Species interactions shape biodiversity patterns, community assemblage, and the dynamics of wildlife populations. Ecological theory posits that the strength of interspecific interactions is fundamentally underpinned by the population sizes of the involved species. Nonetheless, prevalent approaches for modeling species interactions predominantly center around occupancy states. Here, we use simulations to illuminate the inadequacies of modeling species interactions solely as a function of occupancy, as is common practice in ecology. We demonstrate erroneous inference into species interactions due to error in parameter estimates when considering species occupancy alone. To address this critical issue, we propose, develop, and demonstrate an abundance-mediated interaction framework designed explicitly for modeling species interactions involving two or more species from detection/non-detection data. We present Markov chain Monte Carlo (MCMC) samplers tailored for diverse ecological scenarios, including intraguild predation, disease- or predator-mediated competition, and trophic cascades. Illustrating the practical implications of our approach, we compare inference from modeling the interactions in a three-species network involving coyotes (Canis latrans), fishers (Pekania pennanti), and American marten (Martes americana) in North America as a function of occupancy states and as a function of abundance. When modeling interactions as a function of abundance rather than occupancy, we uncover previously unidentified interactions. Our study emphasizes that accounting for abundance-mediated interactions rather than simple co-occurrence patterns can fundamentally alter our comprehension of system dynamics. Through an empirical case study and comprehensive simulations, we demonstrate the importance of accounting for abundance when modeling species interactions, and we present a statistical framework equipped with MCMC samplers to achieve this paradigm shift in ecological research.
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As part of our work with the North American Classification Committee (NACC) of the American Ornithological Society, we here assess species limits in 11 such species complexes of North American birds: Spruce Grouse (Canachites canadensis), Band-tailed Pigeon (Patagioenas fasciata), Antillean Mango (Anthracothorax dominicus), Greenish Puffleg (Haplophaedia aureliae), Black Oystercatcher (Haematopus bachmani), Hook-billed Kite (Chondrohierax uncinatus), Sharp-shinned Hawk (Accipiter striatus), Elegant Trogon (Trogon elegans), American Three-toed Woodpecker (Picoides dorsalis), Golden-olive Woodpecker (Colaptes rubiginosus), and Olive-throated Parakeet (Eupsittula nana). We provide updated information on the taxonomic history of these species, and recommend updated taxonomic treatments by using published works, analysis of museum specimens, and citizen/community science databases. 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EIS accommodates variable geometry and illumination during rapid, low-altitude flybys with both framing and pushbroom imaging capability using rapid-readout, 8-megapixel (4k × 2k) detectors. Color observations are acquired using pushbroom imaging with up to six broadband filters. The data processing units (DPUs) perform digital time delay integration (TDI) to enhance signal-to-noise ratios and use readout strategies to measure and correct spacecraft jitter. The NAC has a 2.3° × 1.2° field of view (FOV) with a 10-μrad instantaneous FOV (IFOV), thus achieving 0.5-m pixel scale over a swath that is 2 km wide and several km long from a range of 50 km. The NAC is mounted on a 2-axis gimbal, ±30° cross- and along-track, that enables independent targeting and near-global (≥90%) mapping of Europa at ≤100-m pixel scale (to date, only ∼15% of Europa has been imaged at ≤900 m/pixel), as well as stereo imaging from as close as 50-km altitude to generate digital terrain models (DTMs) with ≤4-m ground sample distance (GSD) and ≤0.5-m vertical precision. The NAC will also perform observations at long range to search for potential erupting plumes, achieving 10-km pixel scale at a distance of one million kilometers. The WAC has a 48° × 24° FOV with a 218-μrad IFOV, achieving 11-m pixel scale at the center of a 44-km-wide swath from a range of 50 km, and generating DTMs with 32-m GSD and ≤4-m vertical precision. The WAC is designed to acquire three-line pushbroom stereo and color swaths along flyby ground-tracks.
","language":"English","publisher":"Springer","doi":"10.1007/s11214-024-01115-9","usgsCitation":"Turtle, E., McEwen, A., Patterson, G., Ernst, C.M., Elder, C., Slack, K., Hawkins, S., McDermott, J., Meyer, H.M., DeMajistre, R., Espiritu, R., Seifert, H., Niewola, J., Bland, M.T., Becker, M., Centurelli, J., Collins, G., Corlies, P., Darlington, H., Daubar, I.J., Derr, C., Detelich, C., Donald, E., Edens, W., Fletcher, L., Gardner, C., Graham, F., Hansen, C., Haslebacher, C., Hayes, A., Humm, D., Hurford, T., Kirk, R.L., Kutsop, N.W., Lees, W., Lewis, D.T., London, S., Magner, A., Mills, M., Barr Mlinar, A., Morgan, F., Nimmo, F., Ocasio Milanes, A., Osterman, S., Phillips, C., Pommerol, A., Prockter, L., Quick, L., Robbins, G., Soderblom, J., Stewart, B., Stickle, A., Sutton, S., Thomas, N., Torres, I., Tucker, O., Van Auken, R., and Wilk, K., 2024, The Europa Imaging System (EIS) investigation: Space Science Reviews, v. 220, 91, 68 p., https://doi.org/10.1007/s11214-024-01115-9.","productDescription":"91, 68 p.","ipdsId":"IP-165720","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":487716,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11214-024-01115-9","text":"Publisher Index Page"},{"id":482734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"220","noUsgsAuthors":false,"publicationDate":"2024-12-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Turtle, E.P.","contributorId":351657,"corporation":false,"usgs":false,"family":"Turtle","given":"E.P.","affiliations":[{"id":84025,"text":"Johns Hopkins APL","active":true,"usgs":false}],"preferred":false,"id":929251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McEwen, A.S.","contributorId":202347,"corporation":false,"usgs":false,"family":"McEwen","given":"A.S.","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":929252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patterson, G.W.","contributorId":238743,"corporation":false,"usgs":false,"family":"Patterson","given":"G.W.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":929253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ernst, C. 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A.","contributorId":351694,"corporation":false,"usgs":false,"family":"Stickle","given":"A.","affiliations":[{"id":84025,"text":"Johns Hopkins APL","active":true,"usgs":false}],"preferred":false,"id":929299,"contributorType":{"id":1,"text":"Authors"},"rank":52},{"text":"Sutton, S.S.","contributorId":239566,"corporation":false,"usgs":false,"family":"Sutton","given":"S.S.","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":929301,"contributorType":{"id":1,"text":"Authors"},"rank":53},{"text":"Thomas, N.","contributorId":241711,"corporation":false,"usgs":false,"family":"Thomas","given":"N.","affiliations":[{"id":48404,"text":"Universität Bern","active":true,"usgs":false}],"preferred":false,"id":929302,"contributorType":{"id":1,"text":"Authors"},"rank":54},{"text":"Torres, I.","contributorId":351695,"corporation":false,"usgs":false,"family":"Torres","given":"I.","affiliations":[{"id":36390,"text":"Massachussets Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":929303,"contributorType":{"id":1,"text":"Authors"},"rank":55},{"text":"Tucker, O.J.","contributorId":351696,"corporation":false,"usgs":false,"family":"Tucker","given":"O.J.","affiliations":[{"id":84032,"text":"Goddard Space Flight, NASA","active":true,"usgs":false}],"preferred":false,"id":929304,"contributorType":{"id":1,"text":"Authors"},"rank":56},{"text":"Van Auken, R.B.","contributorId":351697,"corporation":false,"usgs":false,"family":"Van Auken","given":"R.B.","affiliations":[{"id":84025,"text":"Johns Hopkins APL","active":true,"usgs":false}],"preferred":false,"id":929305,"contributorType":{"id":1,"text":"Authors"},"rank":57},{"text":"Wilk, K.A.","contributorId":351698,"corporation":false,"usgs":false,"family":"Wilk","given":"K.A.","affiliations":[{"id":16929,"text":"Brown University","active":true,"usgs":false}],"preferred":false,"id":929306,"contributorType":{"id":1,"text":"Authors"},"rank":58}]}} ,{"id":70261455,"text":"70261455 - 2024 - Microfossil biostratigraphy and paleoenvironments of Cretaceous and Pliocene sediments along Greens Mill Run, North Carolina, USA","interactions":[],"lastModifiedDate":"2024-12-11T17:13:11.800838","indexId":"70261455","displayToPublicDate":"2024-12-04T08:25:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3481,"text":"Stratigraphy","active":true,"publicationSubtype":{"id":10}},"title":"Microfossil biostratigraphy and paleoenvironments of Cretaceous and Pliocene sediments along Greens Mill Run, North Carolina, USA","docAbstract":"Cretaceous sediments are disconformably overlain by Pliocene sediments along the banks of Greens Mill Run, Greenville, North Carolina, located in the central coastal plain. The Cretaceous sediments, composed of glauconitic sand and clay, have previously been informally considered part of the Maastrichtian Peedee Formation. The Pliocene sediments are assigned to the Yorktown Formation and consist of shelly, muddy sand overlain with a gradational contact by shell-poor, muddy sand. Foraminifera are abundant in the Cretaceous unit and are dominated by the planktic foraminifera Guembelitria cretacea. Low planktic foraminiferal diversity, the absence of single and double-keeled species, the dominance of planktic over benthic foraminifera, and the occurrence of glauconite and phosphorite grains indicates a middle neritic environment. The age of the Cretaceous unit is late Campanian (Zone CC22c), as indicated by the co-occurrence of the calcareous nannofossils Reinhardtites levis and Reinhardtites anthophorus. For this reason, we assign these deposits to the Donoho Creek Formation of the Black Creek Group rather than to the lithologically similar and younger Peedee Formation. Benthic foraminifera dominate the lower part of the Yorktown Formation, are similar to those from the Rushmere Member of the Yorktown Formation in the southern Salisbury Embayment, and indicate deposition in an inner to middle neritic environment. Phosphorite peloids and intraclasts are likely reworked from underlying strata and suggest wave ravinement and deposition during transgression, which is further supported by a fining upward trend. The upper part of the Yorktown Formation exposure, characterized by abundant molds of small bivalves and barren of foraminifera, represents the Morgarts Beach Member. Sedimentological data are consistent with foraminiferal data as far as expected depositional energy. A restricted inner neritic or estuarine environment is indicated.
","language":"English","publisher":"Micropaleontology Press","doi":"10.47894/stra.21.4.03","usgsCitation":"Dixon, M., Culver, S.J., Mallinson, D.J., Huber, B.T., Self-Trail, J., Spivey, W., and Harris, W., 2024, Microfossil biostratigraphy and paleoenvironments of Cretaceous and Pliocene sediments along Greens Mill Run, North Carolina, USA: Stratigraphy, v. 21, no. 4, p. 323-335, https://doi.org/10.47894/stra.21.4.03.","productDescription":"13 p.","startPage":"323","endPage":"335","ipdsId":"IP-160545","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":465024,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.micropress.org/microaccess/stratigraphy/issue-407/article-2397","linkFileType":{"id":5,"text":"html"}},{"id":465025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","city":"Greenville","otherGeospatial":"Greens Mill Run","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.35352669848585,\n 35.60617951552658\n ],\n [\n -77.35352669848585,\n 35.6031089778176\n ],\n [\n -77.34026585711604,\n 35.6031089778176\n ],\n [\n -77.34026585711604,\n 35.60617951552658\n ],\n [\n -77.35352669848585,\n 35.60617951552658\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"21","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dixon, Mikayla","contributorId":347037,"corporation":false,"usgs":false,"family":"Dixon","given":"Mikayla","email":"","affiliations":[{"id":83044,"text":"ECU","active":true,"usgs":false}],"preferred":false,"id":920610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culver, Stephen J.","contributorId":198984,"corporation":false,"usgs":false,"family":"Culver","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":27911,"text":"East Carolina University Greenville, North Carolina,USA","active":true,"usgs":false}],"preferred":false,"id":920611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mallinson, David J.","contributorId":198986,"corporation":false,"usgs":false,"family":"Mallinson","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":27911,"text":"East Carolina University Greenville, North Carolina,USA","active":true,"usgs":false}],"preferred":false,"id":920612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huber, Brian T.","contributorId":270771,"corporation":false,"usgs":false,"family":"Huber","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":36858,"text":"Smithsonian","active":true,"usgs":false}],"preferred":false,"id":920613,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Self-Trail, Jean 0000-0002-3018-4985 jstrail@usgs.gov","orcid":"https://orcid.org/0000-0002-3018-4985","contributorId":147370,"corporation":false,"usgs":true,"family":"Self-Trail","given":"Jean","email":"jstrail@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":920614,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spivey, Whittney 0000-0003-1111-3361 wspivey@usgs.gov","orcid":"https://orcid.org/0000-0003-1111-3361","contributorId":214849,"corporation":false,"usgs":true,"family":"Spivey","given":"Whittney","email":"wspivey@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":920615,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harris, W. 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The maps depict estimates of the extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 04206425 on the Cuyahoga River at Jaite, Ohio.
Water-surface profiles were computed for the stream reach by using a one-dimensional steady-state step-backwater model. The hydraulic model was calibrated to the current USGS streamgage data and then used to compute 15 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from 6 to 20 feet, which correspond to below “action stage” to “major flood stage” as reported by the National Weather Service. The simulated water-surface profiles were then used with a geographic information system digital elevation model derived from light detection and ranging data to delineate the areas flooded at each stage.
These maps, along with current stage data from the USGS streamgage and forecasted high-flow stages from the National Weather Service, can provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245115","collaboration":"Prepared in cooperation with the Northeast Ohio Regional Sewer District Board of Trustees","usgsCitation":"Whitehead, M.T., and Ostheimer, C.J., 2024, Flood-inundation maps for the Cuyahoga River at Jaite, Ohio, 2024: U.S. Geological Survey Scientific Investigations Report 2024–5115, 12 p., https://doi.org/10.3133/sir20245115.","productDescription":"Report: vi, 12 p.; 1 Data Release","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158402","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":497897,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118059.htm","linkFileType":{"id":5,"text":"html"}},{"id":464690,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O3MYQ0","text":"USGS data release","linkHelpText":"Geospatial datasets and hydraulic model for flood-inundation maps of Cuyahoga River at Jaite, Ohio"},{"id":464689,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5115/images/"},{"id":464688,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5115/sir20245115.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5115 XML"},{"id":464685,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5115/coverthb.jpg"},{"id":464686,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5115/sir20245115.pdf","text":"Report","size":"3.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5115 PDF"},{"id":464687,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245115/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5115 HTML"}],"country":"United States","state":"Ohio","city":"Jaite","otherGeospatial":"Cuyahoga River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.6,\n 41.3167\n ],\n [\n -81.6,\n 41.25\n ],\n [\n -81.5,\n 41.25\n ],\n [\n -81.5,\n 41.3167\n ],\n [\n -81.6,\n 41.3167\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
Conducting detailed investigations of large landslides is difficult, especially in the subsurface, largely due to environmental factors such as steep slopes, difficult access, and numerous objective hazards. These factors have made it challenging to accurately estimate the depth to the failure surface of the Barry Arm landslide, a large (roughly 108 cubic meters), deep-seated bedrock landslide in Prince William Sound, Alaska, recognized in 2019. The landslide has exhibited accelerated movement in recent years and poses a potential tsunamigenic hazard if rapid failure occurs. Failure surface depth, equivalent to landslide thickness, is a necessary metric for landslide-volume calculations and associated tsunami wave models. In this report, we used seismic noise recorded by a seismometer located on the Barry Arm landslide in Alaska to calculate the horizontal-to-vertical spectral ratio (HVSR) to investigate the site fundamental frequency (f0) and depth of the failure surface. To ensure that observed peak frequencies in the spectral ratio were related to the underlying stratigraphy (and not caused by other noise sources like nearby glaciers, topographic resonance, weather, or human activities), we also calculated HVSRs using earthquake signals, HVSRs at other seismic stations within a 2.5-kilometer radius, and a standard spectral ratio between the landslide station and other sites. We observed multiple peaks in the landslide HVSR curves at 1.5 hertz (Hz), 4–5 Hz, and 7–11 Hz. The frequencies of these peaks were consistent at the landslide site through time and across methods and were dissimilar to those identified at other seismic stations in the area, making it unlikely the peaks were caused by local noise.
Directional HVSRs calculated at 15-degree intervals showed amplification of the higher frequency peaks in the direction parallel to slip, indicating two-dimensional site effects. We used the distinct frequency peaks in the seismic record to develop a 4-layer conceptual model of the landslide wherein the top of the deepest layer represents the primary failure surface, or the boundary between damaged (mobile) and undamaged material. We inverted Rayleigh wave ellipticity curves within this 4-layer configuration with constraints on S-wave velocity and layer thickness based on analogous material properties identified in the literature. This was necessary absent any site-specific subsurface S-wave velocity data. The best-fitting models indicate a mean slope-normal depth to the failure surface of 188 (±9) meters (m), with additional stratigraphic boundaries at 4 and 20 m below ground surface, potentially representing layered motion. These results agree with and improve upon ranges estimated by previous studies and can support future modeling and assessment efforts at Barry Arm.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20241071","programNote":"Landslide Hazards Program","usgsCitation":"Collins, A.L., Allstadt, K.E., and Staley, D.M., 2024, Using the horizontal-to-vertical spectral ratio method to estimate thickness of the Barry Arm landslide, Prince William Sound, Alaska: U.S. Geological Survey Open-File Report 2024–1071, 25 p., https://doi.org/10.3133/ofr20241071.","productDescription":"vii, 25 p.","onlineOnly":"Y","ipdsId":"IP-164227","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":464747,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241071/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1043"},{"id":464705,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1071/ofr20241071.xml"},{"id":464704,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1071/images"},{"id":464670,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1071/ofr20241071.pdf","text":"Report","size":"12.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1071"},{"id":464669,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1071/coverthb.jpg"},{"id":497896,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118058.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Alaska","otherGeospatial":"Barry Arm landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.08474908024374,\n 61.17012644718048\n ],\n [\n -148.15558320312988,\n 61.17012644718048\n ],\n [\n -148.1702846248608,\n 61.12691732704323\n ],\n [\n -148.11415192370606,\n 61.12498100886924\n ],\n [\n -148.08474908024374,\n 61.17012644718048\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, Mail Stop 966
Denver, CO 80225
Sea otters (Enhydra lutris) were extirpated from much of their range in the North Pacific by the early 1900s but have made a remarkable recovery in Southeast Alaska. Sea otter populations have been particularly successful in Glacier Bay, Alaska, a protected tidewater glacier fjord with a diverse and productive nearshore habitat. Collection of sea otter foraging observations in Glacier Bay began in 1993, along with high-resolution aerial surveys that provide estimates of sea otter abundance and distribution. We integrated these two data sources to investigate how sea otter diet changed in space and time as sea otters established and spread across Glacier Bay. Specifically, we developed a multilevel Bayesian model to capture how sea otter diet at a location (the number, type, and size of prey collected) changed as a function of local cumulative otter abundance and the year in which the location was first occupied. This framework enabled us to estimate the sequence of sea otter prey selection and switching as prey populations responded to sea otter foraging pressure. We found that local sea otter diet changed substantially as the population established, shifting away from large urchins, crabs, and clams to Modiolus mussels and small urchins, and lastly to small clams and Mytilus mussels. We also found that sea otter diet at newly occupied sites changed as otters spread over the main channel and into the arms of Glacier Bay. Further, by 2019, sea otters across the bay were primarily foraging on small prey, regardless of the local occupancy history. The absence of a spatial gradient in the size of prey captured late in the study suggests that feedbacks between the top-down effects of sea otter foraging, sea otter dispersal processes, and local variation in habitat productivity may have homogenized the size structure of available prey across Glacier Bay.
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2024 - Data and knowledge gaps of a water bottling facility inventory and select water-use dataset, United States","interactions":[],"lastModifiedDate":"2025-12-22T21:02:44.45671","indexId":"sir20245106","displayToPublicDate":"2024-12-02T15:45:09","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":"2024-5106","displayTitle":"Data and Knowledge Gaps of a Water Bottling Facility Inventory and Select Water-Use Dataset, United States","title":"Data and knowledge gaps of a water bottling facility inventory and select water-use dataset, United States","docAbstract":"In 2023, the U.S. Geological Survey developed a national inventory of water bottling facilities for the United States, including information about locations, water sources, water use, and a collection of other attributes. The purpose of the inventory was to provide information about water bottling facilities needed to assess and improve understanding of local-, regional-, and national-scale hydrologic and socioeconomic effects resulting from water extraction for bottling. Beverage types coded in the North American Industry Classification System under subsector 312 (Beverage and Tobacco Product Manufacturing) were compiled; however, facility information was often not publicly available, time intensive to search manually, and difficult to verify because there are no nationally available facility lists. A separate evaluation of facilities in the Great Lakes region identified some facilities that were missing and some with incorrect information. Ancillary facility attributes were primarily available from a proprietary business dataset and water-use data could only be acquired for a small subset of facilities. These limitations and deficiencies may affect the types of analyses that can be done using the inventory information. Therefore, the following data-quality aspects are used to describe the information compiled in the facility and water-use tables: completeness, uniqueness, validity, timeliness, accuracy, consistency, and accessibility. The resulting implications of the data and knowledge gaps are that users of the data may need to make additional evaluations of the inventory information for some analyses.
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U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Inland fisheries face multiple, intensifying threats (i.e., proximate human pressures causing degraded ecological attributes) from land development, climate change, resource extraction, and competing demands for water resources. Planning for resiliency amidst these pressures requires understanding the factors that influence an inland fishery’s capacity to adapt to system changes under multiple threats. Incorporating expert knowledge can illuminate priority fisheries and provide important insights where data are otherwise limited. Using data from a global survey of 536 fishery professionals, this study examines perceptions of threats and adaptive capacity (i.e., ability to mitigate or respond to change) in major inland fisheries. We assessed associations across 29 different perceived threats and their ranked influence scores, tested agreement among five adaptive capacity domains (i.e., agency, assets, flexibility, learning, organization), and examined relationships between threats and adaptive capacity domains. Results provide quantitative evidence that the greatest threats to inland fisheries come from outside the fishing sector and that most inland fisheries face multiple threats. Results also support the five domains as a collective measure of adaptive capacity and illuminate a negative association between the threats to a fishery and a fishery’s adaptive capacity. These findings highlight the need for fishery managers to engage in decision making with non-fishery sectors (e.g., multi-sectoral management) and the prioritization of habitat and watershed-scale conservation and rehabilitation efforts for improved adaptability amidst ecological transformation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloenvcha.2024.102949","usgsCitation":"Stokes, G.L., Smidt, S.J., Tucker, E.L., Cleary, M., Funge-Smith, S., Valbo-Jorgensen, J., Lowe, B.S., and Lynch, A., 2024, Adaptive capacities of inland fisheries facing anthropogenic pressures: Global Environmental Change, v. 90, 102949, 11 p., https://doi.org/10.1016/j.gloenvcha.2024.102949.","productDescription":"102949, 11 p.","ipdsId":"IP-146936","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":489873,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gloenvcha.2024.102949","text":"Publisher Index Page"},{"id":464701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"90","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stokes, Gretchen L. 0000-0003-4202-6527","orcid":"https://orcid.org/0000-0003-4202-6527","contributorId":245640,"corporation":false,"usgs":false,"family":"Stokes","given":"Gretchen","email":"","middleInitial":"L.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":920016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smidt, Samuel J. 0000-0001-7728-2083","orcid":"https://orcid.org/0000-0001-7728-2083","contributorId":192816,"corporation":false,"usgs":false,"family":"Smidt","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":920017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, Emily L.","contributorId":346851,"corporation":false,"usgs":false,"family":"Tucker","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":920018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cleary, Matteo","contributorId":346852,"corporation":false,"usgs":false,"family":"Cleary","given":"Matteo","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":920019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funge-Smith, Simon 0000-0001-9974-5333","orcid":"https://orcid.org/0000-0001-9974-5333","contributorId":245642,"corporation":false,"usgs":false,"family":"Funge-Smith","given":"Simon","email":"","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":920020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Valbo-Jorgensen, John","contributorId":346853,"corporation":false,"usgs":false,"family":"Valbo-Jorgensen","given":"John","email":"","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":920021,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lowe, Benjamin S. 0000-0002-1879-254X","orcid":"https://orcid.org/0000-0002-1879-254X","contributorId":245641,"corporation":false,"usgs":false,"family":"Lowe","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":920022,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lynch, Abigail J. 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":207361,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","middleInitial":"J.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":920023,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70262202,"text":"70262202 - 2024 - Monitoring animal populations with cameras using open, multistate, N-mixture models","interactions":[],"lastModifiedDate":"2025-01-15T16:11:49.912007","indexId":"70262202","displayToPublicDate":"2024-12-02T09:06:48","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring animal populations with cameras using open, multistate, N-mixture models","docAbstract":"Remote cameras have become a mainstream tool for studying wildlife populations. For species whose developmental stages or states are identifiable in photographs, there are opportunities for tracking population changes and estimating demographic rates. Recent developments in hierarchical models allow for the estimation of ecological states and rates over time for unmarked animals whose states are known. However, this powerful class of models has been underutilized because they are computationally intensive, and model outputs can be difficult to interpret. Here, we use simulation to show how camera data can be analyzed with multistate, Dail-Madsen (hereafter multistate DM) models to estimate abundance, survival, and recruitment. We evaluated four commonly encountered scenarios arising from camera trap data (low and high abundance and 25% and 50% missing data) each with 18 different sample size combinations (camera sites = 40, 250; surveys = 4, 8, and 12; and years = 2, 5, 10) and evaluated the bias and precision of abundance, survival, and recruitment estimates. We also analyzed our empirical camera data on moose (Alces alces) with multistate DM models and compared inference with telemetry studies from the same time and region to assess the accuracy of camera studies to track moose populations. Most scenarios recovered the known parameters from our simulated data with higher accuracy and increased precision for scenarios with more sites, surveys, and/or years. Large amounts of missing data and fewer camera sites, especially at higher abundances, reduced accuracy, and precision of survival and recruitment. Our empirical analysis provided biologically realistic estimates of moose survival and recruitment and recovered the pattern of moose abundance across the region. Multistate DM models can be used for estimating demographic parameters from camera data when developmental states are clearly identifiable. We discuss several avenues for future research and caveats for using multistate DM models for large-scale population monitoring.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.70583","usgsCitation":"Siren, A.P., Hallworth, M.T., Kilborn, J.R., Bernier, C., Fortin, N., Geider, K., Patry, R., Cliche, R.M., Prout, L.S., Gifford, S., Wixsom, S., Morelli, T.L., and Wilson, T.L., 2024, Monitoring animal populations with cameras using open, multistate, N-mixture models: Ecology and Evolution, v. 14, no. 12, e70583, 13 p., https://doi.org/10.1002/ece3.70583.","productDescription":"e70583, 13 p.","ipdsId":"IP-171292","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466731,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.70583","text":"Publisher Index Page"},{"id":466423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire, 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Temuco, Chile","active":true,"usgs":false}],"preferred":false,"id":923484,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bernier, Chris A.","contributorId":348499,"corporation":false,"usgs":false,"family":"Bernier","given":"Chris A.","affiliations":[{"id":39587,"text":"Vermont Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":923485,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fortin, Nicholas L.","contributorId":348500,"corporation":false,"usgs":false,"family":"Fortin","given":"Nicholas L.","affiliations":[{"id":39587,"text":"Vermont Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":923486,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Geider, Katherina D.","contributorId":348501,"corporation":false,"usgs":false,"family":"Geider","given":"Katherina D.","affiliations":[{"id":39587,"text":"Vermont Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":923487,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Patry, Riley K.","contributorId":348502,"corporation":false,"usgs":false,"family":"Patry","given":"Riley K.","affiliations":[{"id":83367,"text":"Dartmouth College Woodlands","active":true,"usgs":false}],"preferred":false,"id":923488,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cliche, Rachel M.","contributorId":331624,"corporation":false,"usgs":false,"family":"Cliche","given":"Rachel","email":"","middleInitial":"M.","affiliations":[{"id":79254,"text":"United States Fish and Wildlife Service, Silvio O. Conte National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":923489,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Prout, Leighlan S.","contributorId":331625,"corporation":false,"usgs":false,"family":"Prout","given":"Leighlan","email":"","middleInitial":"S.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":923490,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Gifford, Suzanne J.","contributorId":348504,"corporation":false,"usgs":false,"family":"Gifford","given":"Suzanne J.","affiliations":[{"id":40027,"text":"United States Forest Service","active":true,"usgs":false}],"preferred":false,"id":923491,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wixsom, Scott","contributorId":339522,"corporation":false,"usgs":false,"family":"Wixsom","given":"Scott","affiliations":[{"id":81331,"text":"USDA Green Mountain National Forest","active":true,"usgs":false}],"preferred":false,"id":923492,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":923493,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wilson, Tammy L. 0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923494,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70261108,"text":"sir20245073 - 2024 - Assessment of the interconnection between Tampa Bay and the Floridan aquifer system: Historical groundwater data compilation and analysis, 1976–2022","interactions":[],"lastModifiedDate":"2025-12-22T21:06:29.381777","indexId":"sir20245073","displayToPublicDate":"2024-12-02T08:51:01","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":"2024-5073","displayTitle":"Assessment of the Interconnection Between Tampa Bay and the Floridan Aquifer System: Historical Groundwater Data Compilation and Analysis, 1976–2022","title":"Assessment of the interconnection between Tampa Bay and the Floridan aquifer system: Historical groundwater data compilation and analysis, 1976–2022","docAbstract":"The U.S. Geological Survey used existing data collected after the last major navigational channel modification in the mid-1980s to investigate groundwater levels and chloride concentrations in wells in the Floridan aquifer system and other aquifers beneath and near Tampa Bay. Tampa Bay is located on the west-central coast of Florida and provides access for commercial shipping. In 2021, the U.S. Army Corps of Engineers began to investigate alternatives to improve the efficiency of the deep-draft navigation channels within Tampa Bay, reduce costs, improve safety, and analyze the implications of modifying navigational channels. The Floridan aquifer system underlies Tampa Bay and is the primary source of public water supply in the region. Modifications to the channels have the potential to expose the Floridan aquifer system to the saltwater in Tampa Bay, with the potential to increase salinity in wells in the region. Other factors affecting the groundwater levels and the location of the freshwater/saltwater interface were also examined, including changes in sea level, groundwater extraction, and variations in climate.
Groundwater levels and well-construction reports were used to identify whether different aquifer units are well-connected. Twelve wells had available data before and after the last major channel modifications, which took place in the 1980s, with six datasets of chloride concentration available in areas along the northern and eastern coastline of Tampa Bay, which is nearest to historical dredging activities. Of these six, Kendall’s τ and p-values indicated increasing trends in chloride concentration for three datasets (TR 11-2, TR 10-2, 51), no trend in chloride concentration for two datasets (TR 9-3, 50), and a decreasing trend in chloride concentration for one dataset (TR 9-1). The upward trends in chloride concentration observed for TR 10-2 and 51 are likely the result of changes in local groundwater withdrawals. Well TR 11-2 had a gradual increasing trend in chloride concentration, fresh groundwater throughout the period of record, and a 3- to 4-foot increase in hydraulic head during the period of record, possibly caused by the construction and control of the Tampa Bypass Canal, resulting in changes to the regional potentiometric surface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245073","issn":"2328-0328","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Decker, J.D., 2024, Assessment of the interconnection between Tampa Bay and the Floridan aquifer system—Historical groundwater data compilation and analysis, 1976–2022: U.S. Geological Survey Scientific Investigations Report 2024–5073, 95 p., https://doi.org/10.3133/sir20245073.","productDescription":"Report: x, 95 p.; Data Release","numberOfPages":"110","onlineOnly":"Y","ipdsId":"IP-160506","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":464929,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245073/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5073 HTML"},{"id":464447,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://waterdata.usgs.gov/nwis/","text":"USGS water data for the Nation","linkHelpText":"- USGS National Water Information System database"},{"id":464444,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5073/sir20245073.pdf","size":"7.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5073"},{"id":464443,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5073/images"},{"id":464442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5073/coverthb.jpg"},{"id":464624,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5073/sir20245073.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5073 XML"},{"id":497902,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118056.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"Tampa Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.88462108750781,\n 28.199458304380144\n ],\n [\n -82.88462108750781,\n 27.315234231945368\n ],\n [\n -82.28125819751263,\n 27.315234231945368\n ],\n [\n -82.28125819751263,\n 28.199458304380144\n ],\n [\n -82.88462108750781,\n 28.199458304380144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Contact Us- USGS Publications Warehouse
","tableOfContents":"Energy consumption in the United States (U.S.) and across the world is shifting away from traditional fossil fuels like coal and oil, and towards natural gas and renewable sources, including hydrogen. Because gas demand is typically greatest during cold seasons and renewable sources sometimes produce variable supplies, it is important to store energy for use when demand exceeds supply. Whereas batteries and tanks typically store energy above ground, geologic (underground) storage may be able to retain much greater quantities of energy over much longer durations (e.g., Matos et al. 2019; Buursink et al. 2023). Consequently, the U.S. Geological Survey (USGS) is developing a methodology to locate new geologic energy storage sites with an initial focus on depleted hydrocarbon reservoirs followed by estimates of associated pore space or gas storage capacity.
Depleted hydrocarbon fields in Michigan Basin in the U.S. already host large quantities of injected natural gas (about 681 billion cubic feet of seasonal gas capacity in 42 gas storage facilities; U.S. Energy Information Administration 2024). Therefore, we used this basin as an example to assess potential new storage resources alongside existing storage capacity and carbon dioxide sequestration resources (e.g., Katz and Coats 1968; Haagsma et al. 2020). In Michigan Basin gas storage is taking place in reservoirs within several key hydrocarbon producing formations with different lithologies, including the informal Stray sandstone of the Michigan Formation and the pinnacle reefs of the Niagara Group (Gautier et al. 1995; Swezey et al. 2015). The assessment methodology introduced here consists of two steps relying on both well information and reservoir data.
","conferenceTitle":"Fifth EAGE Global Energy Transition Conference & Exhibition (GET 2024)","conferenceDate":"November 4-7, 2024","conferenceLocation":"Rotterdam, Netherlands","language":"English","publisher":"European Association of Geoscientists & Engineers","doi":"10.3997/2214-4609.202421307","usgsCitation":"Buursink, M., Wiens, A.M., Varela, B.A., Jones, M.M., and Freeman, P., 2024, New methodology for assessing underground natural gas storage resources – Example from Michigan Basin, United States, Fifth EAGE Global Energy Transition Conference & Exhibition (GET 2024), Rotterdam, Netherlands, November 4-7, 2024, 4 p., https://doi.org/10.3997/2214-4609.202421307.","productDescription":"4 p.","ipdsId":"IP-170280","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":482497,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Michigan basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -85.49094725089505,\n 43.986481126427975\n ],\n [\n -85.49094725089505,\n 43.8039809940218\n ],\n [\n -84.90247994380951,\n 43.8039809940218\n ],\n [\n -84.90247994380951,\n 43.986481126427975\n ],\n [\n -85.49094725089505,\n 43.986481126427975\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Buursink, Marc L. 0000-0001-6491-386X","orcid":"https://orcid.org/0000-0001-6491-386X","contributorId":203357,"corporation":false,"usgs":true,"family":"Buursink","given":"Marc L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":913225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiens, Ashton M. 0000-0002-7030-0602","orcid":"https://orcid.org/0000-0002-7030-0602","contributorId":271176,"corporation":false,"usgs":true,"family":"Wiens","given":"Ashton","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":913226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Varela, Brian A. 0000-0001-9849-6742 bvarela@usgs.gov","orcid":"https://orcid.org/0000-0001-9849-6742","contributorId":178091,"corporation":false,"usgs":true,"family":"Varela","given":"Brian","email":"bvarela@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":913227,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, Matthew M. 0000-0001-5996-1728","orcid":"https://orcid.org/0000-0001-5996-1728","contributorId":344228,"corporation":false,"usgs":true,"family":"Jones","given":"Matthew","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":913228,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":913229,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256975,"text":"70256975 - 2024 - A partially nonergodic ground-motion model for Fourier amplitude spectra for the San Francisco Bay area, California, USA","interactions":[],"lastModifiedDate":"2026-03-27T19:14:38.860534","indexId":"70256975","displayToPublicDate":"2024-12-01T13:54:15","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A partially nonergodic ground-motion model for Fourier amplitude spectra for the San Francisco Bay area, California, USA","docAbstract":"We develop a partially nonergodic ground-motion model (GMM) for Fourier amplitude spectra for the San Francisco Bay Area, California, USA, using the Bayless and Abrahamson (2019) GMM as a reference ergodic GMM and developing location-dependent adjustments to the predicted median and variance. We compile regional ground-motion data from moment magnitude (\uD835\uDC40w) >3 earthquakes occurring during 2000–2022 for which magnitude information is available in the U.S. Geological Survey Comprehensive Catalog (Guy et al., 2015). The data set predominantly consists of records from \uD835\uDC40w3.5–4.5 earthquakes but includes three well-recorded \uD835\uDC40w > 5 events. Ground-motion residuals are evaluated using the time-averaged shear-wave velocity in the top 30 m (\uD835\uDC49S30) from the California-specific map of Thompson et al. (2018) and basin-depth site parameters from the seismic velocity model of Aagaard and Hirakawa (2021). The \uD835\uDC49S30 dependence and basin-depth scaling of the reference ergodic GMM of Bayless and Abrahamson (2019) are evaluated and modified with the updated data set. We compute maps of site adjustments using a varying-coefficient model that considers the spatial correlation structure and uncertainties at each observation location. The spatial covariance model is developed using ground-motion residuals that are standardized by the uncertainty model, which allows for consideration of the aleatory variability in developing the site adjustments. The covariance model is fit considering the means and standard deviations of the site terms at all locations. The use of partially nonergodic median adjustments results in modified variance components of the within-event variability. Due to the low number of large-magnitude earthquakes that control seismic hazard in the data set, we do not modify between-event variance; however, we present adjustments to site-to-site variability for use in partially nonergodic hazard assessments.
The State Wildlife Action Plans (SWAPs) are proactive planning documents, known as “comprehensive wildlife conservation strategies,” that assess the health of each state’s wildlife and habitats, identify current management and conservation challenges, and outline needed actions to conserve natural resources over the long term. SWAPs are revised every 10 years, with the last revision in 2015 and the next revision anticipated in 2025. State managers have a long history of managing for threats such as land-use change, pollution, and harvest. However, they have expressed a lack of expertise and capacity to keep pace with the rapid advances in climate science and noted that much of the information available is not scaled to meet their needs; thus making the prospect of integrating climate information into SWAPs a daunting task. (Johnson, 2018; Yocum et al., 2021; Blandford, 2022).
This report, led by the Northeast Climate Adaptation Science Center (NE CASC), directly addresses SWAP climate science data needs through a Northeast regional synthesis across four key areas of climate science: 1) observed and projected climate changes, 2) species responses to climate change, 3) climate vulnerabilities and risks, and 4) scale-specific adaptation strategies and actions. In addition, case studies of climate adaptation efforts and climate threat to-action narratives provide illustrative examples of how climate change frameworks and tools are being operationalized in decision-making processes related to Regional Species of Greatest Conservation Need (RSGCN) and their habitats across the region. Lists of recent climate resources were also synthesized into extensive data tables to provide SWAP writing teams with a comprehensive platform of information to support content development (AFWA 2022).
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These disturbances can change riparian land cover by limiting water availability for riparian plant species, increasing fire intensity and frequency, and increasing soil and water salinities. In response to these forms of degradation, restoration efforts have begun to restore riparian habitats and native plant health, but vegetation greenness and corresponding plant water use continue to decline in unrestored reaches. Researchers from the U.S. Geological Survey (USGS) Southwest Biological Science Center are monitoring riparian plant health along the Colorado River Delta to support better ecohydrological decision-making. The researchers are helping a binational team to protect, restore, and maintain native vegetation within the 150-km long riparian corridor. Researchers are using Landsat 8 Operational Land Imager (OLI) data spanning 2014–2022 to measure greenness, a proxy for plant health, and actual evapotranspiration (ETa). Using an empirical model for ETa, evapotranspiration is estimated over each 16-day Landsat 8 OLI overpass period by considering the 8 days before and after the overpass date.
In their paper, researchers noted an increase in vegetation greenness within the restoration sites over nine years, with an average increase of 41.3%, which may be partially due to targeted water deliveries at the restoration sites. Conversely, greenness in adjacent, unrestored control areas declined by 27.3%. The study showed a 22.1% increase in ETa in restored areas, compared to a 30.8% reduction in unrestored regions. Restored sites in one restored area experienced ETa increases up to 12.2%, whereas their unrestored counterparts showed a decline of 21.4%. These estimates of riparian greenness and water use may assist natural resource managers who are tasked with allocating water and managing habitats within similar riparian corridors.
","language":"English","publisher":"Department of Interior","usgsCitation":"Nagler, P.L., 2024, Colorado Delta riparian plant health improvement, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-169348","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":491582,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://eros.usgs.gov/doi-remote-sensing-activities/2024/usgs/colorado-delta-riparian-plant-health-improvement"},{"id":498747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.10014888935096,\n 34.16115185091701\n ],\n [\n -116.10014888935096,\n 28.217554494522687\n ],\n [\n -109.77287116074022,\n 28.217554494522687\n ],\n [\n -109.77287116074022,\n 34.16115185091701\n ],\n [\n -116.10014888935096,\n 34.16115185091701\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":941661,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70251213,"text":"70251213 - 2024 - A journey to the center of the USGS National Strong-motion Project processing and beyond","interactions":[],"lastModifiedDate":"2026-03-23T15:48:01.142815","indexId":"70251213","displayToPublicDate":"2024-12-01T10:14:12","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"A journey to the center of the USGS National Strong-motion Project processing and beyond","docAbstract":"The United States Geological Survey (USGS) National Strong Motion Project (NSMP) has the primary U.S. government responsibility to acquire, process, and disseminate significant strong-motion earthquake ground motion records measured at surficial free-field stations, structures (buildings, dams, and bridges, and geotechnical arrays to the earthquake engineering community. As a result of the deployment of modern seismic instrumentation and growth of tools such as web-services, earthquake data from U.S. and international seismic networks are more accessible than ever. Our mission is to provide raw and processed strong-motion waveforms with PGA values greater than 0.1%g for M3.0 earthquakes and larger in California and M4.0 and larger within the conterminous US, Hawaii, Puerto Rico, and Alaska. Datasets of interest to the engineering and geophysics communities, such as event sequences in areas of induced seismicity and significant global events, are also processed and posted at the Center for Engineering Strong Motion Data (CESMD) at strongmotioncenter.org when available through collaboration with the international strong-motion data community. Here we outline (1) the NSMP’s current workflow to acquire, process, and distribute data at CESMD; (2) our new endeavours and collaborations focusing on comparison and integration of waveform processing software, development of techniques for metadata quality checks before and after earthquakes, and construction of a dynamic site characterization repository; and (3) our topics for possible collaboration topics across the global strong-motion community.","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30- July 5, 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association for Earthquake Engineering","usgsCitation":"Schleicher, L.S., Steidl, J.H., Thompson, E.M., Yong, A.K., Brody, J., Blair, J., Hearne, M., Aagaard, B.T., Hough, S.E., Shao, H., Huddleston, G., Heilpern, K., Marano, K., Ferragut, G., Worden, B., Wald, D.J., De Cristofaro, J., McClain, A.R., Dunham, B., Nget, D., Aragon, J., Gomez, J., Amador, V., Carrasco Rodriquez, V., Luna, E.E., Cembalski, D., Childs, D., Smith, J., Croker, D., and Gee, L., 2024, A journey to the center of the USGS National Strong-motion Project processing and beyond, 18th World Conference on Earthquake Engineering, Milan, Italy, June 30- July 5, 2024, 12 p.","productDescription":"12 p.","ipdsId":"IP-161746","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":501394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501393,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=25558&conference=18WCEE"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schleicher, Lisa Sue 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E.","contributorId":367246,"corporation":false,"usgs":false,"family":"Luna","given":"E.","middleInitial":"E.","affiliations":[],"preferred":false,"id":957209,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Cembalski, D.","contributorId":367247,"corporation":false,"usgs":false,"family":"Cembalski","given":"D.","affiliations":[],"preferred":false,"id":957210,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Childs, D.","contributorId":367248,"corporation":false,"usgs":false,"family":"Childs","given":"D.","affiliations":[],"preferred":false,"id":957211,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Smith, J.","contributorId":95013,"corporation":false,"usgs":true,"family":"Smith","given":"J.","email":"","affiliations":[],"preferred":false,"id":957212,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Croker, D.","contributorId":367249,"corporation":false,"usgs":false,"family":"Croker","given":"D.","affiliations":[],"preferred":false,"id":957213,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Gee, L.","contributorId":101066,"corporation":false,"usgs":true,"family":"Gee","given":"L.","email":"","affiliations":[],"preferred":false,"id":957214,"contributorType":{"id":1,"text":"Authors"},"rank":30}]}} ,{"id":70261410,"text":"70261410 - 2024 - Salmon data mobilization","interactions":[],"lastModifiedDate":"2024-12-09T16:12:58.122735","indexId":"70261410","displayToPublicDate":"2024-12-01T10:07:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5527,"text":"North Pacific Anadromous Fish Commission Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Salmon data mobilization","docAbstract":"Despite substantial research and conservation efforts, many salmon populations are in decline. Globally, salmon research is not delivering effective decision support products to help managers apply research insights as informed management actions. Data Mobilization (DM) is a key step towards building the wider evidence base required to deliver accountable, reliable, and usable scientific advice to managers. Best practices for DM are being adopted throughout the scientific community but have not permeated deeply into the culture of salmon research and conservation. To address this, we present a strategy for Salmon Data Mobilization (SDM). This strategy defines three spheres of agencies and practitioners that must interact to advance SDM: (1) authoritative bodies that can create policies to support SDM uptake; (2) agencies that can promote, fund, and implement those policies; and (3) the broad salmon community of practice that can support uptake of SDM within focused interest groups. We sketch a future for SDM and propose functional changes required to improve it throughout the community.
","language":"English","publisher":"North Pacific Anadromous Fish Commission","doi":"10.23849/npafcb7/x3rlpo23a","usgsCitation":"Diack, G., Bird, T., Akenhead, S.A., Bayer, J.M., Brophy, D., Bull, C., de Eyto, E., Hanson, N., Johnson, B.T., Jones, M., Knight, A., Nevoux, M., van der Strap, T., and Walker, A., 2024, Salmon data mobilization: North Pacific Anadromous Fish Commission Bulletin, p. 61-76, https://doi.org/10.23849/npafcb7/x3rlpo23a.","productDescription":"16 p.","startPage":"61","endPage":"76","ipdsId":"IP-150943","costCenters":[{"id":65563,"text":"Northwest Pacific Islands Regional Director's Office","active":true,"usgs":true}],"links":[{"id":466735,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.23849/npafcb7/x3rlpo23a","text":"Publisher Index 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Matt","contributorId":218164,"corporation":false,"usgs":false,"family":"Jones","given":"Matt","email":"","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":920509,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Knight, Alexis 0000-0001-8646-5324","orcid":"https://orcid.org/0000-0001-8646-5324","contributorId":346996,"corporation":false,"usgs":false,"family":"Knight","given":"Alexis","email":"","affiliations":[{"id":83027,"text":"Dept. of Fisheries and Oceans, Canada","active":true,"usgs":false}],"preferred":false,"id":920499,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Nevoux, Marie 0000-0003-1451-7732","orcid":"https://orcid.org/0000-0003-1451-7732","contributorId":347000,"corporation":false,"usgs":false,"family":"Nevoux","given":"Marie","email":"","affiliations":[{"id":83032,"text":"Ecosystem Dynamics and Sustainability, INRAE, Institut Agro, IFREMER, France","active":true,"usgs":false}],"preferred":false,"id":920505,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"van der Strap, Tim 0000-0002-0053-0795","orcid":"https://orcid.org/0000-0002-0053-0795","contributorId":346999,"corporation":false,"usgs":false,"family":"van der Strap","given":"Tim","email":"","affiliations":[{"id":83031,"text":"Hakai Institute, Canada","active":true,"usgs":false}],"preferred":false,"id":920504,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Walker, Alan 0000-0001-5934-1449","orcid":"https://orcid.org/0000-0001-5934-1449","contributorId":346997,"corporation":false,"usgs":false,"family":"Walker","given":"Alan","email":"","affiliations":[{"id":83030,"text":"Centre for Environment, Fisheries, and Aquaculture Science, UK","active":true,"usgs":false}],"preferred":false,"id":920502,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70264770,"text":"70264770 - 2024 - Models no not provide proof: An example of model ambiguity and application of isotopic data in a mine pit lake","interactions":[],"lastModifiedDate":"2026-02-11T16:11:52.104027","indexId":"70264770","displayToPublicDate":"2024-12-01T10:02:17","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Models no not provide proof: An example of model ambiguity and application of isotopic data in a mine pit lake","docAbstract":"Geochemical and hydrologic models of pit lakes are commonly used in environmental regulatory decisions to predict future water quality and hydrologic conditions and to understand existing pit lakes. Models may be used to quantify sulfide oxidation, predict thermal/chemical stratification and mixing, and better understand connections between pit lakes and aquifers. One concern related to the hydrologic character of pit lakes is if they are terminal (a groundwater sink with no outflow) or flowthrough (both receiving groundwater inflow and discharging to groundwater). This question was pertinent to the Liberty pit lake, a small acidic pit lake formed in a former Cu deposit in south-central Nevada where potentiometric and geochemical data potentially indicate pit-lake outflow. Potential discharge to groundwater from the pit lake was evaluated using a water-balance model, but uncertainty in hydraulic parameters led to ambiguity in the hydrologic character. Stable isotopes of water were then sampled from the pit lake and adjacent groundwater wells, which unambiguously indicated the lack of an evaporative signature in downgradient groundwater because the groundwater did not plot on a hypothetical mixing line between evaporated pit lake water and observed meteoric recharge. This methodology provided a more effective and more data-driven approach for understanding pit-lake hydrology. Although predictive models are required to quantify reasonable bounds on future conditions, many models contain substantial uncertainty and are not well suited in some environments. Datasets that provide more clear lines of evidence could be collected from existing pit lakes whenever possible to inform water-rock interaction, limnological behavior, and connectivity to adjacent groundwater.
","conferenceTitle":"International Conference on Acid Rock Drainage","conferenceDate":"September 16-20, 2024","conferenceLocation":"Halifax, Nova Scotia, Canada","language":"English","publisher":"Canadian Institute of Mining, Metallurgy and Petroleum","usgsCitation":"Newman, C.P., 2024, Models no not provide proof: An example of model ambiguity and application of isotopic data in a mine pit lake, International Conference on Acid Rock Drainage, Halifax, Nova Scotia, Canada, September 16-20, 2024, p. 1345-1356.","productDescription":"12 p.","startPage":"1345","endPage":"1356","ipdsId":"IP-164239","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":499757,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Liberty pit lake","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":931595,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70261846,"text":"70261846 - 2024 - A metapopulation strategy to support long term conservation of genetic diversity in Department of the Interior bison","interactions":[],"lastModifiedDate":"2024-12-30T16:03:04.072363","indexId":"70261846","displayToPublicDate":"2024-12-01T09:52:45","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":18517,"text":"Science Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SR—2024/229","title":"A metapopulation strategy to support long term conservation of genetic diversity in Department of the Interior bison","docAbstract":"Once numbering in the tens of millions, plains bison (Bison bison bison) were nearly driven to extinction with only a few hundred individuals remaining by the late 19th century. Plains bison have since recovered to approximately 20,000 animals managed in conservation herds throughout North America, yet substantial challenges to their recovery remain.
The Department of the Interior (DOI) is working with diverse partners to steward approximately 11,000 bison in 18 conservation herds across 12 states. Most herds exist in areas without native predators, and removals are required to keep herd sizes at or below carrying capacity. The loss of genetic diversity within bison, and the fact that most DOI herds are relatively small and isolated from each other with no opportunity for natural gene flow, raises concerns about maintaining genetic diversity over the long term. Connecting populations through gene flow (i.e., creating a metapopulation) can minimize loss of genetic diversity, both within and across populations.
Management of DOI bison conservation herds has historically varied across bureaus and conservation units. Adopting a national perspective on bison conservation was identified as a priority in the 2008 Department of the Interior Bison Conservation Initiative (BCI). The concept of metapopulation management as a potential tool to maximize the conservation of genetic diversity among DOI herds was first described in this 2008 Initiative and was specifically encouraged in the 2010 DOI Bison Conservation Genetics Workshop report (Dratch and Gogan 2010). In the 2020 BCI, the DOI re-affirmed its commitment to conserving bison as native, North American wildlife.
This document establishes a framework for a nationally coordinated strategy for bison managed by the DOI to support the genetic conservation goals outlined in the 2020 BCI. This is a decisionmaking framework that guides managers through the process of determining when and how to consider translocations. Decisions and actions within the framework are informed by analysis and interpretation of data housed in an integrated, relational database that will be initially populated with the most current data and updated annually thereafter. It provides science-based guidance on how to conserve DOI bison genetic diversity through strategic translocations, while also considering cattle introgression and bison health. We illustrate how this Strategy can be used to guide the establishment of new conservation herds and discuss what it means to be a DOI partner. Finally, this is intended to be used as a living document that will evolve as needs and technologies change.
","language":"English","publisher":"National Park Service","doi":"10.36967/2307352","usgsCitation":"Oyler-McCance, S.J., Jones, L.C., McCann, B., Zimmerman, S.J., Schoenecker, K., Santavy, P., and Moynahan, B., 2024, A metapopulation strategy to support long term conservation of genetic diversity in Department of the Interior bison: Science Report NPS/SR—2024/229, vi, 47 p., https://doi.org/10.36967/2307352.","productDescription":"vi, 47 p.","ipdsId":"IP-170017","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":465531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":922015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Lee C.","contributorId":149998,"corporation":false,"usgs":false,"family":"Jones","given":"Lee","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":922016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCann, Blake","contributorId":347580,"corporation":false,"usgs":false,"family":"McCann","given":"Blake","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":922017,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zimmerman, Shawna J 0000-0003-3394-6102 szimmerman@usgs.gov","orcid":"https://orcid.org/0000-0003-3394-6102","contributorId":238076,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Shawna","email":"szimmerman@usgs.gov","middleInitial":"J","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":922018,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":922019,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santavy, Paul","contributorId":347091,"corporation":false,"usgs":false,"family":"Santavy","given":"Paul","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":922020,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Moynahan, Brendan J","contributorId":347582,"corporation":false,"usgs":false,"family":"Moynahan","given":"Brendan J","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":922021,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270848,"text":"70270848 - 2024 - Climate vulnerability assessment of Oregon hatchery programs","interactions":[],"lastModifiedDate":"2025-08-28T14:57:36.722075","indexId":"70270848","displayToPublicDate":"2024-12-01T09:51:57","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Climate vulnerability assessment of Oregon hatchery programs","docAbstract":"The goal of this project was to assess the vulnerability to climate change impacts for a sample set of hatchery programs representing different geographic areas and primary anadromous species raised in state-managed Oregon hatcheries (Summary Figure 1). Freshwater and marine ecosystem processes can significantly influence salmon and steelhead survival, and understanding how these factors have affected historical returns can help managers evaluate the climate vulnerability of hatchery stocks. We examined stock-specific trends in smolt-to-adult returns (SARs), which represent the proportion of smolts released from the hatchery that are recovered in fisheries or as returning adult spawners. SARs are among the most consistent long term estimators of survival for hatchery-origin stocks. Depending on the stock, adult recoveries could occur in marine fisheries, freshwater fisheries, returns to the hatchery or another collection facility, and spawning ground surveys. We collected time series data on relevant ecological indicators and used generalized additive models (GAMs) to explore both univariate and multivariate relationships with SARs for each hatchery stock.
An additional aspect of this assessment was to evaluate the climate vulnerability of resident trout stocking programs in the Department's East and West regions, incorporating insights from Oregon Department of Fish and Wildlife (ODFW) staff interviews and published data on the thermal tolerance of hatchery trout stocks. This assessment is provided in the ‘Climate vulnerability of trout stocking programs’ subsection below.
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\"}}]}","noUsgsAuthors":false,"publicationDate":"2024-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrett, Hannah","contributorId":332245,"corporation":false,"usgs":false,"family":"Barrett","given":"Hannah","email":"","affiliations":[{"id":79431,"text":"Oregon State University, Department of Fisheries, Wildlife, and Conservation Sciences","active":true,"usgs":false}],"preferred":false,"id":947579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":947212,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70263666,"text":"70263666 - 2024 - Restoration monitoring metric framework: Integrating innovative remote-sensing technologies: Comparisons between field and remotely sensed vegetation surveys of restored forested and grassland sites in Ohio","interactions":[],"lastModifiedDate":"2025-02-19T15:52:43.381831","indexId":"70263666","displayToPublicDate":"2024-12-01T09:45:54","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9958,"text":"Final Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"ERDC/EL TR-24-19","title":"Restoration monitoring metric framework: Integrating innovative remote-sensing technologies: Comparisons between field and remotely sensed vegetation surveys of restored forested and grassland sites in Ohio","docAbstract":"Restoration monitoring is generally perceived as costly and time-consuming, yet the concept of universal restoration monitoring metrics is trending for evaluation of restoration performance across spatial scales, project boundaries, and jurisdictions. Natural Resource Damage Assessment and Restoration (NRDAR) practitioners seek to restore natural resources injured by oil spills or hazardous substance releases into the environment. Therefore, a multiagency team [US Army Engineer Research and Development Center (ERDC), US Department of the Interior (DOI), and US Department of Energy (DOE)] developed and field-tested a multitiered monitoring framework, illustrating a range of field and remote-sensing techniques and methodologies. The restoration monitoring framework and field demonstration offer a unique methodology to acquire and evaluate simultaneously collected, multiscale/multiplatform data. The result of this research provides new insights to (1) assist planning, implementing, and monitoring restoration progress and effectiveness; and (2) apply common monitoring methods, endpoints, and metrics to other types of ecosystem restoration initiatives. Although the aim was to inform monitoring and management of areas that had been injured, these methods could also be used to inform restoration monitoring practices in a broader context, benefiting environmental stewardship missions of all project partners.","language":"English","publisher":"US Army Corps of Engineers, Engineer Research and Development Center","doi":"10.21079/11681/49490","collaboration":"DOI Office of Restoration and Damage Assessment; U.S. Fish and Wildlife Service; National Park Service; Bureau of Reclamation; Bureau of Land Management; DOI Office of the Solicitor","usgsCitation":"Theel, H., Reif, M., Altman, S., Saltus, C., Beane, N.R., Jackson, S.S., Bourne, S., Laird, J., Hammond, S., Matheson, K., Berry, T., Hinck, J.E., Grabner, K., Stroh, E., Tillitt, R., and Skrabis, K., 2024, Restoration monitoring metric framework: Integrating innovative remote-sensing technologies: Comparisons between field and remotely sensed vegetation surveys of restored forested and 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,{"id":70273175,"text":"70273175 - 2024 - Global Food Security Support Analysis Data (GFSAD) using remote sensing in support of food and water security in the 21st century","interactions":[],"lastModifiedDate":"2025-12-18T15:48:13.209769","indexId":"70273175","displayToPublicDate":"2024-12-01T09:43:09","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Global Food Security Support Analysis Data (GFSAD) using remote sensing in support of food and water security in the 21st century","docAbstract":"The overarching goal of this chapter is to provide a comprehensive overview of the state-of-art of global cropland mapping procedures using remote sensing as characterized and envisioned by the “Global Food Security Support Analysis Data @ 30 m (GFSAD30)” project working group team. First, the chapter will provide an overview of existing cropland maps and their characteristics along with establishing the gaps in knowledge related to global cropland mapping. Second, definitions of cropland mapping along with key parameters involved in cropland mapping based on their importance in food security analysis, and cropland naming conventions for standardized cropland mapping using remote sensing will be presented. Third, existing methods and approaches for cropland mapping will be discussed. This will include the type of remote sensing data used in cropland mapping and their characteristics along with discussions on the secondary data, field-plot data, and cropland mapping algorithms. Fourth, currently existing global cropland products derived using remote sensing will be presented and discussed. Fifth, a synthesis of all existing products leading to a composite global cropland extent version 1.0 (GCE V1.0) is presented and discussed. Sixth, a way forward for advanced global cropland mapping is visualized.
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Alexander Drive, Research Triangle Park, NC, 27709, USA","active":true,"usgs":false}],"preferred":false,"id":952601,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Milesi, Cristina","contributorId":364370,"corporation":false,"usgs":false,"family":"Milesi","given":"Cristina","affiliations":[],"preferred":false,"id":952602,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ozdogan, Mutlu","contributorId":138721,"corporation":false,"usgs":false,"family":"Ozdogan","given":"Mutlu","email":"","affiliations":[{"id":12508,"text":"Department of Forest and Wildlife Ecology, University of Wisconsin, 1710 University Ave., Room 285, Madison, WI 53726, USA","active":true,"usgs":false}],"preferred":false,"id":952603,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Congalton, Russell G.","contributorId":364371,"corporation":false,"usgs":false,"family":"Congalton","given":"Russell","middleInitial":"G.","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":952604,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tilton, James","contributorId":149289,"corporation":false,"usgs":false,"family":"Tilton","given":"James","email":"","affiliations":[],"preferred":false,"id":952605,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sankey, Temuulen Tsagaan","contributorId":364373,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","middleInitial":"Tsagaan","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":952606,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Massey, Richard","contributorId":149291,"corporation":false,"usgs":false,"family":"Massey","given":"Richard","affiliations":[],"preferred":false,"id":952607,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Phalke, Aparna","contributorId":149292,"corporation":false,"usgs":false,"family":"Phalke","given":"Aparna","email":"","affiliations":[],"preferred":false,"id":952608,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Yadav, Kamini","contributorId":138720,"corporation":false,"usgs":false,"family":"Yadav","given":"Kamini","affiliations":[{"id":12507,"text":"Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA","active":true,"usgs":false}],"preferred":false,"id":952609,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70264692,"text":"70264692 - 2024 - Separating signals in elevation data improves supervised machine learning predictions for hydrothermal favorability","interactions":[],"lastModifiedDate":"2025-03-19T14:39:10.244939","indexId":"70264692","displayToPublicDate":"2024-12-01T09:27:09","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Separating signals in elevation data improves supervised machine learning predictions for hydrothermal favorability","docAbstract":"A recent study identified topography (land surface elevation above sea level) as an important input dataset (feature) for predicting the location of hydrothermal systems in the Great Basin in Nevada. Yet, topography is generally a result of more than one geological process and may consequently contain multiple distinct signals. For example, the geologic evolution of the Great Basin has produced both crustal thickening (i.e., regional-scale trends in elevation) and thinning via Basin and Range extensional faulting (i.e., valley-scale topographic relief). We postulate that these geologic processes may affect the occurrence of hydrothermal systems differently. Therefore, we separate the regional trend from the valley-scale signal in the Great Basin, and then use them separately to evaluate the importance of each as predictors for hydrothermal favorability.\n\nOur prior work applying supervised machine learning (ML) using the data from the Nevada Machine Learning Project demonstrated that employing a training strategy that randomly selects negative training sites produces better performing models for predicting hydrothermal favorability than a training strategy that uses expert-selected negatives. The models created using both training strategies exhibited a west-east geographic trend in the predictions for the favorability of hydrothermal resources. These models generally predicted higher favorability in western Nevada and lower favorability in eastern Nevada. This west-east trend in predicted favorability correlates with elevation across the Great Basin, which trends higher from west to east.\n \nBy separating the original elevation feature into distinct features for elevation trend (i.e., regional-scale topography) and detrended elevation (i.e., valley-scale or local relative topography), we find that models using the separated topographic signals consistently outperform competing models that use the original elevation feature. Although western Nevada still exhibits higher favorability than eastern Nevada, using separated signals for regional elevation and local structure reduces the west-east prediction trend in the region and emphasizes structures associated with hydrothermal upflow. This work emphasizes how carefully engineering features to represent geological conditions relevant to hydrothermal systems allows ML algorithms to detect important patterns for predicting hydrothermal resource favorability and leads to better model performance.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Using the Earth to save the Earth: Geothermal Resources Council transactions","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geothermal Rising","usgsCitation":"Caraccioli Salinas, P., Mordensky, S.P., DeAngelo, J., Burns, E., and Lipor, J., 2024, Separating signals in elevation data improves supervised machine learning predictions for hydrothermal favorability, in Using the Earth to save the Earth: Geothermal Resources Council transactions, v. 48, p. 2217-2236.","productDescription":"20 p.","startPage":"2217","endPage":"2236","ipdsId":"IP-165662","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":483520,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483519,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1035032"}],"volume":"48","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Caraccioli Salinas, Pascal Domingo 0009-0003-6711-8257","orcid":"https://orcid.org/0009-0003-6711-8257","contributorId":352438,"corporation":false,"usgs":true,"family":"Caraccioli Salinas","given":"Pascal Domingo","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931277,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931278,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":931279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":931280,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}