Large amounts of video images have been collected for decades by scientific and governmental organizations in deep (>1000 m) water using manned and unmanned submersibles and towed cameras. The collected images were analyzed individually or were mosaiced in small areas with great effort. Here, we provide a workflow for utilizing modern photogrammetry to construct virtual geological outcrops hundreds or thousands of meters in length from these archived video images. The photogrammetry further allows quantitative measurements of these outcrops, which were previously unavailable. Although photogrammetry had been carried out in recent years in the deep sea, it had been limited to small areas with pre-defined overlapping dive paths. Here, we propose a workflow for constructing virtual outcrops from archived exploration dives, which addresses the complicating factors posed by single non-linear and variable-speed vehicle paths. These factors include poor navigation, variable lighting, differential color attenuation due to variable distance from the seafloor, and variable camera orientation with respect to the vehicle. In particular, the lack of accurate navigation necessitates reliance on image quality and the establishment of pseudo-ground-control points to build the photogrammetry model. Our workflow offers an inexpensive method for analyzing deep-sea geological environments from existing video images, particularly when coupled with rock samples.
","language":"English","publisher":"MDPI","doi":"10.3390/jmse12081250","usgsCitation":"Flores, C., and ten Brink, U.S., 2024, Photogrammetry of the deep seafloor from archived unmanned submersible exploration dives: Journal of Marine Science and Engineering, v. 12, no. 8, 1250, 19 p., https://doi.org/10.3390/jmse12081250.","productDescription":"1250, 19 p.","ipdsId":"IP-159627","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":439255,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse12081250","text":"Publisher Index Page"},{"id":432271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Atlantic Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.1,\n 19.2\n ],\n [\n -68.1,\n 18.3\n ],\n [\n -66.8,\n 18.3\n ],\n [\n -66.8,\n 19.2\n ],\n [\n -68.1,\n 19.2\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Flores, Claudia 0000-0003-0676-7061 cflores@usgs.gov","orcid":"https://orcid.org/0000-0003-0676-7061","contributorId":304396,"corporation":false,"usgs":true,"family":"Flores","given":"Claudia","email":"cflores@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":909080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"ten Brink, Uri S. 0000-0001-6858-3001","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":201741,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri","email":"","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":909081,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264796,"text":"70264796 - 2024 - Gape-limited invasive predator frequently kills avian prey that are too large to swallow","interactions":[],"lastModifiedDate":"2025-03-24T15:08:54.248637","indexId":"70264796","displayToPublicDate":"2024-07-24T08:02:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Gape-limited invasive predator frequently kills avian prey that are too large to swallow","docAbstract":"Gape-limited predators (e.g., snakes, many fish) are not generally expected to pose a predation threat to prey that are too large for them to swallow. However, the extent to which snakes predate on prey that exceed their gape limitation remains largely unknown. We conducted the first study to investigate the influence of both prey and predator sizes on the frequency of ingestion success by snakes in a natural system. We combined survival monitoring of an avian prey species (Aplonis opaca) via radio-telemetry with a survey of the size distribution of their major predator (Boiga irregularis) on Guam. This allowed us to assess (1) the frequency of unsuccessful ingestion by the predator, (2) whether the size of the prey predicts ingestion success, (3) whether the size of the predator predicts ingestion success, and (4) the relationship between prey and predator sizes in successful ingestion attempts. We found that nearly half (47.95%) of ingestion attempts by snakes on fledgling birds were unsuccessful, and no instances where unsuccessful ingestion caused the mortality of the snake. Attempts to consume smaller fledglings were as likely to be unsuccessful as attempts to swallow larger fledglings. However, snakes that successfully ingested fledglings were among the largest snakes in the population, and larger than average conspecifics attracted to endothermic prey. The smallest snakes that successfully ingested fledglings attained remarkably high relative prey mass values for their species, consuming prey weighing up to 79.9% of their own mass. Our study indicates that B. irregularis routinely predate prey that are too large for them to successfully ingest, which causes mortality to the prey but poses little risk to the predator. The potential reward for snakes in consuming oversized prey may outweigh the inherent risks, while instances of predation that do not result in consumption may have considerable impacts on prey populations.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11598","usgsCitation":"Kastner, M., Goetz, S.M., Baker, K.M., Siers, S.R., Paxton, E.H., Nafus, M., and Rogers, H., 2024, Gape-limited invasive predator frequently kills avian prey that are too large to swallow: Ecology and Evolution, v. 14, no. 7, e11598, 11 p., https://doi.org/10.1002/ece3.11598.","productDescription":"e11598, 11 p.","ipdsId":"IP-159071","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":488373,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11598","text":"Publisher Index Page"},{"id":483715,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.52940654060023,\n 13.730269126756554\n ],\n [\n 144.52940654060023,\n 13.229219717321797\n ],\n [\n 145.03819941430874,\n 13.229219717321797\n ],\n [\n 145.03819941430874,\n 13.730269126756554\n ],\n [\n 144.52940654060023,\n 13.730269126756554\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Kastner, Martin","contributorId":293508,"corporation":false,"usgs":false,"family":"Kastner","given":"Martin","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":931729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goetz, Scott Michael 0000-0002-8705-5316","orcid":"https://orcid.org/0000-0002-8705-5316","contributorId":228868,"corporation":false,"usgs":true,"family":"Goetz","given":"Scott","email":"","middleInitial":"Michael","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":931730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, Kayla M","contributorId":279515,"corporation":false,"usgs":false,"family":"Baker","given":"Kayla","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":931731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siers, Shane R.","contributorId":152305,"corporation":false,"usgs":false,"family":"Siers","given":"Shane","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":931732,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":931733,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nafus, Melia Gail 0000-0002-7325-3055","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":245717,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia Gail","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":931734,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rogers, Haldre","contributorId":279510,"corporation":false,"usgs":false,"family":"Rogers","given":"Haldre","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":931735,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70259685,"text":"70259685 - 2024 - Cathodoluminescence imaging and spectrometry of a jadeite microbeam reference crystal: Detection of Ce3+","interactions":[],"lastModifiedDate":"2024-10-21T12:26:18.509509","indexId":"70259685","displayToPublicDate":"2024-07-24T07:22:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2738,"text":"Microscopy and Microanalysis","active":true,"publicationSubtype":{"id":10}},"title":"Cathodoluminescence imaging and spectrometry of a jadeite microbeam reference crystal: Detection of Ce3+","docAbstract":"Options for selecting a high Na concentration mineral for instrument calibration that are suitably stable under the electron beam are limited [1]. NaCl (approximately a mass fraction of 39 % Na) is not practical for use alongside other embedded and polished materials in a mounted block of standards. While albite (NaAlSi3O8; approximately a mass fraction of 8 % Na) represents a typical choice for some microanalysts, it may be subject to time dependent signal intensity issues, such as those observed for Na and other cations during analysis of hydrous glasses [2]. Near endmember jadeite pyroxene (NaAlSi2O6; approximately mass fraction of 11 % Na) represents a desirable option for calibration given its relatively large concentration of Na and insensitivity to electron beam radiation with respect to X-ray output. Despite these desirable characteristics, jadeites have been shown to contain complex microstructures as the result of intracrystalline zoning [3, 4], and the impact of chemical inhomogeneity within mineral standards and natural glasses has been documented [5-7]. In this study, a commercially obtained jadeite crystal (source location “China”) was studied to assess its microscale uniformity using a suite of microbeam methods, including dispersive red-green-blue multispectral cathodoluminescence imaging (CLRGB), hyperspectral CL imaging spectrometry, energy dispersive X-ray spectrometry (EDS), SEM-based micro-X-ray fluorescence (μXRF), and wavelength dispersive X-ray spectrometry (WDS).
","language":"English","publisher":"Oxford University Press","doi":"10.1093/mam/ozae044.012","usgsCitation":"Lameris, T., Lowers, H.A., Wight, S.A., and Vicenzi, E.P., 2024, Cathodoluminescence imaging and spectrometry of a jadeite microbeam reference crystal: Detection of Ce3+: Microscopy and Microanalysis, v. 30, p. 25-28, https://doi.org/10.1093/mam/ozae044.012.","productDescription":"ozae044.012, 4 p.","startPage":"25","endPage":"28","ipdsId":"IP-162139","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":498020,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1093/mam/ozae044.012","text":"Publisher Index Page"},{"id":463062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","noUsgsAuthors":false,"publicationDate":"2024-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Lameris, Thomas","contributorId":270786,"corporation":false,"usgs":false,"family":"Lameris","given":"Thomas","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":916236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":916237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wight, Scott A.","contributorId":345300,"corporation":false,"usgs":false,"family":"Wight","given":"Scott","email":"","middleInitial":"A.","affiliations":[{"id":47720,"text":"NIST","active":true,"usgs":false}],"preferred":false,"id":916238,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vicenzi, Edward P.","contributorId":345301,"corporation":false,"usgs":false,"family":"Vicenzi","given":"Edward","email":"","middleInitial":"P.","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":916239,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259724,"text":"70259724 - 2024 - Correlating quantified cathodoluminescence spectra in jadeite with micro-scale color measurements in visible-near infrared reflectance spectrometry","interactions":[],"lastModifiedDate":"2024-10-21T12:17:42.212175","indexId":"70259724","displayToPublicDate":"2024-07-24T07:16:21","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2738,"text":"Microscopy and Microanalysis","active":true,"publicationSubtype":{"id":10}},"title":"Correlating quantified cathodoluminescence spectra in jadeite with micro-scale color measurements in visible-near infrared reflectance spectrometry","docAbstract":"Cultures throughout history have valued jadeite jade (hereinafter jade), a natural material assemblage composed predominately of the NaAl endmember pyroxene, jadeite (NaAlSi2O6) that is prized for its mechanical properties and enticing coloration [1, 2]. The geological setting for the formation of jadeite-rich rocks and associated complex geochemical phenomena is well documented in the literature [3-5]. Unlike single crystal gems, jade often contains a mixture of colors, but the stone is perhaps best known for exhibiting a range of green hues from pale green to blue green, and to deep green [1, 6]. Efforts to classify jade color include qualitative matching to a set of reference color tiles, and less commonly, use of a more quantitative set of color perception values first established by International Commission on Illumination’s (CIE 1931) color space, which includes values for light-dark luminance level (L*), a green-red vector (a*), and a blue-yellow vector (b*) [7, 8]. Two principal mechanisms have been identified as the cause of green coloration in jadeite: 1) the chemical state of impurity Fe, either 2+ or 3+, and 2) minor to trace Cr in the crystal structure [9, 10]. Previous studies have associated Cr3+ with cathodoluminescence (CL) spectral features in jadeite using point spectrometry, with more recent connections made via 2D CL spectrum imaging [11-13]. In this effort, we are seeking to establish whether Cr impurities are correlated with green coloration in jadeite using micro-scale (≤110 μm) visible-near infrared (VIS-NIR) reflectance spectrometry.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/mam/ozae044.009","usgsCitation":"Vicenzi, E.P., Lameris, T., Lowers, H.A., and MacRae, C., 2024, Correlating quantified cathodoluminescence spectra in jadeite with micro-scale color measurements in visible-near infrared reflectance spectrometry: Microscopy and Microanalysis, v. 30, https://doi.org/10.1093/mam/ozae044.009.","productDescription":"ozae044.009, 4 p.","startPage":"21","ipdsId":"IP-162140","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":463061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","edition":"18","noUsgsAuthors":false,"publicationDate":"2024-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Vicenzi, Edward P.","contributorId":345301,"corporation":false,"usgs":false,"family":"Vicenzi","given":"Edward","email":"","middleInitial":"P.","affiliations":[{"id":36606,"text":"Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":916460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lameris, Thomas","contributorId":270786,"corporation":false,"usgs":false,"family":"Lameris","given":"Thomas","email":"","affiliations":[{"id":36570,"text":"NIOZ Royal Netherlands Institute for Sea Research","active":true,"usgs":false}],"preferred":false,"id":916461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":916462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacRae, Colin","contributorId":295342,"corporation":false,"usgs":false,"family":"MacRae","given":"Colin","email":"","affiliations":[{"id":63849,"text":"CSIRO Minerals","active":true,"usgs":false}],"preferred":false,"id":916463,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259682,"text":"70259682 - 2024 - Compositional and structural mapping of Northwest Africa 15507 angrite","interactions":[],"lastModifiedDate":"2024-10-21T12:15:43.068912","indexId":"70259682","displayToPublicDate":"2024-07-24T07:13:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2738,"text":"Microscopy and Microanalysis","active":true,"publicationSubtype":{"id":10}},"title":"Compositional and structural mapping of Northwest Africa 15507 angrite","docAbstract":"Angrite meteorites represent interesting sampling of planetary crustal environments. Quench-textured angrites with strong crystal zoning originated from the shallow surface region, with evidence of reducing conditions during solidification. Plutonic angrites have more coarse-grained igneous and metamorphic textures with comparatively less zoning and are interpreted as having equilibrated at greater depth. Plutonic angrites contain the minerals magnetite Fe3O4 comprised of Fe2+ and Fe3+, and rhönite, where Fe3+ is required by inspection of mineral stoichiometry. NWA 15507 is a plutonic angrite with a microgabbroic texture (mean grainsize ∼1.4 mm) composed of zoned Al-Ti-augite, Ca-bearing olivine, anorthite, with accessory kirschsteinite, rhönite, hercynite, low-Ni kamacite, merrillite, Ti-free magnetite and troilite [1]. Rhönite analyzed by electron-probe microanalysis (EPMA) has the formula Ca2.04(Mg0.32Fe2+4.25Fe3+0.47Ti0.33Al0.61)(Si3.74Al2.26)O20, where Fe3+ was estimated by stoichiometric analysis. During preliminary cathodoluminescence (CL) analysis, complex subgrain and oscillatory zoning was observed in the anorthite. In this study we use a combined approach of electron probe microanalysis (EPMA), cathodoluminescence (CL), electron-backscatter diffraction (EBSD), and laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) to further investigate the anorthite and distribution of Fe3+ in NWA 15507.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/mam/ozae044.008","usgsCitation":"Lowers, H.A., Carpenter, P., Thompson, J.M., and Irving, A., 2024, Compositional and structural mapping of Northwest Africa 15507 angrite: Microscopy and Microanalysis, v. 30, p. 15-17, https://doi.org/10.1093/mam/ozae044.008.","productDescription":"ozae044.008, 3 p.","startPage":"15","endPage":"17","ipdsId":"IP-162946","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":498021,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/mam/ozae044.008","text":"Publisher Index Page"},{"id":463060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","noUsgsAuthors":false,"publicationDate":"2024-07-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":916232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carpenter, Paul C.","contributorId":345297,"corporation":false,"usgs":false,"family":"Carpenter","given":"Paul C.","affiliations":[{"id":35028,"text":"Washington University in St. Louis","active":true,"usgs":false}],"preferred":false,"id":916233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Jay M. 0000-0003-3322-0870","orcid":"https://orcid.org/0000-0003-3322-0870","contributorId":329664,"corporation":false,"usgs":true,"family":"Thompson","given":"Jay","middleInitial":"M.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":916234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irving, Anthony","contributorId":332496,"corporation":false,"usgs":false,"family":"Irving","given":"Anthony","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":916235,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256190,"text":"70256190 - 2024 - Mercury concentrations in Seaside Sparrows and Marsh Rice Rats differ across the Mississippi River Estuary","interactions":[],"lastModifiedDate":"2024-09-23T16:12:38.890928","indexId":"70256190","displayToPublicDate":"2024-07-24T06:36:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"Mercury concentrations in Seaside Sparrows and Marsh Rice Rats differ across the Mississippi River Estuary","docAbstract":"Mercury (Hg) concentrations and their associated toxicological effects in terrestrial ecosystems of the Gulf of Mexico are largely unknown. Compounding this uncertainty, a large input of organic matter from the 2010 Deepwater Horizon oil spill may have altered Hg cycling and bioaccumulation dynamics. To test this idea, we quantified blood concentrations of total mercury (THg) in Seaside Sparrows (Ammospiza maritima) and Marsh Rice Rats (Oryzomys palustris) in marshes west and east of the Mississippi River in 2015 and 2016. We also tested for a difference in THg concentrations between oiled and non-oiled sites. To address the potential confounding effect of diet variation on Hg transfer, we used stable nitrogen (δ15N) and carbon (δ13C) isotope values as proxies of trophic position and the source of primary production, respectively. Our results revealed that five to six years after the spill, THg concentrations were not higher in sites oiled by the spill compared to non-oiled sites. In both species, THg was higher at sites east of the Mississippi River compared to control and oiled sites, located west. In Seaside Sparrows but not in Marsh Rice Rats, THg increased with δ15N values, suggesting Hg trophic biomagnification. Overall, even in sites with the most elevated THg, concentrations were generally low. In Seaside Sparrows, THg concentrations were also lower than previously reported in this and other closely related passerines, with only 7% of tested birds exceeding the lowest observed effect concentration associated with toxic effects across bird species (0.2 µg/g ww). The factors associated with geographic heterogeneity in Hg exposure remain uncertain. Clarification could inform risk assessment and future restoration and management actions in a region facing vast anthropogenic changes.
From April-May 2024, the NASA Mentors who span eleven Distributed Active Archive Centers (DAACs) co-led the third Champions Cohort with the NASA Openscapes project team, this year focusing on, teaching lessons they adapted for geospatial and cloud analysis. The Cohort included nine international research teams from academia and government that were curious about working with NASA Earthdata in the Cloud. Many teams were interested in using data from multiple DAACs. User cloud adaption takes time, given the new conceptual mindsets and technical skillsets it requires. During the ten weeks we worked together, NASA Mentors refined and extended previous lessons to focus on thinking through and planning the transition to using the Cloud for science research and applications, and initial experiments using the Cloud through our 2i2c JupyterHub. Below are these updates and YouTube clips!
There were also recurring themes/questions that we have heard before, some of which remain as open questions and continue to remain a challenge. Importantly, Amazon Web Services (AWS) Cloud onboarding, when to use what resources, how to set them up, and how to discuss needs with organizational leadership and IT staff, which often falls outside the scope of NASA DAACs, yet it’s a key element of helping users adopt the Cloud and use NASA data in the Cloud. It is encouraging to hear some of the champions starting to have conversations with their institutions, IT departments, and making their needs known, which is likely a big part of the solution, too. We are thankful to NASA Openscapes Champions for informing and nudging these conversations! All of this work is underpinned by Openscapes and NASA’s commitment to open science practices and a kinder collaborative culture. This cohort is funded by NASA and is part of our NASA Openscapes Framework project.
","language":"English","publisher":"NASA","usgsCitation":"Thornton, M., Taglialatela, C., Lopez, L., Fisher, M., Hunzinger, A., Jami, M., Lind, B.M., Nickles, C., Teucher, A., Merrelli, A., Robinson, E., and Lowndes, J., 2024, NASA Champions 2024: Data strategies for when to use cloud, coding strategies for parallelization, & first examples of big science in the Cloud, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-167616","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":431422,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://nasa-openscapes.github.io/news/2024-07-24-2024-nasa-champions-cohort/"},{"id":431457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thornton, 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The U.S. Geological Survey can be a key partner with Tribal Nations in the further development of network capacity. A first step is the internship opportunity available through the partnership between the USGS and The Wildlife Society: The Native American Research Assistantship Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243026","usgsCitation":"Hare-Red Corn, E., Breault, R.F., and Sorenson, J.R., 2024, The Native American Research Assistantship Program—Building capacity for Indigenous water-resources monitoring: U.S. Geological Survey Fact Sheet 2024–3026, 2 p., https://doi.org/10.3133/fs20243026.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158895","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":431260,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3026/images/"},{"id":431259,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3026/fs20243026.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2024-3026 XML"},{"id":431258,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243026/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3026 HTML"},{"id":431257,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3026/fs20243026.pdf","text":"Report","size":"5.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3026 PDF"},{"id":431256,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3026/coverthb.jpg"}],"contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
As of 2024, there are 32 National Scenic and Historic Trails (NSHTs) in the system administered by the Bureau of Land Management (BLM), National Park Service, and U.S. Department of Agriculture Forest Service. The BLM administers, manages, and protects 19 of these trails as part of its system of national conservation lands. Various laws, regulations, and policies require that the BLM conduct and maintain an inventory to protect trail-related resources, qualities, values, associated settings, and primary use or uses. There are set procedures for conducting inventory, assessment, and monitoring (IAM) of NSHTs, as outlined in volumes 1 and 2 of BLM Technical Reference 6280-1. One of the first steps in the IAM process is deciding the area along a trail to inventory. However, volumes 1 and 2 of BLM Technical Reference 6280-1 do not specify how the land area to be inventoried should be delineated. The BLM calls these focus areas for IAM efforts “inventory analysis units” (IAUs), which are defined as the geospatial boundary for the location of an inventory along a trail. This report reviews the approach used to delineate the IAUs for an inventory effort and identifies best practices for creating initial IAUs, termed “draft IAUs.” Draft IAUs would provide standardization across multiple management jurisdictions by applying the same parameters for their delineation. These draft IAUs would provide trail managers with an area surrounding NSHTs that would trigger the need for an inventory if a project were proposed within it and are meant to be refined during localized inventory efforts. The best practices herein are for creating draft IAUs using standard parameters for performing a viewshed analysis to identify a proxy of land to include in an initial inventory effort.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245060","collaboration":"Prepared in cooperation with the Bureau of Land Management","programNote":"Land Management Research Program","usgsCitation":"Lindley, S.M., Wilkins, E.J., Farley, C., Rogers, K., and Schuster, R., 2024, Delineating draft inventory analysis units for National Scenic and Historic Trails inventory, assessment, and monitoring programs: U.S. Geological Survey Scientific Investigations Report 2024–5060, 14 p., https://doi.org/10.3133/sir20245060.","productDescription":"vi, 14 p.","onlineOnly":"Y","ipdsId":"IP-167546","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":431360,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5060/sir20245060.xml"},{"id":431359,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5060/images"},{"id":431310,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5060/sir20245060.pdf","text":"Report","size":"8.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5060"},{"id":431445,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5060"},{"id":431309,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5060/coverthb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.32269505673072,\n 37.12200862948433\n ],\n [\n -109.32269505673072,\n 31.181092298048213\n ],\n [\n -105.30169896298118,\n 31.181092298048213\n ],\n [\n -105.30169896298118,\n 37.12200862948433\n ],\n [\n -109.32269505673072,\n 37.12200862948433\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
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Decades of human activities and fire suppression have adversely affected longleaf pine (Pinus palustris) ecosystems, which are home to high levels of diversity and endemism. These iconic ecosystems also now face challenges from urbanization and climate change, which will alter conservation outcomes over the remainder of the 21st century. To explore how long-term, large-scale human influences could affect the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome, we extracted a set of 2400 longleaf pine patches ≥40 ha in size from the Florida Longleaf Pine Ecosystem Geodatabase. Projections from the FUTURES urban growth model and the Florida 2070 project indicate that development will lead to losses of existing longleaf pine habitat, reductions in longleaf pine patch size, and patches that are predominantly located in close proximity to developed areas. Finer-scale patterns of longleaf pine loss in three focal landscapes highlighted differences in land protection, ecological setting, and development pressure and the value of using of multiple urbanization iterations. The occurrence of suitable conditions to conduct prescribed fires, a crucial tool for maintaining, improving, and restoring longleaf pine ecosystems, is projected to decrease seasonally throughout the study area. As a result, the functional persistence of ecosystems is at risk due to climate changes that increase barriers to the safe and reliable application of intentional fire. The long-term viability of this critical ecosystem will warrant the evaluation of adaptive strategies that explicitly account for the individual and compounding effects of urban development and changing fire management conditions when considering options for ecosystem protection, management, and restoration.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.13187","usgsCitation":"Hutchens, L., Kupfer, J.A., Gao, P., Sanchez, G.M., Meentemeyer, R.K., Terando, A., and Hiers, J.K., 2024, Projecting the long-term effects of large-scale human influence on the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome: Conservation Science and Practice, v. 6, e13187, 17 p., https://doi.org/10.1111/csp2.13187.","productDescription":"e13187, 17 p.","ipdsId":"IP-164797","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439258,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.13187","text":"Publisher Index Page"},{"id":433552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.6778324075661,\n 30.937698915049367\n ],\n [\n -82.87914407681765,\n 31.52377445868447\n ],\n [\n -85.48595743814668,\n 29.870880095396842\n ],\n [\n -84.9679549020707,\n 29.721163342172446\n ],\n [\n -83.73921448060787,\n 29.975699066098443\n ],\n [\n -83.09802043717049,\n 29.013903692421295\n ],\n [\n -82.21492940678012,\n 26.67873354422413\n ],\n [\n -81.88136124157192,\n 26.33869742309807\n ],\n [\n -80.97600764340528,\n 26.60948691174565\n ],\n [\n -80.80502196139402,\n 27.153838982412836\n ],\n [\n -80.23615831927407,\n 27.044945104314294\n ],\n [\n -81.6778324075661,\n 30.937698915049367\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hutchens, Lilian","contributorId":343967,"corporation":false,"usgs":false,"family":"Hutchens","given":"Lilian","email":"","affiliations":[],"preferred":false,"id":912487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kupfer, John A.","contributorId":339801,"corporation":false,"usgs":false,"family":"Kupfer","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":912488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gao, Peng","contributorId":224731,"corporation":false,"usgs":false,"family":"Gao","given":"Peng","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":912489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanchez, Georgina M. 0000-0002-2365-6200","orcid":"https://orcid.org/0000-0002-2365-6200","contributorId":303829,"corporation":false,"usgs":false,"family":"Sanchez","given":"Georgina","email":"","middleInitial":"M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":true,"id":912490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meentemeyer, Ross K.","contributorId":179341,"corporation":false,"usgs":false,"family":"Meentemeyer","given":"Ross","email":"","middleInitial":"K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":912491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":912492,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hiers, J. Kevin","contributorId":224733,"corporation":false,"usgs":false,"family":"Hiers","given":"J.","email":"","middleInitial":"Kevin","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":912493,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256127,"text":"70256127 - 2024 - Global variability of the composition and temperature at the 410-km discontinuity from receiver function analysis of dense arrays","interactions":[],"lastModifiedDate":"2024-07-23T13:59:40.007319","indexId":"70256127","displayToPublicDate":"2024-07-23T08:51:29","publicationYear":"2024","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":"Global variability of the composition and temperature at the 410-km discontinuity from receiver function analysis of dense arrays","docAbstract":"Seismic boundaries caused by phase transitions between olivine polymorphs in Earth's mantle provide thermal and compositional markers that inform mantle dynamics. Seismic studies of the mantle transition zone often use either global averaging with sparse arrays or regional sampling from a single dense array. The intermediate approach of this study utilizes many densely spaced seismic arrays distributed around the globe. We systematically compute teleseismic P-to-S receiver functions for each seismic array and invert for the 1-D seismic velocity structure of the mantle transition zone beneath each array to facilitate a comparison between densely sampled regions. We stack 3,600 receiver functions on average at 67 arrays in total. The stack is used in a probabilistic inversion to estimate the mantle transition zone interface depths and velocities beneath each array. We focus on the 410-km discontinuity (410) because it is a prominent seismic interface that is clearly linked to a single mineral phase transition between olivine and wadsleyite. The depths and velocity contrasts of the 410 are mapped to temperatures and compositions using mineral physics constraints. The depth of the 410 ranges from ∼405–440 km, which is consistent with a ∼360 K temperature range in a dry mantle and a ∼260 K temperature range in a wet mantle (2 wt. % water). The Vs contrast across the 410 ranges from ∼2.5–8 %, which is consistent with ∼20–70 vol. % olivine composition in a dry mantle and ∼25–80 vol. % in a wet mantle. The bulk composition of the upper mantle near the 410-km discontinuity is typically considered to be well-mixed because there is no thermodynamic impediment to convection at the olivine to wadsleyite phase transition. However, the wide range of inferred olivine content from our study suggests that there are large lateral variations in the bulk composition of the upper mantle near the 410-km discontinuity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2024.118889","usgsCitation":"Glasgow, M.E., Zhang, H.K., Schmandt, B., Zhou, W., and Zhang, J., 2024, Global variability of the composition and temperature at the 410-km discontinuity from receiver function analysis of dense arrays: Earth and Planetary Science Letters, v. 643, 118889, 12 p., https://doi.org/10.1016/j.epsl.2024.118889.","productDescription":"118889, 12 p.","ipdsId":"IP-162736","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489835,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2024.118889","text":"Publisher Index Page"},{"id":431352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"643","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glasgow, Margaret Elizabeth 0000-0001-5637-5918","orcid":"https://orcid.org/0000-0001-5637-5918","contributorId":340268,"corporation":false,"usgs":true,"family":"Glasgow","given":"Margaret","email":"","middleInitial":"Elizabeth","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Hankui K.","contributorId":211965,"corporation":false,"usgs":false,"family":"Zhang","given":"Hankui","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":906785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmandt, Brandon","contributorId":202750,"corporation":false,"usgs":false,"family":"Schmandt","given":"Brandon","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":906786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhou, Wen-Yi","contributorId":340269,"corporation":false,"usgs":false,"family":"Zhou","given":"Wen-Yi","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":906787,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Jinchi","contributorId":191970,"corporation":false,"usgs":false,"family":"Zhang","given":"Jinchi","email":"","affiliations":[],"preferred":false,"id":906788,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261445,"text":"70261445 - 2024 - A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands","interactions":[],"lastModifiedDate":"2024-12-10T14:41:05.274772","indexId":"70261445","displayToPublicDate":"2024-07-23T08:36:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands","docAbstract":"Popular evapotranspiration (ET) partitioning methods make assumptions that might not be well-suited to dryland ecosystems, such as high sensitivity of plant water-use efficiency (WUE) to vapor pressure deficit (VPD). Our objectives were to (a) create an ET partitioning model that can produce fine-scale estimates of transpiration (T) in drylands, and (b) use this approach to evaluate how climate controls T and WUE across ecosystem types and timescales along a dryland aridity gradient. We developed a novel, semi-mechanistic ET partitioning method using a Bayesian approach that constrains abiotic evaporation using process-based models, and loosely constrains time-varying WUE within an autoregressive framework. We used this method to estimate daily T and weekly WUE across seven dryland ecosystem types and found that T dominates ET across the aridity gradient. Then, we applied cross-wavelet coherence analysis to evaluate the temporal coherence between focal response variables (WUE and T/ET) and environmental variables. At yearly scales, we found that WUE at less arid, higher elevation sites was primarily limited by atmospheric moisture demand, and WUE at more arid, lower elevation sites was primarily limited by moisture supply. At sub-yearly timescales, WUE and VPD were sporadically correlated. Hence, ecosystem-scale dryland WUE is not always sensitive to changes in VPD at short timescales, despite this being a common assumption in many ET partitioning models. This new ET partitioning method can be used in dryland ecosystems to better understand how climate influences physically and biologically driven water fluxes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007914","usgsCitation":"Reich, E., Samuels-Crow, K., Bradford, J., Litvak, M., Schlaepfer, D.R., and Ogle, K., 2024, A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands: Journal of Geophysical Research: Biogeosciences, v. 129, no. 7, e2023JG007914, 18 p., https://doi.org/10.1029/2023JG007914.","productDescription":"e2023JG007914, 18 p.","ipdsId":"IP-166030","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":464941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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Mexico\",\"nation\":\"USA \"}}]}","volume":"129","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Reich, E.G.","contributorId":347030,"corporation":false,"usgs":false,"family":"Reich","given":"E.G.","email":"","affiliations":[{"id":83041,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":920581,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Samuels-Crow, K.","contributorId":347031,"corporation":false,"usgs":false,"family":"Samuels-Crow","given":"K.","affiliations":[{"id":83041,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":920582,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":920583,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Litvak, M.","contributorId":127830,"corporation":false,"usgs":false,"family":"Litvak","given":"M.","email":"","affiliations":[{"id":7164,"text":"Department of Biology, University of New Mexico, Albuquerque, NM 87131 USA","active":true,"usgs":false}],"preferred":false,"id":920584,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":920585,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ogle, K.","contributorId":347032,"corporation":false,"usgs":false,"family":"Ogle","given":"K.","affiliations":[{"id":83043,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA; Center of Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":920586,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259218,"text":"70259218 - 2024 - Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better","interactions":[],"lastModifiedDate":"2024-10-02T13:35:21.858349","indexId":"70259218","displayToPublicDate":"2024-07-23T08:15:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better","docAbstract":"Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR035982","usgsCitation":"Barnhart, T.B., Putman, A.L., Heldmyer, A.J., Rey, D., Hammond, J., Driscoll, J.M., and Sexstone, G., 2024, Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better: Water Resources Research, v. 60, no. 7, e2023WR035982, 21 p., https://doi.org/10.1029/2023WR035982.","productDescription":"e2023WR035982, 21 p.","ipdsId":"IP-154162","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":466979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr035982","text":"Publisher Index Page"},{"id":462477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.35,\n 40\n ],\n [\n -107.5,\n 40\n ],\n [\n -107.5,\n 37.75\n ],\n [\n -105.35,\n 37.75\n ],\n [\n -105.35,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":219010,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore","email":"","middleInitial":"B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Putman, Annie L. 0000-0002-9424-1707","orcid":"https://orcid.org/0000-0002-9424-1707","contributorId":225134,"corporation":false,"usgs":true,"family":"Putman","given":"Annie","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heldmyer, Aaron Joseph 0000-0001-8608-4927","orcid":"https://orcid.org/0000-0001-8608-4927","contributorId":302944,"corporation":false,"usgs":true,"family":"Heldmyer","given":"Aaron","email":"","middleInitial":"Joseph","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":914515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":914517,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914518,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256166,"text":"70256166 - 2024 - Isotopic evidence against North Pacific Deep Water formation during late Pliocene warmth","interactions":[],"lastModifiedDate":"2024-08-13T14:40:37.63952","indexId":"70256166","displayToPublicDate":"2024-07-23T07:28:38","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic evidence against North Pacific Deep Water formation during late Pliocene warmth","docAbstract":"Several modelling and observational studies suggest deep water formation in the subpolar North Pacific as a possible alternative mode of thermohaline circulation that occurred in the warm Pliocene, a time when global atmospheric partial pressure of carbon dioxide was like the modern atmosphere (~400 ppm). We test this hypothesis by measuring the δ13C of the benthic foraminifer Cibicidoides wuellerstorfi collected from northernmost Pacific mid-Piacenzian Warm Period (3.264–3.025 Myr ago) sediments. The data reveal progressively more isotopically negative dissolved inorganic carbon along a northward Equator-to-pole transect, the opposite of the expected Pliocene Pacific meridional overturning circulation signal. C. wuellerstorfi δ13C is also often more positive at the deeper Ocean Drilling Program (ODP) site 887 compared with the shallower ODP site 883, suggesting ‘bottom-up’ ventilation of the deep Pacific Ocean. We then present alkenone sea surface temperature and export-productivity data from ODP site 883, which suggest that late Pliocene subarctic North Pacific carbonate sedimentation was, at least in part, probably due to higher coccolithophore export production, rather than North Pacific Deep Water formation as previously argued. Therefore, we suggest it is unlikely that North Pacific Deep Water formation occurred in the mid-Piacenzian Warm Period, although a shallower overturning cell cannot be ruled out.
Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.
In 2020, natural resource managers at Camp Edwards, Barnstable County, MA, observed Terrapene carolina carolina (Eastern Box Turtle) individuals infected by myiasis, where parasitic flesh flies larviposit into the living tissue of a host. The hypothesized parasite was Dexosarcophaga cistudinis, but its impacts on the host's body condition, movement, and habitat use were unknown. Our objectives were to identify the parasite at Camp Edwards and to compare the body condition, movement, and habitat characteristics at capture locations of Eastern Box Turtles for infected and noninfected individuals. We radio-tracked turtles weekly and encountered 48 individuals from May to August 2022 at Camp Edwards, MA. Upon capture, we recorded turtle infection status, mass, carapace length, shell surface temperature, GPS location, and habitat characteristics of the capture location. We confirmed D. cistudinis as the parasite and found that myiasis-infected turtles had a significantly higher shell temperature (27.92 ± 5.28 °C) than noninfected turtles (26.77 ± 5.64 °C). However, we did not find an effect of myiasis on body condition, habitat use, or average daily distance moved. Collectively, our results suggest that infected turtles may exhibit behavioral fever, a mechanism by which ectotherms move to warmer microclimates to raise their body temperature in response to infections. Eastern Box Turtles at Camp Edwards may be able to use behavioral fever in response to myiasis infection because of the habitat mosaic made available through detailed habitat-management regimes.
In this paper, we review the derivation of the Gauss–Levenberg–Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.
The U.S. Geological Survey Cascades Volcano Observatory (CVO) recently expanded its continuous geophysical monitoring at Mount Rainier, an active stratovolcano in Washington state. CVO monitors volcanoes in Oregon, Washington, and Idaho to characterize volcanic systems and detect unrest. Mount Rainier has a history of large lahar occurrences in the Holocene, including at least one that may not have been associated with volcanic activity. Pierce County, Washington, is one of the areas most at risk from large lahars. In the 1990s, CVO collaborated with Pierce County to install the Rainier lahar detection system (RLDS), an automated system designed to detect large lahars in high‐risk drainages and mitigate hazards to heavily populated areas. The system was designed to detect lahars within 5–10 min of their occurrence and alert authorities of the need to evacuate populated low‐lying areas before lahar arrival. In addition, CVO and the Pacific Northwest Seismic Network (PNSN) maintained and expanded a network of seismic and geodetic monitoring stations on and near the edifice to provide adequate volcano monitoring capabilities. Since 2016, CVO has worked to upgrade the existing RLDS and to expand its capabilities into other drainages around Mount Rainier. This expansion includes installation of 25 new broadband seismic stations with many including infrasound along high‐risk drainages, as well as support for equipment upgrades at existing PNSN and CVO volcano monitoring sites. All stations transmit continuous, near‐real‐time data with dramatically improved spatial coverage for volcano monitoring and lahar hazard mitigation compared to the previous system.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240112","usgsCitation":"Kramer, R., Thelen, W., Iezzi, A.M., Moran, S.C., and Pauk, B., 2024, Recent expansion of the Cascades Volcano Observatory geophysical network at Mount Rainier for improved volcano and lahar monitoring: Seismological Research Letters, v. 95, no. 5, p. 2707-2721, https://doi.org/10.1785/0220240112.","productDescription":"15 p.","startPage":"2707","endPage":"2721","ipdsId":"IP-163746","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":484133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Ranier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.99784760683576,\n 47.05336015194894\n ],\n [\n -121.99784760683576,\n 46.64802462725589\n ],\n [\n -121.42808425851092,\n 46.64802462725589\n ],\n [\n -121.42808425851092,\n 47.05336015194894\n ],\n [\n -121.99784760683576,\n 47.05336015194894\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"95","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Kramer, Rebecca 0000-0002-4873-1983 rkramer@usgs.gov","orcid":"https://orcid.org/0000-0002-4873-1983","contributorId":195070,"corporation":false,"usgs":true,"family":"Kramer","given":"Rebecca","email":"rkramer@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":304206,"corporation":false,"usgs":true,"family":"Iezzi","given":"Alexandra","email":"","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":224629,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932571,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pauk, Benjamin 0000-0003-3036-5927 bpauk@usgs.gov","orcid":"https://orcid.org/0000-0003-3036-5927","contributorId":195069,"corporation":false,"usgs":true,"family":"Pauk","given":"Benjamin","email":"bpauk@usgs.gov","affiliations":[],"preferred":true,"id":932572,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257027,"text":"70257027 - 2024 - Feedbacks: A new synthesis of causal loops across ecology","interactions":[],"lastModifiedDate":"2024-11-22T15:56:13.864419","indexId":"70257027","displayToPublicDate":"2024-07-22T08:28:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Feedbacks: A new synthesis of causal loops across ecology","docAbstract":"Feedbacks are the basic linkages of living systems. In organisms, they regulate the processes of growth and homeostasis, as well as their interactions with their world. Feedback, which Judson (1980) called ‘one of the chief themes of scientific understanding,' is equally important in ecological systems. The ecological literature is rich in papers dealing with the role of feedback in various phenomena. However, we know of no comprehensive synthesis of feedbacks in ecology. Pichon et al. (2024) accomplish this, and for the first time show that ecological feedbacks can be categorized in terms of a small number of fundamental attributes. The paper brings the array of different types of feedbacks into a manageable order, providing not only the relevant theoretical framework but also guidance on methods for applying understanding to practical issues.
","language":"English","publisher":"Nordic Society Oikos","doi":"10.1111/ecog.07460","usgsCitation":"DeAngelis, D.L., and Xu, L., 2024, Feedbacks: A new synthesis of causal loops across ecology: Ecography, v. 2024, no. 11, e07460, 3 p., https://doi.org/10.1111/ecog.07460.","productDescription":"e07460, 3 p.","ipdsId":"IP-164923","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439260,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.07460","text":"Publisher Index Page"},{"id":432334,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2024","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","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":909198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Linhao","contributorId":221358,"corporation":false,"usgs":false,"family":"Xu","given":"Linhao","email":"","affiliations":[{"id":40353,"text":"Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key","active":true,"usgs":false}],"preferred":false,"id":909199,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257094,"text":"70257094 - 2024 - Predictor importance in habitat suitability models for invasive terrestrial plants","interactions":[],"lastModifiedDate":"2024-09-16T16:12:33.446909","indexId":"70257094","displayToPublicDate":"2024-07-22T07:13:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Predictor importance in habitat suitability models for invasive terrestrial plants","docAbstract":"Due to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs.
The contiguous United States.
We explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness.
Human modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest-performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non-woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness.
This study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13906","usgsCitation":"Williams, D.A., Shadwell, K.S., Pearse, I., Prevey, J.S., Engelstad, P., Henderson, G., and Jarnevich, C.S., 2024, Predictor importance in habitat suitability models for invasive terrestrial plants: Diversity and Distributions, v. 30, no. 9, e13906, 13 p., https://doi.org/10.1111/ddi.13906.","productDescription":"e13906, 13 p.","ipdsId":"IP-160521","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439261,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13906","text":"Publisher Index Page"},{"id":432432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.","language":"English","publisher":"Elsevier","doi":"10.1016/j.spasta.2024.100850","usgsCitation":"Reyes, J., Ma, T., McGahan, I., Storm, D., Walsh, D.P., and Zhu, J., 2024, Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease: Spatial Statistics, v. 62, 100850, 15 p., https://doi.org/10.1016/j.spasta.2024.100850.","productDescription":"100850, 15 p.","ipdsId":"IP-155343","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1016/j.spasta.2024.100850","text":"Publisher Index Page"},{"id":485559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":354688,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":354689,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":936164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian P.","contributorId":354690,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":354692,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936167,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Jun","contributorId":354695,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936168,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270064,"text":"70270064 - 2024 - Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures","interactions":[],"lastModifiedDate":"2025-08-18T15:27:11.945136","indexId":"70270064","displayToPublicDate":"2024-07-22T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22165,"text":"Reliability Engineering and System Safety (RESS)","active":true,"publicationSubtype":{"id":10}},"title":"Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures","docAbstract":"Probabilistic seismic performance assessments of engineered structures can be highly sensitive to the seismic input excitation and its variability. In the present study, the scenario-based performance assessment recommended by Federal Emergency Management Agency (FEMA) P-58 guidelines is adopted to estimate seismic fragility of concrete dams for various seismic hazard scenarios. Due to the scarcity of recorded ground motions and thereby their poor representation of uncertainties, stochastic ground motion simulation methods are utilized to obtain the required input excitations. Moreover, to understand the uncertainty in ground motion simulation models, two broadband stochastic simulation models are used to generate input excitations representing six seismic hazard scenarios defined by earthquake magnitude, source-to-site distance, and soil conditions.
Dams have negatively affected freshwater biodiversity throughout the world. These negative effects tend to be exacerbated for aquatic taxa with migratory life histories, and for taxa whose habitat is fundamentally altered by the formation of large reservoirs. Sauger (Sander candadensis; Percidae), large-bodied migratory fishes native to North America, have seen population declines over much of the species' range, and dams are often implicated for their role in blocking access to spawning habitat and otherwise negatively affecting river habitat. Furthermore, hybridization appears to be more frequent between sauger and walleye in the reservoirs formed by large dams. In this study, we examine the role of dams in altering sauger population connectivity and facilitating hybridization with introduced walleye in Wyoming's Wind River and Bighorn River systems. We collected genomic data from individuals sampled over a large spatial scale and replicated sampling throughout the spawning season, with the intent to capture potential variation in hybridization prevalence or genomic divergence between sauger with different life histories. The timing of sampling was not related to hybridization prevalence or population divergence, suggesting limited genetic differences between sauger spawning in different time and places. Overall, there was limited hybridization detected, however, hybridization was most prevalent in Boysen Reservoir (a large impounded section of the Wind River). Dams in the lower Wind River and upper Bighorn River were associated with population divergence between sauger upstream and downstream of the dams, and demographic models suggest that this divergence has occurred in concordance with the construction of the dam. Sauger upstream of the dams exhibited substantially lower estimates of genetic diversity, which implies that disrupted connectivity between Wind River and Bighorn River sauger populations may already be causing negative demographic effects. This research points towards the importance of considering the evolutionary consequences of dams on fish populations in addition to the threats they pose to population persistence.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11706","usgsCitation":"Rosenthal, W., Mandeville, E., Pilkerton, A., Gerrity, P.C., Skorupski, J., Walters, A.W., and Wagner, C., 2024, Influence of dams on sauger population structure and hybridization with introduced walleye, v. 14, no. 7, e11706, 16 p., https://doi.org/10.1002/ece3.11706.","productDescription":"e11706, 16 p.","ipdsId":"IP-145629","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11706","text":"Publisher Index Page"},{"id":485447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bighorn River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.2,\n 45\n ],\n [\n -109.2,\n 42.8\n ],\n [\n -107.6,\n 42.8\n ],\n [\n -107.6,\n 45\n ],\n [\n -109.2,\n 45\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenthal, William C.","contributorId":244630,"corporation":false,"usgs":false,"family":"Rosenthal","given":"William C.","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":935772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":935773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilkerton, Ashleigh","contributorId":346434,"corporation":false,"usgs":false,"family":"Pilkerton","given":"Ashleigh","affiliations":[{"id":63974,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":935774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerrity, Paul C.","contributorId":104198,"corporation":false,"usgs":true,"family":"Gerrity","given":"Paul","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":935775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skorupski, Joseph A.","contributorId":354495,"corporation":false,"usgs":false,"family":"Skorupski","given":"Joseph A.","affiliations":[],"preferred":false,"id":935776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935777,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Catherine E.","contributorId":337377,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine E.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":935778,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}