Developing conservation strategies for highly migratory marine species relies on understanding their spatial distributions. Nesting populations of female loggerhead (Caretta caretta) turtles typically travel from widely dispersed foraging areas and make use of common internesting areas between nesting events. Protection of these areas is essential to the conservation of this species. In this study, we used satellite tracking and behavioral switching state-space movement modeling to examine the internesting use-areas, migration patterns, and foraging area distribution of a previously uninvestigated nesting loggerhead population in southeast Florida. While these turtles spent much of their internesting period close to their nesting site, only 17.4% of the identified internesting area is within the boundaries currently designated under the US Endangered Species Act as critical loggerhead ‘nearshore reproductive habitat’. Additionally, 72% of turtles in this study (17 of 21) that were tracked to foraging grounds have foraging home ranges outside of the USA, with 62% of turtles (n = 13) in The Bahamas. Considering the proximity of their internesting areas to a large human population center and their largely international foraging distribution, this population could benefit from expanding federally designated critical habitat, along with developing collaborative conservation strategies between the USA and The Bahamas.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01339","usgsCitation":"Goodwin, G.D., Hart, K., Evans, A.C., and Burkholder, D.A., 2024, Satellite telemetry reveals high-use internesting areas and international foraging extent for loggerhead turtles tagged in southeast Florida, USA: Endangered Species Research, v. 54, p. 245-259, https://doi.org/10.3354/esr01339.","productDescription":"15 p.","startPage":"245","endPage":"259","ipdsId":"IP-160712","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01339","text":"Publisher Index Page"},{"id":431234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.97747397991283,\n 26.62851048726462\n ],\n [\n -81.97747397991283,\n 24.887138979175802\n ],\n [\n -79.64837241741277,\n 24.887138979175802\n ],\n [\n -79.64837241741277,\n 26.62851048726462\n ],\n [\n -81.97747397991283,\n 26.62851048726462\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"54","noUsgsAuthors":false,"publicationDate":"2024-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Goodwin, Glenn D. 0000-0001-6802-9924","orcid":"https://orcid.org/0000-0001-6802-9924","contributorId":316773,"corporation":false,"usgs":false,"family":"Goodwin","given":"Glenn","email":"","middleInitial":"D.","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":906650,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":906651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Abby C. 0009-0001-6097-6078","orcid":"https://orcid.org/0009-0001-6097-6078","contributorId":340233,"corporation":false,"usgs":false,"family":"Evans","given":"Abby","email":"","middleInitial":"C.","affiliations":[{"id":81512,"text":"Halmos College of Arts and Sciences, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":906652,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burkholder, Derek A. 0000-0001-6315-6932","orcid":"https://orcid.org/0000-0001-6315-6932","contributorId":289783,"corporation":false,"usgs":false,"family":"Burkholder","given":"Derek","email":"","middleInitial":"A.","affiliations":[{"id":62249,"text":"Halmos College of Natural Sciences and Oceanography, Department of Marine and Environmental Science, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":906653,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256562,"text":"70256562 - 2024 - Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2024-08-22T16:05:19.777014","indexId":"70256562","displayToPublicDate":"2024-06-26T10:59:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3784,"text":"Wilson Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico","title":"Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico","docAbstract":"North American Black Terns (Chlidonias niger) breed primarily in the Prairie Pothole region of southern Canada and the northern United States, winter in Central and South American waters, and often migrate through the northern Gulf of Mexico (nGoM). This species has exhibited long-term population declines and is exposed to a myriad of anthropogenic threats in the nGoM, including oil spills, with an estimated 800–1,000 injured during the Deepwater Horizon oil spill, yet historical studies of Black Terns' use of the nGoM are sparse, with inconsistent spatial and temporal coverage. Using vessel-based observations collected from 2017 to 2019, we characterize Black Tern spatial and temporal occurrence in marine waters of the nGoM. We develop 2 separate habitat models: one describing spatial and temporal aspects of Black Terns occurrence and the other describing the relative density when present. In 10 months of survey effort, January–October, we observed Black Terns in 7 (Mar–May and Jul–Oct), predominantly on the continental shelf at <200 m depth. Relative densities were greatest in the fall, coinciding with Black Terns' southward migration. Spatial distribution and habitat models suggest an association with river mouths or ports, as well as cool, productive waters, frequently associated near the outflow of the Mississippi River and just off the coast from Corpus Christi, Texas. The enhanced understanding of Black Terns in the nGoM could inform the preparation for, and response to, future oiling events or provide insight into potential interactions with the installation of offshore wind farms and aquaculture.
","language":"English","publisher":"Wilson Ornithological Society","doi":"10.1676/23-00069","usgsCitation":"Michael, P.E., Gleason, J., Haney, J., Hixson, K.M., Satgé, Y., and Jodice, P.G., 2024, Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico: Wilson Journal of Ornithology, v. 136, no. 2, p. 220-236, https://doi.org/10.1676/23-00069.","productDescription":"17 p.","startPage":"220","endPage":"236","ipdsId":"IP-155261","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.06640768994139,\n 25.968336212595545\n ],\n [\n -81.56410467268525,\n 24.655591782200545\n ],\n [\n -81.0728587401813,\n 25.246086829050483\n ],\n [\n -82.48259218249659,\n 27.272254831037472\n ],\n [\n -82.73152989277146,\n 27.960240807935207\n ],\n [\n -82.69786543399637,\n 28.715489724979037\n ],\n [\n -84.01930283028096,\n 30.13642592586335\n ],\n [\n -85.21352357055073,\n 29.683019111608573\n ],\n [\n -86.43868847529218,\n 30.499185485782192\n ],\n [\n -87.48025217176053,\n 30.277107124160494\n ],\n [\n -88.01869425708601,\n 30.521051975468964\n ],\n [\n -89.17037649125658,\n 30.240796867164235\n ],\n [\n -89.90702908193344,\n 29.518977092248832\n ],\n [\n -90.49189050390542,\n 29.36185843606789\n ],\n [\n -91.74270448217844,\n 29.90068888910362\n ],\n [\n -93.01117136756345,\n 29.866650987283208\n ],\n [\n -94.66379808733016,\n 29.61102885408677\n ],\n [\n -94.72573793650419,\n 29.93320591649301\n ],\n [\n -94.98767798427114,\n 29.573223193987033\n ],\n [\n -95.28662091934446,\n 28.98698052519731\n ],\n [\n -96.75825455051769,\n 28.63898925070241\n ],\n [\n -97.57160445145992,\n 27.963607001385867\n ],\n [\n -97.86987981335206,\n 27.03862279886482\n ],\n [\n -97.06640768994139,\n 25.968336212595545\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"136","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Michael, Pamela E.","contributorId":341152,"corporation":false,"usgs":false,"family":"Michael","given":"Pamela","email":"","middleInitial":"E.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gleason, Jeffrey S.","contributorId":341153,"corporation":false,"usgs":false,"family":"Gleason","given":"Jeffrey S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haney, J. Christopher","contributorId":341154,"corporation":false,"usgs":false,"family":"Haney","given":"J. Christopher","affiliations":[{"id":81710,"text":"Terra Mar Applied Science","active":true,"usgs":false}],"preferred":false,"id":908008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hixson, Kathy M.","contributorId":341155,"corporation":false,"usgs":false,"family":"Hixson","given":"Kathy","email":"","middleInitial":"M.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908009,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Satgé, Yvan G.","contributorId":341156,"corporation":false,"usgs":false,"family":"Satgé","given":"Yvan G.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908010,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908011,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257777,"text":"70257777 - 2024 - Perspectives on the future of host-microbe biology from the Council on Microbial Sciences of the American Society for Microbiology","interactions":[],"lastModifiedDate":"2024-08-27T13:44:58.537194","indexId":"70257777","displayToPublicDate":"2024-06-26T08:40:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5160,"text":"mSphere","active":true,"publicationSubtype":{"id":10}},"title":"Perspectives on the future of host-microbe biology from the Council on Microbial Sciences of the American Society for Microbiology","docAbstract":"Host-microbe biology (HMB) stands on the cusp of redefinition, challenging conventional paradigms to instead embrace a more holistic understanding of the microbial sciences. The American Society for Microbiology (ASM) Council on Microbial Sciences hosted a virtual retreat in 2023 to identify the future of the HMB field and innovations needed to advance the microbial sciences. The retreat presentations and discussions collectively emphasized the interconnectedness of microbes and their profound influence on humans, animals, and environmental health, as well as the need to broaden perspectives to fully embrace the complexity of these interactions. To advance HMB research, microbial scientists would benefit from enhancing interdisciplinary and transdisciplinary research to utilize expertise in diverse fields, integrate different disciplines, and promote equity and accessibility within HMB. Data integration will be pivotal in shaping the future of HMB research by bringing together varied scientific perspectives, new and innovative techniques, and ’omics approaches. ASM can empower under-resourced groups with the goal of ensuring that the benefits of cutting-edge research reach every corner of the scientific community. Thus, ASM will be poised to steer HMB toward a future that champions inclusivity, innovation, and accessible scientific progress.
","language":"English","publisher":"American Society for Microbiology","doi":"10.1128/msphere.00256-24","usgsCitation":"Gestal, M., Oates, A.E., Akob, D., Criss, A., Committee, H.R., and Speakers, H.R., 2024, Perspectives on the future of host-microbe biology from the Council on Microbial Sciences of the American Society for Microbiology: mSphere, v. 9, no. 7, e00256-24, 16 p., https://doi.org/10.1128/msphere.00256-24.","productDescription":"e00256-24, 16 p.","ipdsId":"IP-162159","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":439345,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1128/msphere.00256-24","text":"Publisher Index Page"},{"id":433191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gestal, Monica","contributorId":343672,"corporation":false,"usgs":false,"family":"Gestal","given":"Monica","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":911654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oates, A. Elizabeth","contributorId":343687,"corporation":false,"usgs":false,"family":"Oates","given":"A.","email":"","middleInitial":"Elizabeth","affiliations":[{"id":82151,"text":"American Society for Microbiology","active":true,"usgs":false}],"preferred":false,"id":911655,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Akob, Denise M. 0000-0003-1534-3025","orcid":"https://orcid.org/0000-0003-1534-3025","contributorId":204701,"corporation":false,"usgs":true,"family":"Akob","given":"Denise M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":911656,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Criss, Alison","contributorId":343676,"corporation":false,"usgs":false,"family":"Criss","given":"Alison","email":"","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":911657,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Committee, Host-Microbe Retreat Planning","contributorId":343677,"corporation":false,"usgs":false,"family":"Committee","given":"Host-Microbe","email":"","middleInitial":"Retreat Planning","affiliations":[],"preferred":false,"id":911658,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Speakers, Host-Microbe Retreat","contributorId":343678,"corporation":false,"usgs":false,"family":"Speakers","given":"Host-Microbe","email":"","middleInitial":"Retreat","affiliations":[],"preferred":false,"id":911659,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255599,"text":"sir20245056 - 2024 - Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois","interactions":[],"lastModifiedDate":"2026-02-03T19:43:57.736197","indexId":"sir20245056","displayToPublicDate":"2024-06-25T15:43:18","publicationYear":"2024","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":"2024-5056","displayTitle":"Two-Dimensional Hydraulic Model for the Chain of Lakes on the Fox River near McHenry, Illinois","title":"Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois","docAbstract":"Forecasts of flows entering and leaving the Chain of Lakes on the Fox River in northeastern Illinois are critical information to water-resource managers operating the Stratton Dam at McHenry, Illinois. These managers determine the optimal operation of the Stratton Dam at McHenry, Ill., to manage Chain of Lakes pool levels and to help mitigate flooding in the Chain of Lakes system. In 2020, the U.S. Geological Survey (USGS) and the Illinois Department of Natural Resources–Office of Water Resources (IDNR–OWR) began a cooperative study to develop a system to enable engineers and planners to simulate and communicate water-surface elevations and flows and to proactively prepare for runoff events forecasted for the Chain of Lakes. The hydraulic model described in this report may be helpful to the IDNR–OWR for optimizing the operation of the Stratton Dam and includes the implementation of three newly installed torque-tube crest gates that became operational in 2020.
The hydraulic model for the Chain of Lakes was developed using the Hydrologic Engineering Center–River Analysis System program (version 6.5). The hydraulic model was used to simulate water-surface elevations and flows through the 18.5-mile Chain of Lakes system to 1.7 miles downstream from the Stratton Dam. Five USGS streamgages within the study area were used as reference points for model calibration and initial water-surface elevations for beginning a simulation. The hydraulic model was calibrated to three runoff events that incorporated the design specifications and observed gate operations of the Stratton Dam; furthermore, the hydraulic model simulated a validation event and a substantial flooding event during July 2017. The July 2017 event predated the torque-tube crest gate installation but nevertheless tested the performance of the model for such a substantial event. The model simulation results were a good fit to observed records at USGS streamgages with simulated peak water-surface elevations within −0.36–0.15 foot of observed events. The hydraulic model was then implemented into a forecast workflow that streamlines implementation of model inputs and documents the model outputs tailored to IDNR–OWS Stratton Dam operations and interpretations of simulated water-surface elevations and flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245056","collaboration":"Prepared in cooperation with the Illinois Department of Natural Resources–Office of Water Resources","usgsCitation":"Cigrand, C.V., and Ament, M.R., 2024, Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois: U.S. Geological Survey Scientific Investigations Report 2024–5056, 20 p., https://doi.org/10.3133/sir20245056.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137180","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499478,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117099.htm","linkFileType":{"id":5,"text":"html"}},{"id":430505,"rank":7,"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":430504,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P16H3TDH","text":"USGS data release","linkHelpText":"Archive of the hydraulic model used in the two-dimensional simulation of the Chain of Lakes on the Fox River near McHenry, Illinois:"},{"id":430503,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245056/full"},{"id":430502,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5056/images/"},{"id":430501,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5056/sir20245056.XML"},{"id":430500,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5056/sir20245056.pdf","text":"Report","size":"3.5 MB","description":"SIR 2024–5056"},{"id":430499,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5056/coverthb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Fox River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.3061687403806,\n 42.29838954847517\n ],\n [\n -88.08497136642455,\n 42.29838954847517\n ],\n [\n -88.08497136642455,\n 42.4987780744203\n ],\n [\n -88.3061687403806,\n 42.4987780744203\n ],\n [\n -88.3061687403806,\n 42.29838954847517\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes (
Introduction: Terrestrial species in riverine ecosystems face unique constraints leading to diverging patterns of population structure, connectivity, and disease dynamics. Desert bighorn sheep (Ovis canadensis nelsoni) in Grand Canyon National Park, a large native population in the southwestern USA, offer a unique opportunity to evaluate population patterns and processes in a remote riverine system with ongoing anthropogenic impacts. We integrated non-invasive, invasive, and citizen-science methods to address questions on abundance, distribution, disease status, genetic structure, and habitat fragmentation.
Methods: We compiled bighorn sightings collected during river trips by park staff, commercial guides, and private citizens from 2000–2018 and captured bighorn in 2010–2016 to deploy GPS collars and test for disease. From 2011–2015, we non-invasively collected fecal samples and genotyped them at 9–16 microsatellite loci for individual identification and genetic structure. We used assignment tests to evaluate genetic structure and identify subpopulations, then estimated gene flow and recent migration to evaluate fragmentation. We used spatial capture-recapture to estimate annual population size, distribution, and trends after accounting for spatial variation in detection with a resource selection function model.
Results and discussion: From 2010–2018, 3,176 sightings of bighorn were reported, with sightings of 56–145 bighorn annually on formal surveys. From 2012–2016, bighorn exhibiting signs of respiratory disease were observed along the river throughout the park. Of 25 captured individuals, 56% were infected by Mycoplasma ovipneumoniae, a key respiratory pathogen, and 81% were recently exposed. Pellet sampling for population estimation from 2011–2015 yielded 1,250 genotypes and 453 individuals. We detected 6 genetic clusters that exhibited mild to moderate genetic structure (FST 0.022–0.126). The river, distance, and likely topography restricted recent gene flow, but we detected cross-river movements in one section via genetic recaptures, no subpopulation appeared completely isolated, and genetic diversity was among the highest reported. Recolonization of one large stretch of currently empty habitat appears limited by the constrained topology of this system. Annual population estimates ranged 536–552 (95% CrI range 451–647), lamb:ewe ratios varied, and no significant population decline was detected. We provide a multi-method sampling framework useful for sampling other wildlife in remote riverine systems.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2024.1377214","usgsCitation":"Epps, C.W., Holton, P.B., Monello, R.J., Crowhurst, R.S., Gaulke, S.M., Janousek, W.M., Creech, T.G., and Graves, T., 2024, Population and spatial dynamics of desert bighorn sheep in Grand Canyon during an outbreak of respiratory pneumonia: Frontiers in Ecology and Evolution, v. 12, 1377214, 22 p., https://doi.org/10.3389/fevo.2024.1377214.","productDescription":"1377214, 22 p.","ipdsId":"IP-137271","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":439348,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/fevo.2024.1377214","text":"Publisher Index Page"},{"id":434937,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K89AA3","text":"USGS data release","linkHelpText":"Desert bighorn sheep (Ovis canadensis nelsoni) datasets from Grand Canyon National Park, 2010-2016"},{"id":430966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.5504498659519,\n 36.84244671894457\n ],\n [\n -114.04234802909026,\n 36.84244671894457\n ],\n [\n -114.04234802909026,\n 35.72909582502355\n ],\n [\n -111.5504498659519,\n 35.72909582502355\n ],\n [\n -111.5504498659519,\n 36.84244671894457\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Epps, Clinton W.","contributorId":198148,"corporation":false,"usgs":false,"family":"Epps","given":"Clinton","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":906239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holton, P. Brandon","contributorId":340119,"corporation":false,"usgs":false,"family":"Holton","given":"P.","email":"","middleInitial":"Brandon","affiliations":[],"preferred":false,"id":906240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monello, Ryan J.","contributorId":184143,"corporation":false,"usgs":false,"family":"Monello","given":"Ryan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":906241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crowhurst, Rachel S.","contributorId":198153,"corporation":false,"usgs":false,"family":"Crowhurst","given":"Rachel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":906242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gaulke, Sarah Mccrimmon 0000-0002-2657-5844","orcid":"https://orcid.org/0000-0002-2657-5844","contributorId":225564,"corporation":false,"usgs":true,"family":"Gaulke","given":"Sarah","email":"","middleInitial":"Mccrimmon","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":906243,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Janousek, William Michael 0000-0003-3978-1775","orcid":"https://orcid.org/0000-0003-3978-1775","contributorId":237980,"corporation":false,"usgs":true,"family":"Janousek","given":"William","email":"","middleInitial":"Michael","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":906244,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Creech, Tyler G.","contributorId":198152,"corporation":false,"usgs":false,"family":"Creech","given":"Tyler","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":906245,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":906246,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70259266,"text":"70259266 - 2024 - An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","interactions":[],"lastModifiedDate":"2024-10-03T14:48:46.639349","indexId":"70259266","displayToPublicDate":"2024-06-25T09:46:42","publicationYear":"2024","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":"An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA)","docAbstract":"Detailed, accurate information on flow patterns in river channels can improve understanding of habitat conditions, geomorphic processes, and potential hazards to help inform water management. Data describing flow patterns in river channels can be obtained efficiently via image-based techniques that have become more widely used in recent years as the number of platforms for acquiring images has expanded and the number of algorithms for inferring velocities has grown. Image-based techniques have been incorporated into various software packages, including the Toolbox for River Velocimetry using Images from Aircraft (TRiVIA). TRiVIA is a freely available, standalone computer program that provides a comprehensive workflow for performing particle image velocimetry (PIV)-based analyses within a graphical interface. This paper summarizes major enhancements incorporated into the latest release of TRiVIA, version 2.1. For example, a new Tool for Input Parameter Selection (TIPS) provides guidance for specifying key inputs to the PIV algorithm by allowing users to explore relationships between flow velocity, pixel size, output vector spacing, and frame interval. Improved visualization capabilities include the ability to create streamlines and display PIV output on an interactive web map. The program now provides greater flexibility for importing field data in various formats and selecting which observations to use for accuracy assessment. The most substantial additions to TRiVIA 2.1 are the ability to integrate bathymetric information with image-derived velocity estimates to calculate river discharge and to use images acquired from moving aircraft to efficiently map long segments of large rivers to support habitat assessment, contaminant transport studies, and a range of other applications.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4333","usgsCitation":"Legleiter, C.J., and Kinzel, P.J., 2024, An enhanced and expanded Toolbox for River Velocimetry using Images from Aircraft (TRiVIA): River Research and Applications, v. 40, no. 8, p. 1602-1616, https://doi.org/10.1002/rra.4333.","productDescription":"15 p.","startPage":"1602","endPage":"1616","ipdsId":"IP-163908","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":466990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.4333","text":"Publisher Index Page"},{"id":462540,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":914715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914716,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255575,"text":"sir20245041 - 2024 - Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network","interactions":[],"lastModifiedDate":"2026-02-03T19:22:10.1439","indexId":"sir20245041","displayToPublicDate":"2024-06-25T09:45:01","publicationYear":"2024","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":"2024-5041","displayTitle":"Representation of Surface-Water Flows Using Gradient-Related Discharge in an Everglades Network","title":"Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network","docAbstract":"The Everglades Depth Estimation Network interpolates water-level gage data to produce daily water-level elevations for the Everglades in south Florida. These elevations were used to estimate flow vectors (gradients and directions) and volumetric flow rates using the Gradient-Related Discharge in an Everglades Network (GARDEN) application developed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. Flow rates in both the east-west and north-south directions were computed on a 400-meter square grid using modified parameters in the Manning’s equation. The frictional resistance parameter in the Manning’s equation was calibrated to measured flow rates at coastal creeks fed by Everglades Depth Estimation Network boundary flows. Levees and other features that act as barriers to flow were defined as “no-flow” grid cells where vectors were set to zero.
The flow volume magnitudes were calibrated with 2020 daily values of coastal river flows, and verification was performed using 2021 data. Within a given day, the measured coastal river flows fluctuate more than the GARDEN boundary flows because of tidal and wind forcings. Because the GARDEN boundary flows were the upstream water source for the coastal rivers, calibration focused on matching average daily flow volumes rather than daily fluctuations. The Pearson’s correlation coefficient is 0.766 for the 2020 calibration period and 0.566 for the 2021 verification period.
Applying GARDEN to periods with hydraulic-control-structure releases allows the propagation of structure flows to be seen in the daily flow-vector maps along with the multiday response of flows farther downgradient. Flow vectors may be overestimated near control structures because of difficulties in resolving the water gradient downstream from the structure. Flow vectors farther from the structure are more accurate than those near the structure.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
The U.S. Geological Survey (USGS) provided technical assistance to the National Park Service (NPS) as part of the USGS-NPS Water-Quality Partnership, by gathering references related to water-quality research conducted in the three units of Gateway National Recreation Area (GATE): Jamaica Bay and Staten Island in New York, and Sandy Hook in New Jersey. As part of this effort, a literature search was performed to compile previous water-quality research conducted within the boundaries of GATE. The resulting bibliography is meant to assist GATE resource managers in understanding the extent of available data and developing plans to close data gaps.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241035","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Savoy, P., Marionkova, M., and Schubert, C., 2024, Bibliography of water-quality studies in Gateway National Recreation Area, New York and New Jersey: U.S. Geological Survey Open-File Report 2024–1035, 7 p., https://doi.org/10.3133/ofr20241035.","productDescription":"iii, 7 p.","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-161856","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":499256,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117097.htm","linkFileType":{"id":5,"text":"html"}},{"id":430428,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1035/images/"},{"id":430427,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1035/ofr20241035.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2024-1035 XML"},{"id":430426,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241035/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1035 HTML"},{"id":430425,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1035/ofr20241035.pdf","text":"Report","size":"1.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1035 PDF"},{"id":430424,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1035/coverthb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Gateway National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.85513729439843,\n 40.654551507838846\n ],\n [\n -73.89381273583244,\n 40.62388973104002\n ],\n [\n -73.9579556996699,\n 40.537441746266865\n ],\n [\n -73.8554944605876,\n 40.56042638081682\n ],\n [\n -73.85236012115627,\n 40.58267541112733\n ],\n [\n -73.83876809711201,\n 40.58418418537727\n ],\n [\n -73.82517607306771,\n 40.58569296761614\n ],\n [\n -73.7681617378135,\n 40.606390031110664\n ],\n [\n -73.7722763223719,\n 40.62727653623955\n ],\n [\n -73.81698658676503,\n 40.65139922165838\n ],\n [\n -73.85513729439843,\n 40.654551507838846\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.03359844094561,\n 40.48300783959223\n ],\n [\n -74.00884376460158,\n 40.40869619490783\n ],\n [\n -73.96483206082176,\n 40.405554701601744\n ],\n [\n -73.99371377377663,\n 40.48823772192023\n ],\n [\n -74.03359844094561,\n 40.48300783959223\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.13918165624833,\n 40.5462145867333\n ],\n [\n -74.13082225121698,\n 40.54483184944499\n ],\n [\n -74.1300961800887,\n 40.53681505828524\n ],\n [\n -74.1417287594383,\n 40.53488174204816\n ],\n [\n -74.13701209880821,\n 40.526583892279575\n ],\n [\n -74.04424809693685,\n 40.58020576969048\n ],\n [\n -74.03879133708992,\n 40.602581719859955\n ],\n [\n -74.05916439950865,\n 40.60892165585267\n ],\n [\n -74.06716503242002,\n 40.602004600980564\n ],\n [\n -74.0631653942842,\n 40.59593339538253\n ],\n [\n -74.09372089641063,\n 40.56998116301571\n ],\n [\n -74.10679839509868,\n 40.576050498707474\n ],\n [\n -74.10971340310442,\n 40.56970029742831\n ],\n [\n -74.09954055858626,\n 40.56307272953646\n ],\n [\n -74.11445006764852,\n 40.558650678011816\n ],\n [\n -74.1293363756321,\n 40.56141089836322\n ],\n [\n -74.13918165624833,\n 40.5462145867333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Southeast Alaska (SE) stock of northern sea otters (Enhydra lutris kenyoni) ranges from Cape Yakataga on the north to the Dixon Entrance on the south. During the maritime fur trade, sea otters were commercially harvested to near extinction in SE for their pelts and were presumed unlikely to naturally repopulate the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243007","collaboration":"Prepared in cooperation with the National Park Service and U.S. Fish and Wildlife Service","usgsCitation":"Eisaguirre, J.M., Matsuoka, T.D., Esslinger, G.G., Weitzman, B.P., Womble, J.N., and Schuette, P.A., 2024, Understanding sea otter population change in southeast Alaska: U.S. Geological Survey Fact Sheet 2024-3007, 4 p., https://doi.org/10.3133/fs20243007.","productDescription":"4 p.","ipdsId":"IP-158806","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":430484,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3007/fs20243007.pdf","text":"Report","size":"3.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3007"},{"id":430483,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3007/fs20243007.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -145.9241672313032,\n 60.97708790226284\n ],\n [\n -145.9241672313032,\n 54.16723740167396\n ],\n [\n -129.22494848130341,\n 54.16723740167396\n ],\n [\n -129.22494848130341,\n 60.97708790226284\n ],\n [\n -145.9241672313032,\n 60.97708790226284\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
4210 University Drive
Anchorage, Alaska 99508
We present a hydroclimate synthesis of the southern Great Basin over the last two glacial-interglacial cycles focused on paleolakes in Death Valley (core DV93-1), Searles Valley (core SLAPP-SRLS17), Owens Valley (core OL92), and the Devils Hole cave. There is close agreement between the occurrence of lakes in Death Valley and the height of the water table in the Devils Hole (50 km east of Death Valley) during the last 200 kyr. Death Valley and Devils Hole have adjacent, partly overlapping, drainage areas and most likely did over the last 200 kyr. When the water table in the Devils Hole was above the threshold level of ∼5 m higher than the modern, permanent lakes existed in Death Valley. At water table elevations less than 5 m above the modern, ephemeral lakes, saline pans, and mudflats occurred in Death Valley. The close temporal agreement between inferred paleoenvironments from the sediments in the Death Valley core and the paleowater table elevation in Devils Hole suggests a common forcing and provides insight into climate variability in the southwestern United States over the last 200 kyr. Owens Valley and Searles Valley, which derived inflow waters from the Sierra Nevada via the Owens River, contain paleohydrologic records which match those from Death Valley and the Devils Hole in terms of timing and direction of water availability over the last 200 kyr, indicating a similar paleohydrologic history for the entire southern Great Basin region. Near the end of Marine Oxygen Isotope Stage 6 (MIS 6), 140 ka - 130 ka, Lake Manly in Death Valley became shallow and hypersaline, and ultimately dried up at 127.1 ka ±4.3 ka. The transition from glacial to interglacial vegetation, which involved the loss of Juniperus pollen and an increase in Quercus (oak) pollen, occurred in Death Valley core DV93-1 at 131.3 ka ±4.0 ka. Following the glacial to interglacial pollen shift, a large alkaline lake formed in Death Valley. Similar conditions (freshwater, high productivity, and a mixed, deeply oxygenated water column indicated by biomarkers) existed in Searles Lake between 135.3 +2.7/-2.9 ka and 130.1+2.7/-2.6 ka, also following the juniper-oak pollen transition. Sr isotopes in calcite and sulfate minerals (gypsum, glauberite, thenardite), and the rare occurrence of the sodium carbonate mineral northupite with a low 87Sr/86Sr ratio in core DV93-1, together with organic geochemical proxies from Searles core SLAPP-SRLS17, all suggest that at this time, late MIS 6 Lake Manly in Death Valley received alkaline water via spillover from Searles Valley into Death Valley through Panamint Valley. The hydrologic connection between Searles Valley, Panamint Valley, and Death Valley at Termination II (130 ka) is documented here for this system of pluvial lakes for the first time. The Devils Hole water table decreased to +6.5 m at 140.8 ka ±3.2 ka, rose briefly to +8 m at 137.6 ka ±0.5 ka, and then dropped 8 m by 120.36 ka ±0.45 ka, when it reached an elevation similar to the modern. The pluvial lakes in Death Valley and Searles Valley may have coincided with the rise of the Devils Hole water table at ∼137.6 ka ±0.5 ka years ago, although the age models for core DV93-1 and core SLAPP-SLRS17 during the end of MIS 6 carry large uncertainties.
Overexposure to respirable coal mine dust can cause severe lung disease including progressive massive fibrosis (PMF). Field emission scanning electron microscopy with energy dispersive x-ray spectroscopy (FESEM-EDS) has been used for in situ lung dust particle analysis for evaluation of disease etiology. Automating such work can reduce time, costs, and user bias.
To develop and test an automated FESEM-EDS method for in situ analysis of inorganic particles in coal miner lung tissue.
We programmed an automated FESEM-EDS procedure to collect particle size and elemental data, using lung tissue from 10 underground coal miners with PMF and 4 control cases. A statistical clustering approach was used to establish classification criteria based on particle chemistry. Data were correlated to PMF/non-PMF areas of the tissue, using corresponding brightfield microscopy images. Results for each miner case were compared with a separate corresponding analysis of particles recovered following tissue digestion.
In situ analysis of miner tissues showed higher particle number densities than controls and densities were generally higher in PMF than non-PMF areas. Particle counts were typically dominated by aluminum silicates with varying percentages of silica. Compared to digestion results for the miner tissues, in situ results indicated lower density of particles (number per tissue volume), larger size, and a lower ratio of silica to total silicates—probably due to frequent particle clustering in situ.
Automated FESEM-EDS analysis of lung dust is feasible in situ and could be applied to a larger set of mineral dust–exposed lung tissues to investigate specific histologic features of PMF and other dust-related occupational diseases.
","language":"English","publisher":"Allen Press","doi":"10.5858/arpa.2024-0002-OA","usgsCitation":"Sarver, E.A., Keles, C., Lowers, H.A., Zell-Baran, L., Go, L.H., Hua, J., Cool, C., Rose, C., Green, F., Almberg, K.S., and Cohen, R.A., 2024, In situ lung dust analysis by automated Field Emission Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy: A method for assessing inorganic particles in tissue from coal miners: Archives of Pathology, v. 148, no. 7, p. e154-e169, https://doi.org/10.5858/arpa.2024-0002-OA.","productDescription":"16 p.","startPage":"e154","endPage":"e169","ipdsId":"IP-160543","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":439351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5858/arpa.2024-0002-oa","text":"Publisher Index Page"},{"id":430713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Sarver, Emily A.","contributorId":265758,"corporation":false,"usgs":false,"family":"Sarver","given":"Emily","email":"","middleInitial":"A.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":905409,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keles, C.","contributorId":339857,"corporation":false,"usgs":false,"family":"Keles","given":"C.","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":905410,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":905418,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zell-Baran, L.","contributorId":339860,"corporation":false,"usgs":false,"family":"Zell-Baran","given":"L.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":905413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Go, Leonard H. T.","contributorId":306069,"corporation":false,"usgs":false,"family":"Go","given":"Leonard","email":"","middleInitial":"H. T.","affiliations":[],"preferred":false,"id":905485,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hua, J.","contributorId":339859,"corporation":false,"usgs":false,"family":"Hua","given":"J.","email":"","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":905412,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cool, C.","contributorId":339858,"corporation":false,"usgs":false,"family":"Cool","given":"C.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":905411,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rose, Cecile","contributorId":204557,"corporation":false,"usgs":false,"family":"Rose","given":"Cecile","email":"","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":905414,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Green, F.H.","contributorId":265748,"corporation":false,"usgs":false,"family":"Green","given":"F.H.","email":"","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":905419,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Almberg, K. S.","contributorId":265745,"corporation":false,"usgs":false,"family":"Almberg","given":"K.","email":"","middleInitial":"S.","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":905417,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cohen, R. A.","contributorId":290338,"corporation":false,"usgs":false,"family":"Cohen","given":"R.","email":"","middleInitial":"A.","affiliations":[{"id":18133,"text":"University of Illinois Chicago","active":true,"usgs":false}],"preferred":false,"id":905416,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70255668,"text":"70255668 - 2024 - Application of normalized radar backscatter and hyperspectral data to augment rangeland vegetation fractional classification","interactions":[],"lastModifiedDate":"2024-06-28T11:44:29.88845","indexId":"70255668","displayToPublicDate":"2024-06-25T06:36:12","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Application of normalized radar backscatter and hyperspectral data to augment rangeland vegetation fractional classification","docAbstract":"The East Anatolian Fault Zone (EAFZ) is a plate-bounding strike-slip fault capable of hosting large earthquakes, as demonstrated by the extremely damaging February 2023 Mw 7.8 and Mw 7.7 mainshocks of the Kahramanmaraş earthquake sequence. Deformation related to this boundary, part of the Anatolia-Arabia-Africa (A3) Triple Junction, is diffuse, as was shown by part of this earthquake sequence occurring on a northern splay of the EAFZ (the Sürgü-Çardak Fault Zone; SCFZ). Controls on surface deformation are commonly linked to stress in the brittle upper crust, but the complex deformation and seismicity patterns in this region may also reflect deeper processes, such as variations in the location and extent of the strong Arabian Plate lithospheric mantle. Seismic tomography indicates that the Arabian Plate underthrusts Anatolia as far north as the SCFZ and extends as far west as the central Adana Basin, coincident with a zone of relatively deep (>30 km) strike-slip seismogenesis that has produced Mw >6 earthquakes. By investigating the relationship between deformation since the inception of the EAFZ (ca. 5 Ma), seismic structure, and seismicity, we infer that the SCFZ will become the future SE boundary of the Anatolian Plate as part of the evolving A3 Triple Junction.
Coyotes (Canis latrans) are believed to contribute to declining kit fox (Vulpes macrotis) numbers in the Great Basin desert through intraguild predation. Intraguild prey have been shown to exhibit adaptive compromise, whereby an animal increases selection for risky, but food-rich areas during times of food stress (i.e. winter). We evaluated the habitat selection of kit foxes in the Great Basin desert to elucidate if they demonstrated adaptive compromise as a method of coexisting with coyotes. We created 2nd order resource selection functions to analyze kit fox habitat selection associated with coyote relative probability of use (RPU), prey abundance, and type of soil substrate. In the summer, we found that kit fox selection for areas of relatively more abundant prey was not significant, and there was a small positive selection for coyote RPU. In the winter, we found a positive relationship between kit fox selection and prey abundance as well as a stronger selection for coyote RPU. These findings do follow the pattern of adaptive compromise. We also found kit foxes selected for silty and sandy soils, which are conducive to den construction, as they use dens seasonally for breeding but also year-round for multiple uses, including refugia from predators and extreme heat. Soil substrate appeared to be an important factor impacting kit fox habitat selection.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-61692-1","usgsCitation":"Pershyn, N., Gese, E.M., Stuber, E.F., and Kluever, B.M., 2024, Kit foxes demonstrate adaptive compromise characteristics under intraguild predation pressure by coyotes in the Great Basin desert: Scientific Reports, v. 14, 14446, 12 p., https://doi.org/10.1038/s41598-024-61692-1.","productDescription":"14446, 12 p.","ipdsId":"IP-164159","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488267,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-61692-1","text":"Publisher Index Page"},{"id":485842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.25,\n 40.5\n ],\n [\n -113.25,\n 39.75\n ],\n [\n -112.5,\n 39.75\n ],\n [\n -112.5,\n 40.5\n ],\n [\n -113.25,\n 40.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Pershyn, Nadine A.","contributorId":354962,"corporation":false,"usgs":false,"family":"Pershyn","given":"Nadine A.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":936736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gese, Eric M","contributorId":222151,"corporation":false,"usgs":false,"family":"Gese","given":"Eric","email":"","middleInitial":"M","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":936737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stuber, Erica Francis 0000-0002-2687-6874","orcid":"https://orcid.org/0000-0002-2687-6874","contributorId":298084,"corporation":false,"usgs":true,"family":"Stuber","given":"Erica","email":"","middleInitial":"Francis","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":936738,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kluever, Bryan M.","contributorId":257061,"corporation":false,"usgs":false,"family":"Kluever","given":"Bryan","email":"","middleInitial":"M.","affiliations":[{"id":51974,"text":"US Department of Agriculture, National Wildlife Research Center, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":936739,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255940,"text":"70255940 - 2024 - Mammalian lures monitored with time-lapse cameras increase detection of pythons and other snakes","interactions":[],"lastModifiedDate":"2024-07-11T14:35:28.931946","indexId":"70255940","displayToPublicDate":"2024-06-24T09:31:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Mammalian lures monitored with time-lapse cameras increase detection of pythons and other snakes","docAbstract":"Enhancing detection of cryptic snakes is critical for the development of conservation and management strategies; yet, finding methods that provide adequate detection remains challenging. Issues with detecting snakes can be particularly problematic for some species, like the invasive Burmese python (Python bivittatus) in the Florida Everglades.
Using multiple survey methods, we predicted that our ability to detect pythons, larger snakes and all other snakes would be enhanced with the use of live mammalian lures (domesticated rabbits; Oryctolagus cuniculus). Specifically, we used visual surveys, python detection dogs, and time-lapse game cameras to determine if domesticated rabbits were an effective lure.
Time-lapse game cameras detected almost 40 times more snakes (n = 375, treatment = 245, control = 130) than visual surveys (n = 10). We recorded 21 independent detections of pythons at treatment pens (with lures) and one detection at a control pen (without lures). In addition, we found larger snakes, and all other snakes were 165% and 74% more likely to be detected at treatment pens compared to control pens, respectively. Time-lapse cameras detected almost 40 times more snakes than visual surveys; we did not detect any pythons with python detection dogs.
Our study presents compelling evidence that the detection of snakes is improved by coupling live mammalian lures with time-lapse game cameras. Although the identification of smaller snake species was limited, this was due to pixel resolution, which could be improved by changing the camera focal length. For larger snakes with individually distinctive patterns, this method could potentially be used to identify unique individuals and thus allow researchers to estimate population dynamics.
","language":"English","publisher":"PeerJ, Inc.","doi":"10.7717/peerj.17577","usgsCitation":"McCampbell, M.E., Spencer, M.M., Hart, K., Link, G., Watson, A.J., and McCleery, R.A., 2024, Mammalian lures monitored with time-lapse cameras increase detection of pythons and other snakes: PeerJ, v. 12, e17577, 21 p., https://doi.org/10.7717/peerj.17577.","productDescription":"e17577, 21 p.","ipdsId":"IP-154422","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439354,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.17577","text":"Publisher Index Page"},{"id":430963,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.06716804534993,\n 26.337274298622603\n ],\n [\n -81.34055052477085,\n 26.337274298622603\n ],\n [\n -81.34055052477085,\n 25.103410837459833\n ],\n [\n -80.06716804534993,\n 25.103410837459833\n ],\n [\n -80.06716804534993,\n 26.337274298622603\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"McCampbell, Marina E.","contributorId":331286,"corporation":false,"usgs":false,"family":"McCampbell","given":"Marina","email":"","middleInitial":"E.","affiliations":[{"id":40348,"text":"Department of Wildlife Ecology and Conservation, University of Florida","active":true,"usgs":false}],"preferred":false,"id":906086,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spencer, McKayla M.","contributorId":301071,"corporation":false,"usgs":false,"family":"Spencer","given":"McKayla","email":"","middleInitial":"M.","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":906087,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":222407,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":906088,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Link, Gabrielle","contributorId":340076,"corporation":false,"usgs":false,"family":"Link","given":"Gabrielle","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":906089,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Watson, Andrew J.","contributorId":176461,"corporation":false,"usgs":false,"family":"Watson","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":906090,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCleery, Robert A.","contributorId":139849,"corporation":false,"usgs":false,"family":"McCleery","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":906091,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262884,"text":"70262884 - 2024 - Estimating biogeochemical rates using a computationally efficient Lagrangian approach","interactions":[],"lastModifiedDate":"2025-01-27T15:38:46.616909","indexId":"70262884","displayToPublicDate":"2024-06-24T08:29:40","publicationYear":"2024","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":"Estimating biogeochemical rates using a computationally efficient Lagrangian approach","docAbstract":"Nutrient concentrations in many estuaries have increased over the past century due to increases in wastewater discharge and increased agricultural intensity, contributing to multiple environmental problems. Numerous biogeochemical and physical processes in estuaries influence nutrient concentrations during transport, resulting in complex spatial and temporal variability and challenges identifying predominant processes and their rates. Mechanistic models which require these rates to quantify biogeochemical processes become complex and difficult to calibrate as the number of processes and parameters grows, owing to the high dimensionality of the parameter space and the computational cost of simultaneously modeling the transport and transformations of constituents. We developed a modeling approach that decouples transport from transformations, enabling fast, data-driven exploration of the parameter space. The approach extracted information including water age, cumulative exposure to specific habitats, and mean water depth exposure from a hydrodynamic model. Using this information, a biogeochemical model was implemented to predict ammonium and nitrate concentrations in a Lagrangian frame. The model performed each simulation in milliseconds on a laptop computer, allowing the fitting of rate parameters for key transformations by optimization. The optimization used fixed station nitrate observations and the model was then validated against high-resolution mapping observations of ammonium and nitrate. The results suggest that the observed spatial and temporal variation can be largely represented with five transformation processes and their associated rates. Dissolved inorganic nitrogen (DIN) losses occurred only in shallow vegetated areas in the model, highlighting that biogeochemical processes in these areas should be included in DIN models.
With growing global capability for satellite measurement of river discharge (flow) comes a need to understand and reduce error in satellite-based discharge measurements. Satellite-based discharge estimates are based on measurements of water surface width, elevation, slope, and potentially velocity. Site selection is important for reducing error and uncertainty in both conventional and satellite-based discharge measurements because geomorphic river characteristics have strong control over the relationships between discharge and width, water surface elevation (or depth), slope, and velocity. A large ground-truth data set of 8,445 conventional hydraulic measurements, collected by acoustic Doppler current profilers at 503 stations in the United States, was developed and quality assured to examine correlation between river discharge and water surface width, depth, velocity, and cross-sectional area. A separate database of river surface slope and discharge time-series was developed from paired continuous monitoring stations to examine slope-discharge correlations. Results show that discharge correlates most strongly with velocity, cross-sectional area, depth, width, and slope, in that order. Uncertainty of satellite discharge estimates is affected by observed hydraulic variable and reach-specific variability in observed variable(s) characteristics including range of variability, georegistration accuracy, and stability over time of relationships between discharge and observed hydraulic variable.
Scientists and resource managers increasingly use Markdown-based tools to create reproducible reports and manuscripts. These workflows allow people to use standardized methods that are more reproducible, efficient, and transparent than other standard office tools. We present a Quarto template and demonstrate how this template may be used for a journal, the Journal of Fish and Wildlife Management, in our article. This template may also be readily adapted to other journals that use Microsoft Word-based workflows and for other product types such as annual reports. We also provide a high-level overview of Quarto and other Markdown-based workflows as part of the document. Lastly, we provide examples of some features of the Quarto publishing system that may be helpful for authors when customizing Quarto templates for specific journal formatting requirements and other product types.
The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here, we collated over 5200 contractor elevation checkpoints along the Atlantic and Gulf of Mexico coasts of the United States that were collected for project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey’s Three-Dimensional Elevation Program. We used land cover data to quantify non-vegetated vertical accuracy and vegetated vertical accuracy statistics (overall and by point spacing bins) and assessed elevation error by land cover class. We found the non-vegetated vertical accuracy had an overall root mean square error of 6.9 cm and vegetated areas had a 95th percentile vertical error of 22.3 cm. Point spacing was generally positively correlated to elevation accuracy, but sample size limited the ability to interpret results from accuracy by land cover, particularly in wetlands. Based on the specific questions a researcher may be asking, use of literature or fieldwork could assist with enhancing error statistics in underrepresented classes.
Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R2) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2 km. Hence, we interpret our point-scale R2 of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106124","usgsCitation":"Goodling, P.J., Belitz, K., Stackelberg, P.E., and Fleming, B.J., 2024, A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA: Environmental Modelling & Software, v. 179, 106124, 12 p., https://doi.org/10.1016/j.envsoft.2024.106124.","productDescription":"106124, 12 p.","ipdsId":"IP-160581","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":439361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106124","text":"Publisher Index Page"},{"id":430446,"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 -74.88099946089724,\n 38.58741180591247\n ],\n [\n -74.71321333503417,\n 39.379784628066616\n ],\n [\n -74.91854009408658,\n 39.623906471535875\n ],\n [\n -74.56682974019151,\n 39.83490997578861\n ],\n [\n -74.83087158557463,\n 40.43445755432647\n ],\n [\n -74.69462804413362,\n 42.31099383658801\n ],\n [\n -75.89851464683143,\n 42.243461978517985\n ],\n [\n -76.67474108243883,\n 40.45538711590305\n ],\n [\n -76.4341425039691,\n 39.732367924983954\n ],\n [\n -75.81256791410406,\n 39.70992285882126\n ],\n [\n -75.68892404289328,\n 38.72600697054469\n ],\n [\n -74.88099946089724,\n 38.58741180591247\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":904793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":904794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fleming, Brandon J. 0000-0001-9649-7485 bjflemin@usgs.gov","orcid":"https://orcid.org/0000-0001-9649-7485","contributorId":4115,"corporation":false,"usgs":true,"family":"Fleming","given":"Brandon","email":"bjflemin@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904795,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255961,"text":"70255961 - 2024 - Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data","interactions":[],"lastModifiedDate":"2024-07-11T14:25:45.706092","indexId":"70255961","displayToPublicDate":"2024-06-22T09:17:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data","docAbstract":"Evaluating the status and trends of natural-origin anadromous fish populations over time requires robust estimates of out-migrating juvenile abundance. Information on abundance is typically acquired by capturing actively migrating fish as they pass stationary monitoring platforms. Challenges to estimation include protracted migration timing, temporally varying capture probabilities and the contemporaneous presence of unmarked hatchery-origin fish. The confounding effects of unmarked hatchery fish are especially pernicious in systems hosting multiple hatchery programs with variable mark-rates among releases. Here, we address this problem for a regionally and culturally important population of Chinook salmon (Oncorhynchus tshawytscha) supported by a hatchery-supplementation program implemented in response to the listing of this population under the U.S. Endangered Species Act. We developed a model to estimate daily and annual abundance of naturally produced age-0 fall Chinook salmon passing Lower Granite Dam (Snake River, USA) for each of the last 30 years. We accounted for variable hatchery marking rates by integrating two related data sources: 1) release-recapture data of fish with individually identifiable tags and 2) counts of marked and unmarked sample of fish captured each day. We fit joint parameters for daily fish arrival and capture probabilities to these data to estimate the daily abundance of hatchery- and natural-origin fish. Our results show that from 1992 to 2021, the annual abundance of juvenile natural-origin Snake River fall Chinook salmon increased by two orders of magnitude. These results are the first comprehensive evaluation of multi-decadal trends in abundance and run-timing for this population. Our approach can be adapted to other runs and locations within the Columbia River basin or similar systems where out-migrating fish are monitored at fixed locations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2024.107098","usgsCitation":"Hance, D., Plumb, J., Perry, R., and Tiffan, K., 2024, Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data: Fisheries Research, v. 278, 107098, 18 p., https://doi.org/10.1016/j.fishres.2024.107098.","productDescription":"107098, 18 p.","ipdsId":"IP-160680","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":434942,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EKDXW3","text":"USGS data release","linkHelpText":"Daily and annual abundances of natural- and hatchery-origin age-0 fall Chinook salmon (Oncorhynchus tshawytscha) passing Lower Granite Dam, Washington 1992 - 2021"},{"id":430961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.97264940326166,\n 47.24310996536303\n ],\n [\n -118.075231715107,\n 47.27051186495126\n ],\n [\n -118.03216123599826,\n 44.88742975557119\n ],\n [\n -115.98127625140233,\n 44.90409917890665\n ],\n [\n -115.97264940326166,\n 47.24310996536303\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"278","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plumb, John 0000-0003-4255-1612","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":220178,"corporation":false,"usgs":true,"family":"Plumb","given":"John","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tiffan, Kenneth 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":217812,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906154,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256164,"text":"70256164 - 2024 - Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS)","interactions":[],"lastModifiedDate":"2025-01-17T15:55:36.342599","indexId":"70256164","displayToPublicDate":"2024-06-22T06:52:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1248,"text":"Climate Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS)","docAbstract":"The mid-Piacenzian Warm Period (MPWP, ~ 3.264–3.025 Ma) is the most recent example of a persistently warmer climate in equilibrium with atmospheric CO2 concentrations similar to today. Towards studying patterns and dynamics of a warming climate the MPWP is often compared to today. Following the Pliocene Model Intercomparison Project, Phase 2 (PlioMIP2) protocol we prepare a water isotope-enabled Community Earth System Model (iCESM1.2) simulation that is warmer and wetter than the PlioMIP2 multi-model ensemble (MME). While our simulation resembles PlioMIP2 MME in many aspects we find added insights. (1) Considerable warmth at high latitudes exceeds previous simulations. Polar amplification (PA) is comparable to proxies, enabled by iCESM1.2’s high climate sensitivity and a distinct method of ocean initialization. (2) Major driver of warmth is the downward component of clear-sky surface long-wave radiation (ΔTrlds_clearsky). (3) In iCESM1.2 modulated dominance of dynamic (δDY) processes causes different low-latitude (~ 30 S°–10°N) precipitation response than the PlioMIP2 MME, where thermodynamic processes (δTH) dominate. (4) Modulated local condensation leads to lower δ18Op across tropical Indian Ocean and surrounding Asian-African-Australian monsoon regions. (5) We find contrasting changes in tropical atmospheric circulations (Hadley and Walker cells). Anomalous regional meridional (zonal) circulation, forced by changes in tropical-subtropical (tropical) diabatic processes, presents a more comprehensive perspective than explaining weakened and expanded Hadley circulation (strengthened and westward-shifted Walker circulation) via static stability. (6) Enhanced Atlantic meridional overturning circulation owes to a closed Bering Strait.
","language":"English","publisher":"Springer","doi":"10.1007/s00382-024-07304-0","usgsCitation":"Sun, Y., Su, B., Dowsett, H.J., Wu, H., Hu, J., Stepanek, C., Xiong, Z., Yuan, X., and Ramstein, G., 2024, Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS): Climate Dynamics, v. 62, p. 7741-7761, https://doi.org/10.1007/s00382-024-07304-0.","productDescription":"21 p.","startPage":"7741","endPage":"7761","ipdsId":"IP-145565","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":439362,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00382-024-07304-0","text":"Publisher Index Page"},{"id":431436,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","noUsgsAuthors":false,"publicationDate":"2024-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Sun, Yong","contributorId":336900,"corporation":false,"usgs":false,"family":"Sun","given":"Yong","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":906957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Su, Baohuang","contributorId":338746,"corporation":false,"usgs":false,"family":"Su","given":"Baohuang","email":"","affiliations":[],"preferred":false,"id":907051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":907052,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Haibin","contributorId":338744,"corporation":false,"usgs":false,"family":"Wu","given":"Haibin","email":"","affiliations":[],"preferred":false,"id":907053,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hu, Jun","contributorId":340390,"corporation":false,"usgs":false,"family":"Hu","given":"Jun","email":"","affiliations":[],"preferred":false,"id":907054,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stepanek, Christian","contributorId":220691,"corporation":false,"usgs":false,"family":"Stepanek","given":"Christian","email":"","affiliations":[{"id":40240,"text":"Alfred Wegener Institute-Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":907055,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xiong, Zhongyu","contributorId":340391,"corporation":false,"usgs":false,"family":"Xiong","given":"Zhongyu","email":"","affiliations":[],"preferred":false,"id":907056,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yuan, Xiayu","contributorId":338747,"corporation":false,"usgs":false,"family":"Yuan","given":"Xiayu","email":"","affiliations":[],"preferred":false,"id":907057,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ramstein, Gilles","contributorId":269585,"corporation":false,"usgs":false,"family":"Ramstein","given":"Gilles","email":"","affiliations":[{"id":55994,"text":"Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France","active":true,"usgs":false}],"preferred":false,"id":907058,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256471,"text":"70256471 - 2024 - Viability modeling for decision support with limited data: A lizard case study","interactions":[],"lastModifiedDate":"2024-12-10T15:01:27.166066","indexId":"70256471","displayToPublicDate":"2024-06-21T15:27:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17797,"text":"Journal of Fish and Wildlife Managment","active":true,"publicationSubtype":{"id":10}},"title":"Viability modeling for decision support with limited data: A lizard case study","docAbstract":"Plateau spot-tailed earless lizards, Holbrookia lacerata, are a species of ground lizard in central Texas that are under review for listing as endangered under the US Endangered Species Act, but heretofore no predictive models of population dynamics or viability have been developed. We used limited available data and published demographic rates in a PVA model to predict future status of these lizards under parametric and ecological uncertainty and temporal variability. Even in cases where data are sparse and life history information are limited, viability models can help clarify the consequences of management choices given the uncertainty. Our model predicted that on average populations will decline in in the future. Quasi-extinction probability was low 20 years into the future but up to 0.60. Extinction risk was highly dependent on the road mortality effect and the proportion of the population exposed to roadways, both of which are currently uncertain quantities. Despite these unknowns, our model enables managers to consider the future abundance and extinction risk for the species and make decisions about management to project the populations and also identifies key uncertainties for future research and monitoring.
","language":"English","publisher":"US Fish and Wildlife Service","doi":"10.3996/JFWM-23-024","usgsCitation":"Goode, A.B., Allan, N., and McGowan, C., 2024, Viability modeling for decision support with limited data: A lizard case study: Journal of Fish and Wildlife Managment, v. 15, no. 1, p. 70-86, https://doi.org/10.3996/JFWM-23-024.","productDescription":"17 p.","startPage":"70","endPage":"86","ipdsId":"IP-152348","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487513,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-024","text":"Publisher Index Page"},{"id":432057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Goode, Ashley B.C.","contributorId":340756,"corporation":false,"usgs":false,"family":"Goode","given":"Ashley","email":"","middleInitial":"B.C.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":907519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allan, Nathan","contributorId":340757,"corporation":false,"usgs":false,"family":"Allan","given":"Nathan","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":3381,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":907521,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}