are implemented, lack of floods and ongoing flow regulation will enable the continued spread of redcedar along the MNRR and other regulated rivers in the eastern Great Plains.
The Muddy River provides habitat for several wildlife and endemic aquatic species protected under the Endangered Species Act. Near Moapa, Nevada, in the Bureau of Land Management’s Muddy River Floodplain Restoration Project Area, a previously constructed levee on the east side of the river alters the natural hydrology and decreases connectivity between the river and its floodplain. The Bureau of Land Management is interested in restoring the project area to a more natural state and proposed removing the existing levee (at the time of this study in 2019) on the east bank of the river and replacing it with a new levee farther away from the river. The 50-, 20-, 10-, 4-, 2-, and 1-percent annual exceedance probability flood streamflows were estimated based on a flood-frequency analysis of a streamgage in the study area. River cross-sections were surveyed and combined with a digital elevation model of the floodplains to create a coupled one- and two-dimensional hydraulic model of the study area. The estimated flood streamflows were used as inputs in the hydraulic model to simulate how flood-inundation extents would change with the proposed restoration. Simulated inundation extents expand with increasing flood magnitudes, with nearly the entire valley inundated by the 2-percent flood streamflow. Within the project area, inundation extents with restoration increased on the east floodplain and decreased on the west floodplain for the 20-, 10-, 4-, 2-, and 1-percent flood streamflows. Outside the Muddy River Floodplain Restoration Project Area, inundation extents decreased with restoration east of the project area for the 20- and 10-percent flood streamflows, but changes in extent for larger streamflows were minor because most of the streamflow leaves the main river channel upstream of the restoration area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235033","usgsCitation":"Morris, C.M., and Childres, H.K., 2023, Flood-inundation maps for the Muddy River, near Moapa, Nevada: U.S. Geological Survey Scientific Investigations Report 2023–5033, 22 p., https://doi.org/10.3133/sir20235033.","productDescription":"Report: viii, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104618","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":416999,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5033/sir20235033.pdf","text":"Report","size":"23 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417002,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5033/images"},{"id":416997,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K68IWI","text":"Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada","description":"Morris, C.M., 2023, Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada: U.S. Geological Survey data release, at https://doi.org/10.5066/P9K68IWI."},{"id":417000,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5033/covrthb.jpg"},{"id":417001,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5033/sir20235033.xml"},{"id":417003,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235033/full"},{"id":500898,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114737.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nevada","city":"Moapa","otherGeospatial":"Muddy River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.716667,\n 36.723611\n ],\n [\n -114.716667,\n 36.675\n ],\n [\n -114.675,\n 36.675\n ],\n [\n -114.675,\n 36.723611\n ],\n [\n -114.716667,\n 36.723611\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
In contrast to many other arid region rivers, streamflow in the South Platte River is heavily augmented by trans-basin water imports and irrigation return flows. Hydrological changes began in the 1880s, resulting in channel narrowing and the development of a continuous Populus-Salix forest by the mid-twentieth century. We assessed the composition, structure and regeneration status of the riparian forest and identified environmental variables affecting annual Populus deltoides tree growth. We sampled forest structure at four sites in 2015, and conducted dendroecological analysis at seven additional sites in 2019. The riparian forest was dominated by P. deltoides, which occurred at all sites, comprising 79% of total tree basal area and 62% of total tree density. Age structure data indicated ongoing though episodic recruitment of P. deltoides, at least over the past ~130 years. We tested 14 linear mixed effects models to describe the effect of climate and streamflow on individual tree growth (modeled as the log of BAI, n = 237 trees). The most parsimonious model selected with AICc explained 28.6% of BAI variability, and included hydrology and climate factors during the growing season (i.e., June–August streamflow, June–July PDSI), some aspects of off-season (i.e., previous November and March) streamflow, along with tree age and study site effects. The riparian forest developed in response to, and has been maintained by, current climate conditions and water management regimes. It may be negatively affected by future climate change and increased urban water demand in the basin.
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Despite the importance of early warning systems for these hazards, existing real-time meteorological and oceanographic forecast systems at regional and national scales, until now, have lacked estimates of runup necessary to predict wave-driven overwash and erosion. To address this need, we present an approach that includes wave runup in an operational, national-scale modeling system. Using this system, we quantify the contribution of waves to potential dune erosion events along 4,700 km of U.S. Atlantic and Gulf of Mexico sandy coastlines for a one-year period. Dune erosion events were predicted to occur at over 80% of coastal locations, where waves dominated shoreline total water levels, representing 73% of the signal. This shows that models that neglect the wave component underestimate the hazard. This new, national-scale operational modeling system provides communities with timely, local-scale (0.5 km resolution) coastal hazard warnings for all wave conditions, allowing for rapid decision-making related to safety and emergency management. The modeling system also enables continued research into wave-driven processes at a broad range of coastal areas.","language":"English","publisher":"Springer","doi":"10.1038/s43247-023-00817-2","usgsCitation":"Stockdon, H.F., Long, J.W., Palmsten, M.L., Van der Westhuysen, A., Doran, K., and Snell, R.J., 2023, Operational forecasts of wave-driven water levels and coastal hazards for US Gulf and Atlantic coasts: Communications Earth & Environment, v. 4, 169, 8 p., https://doi.org/10.1038/s43247-023-00817-2.","productDescription":"169, 8 p.","ipdsId":"IP-140230","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443430,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-023-00817-2","text":"Publisher Index Page"},{"id":417327,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":219235,"corporation":false,"usgs":false,"family":"Long","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":873505,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":873506,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van der Westhuysen, Andre","contributorId":305676,"corporation":false,"usgs":false,"family":"Van der Westhuysen","given":"Andre","affiliations":[{"id":36262,"text":"NOAA National Centers for Environmental Prediction","active":true,"usgs":false}],"preferred":false,"id":873507,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doran, Kara S. 0000-0001-8050-5727","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":292448,"corporation":false,"usgs":true,"family":"Doran","given":"Kara S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":873508,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Snell, Richard J. rsnell@usgs.gov","contributorId":5554,"corporation":false,"usgs":true,"family":"Snell","given":"Richard","email":"rsnell@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":873509,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243807,"text":"70243807 - 2023 - Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California","interactions":[],"lastModifiedDate":"2023-05-25T15:59:15.380077","indexId":"70243807","displayToPublicDate":"2023-05-22T08:29:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California","docAbstract":"In this study, we compare the Southern California Earthquake Center CyberShake platform against the Next Generation Attenuation‐West2 empirical datasets. Because the CyberShake and empirical datasets cover very different magnitude ranges and site conditions, we develop ground‐motion models (GMMs) for CyberShake datasets to compare trends with empirical GMMs and decompose the residuals for further analysis. We apply mixed effects regression to four CyberShake datasets in southern, central, and northern California at 2, 3, 5, and 10 s periods, and compare the results with the empirical datasets using the same approach. CyberShake captures the total variability of ground motions in the empirical datasets but tends to predict larger median ground motions relative to the empirical GMMs. We then calculate and compare the repeatable source‐specific location, site, and path effects between CyberShake and empirical datasets. We find that the correlations of site effects between the CyberShake and empirical datasets are generally satisfactory, but the variability of site effects is slightly smaller for CyberShake datasets. There is no apparent correlation of source‐specific location effects between the CyberShake and empirical datasets. Comparison of path effects shows a wide range of correlation coefficients. Finally, we investigate the source of observed differences between the CyberShake and empirical datasets. We attribute the larger median ground‐motion levels in CyberShake to a combination of the homogeneous slip patterns of the earthquake ruptures, the low resolution of near‐surface materials in the velocity models, and strong reflections at high‐contrast boundaries in the velocity models. These factors also impact the correlations of site and path effects between the CyberShake and empirical datasets. Moreover, the leakage from location effects into site and path terms further weakens the correlations. In summary, we find that CyberShake could be improved, but it is still very useful to supplement empirical datasets for ground‐motion studies, especially to inform their nonergodic components.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220049","usgsCitation":"Meng, X., Goulet, C., Milner, K.R., Graves, R., and Callaghan, S., 2023, Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1152-1175, https://doi.org/10.1785/0120220049.","productDescription":"24 p.","startPage":"1152","endPage":"1175","ipdsId":"IP-140662","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":417292,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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US","interactions":[],"lastModifiedDate":"2023-05-22T13:08:24.434575","indexId":"70243816","displayToPublicDate":"2023-05-22T07:57:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Generating a reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US","docAbstract":"This report presents a reference flow network for the conterminous United States that is built from the best available information from the U.S. Geological Survey, the National Oceanic and Atmospheric Administration National Weather Service, and the U.S. Environmental Protection Agency. The work is intended to support durable data integration and reproducibility. Originating from the National Hydrography Dataset Plus (NHDPlus) V2.1, the reference flow network incorporates network connectivity enhancements from federal agency efforts. After incorporating these network improvements, many original NHDPlus attributes were regenerated to enable network navigation and related operations. After introducing the motivation and background for this work, this report describes the attribute generation workflow and data quality checks that were performed in preparation of the dataset. The reference flow network follows the NHDPlus data model and is described using terms defined in the Mainstem and Drainage Basin logical model and WaterML2 Part3: Surface Hydrology Features conceptual model.
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Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":873360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, J. Michael","contributorId":304963,"corporation":false,"usgs":false,"family":"Johnson","given":"J. Michael","affiliations":[{"id":66193,"text":"NOAA-NWS-OWP","active":true,"usgs":false}],"preferred":false,"id":873361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873362,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70247515,"text":"70247515 - 2023 - High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands","interactions":[],"lastModifiedDate":"2023-08-10T11:38:17.536908","indexId":"70247515","displayToPublicDate":"2023-05-22T06:36:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands","docAbstract":"Translocation, often a management solution reserved for at-risk species, is a highly time-sensitive intervention in the face of a rapidly changing climate. The definition of abiotic and biotic habitat requirements is essential to the selection of appropriate release sites in novel environments. However, field-based approaches to gathering this information are often too time intensive, especially in areas of complex topography where common, coarse-scale climate models lack essential details. We apply a fine-scale remote sensing-based approach to study the 'akikiki (Oreomystis bairdi) and 'akeke'e (Loxops caeruleirostris), Hawaiian honeycreepers endemic to Kaua'i that are experiencing large-scale population declines due to warming-induced spread of invasive disease. We use habitat suitability modeling based on fine-scale light detection and ranging (lidar)-derived habitat structure metrics to refine coarse climate ranges for these species in candidate translocation areas on Maui. We found that canopy density was consistently the most important variable in defining habitat suitability for the two Kaua'i species. Our models also corroborated known habitat preferences and behavioral information for these species that are essential for informing translocation. We estimated a nesting habitat that will persist under future climate conditions on east Maui of 23.43 km2 for 'akikiki, compared to the current Kaua'i range of 13.09 km2. In contrast, the novel nesting range for 'akeke'e in east Maui was smaller than its current range on Kaua'i (26.29 vs. 38.48 km2, respectively). We were also able to assess detailed novel competitive interactions at a fine scale using models of three endemic Maui species of conservation concern: 'ākohekohe (Palmeria dolei), Maui 'alauahio (Paroreomyza montana), and kiwikiu (Pseudonestor xanthophrys). Weighted overlap areas between the species from both islands were moderate (<12 km2), and correlations between Maui and Kaua'i bird habitat were generally low, indicating limited potential for competition. Results indicate that translocation to east Maui could be a viable option for 'akikiki but would be more uncertain for 'akeke'e. Our novel multifaceted approach allows for the timely analysis of both climate and vegetation structure at informative scales for the effective selection of appropriate translocation sites for at-risk species.
Rapid and targeted management actions are a prerequisite to efficiently mitigate disease outbreaks. Targeted actions, however, require accurate spatial information on disease occurrence and spread. Frequently, targeted management actions are guided by non-statistical approaches that define the affected area by a pre-determined distance surrounding a small number of disease detections. As an alternative, we present a long-recognized but underutilized Bayesian technique that uses limited local data and informative priors to make statistically valid predictions and forecasts about disease occurrence and spread. As a case study, we use limited local data that were available after the detection of chronic wasting disease in Michigan, U.S. along with information rich priors obtained from a previous study in a neighboring state. Using these limited local data and informative priors, we generate statistically valid predictions of disease occurrence and spread for the Michigan study area. This Bayesian technique is conceptually and computationally simple, relies on little to no local data, and is competitive with non-statistical distance-based metrics in all performance evaluations. Bayesian modeling has added benefits because it allows practitioners to generate immediate forecasts of future disease conditions and provides a principled framework to incorporate new data as they accumulate. We contend that the Bayesian technique offers broad-scale benefits and opportunities to make statistical inference across a diversity of data-deficient systems, not limited to disease.
The 25 October 2022 Mw 5.1 Alum Rock earthquake shows strong evidence for southeast rupture directivity along the central Calaveras fault (CCF), as indicated by observed ground motions and simulated kinematic ruptures. Peak ground accelerations (PGAs) and peak ground velocities (PGVs) are notably higher to the southeast, with an order of magnitude difference for stations at the same distance but different azimuths. In addition, PGAs are lower than that predicted by ground‐motion models by a factor of 3 on average in all the directions, indicating a low stress drop (∼1.57 MPa). Directivity function modeling and ground‐motion simulations both indicate rupture propagation to the southeast with rupture velocity between 2.3 and 2.5 km/s. We suggest that the southward rupture propagation and relatively low stress drop may be typical of M ∼5 earthquakes on this portion of the CCF.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230013","usgsCitation":"Hirakawa, E.T., Parker, G.A., Baltay Sundstrom, A.S., and Hanks, T.C., 2023, Rupture directivity of the 25 October 2022 Mw 5.1 Alum Rock earthquake: The Seismic Record, v. 3, no. 2, p. 144-155, https://doi.org/10.1785/0320230013.","productDescription":"12 p.","startPage":"144","endPage":"155","ipdsId":"IP-151768","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230013","text":"Publisher Index Page"},{"id":482034,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Califronia","otherGeospatial":"Alum Rock earthquake area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122,\n 37.5\n ],\n [\n -122,\n 37\n ],\n [\n -121.4,\n 37\n ],\n [\n -121.4,\n 37.5\n ],\n [\n -122,\n 37.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hirakawa, Evan Tyler 0000-0002-5720-0850","orcid":"https://orcid.org/0000-0002-5720-0850","contributorId":295776,"corporation":false,"usgs":true,"family":"Hirakawa","given":"Evan","email":"","middleInitial":"Tyler","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baltay Sundstrom, Annemarie S. 0000-0002-6514-852X abaltay@usgs.gov","orcid":"https://orcid.org/0000-0002-6514-852X","contributorId":4932,"corporation":false,"usgs":true,"family":"Baltay Sundstrom","given":"Annemarie","email":"abaltay@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Thomas C. 0000-0003-0928-0056 thanks@usgs.gov","orcid":"https://orcid.org/0000-0003-0928-0056","contributorId":3065,"corporation":false,"usgs":true,"family":"Hanks","given":"Thomas","email":"thanks@usgs.gov","middleInitial":"C.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927342,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243845,"text":"70243845 - 2023 - Nest attendance, incubation constancy, and onset of incubation in dabbling ducks","interactions":[],"lastModifiedDate":"2023-05-23T13:56:58.32871","indexId":"70243845","displayToPublicDate":"2023-05-19T08:52:01","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Nest attendance, incubation constancy, and onset of incubation in dabbling ducks","docAbstract":"In birds, parents must provide their eggs with a safe thermal environment suitable for embryonic development. Species with uniparental incubation must balance time spent incubating eggs with time spent away from the nest to satisfy self-maintenance needs. Patterns of nest attendance, therefore, influence embryonic development and the time it takes for eggs to hatch. We studied nest attendance (time on the nest), incubation constancy (time nests were at incubation temperatures), and variation in nest temperature of 1,414 dabbling duck nests of three species in northern California. Daily nest attendance increased from only 1–3% on the day the first egg was laid to 51–57% on the day of clutch completion, and 80–83% after clutch completion through hatch. Variation in nest temperature also decreased gradually during egg-laying, and then dropped sharply (33–38%) between the day of and the day after clutch completion because increased nest attendance, particularly at night, resulted in more consistent nest temperatures. During the egg-laying stage, nocturnal nest attendance was low (13–25%), whereas after clutch completion, nest attendance was greater at night (≥87%) than during the day (70–77%) because most incubation recesses occurred during the day. Moreover, during egg-laying, nest attendance and incubation constancy increased more slowly among nests with larger final clutch sizes, suggesting that the number of eggs remaining to be laid is a major driver of incubation effort during egg-laying. Although overall nest attendance after clutch completion was similar among species, the average length of individual incubation bouts was greatest among gadwall (Mareca strepera; 779 minutes), followed by mallard (Anas platyrhynchos; 636 minutes) and then cinnamon teal (Spatula cyanoptera; 347 minutes). These results demonstrate that dabbling ducks moderate their incubation behavior according to nest stage, nest age, time of day, and clutch size and this moderation likely has important implications for egg development and overall nest success.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0286151","usgsCitation":"Hartman, C.A., Ackerman, J.T., Peterson, S.H., Fettig, B.L., Casazza, M.L., and Herzog, M.P., 2023, Nest attendance, incubation constancy, and onset of incubation in dabbling ducks: PLoS ONE, v. 18, no. 5, e0286151, 28 p., https://doi.org/10.1371/journal.pone.0286151.","productDescription":"e0286151, 28 p.","ipdsId":"IP-147141","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443460,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0286151","text":"Publisher Index Page"},{"id":435323,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NSAKP8","text":"USGS data release","linkHelpText":"Nest Attendance, Incubation Constancy, and Onset of Incubation in Dabbling Ducks"},{"id":417335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Grizzly Island Wildlife Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.93120904361,\n 38.087026017844266\n ],\n [\n -121.92222832467957,\n 38.09123333537573\n ],\n [\n -121.90341158025386,\n 38.0839965992823\n ],\n [\n -121.88502248911058,\n 38.09022360125243\n ],\n [\n -121.89250642155254,\n 38.09846935507156\n ],\n [\n -121.88801606208753,\n 38.105031637082874\n ],\n [\n -121.88801606208753,\n 38.12353746197775\n ],\n [\n -121.89207876827012,\n 38.13463870601953\n ],\n [\n -121.90170096712419,\n 38.1351432679065\n ],\n [\n -121.9083295930017,\n 38.14170225498961\n ],\n [\n -121.93120904361,\n 38.1319476503748\n ],\n [\n -121.93698236292227,\n 38.13043388797155\n ],\n [\n -121.94788752162361,\n 38.14035686980549\n ],\n [\n -121.95344701429467,\n 38.13985234396577\n ],\n [\n -121.97376054520885,\n 38.156836128843764\n ],\n [\n -121.99236346299335,\n 38.1591900047855\n ],\n [\n -122.00391010161832,\n 38.15330517246224\n ],\n [\n -121.99835060894684,\n 38.142374938278834\n ],\n [\n -121.98209978421568,\n 38.13026569021102\n ],\n [\n -121.98209978421568,\n 38.1122662905006\n ],\n [\n -121.96371069307239,\n 38.105031637082874\n ],\n [\n -121.95644058727163,\n 38.09443073474577\n ],\n [\n -121.9457492552115,\n 38.09594524352207\n ],\n [\n -121.94296950887617,\n 38.08652112346789\n ],\n [\n -121.93120904361,\n 38.087026017844266\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131157,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Sarah H. 0000-0003-2773-3901 sepeterson@usgs.gov","orcid":"https://orcid.org/0000-0003-2773-3901","contributorId":167181,"corporation":false,"usgs":true,"family":"Peterson","given":"Sarah","email":"sepeterson@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fettig, Brady Lynn 0000-0002-3124-2606","orcid":"https://orcid.org/0000-0002-3124-2606","contributorId":302106,"corporation":false,"usgs":true,"family":"Fettig","given":"Brady","email":"","middleInitial":"Lynn","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873484,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243759,"text":"70243759 - 2023 - Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","interactions":[],"lastModifiedDate":"2023-05-19T12:54:11.017578","indexId":"70243759","displayToPublicDate":"2023-05-19T07:29:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","docAbstract":"Objective: The American Eel Anguilla rostrata historically was one of the most common fish species in Atlantic coast watersheds, but extensive dam construction and other factors caused a widespread population decline. One of the watersheds where American Eels have declined considerably is the Mohawk River in eastern and central New York. Recent attempts to characterize the distribution and abundance of American Eels in this watershed have been ineffective, and the extent to which a series of locks and dams on the Hudson River and lower Mohawk River limits use of the watershed is unclear.
Methods: We developed a model between environmental DNA (eDNA) quantity and American Eel abundance in the Hudson River watershed in which the DNA concentration in water samples explained up to 65% of the variability in eel density and 56% of the variability in eel biomass. We then used this relationship to interpret eDNA data collected twice from 36 sites across the Mohawk River watershed in 2021 and make inferences about the distribution and abundance of American Eels.
Result: American Eel DNA was detected almost exclusively in the downstream-most 4 km of the Mohawk River within a series of barriers. The concentration of DNA was reduced by approximately 80% across each successive upstream barrier before becoming too low to detect consistently. Our data suggest that eel population density was high in the Hudson River estuary and declined rapidly in the lower Mohawk River, and the species was nearly absent or undetectable in the Mohawk River and its tributaries upstream of the Crescent Dam and the Waterford Flight of Locks.
Conclusion: Barriers appear to be largely restricting American Eels from using over 99% of the Mohawk River watershed. Therefore, improvements in fish passage at dams and hydroelectric facilities in the region could help the American Eel to regain access to this part of its native range.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10404","usgsCitation":"George, S.D., Baldigo, B., Rees, C., Bartron, M.L., Wiley, J.J., Stich, D.S., Wells, S.M., and Winterhalter, D., 2023, Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system: Transactions of the American Fisheries Society, v. 152, no. 3, p. 310-326, https://doi.org/10.1002/tafs.10404.","productDescription":"17 p.","startPage":"310","endPage":"326","ipdsId":"IP-143996","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":443462,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10404","text":"Publisher Index Page"},{"id":417238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.46545959281143,\n 43.193210843337965\n ],\n [\n -75.36068780837059,\n 43.1807103892566\n ],\n [\n -75.25020119932465,\n 43.095917619682325\n ],\n [\n -75.11304540878423,\n 43.06391605114166\n ],\n [\n -75.0406576304439,\n 43.00264908923049\n ],\n [\n -74.84063876923905,\n 43.01240019708166\n ],\n [\n -74.66347920645833,\n 42.963629178932905\n ],\n [\n -74.58156672044112,\n 42.881327587792015\n ],\n [\n -74.45774552064795,\n 42.879931698082856\n ],\n [\n -74.25772665944312,\n 42.84083396491155\n ],\n [\n -74.11866592736799,\n 42.86317855831385\n ],\n [\n -74.03865838288596,\n 42.83524655344701\n ],\n [\n -74.02532379213918,\n 42.75417231574653\n ],\n [\n -74.00627437678641,\n 42.707996466290325\n ],\n [\n -73.81976366766865,\n 42.62025113498041\n ],\n [\n -73.79690436924501,\n 42.550123385716574\n ],\n [\n -73.8254784922747,\n 42.384309188935845\n ],\n [\n -73.81404884306264,\n 42.31351753996279\n ],\n [\n -73.93977498439143,\n 42.2035468271101\n ],\n [\n -73.97025404895584,\n 42.10469190424271\n ],\n [\n -74.00454299659106,\n 41.89801206244189\n ],\n [\n -73.91310580289745,\n 41.86681033353875\n ],\n [\n -73.88262673833307,\n 42.11317123319691\n ],\n [\n -73.72642153243986,\n 42.28251957771869\n ],\n [\n -73.74547094779265,\n 42.37828214920688\n ],\n [\n -73.71880176629864,\n 42.619854148073244\n ],\n [\n -73.61212504032306,\n 42.82697116015191\n ],\n [\n -73.66165352024026,\n 42.88841312774437\n ],\n [\n -73.94442157442768,\n 42.88448522793766\n ],\n [\n -74.22444798011406,\n 42.97932677531503\n ],\n [\n -74.37874824447162,\n 42.982114021563405\n ],\n [\n -74.51971391808242,\n 42.93192425763374\n ],\n [\n -74.65305982555196,\n 43.01415827143066\n ],\n [\n -74.86679009610465,\n 43.05871377504309\n ],\n [\n -75.00966071125109,\n 43.05314610631808\n ],\n [\n -75.22491910473742,\n 43.151897073297846\n ],\n [\n -75.36207489527783,\n 43.21717980329879\n ],\n [\n -75.47065656278859,\n 43.221344415722996\n ],\n [\n -75.46545959281143,\n 43.193210843337965\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"152","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rees, Christopher B.","contributorId":196308,"corporation":false,"usgs":false,"family":"Rees","given":"Christopher B.","affiliations":[],"preferred":false,"id":873175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartron, Meredith L.","contributorId":149109,"corporation":false,"usgs":false,"family":"Bartron","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false},{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":873176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiley, John J. Jr.","contributorId":305554,"corporation":false,"usgs":false,"family":"Wiley","given":"John","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":873177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":873178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wells, Scott M.","contributorId":305556,"corporation":false,"usgs":false,"family":"Wells","given":"Scott","email":"","middleInitial":"M.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":873179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Winterhalter, Dylan R. 0000-0003-1774-8034","orcid":"https://orcid.org/0000-0003-1774-8034","contributorId":251765,"corporation":false,"usgs":true,"family":"Winterhalter","given":"Dylan R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873180,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243768,"text":"70243768 - 2023 - Watershed carbon yield derived from gauge observations and river network connectivity in the United States","interactions":[],"lastModifiedDate":"2023-05-19T12:28:27.653939","indexId":"70243768","displayToPublicDate":"2023-05-19T07:04:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Watershed carbon yield derived from gauge observations and river network connectivity in the United States","docAbstract":"River networks play a critical role in the global carbon cycle. Although global/continental scale riverine carbon cycle studies demonstrate the significance of rivers and streams for linking land and coastal regions, the lack of spatially distributed riverine carbon load data represents a gap for quantifying riverine carbon net gain or net loss in different regions, understanding mechanisms and factors that influence the riverine carbon cycle, and testing simulations of aquatic carbon cycle models at fine scales. Here, we (1) derive the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) for over 1,000 hydrologic stations across the Conterminous United States (CONUS) and (2) use the river network connectivity information for over 80,000 catchment units within the National Hydrography Dataset Plus (NHDPlus) to estimate riverine POC and DOC net gain or net loss for watersheds controlled between upstream-downstream hydrologic stations. The new riverine carbon load and watershed net gain/loss represent a unique contribution to support future studies for better\nunderstanding and quantification of riverine carbon cycles.","language":"English","publisher":"Springer","doi":"10.1038/s41597-023-02162-7","usgsCitation":"Qiu, H., Zhang, X., Yang, A., Wickland, K., Stets, E.G., and Chen, M., 2023, Watershed carbon yield derived from gauge observations and river network connectivity in the United States: Scientific Data, v. 10, 278, 13 p., https://doi.org/10.1038/s41597-023-02162-7.","productDescription":"278, 13 p.","ipdsId":"IP-150043","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":443465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-023-02162-7","text":"Publisher Index 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Jersey","interactions":[],"lastModifiedDate":"2026-02-23T19:12:37.435815","indexId":"sir20225047","displayToPublicDate":"2023-05-18T10:55:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5047","displayTitle":"Simulation of Flow and Eutrophication in the Central Salem River, New Jersey","title":"Simulation of flow and eutrophication in the central Salem River, New Jersey","docAbstract":"The central Salem River in New Jersey is subject to periods of water-quality impairment, marked by elevated concentrations of phosphorus and chlorophyll-a, and low concentrations of and large diurnal swings in concentrations of dissolved oxygen. These seasonal eutrophic conditions are controlling factors for water quality in lower reaches, where the river is more lacustrine than in upper reaches, as a result of downstream damming. This biological productivity is supported by nutrient wash-off from agricultural areas in the surrounding watershed. To investigate this impairment, flow measurement and water-quality sampling were conducted during 2007–08 in support of development of a one-dimensional surface-water-quality model that simulates nutrient cycling and transformation processes.
The U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, used the U.S. Environmental Protection Agency Water Quality Analysis Simulation Program (WASP) to develop a receiving-water-quality model of the central Salem River between Woodstown and Deepwater, New Jersey, from April 2007 to October 2008. The main-stem river and largest tributary were simulated. In the flow model, kinematic wave flow is used to simulate flow in upper reaches and ponded weir flow is used to simulate flow in lower reaches. The water-quality model makes use of a mass-balance equation to simulate the fate and transport of nutrients, phytoplankton chlorophyll-a, dissolved oxygen, and oxygen demands (an indicator rather than a substance) in the river. Model input included channel characteristics, boundary conditions for flow and water quality, environmental parameters, vertical dispersion coefficients, settling rates, and kinetic constants. Inputs were estimated where field data were lacking, notably for tributary flows and nutrient loads.
The model was calibrated to observed flow variables and concentrations of dissolved oxygen, chlorophyll-a, and nutrients at sampling locations, with emphasis on growing-season conditions. Calibration was achieved through graphical and statistical comparison of simulated results to observed data. Sensitivity analyses were performed, and model limitations and applicability were evaluated. Simulated results closely matched observed data in most cases, although some were overpredicted slightly. The most important causes of overprediction were estimated tributary flows for the flow model and estimated tributary watershed loads for the water-quality model. Calibration of dissolved-oxygen concentrations was closer, and predicted diurnal variations were consistent with high algal photosynthesis/respiration, although lack of continuous dissolved-oxygen data precluded verifying these predictions. A similar caveat applies to predicted diurnal variations in chlorophyll-a. Simulated limitations on algal growth were consistent with those based on observed data and indicated phosphorus was the main limiting nutrient, except during certain periods when nitrogen was limiting.
Two water-quality management scenarios were simulated with the model to assess the effect of point- and nonpoint-source nutrient reductions on water-quality conditions in the river. Scenarios involved (1) a return of watershed land use to predevelopment natural conditions and (2) an extreme reduction in nutrient input. Although the extreme-nutrient-reduction scenario yielded improvements in water quality, the natural-conditions scenario yielded the largest improvements as indicated by minimal violations of surface-water-quality standards or thresholds. However, years may be needed to attain the full benefit of these management scenarios as a result of accumulation of phosphorus and organic carbon in riverbed sediments in lacustrine reaches. The results of this study indicate that the quality of water in the central Salem River will improve if management policies that mitigate the effects of nutrient-loading practices in the watershed, particularly those related to agriculture, are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225047","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Spitz, F.J., and DePaul, V.T., 2023, Simulation of flow and eutrophication in the central Salem River, New Jersey: U.S. Geological Survey Scientific Investigations Report 2022–5047, 72 p., https://doi.org/10.3133/sir20225047.","productDescription":"Report: x, 72 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109225","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500449,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114735.htm","linkFileType":{"id":5,"text":"html"}},{"id":417027,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JPJ","text":"USGS data release","linkHelpText":"WASP model used to simulate flow and eutrophication in the central Salem River, New Jersey"},{"id":417026,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5047/images/"},{"id":417025,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.XML"},{"id":417024,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5047"},{"id":417023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5047"},{"id":417022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5047/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Central Salem River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.51261142665348,\n 39.66506027345514\n ],\n [\n -75.13011677160785,\n 39.493754929673486\n ],\n [\n -75.01123329774249,\n 39.637202213256444\n ],\n [\n -75.41569555121957,\n 39.76545497451639\n ],\n [\n -75.51261142665348,\n 39.66506027345514\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006810","usgsCitation":"Woodard, J.B., Mirus, B., Crawford, M., Or, D., Leshchinsky, B., Allstadt, K.E., and Wood, N.J., 2023, Mapping landslide susceptibility over large regions with limited data: Journal of Geophysical Research: Earth Surface, v. 128, no. 5, e2022JF006810, 21 p., https://doi.org/10.1029/2022JF006810.","productDescription":"e2022JF006810, 21 p.","ipdsId":"IP-142367","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":443478,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006810","text":"Publisher Index Page"},{"id":435324,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P959G9JN","text":"USGS data 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]\n}","volume":"128","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Woodard, Jacob Bryson 0000-0002-3095-0774","orcid":"https://orcid.org/0000-0002-3095-0774","contributorId":305507,"corporation":false,"usgs":true,"family":"Woodard","given":"Jacob","email":"","middleInitial":"Bryson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Matthew","contributorId":224687,"corporation":false,"usgs":false,"family":"Crawford","given":"Matthew","email":"","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":873054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Or, Dani","contributorId":267915,"corporation":false,"usgs":false,"family":"Or","given":"Dani","affiliations":[{"id":55530,"text":"ETH / DRI","active":true,"usgs":false}],"preferred":false,"id":873055,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leshchinsky, Ben","contributorId":267910,"corporation":false,"usgs":false,"family":"Leshchinsky","given":"Ben","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":873056,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873057,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873058,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243709,"text":"70243709 - 2023 - Heavy: Software for forward-modeling gravity change from MODFLOW output","interactions":[],"lastModifiedDate":"2023-05-18T12:45:59.131321","indexId":"70243709","displayToPublicDate":"2023-05-18T07:43:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Heavy: Software for forward-modeling gravity change from MODFLOW output","docAbstract":"Fortran software, named Heavy, was developed to simulate gravity change due to water-storage change in MODFLOW groundwater models. Heavy is compatible with MODFLOW-2005 and MODFLOW-NWT models using the layer-property flow or upstream weighting packages. All of the necessary information for the gravity calculation—the geometry of the model cells, the storage coefficient, and head change—is present within the existing MODFLOW model files and no additional information is necessary. Gravity change is calculated at each time step, for each layer, at user specified locations or at a grid of hypothetical positions across the model. The software has been validated using analytical gravity solutions and three example MODFLOW models are included for demonstration. Heavy leverages the input/output routines from MODFLOW and is orders of magnitude faster than previous efforts using interpreted languages such as Python or MATLAB. The objective of the software is to facilitate repeat microgravity field measurements for groundwater-flow model calibration.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2023.105714","usgsCitation":"Kennedy, J.R., and Larsen, J., 2023, Heavy: Software for forward-modeling gravity change from MODFLOW output: Environmental Modelling and Software, v. 165, 105714, 7 p., https://doi.org/10.1016/j.envsoft.2023.105714.","productDescription":"105714, 7 p.","ipdsId":"IP-137200","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":435325,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IIHXN3","text":"USGS data release","linkHelpText":"Heavy"},{"id":417201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":176478,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":873014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Joshua 0000-0002-1218-800X jlarsen@usgs.gov","orcid":"https://orcid.org/0000-0002-1218-800X","contributorId":272403,"corporation":false,"usgs":true,"family":"Larsen","given":"Joshua","email":"jlarsen@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873015,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244257,"text":"70244257 - 2023 - Future climate-induced changes in mixing and deep oxygen content of a caldera lake with hydrothermal heat and salt inputs","interactions":[],"lastModifiedDate":"2023-06-09T12:01:12.277301","indexId":"70244257","displayToPublicDate":"2023-05-18T06:55:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Future climate-induced changes in mixing and deep oxygen content of a caldera lake with hydrothermal heat and salt inputs","docAbstract":"Vertical profiles of temperature, salinity and dissolved oxygen in Crater Lake, a caldera lake in the Oregon Cascade Range that receives hydrothermal inputs of heat and salt, were simulated with a 1-dimensional model. Twelve Global Circulation Models and two Representative Concentration Pathways (RCPs) were used to develop boundary conditions from 1950 to 2099. The model simulated the ventilation of deep water initiated by reverse stratification and subsequent thermobaric instability. All models predicted a reduction in the frequency of deep ventilation events, from an ensemble median frequency of 5.4 winters decade−1 during 1950–2005 to 4.3 (RCP4.5) or 2.5 (RCP8.5) winters decade−1 during 2045–2099. Favorable conditions for thermobaric instability-induced mixing currently occur infrequently and will become rare in the future. The salinity gradient resulting from hydrothermal inputs presents an additional barrier to thermobaric instability that will continue through 2099. A redistribution of salt to the deep lake may prevent ventilation all the way to the bottom in the future. Hypolimnetic dissolved oxygen percent saturation remained above 75% within the 21st century, consistent with oligotrophy and very small oxygen demands. The rate of change in all variables accelerated approaching 2099, coincident with elimination of winter reverse stratification. Historically, about half of the hydrothermal heat added to Crater Lake has been vented to the atmosphere. In the RCP8.5 scenario, the hydrothermal heat will cease to be vented to the atmosphere by the end of the 21st century, and then the temperature of the deep waters will increase rapidly.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.03.014","usgsCitation":"Wood, T.M., Wherry, S., Piccolroaz, S., and Girdner, S.F., 2023, Future climate-induced changes in mixing and deep oxygen content of a caldera lake with hydrothermal heat and salt inputs: Journal of Great Lakes Research, v. 49, no. 3, p. 563-580, https://doi.org/10.1016/j.jglr.2023.03.014.","productDescription":"18 p.","startPage":"563","endPage":"580","ipdsId":"IP-150869","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":443494,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2023.03.014","text":"Publisher Index Page"},{"id":435329,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96NLDLX","text":"USGS data release","linkHelpText":"1-D Deep Ventilation (1DDV) model for Crater Lake, Oregon, 1950-2100"},{"id":417960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Crater Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.2356963721314,\n 43.03291163525574\n ],\n [\n -122.2356963721314,\n 42.848854312940176\n ],\n [\n -121.96965348050013,\n 42.848854312940176\n ],\n [\n -121.96965348050013,\n 43.03291163525574\n ],\n [\n -122.2356963721314,\n 43.03291163525574\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Tamara M. 0000-0001-6057-8080 tmwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6057-8080","contributorId":1164,"corporation":false,"usgs":true,"family":"Wood","given":"Tamara","email":"tmwood@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875050,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piccolroaz, Sebastiano","contributorId":297277,"corporation":false,"usgs":false,"family":"Piccolroaz","given":"Sebastiano","affiliations":[{"id":64342,"text":"University of Trento, Department of Civil, Environmental and Mechanical Engineering, Trento, Italy","active":true,"usgs":false}],"preferred":false,"id":875051,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Girdner, Scott F","contributorId":168526,"corporation":false,"usgs":false,"family":"Girdner","given":"Scott","email":"","middleInitial":"F","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":875052,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250988,"text":"70250988 - 2023 - Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes","interactions":[],"lastModifiedDate":"2024-01-18T11:54:48.157094","indexId":"70250988","displayToPublicDate":"2023-05-18T05:53:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes","docAbstract":"Small waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO2 and CH4. Here, we measured partial pressures of CO2 (pCO2) and CH4 (pCH4) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability of pCO2 and pCH4 in space and time. Summer means of pCO2 and pCH4 were inversely related to waterbody size and positively related to floating vegetative cover; pCO2 was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonal pCO2 by up to 26%, mean seasonal pCH4 could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability in pCH4 and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities.
Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land-use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with best-available science to help guide current management and conservation plans aimed at benefitting sage-grouse populations. This analysis relied on previously published population trend modeling methodology from Coates and others (2021, 2022a) and incorporated population lek count data through 2022. Bayesian state-space models estimated 2.9 percent average annual decline in sage-grouse populations across their geographical range, which varied among subpopulations at the largest scale of analysis, termed climate clusters (2.2–4.7). Cumulative declines were 40.9, 65.0, and 79.6 percent range-wide across short (19 years), medium (35 years), and long (55 years) temporal periods, respectively. These results indicate that the most recent nadir for range-wide populations occurred during 2021. However, growth during 2022 was modest, making 2021 a tentative final nadir at this point.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1175","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","programNote":"Species Management Research Program","usgsCitation":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2023, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2022: U.S. Geological Survey Data Report 1175, 17 p., https://doi.org/10.3133/dr1175.","productDescription":"Report: viii, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-151795","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":417065,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1175/full"},{"id":417060,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OQWGIV","text":"Trends and a targeted annual warning system for greater sage-grouse in the western United States (ver. 2.0, May 2023)","description":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2023, Trends and a targeted annual warning system for greater sage-grouse in the western United States (ver. 2.0, May 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9OQWGIV."},{"id":417061,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1175/covrthb.jpg"},{"id":417062,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1175/dr1175.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417063,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1175/dr1175.xml"},{"id":417064,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1175/images"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103,\n 49\n ],\n [\n -122,\n 49\n ],\n [\n -122,\n 36\n ],\n [\n -103,\n 36\n ],\n [\n -103,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
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Bioaccumulation of ionizable pharmaceuticals has been increasingly studied, with most reported aquatic tissue concentrations in field or laboratory experiments being from fish. However, higher levels of antidepressants have been observed in bivalves compared with fish from effluent-dominated and dependent surface waters. Such observations may be important for biodiversity because approximately 70% of freshwater bivalves in North America are considered to be vulnerable to extinction. Because experimental bioaccumulation information for freshwater bivalves is lacking, we examined accumulation dynamics in the freshwater pondmussel, Sagittunio subrostratus, following exposure to a model weak acid, acetaminophen (mean (±SD) = 4.9 ± 1 µg L–1), and a model weak base, sertraline (mean (±SD) = 1.1 ± 1.1 µg L–1) during 14-day uptake and 7-day depuration experiments. Pharmaceutical concentrations were analyzed in water and tissue using isotope dilution liquid chromatography–tandem mass spectrometry. Mussels accumulated two orders of magnitude higher concentrations of sertraline (31.7 ± 9.4 µg g–1) compared to acetaminophen (0.3 ± 0.1 µg g–1). Ratio and kinetic-based bioaccumulation factors of 28,836.4 (L kg–1) and 34.9 (L kg–1) were calculated for sertraline and for acetaminophen at 65.3 (L kg–1) and 0.13 (L kg–1), respectively. However, after 14 days sertraline did not reach steady-state concentrations, although it was readily eliminated by S. subrostratus. Acetaminophen rapidly reached steady-state conditions but was not depurated over a 7-day period. Future bioaccumulation studies of ionizable pharmaceuticals in freshwater bivalves appear warranted. Environ Toxicol Chem 2023;00:1–7. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5590","usgsCitation":"Burket, S., Sims, J.L., Dorman, R.A., Kemble, N.E., Brunson, E., Steevens, J.A., and Brooks, B.W., 2023, Bioaccumulation kinetics of model pharmaceuticals in the freshwater unionid pondmussel, Sagittunio subrostratus: Environmental Toxicology and Chemistry, v. 42, no. 6, p. 1183-1189, https://doi.org/10.1002/etc.5590.","productDescription":"7 p.","startPage":"1183","endPage":"1189","ipdsId":"IP-136216","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":499335,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5590","text":"Publisher Index Page"},{"id":435331,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HVMQJL","text":"USGS data release","linkHelpText":"Morphometric measurements from unionid Pondmussel (Ligumia subrostrata) and concentrations of four per- and polyfluoroalkyl substances (PFAS) in water and mussels collected from a 14-day accumulation and 7-day elimination study"},{"id":435330,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OUHJ8N","text":"USGS data release","linkHelpText":"Concentration of sertraline and acetaminophen in freshwater mussel (Sagittunio subrostratus) and water from an exposure bioassay"},{"id":417132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","county":"Boone County","otherGeospatial":"Lake Paragon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.214285899655,\n 38.856471915263995\n ],\n [\n -92.21393836929818,\n 38.85738644821262\n ],\n [\n -92.21326727619508,\n 38.85836629189188\n ],\n [\n -92.21301561628127,\n 38.85853426373845\n ],\n [\n -92.21300363247597,\n 38.858711566924285\n ],\n [\n -92.21312347053026,\n 38.85874889385451\n ],\n [\n -92.21278792397871,\n 38.85912216207768\n ],\n [\n -92.21269205353535,\n 38.859579412980565\n ],\n [\n -92.21283585920021,\n 38.85985002947703\n ],\n [\n -92.21317140575175,\n 38.85992468212277\n ],\n [\n -92.21335116283278,\n 38.8598686926461\n ],\n [\n -92.21345901708142,\n 38.85989668738969\n ],\n [\n -92.21386646646538,\n 38.85952342323205\n ],\n [\n -92.2140102721306,\n 38.85923414216251\n ],\n [\n -92.21423796443312,\n 38.85900085011963\n ],\n [\n -92.21499294417428,\n 38.85852493197967\n ],\n [\n -92.21517270125533,\n 38.85817965604173\n ],\n [\n -92.21585577816369,\n 38.85761041367235\n ],\n [\n -92.21593966480175,\n 38.85737711630321\n ],\n [\n -92.2160834704666,\n 38.85737711630321\n ],\n [\n -92.21628719515857,\n 38.85686385939647\n ],\n [\n -92.21625124374236,\n 38.85667721960269\n ],\n [\n -92.21640703321249,\n 38.85621061797582\n ],\n [\n -92.21664670932068,\n 38.85589332712095\n ],\n [\n -92.21614338949448,\n 38.85483205731646\n ],\n [\n -92.21580784294294,\n 38.854421437393256\n ],\n [\n -92.21558015064004,\n 38.85435611127798\n ],\n [\n -92.21512476603421,\n 38.85438410819165\n ],\n [\n -92.21462144620727,\n 38.854692073517924\n ],\n [\n -92.21435780248817,\n 38.85506536303453\n ],\n [\n -92.21439375390437,\n 38.85558796506638\n ],\n [\n -92.21435780248817,\n 38.85575594347523\n ],\n [\n -92.21441772151529,\n 38.855867928860704\n ],\n [\n -92.21442970532058,\n 38.85609189910329\n ],\n [\n -92.2142139968233,\n 38.85612922740793\n ],\n [\n -92.21413011018525,\n 38.85629720453841\n ],\n [\n -92.214285899655,\n 38.856471915263995\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-02-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Brunson, Eric 0000-0001-6624-0902","orcid":"https://orcid.org/0000-0001-6624-0902","contributorId":201761,"corporation":false,"usgs":true,"family":"Brunson","given":"Eric","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":872908,"contributorType":{"id":2,"text":"Editors"},"rank":5}],"authors":[{"text":"Burket, S. 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Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised (n = 6), semi-supervised (n = 1), and unsupervised (n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors (Gymnogyps californianus). Unsupervised K-means and EM (expectation–maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: −0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10035","usgsCitation":"Sur, M., Hall, J.C., Brandt, J., Astell, M., Poessel, S.A., and Katzner, T., 2023, Supervised versus unsupervised approaches to classification of accelerometry data: Ecology and Evolution, v. 13, no. 5, e10035, 11 p., https://doi.org/10.1002/ece3.10035.","productDescription":"e10035, 11 p.","ipdsId":"IP-144075","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":443508,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10035","text":"Publisher Index Page"},{"id":435332,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PAVUEZ","text":"USGS data release","linkHelpText":"Tri-axial acceleration data from California condors (Gymnogyps californianus), California, USA"},{"id":417910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Sur, Maitreyi","contributorId":191354,"corporation":false,"usgs":false,"family":"Sur","given":"Maitreyi","email":"","affiliations":[],"preferred":false,"id":874874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Jonathan C.","contributorId":202606,"corporation":false,"usgs":false,"family":"Hall","given":"Jonathan","email":"","middleInitial":"C.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":874875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, Joseph","contributorId":127742,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":874876,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Astell, Molly","contributorId":199753,"corporation":false,"usgs":false,"family":"Astell","given":"Molly","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":874877,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":874878,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":874879,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243691,"text":"70243691 - 2023 - Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast","interactions":[],"lastModifiedDate":"2023-06-27T16:55:28.533795","indexId":"70243691","displayToPublicDate":"2023-05-17T08:23:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast","docAbstract":"A 38-year hindcast water level product is developed for the U.S. Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water level modelling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modelled water level components. Corrected water level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.","language":"English","publisher":"Springer","doi":"10.1007/s11069-023-05939-6","usgsCitation":"Parker, K.A., Erikson, L.H., Thomas, J.A., Nederhoff, C.M., Barnard, P.L., and Muis, S., 2023, Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast: Natural Hazards, v. 117, p. 2219-2248, https://doi.org/10.1007/s11069-023-05939-6.","productDescription":"30 p.","startPage":"2219","endPage":"2248","ipdsId":"IP-145520","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443513,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11069-023-05939-6","text":"Publisher Index 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