Pseudoloma neurophilia is a critical threat to the zebrafish (Danio rerio) model, as it is the most common infectious agent found in research facilities. In this study, our objectives were two-fold: (1) compare the application of diagnostic tools for P. neurophilia and (2) track the progression of infection using PCR and histology. The first experiment showed that whole-body analysis by qPCR (WB-qPCR) can be a standardized process, providing a streamlined diagnostic protocol, without the need for extraction of specific tissues. Evaluating the course of infection in experimentally infected fish, we showed key dynamics in infection. Starting with a low dose exposure of 8000 spores/fish, the prevalence remained low until 92 days post-exposure (dpe), followed by a 30%–40% prevalence by histology or 40%–90% by PCR until the end of the experiment at 334 dpe. WB-qPCR positively detected infection in more fish than histology throughout the study, as WB-qPCR detected the parasite as early as 4 dpe, whereas it was undetected by histology until 92 dpe. We also added a second slide for histologic analyses, showing an increase in detection rate from 24% to 26% when we combined all data from our experiments, but this increase was not statistically significant.
","language":"English","publisher":"Wiley","doi":"10.1111/jfd.13675","usgsCitation":"Schuster, C.J., Kreul, T., Al-Samarrie, C.E., Peterson, J., Sanders, J., and Kent, M., 2022, Progression of infection and detection of Pseudoloma neurophilia in zebrafish Danio rerio Hamilton by PCR and histology: Journal of Fish Diseases, v. 45, no. 10, p. 1463-1475, https://doi.org/10.1111/jfd.13675.","productDescription":"13 p.","startPage":"1463","endPage":"1475","ipdsId":"IP-142137","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":429882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"45","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Schuster, Corbin J.","contributorId":338307,"corporation":false,"usgs":false,"family":"Schuster","given":"Corbin","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903101,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreul, Taylor","contributorId":338308,"corporation":false,"usgs":false,"family":"Kreul","given":"Taylor","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903102,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Al-Samarrie, Colleen E.","contributorId":338309,"corporation":false,"usgs":false,"family":"Al-Samarrie","given":"Colleen","email":"","middleInitial":"E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903103,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903104,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sanders, Justin L.","contributorId":338310,"corporation":false,"usgs":false,"family":"Sanders","given":"Justin L.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903105,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kent, Michael L.","contributorId":338311,"corporation":false,"usgs":false,"family":"Kent","given":"Michael L.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903106,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232283,"text":"70232283 - 2022 - Provenance of Devonian-Carboniferous strata of Colorado: The influence of the Cambrian and the Proterozoic","interactions":[],"lastModifiedDate":"2022-06-24T15:42:08.004746","indexId":"70232283","displayToPublicDate":"2022-06-24T10:24:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3310,"text":"Rocky Mountain Geology","active":true,"publicationSubtype":{"id":10}},"title":"Provenance of Devonian-Carboniferous strata of Colorado: The influence of the Cambrian and the Proterozoic","docAbstract":"We report new LA-ICPMS U-Pb detrital zircon ages and sedimentary petrology of silty to sandy limestones and dolostones, as well as calcareous to dolomitic sandstones of the Devonian-Carboniferous (Mississippian) Chaffee Group, as well as detrital zircon ages from the Late Cambrian Sawatch Quartzite and a U-Pb zircon crystallization age on a late Mesoproterozoic (1087.9 13.5 Ma) granitoid of underlying basement from the Eagle basin of Colorado. Grain populations in the Chaffee Group are mostly bimodal, with over 84% of zircons centered around a Paleoproterozoic (ca. 1.78 Ga) mode typical of the Yavapai Province that forms much of the basement of Colorado and an early Mesoproterozoic (ca. 1.42 Ga) mode typical of A-type granites that intrude this region. A notable late Mesoproterozoic (ca. 1.08 Ga) mode exists in some Chaffee samples, giving those samples a trimodal detrital zircon age distribution. These bipartite or tripartite detrital zircon age modes exist in Cambrian, Devonian and Carboniferous strata from paleogeographically adjacent successions, but the correlation between the Chaffee zircons is highest with the regions basal Cambrian sandstones (Sawatch, Flathead, Ignacio formations) which have similar (1.08 Ga, 1.43 Ga, 1.71 Ga) zircon populations and a paucity of >1.8 Ga grains. This similarity suggests that the majority of grains in the Chaffee Group derive from recycling of these basal sandstones, and that little sediment was derived directly from then-exposed Precambrian basement highs, from the Wyoming Craton to the north, or from Paleoproterozoic arcs and orogens to the west and northeast. Minor Mesoarchean to early Paleoproterozoic (ca. 3.00 to 2.40 Ga) grains exist in the Chaffee Group, an attribute shared by the Late Ordovician Harding Sandstone of Colorados Front Range, but that is absent from the regions underlying Cambrian sandstonessuggesting some recycled mixture of Cambrian and Ordovician sedimentary rocks. No near-depositional age grains are present in the Chaffee Group; the youngest grain is Early Devonian (~417 Ma), >45 m.y. older than these strata, and Paleozoic grains are extremely uncommon (<0.1%; n=2927 grains).","language":"English","publisher":"University of Wyoming","doi":"10.24872/rmgjournal.57.1.1","usgsCitation":"Holm-Denoma, C., Matthews, W.A., Soar, L., Longman, M.W., and Hagadorn, J.W., 2022, Provenance of Devonian-Carboniferous strata of Colorado: The influence of the Cambrian and the Proterozoic: Rocky Mountain Geology, v. 57, no. 1, p. 1-21, https://doi.org/10.24872/rmgjournal.57.1.1.","productDescription":"21 p.","startPage":"1","endPage":"21","ipdsId":"IP-134077","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":402474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Eagle Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.10983276367186,\n 39.40861097325807\n ],\n [\n -106.16500854492188,\n 39.40861097325807\n ],\n [\n -106.16500854492188,\n 39.890772566959534\n ],\n [\n -107.10983276367186,\n 39.890772566959534\n ],\n [\n -107.10983276367186,\n 39.40861097325807\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":845008,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matthews, William A.","contributorId":292542,"corporation":false,"usgs":false,"family":"Matthews","given":"William","email":"","middleInitial":"A.","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":845009,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Soar, Linda","contributorId":292543,"corporation":false,"usgs":false,"family":"Soar","given":"Linda","email":"","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":845010,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Longman, Mark W.","contributorId":292544,"corporation":false,"usgs":false,"family":"Longman","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":27833,"text":"Denver Museum of Nature and Science","active":true,"usgs":false}],"preferred":false,"id":845011,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hagadorn, James W.","contributorId":219765,"corporation":false,"usgs":false,"family":"Hagadorn","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":40069,"text":"Denver Museum of Nature and Science, Department of Earth Sciences","active":true,"usgs":false}],"preferred":false,"id":845012,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255102,"text":"70255102 - 2022 - Integrating monitoring and optimization modeling to inform flow decisions for Chinook salmon smolts","interactions":[],"lastModifiedDate":"2024-06-17T13:42:40.49227","indexId":"70255102","displayToPublicDate":"2022-06-24T08:33:11","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Integrating monitoring and optimization modeling to inform flow decisions for Chinook salmon smolts","docAbstract":"Monitoring is usually among the first actions taken to help inform recovery planning for declining species, but these data are rarely used formally to inform conservation decision making. For example, Central Valley Chinook salmon were once abundant, but anthropogenic activities have led to widespread habitat loss and degradation resulting in significant population declines. Monitoring data suggest survival through the southern Sacramento-San Joaquin River Delta, in particular, may be a limiting factor for juvenile Chinook salmon outmigrating from the San Joaquin River and its tributaries. However, survival and routing monitoring data have not been formally used to inform water management in a decision analytic framework. Here, we illustrate how estimates derived from disjunct monitoring data can be used to inform water management and as a basis for adaptively managing flows. We aggregated a meta-analysis of Chinook salmon smolt survival and routing estimates through the south Delta with other sources of data to develop a survival and routing simulation model to estimate optimal flows for the San Joaquin River during smolt outmigration from February–May. We found that large flow pulses at predictable times during the spring are projected to be optimal for increasing Chinook salmon smolt survival to the San Francisco Bay and that optimal scenarios differed somewhat with water year type. Sensitivity analysis revealed temperature and smolt outmigration timing are driving optimal pulse distribution and that water allocation changes little with parameter uncertainty. This case study highlights the utility of the decision-analytic framework for solving conservation problems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2022.110058","usgsCitation":"Wohner, P.J., Duarte, A., Wikert, J., Cavallo, B., Zeug, S.C., and Peterson, J., 2022, Integrating monitoring and optimization modeling to inform flow decisions for Chinook salmon smolts: Ecological Modelling, v. 471, 110058, 17 p., https://doi.org/10.1016/j.ecolmodel.2022.110058.","productDescription":"110058, 17 p.","ipdsId":"IP-133558","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467178,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2022.110058","text":"Publisher Index Page"},{"id":430269,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.22739494059627,\n 38.193612778746115\n ],\n [\n -122.22739494059627,\n 37.63171263541648\n ],\n [\n -120.98640474009068,\n 37.63171263541648\n ],\n [\n -120.98640474009068,\n 38.193612778746115\n ],\n [\n -122.22739494059627,\n 38.193612778746115\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"471","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wohner, Patti J.","contributorId":338611,"corporation":false,"usgs":false,"family":"Wohner","given":"Patti","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":338612,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wikert, John","contributorId":338613,"corporation":false,"usgs":false,"family":"Wikert","given":"John","email":"","affiliations":[{"id":25470,"text":"U.S. Fish & Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":903401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cavallo, Brad","contributorId":338615,"corporation":false,"usgs":false,"family":"Cavallo","given":"Brad","email":"","affiliations":[{"id":81177,"text":"Cramer Fish Sciences, Modeling, Analysis, and Synthesis Lab","active":true,"usgs":false}],"preferred":false,"id":903402,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zeug, Steven C.","contributorId":338617,"corporation":false,"usgs":false,"family":"Zeug","given":"Steven","email":"","middleInitial":"C.","affiliations":[{"id":81177,"text":"Cramer Fish Sciences, Modeling, Analysis, and Synthesis Lab","active":true,"usgs":false}],"preferred":false,"id":903403,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903398,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70231865,"text":"70231865 - 2022 - Wading bird foraging on a wetland landscape: A comparison of two strategies","interactions":[],"lastModifiedDate":"2022-06-01T13:30:54.834529","indexId":"70231865","displayToPublicDate":"2022-06-24T08:28:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2699,"text":"Mathematical Biosciences and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Wading bird foraging on a wetland landscape: A comparison of two strategies","docAbstract":"Tactile-feeding wading birds, such as wood storks and white ibises, require high densities of prey such as small fishes and crayfish to support themselves and their offspring during the breeding season. Prey availability in wetlands is often determined by seasonal hydrologic pulsing, such as in the subtropical Everglades, where spatial distributions of prey can vary through time, becoming heterogeneously clumped in patches, such as ponds or sloughs, as the wetland dries out. In this mathematical modeling study, we selected two possible foraging strategies to examine how they impact total energetic intake over a time scale of one day. In the first, wading birds sample prey patches without a priori knowledge of the patches' prey densities, moving from patch to patch, staying long enough to estimate the prey density, until they find one that meets a predetermined satisfactory threshold, and then staying there for a longer period. For this case, we solve for a wading bird's expected prey intake over the course of a day, given varying theoretical probability distributions of patch prey densities across the landscape. In the second strategy considered, it is assumed that the wading bird samples a given number of patches, and then uses memory to return to the highest quality patch. Our results show how total intake over a day is impacted by assumptions of the parameters governing the spatial distribution of prey among patches, which is a key source of parameter uncertainty in both natural and managed ecosystems. Perhaps surprisingly, the foraging strategy that uses a prey density threshold generally led to higher maximum potential prey intake than the strategy for using memory to return to the best patch sampled. These results will contribute to understanding the foraging of wading birds and to the management of wetlands.
","language":"English","publisher":"AIMS Press","doi":"10.3934/mbe.2022361","usgsCitation":"Lee, H.W., DeAngelis, D.L., Yurek, S., and Tennenbaum, S., 2022, Wading bird foraging on a wetland landscape: A comparison of two strategies: Mathematical Biosciences and Engineering, v. 19, no. 8, p. 7687-7718, https://doi.org/10.3934/mbe.2022361.","productDescription":"32 p.","startPage":"7687","endPage":"7718","ipdsId":"IP-138910","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":447339,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3934/mbe.2022361","text":"Publisher Index Page"},{"id":401534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lee, Hyo Won","contributorId":292184,"corporation":false,"usgs":false,"family":"Lee","given":"Hyo","email":"","middleInitial":"Won","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":844003,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":844004,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yurek, Simeon 0000-0002-6209-7915","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":216733,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":844005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tennenbaum, Stephen","contributorId":292180,"corporation":false,"usgs":false,"family":"Tennenbaum","given":"Stephen","email":"","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":844006,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70254298,"text":"70254298 - 2022 - Satellite remote sensing of crop water use across the Missouri River Basin for 1986–2018 period","interactions":[],"lastModifiedDate":"2024-05-17T11:43:50.362857","indexId":"70254298","displayToPublicDate":"2022-06-24T06:42:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Satellite remote sensing of crop water use across the Missouri River Basin for 1986–2018 period","docAbstract":"Understanding historical crop water use (CWU) dynamics is important to improve land and water management. In this study, well-validated (coefficient of determination = 0.91, percent bias = 4%, and percent root mean square error = 11.8%) Landsat-based actual evapotranspiration (ETa) time-series estimations were used to (1) assess summer season CWU (CWU-Su) dynamics, (2) investigate CWU-Su trends over the study period (1986–2018; 33 years) at the regional- and pixel-scales, and (3) attribute CWU-Su driving factors across Missouri River Basin. Spatial variability of the ETa estimations along with the observed bimodal probability density distribution of ETa highlighted a strong relation between land cover and water uses across the basin. The bimodal distribution of ETa also indicated the presence of two major landcovers in the basin. The drier foothill regions in northwestern Missouri River Basin, dominated by grassland/shrubland, showed lower ETa (< 500 mm/year), whereas cropland dominated regions in lower semi-humid basin and forested subbasins exhibited higher ETa (> 600 mm/year). The CWU-Su anomalies revealed the vulnerability of the basin to year-to-year weather conditions. The CWU-Su trend analysis revealed a significant positive trend (p < 0.1) at the regional-scale affecting 30% of basin’s cropland pixels. The cropland pixels under positive CWU-Su trend were found to be clustered in the eastern and central Missouri River Basin as a result of the combined effect of increased crop production area, increased crop yields, crop practice shifts to higher biomass crops, and increased irrigated land. The effect of improved irrigation and water management practices on reducing CWU-Su was observed in western Missouri River Basin, which had a stable major crop throughout the study period. Overall, the study highlights the usefulness of Landsat imagery and remote sensing-based ETa modeling approaches in generating historical time-series ETa maps over a wide range of elevation, vegetation, and climate.
Understanding the role that an environmental prion reservoir plays in the outbreak dynamics of chronic wasting disease (CWD) in free ranging white-tailed deer (Odocoileus virginianus) is critical for the allocation of disease surveillance resources by state and provincial wildlife agencies. We hypothesized that demographic, ecological, and epidemiological configurations naturally attenuate epidemic risk despite the introduction of infectious prions into a susceptible population of deer, but the magnitude of infectious prions in the environmental prion reservoir complicate outbreak expectations. We developed a Susceptible-Latent-Exposed-Infective (SLEI) compartment model to represent the dynamics of CWD epidemics in free-ranging white-tailed deer, then used the basic reproductive ratio (
In oil spill research, a topic of increasing attention during the last decade has been the environmental impact of the partial oxidation products that result from transformation of the petroleum in freshwater, marine, and terrestrial ecosystems. This report describes the isolation and characterization of the partial oxidation products from crude oil contaminating groundwater at the long-term U.S. Geological Survey Bemidji research site in Minnesota. As the result of a pipeline burst in August 1979, a body of light aliphatic crude oil is present from the land surface to 2 meters below the water table in a shallow sand and gravel aquifer in a remote area outside Bemidji, Minnesota, United States. Biodegradation has resulted in the formation of a plume of dissolved organic carbon (DOC) downgradient from the oil body. Groundwater has also been contaminated in an area known as the spray zone, from vertical infiltration of DOC resulting from biodegradation of oil in the soil column, and possibly from photooxidation of oil at the soil surface. The majority of DOC in the contaminated groundwater is in the form of nonvolatile organic acids (NVOAs) which represent the partial oxidation products of the crude oil constituents. The NVOAs have been classified into three fractions according to their isolation on XAD resins: hydrophobic neutrals (HPON), hydrophobic acids (HPOA), and hydrophilic acids (HPIA). These fractions of NVOAs were isolated from wells downgradient from the oil body (sampling well numbers 533, 532, 530, 515), in the spray zone (603), and from an uncontaminated well upgradient of the oil body (310) between the years 1986 and 1989, and again from wells 530 and 603 in 1998. The samples have been characterized by elemental analysis, radiocarbon dating, carbon-13 nuclear magnetic resonance spectroscopy (13C NMR), and negative-mode (-) electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FTICR-MS), with a particular focus on fractions from wells 310, 530, and 603.
All the characterization data indicate that the NVOAs from contaminated wells are distinguishable from the background DOC. Carbon-14 (14C) ages of NVOAs from contaminated wells ranged from 3,615 to 18,985 years before the present, whereas the background DOC from the aquifer was post-bomb (post 1950). By elemental analysis, NVOAs from contaminated wells had higher sulfur but lower nitrogen contents than the background. By electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry, number average molecular weights determined from assigned molecular formulas ranged from 416 to 486 daltons for the HPOA and HPIA fractions from both background and contaminant wells. NVOAs from contaminated wells had significantly greater numbers of assigned molecular formulas containing sulfur, with elevated concentrations of the S1O4-10 species in particular. Compared to the background, HPOA and HPIA fractions from contaminant wells had a broader range of double bond equivalents (DBEs) within On compound classes (n is number of atoms). Additionally, within On compound classes, contaminant well HPOA fractions had a greater abundance of lower n (less than eight) than the background. Contaminant well double bond equivalents versus carbon number (C#) plots of oxygen compound classes suggest oil-derived aliphatic compounds in the range from C12 to C22 in HPOA and HPIA fractions and oil-derived compounds containing aromatic or saturated rings in the approximate range from C20 to C30 are present in HPOA fractions.
The data suggest the NVOAs originate from biodegradation of several classes of C12 and greater crude oil constituents: sulfur-containing constituents, including possibly the resins and asphaltenes; constituents containing aromatic rings substituted with methyl groups, including alkylaromatic and naph-
thenoaromatic compounds, and C12 to C22 alkyl constituents. The overall similarities of the carbon-13 nuclear magnetic resonance spectra for the well 603 and 530 samples from the two sampling dates suggest that a steady state in the composition of the partial oxidation products in each of the two wells had been reached between 1986–1989 and 1998.
Chief, USGS National Water Quality Laboratory
U.S. Geological Survey
Box 25585, Mail Stop 407
Denver, CO 80225-0585
Traditional methods to assess the probability of storm-induced erosion and flooding from extreme water levels have limited use along the U.S. West Coast where swell dominates erosion and storm surge is limited. This effort presents methodology to assess the probability of erosion and flooding for the U.S. West Coast from extreme total water levels (TWLs), but the approach is applicable to coastal settings worldwide. TWLs were derived from 61 years of wave and water level data at shore-perpendicular transects every 100-m along open coast shorelines. At each location, wave data from the Global Ocean Waves model were downscaled to the nearshore and used to empirically calculate wave run-up. Tides were simulated using the Oregon State University’s tidal data inversion model and non-tidal residuals were calculated from sea-surface temperature and pressure anomalies. Wave run-up was combined with still water levels to generate hourly TWL estimates and extreme TWLs for multiple return periods. Extremes were compared to onshore morphology to determine erosion hazards and define the probability of collision, overwash, and inundation.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41597-022-01313-6","usgsCitation":"Shope, J.B., Erikson, L.H., Barnard, P.L., Storlazzi, C.D., Serafin, K.A., Doran, K., Stockdon, H.F., Reguero, B.G., Mendez, F.J., Castanedo, S., Cid, A., Cagigal, L., and Ruggiero, P., 2022, Characterizing storm-induced coastal change hazards along the United States West Coast: Nature--Scientific Data, v. 9, 224, 20 p., https://doi.org/10.1038/s41597-022-01313-6.","productDescription":"224, 20 p.","ipdsId":"IP-122757","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":447347,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-022-01313-6","text":"Publisher Index Page"},{"id":435798,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LBJEY1","text":"USGS data release","linkHelpText":"Chesapeake Bay Nontidal Network 1985 - 2018: 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Sonia","contributorId":292450,"corporation":false,"usgs":false,"family":"Castanedo","given":"Sonia","email":"","affiliations":[{"id":41638,"text":"University of Cantabria","active":true,"usgs":false}],"preferred":false,"id":844595,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cid, Alba","contributorId":292451,"corporation":false,"usgs":false,"family":"Cid","given":"Alba","email":"","affiliations":[{"id":41638,"text":"University of Cantabria","active":true,"usgs":false}],"preferred":false,"id":844596,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cagigal, Laura","contributorId":264473,"corporation":false,"usgs":false,"family":"Cagigal","given":"Laura","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":844597,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ruggiero, Peter","contributorId":15709,"corporation":false,"usgs":false,"family":"Ruggiero","given":"Peter","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":844598,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70236762,"text":"70236762 - 2022 - Response study of a tall San Diego, California building inferred from the M7.1 July 5, 2019 Ridgecrest, California earthquake motions","interactions":[],"lastModifiedDate":"2022-09-19T13:29:55.472618","indexId":"70236762","displayToPublicDate":"2022-06-23T08:23:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12589,"text":"The Open Construction & Building Technology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Response study of a tall San Diego, California building inferred from the M7.1 July 5, 2019 Ridgecrest, California earthquake motions","docAbstract":"The shaking of a new 24-story tall building in San Diego, California, was recorded by its seismic monitoring array during the M7.1 Ridgecrest, California earthquake of July 5, 2019. The building is located ~340 km from the epicenter of the event. The building is a special moment framed (SMF) steel structure with reduced beam sections (RBS) and viscous damper systems (DS). Peak accelerations recorded by the array indicate 0.007 g at the basement level and 0.044 g at the roof level. Spectral analyses and system identification methods indicate coupled NS, EW, and torsinal fundamental modes at ~ 0.30 Hz frequency and critical damping percentages < 5%. For the EW and fundamental torsional modes, critical damping percentages are < 2.5%. At the low-level shaking, the computed largest average drift ratio is ~ 0.065%, less than 0.5% of the value considered to be the starting threshold of nonlinear behavior or damage.
","language":"English","publisher":"Bentham Open","doi":"10.2174/18748368-v16-e2108040","usgsCitation":"Celebi, M., and Swensen, D., 2022, Response study of a tall San Diego, California building inferred from the M7.1 July 5, 2019 Ridgecrest, California earthquake motions: The Open Construction & Building Technology Journal, v. 16, no. Suppl 1, e187483682108040, 14 p., https://doi.org/10.2174/18748368-v16-e2108040.","productDescription":"e187483682108040, 14 p.","ipdsId":"IP-122744","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":447350,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2174/18748368-v16-e2108040","text":"Publisher Index Page"},{"id":406953,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Diego","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.17279434204102,\n 32.69226560224167\n ],\n [\n -117.11975097656249,\n 32.69226560224167\n ],\n [\n -117.11975097656249,\n 32.73703926550904\n ],\n [\n -117.17279434204102,\n 32.73703926550904\n ],\n [\n -117.17279434204102,\n 32.69226560224167\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"Suppl 1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":852114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swensen, Daniel","contributorId":296667,"corporation":false,"usgs":false,"family":"Swensen","given":"Daniel","email":"","affiliations":[{"id":64121,"text":"CSMIP-CGS","active":true,"usgs":false}],"preferred":false,"id":852115,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241792,"text":"70241792 - 2022 - Diverse aging rates in ectothermic tetrapods provide insights for the evolution of aging and longevity","interactions":[],"lastModifiedDate":"2023-03-27T12:16:37.620756","indexId":"70241792","displayToPublicDate":"2022-06-23T07:14:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Diverse aging rates in ectothermic tetrapods provide insights for the evolution of aging and longevity","docAbstract":"Mauka-to-makai (mountain to sea in the Hawaiian language) hydrologic connectivity – commonly referred to as ridge-to-reef – directly affects biogeochemical processes and socioecological functions across terrestrial, freshwater, and marine systems. The supply of freshwater to estuarine and nearshore environments in a ridge-to-reef system supports the food, water, and habitats utilized by marine fauna. In addition, the ecosystem services derived from this land-to-sea connectivity support social and cultural practices (hereafter referred to as socio-cultural) including fishing, aquaculture, wetland agriculture, religious ceremonies, and recreational activities. To effectively guide island resource management, a better understanding of the linkages from ridge-to-reef across natural and social usages is critical, particularly in the context of climate change, with anticipated increasing temperature and shifting precipitation patterns. The objective of this study was to identify spatial linkages that promote multiple and diverse uses, following the ridge-to-reef concept, at an island-wide scale to identify regions of high conservation importance for aquatic resources. We selected the Island of Maui as a study representative of many Pacific islands. Diverse datasets, including agricultural lands within watersheds, wetland locations, presence of stream species, indicators of freshwater input from streams, coral cover, nearshore fish biomass, socio-cultural data such as fishpond locations, wetland taro cultivation, beach recreation use, and lastly the dynamically downscaled Coupled Model Intercomparison Project Phase (CMIP5) future climate projections scenarios (Representative Concentration Pathway (RCP) 4.5 & 8.5) were used to examine the spatial linkages through hydrological connectivity from land to the sea. Zonation spatial planning software was used to prioritize areas of high management and conservation value and to help inform aquatic resources management. The resulting prioritized areas included many minimally disturbed watersheds in east Maui and western nearshore and coastal zones that are adjacent to diverse coral reefs. These results are driven by the importance of fish biomass and coral reef distribution as well as traditional wetland taro cultivation and coastal access points for recreation. These results underline the importance of examining ridge-to-reef systems for aquatic resource management and including important social and cultural values in resource management upon planning adaptation strategies for climate change. Improving our understanding of diverse natural and socio-cultural influences on habitat conditions and their values in these areas provides an opportunity to strategically plan future management and conservation actions.
This report assesses the status and trends of selected ecological health indicators of the Upper Mississippi River System (UMRS) based on the data collected and analyzed by the Long Term Resource Monitoring element of the Upper Mississippi River Restoration program, supplemented with data from other sources. This report has four objectives: providing a brief introduction of the UMRS, including its significance, history, modern-day stressors, and recent research; using ecological indicators to describe the status of the river system and where and how it has changed from circa 1993 to 2019; discussing management and restoration implications of these changes; and highlighting the fundamental role of long-term monitoring in the understanding, management, and restoration of large-floodplain rivers.
The data were collected in the six Long Term Resource Monitoring element study reaches that spanned much of the UMRS and the various gradients contained therein. These study reaches included Navigation Pools 4, 8, 13, and 26; the part of the Unimpounded Reach of the Upper Mississippi River between Grand Tower and Cairo, Illinois; and the La Grange Pool on the Illinois River. The indicators included in this report describe the status and trends for the hydrology, geomorphology, floodplain vegetation, water quality, vegetation, and fishes of the UMRS. Many of the indicators of river ecosystem health changed significantly over the nearly 30 years of our evaluation. However, there was substantial spatial variability in the magnitude and timing of those changes among study reaches. Few indicators changed everywhere or nowhere; most indicators changed in some reaches but not others. The quantitative assessments of these indicators describe how the conditions of the river differ across hydrogeomorphic and climate gradients and through time and are intended to support the restoration and management of the UMRS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221039","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","programNote":"Species Management Research Program and Land Management Research Program","usgsCitation":"Houser, J.N., ed., 2022, Ecological status and trends of the Upper Mississippi and Illinois Rivers (ver. 1.1, July 2022): U.S. Geological Survey Open-File Report 2022–1039, 199 p., https://doi.org/10.3133/ofr20221039.","productDescription":"xiv, 199 p.","numberOfPages":"220","onlineOnly":"N","ipdsId":"IP-125605","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":402335,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1039/coverthb2.jpg"},{"id":402336,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1039/ofr20221039.pdf","text":"Report","size":"41.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1039"},{"id":403771,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2022/1039/versionHist.txt","size":"3.11 kB","linkFileType":{"id":2,"text":"txt"}},{"id":501772,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113198.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, North Dakota, South Dakota, Wisconsin","otherGeospatial":"Illinois River, upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.329833984375,\n 46.172222978455395\n ],\n [\n -90.340576171875,\n 46.30140615437332\n ],\n [\n -92.384033203125,\n 46.49082901981415\n ],\n [\n -93.087158203125,\n 46.74738913515841\n ],\n [\n -93.262939453125,\n 47.42065432071318\n ],\n [\n -94.3505859375,\n 47.76148371616669\n ],\n [\n -95.712890625,\n 47.30903424774781\n ],\n [\n -96.1083984375,\n 46.912750956378915\n ],\n [\n -96.1083984375,\n 46.18743678432541\n ],\n [\n -96.6357421875,\n 45.89000815866184\n ],\n [\n -97.393798828125,\n 45.867062714815475\n ],\n [\n -97.591552734375,\n 45.72152152227954\n ],\n [\n -96.50390625,\n 44.56699093657141\n ],\n [\n -95.43823242187499,\n 43.51668853502906\n ],\n [\n -95.042724609375,\n 42.70665956351041\n ],\n [\n -94.559326171875,\n 41.82045509614034\n ],\n [\n -94.02099609375,\n 41.20345619205131\n ],\n [\n -93.043212890625,\n 39.66491373749128\n ],\n [\n -92.61474609375,\n 39.172658670429946\n ],\n [\n -91.790771484375,\n 38.84826438869913\n ],\n [\n -90.977783203125,\n 38.762650338334154\n ],\n [\n -90.76904296874999,\n 38.736946065676\n ],\n [\n -91.14257812499999,\n 38.35888785866677\n ],\n [\n -91.58203125,\n 37.43997405227057\n ],\n [\n -90.8349609375,\n 36.86204269508728\n ],\n [\n -89.18701171875,\n 37.046408899699564\n ],\n [\n -88.72558593749999,\n 37.87485339352928\n ],\n [\n -88.87939453125,\n 38.685509760012\n ],\n [\n -87.69287109375,\n 40.863679665481676\n ],\n [\n -86.748046875,\n 41.178653972331674\n ],\n [\n -86.781005859375,\n 41.5579215778042\n ],\n [\n -87.593994140625,\n 41.46742831254425\n ],\n [\n -87.86865234374999,\n 42.374778361114195\n ],\n [\n -88.494873046875,\n 43.31718491566705\n ],\n [\n -89.3408203125,\n 43.810747313446996\n ],\n [\n -89.6484375,\n 43.937461690316646\n ],\n [\n -89.40673828125,\n 44.50434127765394\n ],\n [\n -89.329833984375,\n 46.172222978455395\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: June 22, 2022; Version 1.1: July 14, 2022","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
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In Spring 2021, the highly transmissible SARS-CoV-2 Delta variant began to cause increases in cases, hospitalizations, and deaths in parts of the United States. At the time, with slowed vaccination uptake, this novel variant was expected to increase the risk of pandemic resurgence in the US in summer and fall 2021. As part of the COVID-19 Scenario Modeling Hub, an ensemble of nine mechanistic models produced 6-month scenario projections for July–December 2021 for the United States. These projections estimated substantial resurgences of COVID-19 across the US resulting from the more transmissible Delta variant, projected to occur across most of the US, coinciding with school and business reopening. The scenarios revealed that reaching higher vaccine coverage in July–December 2021 reduced the size and duration of the projected resurgence substantially, with the expected impacts was largely concentrated in a subset of states with lower vaccination coverage. Despite accurate projection of COVID-19 surges occurring and timing, the magnitude was substantially underestimated 2021 by the models compared with the of the reported cases, hospitalizations, and deaths occurring during July–December, highlighting the continued challenges to predict the evolving COVID-19 pandemic. Vaccination uptake remains critical to limiting transmission and disease, particularly in states with lower vaccination coverage. Higher vaccination goals at the onset of the surge of the new variant were estimated to avert over 1.5 million cases and 21,000 deaths, although may have had even greater impacts, considering the underestimated resurgence magnitude from the model.
","language":"English","publisher":"eLife Sciences Publications, Ltd","doi":"10.7554/eLife.73584","usgsCitation":"Truelove, S., Smith, C.P., Qin, M., Mullany, L., Borchering, R.K., Lessler, J., Shea, K., Howerton, E., Contamin, L., Levander, J., Kerr, J., Hochheiser, H., Kinsey, M., Tallaksen, K., Wilson, S., Shin, L., Rainwater-Lovett, K., Lemaitre, J., Dent, J., Kaminsky, J., Lee, E.C., Perez-Saez, J., Hill, A., Karlen, D., Chinazzi, M., Davis, J., Mu, K., Xiong, X., Pastore y Piontti, A., Vespignani, A., Srivastava, A., Porebski, P., Venkatramanan, S., Adiga, A., Lewis, B., Klahn, B., Outten, J., Orr, M., Harrison, G., Hurt, B., Chen, J., Vullikanti, A., Marathe, M., Hoops, S., Bhattacharya, P., Machi, D., Chen, S., Paul, R., Janies, D., Thill, J., Galanti, M., Yamana, T., Pei, S., Shaman, J.L., Healy, J., Slayton, R.B., Biggerstaff, M., Johansson, M.A., Runge, M.C., and Viboud, C., 2022, Projected resurgence of COVID-19 in the United States in July—December 2021 resulting from the 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season","interactions":[],"lastModifiedDate":"2022-06-20T16:34:49.404382","indexId":"70232259","displayToPublicDate":"2022-06-20T11:04:17","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Predictive models of phosphorus concentration and load in stormwater runoff from small urban residential watersheds in fall season","docAbstract":"Urban street trees are a key part of public green infrastructure in many cities, however, leaf litter on streets is a critical biogenic source of phosphorus (P) in urban stormwater runoff during Fall. This study identified mass of street leaf litter (Mleaf) and antecedent dry days (ADD) as the top two explanatory parameters that have significant predictive power of event end-of-pipe P concentrations through multiple linear regression (MLR) analysis. Mleaf and volume of runoff (Vol) were the top two key explanatory parameters of event end-of-pipe P loads. Two-predictor MLR models were developed with these explanatory parameters using a 40-storm dataset derived from six small urban residential watersheds in Wisconsin, USA, and evaluated using storms specific to each study basin. The MLR model validation results indicated sensitivity to storm composition in the datasets. Our analysis shows selected parameters can be used by environmental managers to facilitate end-of-pipe P prediction in urban areas. This information can be used to reduce the amount of P in stormwater runoff by adjusting the timing and frequency of municipal leaf collection and street cleaning programs in urban areas.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2022.115171","usgsCitation":"Wang, Y., Thompson, A., and Selbig, W.R., 2022, Predictive models of phosphorus concentration and load in stormwater runoff from small urban residential watersheds in fall season: Journal of Environmental Management, v. 315, 115171, 8 p., https://doi.org/10.1016/j.jenvman.2022.115171.","productDescription":"115171, 8 p.","ipdsId":"IP-122605","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jenvman.2022.115171","text":"Publisher Index Page"},{"id":402375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fond du Lac, Madison, Oshkosh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.58389282226561,\n 42.97752543508356\n ],\n [\n -89.25018310546875,\n 42.97752543508356\n ],\n [\n -89.25018310546875,\n 43.206176810164784\n ],\n [\n -89.58389282226561,\n 43.206176810164784\n ],\n [\n -89.58389282226561,\n 42.97752543508356\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.62533569335938,\n 43.94042832696309\n ],\n [\n -88.48251342773438,\n 43.94042832696309\n ],\n [\n -88.48251342773438,\n 44.11125397357155\n ],\n [\n -88.62533569335938,\n 44.11125397357155\n ],\n [\n -88.62533569335938,\n 43.94042832696309\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.51066589355469,\n 43.72694838956604\n ],\n [\n -88.36578369140625,\n 43.72694838956604\n ],\n [\n -88.36578369140625,\n 43.823629034783124\n ],\n [\n -88.51066589355469,\n 43.823629034783124\n ],\n [\n -88.51066589355469,\n 43.72694838956604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"315","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Yi 0000-0003-3638-7940","orcid":"https://orcid.org/0000-0003-3638-7940","contributorId":236843,"corporation":false,"usgs":false,"family":"Wang","given":"Yi","email":"","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":844871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Anita 0000-0002-6202-1742","orcid":"https://orcid.org/0000-0002-6202-1742","contributorId":236844,"corporation":false,"usgs":false,"family":"Thompson","given":"Anita","email":"","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":844872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selbig, William R. 0000-0003-1403-8280 wrselbig@usgs.gov","orcid":"https://orcid.org/0000-0003-1403-8280","contributorId":877,"corporation":false,"usgs":true,"family":"Selbig","given":"William","email":"wrselbig@usgs.gov","middleInitial":"R.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844873,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232258,"text":"70232258 - 2022 - Demonstration of a novel quantitative microscopy technique for automated characterization of in situ particulate matter in coal miners with progressive massive fibrosis","interactions":[],"lastModifiedDate":"2022-06-28T13:56:28.826441","indexId":"70232258","displayToPublicDate":"2022-06-20T10:55:33","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Demonstration of a novel quantitative microscopy technique for automated characterization of in situ particulate matter in coal miners with progressive massive fibrosis","docAbstract":"Rationale: Increasing exposure to respirable crystalline silica (RCS) linked to changes in mining production processes has been implicated in the resurgence of severe lung disease in U.S. coal miners. Lung mineralogy can provide insight into particle pathogenesis. However, standard approaches to characterizing in situ particulate matter (PM) by pulmonary pathologists have poor inter-rater comparability, and consensus agreement is time consuming. Scanning electron microscopy/energy-dispersive spectroscopy (SEM/EDX) is technically complex and labor intensive. We developed a method for quantitative in situ PM characterization using conventional polarized light microscopy (PLM) and explored PM features in lung tissue of coal miners with progressive massive fibrosis (PMF). \nMethods: With institutional review board approval, PLM images were obtained from 30 miners with PMF, classified by pathologists consensus based on PM profusion; 10 from each profusion group (mild/moderate/severe) were selected. Automated PM counting and characterization (including dimensions and grayscale intensity of PM > 0.3 m diameter) was performed on image samples using PLM with modified cell-counting software (BZ-X800 light microscope, Keyence Corporation, Osaka, Japan) (Figure 1). Quantitative PM density loge(PM count)/mm3 tissue was calculated for each sample and compared to pathologist PM profusion groups. PMF lesion type using consensus pathologist classification (13 coal-type, 9 mixed-type, and 8 silicotic-type) was compared to automated PM birefringence level (% particles with mean grayscale intensity <65, range 0-255). RCS particles are expected to be weakly birefringent (lower intensity) relative to other common minerals (e.g., silicates) contained in coal mine dust. PM features were analyzed in R 4.0.3 using one-way ANOVA for between-group comparisons.\nResults: Measured PM log-density increased significantly with higher qualitative profusion group (mild=10.480.98/mm3, moderate=11.460.81/mm3, severe=12.520.86/mm3, p<0.0001). Prevalence of weakly birefringent particles was significantly higher among silicotic-type PMF samples (31.57.9%) compared to either coal-type (21.510.1%, p=0.022) or mixed-type lesions (21.510.5%, p=0.025). \nConclusion: This pilot study demonstrates the feasibility of a novel quantitative microscopy technique for counting and characterizing in situ lung PM in coal miners with PMF. Quantitative PM burden was comparable to pulmonary pathologists consensus profusion classification, but this method was substantially less time consuming and labor intensive and provided additional information about relevant PM features. The higher prevalence of weakly birefringent particles seen in silicotic-type PMF lesions may help inform mineralogic pathogenesis of RCS. Future efforts will expand the number of PMF cases analyzed, further validate our mineralogic findings using data from SEM/EDX and lung tissue digestate methods, and compare findings in historical versus contemporary coal miners with PMF.","largerWorkTitle":"American Thoracic Society 2022 proceedings","language":"English","publisher":"American Thoracic Society","doi":"10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A2489","usgsCitation":"Hua, J.T., Zell-Baran, L.M., Go, L.H., Cool, C.D., Lowers, H.A., Almberg, K.S., Sarver, E.A., Majka, S.M., Pang, K.D., Cohen, R.A., and Rose, C.S., 2022, Demonstration of a novel quantitative microscopy technique for automated characterization of in situ particulate matter in coal miners with progressive massive fibrosis, in American Thoracic Society 2022 proceedings, 2 p., https://doi.org/10.1164/ajrccm-conference.2022.205.1_MeetingAbstracts.A2489.","productDescription":"2 p.","ipdsId":"IP-133920","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":402374,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Hua, Jeremy T.","contributorId":292496,"corporation":false,"usgs":false,"family":"Hua","given":"Jeremy","email":"","middleInitial":"T.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":844860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zell-Baran, Lauren M.","contributorId":265756,"corporation":false,"usgs":false,"family":"Zell-Baran","given":"Lauren","email":"","middleInitial":"M.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":844861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Go, L. H.","contributorId":190733,"corporation":false,"usgs":false,"family":"Go","given":"L.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":844862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cool, Carlyne D.","contributorId":265746,"corporation":false,"usgs":false,"family":"Cool","given":"Carlyne","email":"","middleInitial":"D.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":844863,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":844864,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Almberg, K. S.","contributorId":265745,"corporation":false,"usgs":false,"family":"Almberg","given":"K.","email":"","middleInitial":"S.","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":844865,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sarver, Emily A.","contributorId":265758,"corporation":false,"usgs":false,"family":"Sarver","given":"Emily","email":"","middleInitial":"A.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":844866,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Majka, Susan M.","contributorId":292497,"corporation":false,"usgs":false,"family":"Majka","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":844867,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pang, Kathy D.","contributorId":292498,"corporation":false,"usgs":false,"family":"Pang","given":"Kathy","email":"","middleInitial":"D.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":844868,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cohen, R. A.","contributorId":290338,"corporation":false,"usgs":false,"family":"Cohen","given":"R.","email":"","middleInitial":"A.","affiliations":[{"id":18133,"text":"University of Illinois Chicago","active":true,"usgs":false}],"preferred":false,"id":844869,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Cecil S.","contributorId":265751,"corporation":false,"usgs":false,"family":"Rose","given":"Cecil","email":"","middleInitial":"S.","affiliations":[{"id":36955,"text":"National Jewish Health","active":true,"usgs":false}],"preferred":false,"id":844870,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70239285,"text":"70239285 - 2022 - Geoenvironmental model for roll-type uranium deposits in the Texas Gulf Coast","interactions":[],"lastModifiedDate":"2023-01-06T12:49:39.192331","indexId":"70239285","displayToPublicDate":"2022-06-20T06:46:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Geoenvironmental model for roll-type uranium deposits in the Texas Gulf Coast","docAbstract":"The transition of sagebrush-dominated (Artemisia spp.) shrublands to pinyon (Pinus spp.) and juniper (Juniperus spp.) woodlands markedly alters resource-conserving vegetation structure typical of these landscapes. Land managers and scientists in the western United States need knowledge and predictive tools for assessment and effective targeting of tree-removal treatments to conserve or restore sagebrush vegetation and associated hydrologic function. This study developed modeling approaches to quantify the hydrologic vulnerability and erosion potential of sagebrush rangelands in the later stages of woodland encroachment and in response to commonly applied tree-removal treatments. Using experimental data from multiple sites in the Great Basin Region, USA, and process-based knowledge from decade-long vegetation and rainfall simulation studies at those sites, we (1) assessed the capability of the Rangeland Hydrology and Erosion Model (RHEM) to accurately predict patch-scale (12 m2) measured runoff and erosion from tree canopy and intercanopy hydrologic functional units in untreated and burned woodlands 9 years postfire, and (2) developed and evaluated multiple RHEM approaches/frameworks to model aggregated effects of tree canopy and intercanopy areas on patch- and hillslope-scale (50 m length) runoff and erosion processes in untreated and treated (burned, cut, and masticated) woodlands. The RHEM accurately predicted measured runoff and sediment yield from patch-scale rainfall simulations as partitioned on untreated and treated tree canopy and intercanopy areas and effectively parameterized the dominant controls on runoff and erosion process in woodlands. With few exceptions, evaluated hillslope-scale RHEM frameworks similarly predicted reduced hydrologic vulnerability and erosion potential for conditions 9 years following tree removal by burning, cutting, and mastication treatments. Regressions of RHEM-predicted hillslope runoff, sediment, and hydraulic/erosion parameters with bare ground and ground cover attributes indicate all RHEM frameworks effectively represented the dominant controls on hydrologic and erosion processes for rangelands and woodlands. The results provide RHEM frameworks and recommendations for assessing hydrologic vulnerability and erosion potential on woodland-encroached sites and predicting the effectiveness of tree removal to reestablish a water and soil resource-conserving vegetation structure on sagebrush rangelands. We anticipate our RHEM or similar modeling approaches may be applicable to analogous water-limited landscapes elsewhere subject to woody plant encroachment.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4145","usgsCitation":"Williams, C.J., Pierson, F.B., Al-Hamdan, O.Z., Nouwakpo, S.K., Johnson, J.C., Polyakov, V.O., Kormos, P.R., Shaff, S., and Spaeth, K.E., 2022, Assessing runoff and erosion on woodland-encroached sagebrush steppe using the Rangeland Hydrology and Erosion Model: Ecosphere, v. 13, no. 6, e4145, 32 p., https://doi.org/10.1002/ecs2.4145.","productDescription":"e4145, 32 p.","ipdsId":"IP-137960","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447392,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecs2.4145","text":"External Repository"},{"id":402401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.14427185058592,\n 39.44679856427205\n ],\n [\n -115.10032653808594,\n 39.44679856427205\n ],\n [\n -115.10032653808594,\n 39.47807557129829\n ],\n [\n -115.14427185058592,\n 39.47807557129829\n ],\n [\n -115.14427185058592,\n 39.44679856427205\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.47871398925781,\n 40.20824570152502\n ],\n [\n -112.46429443359375,\n 40.20824570152502\n ],\n [\n -112.46429443359375,\n 40.2203056748532\n ],\n [\n -112.47871398925781,\n 40.2203056748532\n ],\n [\n -112.47871398925781,\n 40.20824570152502\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, C. Jason","contributorId":12774,"corporation":false,"usgs":true,"family":"Williams","given":"C.","email":"","middleInitial":"Jason","affiliations":[],"preferred":false,"id":844919,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pierson, Frederick B.","contributorId":195933,"corporation":false,"usgs":false,"family":"Pierson","given":"Frederick","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":844920,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Al-Hamdan, Osama Z.","contributorId":292513,"corporation":false,"usgs":false,"family":"Al-Hamdan","given":"Osama","email":"","middleInitial":"Z.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":844921,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nouwakpo, S. Kossi","contributorId":292514,"corporation":false,"usgs":false,"family":"Nouwakpo","given":"S.","email":"","middleInitial":"Kossi","affiliations":[{"id":62926,"text":"Agricultural Research Service, U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":844922,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Justin C.","contributorId":261635,"corporation":false,"usgs":false,"family":"Johnson","given":"Justin","email":"","middleInitial":"C.","affiliations":[{"id":47959,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":844923,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Polyakov, Viktor O.","contributorId":292516,"corporation":false,"usgs":false,"family":"Polyakov","given":"Viktor","email":"","middleInitial":"O.","affiliations":[{"id":62926,"text":"Agricultural Research Service, U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":844924,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kormos, Patrick R.","contributorId":292517,"corporation":false,"usgs":false,"family":"Kormos","given":"Patrick","email":"","middleInitial":"R.","affiliations":[{"id":62927,"text":"National Oceanic and Atmospheric Administration - National Weather Service, US Department of Commerce","active":true,"usgs":false}],"preferred":false,"id":844925,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaff, Scott 0000-0001-8978-9260 sshaff@usgs.gov","orcid":"https://orcid.org/0000-0001-8978-9260","contributorId":5126,"corporation":false,"usgs":true,"family":"Shaff","given":"Scott","email":"sshaff@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844926,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spaeth, Kenneth E.","contributorId":9387,"corporation":false,"usgs":true,"family":"Spaeth","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":844927,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70232264,"text":"70232264 - 2022 - What is a biocrust? A refined, contemporary definition for a broadening research community","interactions":[],"lastModifiedDate":"2022-09-01T14:40:41.902044","indexId":"70232264","displayToPublicDate":"2022-06-18T11:20:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1023,"text":"Biological Reviews","active":true,"publicationSubtype":{"id":10}},"title":"What is a biocrust? A refined, contemporary definition for a broadening research community","docAbstract":"Studies of biological soil crusts (biocrusts) have proliferated over the last few decades. The biocrust literature has broadened, with more studies assessing and describing the function of a variety of biocrust communities in a broad range of biomes and habitats and across a large spectrum of disciplines, and also by the incorporation of biocrusts into global perspectives and biogeochemical models. As the number of biocrust researchers increases, along with the scope of soil communities defined as ‘biocrust’, it is worth asking whether we all share a clear, universal, and fully articulated definition of what constitutes a biocrust. In this review, we synthesize the literature with the views of new and experienced biocrust researchers, to provide a refined and fully elaborated definition of biocrusts. In doing so, we illustrate the ecological relevance and ecosystem services provided by them. We demonstrate that biocrusts are defined by four distinct elements: physical structure, functional characteristics, habitat, and taxonomic composition. We describe outgroups, which have some, but not all, of the characteristics necessary to be fully consistent with our definition and thus would not be considered biocrusts. We also summarize the wide variety of different types of communities that fall under our definition of biocrusts, in the process of highlighting their global distribution. Finally, we suggest the universal use of the Belnap, Büdel & Lange definition, with minor modifications: Biological soil crusts (biocrusts) result from an intimate association between soil particles and differing proportions of photoautotrophic (e.g. cyanobacteria, algae, lichens, bryophytes) and heterotrophic (e.g. bacteria, fungi, archaea) organisms, which live within, or immediately on top of, the uppermost millimetres of soil. Soil particles are aggregated through the presence and activity of these often extremotolerant biota that desiccate regularly, and the resultant living crust covers the surface of the ground as a coherent layer. With this detailed definition of biocrusts, illustrating their ecological functions and widespread distribution, we hope to stimulate interest in biocrust research and inform various stakeholders (e.g. land managers, land users) on their overall importance to ecosystem and Earth system functioning.
","language":"English","publisher":"Wiley","doi":"10.1111/brv.12862","usgsCitation":"Weber, B., Belnap, J., Budel, B., Antoninka, A.J., Barger, N.N., Chaudhary, V., Darrouzet-Nardi, A., Eldridge, D.J., Faist, A.M., Ferrenberg, S., Havrilla, C., Huber-Sannwald, E., Issa, O.M., Maestre, F.T., Reed, S., Rodriguez-Caballero, E., Tucker, C.L., Young, K., Zhang, Y., Zhao, Y., Zhou, X., and Bowker, M.A., 2022, What is a biocrust? A refined, contemporary definition for a broadening research community: Biological Reviews, v. 97, no. 5, p. 1768-1785, https://doi.org/10.1111/brv.12862.","productDescription":"18 p.","startPage":"1768","endPage":"1785","ipdsId":"IP-139133","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447393,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/brv.12862","text":"External Repository"},{"id":402399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"97","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Weber, Bettina","contributorId":196800,"corporation":false,"usgs":false,"family":"Weber","given":"Bettina","email":"","affiliations":[],"preferred":false,"id":844885,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belnap, Jayne 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J.","contributorId":240674,"corporation":false,"usgs":false,"family":"Antoninka","given":"Anita","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":844888,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barger, Nichole N.","contributorId":193039,"corporation":false,"usgs":false,"family":"Barger","given":"Nichole","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":844889,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chaudhary, V Bala","contributorId":240984,"corporation":false,"usgs":false,"family":"Chaudhary","given":"V Bala","affiliations":[{"id":36623,"text":"DePaul University","active":true,"usgs":false}],"preferred":false,"id":844890,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Darrouzet-Nardi, Anthony adarrouzet-nardi@usgs.gov","contributorId":207292,"corporation":false,"usgs":false,"family":"Darrouzet-Nardi","given":"Anthony","email":"adarrouzet-nardi@usgs.gov","affiliations":[],"preferred":false,"id":844891,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Eldridge, David J. 0000-0002-2191-486X","orcid":"https://orcid.org/0000-0002-2191-486X","contributorId":207298,"corporation":false,"usgs":false,"family":"Eldridge","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":37514,"text":"Center for Ecosystem Science, University of New South Wales, Sydney, NSW 2052, Australia","active":true,"usgs":false}],"preferred":false,"id":844892,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Faist, Akasha M.","contributorId":193038,"corporation":false,"usgs":false,"family":"Faist","given":"Akasha","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":844893,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ferrenberg, Scott","contributorId":217143,"corporation":false,"usgs":false,"family":"Ferrenberg","given":"Scott","affiliations":[{"id":39569,"text":"Department of Biology, New Mexico State University, Las Cruces, NM 88001, USA","active":true,"usgs":false}],"preferred":false,"id":844894,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Havrilla, Caroline 0000-0003-3913-0980","orcid":"https://orcid.org/0000-0003-3913-0980","contributorId":245368,"corporation":false,"usgs":false,"family":"Havrilla","given":"Caroline","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":844895,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Huber-Sannwald, Elisabeth","contributorId":292502,"corporation":false,"usgs":false,"family":"Huber-Sannwald","given":"Elisabeth","affiliations":[{"id":62916,"text":"Division of Environmental Sciences, Instituto Potosino de Investigación Científica y Tecnológica, San Luis Potosi, SLP, 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0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844899,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rodriguez-Caballero, Emilio","contributorId":292504,"corporation":false,"usgs":false,"family":"Rodriguez-Caballero","given":"Emilio","affiliations":[{"id":62918,"text":"Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany; Departamento de Agronomía and Centro de Investigación de Colecciones Científicas de la Universidad de Almería, Almería, Spain","active":true,"usgs":false}],"preferred":false,"id":844900,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Tucker, Colin L","contributorId":270737,"corporation":false,"usgs":false,"family":"Tucker","given":"Colin","email":"","middleInitial":"L","affiliations":[{"id":56205,"text":"U.S. National Forest Service, Northern Research Station, Houghton, MI 49931","active":true,"usgs":false}],"preferred":false,"id":844901,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Young, Kristina E.","contributorId":195945,"corporation":false,"usgs":false,"family":"Young","given":"Kristina E.","affiliations":[],"preferred":false,"id":844902,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Zhang, Yuanming","contributorId":173232,"corporation":false,"usgs":false,"family":"Zhang","given":"Yuanming","email":"","affiliations":[{"id":27200,"text":"Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China","active":true,"usgs":false}],"preferred":false,"id":844903,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Zhao, Yunge","contributorId":224390,"corporation":false,"usgs":false,"family":"Zhao","given":"Yunge","email":"","affiliations":[],"preferred":false,"id":844904,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Zhou, Xiaobing","contributorId":181757,"corporation":false,"usgs":false,"family":"Zhou","given":"Xiaobing","email":"","affiliations":[],"preferred":false,"id":844905,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Bowker, Matthew A.","contributorId":196428,"corporation":false,"usgs":false,"family":"Bowker","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":844906,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70266740,"text":"70266740 - 2022 - Cryptic population decrease due to invasive species predation in a long-lived seabird supports need for eradication","interactions":[],"lastModifiedDate":"2025-05-12T14:55:29.381762","indexId":"70266740","displayToPublicDate":"2022-06-18T09:44:46","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":"Cryptic population decrease due to invasive species predation in a long-lived seabird supports need for eradication","docAbstract":"The presence of vitrinite in sedimentary rocks of post-Silurian age allows its reflectance to be used to estimate the thermal maturation of organic matter in petroleum systems. Increasing reflectance of vitrinite, which is primarily driven by aromaticity, depends primarily on the time and temperature attributes of its evolutionary pathway. This study evaluated carbonaceous shales proximal to coal measures and coal samples via isothermal hydrous pyrolysis (HP) to compare differences in the maturation pathways of vitrinite. Sample residues were analysed via vitrinite reflectance (VRo), geochemical screening tests (organic carbon and programmed temperature pyrolysis), and infrared spectroscopy. The study included samples from Indian and North American basins, to observe differences in vitrinite evolution with respect to enclosing mineral matrix, starting degree of aromaticity, organic matter types, stratigraphic age, and depositional environment. The organic content of HP residues shows an intuitive response to the thermal stress of HP, e.g., a general depletion of total organic carbon (TOC) content, pyrolyzate (S2), and hydrogen index with increasing HP temperature. Infrared proxies including C-factor and CH2/CH3 generally decrease with increasing thermal maturity indicating loss of O via CO2 generation and the thermal cracking of aliphatic organic matter. Tmax, production index (PI), and VRo show intuitive increasing values with respect to HP temperature. The least mature sample (0.48 ± 0.05% VRo) generally experienced the maximum change in these parameters during maturation, whereas the most mature sample (0.99 ± 0.06% VRo) generally showed the least change. This observation is consistent with higher kinetic barriers to reaction in more aromatic vitrinite which contains higher bond dissociation energies. Devolatilization of vitrinite during HP causes formation of gas evacuation vacuoles and contraction cracks in the vitrinite grains of both coal and carbonaceous shale. Similarities in vitrinite response to HP between coal and carbonaceous shale suggest that thermal evolution of the vitrinite maceral is principally controlled by inherent rate-limiting kinetic parameters related to its molecular structure. Whereas, the stratigraphic age, sedimentary environment, surrounding organic matter, lithology, and catalysis by mineral composition have less effect. To further improve our understanding of vitrinite aromatization and kinetic parameters, future studies of vitrinite reflectance thermal evolution with temperature should include coal and carbonaceous shale from the same stratigraphic section and extant woody tissue from modern vascular plants.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2022.104044","usgsCitation":"Mishra, D.K., Hackley, P.C., Jubb, A., Sanders, M.M., Agrawal, S., and Varma, A.K., 2022, Maturation study of vitrinite in carbonaceous shales and coals: Insights from hydrous pyrolysis: International Journal of Coal Geology, v. 259, 104044, 13 p., https://doi.org/10.1016/j.coal.2022.104044.","productDescription":"104044, 13 p.","ipdsId":"IP-138029","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":408530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mishra, Divya K.","contributorId":290218,"corporation":false,"usgs":false,"family":"Mishra","given":"Divya","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":855038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":855039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanders, Margaret M. 0000-0003-3505-874X","orcid":"https://orcid.org/0000-0003-3505-874X","contributorId":248709,"corporation":false,"usgs":true,"family":"Sanders","given":"Margaret","email":"","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":855041,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Agrawal, Shailesh","contributorId":261453,"corporation":false,"usgs":false,"family":"Agrawal","given":"Shailesh","email":"","affiliations":[],"preferred":false,"id":855042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Varma, Atul K.","contributorId":290219,"corporation":false,"usgs":false,"family":"Varma","given":"Atul","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":855043,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254718,"text":"70254718 - 2022 - Reproductive indices and observations of mass ovarian follicular atresia in hatchery-origin pallid sturgeon","interactions":[],"lastModifiedDate":"2024-06-10T16:05:35.788952","indexId":"70254718","displayToPublicDate":"2022-06-17T10:59:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2166,"text":"Journal of Applied Ichthyology","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive indices and observations of mass ovarian follicular atresia in hatchery-origin pallid sturgeon","docAbstract":"The Pallid Sturgeon (Scaphirhynchus albus) Conservation Propagation and Stocking Program began stocking in the Missouri River above Fort Peck Reservoir in 1998 with 1997-year-class pallid sturgeon. Within the 1997-year class, all hatchery-origin pallid sturgeon females that reached reproductive maturation by 2016 underwent mass ovarian follicular atresia. Using combined historical and contemporary data, we described the spawning periodicity for female and male pallid sturgeon, characterized age- and size-at-first spawning, and evaluated what proportion of females experience mass ovarian follicular atresia during the first and subsequent reproductive cycles. Pallid sturgeon reached their first reproductive cycle at older ages and larger sizes than described for other populations. Females were functionally and physiologically capable of spawning at 21 years and males at 15 years. Immature pallid sturgeon as old as 20 years were documented. We found that more female pallid sturgeon underwent mass ovarian follicular atresia during the presumed-first reproductive cycle or known-first reproductive cycle than females during subsequent reproductive cycles (62.5% compared to 33.3%) indicating that effects related to reproductive maturation may be occurring. Nonetheless, mass ovarian follicular atresia appears to also occur for reasons not related to reproductive maturation. Females had biennial reproductive cycles, and males had annual and biennial reproductive cycles. Population models should account for females undergoing mass ovarian follicular atresia in their first reproductive cycle and subsequent cycles thereby increasing the age at first-successful spawning and reducing the estimated size of the spawning stock.
","language":"English","publisher":"Wiley","doi":"10.1111/jai.14339","usgsCitation":"Cox, T., Guy, C.S., Holmquist, L., and Webb, M., 2022, Reproductive indices and observations of mass ovarian follicular atresia in hatchery-origin pallid sturgeon: Journal of Applied Ichthyology, v. 38, p. 391-402, https://doi.org/10.1111/jai.14339.","productDescription":"12 p.","startPage":"391","endPage":"402","ipdsId":"IP-137205","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":447396,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jai.14339","text":"Publisher Index Page"},{"id":429772,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationDate":"2022-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Cox, Tanner L.","contributorId":337305,"corporation":false,"usgs":false,"family":"Cox","given":"Tanner L.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":902340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":902341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmquist, Luke M.","contributorId":337306,"corporation":false,"usgs":false,"family":"Holmquist","given":"Luke M.","affiliations":[{"id":37431,"text":"Montana Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":902342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Webb, Molly A. H.","contributorId":337308,"corporation":false,"usgs":false,"family":"Webb","given":"Molly A. H.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":902343,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232218,"text":"sir20215143 - 2022 - Application of a soil-water-balance model to estimate annual groundwater recharge for Long Island, New York, 1900–2019","interactions":[],"lastModifiedDate":"2026-04-08T16:38:21.182332","indexId":"sir20215143","displayToPublicDate":"2022-06-17T10:44:25","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5143","displayTitle":"Application of a Soil-Water-Balance Model to Estimate Annual Groundwater Recharge for Long Island, New York, 1900–2019","title":"Application of a soil-water-balance model to estimate annual groundwater recharge for Long Island, New York, 1900–2019","docAbstract":"A soil-water-balance (SWB) model was developed for Long Island, New York, to estimate the potential amount of annual groundwater recharge to the Long Island aquifer system from 1900 to 2019. The SWB model program is a computer code based on a modified Thornthwaite-Mather SWB approach and uses spatially and temporally distributed meteorological, land-cover, and soil properties as input to compute potential daily groundwater recharge. Simulated outputs indicate that island-wide potential groundwater recharge trends, as a percentage of precipitation, have increased approximately 3 percent during the 120-year period. The simulated results account for both climatic and land-cover changes that have occurred during the period. A change from undeveloped (forested land cover) to low- and medium-density residential land cover or land use increased potential groundwater recharge because of a decrease in evapotranspiration. During the 30-year period from 1900 to 1930, the simulated potential average groundwater recharge rate on Long Island was estimated to be 18.50 inches per year (in/yr), or a total of 1,243 million gallons per day, during the 30-year period from 1985 to 2015, the simulated potential average groundwater recharge rate estimate increased to 20.73 in/yr (a total of around 1,393 million gallons per day).
During the 1900–2019 simulation period, the potential average annual groundwater recharge rate was about 19.24 in/yr. The data for that period included values for a 3-year meteorological drought from 1963 to 1965, where the mean precipitation was about 26.5 percent lower than the long-term average of 46.7 in/yr, and the potential groundwater recharge rate was about 12.3 in/yr. During a 3-year wet period from 1982 to 1984, where mean precipitation was about 19.6 percent higher than the long-term average, the estimated potential groundwater recharge rate was about 26.8 in/yr.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215143","usgsCitation":"Finkelstein, J.S., Monti, J., Jr., Masterson, J.P., and Walter, D.A., 2022, Application of a soil-water-balance model to estimate annual groundwater recharge for Long Island, New York, 1900–2019: U.S. Geological Survey Scientific Investigations Report 2021–5143, 25 p., https://doi.org/10.3133/sir20215143.","productDescription":"Report: v, 25 p.; 2 Data Releases","numberOfPages":"25","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103053","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":435800,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93Z8Y8L","text":"USGS data release","linkHelpText":"Soil-water-balance model archive for Long Island, NY, 1900-2019"},{"id":402201,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V2NMUB","text":"USGS data release","linkHelpText":"Soil-water-balance groundwater recharge model results for Long Island, NY, 1900-2019"},{"id":402197,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5143/coverthb.jpg"},{"id":402196,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5143/sir20215143.pdf","text":"Report","size":"5.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5143"},{"id":402199,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5143/images/"},{"id":402217,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20215143/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":402198,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5143/sir20215143.XML"},{"id":402200,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94OLK6Z","text":"USGS data release","linkHelpText":"Soil-water-balance model archive for Long Island, NY, 1900-2019"},{"id":502287,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113197.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.036865234375,\n 40.55554790286311\n ],\n [\n -73.1744384765625,\n 40.56806745430726\n ],\n [\n -71.795654296875,\n 41.03378713521864\n ],\n [\n -72.2186279296875,\n 41.21998578493921\n ],\n [\n -72.6800537109375,\n 41.02135510866602\n ],\n [\n -73.10302734375,\n 40.98819156349393\n ],\n [\n -73.2513427734375,\n 40.93426521177941\n ],\n [\n -73.5369873046875,\n 40.95501133048621\n ],\n [\n -73.7896728515625,\n 40.8595252289932\n ],\n [\n -73.93798828125,\n 40.763901280945866\n ],\n [\n -74.036865234375,\n 40.66397287638688\n ],\n [\n -74.036865234375,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
In densely populated coastal areas with sea-level rise (SLR), protecting the shorelines against erosion due to the wave impact is crucial. Along with many engineered structures like seawalls and breakwaters, there are also green structures like constructed oyster reefs (CORs) that can not only attenuate the incident waves but also grow and maintain pace with SLR. However, there is a lack of data and understanding of the long-term wave attenuation capacity of the living shoreline structures under SLR. In this study, we used the phase-resolving Boussinesq model, FUNWAVE-TVD, to examine the hydrodynamics including wave height and wave-induced currents around the CORs in the Gandys Beach living shoreline project area in the upper Delaware Bay, United States. Waves were measured at six locations (offshore to onshore, with and without CORs) in the Gandys Beach living shoreline project area for two winter months, during which four nor’easters occurred. We selected three cases that represent prevailing wind, wave, and tide conditions to examine the fine spatial and temporal changes in wave height and current velocity by the construction of the reefs. Wave heights and wave energy spectra generated from FUNWAVE-TVD were then validated with field observations. It is found that FUNWAVE-TVD is capable of simulating waves and associated hydrodynamic processes that interact with CORs. The model results show that wave attenuation rates vary with the incident wave properties and water depth, and wave-induced circulation patterns are affected by the CORs. The wave attenuation capacity of CORs over the next 100 years was simulated with the incorporation of the oyster reef optimal growth zone. Our study found that sustainable wave attenuation capacity can only be achieved when suitable habitat for COR is provided, thus it can vertically grow with SLR. Suitable habitat includes optimal intertidal inundation duration, current velocity for larval transport and settlement, on-reef oyster survival and growth, and other environmental conditions including salinity, temperature, and nutrient availability. Furthermore, the model results suggest that it would take CORs approximately 9 years after construction to reach and maintain the maximum wave attenuation capacity in sustainable living shorelines.