Engineered and natural submerged coastal structures (e.g., submerged breakwaters and reefs) modify incident wave fields and thus can alter hydrodynamic processes adjacent to coastlines. Although submerged structures are generally assumed to promote beach protection by dissipating waves offshore and creating sheltered conditions in their lee, their interaction with waves can result in mean wave-driven circulation patterns that may either promote shoreline accretion or erosion. Here, we analyse the mean flow patterns and shoreline water levels (wave runup) in the lee of idealised impermeable submerged structures with a phase-resolved nonhydrostatic numerical model. Waves propagating over submerged structures can drive either a 2-cell mean (wave-averaged) circulation, which is characterised by diverging flows behind the structure and at the shoreline, or 4-cell circulation, with converging flows at the shoreline and diverging flows in the immediate lee of the structure. The numerical results show that the mode of circulation can be predicted with a set of relationships depending on the incoming wave heights, the structure crest level, and distance to the shoreline (or structure depth). Qualitative agreement between the mean flow and proxies for the sediment transport using an energetics approach suggest that the mean flow can be a robust proxy for inferring sediment transport patterns. For the cases considered, the submerged structures had a minimal influence on shoreline wave setup and wave runup despite the wave energy dissipation by the structures due to alongshore wave energy fluxes in the lee. Consequently, these results suggest that the coastal protection provided by the range of impermeable submerged structures we modelled is primarily due to their capacity to promote beach accretion.
The fathead minnow (Pimephales promelas) is a model species for toxicological research. A high-quality genome reference sequence is available, and genomic methods are increasingly used in toxicological studies of the species. However, phylogenetic relationships within the genus remain incompletely known and little population-genomic data are available for fathead minnow despite the potential effects of genetic background on toxicological responses. On the other hand, a wealth of extant samples is stored in museum collections that in principle allow fine-scale analysis of contemporary and historical genetic variation.
Here we use short-read shotgun resequencing to investigate sequence variation among and within Pimephales species. At the genus level, our objectives were to resolve phylogenetic relationships and identify genes with signatures of positive diversifying selection. At the species level, our objective was to evaluate the utility of archived-sample resequencing for detecting selective sweeps within fathead minnow, applied to a population introduced to the San Juan River of the southwestern United States sometime prior to 1950.
We recovered well-supported but discordant phylogenetic topologies for nuclear and mitochondrial sequences that we hypothesize arose from mitochondrial transfer among species. The nuclear tree supported bluntnose minnow (P. notatus) as sister to fathead minnow, with the slim minnow (P. tenellus) and bullhead minnow (P. vigilax) more closely related to each other. Using multiple methods, we identified 11 genes that have diversified under positive selection within the genus. Within the San Juan River population, we identified selective-sweep regions overlapping several sets of related genes, including both genes that encode the giant sarcomere protein titin and the two genes encoding the MTORC1 complex, a key metabolic regulator. We also observed elevated polymorphism and reduced differentation among populations (FST) in genomic regions containing certain immune-gene clusters, similar to what has been reported in other taxa. Collectively, our data clarify evolutionary relationships and selective pressures within the genus and establish museum archives as a fruitful resource for characterizing genomic variation. We anticipate that large-scale resequencing will enable the detection of genetic variants associated with environmental toxicants such as heavy metals, high salinity, estrogens, and agrichemicals, which could be exploited as efficient biomarkers of exposure in natural populations.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.13954","usgsCitation":"Klymus, K.E., Hrabik, R.A., Thompson, N., and Cornman, R.S., 2022, Genome resequencing clarifies phylogeny and reveals patterns of selection in the toxicogenomics model Pimephales promelas: PeerJ, v. 10, e13954, 34 p., https://doi.org/10.7717/peerj.13954.","productDescription":"e13954, 34 p.","ipdsId":"IP-138759","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":446657,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.13954","text":"Publisher Index Page"},{"id":435715,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XXEUNR","text":"USGS data release","linkHelpText":"Genomic variation in the genus Pimephales: raw sequence data and single-nucleotide polymorphisms"},{"id":405677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":849724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hrabik, Robert A.","contributorId":148008,"corporation":false,"usgs":false,"family":"Hrabik","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":849725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":849726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":849727,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239304,"text":"70239304 - 2022 - Methods for robust estimates of tree biomass from pollen accumulation rates: Quantifying paleoecological reconstruction uncertainty","interactions":[],"lastModifiedDate":"2023-01-09T12:40:19.984396","indexId":"70239304","displayToPublicDate":"2022-08-25T06:38:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Methods for robust estimates of tree biomass from pollen accumulation rates: Quantifying paleoecological reconstruction uncertainty","docAbstract":"Pollen accumulation rates (PAR, grains cm–2 year–1) have been shown to be a reliable but methodologically complex bioproxy for quantitative reconstruction of past tree abundance. In a prior study, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus, and the pollen group TC (Taxaceae and Cupressaceae families) – were robust and precise estimators of contemporary tree biomass. This paper expands our earlier work. Here, we more fully evaluate the errors associated with biomass reconstructions to identify weaknesses and recommend improvements in PAR-based reconstructions of forest biomass. We account for uncertainty in our biomass proxy in a formal, coherent fashion. The greatest error was introduced by the age models, underscoring the need for improved statistical approaches to age-depth modeling. Documenting the uncertainty in pollen vegetation models should be standard practice in paleoecology. We also share insights gained from the delineation of the relevant source area of pollen, advances in Bayesian 210Pb modeling, the importance of site selection, and the use of independent data to corroborate biomass estimates. Lastly, we demonstrate our workflow with a new dataset of reconstructed tree biomass between 1850 and 2018 AD from lakes in the Klamath Mountains, California. Our biomass records followed a broad trend of low mean biomass in the ∼1850s followed by large contemporary increases, consistent with expectations of forest densification due to twentieth century fire suppression policies in the American West. More recent reconstructed tree biomass estimates also corresponded with silviculture treatments occurring within the relevant source area of pollen of our lake sites.
Mercury (Hg) deposition was reconstructed in sediment cores from lakes in two coastal U.S. National Parks: Acadia National Park (ANP) and Cape Cod National Seashore (CCNS), to fill an important spatial gap in Hg deposition records and to explore changing sources of Hg and processes affecting Hg accumulation in these coastal sites. Recent Hg deposition chronology was assessed using (1) a newly developed lead-210 (210Pb) based sediment age model which employs 7Be to constrain deposition and sediment mixing of 210Pb-excess, (2) coinciding Pb flux and isotope ratios (206Pb/207Pb), and (3) Hg isotope ratios and their response to changes in Hg flux. At both sites, Hg flux increased substantially from pre-1850 levels, with accumulation in ANP peaking in the 1970s, whereas in CCNS, Hg levels were highest in recent sediments. Negative values of δ202Hg and Δ199Hg indicated terrestrially-derived Hg was a major constituent of Hg flux to Sargent Mountain Pond, ANP, although recent decreases in Hg flux were in agreement with precipitation Hg records, indicating a rapid watershed response. By contrast, δ202Hg and Δ199Hg profiles in Long Pond, CNNS reflect direct Hg deposition, but disturbances in the sedimentary record were indicated by bomb fallout radionuclide inventories and by peaks in both Pb and Hg isotope depth profiles. These cores provided poor reconstructions of atmospheric deposition and reveal responses that are decoupled from emissions reduction due to complex post-depositional redistribution of atmospheric metals including Hg. The application of multiple tracers of Hg deposition provide insight into the sources and pathways governing Hg accumulation in these lakes.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D2EM00214K","usgsCitation":"Taylor, V., Landis, J.D., and Janssen, S., 2022, Tracing the sources and depositional history of mercury to coastal northeastern U.S. lakes: Environmental Science: Processes and Impacts, v. 24, p. 1805-1820, https://doi.org/10.1039/D2EM00214K.","productDescription":"16 p.","startPage":"1805","endPage":"1820","ipdsId":"IP-142940","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, Massachusetts","otherGeospatial":"Acadia National Park, Cape Cod National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.40568542480469,\n 44.21863119293724\n ],\n [\n -68.17,\n 44.21863119293724\n ],\n [\n -68.17,\n 44.44995770844175\n ],\n [\n -68.40568542480469,\n 44.44995770844175\n ],\n [\n -68.40568542480469,\n 44.21863119293724\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.01380920410156,\n 41.74467659677642\n ],\n [\n -69.88128662109375,\n 41.74467659677642\n ],\n [\n -69.88128662109375,\n 41.949787926673416\n ],\n [\n -70.01380920410156,\n 41.949787926673416\n ],\n [\n -70.01380920410156,\n 41.74467659677642\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Vivien F.","contributorId":296971,"corporation":false,"usgs":false,"family":"Taylor","given":"Vivien F.","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":853015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landis, Joshua D.","contributorId":211459,"corporation":false,"usgs":false,"family":"Landis","given":"Joshua","email":"","middleInitial":"D.","affiliations":[{"id":38249,"text":"Department of Earth Sciences, Dartmouth College, Hanover, NH","active":true,"usgs":false}],"preferred":false,"id":853016,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853017,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235909,"text":"70235909 - 2022 - Trace elements in olivine fingerprint the source of 2018 magmas and shed light on explosive-effusive eruption cycles at Kīlauea Volcano","interactions":[],"lastModifiedDate":"2022-08-25T15:06:33.994252","indexId":"70235909","displayToPublicDate":"2022-08-24T10:03:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Trace elements in olivine fingerprint the source of 2018 magmas and shed light on explosive-effusive eruption cycles at Kīlauea Volcano","docAbstract":"Understanding magma genesis and the evolution of intensive parameters (temperature, pressure, composition, degree of melting) in the mantle source of highly active volcanic systems is crucial for interpreting magma supply changes over time and recognizing cyclic behavior to anticipate future volcanic behavior. Major and trace elements in olivine are commonly used to study variations in mantle lithologies and melting conditions (e.g., temperature, pressure, oxygen fugacity) affecting the mantle over time. Here, we track the temporal evolution of primary melts through the most recent cycle of explosive and effusive eruptions at Kīlauea (Hawai‘i), which spans the last ∼500 years. We report major and trace elements in olivine from the last explosive period (∼1500 – early 1820’s Keanakāko‘i Tephra) and the most recent decade of the current effusive period (2018 LERZ, 2015–2018 Pu‘u‘ō‘ō, 2008–2018 lava lake and 2020 eruption in Halema‘uma‘u). Scandium concentrations in olivine allow characterizing changes in mantle source between 1500 and 2018, and suggest that the recent (2015–2018) magma feeding the Pu‘u‘ō‘ō cone did not significantly interact with the magma that erupted in the LERZ in 2018. The evolution of olivine and melt compositions over the past 500 years is not easily reconcilable with variations in mantle potential temperature, pressure of mantle melt pooling and storage, or oxygen fugacity. Instead, Sc, Mn, and Co concentrations and Ni/Mg ratio in high forsterite (Fo >87) olivine advocate for an increase in the proportion of clinopyroxene in the mantle source associated with a slightly higher degree of partial melting from 1500 to 2018. Changes in primitive melt compositions and degrees of mantle melting may well modulate magma supply to the crust and formation-replenishment of steady or ephemeral summit reservoirs, and thereby control transitions between explosive and effusive periods at Kīlauea. Analyzing trace elements in olivine at Kīlauea and elsewhere could therefore provide important clues on subtle changes occurring at the mantle level that might herald changes in volcanic behavior.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2022.117769","usgsCitation":"Mourey, A., Shea, T., Lynn, K.J., Lerner, A., Lambart, S., Costa, F., Oalmann, J., Lee, R.L., and Gansecki, C., 2022, Trace elements in olivine fingerprint the source of 2018 magmas and shed light on explosive-effusive eruption cycles at Kīlauea Volcano: Earth and Planetary Science Letters, v. 595, 117769, 13 p., https://doi.org/10.1016/j.epsl.2022.117769.","productDescription":"117769, 13 p.","ipdsId":"IP-133433","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446674,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://insu.hal.science/insu-03776398","text":"Publisher Index Page"},{"id":405578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.5224609375,\n 19.199647272639126\n ],\n [\n -154.80148315429688,\n 19.199647272639126\n ],\n [\n -154.80148315429688,\n 19.484718252643216\n ],\n [\n -155.5224609375,\n 19.484718252643216\n ],\n [\n -155.5224609375,\n 19.199647272639126\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"595","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mourey, Adrien","contributorId":264238,"corporation":false,"usgs":false,"family":"Mourey","given":"Adrien","affiliations":[{"id":39163,"text":"University of Hawaii - Manoa","active":true,"usgs":false}],"preferred":false,"id":849658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shea, Thomas","contributorId":236886,"corporation":false,"usgs":false,"family":"Shea","given":"Thomas","affiliations":[{"id":47560,"text":"University of Hawaii Manoa","active":true,"usgs":false}],"preferred":false,"id":849659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lynn, Kendra J. 0000-0001-7886-4376","orcid":"https://orcid.org/0000-0001-7886-4376","contributorId":290327,"corporation":false,"usgs":true,"family":"Lynn","given":"Kendra","email":"","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":849660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lerner, Allan","contributorId":205264,"corporation":false,"usgs":false,"family":"Lerner","given":"Allan","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":849661,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lambart, Sarah","contributorId":295555,"corporation":false,"usgs":false,"family":"Lambart","given":"Sarah","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":849662,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Costa, Fidel","contributorId":184169,"corporation":false,"usgs":false,"family":"Costa","given":"Fidel","email":"","affiliations":[],"preferred":false,"id":849663,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Oalmann, Jeffrey","contributorId":295556,"corporation":false,"usgs":false,"family":"Oalmann","given":"Jeffrey","email":"","affiliations":[{"id":16631,"text":"Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":849664,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lee, R. Lopaka 0000-0002-6352-0340","orcid":"https://orcid.org/0000-0002-6352-0340","contributorId":223777,"corporation":false,"usgs":true,"family":"Lee","given":"R.","email":"","middleInitial":"Lopaka","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":849665,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gansecki, Cheryl 0000-0001-5581-9097","orcid":"https://orcid.org/0000-0001-5581-9097","contributorId":215620,"corporation":false,"usgs":false,"family":"Gansecki","given":"Cheryl","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":849666,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236044,"text":"70236044 - 2022 - Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network","interactions":[],"lastModifiedDate":"2022-08-26T12:04:48.992953","indexId":"70236044","displayToPublicDate":"2022-08-24T07:02:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Democratizing macroecology: Integrating unoccupied aerial systems with the National Ecological Observatory Network","docAbstract":"Macroecology research seeks to understand ecological phenomena with causes and consequences that accumulate, interact, and emerge across scales spanning several orders of magnitude. Broad-extent, fine-grain information (i.e., high spatial resolution data over large areas) is needed to adequately capture these cross-scale phenomena, but these data have historically been costly to acquire and process. Unoccupied aerial systems (UAS or drones carrying a sensor payload) and the National Ecological Observatory Network (NEON) make the broad-extent, fine-grain observational domain more accessible to researchers by lowering costs and reducing the need for highly specialized equipment. Integration of these tools can further democratize macroecological research, as their strengths and weaknesses are complementary. However, using these tools for macroecology can be challenging because mental models are lacking, thus requiring large up-front investments in time, energy, and creativity to become proficient. This challenge inspired a working group of UAS-using academic ecologists, NEON professionals, imaging scientists, remote sensing specialists, and aeronautical engineers at the 2019 NEON Science Summit in Boulder, Colorado, to synthesize current knowledge on how to use UAS with NEON in a mental model for an intended audience of ecologists new to these tools. Specifically, we provide (1) a collection of core principles for collecting high-quality UAS data for NEON integration and (2) a case study illustrating a sample workflow for processing UAS data into meaningful ecological information and integrating it with NEON data collected on the ground—with the Terrestrial Observation System—and remotely—from the Airborne Observation Platform. With this mental model, we advance the democratization of macroecology by making a key observational domain—the broad-extent, fine-grain domain—more accessible via NEON/UAS integration.
The Great Lakes basin was historically populated by multiple, coevolved coregonine species, but much of that diversity has been lost. In Lakes Erie and Ontario, both lake whitefish (Coregonus clupeaformis) and cisco (Coregonus artedi) occurred in high numbers before habitat degradation, overfishing, invasive species, and other factors caused significant declines. There is growing interest in restoring these populations, and suggested actions include restoration of critical habitats such as spawning habitat. Unfortunately, our current understanding of lake whitefish and cisco spawning habitat characteristics and locations in these lakes is limited. To highlight areas of potential importance for conservation and restoration, we used random forest models and data on historical spawning locations to predict lake whitefish and cisco spawning habitats based on hypothesized key factors including wind fetch, ice cover duration, distance from 1st and 6th order tributaries, and lake bottom substrate. Our model accurately predicted spawning habitat locations for 71% and 54% of cases for lake whitefish and cisco, respectively. Fetch was the most important variable in the lake whitefish model, with spawning habitats being most likely to occur in regions of low to moderate fetch. Cisco spawning habitats were most likely to occur in areas of relatively low fetch near a 1st order stream. We used these models to predict spawning habitat locations for both species across Lakes Erie, Ontario, and St. Clair. Our results improve our understanding of lake whitefish and cisco spawning habitat characteristics and will aid in the spatial prioritization of actions to restore these native fishes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.08.014","usgsCitation":"Schaefer, H.M., Honsey, A.E., Bunnell, D., Weidel, B., DeBruyne, R., Diana, J.S., Gorsky, D., and Roseman, E., 2022, Predicting physical and geomorphic habitat associated with historical lake whitefish and cisco spawning locations in Lakes Erie and Ontario: Journal of Great Lakes Research, v. 48, no. 6, p. 1636-1646, https://doi.org/10.1016/j.jglr.2022.08.014.","productDescription":"11 p.","startPage":"1636","endPage":"1646","ipdsId":"IP-136743","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":405591,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Erie, Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.509033203125,\n 44.731125592643274\n ],\n [\n -80.343017578125,\n 44.22158376545796\n ],\n [\n -82.298583984375,\n 42.84375132629021\n ],\n [\n -83.265380859375,\n 42.80346172417078\n ],\n [\n -84.287109375,\n 41.705728515237524\n ],\n [\n -82.452392578125,\n 40.772221877329024\n ],\n [\n -79.376220703125,\n 41.30257109430557\n ],\n [\n -76.35498046875,\n 43.18915769654922\n ],\n [\n -75.498046875,\n 43.76315996157264\n ],\n [\n -75.509033203125,\n 44.731125592643274\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"48","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schaefer, Hannah M","contributorId":216810,"corporation":false,"usgs":false,"family":"Schaefer","given":"Hannah","email":"","middleInitial":"M","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":849641,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Honsey, Andrew Edgar 0000-0001-7535-1321","orcid":"https://orcid.org/0000-0001-7535-1321","contributorId":295468,"corporation":false,"usgs":true,"family":"Honsey","given":"Andrew","email":"","middleInitial":"Edgar","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":849642,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunnell, David 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":217344,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":849643,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":849644,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeBruyne, Robin 0000-0002-9232-7937","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":240598,"corporation":false,"usgs":false,"family":"DeBruyne","given":"Robin","affiliations":[{"id":48111,"text":"Univ. Toledo","active":true,"usgs":false}],"preferred":false,"id":849645,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diana, James S.","contributorId":216547,"corporation":false,"usgs":false,"family":"Diana","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":37387,"text":"University of Michigan","active":true,"usgs":false}],"preferred":false,"id":849646,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gorsky, Dimitry 0000-0003-1708-539X","orcid":"https://orcid.org/0000-0003-1708-539X","contributorId":295528,"corporation":false,"usgs":false,"family":"Gorsky","given":"Dimitry","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":849647,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":849648,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266889,"text":"70266889 - 2022 - Sedimentary geochemistry of deepwater slope deposits in southern Lake Tanganyika (East Africa): Effects of upwelling and minor lake level oscillations","interactions":[],"lastModifiedDate":"2025-05-14T14:38:39.481043","indexId":"70266889","displayToPublicDate":"2022-08-23T09:33:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2451,"text":"Journal of Sedimentary Research","onlineIssn":"1938-3681","printIssn":"1527-1404","active":true,"publicationSubtype":{"id":10}},"title":"Sedimentary geochemistry of deepwater slope deposits in southern Lake Tanganyika (East Africa): Effects of upwelling and minor lake level oscillations","docAbstract":"Lake Tanganyika ranks among the most valuable modern analogs for understanding depositional processes of carbonaceous sediments in ancient tropical rifts. Prior research on Lake Tanganyika has emphasized the importance of bottom-water anoxia, depositional processes (hemipelagic settling versus gravity flows), and large-scale (100s of meters) lake level change on the quality of sedimentary organic matter content. Here, facies analysis and numerous organic geochemical tools (elemental, carbon isotope, and programmed pyrolysis) were applied to a radiocarbon-dated core from southern Lake Tanganyika to investigate the accumulation of carbonaceous sediments in a deepwater slope environment influenced by high-frequency climatic fluctuations accompanied by only minor (10s of meters) lake level changes. Considerable variability in lithofacies and geochemistry characterizes the ∼ 1030-year-long core record, chiefly driven by climate-mediated changes to the lake's upwelling system. Laminated diatom oozes and sapropels with mean total organic carbon (TOC) concentrations and hydrogen indices of 6.9 wt.% and 385 mg hydrocarbon/g TOC, respectively, characterize sediments deposited during periods of strong upwelling and variable water levels. Silty sediments deposited via gravity-flow processes were likewise rich in organic matter, likely due to preservation-enhancing bottom-water anoxia. Dilution by reworked tephra was the chief constraint on organic enrichment at the study site. Data from this study reveal that oscillations in atmospheric and limnological processes in the absence of major shoreline movements can result in geochemically diverse deepwater slope sediments, which have implications for improving depositional models of petroliferous continental rift basins.
","language":"English","publisher":"Society for Sedimentary Geology","doi":"10.2110/jsr.2021.104","usgsCitation":"McGlue, M., Ellis, G.S., Brannon, M., Latimer, J., Stone, J., Ivory, S., Mganza, N., Soreghan, M.J., and Scholz, C., 2022, Sedimentary geochemistry of deepwater slope deposits in southern Lake Tanganyika (East Africa): Effects of upwelling and minor lake level oscillations: Journal of Sedimentary Research, v. 92, no. 8, p. 721-738, https://doi.org/10.2110/jsr.2021.104.","productDescription":"18 p.","startPage":"721","endPage":"738","ipdsId":"IP-128441","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":485933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Tanganyika","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 27.668481366027635,\n -3.1820760248469355\n ],\n [\n 27.668481366027635,\n -9.112434947743992\n ],\n [\n 31.779025954731935,\n -9.112434947743992\n ],\n [\n 31.779025954731935,\n -3.1820760248469355\n ],\n [\n 27.668481366027635,\n -3.1820760248469355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"92","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-23","publicationStatus":"PW","contributors":{"authors":[{"text":"McGlue, Michael M.","contributorId":225229,"corporation":false,"usgs":false,"family":"McGlue","given":"Michael M.","affiliations":[{"id":41081,"text":"Department of Geosciences, The University of Arizona, Tucson AZ","active":true,"usgs":false}],"preferred":false,"id":937045,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellis, Geoffrey S. 0000-0003-4519-3320 gsellis@usgs.gov","orcid":"https://orcid.org/0000-0003-4519-3320","contributorId":1058,"corporation":false,"usgs":true,"family":"Ellis","given":"Geoffrey","email":"gsellis@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":937046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brannon, McKenzie A","contributorId":355181,"corporation":false,"usgs":false,"family":"Brannon","given":"McKenzie A","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":937047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Latimer, Jennifer C","contributorId":355182,"corporation":false,"usgs":false,"family":"Latimer","given":"Jennifer C","affiliations":[{"id":17777,"text":"Indiana State University","active":true,"usgs":false}],"preferred":false,"id":937048,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stone, Jeffery S","contributorId":355183,"corporation":false,"usgs":false,"family":"Stone","given":"Jeffery S","affiliations":[{"id":17777,"text":"Indiana State University","active":true,"usgs":false}],"preferred":false,"id":937049,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ivory, Sarah J.","contributorId":138493,"corporation":false,"usgs":false,"family":"Ivory","given":"Sarah J.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":937050,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mganza, Neema E","contributorId":355184,"corporation":false,"usgs":false,"family":"Mganza","given":"Neema E","affiliations":[{"id":84721,"text":"Tanzania Petroleum Development Corporation","active":true,"usgs":false}],"preferred":false,"id":937051,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Soreghan, Michael J.","contributorId":347062,"corporation":false,"usgs":false,"family":"Soreghan","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":83052,"text":"School of Geosciences, University of Oklahoma, Norman, OK, 73019, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":937052,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Scholz, Christopher A.","contributorId":149267,"corporation":false,"usgs":false,"family":"Scholz","given":"Christopher A.","affiliations":[{"id":17692,"text":"Syracuse University, Syracuse NY","active":true,"usgs":false}],"preferred":false,"id":937053,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70238032,"text":"70238032 - 2022 - NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations","interactions":[],"lastModifiedDate":"2022-11-04T12:26:51.26005","indexId":"70238032","displayToPublicDate":"2022-08-23T07:22:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations","docAbstract":"This report addresses system characterization of the Instituto Nacional de Pesquisas Espaciais Amazônia-1 satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Amazônia-1 is a four-band imager with a 64-meter (m) pixel ground sample distance. Amazônia-1 was launched in February 2021 into a Sun-synchronous orbit of 752 kilometers with an inclination of 98.4 degrees and a swath width of 850 kilometers. The satellite has an expected lifetime of about 4 years. More information on Amazônia-1 is available in the “Land Remote Sensing Satellites Online Compendium” (https://calval.cr.usgs.gov/apps/compendium).
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that the Amazônia-1 satellite has an interior geometric performance in the range of −3.584 m (−0.056 pixel) to 0.320 m (0.005 pixel) in easting and −1.984 m (−0.031 pixel) to 2.048 m (0.032 pixel) in northing in band-to-band registration, an exterior geometric performance of −37.256 m (−0.621 pixel) to 54.758 m (0.913 pixel) in easting and −12.684 m (−0.211 pixel) to 54.898 m (0.915 pixel) in northing offset in comparison to the Landsat 8 Operational Land Imager, a radiometric performance in the range of 0.030 to 0.143 in offset and 0.662 to 0.825 in slope, and a spatial performance in the range of 1.62 to 2.06 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.062 to 0.115.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"System characterization of Earth observation sensors","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030N","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., and Park, S., 2022, System characterization report on the Amazônia-1 multispectral sensor, chap. N of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 33 p., https://doi.org/10.3133/ofr20211030N.","productDescription":"v, 33 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-142103","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/n/ofr20211030n.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030–N"},{"id":405397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/n/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
In 2002 and 2003 a collaborative effort was undertaken between Lawrence Berkeley National Laboratory, Sandia National Laboratories, the U.S. Geological Survey (USGS) Menlo Park, the USGS Hawaiian Volcano Observatory, and Electromagnetic Instruments Inc. to study the Kīlauea volcano in Hawaii using the magnetotelluric (MT) technique. The work was motivated by a desire to improve understanding of the magma reservoirs and conduits within Kīlauea and the East and Southwest Rift zones, which has implications for understanding Kīlauea's plumbing system. An improved understanding of the rift zones has implications in understanding large-scale landslides that are generated in the Hilina Slump, which produce significant impacts on coastal communities. Up to eight stations operated simultaneously, with multiple remote reference sites, and data were processed using multi-station robust processing techniques. In total, data were acquired at 70 sites over the Southwest and East rift zones. Good to excellent quality data were obtained even in the harshest conditions, such as those encountered on the fresh lava flows of the East Rift Zone, where electrical contact resistances are on the order of 100 kΩ. A three-dimensional (3D) MT model study was done to guide interpretation of the observed MT measurements. Synthetic modeling demonstrates that conductive bodies in the upper 3 km can be spatially resolved where MT station sampling is good. Resistivity anomalies in the 3D inversions have a high degree of spatial correlation with previously published seismic velocity anomalies beneath Kīlauea. Melt fractions between 0.096 and 0.117 are calculated for the Kīlauea and Puʻuʻōʻō low resistivity anomalies, respectively.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024418","usgsCitation":"Hoversten, G., Gasperikova, E., Mackie, R., Myer, D., Kauahikaua, J.P., Newman, G.A., and Cuevas, N., 2022, Magnetotelluric investigations of the Kīlauea Volcano, Hawaii: Journal of Geophysical Research: Solid Earth, v. 127, no. 8, e2022JB024418, 24 p., https://doi.org/10.1029/2022JB024418.","productDescription":"e2022JB024418, 24 p.","ipdsId":"IP-135899","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446701,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022jb024418","text":"External Repository"},{"id":405386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawai'i","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.29131889343262,\n 19.396901484778134\n ],\n [\n -155.28496742248535,\n 19.39892544698541\n ],\n [\n -155.2786159515381,\n 19.399087362874425\n ],\n [\n -155.2730369567871,\n 19.39827778181811\n ],\n [\n -155.2676296234131,\n 19.400949384016776\n ],\n [\n -155.26385307312012,\n 19.403944764615613\n ],\n [\n -155.25887489318848,\n 19.40629245679785\n ],\n [\n -155.2511501312256,\n 19.4109877394962\n ],\n [\n -155.24694442749023,\n 19.408882974361152\n ],\n [\n -155.24042129516602,\n 19.408478208711944\n ],\n [\n -155.23784637451172,\n 19.410906787494763\n ],\n [\n -155.23836135864258,\n 19.414306736852605\n ],\n [\n -155.24102210998535,\n 19.414306736852605\n ],\n [\n -155.2434253692627,\n 19.418758943963656\n ],\n [\n -155.24943351745605,\n 19.42353481237166\n ],\n [\n -155.25372505187985,\n 19.427662992293143\n ],\n [\n -155.25853157043457,\n 19.4298484568423\n ],\n [\n -155.26239395141602,\n 19.43081976498137\n ],\n [\n -155.2672004699707,\n 19.430576938491143\n ],\n [\n -155.2708911895752,\n 19.4313863587134\n ],\n [\n -155.27406692504883,\n 19.432033891986865\n ],\n [\n -155.2799892425537,\n 19.429605628899985\n ],\n [\n -155.28857231140134,\n 19.42013505603468\n ],\n [\n -155.29492378234863,\n 19.416492381036047\n ],\n [\n -155.2946662902832,\n 19.413416280797698\n ],\n [\n -155.29646873474118,\n 19.410663931248664\n ],\n [\n -155.29698371887207,\n 19.407021044033193\n ],\n [\n -155.29131889343262,\n 19.396901484778134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hoversten, G.M.","contributorId":295409,"corporation":false,"usgs":false,"family":"Hoversten","given":"G.M.","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":849391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gasperikova, Erika","contributorId":193561,"corporation":false,"usgs":false,"family":"Gasperikova","given":"Erika","affiliations":[],"preferred":false,"id":849392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mackie, Randall","contributorId":295410,"corporation":false,"usgs":false,"family":"Mackie","given":"Randall","email":"","affiliations":[{"id":63861,"text":"CGG Multiphysics","active":true,"usgs":false}],"preferred":false,"id":849393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Myer, David","contributorId":206497,"corporation":false,"usgs":false,"family":"Myer","given":"David","email":"","affiliations":[],"preferred":false,"id":849394,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kauahikaua, James P. 0000-0003-3777-503X jimk@usgs.gov","orcid":"https://orcid.org/0000-0003-3777-503X","contributorId":2146,"corporation":false,"usgs":true,"family":"Kauahikaua","given":"James","email":"jimk@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":849395,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newman, Greg A.","contributorId":295412,"corporation":false,"usgs":false,"family":"Newman","given":"Greg","email":"","middleInitial":"A.","affiliations":[{"id":63862,"text":"Lawrence Berkeley National Laboratory, Sandia National Laboratory","active":true,"usgs":false}],"preferred":false,"id":849396,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cuevas, Nestor","contributorId":295414,"corporation":false,"usgs":false,"family":"Cuevas","given":"Nestor","email":"","affiliations":[{"id":63864,"text":"Electromagnetic Instruments","active":true,"usgs":false}],"preferred":false,"id":849397,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255669,"text":"70255669 - 2022 - GSPy: A new toolbox and data standard for Geophysical Datasets","interactions":[],"lastModifiedDate":"2026-03-10T13:23:35.700958","indexId":"70255669","displayToPublicDate":"2022-08-22T06:45:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17985,"text":"Frontiers in Earth Science - Environmental Informatics and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"GSPy: A new toolbox and data standard for Geophysical Datasets","docAbstract":"The diversity of geophysical methods and datatypes, as well as the isolated nature of various specialties (e.g., electromagnetic, seismic, potential fields) leads to a profusion of separate data file formats and documentation conventions. This can hinder cooperation and reduce the impact of datasets researchers have invested in heavily to collect and prepare. An open, portable, and well-supported community data standard could greatly improve the interoperability, transferability, and long-term archival of geophysical data. Airborne geophysical methods particularly need an open and accessible data standard, and they exemplify the complexity that is common in geophysical datasets where critical auxiliary information on the survey and system parameters are required to fully utilize and understand the data. Here, we propose a new Geophysical Standard, termed the GS convention, that leverages the well-established and widely used NetCDF file format and builds on the Climate and Forecasts (CF) metadata convention. We also present an accompanying open-source Python package, GSPy, to provide methods and workflows for building the GS-standardized NetCDF files, importing and exporting between common data formats, preparing input files for geophysical inversion software, and visualizing data and inverted models. By using the NetCDF format, handled through the Xarray Python package, and following the CF conventions, we standardize how metadata is recorded and directly stored with the data, from general survey and system information down to specific variable attributes. Utilizing the hierarchical nature of NetCDF, GS-formatted files are organized with a root Survey group that contains global metadata about the geophysical survey. Data are then organized into subgroups beneath Survey and are categorized as Tabular or Raster depending on the geometry and point of origin for the data. Lastly, the standard ensures consistency in constructing and tracking coordinate reference systems, which is vital for accurate portability and analysis. Development and adoption of a NetCDF-based data standard for geophysical surveys can greatly improve how these complex datasets are shared and utilized, making the data more accessible to a broader science community. The architecture of GSPy can be easily transferred to additional geophysical datatypes and methods in future releases.
A paucity of detailed relative sea-level (RSL) reconstructions from low latitudes hinders efforts to understand the global, regional, and local processes that cause RSL change. We reconstruct RSL change during the past ~5 ka using cores of mangrove peat at two sites (Snipe Key and Swan Key) in the Florida Keys. Remote sensing and field surveys established the relationship between peat-forming mangroves and tidal elevation in South Florida. Core chronologies are developed from age-depth models applied to 72 radiocarbon dates (39 mangrove wood macrofossils and 33 fine-fraction bulk peat). RSL rose 3.7 m at Snipe Key and 5.0 m at Swan Key in the past 5 ka, with both sites recording the fastest century-scale rate of RSL rise since ~1900 CE (~2.1 mm/a). We demonstrate that it is feasible to produce near-continuous reconstructions of RSL from mangrove peat in regions with a microtidal regime and accommodation space created by millennial-scale RSL rise. Decomposition of RSL trends from a network of reconstructions across South Florida using a spatio-temporal model suggests that Snipe Key was representative of regional RSL trends, but Swan Key was influenced by an additional local-scale process acting over at least the past five millennia. Geotechnical analysis of modern and buried mangrove peat indicates that sediment compaction is not the local-scale process responsible for the exaggerated RSL rise at Swan Key. The substantial difference in RSL between two nearby sites highlights the critical need for within-region replication of RSL reconstructions to avoid misattribution of sea-level trends, which could also have implications for geophysical modeling studies using RSL data for model tuning and validation.
Large-area, high-resolution digital elevation models (DEMs) created from light detection and ranging (LIDAR) and/or multibeam echosounder data sets are commonly used in many scientific disciplines. These DEMs can span thousands of square kilometers, typically with a spatial resolution of 1 m or finer, and can be difficult to process and analyze without specialized computers and software. Such DEMs often can be subsampled to expedite analysis with negligible impact on results for large-scale geospatial analyses. Subsampling can be achieved by creating a grid of points that specify the locations from which to extract elevation values from the DEM. This paper presents a method that can be used to accurately perform subsampling of large-scale, high-resolution DEMs using GIS software. This subsampling method was applied to two LIDAR-derived DEMs encompassing 242 km2 of the northern Florida Reef Tract as an example application and to test subsampling accuracy. Results indicate that subsampling 1-m-resolution DEMs using a 2-m-spaced grid results in no significant difference in mean elevation or other basic statistics for analyses performed over multiple spatial scales ranging from 1 km2 to 242 km2.
An uncrewed aerial vehicle (UAV) multirotor aeromagnetic system using a 5-m sling load for a magnetic sensor system is described and characterized. Four magnetic surveys with identical flight lines were completed, at two nominal altitudes of 25 and 40 m. The surveys were used to assess the repeatability of data collected with the described UAV aeromagnetic system, and comparison with a ground survey was used to assess the precision. The 5-m sling is designed to reduce magnetic interference from the UAV. A magnetic compensation model was developed for this particular UAV aeromagnetic system. This custom compensation model reduces the noise in the collected data by a factor of five over the uncompensated data, and the 5-m sling further reduces the noise by an estimated factor of four over a similar system with a 3-m sling. The precision of the UAV aeromagnetic system was then estimated to be sub-nT, with 50% of the noise component <0.3 nT, and 90% <0.6 nT.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jappgeo.2022.104779","usgsCitation":"Phelps, G., Bracken, R.E., Spritzer, J., and White, D.S., 2022, Achieving sub-nanoTesla precision in multirotor UAV aeromagnetic surveys: Journal of Applied Geophysics, v. 206, 104779, 16 p., https://doi.org/10.1016/j.jappgeo.2022.104779.","productDescription":"104779, 16 p.","ipdsId":"IP-130875","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":446713,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jappgeo.2022.104779","text":"Publisher Index Page"},{"id":435723,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HCY1NQ","text":"USGS data release","linkHelpText":"UASmagpy: Python code for compensating rotary-wing sling-load UAS aeromagnetic data"},{"id":435722,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MXMM5","text":"USGS data release","linkHelpText":"Uncrewed aerial system aeromagnetic test survey at the Boulder Magnetic Observatory"},{"id":411343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"206","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Phelps, Geoffrey 0000-0003-1958-2736 gphelps@usgs.gov","orcid":"https://orcid.org/0000-0003-1958-2736","contributorId":127489,"corporation":false,"usgs":true,"family":"Phelps","given":"Geoffrey","email":"gphelps@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":860859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bracken, Robert E. 0000-0001-7759-2743 rbracken@usgs.gov","orcid":"https://orcid.org/0000-0001-7759-2743","contributorId":2640,"corporation":false,"usgs":true,"family":"Bracken","given":"Robert","email":"rbracken@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":860860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spritzer, John 0000-0002-2147-530X jspritzer@usgs.gov","orcid":"https://orcid.org/0000-0002-2147-530X","contributorId":244361,"corporation":false,"usgs":true,"family":"Spritzer","given":"John","email":"jspritzer@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":860861,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, David S.","contributorId":173069,"corporation":false,"usgs":false,"family":"White","given":"David","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":860862,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237002,"text":"70237002 - 2022 - Quantifying modeling uncertainty in simplified beam models for building response prediction","interactions":[],"lastModifiedDate":"2022-10-17T16:35:12.627089","indexId":"70237002","displayToPublicDate":"2022-08-19T10:28:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5007,"text":"Structural Control and Health Monitoring","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying modeling uncertainty in simplified beam models for building response prediction","docAbstract":"The use of simple models for response prediction of building structures is preferred in earthquake engineering for risk evaluations at regional scales, as they make computational studies more feasible. The primary impediment in their gainful use presently is the lack of viable methods for quantifying (and reducing upon) the modeling errors/uncertainties they bear. This study presents a Bayesian calibration method wherein the modeling error is embedded into the parameters of the model. The method is specifically described for coupled shear-flexural beam models here, but it can be applied to any parametric surrogate model. The major benefit the method offers is the ability to consider the modeling uncertainty in the forward prediction of any degree-of-freedom or composite response regardless of the data used in calibration. The method is extensively verified using two synthetic examples. In the first example, the beam model is calibrated to represent a similar beam model but with enforced modeling errors. In the second example, the beam model is used to represent the detailed finite element model of a 52-story building. Both examples show the capability of the proposed solution to provide realistic uncertainty estimation around the mean prediction.
","language":"English","publisher":"Wiley","doi":"10.1002/stc.3078","usgsCitation":"Ghahari, S., Sargsyan, K., Celebi, M., and Taciroglu, E., 2022, Quantifying modeling uncertainty in simplified beam models for building response prediction: Structural Control and Health Monitoring, v. 29, no. 11, e3078, https://doi.org/10.1002/stc.3078.","productDescription":"e3078","ipdsId":"IP-139979","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":446722,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1882634","text":"Publisher Index Page"},{"id":407408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-08-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Ghahari, S. Farid","contributorId":296977,"corporation":false,"usgs":false,"family":"Ghahari","given":"S. Farid","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":853025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sargsyan, Khachik","contributorId":296978,"corporation":false,"usgs":false,"family":"Sargsyan","given":"Khachik","email":"","affiliations":[{"id":64263,"text":"Sandia Laboratories","active":true,"usgs":false}],"preferred":false,"id":853026,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":853027,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taciroglu, Ertugrul","contributorId":296979,"corporation":false,"usgs":false,"family":"Taciroglu","given":"Ertugrul","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":853028,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259623,"text":"70259623 - 2022 - High-resolution marine seismic imaging of the Seattle fault zone: Near surface insights into fault zone geometry, Quaternary deformation, and long-term evolution","interactions":[],"lastModifiedDate":"2024-10-17T12:00:47.133274","indexId":"70259623","displayToPublicDate":"2022-08-19T06:59:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution marine seismic imaging of the Seattle fault zone: Near surface insights into fault zone geometry, Quaternary deformation, and long-term evolution","docAbstract":"The Seattle fault zone (SFZ) is a north‐directed thrust fault system that underlies the greater Seattle metropolitan area. Evidence of past land level changes, landslides, liquefaction, and a local tsunami indicate that this 70‐km‐long fault system can host up to M 7–7.5 earthquakes. Both the geometry and earthquake recurrence of the SFZ are debated and surveys of the shallow subsurface have not yet been incorporated into deeper crustal‐scale structural interpretations, especially where the SFZ cuts across marine portions of the Puget Lowland. Here we use a new high‐resolution marine seismic reflection dataset to image fault‐related deformation in Quaternary sediments and Tertiary bedrock throughout Puget Sound and Lake Washington. We use this perspective of shallow geology as a link between existing crustal‐scale geophysical insights into fault geometry at depth and paleoseismological observations of faulting at the surface and propose a refined structural model for the SFZ. We interpret that our new seismic reflection data in the Rich Passage area of Puget Sound images evidence of an inactive, south‐dipping strand of the SFZ, which is overprinted by Quaternary folding and slip along north‐dipping backthrusts within the hanging wall of a blind, south‐dipping fault located 6 km farther north. To explain these results, we propose that the SFZ is a normal sequence fault propagation fold that has stepped northward through time, and we show the plausibility of this model through trishear forward modeling. Growth strata and faulting imaged in Quaternary sediments in Lake Washington and Rich Passage are consistent with the spatial distribution of folding and backthrusting that occurred during an M 7–7.5 earthquake in A.D. 900–930, corroborating existing evidence that the SFZ has been active throughout the Quaternary.
In the southern Appalachian Mountains of the southeastern USA, bat communities in high-elevation habitats tend to be relatively under-surveyed. High-elevation habitats may provide important habitat to certain species (i.e., migratory tree bats), and may serve as climate refugia during droughts or high temperatures. We conducted an opportunistic acoustic survey of bat communities in ten survey areas in high elevation (1,585–1,920 m a.s.l.) montane Picea rubens (red spruce)-Abies fraseri (Fraser fir) forest in the southern Appalachian Mountains of western North Carolina. In each survey area, we randomly placed three full spectrum acoustic detectors (N = 30) during three seasons (spring, summer and fall) in 2015. We deployed each detector for two five-day periods during each season (n = 900 survey nights). Although we detected seven bat species/groups during the surveys, 73% of echolocation files were attributed to Lasiurus cinereus (hoary bat) and Lasionycteris noctivagans (silver-haired bat). Generally rare in the Appalachians and typically present only at low densities in the summer at mid- and low-elevations, both species were detected at all sites during all seasons. Overall, mean nightly activity of bats was higher in the summer than the spring or fall. We observed 3.7–5 times greater activity of L. cinereus in spruce-fir forests during the summer compared to spring and fall, whereas L. noctivagans had 1.3–5 times more activity in the summer compared to other seasons. After accounting for precipitation events, our finite mixture models showed that season, temperature, elevation, and canopy height influenced L. cinereus activity, whereas season and temperature affected L. noctivagans activity. Our observations suggest that high-elevation spruce-fir forests are providing summer foraging and possibly day-roosting habitat of tree bats not previously documented this far south in North America.
","language":"English","publisher":"Museum and Institute of Zoology at the Polish Academy of Sciences","doi":"10.3161/15081109acc2022.24.1.007","usgsCitation":"Diggins, C., and Ford, W., 2022, Seasonal activity patterns of bats in high-elevation conifer sky islands: Acta Chiropterologica, v. 24, no. 1, p. 91-101, https://doi.org/10.3161/15081109acc2022.24.1.007.","productDescription":"11 p.","startPage":"91","endPage":"101","ipdsId":"IP-121634","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467168,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://zotero.org/groups/5435545/items/TUMBJJ9Q","text":"External Repository"},{"id":465988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.01925096181063,\n 36.1284749704932\n ],\n [\n -83.75535293883529,\n 36.1284749704932\n ],\n [\n -83.75535293883529,\n 35.0263131090354\n ],\n [\n -82.01925096181063,\n 35.0263131090354\n ],\n [\n -82.01925096181063,\n 36.1284749704932\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diggins, Corinne A.","contributorId":270602,"corporation":false,"usgs":false,"family":"Diggins","given":"Corinne A.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":922940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":922939,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70235758,"text":"70235758 - 2022 - Using machine learning to improve predictions and provide insight into fluvial sediment transport","interactions":[],"lastModifiedDate":"2022-08-18T14:43:16.365462","indexId":"70235758","displayToPublicDate":"2022-08-18T09:36:27","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Using machine learning to improve predictions and provide insight into fluvial sediment transport","docAbstract":"A thorough understanding of fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. Fluvial sediment samples are integral for addressing these environmental concerns but cannot be collected at every river and time of interest. Therefore, to gain a better understanding for rivers where direct measurements have not been made, extreme gradient boosting machine learning (ML) models were developed and trained to predict suspended sediment and bedload from sampling data collected in Minnesota, United States (U.S.), by the U.S. Geological Survey. Approximately 400 watershed (full upstream area), catchment (nearby landscape), near-channel, channel, and streamflow features were retrieved or developed from multiple sources, reduced to approximately 30 uncorrelated features, and used in the final ML models. The results indicate suspended sediment and bedload ML models explain approximately 70% of the variance in the datasets. Important features used in the models were interpreted with Shapley additive explanation (SHAP) plots, which provided insight into sediment transport processes. The most important features in the models were developed to normalize streamflow by the 2-year recurrence interval and quantify the rate of change in streamflow (slope), which helped account for sediment hysteresis. Generally, this study also showed a combination of mostly watershed and catchment geospatial features were important in ML models that predict sediment transport from physical samples. This study is a promising step forward in making fluvial sediment transport predictions using machine learning models trained by physically collected samples. The approach developed here can be used wherever similar datasets exists and will be useful for landscape and water management.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14648","usgsCitation":"Lund, J.W., Groten, J.T., Karwan, D.L., and Babcock, C., 2022, Using machine learning to improve predictions and provide insight into fluvial sediment transport: Hydrological Processes, v. 36, no. 8, e14648, 21 p., https://doi.org/10.1002/hyp.14648.","productDescription":"e14648, 21 p.","ipdsId":"IP-133936","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":446739,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14648","text":"Publisher Index Page"},{"id":435725,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VOPSEJ","text":"USGS data release","linkHelpText":"Extreme gradient boosting machine learning models, suspended sediment, bedload, streamflow, and geospatial data, Minnesota, 2007-2019"},{"id":405305,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"36","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lund, J. William 0000-0002-8830-4468","orcid":"https://orcid.org/0000-0002-8830-4468","contributorId":211157,"corporation":false,"usgs":true,"family":"Lund","given":"J.","email":"","middleInitial":"William","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849194,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Groten, Joel T. 0000-0002-0441-8442 jgroten@usgs.gov","orcid":"https://orcid.org/0000-0002-0441-8442","contributorId":173464,"corporation":false,"usgs":true,"family":"Groten","given":"Joel","email":"jgroten@usgs.gov","middleInitial":"T.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849195,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Karwan, Diana L.","contributorId":207315,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":849196,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Babcock, Chad","contributorId":150502,"corporation":false,"usgs":false,"family":"Babcock","given":"Chad","email":"","affiliations":[{"id":18039,"text":"Department of Geography, Michigan State University, East Lansing, Michigan USA","active":true,"usgs":false}],"preferred":false,"id":849197,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238485,"text":"70238485 - 2022 - The effects of prolonged drought on vegetation dieback and megafires in southern California chaparral","interactions":[],"lastModifiedDate":"2022-11-28T13:51:09.520414","indexId":"70238485","displayToPublicDate":"2022-08-18T07:46:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"The effects of prolonged drought on vegetation dieback and megafires in southern California chaparral","docAbstract":"Drought contributed to extensive dieback of southern California chaparral, and normalized difference vegetation index before drought and near the end of the drought was used to estimate this dieback, after accounting for other disturbances recorded in aerial photographs. Within the perimeters of two megafires that occurred after the drought, the 2017 Thomas Fire and the 2018 Woolsey Fire, there had been extensive areas of dieback. Comparing dieback with Monitoring Trends in Burn Severity measures of fire severity, there was a highly significant negative relationship between drought-caused shrub dieback and fire-caused dieback as measured by fire severity. We interpret this as further support for our remote sensing methodology for prefire dieback. Models of fire behavior suggest that one means by which dieback contributes to fire size is through increasing the density and distance of spot fires, particularly under extreme wind conditions. Lower elevation chaparral associations appear to be most vulnerable and are closer to urban environments, which should be a concern to fire managers in regions subjected to extended droughts.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4203","usgsCitation":"Keeley, J., Brennan-Kane, T.J., and Syphard, A.D., 2022, The effects of prolonged drought on vegetation dieback and megafires in southern California chaparral: Ecosphere, v. 13, no. 8, e4203, 16 p., https://doi.org/10.1002/ecs2.4203.","productDescription":"e4203, 16 p.","ipdsId":"IP-136211","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446744,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4203","text":"Publisher Index Page"},{"id":435726,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91LIW2P","text":"USGS data release","linkHelpText":"The Effect of Prolonged Drought on Chaparral Dieback within the Perimeters of the Thomas and Woolsey Fires in Southern California, USA"},{"id":409688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Los Angeles County, Santa Barbara County, Venture County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.5917796123387,\n 34.052337495398135\n ],\n [\n -118.4772825512335,\n 34.467490859424444\n ],\n [\n -119.38587213290705,\n 34.92905378144492\n ],\n [\n -119.67765496604588,\n 34.46140052062019\n ],\n [\n -119.13102383560852,\n 34.1410346214366\n ],\n [\n -118.79122610587672,\n 34.02785296997229\n ],\n [\n -118.5917796123387,\n 34.052337495398135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brennan-Kane, Theresa J 0000-0002-0646-3298","orcid":"https://orcid.org/0000-0002-0646-3298","contributorId":292871,"corporation":false,"usgs":false,"family":"Brennan-Kane","given":"Theresa","email":"","middleInitial":"J","affiliations":[{"id":63051,"text":"previously WERC","active":true,"usgs":false}],"preferred":false,"id":857605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Syphard, Alexandra D.","contributorId":8977,"corporation":false,"usgs":false,"family":"Syphard","given":"Alexandra","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":857606,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235785,"text":"70235785 - 2022 - Simplifying complex fault data for systems-level analysis: Earthquake geology inputs for U.S. NSHM 2023","interactions":[],"lastModifiedDate":"2022-08-19T13:37:36.641728","indexId":"70235785","displayToPublicDate":"2022-08-18T07:19:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Simplifying complex fault data for systems-level analysis: Earthquake geology inputs for U.S. NSHM 2023","docAbstract":"As part of the U.S. National Seismic Hazard Model (NSHM) update planned for 2023, two databases were prepared to more completely represent Quaternary-active faulting across the western United States: the NSHM23 fault sections database (FSD) and earthquake geology database (EQGeoDB). In prior iterations of NSHM, fault sections were included only if a field-measurement-derived slip rate was estimated along a given fault. By expanding this inclusion criteria, we were able to assess a larger set of faults for use in NSHM23. The USGS Quaternary Fault and Fold Database served as a guide for assessing possible additions to the NSHM23 FSD. Reevaluating available data from published sources yielded an increase of fault sections from ~650 faults in NSHM18 to ~1,000 faults proposed for use in NSHM23. EQGeoDB, a companion dataset linked to NSHM23 FSD, contains geologic slip rate estimates for fault sections included in FSD. Together, these databases serve as common input data used in deformation modeling, earthquake rupture forecasting, and additional downstream uses in NSHM development.
Removal sampling data are the primary source of monitoring information for many populations (e.g., invasive species, fisheries). Population dynamics, temporary emigration, and imperfect detection are common sources of variation in monitoring data and are key parameters for informing management. We developed two open robust-design removal models for simultaneously modeling population dynamics, temporary emigration, and imperfect detection: a random walk linear trend model (estimable without ancillary information), and a 2-age class informed population model (InfoPM, closely related to integrated population models) that incorporated prior information for age-structured vital rates and relative juvenile availability. We applied both models to multiyear, removal trapping time-series of a large invasive lizard (Argentine black and white tegu, Salvator merianae) in three management areas of South Florida to evaluate the effectiveness of management programs. Although estimates of the two models were similar, the InfoPMs generally returned more precise estimates, partitioned dynamics into births, deaths, net migration, and provided a decision support tool to predict population dynamics under different effort scenarios while accounting for uncertainty. Trends in tegu superpopulation abundance estimates were increasing in two management areas despite generally high removal rates. However, tegu abundance appeared to decline in the Core management area, where trapping density was the highest and immigration the lowest. Finally, comparing abundance predictions of no-removal scenarios to those estimated in each management area suggested significant population reductions due to management. These results suggest that local tegu population control via systematic trapping may be feasible with high enough trap density and limited immigration; and highlights the value of these trapping programs. We provided the first estimates of tegu abundance, capture probabilities, and population dynamics, which is critical for effective management. Furthermore, our models are applicable to a wide range of monitoring programs (e.g., carcass recovery or removal point-counts).
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9173","usgsCitation":"Udell, B., Martin, J., Romagosa, C., Waddle, J.H., Johnson, F., Falk, B., Yackel Adams, A.A., Funck, S., Ketterlin Eckles, J., Suarez, E., and Mazzotti, F., 2022, Open removal models with temporary emigration and population dynamics to inform invasive animal management: Ecology and Evolution, v. 12, no. 8, e9173; 19 p.; Data Release, https://doi.org/10.1002/ece3.9173.","productDescription":"e9173; 19 p.; Data Release","ipdsId":"IP-123086","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446750,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9173","text":"Publisher Index Page"},{"id":405442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417829,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NXHC0V"}],"volume":"12","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Udell, Bradley","contributorId":216709,"corporation":false,"usgs":false,"family":"Udell","given":"Bradley","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":849363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Julien 0000-0002-7375-129X julienmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":5785,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","email":"julienmartin@usgs.gov","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":849364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romagosa, Christina","contributorId":178167,"corporation":false,"usgs":false,"family":"Romagosa","given":"Christina","affiliations":[],"preferred":false,"id":849365,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waddle, J. Hardin 0000-0003-1940-2133 waddleh@usgs.gov","orcid":"https://orcid.org/0000-0003-1940-2133","contributorId":138953,"corporation":false,"usgs":true,"family":"Waddle","given":"J.","email":"waddleh@usgs.gov","middleInitial":"Hardin","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":849366,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Fred","contributorId":295463,"corporation":false,"usgs":false,"family":"Johnson","given":"Fred","affiliations":[{"id":6963,"text":"Department of Bioscience, Aarhus University","active":true,"usgs":false}],"preferred":false,"id":849367,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Falk, Bryan 0000-0002-9690-5626 bfalk@usgs.gov","orcid":"https://orcid.org/0000-0002-9690-5626","contributorId":150075,"corporation":false,"usgs":true,"family":"Falk","given":"Bryan","email":"bfalk@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":849368,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":849369,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Funck, Sarah","contributorId":295401,"corporation":false,"usgs":false,"family":"Funck","given":"Sarah","email":"","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":849370,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ketterlin Eckles, Jennifer","contributorId":152302,"corporation":false,"usgs":false,"family":"Ketterlin Eckles","given":"Jennifer","email":"","affiliations":[{"id":18903,"text":"Florida FWC","active":true,"usgs":false}],"preferred":false,"id":849371,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Suarez, Eric","contributorId":295404,"corporation":false,"usgs":false,"family":"Suarez","given":"Eric","email":"","affiliations":[{"id":35758,"text":"FWC","active":true,"usgs":false}],"preferred":false,"id":849372,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mazzotti, Frank","contributorId":138878,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":849373,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70236648,"text":"70236648 - 2022 - Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds","interactions":[],"lastModifiedDate":"2023-01-19T19:24:00.787879","indexId":"70236648","displayToPublicDate":"2022-08-17T09:52:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds","docAbstract":"Roadway deicing agents, including rock salt and brine containing NaCl, have had a profound impact on the water quality and aquatic health of rivers and streams in urbanized areas with temperate climates. Yet, few studies evaluate impacts to watersheds characterized by relatively low impervious surface cover (ISC; < 15 %). Here, we use long-term (1997-2019), monthly streamwater quality data combined with daily streamflow for six exurban and suburban watersheds in southeastern Pennsylvania to examine the relations among chloride (Cl−) concentrations and ISC. Both flow-normalized Cl− concentrations and ISC increased over time in each of the six watersheds, consistent with changes in watershed management (e.g., ISC, road salt application, etc.). The watersheds that experienced the greatest changes in percent ISC (e.g., agriculture replaced by residential and commercial development) experienced the greatest changes in flow-normalized Cl− concentrations. We also utilized a comprehensive mass-balance model (2011–2018) that indicated Cl− inputs exceeded the outputs for the study watersheds. Road salt applied to state roads, non-state roads, and other impervious surfaces accounted for the majority of Cl− inputs to the six watersheds. Furthermore, increasing Cl− concentrations during baseflow conditions confirm impacts to shallow groundwater. Although flow-normalized Cl− concentrations are below the U.S. Environmental Protection Agency's chronic threshold value for impacts to aquatic organisms, year-round exceedances may result before the end of this century based on current trends. Though reduced Cl− loading to streams may be achieved by limiting the expansion of impervious surfaces in exurban and suburban watersheds, changes in baseflow concentrations are likely to be gradual because of the accumulated Cl− in groundwater.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157933","usgsCitation":"Rossi, M., Kremer, P., Cravotta, C., Scheirer, K.E., and Goldsmith, S.T., 2022, Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds: Science of the Total Environment, v. 851, no. Part 2, 157933, 13 p., https://doi.org/10.1016/j.scitotenv.2022.157933.","productDescription":"157933, 13 p.","ipdsId":"IP-139821","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":446757,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.157933","text":"Publisher Index Page"},{"id":406679,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Berks County, Bucks County, Chester County, Delaware County, Lehigh County, Montgomery County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.6,\n 39.812755695478124\n ],\n [\n -74.9542236328125,\n 39.812755695478124\n ],\n [\n -74.9542236328125,\n 40.2\n ],\n [\n -75.6,\n 40.2\n ],\n [\n -75.6,\n 39.812755695478124\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"851","issue":"Part 2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rossi, Marissa L. 0000-0003-2341-0312","orcid":"https://orcid.org/0000-0003-2341-0312","contributorId":296518,"corporation":false,"usgs":false,"family":"Rossi","given":"Marissa L.","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":851695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kremer, Peleg","contributorId":296521,"corporation":false,"usgs":false,"family":"Kremer","given":"Peleg","email":"","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":851696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scheirer, Krista E.","contributorId":296524,"corporation":false,"usgs":false,"family":"Scheirer","given":"Krista","email":"","middleInitial":"E.","affiliations":[{"id":64093,"text":"Aqua Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":851698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldsmith, Steven T.","contributorId":193458,"corporation":false,"usgs":false,"family":"Goldsmith","given":"Steven","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":851699,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240257,"text":"70240257 - 2022 - Seismometer records of ground tilt induced by debris flows","interactions":[],"lastModifiedDate":"2023-02-02T15:41:51.102633","indexId":"70240257","displayToPublicDate":"2022-08-17T09:21:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Seismometer records of ground tilt induced by debris flows","docAbstract":"A change in surface loading causes the Earth’s surface to deform. Mass movements, such as debris flows, can cause a tilt large enough to be recorded by nearby instruments, but the signal is strongly dependent on the mass loading and subsurface parameters. Specifically designed sensors for such measurements (tiltmeters) are cumbersome to install. Alternatively, broadband seismometers record translational motion and also tilt signals, often at periods of tens to hundreds of seconds. Their horizontal components are thereby the most sensitive to tilt. In this study, we show how to obtain tilt caused by the passing by of debris flows from seismic measurements recorded within tens of meters of the flow and investigate the usefulness of this signal for flow characterization. We investigate the problem on three scales (1) large‐scale laboratory experiments at the U.S. Geological Survey debris‐flow flume, where broadband seismometers and tiltmeters were installed for six