The recent development of animal-borne sensors coupled with location data can provide insights into how individuals modify their behaviour with respect to specific habitat features. Animals can express a diverse array of behaviours as they navigate heterogenous landscapes, yet few studies have specifically evaluated the interaction of behaviours with habitat characteristics. We used a novel broadcast acoustic transmitter to investigate the interaction between vocal behaviours of an endemic Hawaiian thrush, the ʻōmaʻo, Myadestes obscurus, and habitat features across a naturally fragmented forest landscape. Through the development of behavioural landscape models that link specific vocalizations with space use, we found that the use of different vocalization types (calls, songs, whisper songs) were highly variable across the landscape but were associated with distinct habitat features. The likelihood of calls increased in an open lava matrix between forest patches, while whisper songs were more strongly associated with the dense interior areas of forest fragments. In contrast, the rate of ʻōmaʻo vocalizations overall decreased in the open lava matrix, suggesting that ʻōmaʻo may shift behaviours from territory defence to foraging as they transition through different habitats. Our study revealed context-specific changes in behaviour across ʻōmaʻo home ranges, including courtship, aggression and social interactions between individuals. Combining the use of a novel acoustic tool with automated radiotelemetry allowed us to overcome challenges associated with detection and analysis of variation in behaviour and resource selection across a highly heterogeneous landscape that would have been otherwise difficult to impossible.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.anbehav.2023.04.006","usgsCitation":"Netoskie, E.C., Paxton, K.L., Paxton, E.H., Asner, G.P., and Hart, P.J., 2023, Linking vocal behaviours to habitat structure to create behavioural landscapes: Animal Behaviour, v. 201, p. 1-11 p., https://doi.org/10.1016/j.anbehav.2023.04.006.","productDescription":"11 p.","startPage":"1","endPage":"11 p.","ipdsId":"IP-136322","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":443605,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.anbehav.2023.04.006","text":"Publisher Index Page"},{"id":420907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Hawaii, Mauna Loa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.58292548105314,\n 19.5421499770886\n ],\n [\n -155.58292548105314,\n 19.33132215765302\n ],\n [\n -155.42705047437903,\n 19.33132215765302\n ],\n [\n -155.42705047437903,\n 19.5421499770886\n ],\n [\n -155.58292548105314,\n 19.5421499770886\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"201","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Netoskie, Erin C","contributorId":329790,"corporation":false,"usgs":false,"family":"Netoskie","given":"Erin","email":"","middleInitial":"C","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":883317,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Kristina L. 0000-0003-2321-5090","orcid":"https://orcid.org/0000-0003-2321-5090","contributorId":41917,"corporation":false,"usgs":false,"family":"Paxton","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false},{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":883318,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":883319,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":883320,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Patrick J.","contributorId":147728,"corporation":false,"usgs":false,"family":"Hart","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false}],"preferred":false,"id":883321,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256482,"text":"70256482 - 2023 - Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan","interactions":[],"lastModifiedDate":"2024-08-07T15:11:02.429549","indexId":"70256482","displayToPublicDate":"2023-05-09T09:49:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan","docAbstract":"Effective fishery management in large systems relies on understanding how individual stocks contribute to a fishery over spatial and temporal scales. The current conceptual model for management of Walleye Sander vitreus in Green Bay designates Walleye in the northern and southern parts of the bay as distinct stocks, with little mixing between the northern and southern fisheries, and assumes that Walleye in both northern and southern Green Bay primarily spawn in tributaries as opposed to shoreline or offshore reef areas. We used acoustic telemetry to test this conceptual model for Walleye management in Green Bay. Telemetry indicated that the majority of Green Bay Walleye use tributaries for spawning. However, many individuals were assigned to open-water spawning locations during consecutive years in both northern (26%) and southern (21%) Green Bay, suggesting that open-water spawners may represent a larger proportion of the Walleye stocks than previously thought. Differential movement was observed between northern and southern portions of Green Bay, with 56% of Walleye tagged in northern Green Bay crossing receiver lines to move south compared to only 19% of Walleye tagged in southern Green Bay crossing receiver lines to move north. Walleye typically transitioned across these boundaries in summer and fall, suggesting that stock contributions to the fishery in each zone may differ seasonally. Differential movements of northern Green Bay Walleye may be influenced by broad-scale differences in habitat and prey availability, which are likely related to the differential effects of dreissenid mussel invasion in Green Bay. Our results suggest that adjustment of monitoring efforts to account for open-water spawners may provide a more complete picture of stock status. Additionally, more research examining potential food web effects of northern Green Bay Walleye moving into southern Green Bay may be needed to determine how these movements might influence other important species.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10883","usgsCitation":"Izzo, L., Dembkowski, D., Hayden, T., Binder, T., Christopher Vandergoot, Hogler, S., Donofrio, M., Zorn, T., Krueger, C., and Isermann, D.A., 2023, Spawning locations, movements, and potential for stock mixing of walleye in Green Bay, Lake Michigan: North American Journal of Fisheries Management, v. 43, no. 3, p. 695-714, https://doi.org/10.1002/nafm.10883.","productDescription":"20 p.","startPage":"695","endPage":"714","ipdsId":"IP-145656","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":443607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10883","text":"Publisher Index Page"},{"id":432339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.32040853136192,\n 44.37812529340982\n ],\n [\n -87.91846002557772,\n 44.3585534932721\n ],\n [\n -86.60288078784984,\n 45.624769896015266\n ],\n [\n -86.99575779919539,\n 45.917890769552486\n ],\n [\n -87.69003998623847,\n 45.15642614432045\n ],\n [\n -88.32040853136192,\n 44.37812529340982\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Izzo, Lisa K.","contributorId":340807,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel","contributorId":340808,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayden, Todd","contributorId":340810,"corporation":false,"usgs":false,"family":"Hayden","given":"Todd","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Binder, Tom","contributorId":340812,"corporation":false,"usgs":false,"family":"Binder","given":"Tom","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christopher Vandergoot","contributorId":340814,"corporation":false,"usgs":false,"family":"Christopher Vandergoot","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907578,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hogler, Steven","contributorId":340817,"corporation":false,"usgs":false,"family":"Hogler","given":"Steven","email":"","affiliations":[{"id":81669,"text":"Wisconsin Department of Natural Resource (retired)","active":true,"usgs":false}],"preferred":false,"id":907579,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donofrio, Michael","contributorId":340818,"corporation":false,"usgs":false,"family":"Donofrio","given":"Michael","email":"","affiliations":[{"id":81669,"text":"Wisconsin Department of Natural Resource (retired)","active":true,"usgs":false}],"preferred":false,"id":907580,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zorn, Troy","contributorId":340819,"corporation":false,"usgs":false,"family":"Zorn","given":"Troy","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907581,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krueger, Charles","contributorId":340820,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907582,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907583,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70249368,"text":"70249368 - 2023 - The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","interactions":[],"lastModifiedDate":"2023-10-05T12:06:20.98252","indexId":"70249368","displayToPublicDate":"2023-05-09T07:05:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"The Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","docAbstract":"Accurate knowledge of the speed at which water moves along a river is essential for understanding ecohydraulic processes and managing natural resources. Measuring flow velocity via remote sensing can be more efficient than conventional field methods, and powerful computational techniques for inferring velocity fields from videos or image time series have been developed. The development of dedicated software tools for particle image velocimetry (PIV) could facilitate greater use of these methods by the river community. This paper introduces a standalone app designed for this exact purpose: the Toolbox for River Velocimetry using Images from Aircraft, or TRiVIA. The program provides a complete workflow for producing spatially distributed velocity vectors from a video or sequence of images, all within an accessible graphical user interface. TRiVIA includes modules for extracting and resampling frames, stabilization and geo-referencing images, defining a region of interest, enhancing images, performing PIV with an efficient ensemble correlation algorithm, visualizing results, assessing accuracy assessment, and exporting PIV output. We illustrate the software's capabilities using an example data set from a large river in Alaska. The initial release of the toolbox is now freely available. Augmenting TRiVIA to incorporate bathymetric information could enable discharge calculation functionality.
The parasitic copepod Salmincola californiensis infects Pacific salmon and trout (Oncorhynchus spp.) and often reaches high prevalence and intensity in reservoirs compared to stream systems. Recent research indicates that temperature plays a fundamental role in copepod development and fish susceptibility. Here, we expand a linked foraging and bioenergetics model to simulate infection risk. Based on juvenile salmon vertical migration patterns, we add estimates of copepod generations produced and thermal strata metrics that appear associated with copepodid aggregations and increased infection. Severe damage on hosts may be caused by the infectious copepodid, a life-stage not readily visible and thus not detectable using traditional fish screenings. We discuss model limitations, opportunities for future research, and the potential for inclusion of copepod expansion equations to existing linked bioenergetics models or observed behaviors of salmonids in other lentic systems. We demonstrate that using a temperature sensitive model framework that includes copepod infection dynamics is useful in interpreting other lines of evidence, such as fish mortality estimates. Collectively, our work provides a testable framework for future comparisons of infection potential and demonstrates how bioenergetics models may be useful in understanding host–parasite interactions.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-023-01420-2","usgsCitation":"Murphy, C.A., Pollock, A., Johnson, S.L., and Arismendi, I., 2023, Linked foraging and bioenergetics modeling may inform fish parasite infection dynamics: Environmental Biology of Fishes, v. 106, p. 1345-1356, https://doi.org/10.1007/s10641-023-01420-2.","productDescription":"12 p.","startPage":"1345","endPage":"1356","ipdsId":"IP-147898","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":433511,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","noUsgsAuthors":false,"publicationDate":"2023-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Christina Amy 0000-0002-3467-6610","orcid":"https://orcid.org/0000-0002-3467-6610","contributorId":335232,"corporation":false,"usgs":true,"family":"Murphy","given":"Christina","email":"","middleInitial":"Amy","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910078,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollock, Amanda","contributorId":150244,"corporation":false,"usgs":false,"family":"Pollock","given":"Amanda","email":"","affiliations":[{"id":17945,"text":"U.S. Fish and Wildlife Service, Pacific Islands Refuge and Monuments","active":true,"usgs":false}],"preferred":false,"id":910079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Sherri L 0000-0002-4223-3465","orcid":"https://orcid.org/0000-0002-4223-3465","contributorId":192210,"corporation":false,"usgs":false,"family":"Johnson","given":"Sherri","email":"","middleInitial":"L","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":910080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arismendi, Ivan 0000-0002-8774-9350","orcid":"https://orcid.org/0000-0002-8774-9350","contributorId":202207,"corporation":false,"usgs":false,"family":"Arismendi","given":"Ivan","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":910081,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243176,"text":"sir20235028 - 2023 - Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico","interactions":[],"lastModifiedDate":"2026-03-06T20:53:37.591262","indexId":"sir20235028","displayToPublicDate":"2023-05-08T11:03:58","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":"2023-5028","displayTitle":"Development of an Integrated Hydrologic Flow Model of the Rio San Jose Basin and Surrounding Areas, New Mexico","title":"Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico","docAbstract":"The Rio San Jose Integrated Hydrologic Model (RSJIHM) was developed to provide a tool for analyzing the hydrologic system response to historical water use and potential changes in water supplies and demands in the Rio San Jose Basin. The study area encompasses about 6,300 square miles in west-central New Mexico and includes the communities of Grants, Bluewater, and San Rafael and three Native American Tribal lands: the Acoma and Laguna Pueblos and the Navajo Nation. Perennial surface water features are sparse in the study area and most water resources consist of groundwater pumped from sedimentary and basalt aquifers.
Calibration of the RSJIHM was performed using PEST++ (version 4.3.20) and BeoPEST (version 13.6). Model parameter values were adjusted during calibration to fit model simulated values to the measured or estimated values for several observation groups: (1) solar radiation, (2) potential evapotranspiration, (3) actual evapotranspiration, (4) precipitation and minimum and maximum air temperature, (5) snow water equivalent, (6) snow-covered area, (7) streamflow, (8) hydraulic head, (9) springflow at Ojo del Gallo, (10) springflow at Horace Springs, (11) surface-water releases from Bluewater Lake, and (12) surface-water diversions for irrigation within the Bluewater-Toltec Irrigation District.
The simulated average annual hydrologic budget from 1950 through 2018 indicated that the majority (greater than 98 percent) of precipitation within the basin was consumed by evapotranspiration, leaving 1.2 percent to recharge the groundwater system, 0.47 percent to direct runoff to streams, and 0.20 percent to infiltrate the soil zone and interflow to streams. The average annual recharge to the groundwater system and runoff to streams simulated by the RSJIHM was about 28,000 and 11,000 acre-feet, respectively. The RSJIHM simulated about 590,000 acre-feet of cumulative aquifer storage depletion from 1950 through 2018.
Additional work that could improve the simulation capability of the RSJIHM includes (1) further data collection (streamflow, head, springflow) in the southwestern subbasin that includes the El Malpais National Monument, (2) incorporating temporally variable vegetation parameters, (3) spatial downscaling of the hydrometeorological input datasets, (4) incorporating additional spatial variability to hydraulic property parameters on the basis of new data collection, and (5) using environmental tracers to verify and calibrate model parameters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235028","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Pueblo of Acoma, and Pueblo of Laguna","usgsCitation":"Ritchie, A.B., Chavarria, S.B., Galanter, A.E., Flickinger, A.K., Robertson, A.J., and Sweetkind, D.S., 2023, Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5028, 76 p., 1 pl., https://doi.org/10.3133/sir20235028.","productDescription":"Report: x, 76 p.; 1 Plate: 25.37 x 40.38 inches; Data Release","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-111893","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":416632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5028/coverthb.jpg"},{"id":416635,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235028/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5028 HTML"},{"id":416634,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.XML","size":"482 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5028 XML"},{"id":416638,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028_plate1.pdf","size":"550 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028 plate 1"},{"id":416633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.pdf","size":"5.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028"},{"id":416637,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRTKTM","text":"USGS data release—GSFLOW, used to run PRMS and MODFLOW-NWT models, to simulate the effects of natural and anthropogenic impacts on water resources in the Rio San Jose Basin and surrounding areas, New Mexico"},{"id":416636,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5028/Images/"},{"id":500886,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114717.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio San Jose Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.5,\n 36\n ],\n [\n -108.5,\n 36\n ],\n [\n -108.5,\n 34\n ],\n [\n -106.5,\n 34\n ],\n [\n -106.5,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
New York City faces accelerating inundation risk from sea level rise, subsidence, and increasing storm intensity from natural and anthropogenic causes. Here we calculate a previously unquantified contribution to subsidence from the cumulative mass and downward pressure exerted by the built environment of the city. We enforce that load distribution in a multiphysics finite element model to calculate expected subsidence. Complex surface geology requires multiple rheological soil models to be applied; clay rich soils and artificial fill are calculated to have the highest post-construction subsidence as compared with more elastic soils. Minimum and maximum calculated building subsidence ranges from 0 to 600 mm depending on soil/rock physical parameters and foundation modes. We compare modeled subsidence and surface geology to observed subsidence rates from satellite data (Interferometric Synthetic Aperture Radar and Global Positioning System). The comparison is complicated because the urban load has accumulated across a much longer period than measured subsidence rates, and there are multiple causes of subsidence. Geodetic measurements show a mean subsidence rate of 1–2 mm/year across the city that is consistent with regional post-glacial deformation, though we find some areas of significantly greater subsidence rates. Some of this deformation is consistent with internal consolidation of artificial fill and other soft sediment that may be exacerbated by recent building loads, though there are many possible causes. New York is emblematic of growing coastal cities all over the world that are observed to be subsiding (Wu et al., 2022, https://doi.org/10.1029/2022GL098477), meaning there is a shared global challenge of mitigation against a growing inundation hazard.
The Paleoproterozoic Nagssugtoqidian Orogen is one of the principal tectonic features related to the assembly of Nuna, extending across Greenland from east to west and forming an orogenic belt separating the North Atlantic Craton on the south from the Rae Craton on the north. In South-East Greenland, the Ammassalik Intrusive Complex (AIC) (∼1910 to 1870 Ma) occupies the central part of the orogenic belt, was formed by subduction- and magmatic arc-related processes, and has significant potential for undiscovered deposits of critical minerals. Previous interpretations of aeromagnetic data have been hindered by terrain effects, and we use a novel mix of geophysical analysis tools to develop new tectonomagmatic interpretations of the central Nagssugtoqidian Orogen in South-East Greenland. These interpretations extend into areas covered by ocean and ice. Results show that Archean rocks of the juxtaposed North Atlantic (Isertoq Terrane) and Rae (Kuummiut Terrane) Cratons are relatively weakly magnetized (with the exception of rocks of the Schweizerland Terrane) and have a NW-striking structural fabric that likely formed or was enhanced during the Nagssugtoqidian Orogeny. The AIC is structurally complex, with weakly magnetized metasedimentary rocks, and both weakly and strongly magnetized intrusions, arrayed in a NW-striking tectonic fabric. The strongly magnetized intrusions are largely concealed and distributed in a broader and more spatially complex fashion than previously known, suggesting that additional areas may be considered for mineral exploration. Strongly magnetized NW-striking dikes are imaged within the AIC, where they are spatially closely related to the strongly magnetized intrusions, and extend southward into the North Atlantic Craton (Isertoq Terrane). This spatial pattern of arc magmatism is consistent with previously developed models of SW-directed subduction that preceded collision during the Nagssugtoqidian Orogeny. The strongly magnetized Ammassalik Batholith (∼1670 Ma) and related intrusions form a cluster of plutons within ∼ 50 km of the Nagssugtoqidian suture. Their tectonomagmatic setting is unknown, although are speculatively related to delamination of a lithospheric keel formed during the Nagssugtoqidian Orogeny ∼ 200 m.y. prior. Numerous strongly magnetized NNE-striking Paleogene dikes, related to the opening of the Atlantic Ocean, are imaged cutting most other geologic units.
Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Accurate estimates of flood magnitude and frequency are needed to (1) optimize the design and location of infrastructure, including dams, culverts, bridges, industrial buildings, and highways, and (2) inform flood-zoning and flood-insurance studies. The U.S. Geological Survey (USGS), in cooperation with the State of Hawaiʻi Department of Transportation, estimated flood magnitudes for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) for unregulated streamgages in Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, using data through water year 2020. Regression equations were developed to estimate flood magnitude and associated frequency at ungaged streams. This study improves upon a previous USGS flood-frequency report (Oki and others, 2010) by including more peak-flow data, implementing new statistical methods in flood-frequency analysis, and using updated techniques to estimate the regional-skewness coefficient (regional skew).
Flood magnitude and frequency at 238 streamgages were estimated—following national guidelines established in Bulletin 17C (England and others, 2019)—by fitting annual peak-flow data to the Log-Pearson Type III distribution using the expected moments algorithm and the PeakFQ flood-frequency software. Potentially influential low outliers in the data were identified and removed using the Multiple Grubbs-Beck Test. An updated regional skew for Hawaiʻi was estimated using the Bayesian weighted least squares/Bayesian generalized least squares method. The updated regional skew employs a constant model for the five islands in the study area and has a value of −0.157 (mean square error of 0.212).
Multiple linear regression techniques were used to develop regression equations that relate basin and climatic characteristics to peak flows at streamgages. The regression equations can be applied to estimate flood magnitude and frequency at ungaged sites. The study area was split into 10 regions—2 regions per island, generally following a leeward/windward division—containing from 9 to 49 streamgages each. The final regression equations for each region were determined with generalized least-squares analysis using the USGS weighted-multiple-linear regression (WREG) program. The standard error of prediction at the 1-percent AEP for the regression equations ranged from 18 to 164 percent; the pseudo coefficient of determination (pseudo-R2) at the 1-percent AEP ranged from 46 to 100 percent. The regression equations performed well for all regions except leeward Molokaʻi and southern Island of Hawaiʻi; for all other regions, the pseudo-R2 values ranged from about 75 to 100 percent. Compared to the regression equations developed by Oki and others (2010), the regression equations in this study generally showed modest improvements, although the magnitude of differences varied for each region.
Peak-flow estimates at the 238 streamgages included in this study are improved by weighting the at-site statistics computed with PeakFQ and the predicted flows based on the regression equations. Results of this study—including the final peak-flow estimates at streamgages and the regional regression equations—are implemented in the USGS StreamStats web application (U.S. Geological Survey, 2023, StreamStats: https://streamstats.usgs.gov/ss/). StreamStats provides a consistent approach for obtaining peak-flow estimates at streamgages and for applying the regional regression equations for estimating peak flows at ungaged locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235014","collaboration":"Prepared in cooperation with the State of Hawaiʻi Department of Transportation","usgsCitation":"Mitchell, J.N., Wagner, D.M., and Veilleux, A.G., 2023, Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020: U.S. Geological Survey Scientific Investigations Report 2023–5014, 66 p. plus 4 appendixes, https://doi.org/10.3133/sir20235014.","productDescription":"Report: vii, ; 8 Tables; 3 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-139812","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":416577,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GGPPV5","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of flood-frequency report—Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020"},{"id":416576,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TOQANM","text":"USGS data release","description":"USGS data release","linkHelpText":"Basin characteristic rasters used in the update of Hawaiʻi StreamStats, 2022"},{"id":416575,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N61WJ7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for watershed delineation used in the update of Hawaiʻi StreamStats, 2022"},{"id":416566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5014/coverthb.jpg"},{"id":416567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014.pdf","text":"Report","size":"7.4 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States","state":"Hawaii","otherGeospatial":"Kauaʻi, Oʻahu, Molokaʻi, Maui, Hawaiʻi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.92521972722102,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 22.39025206306377\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pacific Islands Science Center
U.S. Geological Survey
1845 Wasp Blvd., B176
Honolulu, HI 96818
Geodetic velocity data provide first-order constraints on crustal surface strain rates, which in turn are linked to seismic hazard. Estimating the 2-D surface strain tensor everywhere requires knowledge of the surface velocity field everywhere, while geodetic data such as Global Navigation Satellite System (GNSS) only have spatially scattered measurements on the surface of the Earth. To use these data to estimate strain rates, some type of interpolation is required. In this study, we review methodologies for strain rate estimation and compare a suite of methods, including a new implementation based on the geostatistical method of kriging, to compare variation between methods with uncertainty based on one method. We estimate the velocity field and calculate strain rates in southern California using a GNSS velocity field and five different interpolation methods to understand the sources of variability in inferred strain rates. Uncertainty related to data noise and station spacing (aleatoric uncertainty) is minimal where station spacing is dense and maximum far from observations. Differences between methods, related to epistemic uncertainty, are usually highest in areas of high strain rate due to differences in how gradients in the velocity field are handled by different interpolation methods. Parameter choices, unsurprisingly, have a strong influence on strain rate field, and we propose the traditional L-curve approach as one method for quantifying the inherent trade-off between fit to the data and models that are reflective of tectonic strain rates. Doing so, we find total variability between five representative strain rate models to be roughly 40 per cent, a much lower value than roughly 100 per cent that was found in previous studies (Hearn et al.). Using multiple methods to tune parameters and calculate strain rates provides a better understanding of the range of acceptable models for a given velocity field. Finally, we present an open-source Python package (Materna et al.) for calculating strain rates, Strain_2D, which allows for the same data and model grid to be used in multiple strain rate methods, can be extended with other methods from the community, and provides an interface for comparing strain rate models, calculating statistics and estimating strain rate uncertainty for a given GNSS data set.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggad191","usgsCitation":"Maurer, J., and Materna, K.Z., 2023, Quantification of geodetic strain rate uncertainties and implications for seismic hazard estimates: Geophysical Journal International, v. 234, no. 3, p. 2128-2142, https://doi.org/10.1093/gji/ggad191.","productDescription":"15 p.","startPage":"2128","endPage":"2142","ipdsId":"IP-142818","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443642,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggad191","text":"Publisher Index Page"},{"id":435346,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJW0DY","text":"USGS data release","linkHelpText":"Strain_2D: a package to compute and compare strain rate maps from geodetic velocities"},{"id":417484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"234","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Maurer, Jeremy","contributorId":305786,"corporation":false,"usgs":false,"family":"Maurer","given":"Jeremy","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":873851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Materna, Kathryn Zerbe 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":261337,"corporation":false,"usgs":true,"family":"Materna","given":"Kathryn","email":"","middleInitial":"Zerbe","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873852,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243951,"text":"70243951 - 2023 - Estimated reduction of nitrogen in streams of the Chesapeake Bay in areas with agricultural conservation practices","interactions":[],"lastModifiedDate":"2023-05-26T12:02:55.072234","indexId":"70243951","displayToPublicDate":"2023-05-05T07:01:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11111,"text":"PLOS Water","active":true,"publicationSubtype":{"id":10}},"title":"Estimated reduction of nitrogen in streams of the Chesapeake Bay in areas with agricultural conservation practices","docAbstract":"Spatial data provided by the U.S. Department of Agriculture National Resource Conservation Service representing implementation at the field-level for a selection of agricultural conservation practices were incorporated within a spatially referenced regression model to estimate their effects on nitrogen loads in streams in the Chesapeake Bay watershed. Conservation practices classified as “high-impact” were estimated to be effective (p = 0.017) at reducing contemporary nitrogen loads to streams of the Chesapeake Bay watershed in areas where groundwater ages are estimated to be less than 14-years old. Watershed-wide, high-impact practices were estimated to reduce nitrogen loads to streams by 1.45%, with up to 60% reductions in areas with shorter groundwater ages and larger amounts of implementation. Effects of “other-impact” practices and practices in areas with groundwater ages of 14 years or more showed less evidence of effectiveness. That the discernable impact of high-impact practices was limited to areas with a median groundwater age of less than 14 years does not imply that conservation practices are not effective in areas with older groundwater ages. A model recalibrated using high-impact agricultural conservation practice data summarized by county suggests effects may also be detectable using implementation data available at such coarser resolution. Despite increasing investment, effects of agricultural conservation practices on regional water quality remain difficult to quantify due to factors such as groundwater travel times, varying modes-of-action, and the general lack of high-quality spatial datasets representing practice implementation.
The U.S. Geological Survey and University of Wisconsin–Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at mainstem Plum Creek and west Plum Creek in northeastern Wisconsin. These two subbasins cover 88 percent of the Plum Creek Basin (Hydrologic Unit Code 12), which is a subbasin of the lower Fox River Basin. A published total maximum daily load report for the lower Fox River Basin rated Plum Creek as one of the greatest contributors of total suspended solids (TSS) and total phosphorus (TP) draining into the lower Fox River. To reduce TSS and TP exports from Plum Creek, additional cropland conservation practices and watercourse protections were applied between 2012 and 2020. To detect water-quality trends, data were collected during 2010 to 2020 at mainstem Plum Creek and 2013 to 2020 at west Plum Creek.
The project used two methods to evaluate CMP effectiveness. The first method focused on evaluating water-quality changes between initial and post-CMP implementation periods during rain- or snowmelt-induced runoff events (hereafter referred to as “events”). In this approach random-forest models were developed to account for environmental factors which influence water quality. Model residuals from the two time periods were compared to determine the significance of water-quality changes associated with CMP implementation for mainstem and west Plum Creek Basins. The second method used a Weighted Regressions on Time, Discharge, and Season time-series approach to examine changes in water quality during the entire study period in mainstem Plum Creek. Results from both methods indicated there were minimal water-quality changes in TSS concentrations and flow-normalized delivery during runoff events during the 10-year period from 2010 to 2020; however, TP concentrations during low streamflow (less than 3 cubic feet per second [ft3/s]) may have decreased. The lack of observed improvement may be attributable to any of the following: variability in weather and hydrologic conditions, insufficient post-treatment data, additional cropland being converted to corn production, above average rainfall, streambank degradation, acute and legacy sources of phosphorus from farm fields, excessive/vulnerable manure applications and spills, and point-source discharges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235043","collaboration":"Prepared in cooperation with the University of Wisconsin-Green Bay and Outagamie County, Wisconsin","usgsCitation":"Horwatich, J.A., Fermanich, K., Pronschinske, M.A., Robertson, D.M., Kussow, S., Loken, L.C., Reneau, P.C., Freund, J., and Komiskey, M.J., 2023, Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20: U.S. Geological Survey Scientific Investigations Report 2023–5043, 31 p., https://doi.org/10.3133/sir20235043.","productDescription":"Report: ix, 31 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130579","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5043/coverthb.jpg"},{"id":416707,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500920,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114718.htm","linkFileType":{"id":5,"text":"html"}},{"id":416709,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92A0H98","text":"USGS data release","linkHelpText":"Water quality and estimated changes in the Plum Creek watershed 2010–2020 (data release and model archive)"},{"id":416708,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5043/images"},{"id":416706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5043"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Plum Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.30723441433527,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.40323167054055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
To help support remedial efforts at the former Badger Army Ammunition Plant the U.S. Geological Survey built and calibrated a transient groundwater flow model using the Newton Raphson formulation (MODFLOW–NWT) of the U.S. Geological Survey’s modular three-dimensional finite-difference code. The model simulates the groundwater flow system at the site from 1984 to 2020. The former Badger Army Ammunition Plant is a 7,275-acre site in Sauk County, Wisconsin. The plant produced smokeless gunpower and solid rocket propellent as munitions components. Peak production periods were during World War II, the Korean War, and the Vietnam War. Subsequent groundwater contamination investigations have found four plumes at the site. A health risk assessment identified at least one contaminant of concern for human health risk present in three of the plumes: the propellant burning ground plume, the deterrent burning ground plume, and the central plume. A cooperative study began between the U.S. Army Environmental Command and U.S. Geological Survey to better understand the groundwater flow system at the former Badger Army Ammunition Plant. Field data, including aquifer tests, streamflow measurements, continuous groundwater elevations, and groundwater gradients with the Wisconsin River were collected and used to inform and calibrate the groundwater flow model. The model was used to assess the variability of the groundwater system over the study period, the components of the groundwater budget, and groundwater flow directions from identified source areas towards the Wisconsin River. Model performance assessment focused on using particle tracking to compare groundwater flowpaths that originate in the contaminant source areas to the observed plume footprints. This focus on plume behavior geometry should help constrain the advective component of a future groundwater transport model of the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235040","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Haserodt, M.J., Reeves, H.W., Nielsen, M.G., Schachter, L.A., Corson-Dosch, N.T., and Feinstein, D.T., 2023, Simulation of groundwater flow at the former Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5040, 140 p., https://doi.org/10.3133/sir20235040.","productDescription":"Report: viii, 140 p.; 3 Data Releases; Dataset","numberOfPages":"152","onlineOnly":"Y","ipdsId":"IP-135445","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416676,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2IDV0","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model archive used to simulate potential annual recharge for the former Badger Army Ammunition Plant study area, Prairie du Sac, Wisconsin, 1980 to 2020"},{"id":416672,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":416671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.pdf","text":"Report","size":"106 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5040"},{"id":416670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5040/coverthb.jpg"},{"id":500916,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114707.htm","linkFileType":{"id":5,"text":"html"}},{"id":416678,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":416675,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TSI73","text":"USGS data release","linkHelpText":"Slug test analysis results from unconsolidated and bedrock aquifers at Badger Army Ammunition Plant, Sauk County, Wisconsin, 2020"},{"id":416674,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5040/images"},{"id":416677,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LNRILT","text":"USGS data release","linkHelpText":"Groundwater model archive for the former Badger Army Ammunition Plant, Wisconsin"},{"id":416712,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235040/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"former Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.76922975135027,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.38657213852542\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed field mapping difficult. While recent advances in remote sensing have facilitated mapping of surficial materials and landforms, manual map creation has remained a time-intensive task.
The development of convolutional neural networks (CNNs) for image classification has provided a new opportunity for rapid characterization of digital elevation models, thus enabling efficient mapping of surficial materials and landforms. We have developed a methodology that leverages existing geologic maps and high-resolution (1–3 m) lidar data to train a U-Net CNN to classify alluvium and exposed bedrock in previously glaciated regions. Coupled with U.S. Geological Survey-developed geomorphometry tools capable of approximating stream incision depths, these classifications can be used to estimate the minimum thicknesses of stream-proximal hillslope sediments in areas where streams have undergone minimal incision into bedrock.
We validate this approach in the context of the Neversink River watershed, a subbasin of the Delaware River Basin and significant water source for New York City. Evaluation of deep learning model performance demonstrates substantial agreement with manually drawn maps of alluvium and exposed bedrock. Validation of the minimum sediment thickness map using borehole data and passive seismic measurements shows the greatest performance for shallow materials and decreased performance in deep sediments, as well as in areas where bedrock exposures were too small to be resolved by lidar. To resolve these issues and create more accurate surficial maps, we are training new CNNs with additional geologic data and exploring advanced approaches for estimating depths of stream incision.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2023.100116","usgsCitation":"Odom, W.E., and Doctor, D.H., 2023, Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learning-derived surficial material maps: Applied Computing and Geosciences, v. 18, 100116, 11 p., https://doi.org/10.1016/j.acags.2023.100116.","productDescription":"100116, 11 p.","ipdsId":"IP-146769","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443651,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2023.100116","text":"Publisher Index Page"},{"id":417128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.31774531759645,\n 38.65648371068133\n ],\n [\n -74.83711116001508,\n 39.00784172038104\n ],\n [\n -74.58034795534637,\n 39.29641683375996\n ],\n [\n -74.84560023131485,\n 39.59878756424641\n ],\n [\n -74.57402157309659,\n 39.81435898583538\n ],\n [\n -74.36164212699806,\n 40.42339954973025\n ],\n [\n -74.7398677925508,\n 40.67483290305228\n ],\n [\n -74.20311730788073,\n 41.49081439956416\n ],\n [\n -73.87900349307475,\n 42.134764710063195\n ],\n [\n -74.40404082594178,\n 42.53787114018144\n ],\n [\n -75.19513563542162,\n 42.478156559974536\n ],\n [\n -75.74165548596034,\n 41.860885020202005\n ],\n [\n -76.19243039302005,\n 41.151909429148475\n ],\n [\n -76.53910969789781,\n 40.49637767696336\n ],\n [\n -76.22016361968275,\n 40.00185213900514\n ],\n [\n -75.6922644432239,\n 39.71427259376151\n ],\n [\n -75.61094038798961,\n 39.383028287456796\n ],\n [\n -75.31774531759645,\n 38.65648371068133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, William Elijah 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","email":"","middleInitial":"Elijah","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872923,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244178,"text":"70244178 - 2023 - Bringing the Nature Futures Framework to life: Creating a set of illustrative narratives of nature futures","interactions":[],"lastModifiedDate":"2023-06-06T11:51:36.740586","indexId":"70244178","displayToPublicDate":"2023-05-04T06:49:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5318,"text":"Sustainability Science","active":true,"publicationSubtype":{"id":10}},"title":"Bringing the Nature Futures Framework to life: Creating a set of illustrative narratives of nature futures","docAbstract":"To halt further destruction of the biosphere, most people and societies around the globe need to transform their relationships with nature. The internationally agreed vision under the Convention of Biological Diversity—Living in harmony with nature—is that “By 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people”. In this context, there are a variety of debates between alternative perspectives on how to achieve this vision. Yet, scenarios and models that are able to explore these debates in the context of “living in harmony with nature” have not been widely developed. To address this gap, the Nature Futures Framework has been developed to catalyse the development of new scenarios and models that embrace a plurality of perspectives on desirable futures for nature and people. In this paper, members of the IPBES task force on scenarios and models provide an example of how the Nature Futures Framework can be implemented for the development of illustrative narratives representing a diversity of desirable nature futures: information that can be used to assess and develop scenarios and models whilst acknowledging the underpinning value perspectives on nature. Here, the term illustrative reflects the multiple ways in which desired nature futures can be captured by these narratives. In addition, to explore the interdependence between narratives, and therefore their potential to be translated into scenarios and models, the six narratives developed here were assessed around three areas of the transformative change debate, specifically, (1) land sparing vs. land sharing, (2) Half Earth vs. Whole Earth conservation, and (3) green growth vs. post-growth economic development. The paper concludes with an assessment of how the Nature Futures Framework could be used to assist in developing and articulating transformative pathways towards desirable nature futures.
The National Park Service (NPS), in collaboration with the U.S. Geological Survey (USGS), recognizes the need to quantify the sediment budget of the barrier islands within the Gulf Islands National Seashore (GINS) to understand the coastal processes affecting island resiliency. To achieve this goal, identifying and quantifying the physical parameters that drive long-term change is necessary to model the processes that are both generative and terminal in island evolution and capture island response to long-term human alteration and climatic patterns. For example, measuring change across periods of storminess is more effective at assessing island resiliency than measuring change resulting from a single storm impact. Understanding changes to the physical environment over time is key to successfully predicting island responses to future storm impacts, human alteration, and sea-level rise and is necessary for effective decision making and management response. Yet, the diversity of factors affecting natural and cultural resources necessitates a strategic approach to data collection priorities that can inform sediment budget quantification and integrated resource management.
This study sought to advance sediment budget modeling efforts by conducting a “Needs Assessment Workshop” at the GINS. The purpose of the workshop was to identify and prioritize the specific research and data needs regarding the sediment budget at the GINS that can enhance the NPS efforts to conserve the islands’ natural resources, cultural resources, and the facilities and infrastructure that support both conservation and visitor use of those resources. This effort explored two research questions: (1) “what research and data needs exist for the sediment budget at Gulf Islands National Seashore” (research question 1) and (2) “how can research to address these needs capitalize on regional partnerships to advance natural and cultural resource conservation at Gulf Islands National Seashore” (research question 2)? The workshop was conducted virtually in a two-part, two-day series.
The workshop series was organized by researchers from North Carolina State University in collaboration with NPS and USGS staff and was facilitated by National Oceanographic and Atmospheric Administration staff. The workshop series (two paired, sequential, partial-day workshops) addressed two target audiences: (1) NPS and USGS staff (April workshop) and (2) regional Federal, State, county, and nongovernmental organization staff, including NPS and USGS staff (May workshop). A total of four workshop sessions were held, comprising two sessions with each target audience.
The workshop series intended to identify sediment management research and data needs that could enhance natural and cultural resource stewardship at the GINS. One objective was to share information about regional sediment transport and management, available sediment management plans, and predictive modeling capabilities, including geomorphologic and hydrodynamic predictive models. This information was shared through a series of presentations by park managers and NPS and USGS researchers that identified park issues and available capabilities and data. The second objective was to elicit research and data needs, with a primary goal of assessing the importance and urgency of the identified needs. This assessment was partly determined by requesting that the workshop participants identify and prioritize research themes through polls, comments, and discussion.
The polls explicitly asked participants to qualitatively evaluate the importance (not at all, slightly, somewhat, very, or extremely) and urgency (not at all, slightly, somewhat, very, or extremely) of the thematically grouped research and data needs. These evaluations were plotted and shared during the workshop to visualize how the relative importance (x-axis) and relative urgency (y-axis) of each “need,” relative to other needs, to identify the most necessary (importance) and time-sensitive (urgent) items, thereby allowing an enhanced, holistic understanding of the sediment budget at GINS. Results of the poll are published as a USGS data release.
The assessment results revealed that the most important and urgent research and data needs included mapping (for example, elevation, habitat, and cultural resources), a regional sediment budget and management plan, and the dynamic modeling of sediment processes. During the workshop, these issues were visualized using scatter plots to demonstrate the relative importance and urgency of each theme, provide descriptive statistics, and elicit discussion. This format of iterative presentation, discussion, and prioritization allowed the project team to effectively accomplish their objective of identifying important and urgent research needs for natural and cultural resource stewardship at the GINS. Through the workshop, it was determined that expanded communication with the broader research community was needed to coordinate research activities and streamline potential funding opportunities and that research and policy should be integrated through a structured decision-making process.
At the conclusion of the workshop, an administered poll showed that the presentations effectively identified data and research needs and that the goals of the workshop were achieved. The results suggest that this type of needs-assessment workshop can effectively identify existing research capabilities and data, determine and prioritize research and data needs, and address how these efforts can use regional partnerships to aid natural and cultural resource conservation and management at National Parks.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221087","programNote":"Coastal/Marine Hazards and Resources Program","usgsCitation":"Seekamp, E., Flocks, J., Hotchkiss, C., York, L., and Irick, K., 2023, Gulf Islands National Seashore regional sediment budget research and data needs—Workshop series summary: U.S. Geological Survey Open-File Report 2022–1087, 46 p., https://doi.org/10.3133/ofr20221087.","productDescription":"Report: vii, 46 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127837","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416372,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1087/coverthb.jpg"},{"id":416373,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1087/ofr20221087.pdf","text":"Report","size":"2.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1087"},{"id":416375,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1087/ofr20221087.XML"},{"id":416376,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1087/images/"},{"id":416377,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JG3J7B","text":"USGS data release","linkHelpText":"Gulf Islands National Seashore 2020 workshop-attendee survey results"},{"id":499720,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114708.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida, Mississippi","otherGeospatial":"Gulf Islands National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.47993451563534,\n 30.4372165405142\n ],\n [\n -89.47993451563534,\n 30.14326231135827\n ],\n [\n -86.39410371358161,\n 30.14326231135827\n ],\n [\n -86.39410371358161,\n 30.4372165405142\n ],\n [\n -89.47993451563534,\n 30.4372165405142\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South St.
Petersburg, FL 33701
Although freshwater mussels are imperiled and identified as key conservation priorities, limited bioaccumulation information is available on these organisms for contaminants of emerging concern. In the present study we investigated the bioaccumulation of per- and polyfluoroalkyl substances (PFAS) in the model freshwater pond mussel Sagittunio subrostratus because mussels provide important ecosystem services and are important components of aquatic systems where PFAS occur. In the present study we selected four representative perfluorinated carboxylic acids and sulfonic acids, then determined the bioaccumulation kinetics of freshwater mussels in a controlled laboratory study. Because uptake (ku) and elimination (ke) rate constants and time to steady state are important parameters for food web bioaccumulation models, we derived bioaccumulation kinetic parameters following exposure to perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), and perfluorodecanoic acid (PFDA) at 10 µg/L and perfluoroundecanoic acid (PFUnDA) at 1 µg/L during a 14-day uptake period followed by a 7-day elimination period. Kinetic and ratio-based bioaccumulation factors (BAFs) were subsequently calculated, for example ratio-based BAFs for mussel at day 7 were determined for PFHxS (0.24 ± 0.08 L/kg), PFOS (7.73 ± 1.23 L/kg), PFDA (4.80 ± 1.21 L/kg), and PFUnDA (84.0 ± 14.4 L/kg). We generally observed that, for these four model PFAS, freshwater mussels have relatively low BAF values compared with other aquatic invertebrates and fish.
Groundwater in the karst groundwater system at Area B of Fort Detrick in Frederick County, Maryland, is contaminated with chlorinated solvents from the past disposal of laboratory wastes. In cooperation with U.S. Army Environmental Command and U.S. Army Garrison Fort Detrick, the U.S. Geological Survey performed a 3-year study to refine the conceptual model of groundwater flow in and around Area B of Fort Detrick at the site- to regional-scale. The investigation was designed to review the geologic setting, assess the temporal variability of the hydrologic system, evaluate the potential for interbasin groundwater flow, determine the degree of vertical connectivity of the aquifer, characterize the sources and timing of groundwater recharge, and identify if dyes from previous tracer tests continue to drain from the aquifer. This study established a continuous hydrologic monitoring network of 12 water level gages, 2 streamgages, a precipitation gage, and in situ fluorometric monitoring. A water budget analysis was performed using hydrologic monitoring data and a soil-water balance model constructed for the study. In this study each individual water budget term is calculated using available data or through modeling, and a water budget residual term is calculated. If the water budget residual term is small relative to the uncertainty of the underlying data, then an additional import or export of water (in other words, interbasin transfer) is not needed to fully describe the hydrologic system. Groundwater and spring samples from 20 locations were collected in a 2019 synoptic geochemical sampling event and analyzed for a suite of analytes that included groundwater age tracer constituents.
The karst groundwater system was found to be highly responsive to hydrologic events, with strong water level and stream base flow responses to individual storm events and a historic wet period in 2017 and 2018. The water budget analysis included historic flooding in May 2018, though more typical hydrologic patterns were observed in 2019 and 2020. During most evaluated intervals, the water budget residual was less than the estimated uncertainty on the residual for the two Carroll Creek watersheds, which suggested no substantial net interbasin flow occurs from these watersheds. The watershed difference area, a region that includes Area B, had a significant negative water budget residual, which may be the result of a net interbasin import of groundwater or the result of focused groundwater recharge not simulated by the soil-water balance model. Geochemical analysis and groundwater age dating reveals shallow groundwater (approximately less than [<] 150 feet deep) appears to be relatively young (approximately <30 years) and to be recharged in the vicinity of Area B. In the deep groundwater sampled in this study (approximately greater than [>] 150 feet deep), older groundwater from a differing recharge source, based on stable isotopes and noble gas analyses, is observed and interpreted to represent less direct connectivity to the surface and increased proportions of water recharged to the north and (or) west of Area B. A clustering analysis to reveal groupings within the suite of geochemical data was used to define seven groups. The groupings generally show that wells in similar depths and lateral aquifer positions generally cluster together, with some exceptions. Although limited by suspended sediments, the in situ fluorometric monitoring at springs did not detect any dye leaving the system above the limit of detection for the method. Dye was only detected above the limit of detection in one well, which was used as an injection well during a previous dye tracer test.
The results of this study support and refine the conceptual site model of groundwater hydrology at Area B. The geologic and geophysical log review in this study agrees with prior assessments of physical controls on groundwater flow. A literature review of mid-Atlantic karst studies identified similar controls reported in these environments. The additional characterization of hydrologic responsiveness in this study suggests that hydrologic conditions and events are important considerations when interpreting potentiometric surfaces and contaminant trends over time and highlights the importance of continuous hydrologic monitoring. There is evidence to suggest that either intense focused groundwater recharge occurs in the vicinity of Area B or net along-valley groundwater interbasin flow from the upper study watershed enters the lower watershed and discharges to Carroll Creek. Geochemical analyses also suggest that water recharged from Catoctin Mountain and the elevated areas to the north and (or) west of the site may be present in the older and deeper Area B groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225054","collaboration":"Prepared in cooperation with U.S. Army Environmental Command and U.S. Army Garrison, Fort Detrick","usgsCitation":"Goodling, P.J., Fleming, B.J., Solder, J., Soroka, A., and Raffensperger, J., 2023, Hydrogeologic characterization of Area B, Fort Detrick, Maryland: U.S. Geological Survey Scientific Investigations Report 2022–5054, 128 p., https://doi.org/10.3133/sir20225054.","productDescription":"Report: xiv, 128 p.; 2 Data Releases","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124092","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":435349,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DUFZY7","text":"USGS data release","linkHelpText":"Supporting Datasets for Hydrogeological Characterization of Ft. Detrick Area B, Maryland"},{"id":500936,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114709.htm","linkFileType":{"id":5,"text":"html"}},{"id":416517,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GTTX8Q","text":"USGS data release","linkHelpText":"Soil water balance model developed for Maryland and Pennsylvania"},{"id":416516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AYWBXU","text":"USGS data release","linkHelpText":"Supporting datasets for hydrogeological characterization of Area B, Fort Detrick, Maryland"},{"id":416515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.pdf","text":"Report","size":"51.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5054"},{"id":416514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5054/coverthb.jpg"},{"id":416562,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5054"},{"id":416564,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5054/images/"},{"id":416563,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.XML"}],"country":"United States","state":"Maryland","otherGeospatial":"Fort Detrick","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.4693386578843,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.458154924593\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
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In recent years, coral populations in the western Atlantic have undergone widespread declines from climate change, anthropogenic stressors, and infectious disease outbreaks. The pillar coral, Dendrogyra cylindrus, has been one of the most affected species, prompting its listing as threatened under the United States Endangered Species Act in 2014 and critically endangered under the IUCN Red List in 2022. However, due to its natural rarity, it is particularly difficult to study using conventional long-term monitoring studies or less common paleontological investigations. Here, we document for the first time, the multi-century persistence of D. cylindrus on high-latitude nearshore reefs off southeast Florida during the late Holocene. Using high-precision uranium–thorium (U-Th) dating, we constrain the ages of well-preserved subfossil D. cylindrus colonies recovered from newly described coral death assemblages. We also describe specific morphological characteristics and taphonomic indicators reflecting their unique depositional environment. Our findings demonstrate long-term persistence of D. cylindrus in southeast Florida, despite geographical isolation and historical rarity in the region.
Conservation and management of biological systems involves decision-making over time, with a generic goal of sustaining systems and their capacity to function in the future. We address four persistent and difficult conservation challenges: (1) prediction of future consequences of management, (2) uncertainty about the system's structure, (3) inability to observe ecological systems fully, and (4) nonstationary system dynamics. We describe these challenges in terms of dynamic systems subject to different sources of uncertainty, and we present a basic Markovian framework that can encompass approaches to all four challenges. Finding optimal conservation strategies for each challenge requires issue-specific structural features, including adaptations of state transition models, uncertainty metrics, valuation of accumulated returns, and solution methods. Strategy valuation exhibits not only some remarkable similarities among approaches but also some important operational differences. Technical linkages among the models highlight synergies in solution approaches, as well as possibilities for combining them in particular conservation problems. As methodology and computing software advance, such an integrated conservation framework offers the potential to improve conservation outcomes with strategies to allocate management resources efficiently and avoid negative consequences.
Many microorganisms secrete secondary metabolites with antibiotic properties; however, there is debate whether the secretions evolved as a means to gain a competitive edge or as a chemical signal to coordinate community growth. The objective of this research was to investigate if select antibiotics acted as a weapon or as a chemical signal by exposing communities of aquatic cave bacteria to increasing concentrations of antibiotics. Water samples were collected from six cave locations where actinobacterial mats appeared to be plentiful. Bacterial growth was measured using colony counts on 10 % tryptic soy agar augmented with increasing concentrations of erythromycin, tetracycline, kanamycin, gentamicin, or quaternary ammonia compounds (QAC). Colony counts generally decreased as the gentamicin, kanamycin and QAC dose increased. In contrast, the colony numbers increased on agar plates supplemented with 0.01 mg L−1, 0.10 mg L−1 and 1.00 mg L−1 erythromycin or tetracycline. A 10.00 mg L−1 dose of each antibiotic treatment reduced bacteria colonies by 98 % or more. Community-level physiological capabilities were evaluated using Ecolog plates inoculated with cave water dosed with either 0.00 mg L−1 or 0.10 mg L−1 of erythromycin. Incubation with the antibiotic almost doubled the number of food substrates used in the first 24 hours. There was a significant increase in the use of acetyl glucosamine, arginine, and putrescine when bacteria were exposed to 0.10 mg L−1 erythromycin triggered by the antibiotic acting as a chemical messenger. Principal component analysis confirmed a shift in substrate preferences when erythromycin was added. A conceptual ecological model is proposed based on the response of aquatic cave bacteria to sublethal antibiotics.
","language":"English","publisher":"National Speleological Society","doi":"10.4311/2022MB0106","usgsCitation":"Byl, T.D., Byl, P.K., Byl, J.P., and Toomey, R., 2023, Stimulation of aquatic bacteria from Mammoth Cave, Kentucky, by sublethal concentrations of antibiotics: Journal of Cave and Karst Studies, v. 85, no. 1, p. 16-27, https://doi.org/10.4311/2022MB0106.","productDescription":"12 p.","startPage":"16","endPage":"27","ipdsId":"IP-065135","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":443673,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.4311/2022mb0106","text":"Publisher Index Page"},{"id":435353,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J1018X","text":"USGS data release","linkHelpText":"Average well color development data for water samples from six locations within the historic section of Mammoth Cave National Park, Kentucky"},{"id":418004,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Mammoth Cave","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.2828185616811,\n 37.28844220519747\n ],\n [\n -86.2828185616811,\n 37.09470580139313\n ],\n [\n -85.96018319973803,\n 37.09470580139313\n ],\n [\n -85.96018319973803,\n 37.28844220519747\n ],\n [\n -86.2828185616811,\n 37.28844220519747\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"85","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byl, Thomas D. 0000-0001-6907-9149 tdbyl@usgs.gov","orcid":"https://orcid.org/0000-0001-6907-9149","contributorId":583,"corporation":false,"usgs":true,"family":"Byl","given":"Thomas","email":"tdbyl@usgs.gov","middleInitial":"D.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byl, Petra Kim 0000-0002-9168-2603","orcid":"https://orcid.org/0000-0002-9168-2603","contributorId":304716,"corporation":false,"usgs":false,"family":"Byl","given":"Petra","email":"","middleInitial":"Kim","affiliations":[{"id":66150,"text":"Biological Oceanography University of Hawaii at Mānoa School of Ocean and Earth Science and Technology Department of Oceanography","active":true,"usgs":false}],"preferred":false,"id":875145,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byl, Jacob P. 0000-0001-7998-9795","orcid":"https://orcid.org/0000-0001-7998-9795","contributorId":304724,"corporation":false,"usgs":false,"family":"Byl","given":"Jacob","email":"","middleInitial":"P.","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":875146,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toomey, Rickard III","contributorId":306230,"corporation":false,"usgs":false,"family":"Toomey","given":"Rickard","suffix":"III","email":"","affiliations":[],"preferred":false,"id":875147,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249412,"text":"70249412 - 2023 - Forecasting sea level rise-driven inundation in diked and tidally restricted coastal lowlands","interactions":[],"lastModifiedDate":"2023-10-06T15:44:12.015642","indexId":"70249412","displayToPublicDate":"2023-05-01T10:38:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting sea level rise-driven inundation in diked and tidally restricted coastal lowlands","docAbstract":"Diked and drained coastal lowlands rely on hydraulic and protective infrastructure that may not function as designed in areas with relative sea-level rise. The slow and incremental loss of the hydraulic conditions required for a well-drained system make it difficult to identify if and when the flow structures no longer discharge enough water, especially in tidal settings where two-way flows occur through the dike. We developed and applied a hydraulic mass-balance model to quantify how water levels in the diked and tidally restricted coastal wetlands and water bodies dynamically respond to sea-level rise, specifically applied to the Herring River Estuary in MA, USA, from 2020 to 2100. Sensitivity testing of the model parameters indicated that primary outcomes were not sensitive to many of the chosen input values, though the terrestrial water input rate to the estuary and the flow coefficient for the hydraulic infrastructure were important. The relative importance of parameters, however, is expected to be site specific. We introduced a drainability metric that quantifies the net water volume drained over every tidal cycle to monitor and forecast how rising water levels on either side of the dike affected the net draining or impounding conditions of the system. Ensembles of model results across parameter and sea-level scenario uncertainties indicated that substantial impoundment of the Herring River Estuary was expected within ~ 20 years with the existing flow structures, a sluice and two flap gates. Simulations with up to three additional gates did not dampen this trend toward impoundment, suggesting that rising impounded water levels are likely even with major construction upgrades. Increasingly impounded diked coastal waterbodies present a hydrologic challenge with socioecological implications due to projected flooding and ecosystem impacts. Solutions to this challenge may be to allow coastal wetland restoration pathways or require substantial and recurring infrastructure improvement projects.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-023-01174-1","usgsCitation":"Befus, K.A., Kurnizki, A., Kroeger, K.D., Eagle, M.J., and Smith, T.P., 2023, Forecasting sea level rise-driven inundation in diked and tidally restricted coastal lowlands: Estuaries and Coasts, v. 46, no. 6, p. 1157-1169, https://doi.org/10.1007/s12237-023-01174-1.","productDescription":"13 p.","startPage":"1157","endPage":"1169","ipdsId":"IP-145736","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1007/s12237-023-01174-1","text":"Publisher Index Page"},{"id":421746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Herring River Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.06356706053543,\n 41.92990690084844\n ],\n [\n -70.0599719567331,\n 41.93094108028947\n ],\n [\n -70.05877358879943,\n 41.93322334800044\n ],\n [\n -70.05954054427677,\n 41.93450708770192\n ],\n [\n -70.05915706653809,\n 41.9355411925597\n ],\n [\n -70.05752728614821,\n 41.93611172599333\n ],\n [\n -70.057239677844,\n 41.93678922781737\n ],\n [\n -70.05786282916928,\n 41.937965924404836\n ],\n [\n -70.05656859180117,\n 41.937751981185585\n ],\n [\n -70.0528776185643,\n 41.939463506843225\n ],\n [\n -70.05426772536764,\n 41.94014097305953\n ],\n [\n -70.057239677844,\n 41.93889300339495\n ],\n [\n -70.05862978464737,\n 41.93903562973523\n ],\n [\n -70.05934880540781,\n 41.93871472002016\n ],\n [\n -70.05915706653809,\n 41.93760935197429\n ],\n [\n -70.06025956503724,\n 41.93746672244353\n ],\n [\n -70.06131412881953,\n 41.935826459914495\n ],\n [\n -70.06323151751364,\n 41.93464972385124\n ],\n [\n -70.06275217033973,\n 41.93340164672611\n ],\n [\n -70.06529271035981,\n 41.93233184689518\n ],\n [\n -70.06356706053543,\n 41.92990690084844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"46","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Befus, Kevin A.","contributorId":299488,"corporation":false,"usgs":false,"family":"Befus","given":"Kevin","email":"","middleInitial":"A.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":885528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurnizki, A","contributorId":330654,"corporation":false,"usgs":false,"family":"Kurnizki","given":"A","email":"","affiliations":[{"id":78949,"text":"epartment of Civil and Architectural Engineering, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":885529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":885530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":885531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Timothy P.","contributorId":220144,"corporation":false,"usgs":false,"family":"Smith","given":"Timothy","email":"","middleInitial":"P.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":885532,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243216,"text":"70243216 - 2023 - Incorporating uncertainty in susceptibility criteria into probabilistic liquefaction hazard analysis","interactions":[],"lastModifiedDate":"2023-05-09T15:31:01.80947","indexId":"70243216","displayToPublicDate":"2023-05-01T10:29:02","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Incorporating uncertainty in susceptibility criteria into probabilistic liquefaction hazard analysis","docAbstract":"Most conventional approaches for assessing liquefaction triggering hazards generally rely on simplified procedures that involve identifying liquefaction susceptible layers and calculating a factor of safety against liquefaction (FSL) in each layer. Such procedures utilize deterministic semi-empirical models for standard penetration test (SPT), cone penetrometer test (CPT), or shear wave velocity (Vs)-based subsurface data. This general approach largely neglects considerable uncertainties in ground shaking, as well as aleatory variabilities and epistemic uncertainties inherent to liquefaction susceptibility and triggering prediction. A more robust methodology introduced by Kramer and Mayfield (2007) is known as probabilistic liquefaction hazard analysis (PLHA), which integrates the full ground motion hazard space with probabilistic forms of liquefaction triggering models (e.g., Boulanger and Idriss 2014), resulting in the computation of FSL profiles with consistent return periods. Multiple PLHA computational platforms have been developed over the years, with the computational framework from Makdisi (2021) serving as the basis for a new Liquefaction Hazard Tool under development at the U.S. Geologic Survey (USGS).\nDespite significant improvements in recent years to the availability of seismic hazard data and probabilistic triggering and effects models, the issue of incorporating uncertainty in characterizing liquefaction susceptibility remains a challenge. Most compositional susceptibility criteria (i.e., whether or not the soil exhibits sand-like behavior) currently in use are presented as deterministic bounds based on in-situ or laboratory test data; similarly, determination of soil saturation is often based on a single groundwater level from in-situ testing. As a result, the same types of binary decisions must be made in PLHA as in more conventional methods. With the expansion and availability of field and laboratory data pertaining to liquefaction through resources such as the Next Generation Liquefaction (NGL) project, there exists the potential for an improved set of susceptibility models for CPT, SPT, and Vs-based applications. Presented here is a brief discussion on how probabilistic susceptibility modeling can be accommodated in PLHA calculations, as well as how the use of multiple models can be leveraged within a logic tree to improve the representation of epistemic uncertainty in liquefaction hazard analysis.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"PEER workshop on liquefaction susceptibility, PEER report 2023-02","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Pacific Earthquake Engineering Research Center","doi":"10.55461/BPSK6314","usgsCitation":"Makdisi, A.J., 2023, Incorporating uncertainty in susceptibility criteria into probabilistic liquefaction hazard analysis, in PEER workshop on liquefaction susceptibility, PEER report 2023-02, p. 147-149, https://doi.org/10.55461/BPSK6314.","productDescription":"3 p.","startPage":"147","endPage":"149","ipdsId":"IP-143255","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":443677,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.55461/bpsk6314","text":"Publisher Index Page"},{"id":416863,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2023-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":871493,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70245189,"text":"70245189 - 2023 - Exploring the geology of the Midcontinent Rift under western Lake Superior using a preliminary velocity model of seismic line GLIMPCE C","interactions":[],"lastModifiedDate":"2023-09-28T14:31:13.326929","indexId":"70245189","displayToPublicDate":"2023-05-01T09:24:24","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Exploring the geology of the Midcontinent Rift under western Lake Superior using a preliminary velocity model of seismic line GLIMPCE C","docAbstract":"Seismic-reflection data were collected in the 1980s as part of the Great Lakes International Multidisciplinary Program on Crustal Evolution (GLIMPCE) to investigate the 1.1 Ga Midcontinent Rift System (MRS). GLIMPCE Line C crosses western Lake Superior from north to south shores (Fig. 1 inset). Many previous workers have interpreted the MRS in Line C as an asymmetric central graben filled with 10–20 km of subaerial basalt flows, overlain by 7-10 km of sedimentary section, and underlain by magmatic underplating. The central graben was interpreted to have formed from extensional normal faults, later reactivated as high-angle reverse faults. The northern part of Line C crosses over a prominent gravity low called the Grand Marais Ridge (GMR; Fig. 1 inset), previously interpreted as an Archean granitic basement high.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 69th ILSG annual meeting","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Institute on Lake Superior Geology 69th Annual Meeting","conferenceDate":"April 23-24, 2023","conferenceLocation":"Eau Claire, WI","language":"English","publisher":"Institute on Lake Superior Geology","usgsCitation":"Grauch, V.J., Heller, S.J., Stewart, E.K., and Woodruff, L.G., 2023, Exploring the geology of the Midcontinent Rift under western Lake Superior using a preliminary velocity model of seismic line GLIMPCE C, in Proceedings of the 69th ILSG annual meeting, Eau Claire, WI, April 23-24, 2023, p. 37-38.","productDescription":"2 p.","startPage":"37","endPage":"38","ipdsId":"IP-151610","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":421345,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418275,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.lakesuperiorgeology.org/"}],"country":"United States","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.46510586137026,\n 47.59708757842765\n ],\n [\n -90.52262719746251,\n 47.033420504728184\n ],\n [\n -89.41926338696646,\n 47.03699042088601\n ],\n [\n -89.52384763440693,\n 47.67106063862627\n ],\n [\n -90.46510586137026,\n 47.59708757842765\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grauch, V. J. S. 0000-0002-0761-3489 tien@usgs.gov","orcid":"https://orcid.org/0000-0002-0761-3489","contributorId":886,"corporation":false,"usgs":true,"family":"Grauch","given":"V.","email":"tien@usgs.gov","middleInitial":"J. S.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":875800,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heller, Samuel J. 0000-0002-6579-5620 sheller@usgs.gov","orcid":"https://orcid.org/0000-0002-6579-5620","contributorId":201350,"corporation":false,"usgs":true,"family":"Heller","given":"Samuel","email":"sheller@usgs.gov","middleInitial":"J.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":875801,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Esther K.","contributorId":247878,"corporation":false,"usgs":false,"family":"Stewart","given":"Esther","email":"","middleInitial":"K.","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":875802,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Woodruff, Laurel G. 0000-0002-2514-9923 woodruff@usgs.gov","orcid":"https://orcid.org/0000-0002-2514-9923","contributorId":2224,"corporation":false,"usgs":true,"family":"Woodruff","given":"Laurel","email":"woodruff@usgs.gov","middleInitial":"G.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":875803,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}