In a 1998 paper entitled “Guts don’t fly: small digestive organs in obese bar-tailed godwits,” Piersma and Gill (1998) showed that the digestive organs were tiny and the fat loads huge in individuals suspected of embarking on a non-stop flight from Alaska to New Zealand. It was suggested that prior to migratory departure, these godwits would shrink the digestive organs used during fuel deposition and boost the size and capacity of exercise organs to optimize flight performance. Here we document the verity of the proposed physiomorphic changes by comparing organ sizes and body composition of bar-tailed godwits Limosa lapponica baueri collected in modesty midway during their fueling period (mid-September; fueling, n = 7) with the previously published data for godwits that had just departed on their trans-Pacific flight (October 19; flying, n = 9). Mean total body masses for the two groups were nearly identical, but nearly half of the body mass of fueling godwits consisted of water, while fat constituted over half of total body mass of flying godwits. The two groups also differed in their fat-free mass components. The heart and flight muscles were heavier in fueling godwits, but these body components constituted a relatively greater fraction of the fat-free mass in flying godwits. In contrast, organs related to digestion and homeostasis were heavier in fueling godwits, and most of these organ groups were also relatively larger in fueling godwits compared to flying godwits. These results reflect the functional importance of organ and muscle groups related to energy acquisition in fueling godwits and the consequences of flight-related exertion in flying godwits. The extreme physiomorphic changes apparently occurred over a short time window (≤1 month). We conclude that the inferences made on the basis of the 1998 paper were correct. The cues and stimuli which moderate these changes remain to be studied.
Fate and transport modeling of water-borne contaminants is a data demanding and costly endeavor, requiring considerable expes such, it becomes important to know when a complex modeling approach is required, and when a simpler approach is adequate. This is the main objective herein, where a conservative mixing model is used to characterize the transport of As, Cu, Fe, and SO4. The study area is divided into three sectors, corresponding to the upstream, middle, and downstream portions of the Elqui River Basin, Chile. In Sector 1, acidic conditions result in the conservative transport of constituents that are sourced from acid rock drainage. In Sector 2, pH increases and transport is influenced by pH-dependent reactions and the subsequent settling of the particulate phase. In Sector 3, there are no additional constituent inputs, and the constituents are conservatively transported downstream. Conservative transport within Sector 3 is confirmed through the development of a regression model that provides monthly estimates of SO4 load. Whereas SO4 and Cu concentrations are adequately approximated by the conservative mixing model, estimates of As and Fe concentrations exhibit larger errors, due to the more reactive behavior of these constituents. The fact that the simple, conservative mixing model describes SO4 transport is a valuable result, as this constituent is known to be one of the primary indicators of mining-related contamination in rivers. The approach could also be a useful starting point for further evaluations of the effects of climate change and hydrological variability on the water quality of rivers.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3823","usgsCitation":"Rossi, C., Oyarzun, J., Pasten, P., Runkel, R.L., Núñez, J., Duhalde, D., Maturana, H., Rojas, E., Arumí, J., Castillo, D., and Oyarzun, R., 2021, Assessment of a conservative mixing model for the evaluation of constituent behavior below river confluences, Elqui River Basin, Chile: River Research and Applications, v. 37, no. 7, p. 967-978, https://doi.org/10.1002/rra.3823.","productDescription":"12 p.","startPage":"967","endPage":"978","ipdsId":"IP-117538","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":390393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Elqui River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.4605712890625,\n -30.741835717889778\n ],\n [\n -68.8348388671875,\n -30.741835717889778\n ],\n [\n -68.8348388671875,\n -29.176145182559758\n ],\n [\n -71.4605712890625,\n -29.176145182559758\n ],\n [\n -71.4605712890625,\n -30.741835717889778\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Rossi, Catalina","contributorId":267243,"corporation":false,"usgs":false,"family":"Rossi","given":"Catalina","email":"","affiliations":[{"id":55453,"text":"U. 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La Serena","active":true,"usgs":false}],"preferred":false,"id":824834,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Arumí, José L.","contributorId":267250,"corporation":false,"usgs":false,"family":"Arumí","given":"José L.","affiliations":[{"id":49667,"text":"Universidad de Concepción","active":true,"usgs":false}],"preferred":false,"id":824835,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Castillo, Daniela","contributorId":267251,"corporation":false,"usgs":false,"family":"Castillo","given":"Daniela","email":"","affiliations":[{"id":55455,"text":"Universidad de La Serena","active":true,"usgs":false}],"preferred":false,"id":824837,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Oyarzun, Ricardo","contributorId":267252,"corporation":false,"usgs":false,"family":"Oyarzun","given":"Ricardo","email":"","affiliations":[{"id":55455,"text":"Universidad de La Serena","active":true,"usgs":false}],"preferred":false,"id":824838,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70223276,"text":"70223276 - 2021 - Prioritizing restoration areas to conserve multiple sagebrush-associated wildlife species","interactions":[],"lastModifiedDate":"2021-08-20T12:01:22.603183","indexId":"70223276","displayToPublicDate":"2021-06-17T10:24:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing restoration areas to conserve multiple sagebrush-associated wildlife species","docAbstract":"Strategic restoration of altered habitat is one method for addressing worldwide biodiversity declines. Within the sagebrush steppe of western North America, habitat degradation has been linked to declines in many species, making restoration a priority for managers; however, limited funding, spatiotemporal variation in restoration success, and the need to manage for diverse wildlife species make decision-making regarding restoration actions challenging. To address the challenge of spatial conservation prioritization, we developed the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET). This decision support tool utilizes the prioritizr package in program R and an integer linear programming algorithm to select parcels representing both high biodiversity value and high probability of restoration success. We tested PReSET on a sagebrush steppe system within southwestern Wyoming using distributional data for six species with diverse life histories and a spatial layer of predicted sagebrush recovery times to identify restoration targets at both broad and local scales. While the broad-scale portion of our tool outputs can inform policy, the local-scale results can be applied directly to on-the-ground restoration. We identified restoration priority areas with greater precision than existing spatial prioritizations and incorporated range differences among species. We noted tradeoffs, including that restoring for habitat connectivity may require restoration actions in areas with lower probability of success. Future applications of PReSET will draw from emerging datasets, including spatially-varying economic costs of restoration, animal movement data, and additional species, to further improve our ability to target effective sagebrush restoration.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2021.109212","usgsCitation":"Duchardt, C.J., Monroe, A., Heinrichs, J.A., O’Donnell, M.S., Edmunds, D.R., and Aldridge, C.L., 2021, Prioritizing restoration areas to conserve multiple sagebrush-associated wildlife species: Biological Conservation, v. 260, 109212, 12 p., https://doi.org/10.1016/j.biocon.2021.109212.","productDescription":"109212, 12 p.","ipdsId":"IP-126263","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451836,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2021.109212","text":"Publisher Index Page"},{"id":436303,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VJXJNY","text":"USGS data release","linkHelpText":"Spatial layers generated by the Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET) applied in Southern Wyoming"},{"id":388154,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wyoming Landscape Conservation Initiative","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.083984375,\n 41.07935114946899\n ],\n [\n -106.083984375,\n 42.19596877629178\n ],\n [\n -107.16064453125,\n 42.261049162113856\n ],\n [\n -108.12744140625,\n 41.983994270935625\n ],\n [\n -108.896484375,\n 42.114523952464246\n ],\n [\n -109.75341796875,\n 42.779275360241904\n ],\n [\n -110.32470703125,\n 43.08493742707592\n ],\n [\n -110.89599609375,\n 43.052833917627936\n ],\n [\n -110.98388671874999,\n 42.87596410238256\n ],\n [\n -111.005859375,\n 41.02964338716638\n ],\n [\n -106.083984375,\n 41.07935114946899\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"260","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Duchardt, Courtney Jean 0000-0003-4563-0199","orcid":"https://orcid.org/0000-0003-4563-0199","contributorId":264471,"corporation":false,"usgs":true,"family":"Duchardt","given":"Courtney","email":"","middleInitial":"Jean","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":821568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":821569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034 jheinrichs@usgs.gov","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":193742,"corporation":false,"usgs":true,"family":"Heinrichs","given":"Julie","email":"jheinrichs@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":821570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":140876,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":821571,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":821572,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":821573,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221586,"text":"70221586 - 2021 - Enhanced terrestrial runoff during Oceanic Anoxic Event 2 on the North Carolina Coastal Plain, USA","interactions":[],"lastModifiedDate":"2021-06-24T14:41:03.659695","indexId":"70221586","displayToPublicDate":"2021-06-17T09:29:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"Enhanced terrestrial runoff during Oceanic Anoxic Event 2 on the North Carolina Coastal Plain, USA","docAbstract":"A global increase in the strength of the hydrologic cycle drove an increase in the flux of terrigenous sediments into the ocean during the Cenomanian–Turonian Oceanic Anoxic Event 2 (OAE2) and was an important mechanism driving nutrient enrichment and thus organic carbon burial. This global change is primarily known from isotopic records, but global average data do not tell us anything about changes at any particular location. Reconstructions of local terrigenous flux can help us understand the role of regional shifts in precipitation in driving these global trends. The proto-North Atlantic basin was one of the epicenters of enhanced organic carbon burial during OAE2, so constraining terrigenous flux is particularly important in this region; however, few local records exist. Here, we present two new OAE2 records from the Atlantic Coastal Plain of North Carolina, USA, recognized with calcareous nannoplankton biostratigraphy and organic carbon isotopes. We use carbon
Accurate maps of regional surface water features are integral for advancing ecologic, atmospheric and land development studies. The only comprehensive surface water feature map of Alaska is the National Hydrography Dataset (NHD). NHD features are often digitized representations of historic topographic map blue lines and may be outdated. Here we test deep learning methods to automatically extract surface water features from airborne interferometric synthetic aperture radar (IfSAR) data to update and validate Alaska hydrographic databases. U-net artificial neural networks (ANN) and high-performance computing (HPC) are used for supervised hydrographic feature extraction within a study area comprised of 50 contiguous watersheds in Alaska. Surface water features derived from elevation through automated flow-routing and manual editing are used as training data. Model extensibility is tested with a series of 16 U-net models trained with increasing percentages of the study area, from about 3 to 35 percent. Hydrography is predicted by each of the models for all watersheds not used in training. Input raster layers are derived from digital terrain models, digital surface models, and intensity images from the IfSAR data. Results indicate about 15 percent of the study area is required to optimally train the ANN to extract hydrography when F1-scores for tested watersheds average between 66 and 68. Little benefit is gained by training beyond 15 percent of the study area. Fully connected hydrographic networks are generated for the U-net predictions using a novel approach that constrains a D-8 flow-routing approach to follow U-net predictions. This work demonstrates the ability of deep learning to derive surface water feature maps from complex terrain over a broad area.
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Currently, 15 subspecies are recognized from Borneo, Sumatra, and smaller islands, and many were originally differentiated based on minor pelage differences and small sample sizes. We explored intraspecific variation in T. tana using quantitative osteological data obtained from the hands and skulls of museum specimens. Multivariate analyses reveal extensive overlap among T. tana populations in morphospace, indicating that the majority of currently recognized subspecies are not morphometrically distinct. In contrast, the separation between Bornean and Sumatran populations of T. tana is sufficient to recognize them as different subspecies. Comparisons of Bornean specimens to those on small, offshore islands reveal that the latter average smaller body size. This pattern is inconsistent with Foster’s island rule, which predicts that island populations of small mammals (< 5 kg) will average larger body size relative to mainland forms. A similar lack of support for ecogeographic rules has been noted in T. glis (Diard, 1820), suggesting that these “rules” are poor predictors of geographic variation in treeshrews.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jmammal/gyab059","usgsCitation":"Juman, M.M., Woodman, N., Olson, L.E., and Sargis, E.J., 2021, Ecogeographic variation and taxonomic boundaries in Large Treeshrews (Scandentia, Tupaiidae: Tupaia tana Raffles, 1821) from Southeast Asia: Journal of Mammalogy, v. 102, no. 4, p. 1054-1066, https://doi.org/10.1093/jmammal/gyab059.","productDescription":"13 p.","startPage":"1054","endPage":"1066","ipdsId":"IP-126399","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":386646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Southeast Asia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 88.76953125,\n -11.695272733029402\n ],\n [\n 162.0703125,\n -11.695272733029402\n ],\n [\n 162.0703125,\n 27.059125784374068\n ],\n [\n 88.76953125,\n 27.059125784374068\n ],\n [\n 88.76953125,\n -11.695272733029402\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Juman, Maya M. 0000-0002-0211-0655","orcid":"https://orcid.org/0000-0002-0211-0655","contributorId":260457,"corporation":false,"usgs":false,"family":"Juman","given":"Maya","email":"","middleInitial":"M.","affiliations":[{"id":37550,"text":"Yale University","active":true,"usgs":false}],"preferred":false,"id":817993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woodman, Neal 0000-0003-2689-7373 nwoodman@usgs.gov","orcid":"https://orcid.org/0000-0003-2689-7373","contributorId":3547,"corporation":false,"usgs":true,"family":"Woodman","given":"Neal","email":"nwoodman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":817994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olson, Link E. 0000-0002-2481-5701","orcid":"https://orcid.org/0000-0002-2481-5701","contributorId":203887,"corporation":false,"usgs":false,"family":"Olson","given":"Link","email":"","middleInitial":"E.","affiliations":[{"id":36743,"text":"University of Alaska Museum, University of Alaska Fairbanks, Fairbanks, AK 99775, USA","active":true,"usgs":false}],"preferred":false,"id":817995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sargis, Eric J. 0000-0003-0424-3803","orcid":"https://orcid.org/0000-0003-0424-3803","contributorId":203885,"corporation":false,"usgs":false,"family":"Sargis","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":36741,"text":"Department of Anthropology, Yale University, P.O. Box 208277, New Haven, CT 06520, USA","active":true,"usgs":false}],"preferred":false,"id":817996,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221549,"text":"70221549 - 2021 - Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","interactions":[],"lastModifiedDate":"2021-06-23T12:28:09.472652","indexId":"70221549","displayToPublicDate":"2021-06-17T06:59:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance","docAbstract":"In contrast to abrupt changes caused by land cover conversion, subtle changes driven by a shift in the condition, structure, or other biological attributes of land often lead to minimal and slower alterations of the terrestrial surface. Accurate mapping and monitoring of subtle change are crucial for an early warning of long-term gradual change that may eventually result in land cover conversion. Freely accessible moderate-resolution datasets such as the Landsat archive have great potential to characterize subtle change by capturing low-magnitude spectral changes in long-term observations. However, past studies have reported limited success in accurately extracting subtle changes from satellite-based time series analysis. In this study, we introduce a supervised framework named ‘PIDS’ to detect subtle forest disturbance from a comprehensive Landsat data archive by leveraging disturbance-based calibration sites. PIDS consists of four components: (1) Parameter optimization; (2) Index selection; (3) Dynamic stratified monitoring; and (4) Spatial consideration. PIDS was applied to map the early stage of bark beetle infestations (i.e., a lower per-pixel fraction of trees cover that show visual signs of infestation), which are a typical example of subtle change in conifer forests. Landsat Analysis Ready Data were used as the time series inputs for mapping mountain pine beetle and spruce beetle disturbance between 2001 and 2019 in Colorado, USA. PIDS-detection map assessment showed that the overall performance of PIDS (namely ‘F1 score’) was 0.86 for mountain pine beetle and 0.73 for spruce beetle, making a substantial improvement (> 0.3) compared to other approaches/products including COntinuous monitoring of Land Disturbance, LandTrendr, and the National Land Cover Database forest disturbance product. A sub-pixel analysis of tree canopy mortality percentage was performed by linking classified high-resolution (0.3- and 1-m) aerial imagery and 30-m PIDS-detection maps. Results show that PIDS typically detects mountain pine beetle infestation when ≥56% of a Landsat pixel is occupied by red-stage canopy mortality (one year after initial infestation), and spruce beetle infestation when ≥55% is occupied by gray-stage mortality (two years after initial infestation). This study addresses an important methodological goal pertinent to the utility of event-based reference samples for detecting subtle forest change, which could be potentially applied to other types of subtle land change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112560","usgsCitation":"Ye, S., Rogan, J., Zhu, Z., Hawbaker, T., Hart, S., Andrus, R.A., Meddens, A.J., Hicke, J.A., Eastman, J.R., and Kulakowski, D., 2021, Detecting subtle change from dense landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance: Remote Sensing of Environment, v. 263, 112560, 16 p., https://doi.org/10.1016/j.rse.2021.112560.","productDescription":"112560, 16 p.","ipdsId":"IP-124774","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":488056,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112560","text":"Publisher Index Page"},{"id":386642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0283203125,\n 37.020098201368114\n ],\n [\n -106.14990234375,\n 37.020098201368114\n ],\n [\n -106.14990234375,\n 40.91351257612758\n ],\n [\n -109.0283203125,\n 40.91351257612758\n ],\n [\n -109.0283203125,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"263","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ye, Su","contributorId":260471,"corporation":false,"usgs":false,"family":"Ye","given":"Su","email":"","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818017,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogan, John","contributorId":260472,"corporation":false,"usgs":false,"family":"Rogan","given":"John","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhu, Zhe","contributorId":260473,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":818019,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":818020,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hart, Sarah J.","contributorId":260474,"corporation":false,"usgs":false,"family":"Hart","given":"Sarah J.","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":818021,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andrus, Robert A.","contributorId":260475,"corporation":false,"usgs":false,"family":"Andrus","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":818022,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meddens, Arjan J.H.","contributorId":260476,"corporation":false,"usgs":false,"family":"Meddens","given":"Arjan","middleInitial":"J.H.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":818023,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hicke, Jeffery A.","contributorId":260477,"corporation":false,"usgs":false,"family":"Hicke","given":"Jeffery","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":818024,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eastman, J. Ronald","contributorId":260480,"corporation":false,"usgs":false,"family":"Eastman","given":"J.","email":"","middleInitial":"Ronald","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818025,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kulakowski, Dominik","contributorId":260482,"corporation":false,"usgs":false,"family":"Kulakowski","given":"Dominik","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":818026,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70221385,"text":"cir1484 - 2021 - U.S. Geological Survey landscape science strategy 2020–2030","interactions":[],"lastModifiedDate":"2021-06-17T10:21:11.812779","indexId":"cir1484","displayToPublicDate":"2021-06-16T15:48:57","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1484","displayTitle":"U.S. Geological Survey Landscape Science Strategy 2020–2030","title":"U.S. Geological Survey landscape science strategy 2020–2030","docAbstract":"Across our Nation, multiple Federal, State, Tribal, and local governments are working with stakeholders and landowners to restore, conserve, and manage lands and resources to benefit fish, wildlife, and people. One of the largest Federal efforts is led by the U.S. Department of the Interior (DOI), with multiple DOI agencies working to conserve and manage public lands, resources, and cultural heritage for the benefit and enjoyment of current and future generations. As a science provider within the DOI, the U.S. Geological Survey (USGS) has an important role in developing actionable science products that can inform decision making on public lands and across all of our Nation’s landscapes.
Our landscapes and resources are undergoing continual change from a complex and interacting suite of stressors that include traditional and nontraditional land uses, a changing climate, a dynamic economy, and a culturally diverse and ever-changing society. Landscape science seeks to understand how the physical, biological, and social components of ecosystems and landscapes interact with each other and are affected by these stressors across local to global scales.
The USGS has developed this landscape science strategy to focus and strengthen the agency’s efforts to inform critical conservation, restoration, and management decisions for American landscapes. The strategy directly supports the overarching 21st-century science strategy of the USGS and expands our perspective and focus on partnerships, including with internal, external, traditional, and nontraditional partners.
The vision for USGS landscape science is to integrate multiple disciplines and approaches to create and deliver relevant, timely, and scientifically sound products that enable our partners to make informed decisions about how to manage complex interacting natural and human systems across changing American landscapes. This strategy seeks to leverage the expertise, data, analytical capacity, and tools of the agency and to strengthen integration of science efforts across programs and scales. A cornerstone of the strategy is working closely with landowners and resource managers to identify the science that is needed to inform management actions, and then working collaboratively with those same partners to coproduce science and data products that are timely, practical, and useful for their decision making.
Many individual efforts within the USGS already embrace landscape science ideas and practices. With the development of this strategy, we are seeking to build on these efforts and to focus agency attention and expertise on expanding, connecting, and institutionalizing core practices and approaches for landscape science that produces actionable science products and tools that decision makers can use to help conserve and manage American landscapes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1484","isbn":"978-1-4113-4419-8","programNote":"Land Management Research Program","usgsCitation":"Jenni, K.E., Carter, S.K., Aumen, N.G., Bowen, Z.H., Bradford, J.B., Chotkowski, M.A., Hsu, L., Murdoch, P.S., Phillips, S.W., Pope, K.L., Schuster, R., Steinkamp, M.J., Weltzin, J., and Xian, G.Z., 2021, U.S. Geological Survey landscape science strategy 2020–2030: U.S. Geological Survey Circular 1484, 26 p., https://doi.org/10.3133/cir1484.","productDescription":"v, 26 p.","numberOfPages":"36","onlineOnly":"N","ipdsId":"IP-121985","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":386449,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1484/coverthb.jpg"},{"id":386450,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1484/cir1484.pdf","text":"Report","size":"14.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1484"}],"contact":"Associate Director, Ecosystems Mission Area
U.S. Geological Survey
Mail Stop 300
12201 Sunrise Valley Drive
Reston, VA 20192
Land Management Research Program
","tableOfContents":"New York City maintains an extensive system of reservoirs and aqueducts to provide drinking water to its residents, including 16 reservoirs and controlled lakes in Westchester and Putnam Counties in southern New York, east of the Hudson River (also called “East of Hudson reservoirs and controlled lakes”). These reservoirs were put into service from 1842 to 1915, and their capacities have likely changed since their original construction. To provide updated bathymetric surface, contour, and capacity data, the U.S. Geological Survey, in cooperation with New York City Department of Environmental Protection, surveyed the bathymetry of the 16 East of Hudson reservoirs and controlled lakes from 2017 to 2019 using a multibeam echosounder. The points measured with the multibeam echosounder were combined with light detection and ranging data to generate 3.28-foot raster grids of the bathymetric surfaces, bathymetric contours at 2-foot intervals of elevation, and elevation-area-capacity tables. The results of the bathymetric survey show that the East of Hudson reservoirs range from about 25 feet deep (Kirk Lake) to about 162 feet deep (Kensico Reservoir) and have a total capacity of 142.9 billion gallons, with a combined surface area of more than 11,600 acres.
The accuracy of the mapped bathymetric data was evaluated using quality assurance datasets collected with a single-beam echosounder; about 284,000 quality assurance points were spatially joined with the mapped raster surface to compute measurement errors. The calculated mean point elevation error for the East of Hudson reservoirs was 0.35 foot, the median error was 0.21 foot, and the 95-percent accuracy was 1.68 feet; the 95-percent accuracy of the computed capacity at spillway elevation was 1.6 percent or less. The largest errors occurred in the steepest areas of the reservoirs and in areas where the data were interpolated. Geospatial files of the bathymetry data, including mapped bathymetric surfaces, contours, and capacity tables, quality assurance points, and associated metadata are available for download as part of an accompanying U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215057","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Nystrom, E.A., Huston, C.J., and Welk, R.J., 2021, Bathymetry of New York City’s East of Hudson reservoirs and controlled lakes, 2017 to 2019: U.S. Geological Survey Scientific Investigations Report 2021–5057, 46 p., https://doi.org/10.3133/sir20215057.","productDescription":"Report: viii, 46 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119563","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":386538,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZZQ2OT","text":"USGS data release","linkHelpText":"Geospatial bathymetry datasets for New York City's East of Hudson Reservoirs and Controlled Lakes"},{"id":386537,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5057/sir20215057.pdf","text":"Report","size":"30.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5057"},{"id":386536,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5057/coverthb2.jpg"}],"country":"United States","state":"New York","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.992919921875,\n 40.88029480552824\n ],\n [\n -73.49853515625,\n 40.88029480552824\n ],\n [\n -73.49853515625,\n 41.40153558289846\n ],\n [\n -73.992919921875,\n 41.40153558289846\n ],\n [\n -73.992919921875,\n 40.88029480552824\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
The HANDLEY nuclear test was detonated at about 2,700 feet below the water table on March 26, 1970, in Pahute Mesa, south-central Nevada. Measured tritium concentrations in boreholes ER-20-12 and PM-3 indicate that a shallow tritium plume has migrated more than 1 mile (mi) downgradient from the HANDLEY test within a semi-perched aquifer and deeper tritium plumes have migrated 4.5 miles (mi) within underlying regional aquifers. Boreholes ER-20-12 and PM-3 are in an area of moderate-to-low transmissivity, but observation of tritium moving 4.5 mi within 40 years of the detonation indicates that high-transmissivity intervals exist. However, the location of these permeable pathways is unknown.
This report integrates geologic, hydrologic, and tritium data to infer the location of permeable pathways near and downgradient from the HANDLEY test. Numerical groundwater-flow and tritium-transport models were developed to estimate hydraulic and transport properties between the HANDLEY test and boreholes ER-20-12 and PM-3. Recharge, hydraulic-conductivity, specific-yield, specific-storage, and effective-porosity distributions were estimated with the numerical models by fitting simulated water-level altitudes, vertical-head differences, aquifer-test transmissivities, tritium concentrations, and drawdowns in wells PM-3-1 and PM-3-2 to measured equivalents. Drawdowns were estimated in wells PM-3-1 and PM-3-2 in response to groundwater withdrawals during the drilling of borehole ER-20-12. A modified hydrostratigraphic framework model (mHFM) was developed that incorporates hydrostratigraphic units (HSUs) from the Pahute Mesa–Oasis Valley hydrostratigraphic framework model (PMOV HFM). HSUs in the mHFM were modified from the PMOV HFM by grouping HSUs that, conceptually, are hydraulically similar and splitting HSUs based on water-level, aquifer-test, and tritium data.
Shallow and deeper tritium plumes have migrated to borehole ER-20-12 from the HANDLEY test. The shallow plume migrated from the HANDLEY test through the Timber Mountain welded tuff aquifer, whereas the deeper plumes moved through the Belted Range aquifer (BRA) and modified pre-Belted Range lava flow aquifer (mPBRLFA). Simulated tritium concentrations indicate that the leading edges of tritium plumes reached borehole ER-20-12 by 1990. From 1970 to 2020, the simulated tritium load mostly occurs between borehole ER-20-12 and the HANDLEY test.
An unmapped permeable feature was simulated between borehole ER-20-12 and the downgradient Ribbon Cliff structural zone. This permeable feature hydraulically connects the BRA and mPBRLFA with the Tiva Canyon aquifer (TCA). The TCA is the most transmissive unit in the study area. Simulated tritium from the deeper plumes moves through the permeable feature downgradient from borehole ER-20-12 and then migrates toward well PM-3-1 through the TCA. The leading edge of the deeper simulated tritium plumes reaches well PM-3-1 by 2010.
The mHFM and PMOV HFM do not include a permeable HSU at the water table near borehole PM-3, which is necessary for numerical flow and transport models to match measured water levels, transmissivities, and tritium concentrations in well PM-3-2. Consistently higher measured tritium concentrations in shallow well PM-3-2, compared to deeper well PM-3-1, and a downward vertical gradient between these wells indicate that a permeable feature exists near the water table that causes faster tritium migration toward the shallow well. Reevaluation of the PMOV HFM and geologic investigations, such as drilling another well, are needed to more precisely understand the shallow permeable pathway from the Handley test to well PM-3-2.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215032","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management, under Interagency Agreement DE-EM0004969","usgsCitation":"Jackson, T.R., 2021, Permeable groundwater pathways and tritium migration patterns from the HANDLEY underground nuclear test, Pahute Mesa, Nevada: U.S. Geological Survey Scientific Investigations Report 2021–5032, 49 p., https://doi.org/10.3133/sir20215032.","productDescription":"Report: vii, 49 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-120498","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":386552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5032/coverthb.jpg"},{"id":386553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5032/sir20215032.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5032"},{"id":386554,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRDQSN","text":"USGS data release","description":"USGS data release.","linkHelpText":"MODFLOW-2005 and MT3DMS models and supplemental data used to simulate groundwater flow and tritium transport from the HANDLEY underground nuclear test, Pahute Mesa, southern Nevada"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.333984375,\n 36.491973470593685\n ],\n [\n -115.79589843749999,\n 36.491973470593685\n ],\n [\n -115.79589843749999,\n 37.94419750075404\n ],\n [\n -117.333984375,\n 37.94419750075404\n ],\n [\n -117.333984375,\n 36.491973470593685\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Few States derive as much of their social and economic well-being from the land as South Dakota. Agriculture is the State’s primary industry. Hunting, fishing, tourism, mining, and hydroelectric power are also important economic and cultural drivers. Data from the U.S. Geological Survey Landsat program, archived at the U.S. Geological Survey Earth Resources Observation and Science Center just outside Sioux Falls, South Dakota, has long been a boon to the monitoring, mapping, and management of the State’s land resources.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
A description of historical and ambient water quality conditions is often required as part of navigational studies. This paper describes a series of tools developed by the USGS that can aid navigation managers in developing water quality assessments. The tools use R, a statistical software program, and provide methods to retrieve historical streamflow and water quality data, summarize observations, model concentrations and fluxes, and estimate seasonal, annual, and decadal trends. The utility of these tools is demonstrated by providing an analysis of the seasonal variability and long-term trends of nitrate plus nitrite, orthophosphate, and suspended sediment concentrations and fluxes at nine sites in the Mississippi River Basin. Trends in annual mean concentration and flux showed fairly stable nitrate plus nitrite at most of the nine sites, with increases in the Upper Mississippi and Missouri Rivers and decreases on the Illinois River over a 40-year period beginning in 1980. Orthophosphate concentration or flux increased at almost all sites over a similar time period. Conversely, a concurrent steady decline in suspended sediment concentrations and fluxes was noted at sites throughout the basin.
Post-wildfire changes to hydrologic and geomorphic systems can lead to widespread sediment redistribution. Understanding how sediment moves through a watershed is crucial for assessing hazards, developing debris flow inundation models, engineering sediment retention solutions, and quantifying the role that disturbances play in landscape evolution. In this study, we used terrestrial and airborne lidar to measure sediment redistribution in the 2016 Fish Fire, in the San Gabriel Mountains in southern California, USA. The lidar areas are in two adjacent watersheds, at spatial scales of 900 m2 to 4 km2, respectively. Terrestrial lidar data were acquired prior to rainfall, and two subsequent surveys show erosional change after rainstorms. Two airborne lidar flights occurred (1) 7 months before, and (2) 14 months after the fire ignition, capturing the erosional effects after rainfall. We found hillslope erosion dominated the overall sediment budget in the first rainy season after wildfire. Only 7% of the total erosion came from the active channel bed and channel banks, and the remaining 93% of eroded sediment was derived from hillslopes. Within the channelized portion of the watershed erosion/deposition could be generally described with topographic metrics used in a stream power equation. Observed sediment volumes were compared with four empirical models and one process-based model. We found that the best predictions of sediment volume were obtained from an empirical model developed in the same physiographic region. Moreover, this study showed that post-wildfire erosion rates in the San Gabriel Mountains attain the same magnitude as millennial time scale bedrock erosion rates.
Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear (Ursus americanus), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.
Landscape change in Tuolumne Meadows, Yosemite National Park, California, was characterized using data derived from four lidar surveys: one airborne survey in 2006 and three terrestrial surveys in 2016, 2017, and 2018. These surveys were used to generate a better quantitative understanding of changes associated with fluvial processes along the reach of the Tuolumne River within Tuolumne Meadows. This research was performed to provide a scientific basis for restoration and management decisions made by the National Park Service in accordance with the Tuolumne Wild and Scenic River Final Comprehensive Management Plan. A total of 15 reaches of the streambanks along the Tuolumne River in Tuolumne Meadows were subject to measurable streambank erosion between 2006 and 2018. In these areas, streambank retreat rates ranged between 0 and 2.7 meters per year (m/yr), recorded as an average retreat distance along the length of changing streambank position, with most retreat rates being less than 0.50 m/yr. The highest streambank retreat rates are associated with a year of high spring streamflow in 2017. Based on the data available, it was concluded that deposition on channel and point bars balances streambank erosion over a period of 12 years along the Tuolumne River in Tuolumne Meadows. As such, the river could be considered to be in a state of dynamic equilibrium during this period; erosion and sedimentation occur in distinct pulses in response to hydrological forcing but it is not clear that there is a trend towards sediment accumulation or removal in Tuolumne Meadows nor is there an obvious trend toward channel widening or narrowing. The existence of visible paleochannels in the meadow are an indication that more dramatic channel planform geometry changes have occurred in Tuolumne Meadows over an undetermined period and may occur again in the future. Geomorphic change rates relate to hydrology; during the study period, the high water in 2017 led to the highest rates of geomorphic change. Land managers should anticipate that floods with discharge rates greater than the peak flow in 2017 may cause more substantial landscape change than what was observed in this study, but erosion resulting from these events may be balanced by channel and point-bar deposition over a period of years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215025","collaboration":"Prepared in cooperation with National Park Service","usgsCitation":"DeLong, S.B., Pickering, A.J., and Kuhn, T., 2021, Streambank erosion and related geomorphic change in Tuolumne Meadows, Yosemite National Park, California: U.S. Geological Survey Scientific Investigations Report 2021–5025, 87 p., https://doi.org/10.3133/sir20215025.","productDescription":"viii, 87 p.","numberOfPages":"87","onlineOnly":"Y","ipdsId":"IP-118934","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":386473,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5025/sir20215025.pdf","text":"Report","size":"45 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386472,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5025/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.03936767578124,\n 37.461778479617465\n ],\n [\n -118.85284423828124,\n 37.461778479617465\n ],\n [\n -118.85284423828124,\n 38.0091482264894\n ],\n [\n -120.03936767578124,\n 38.0091482264894\n ],\n [\n -120.03936767578124,\n 37.461778479617465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center—Menlo Park, Calif. Office
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Menlo Park, CA 94025
Globally, anthrax outbreaks pose a serious threat to people, livestock, and wildlife. Furthermore, environmental change can exacerbate these outbreak dynamics by altering the host–pathogen relationship. However, little is known about how the quantitative spatial dynamics of host movement and environmental change may affect the spread of Bacillus anthracis, the causative agent of anthrax. Here, we use real-time observations and high-resolution tracking data from a population of common hippopotamus (Hippopotamus amphibius) in Tanzania to explore the relationship between river hydrology, H. amphibius movement, and the spatiotemporal dynamics of an active anthrax outbreak. We found that extreme river drying, a consequence of anthropogenic disturbances to our study river, indirectly facilitated the spread of B. anthracis by modulating H. amphibius movements. Our findings reveal that anthrax spread upstream in the Great Ruaha River (~3.5 km over a 9-day period), which followed the movement patterns of infected H. amphibius, who moved upstream as the river dried in search of remaining aquatic refugia. These upstream movements can result in large aggregations of H. amphibius. However, despite these aggregations, the density of H. amphibius in river pools did not influence the number of B. anthracis-induced mortalities. Moreover, infection by B. anthracis did not appear to influence H. amphibius movement behaviors, which suggests that infected individuals can vector B. anthracis over large distances right up until their death. Finally, we show that contact rates between H. amphibius- and B. anthracis-infected river pools are highly variable and the frequency and duration of contacts could potentially increase the probability of mortality. While difficult to obtain, the quantitative insights that we gathered during a real-time anthrax outbreak are critical to better understand, predict, and manage future outbreaks.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3540","usgsCitation":"Stears, K., Schmitt, M.H., Turner, W.C., McCauley, D., Muse, E.A., Kiwango, H., Matheyo, D., and Mutayoba, B.M., 2021, Hippopotamus movements structure the spatiotemporal dynamics of an active anthrax outbreak: Ecosphere, v. 12, no. 6, e03540, 14 p., https://doi.org/10.1002/ecs2.3540.","productDescription":"e03540, 14 p.","ipdsId":"IP-121950","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451887,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3540","text":"Publisher Index Page"},{"id":396541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tanzania","otherGeospatial":"Ruaha National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 33.95874023437499,\n -8.697784143504906\n ],\n [\n 34.87884521484374,\n -8.697784143504906\n ],\n [\n 34.87884521484374,\n -7.917793352627911\n ],\n [\n 33.95874023437499,\n -7.917793352627911\n ],\n [\n 33.95874023437499,\n -8.697784143504906\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Stears, Keenan","contributorId":287054,"corporation":false,"usgs":false,"family":"Stears","given":"Keenan","email":"","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":836456,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmitt, Melissa H.","contributorId":287055,"corporation":false,"usgs":false,"family":"Schmitt","given":"Melissa","email":"","middleInitial":"H.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":836457,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":836455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCauley, Douglas J.","contributorId":287056,"corporation":false,"usgs":false,"family":"McCauley","given":"Douglas J.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":836458,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Muse, Epaphras A.","contributorId":287060,"corporation":false,"usgs":false,"family":"Muse","given":"Epaphras","email":"","middleInitial":"A.","affiliations":[{"id":61455,"text":"Tanzania National Parks","active":true,"usgs":false}],"preferred":false,"id":836459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kiwango, Halima","contributorId":287062,"corporation":false,"usgs":false,"family":"Kiwango","given":"Halima","email":"","affiliations":[{"id":61455,"text":"Tanzania National Parks","active":true,"usgs":false}],"preferred":false,"id":836460,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Matheyo, Daniel","contributorId":287063,"corporation":false,"usgs":false,"family":"Matheyo","given":"Daniel","email":"","affiliations":[{"id":61455,"text":"Tanzania National Parks","active":true,"usgs":false}],"preferred":false,"id":836461,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mutayoba, Benezeth M.","contributorId":287064,"corporation":false,"usgs":false,"family":"Mutayoba","given":"Benezeth","email":"","middleInitial":"M.","affiliations":[{"id":61457,"text":"Sokoine University of Agriculture","active":true,"usgs":false}],"preferred":false,"id":836462,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224310,"text":"70224310 - 2021 - Spatial Gaussian processes improve multi-species occupancy models when range boundaries are uncertain and nonoverlapping","interactions":[],"lastModifiedDate":"2021-09-21T12:39:44.167853","indexId":"70224310","displayToPublicDate":"2021-06-14T07:37:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Spatial Gaussian processes improve multi-species occupancy models when range boundaries are uncertain and nonoverlapping","docAbstract":"Ecological forecasts are quantitative tools that can guide ecosystem management. The coemergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of (1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, (2) incorporating multiple data sources in the calibration data set to separate and propagate different sources of uncertainty, and (3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.
A three-dimensional hydrogeologic framework of the Hot Springs anticlinorium beneath Hot Springs National Park, Arkansas, was constructed to represent the complex hydrogeology of the park and surrounding areas to depths exceeding 9,000 feet below ground surface. The framework, composed of 6 rock formations and 1 vertical fault emplaced beneath the thermal springs, was discretized into 19 layers, 429 rows, and 576 columns and incorporated into a 3-dimensional steady-state groundwater-flow model constructed in MODFLOW-2005. Historical daily mean thermal spring flows were simulated for one stress period of approximately 34 years (1980–2014), chosen to represent the period of record for historical climate data used in the quantification of the boundary conditions. The groundwater-flow model was manually calibrated to historical daily mean thermal spring flows of 88,000 cubic feet per day observed over a 12-year period of record (1990–1995 and 1998–2005) at the thermal springs collection system. Calibration was achieved by calculating starting heads and general head boundary conditions from the Bernoulli equation and then adjusting the horizontal and vertical hydraulic conductivities of the rock formations and vertical fault and the hydraulic conductance of head-dependent flux boundaries. The groundwater-flow model was coupled to a surface-water model developed in the Precipitation-Runoff Modeling System (PRMS) by using PRMS-simulated gravity drainage as a specified flux recharge boundary condition in the groundwater-flow model. Together, the coupled models were used to (1) locate the areas of groundwater recharge to the thermal springs in the discretized hydrogeologic framework by using forward and reverse particle-tracking capabilities of MODPATH, (2) simulate the effects of variable recharge rates on the spring flows at the thermal springs, and (3) assess possible effects of climate and land-use change on the long-term variability of spring flows at the thermal springs.
Forward and backward particle-tracking maps indicated that the most prevalent areas of recharge in the discretized hydrogeologic framework used in this study were within about 0.6–0.9 mile of the thermal springs. Forward particle tracking indicated a recharge area southwest of the thermal springs that corresponded to a location where the predominant lithologies are the Arkansas Novaculite, Hot Springs Sandstone, and Bigfork Chert. Backward particle tracking indicated a second localized area of recharge to the northeast of the thermal springs that corresponded to a location where the dominant lithology is the Bigfork Chert. The groundwater-flow model indicated that the most probable recharge formations are the Arkansas Novaculite, Bigfork Chert, and Hot Springs Sandstone.
The simulated effects of climate and land-use changes on the variability of the spring-flow rates at the thermal springs generally resulted in reductions of thermal spring flow attributed to urban development and more extreme climates characterized by elevated mean surface air temperatures. The groundwater-flow model predicted a linear relation between the thermal spring discharge and the cumulative recharge volume applied to the hydrogeologic framework, and the positive slope of the predicted relation between recharge and simulated thermal spring flow indicates that more extreme precipitation events that supply more recharge may in fact increase the thermal spring-flow rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215045","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Hart, R.M., Ikard, S.J., Hays, P.D., and Clark, B.R., 2021, Effects of climate and land-use change on thermal springs recharge—A system-based coupled surface-water and groundwater-flow model for Hot Springs National Park, Arkansas: U.S. Geological Survey Scientific Investigations Report 2021–5045, 38 p., https://doi.org/10.3133/sir20215045.","productDescription":"Report: viii, 38 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-091576","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":386401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5045/coverthb.jpg"},{"id":386402,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5045/sir20215045.pdf","text":"Report","size":"43.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5045"},{"id":386403,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBJVVL","text":"USGS data release","linkHelpText":"Model inputs and outputs for simulating and predicting the effects of climate and land-use changes on thermal springs recharge—A system-based coupled surface-water and groundwater-flow model for Hot Springs National Park, Arkansas"},{"id":386404,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5045/images"}],"country":"United States","state":"Arkansas","otherGeospatial":"Hot Springs National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.1475830078125,\n 34.487881874939866\n ],\n [\n -92.96012878417969,\n 34.487881874939866\n ],\n [\n -92.96012878417969,\n 34.57273337081573\n ],\n [\n -93.1475830078125,\n 34.57273337081573\n ],\n [\n -93.1475830078125,\n 34.487881874939866\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Coral growth anomalies (GAs) are tumor-like protrusions that are detrimental to coral health, affecting both the coral skeleton and soft tissues. These lesions are increasingly found throughout the tropics and are commonly associated with high human population density, yet little is known about the molecular pathology of the disease. Here, we investigate the metabolic impacts of GAs through 1H nuclear magnetic resonance (NMR) metabolomics in Porites compressa tissues from a site of high disease prevalence (Coconut Island, Hawaii). We putatively identified 18 metabolites (8.1% of total annotated features) through complementary 1H and 1H–13C heteronuclear single quantum correlation NMR data that increase confidence in pathway analyses and may bolster future coral metabolite annotation efforts. Extract yield was elevated in both GA and unaffected (normal tissue from a diseased colony) compared to reference (normal tissue from GA-free colony) samples, potentially indicating elevated metabolic activity in GA-impacted colonies. Relatively high variation in metabolomic profiles among coral samples of the same treatment (i.e., inter-colony variation) confounded data interpretation, however, analyses of paired GA and unaffected samples identified 73 features that differed between these respective metabolome types. These features were largely annotated as unknowns, but 1-methylnicotinamide and trigonelline were found to be elevated in GA samples, while betaine, glycine, and histamine were lower in GA samples. Pathway analyses indicate decreased choline oxidation in GA samples, making this a pathway of interest for future targeted studies. Collectively, our results provide unique insights into GA pathophysiology by showing these lesions alter both the absolute and relative metabolism of affected colonies and by identifying features (metabolites and unknowns) and metabolic pathways of interest in GA pathophysiology going forward.
","language":"English","publisher":"Springer","doi":"10.1007/s00338-021-02125-7","usgsCitation":"Andersson, E.R., Day, R.D., Work, T.M., Anderson, P.E., Woodley, C.M., and Schock, T.B., 2021, Identifying metabolic alterations associated with coral growth anomalies using 1H NMR metabolomics: Coral Reefs, v. 40, p. 1195-1209, https://doi.org/10.1007/s00338-021-02125-7.","productDescription":"15 p.","startPage":"1195","endPage":"1209","ipdsId":"IP-126580","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":467239,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s00338-021-02125-7","text":"External Repository"},{"id":386978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","noUsgsAuthors":false,"publicationDate":"2021-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Andersson, Erik R.","contributorId":260786,"corporation":false,"usgs":false,"family":"Andersson","given":"Erik","email":"","middleInitial":"R.","affiliations":[{"id":35839,"text":"College of Charleston","active":true,"usgs":false}],"preferred":false,"id":818739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, Rusty D.","contributorId":260787,"corporation":false,"usgs":false,"family":"Day","given":"Rusty","email":"","middleInitial":"D.","affiliations":[{"id":35839,"text":"College of Charleston","active":true,"usgs":false}],"preferred":false,"id":818740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":818741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Paul E.","contributorId":260788,"corporation":false,"usgs":false,"family":"Anderson","given":"Paul","email":"","middleInitial":"E.","affiliations":[{"id":35839,"text":"College of Charleston","active":true,"usgs":false}],"preferred":false,"id":818742,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woodley, Cheryl M.","contributorId":260789,"corporation":false,"usgs":false,"family":"Woodley","given":"Cheryl","email":"","middleInitial":"M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":818743,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schock, Tracey B.","contributorId":260790,"corporation":false,"usgs":false,"family":"Schock","given":"Tracey","email":"","middleInitial":"B.","affiliations":[{"id":47720,"text":"NIST","active":true,"usgs":false}],"preferred":false,"id":818744,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222099,"text":"70222099 - 2021 - Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams","interactions":[],"lastModifiedDate":"2021-07-20T12:18:00.475837","indexId":"70222099","displayToPublicDate":"2021-06-12T07:15:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2476,"text":"Journal of Thermal Biology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating thermal infrared stream temperature imagery and spatial stream network models to understand natural spatial thermal variability in streams","docAbstract":"Under a warmer future climate, thermal refuges could facilitate the persistence of species relying on cold-water habitat. Often these refuges are small and easily missed or smoothed out by averaging in models. Thermal infrared (TIR) imagery can provide empirical water surface temperatures that capture these features at a high spatial resolution (<1 m) and over tens of kilometers. Our study examined how TIR data could be used along with spatial stream network (SSN) models to characterize thermal regimes spatially in the Middle Fork John Day (MFJD) River mainstem (Oregon, USA). We characterized thermal variation in seven TIR longitudinal temperature profiles along the MFJD mainstem and compared them with SSN model predictions of stream temperature (for the same time periods as the TIR profiles). TIR profiles identified reaches of the MFJD mainstem with consistently cooler temperatures across years that were not consistently captured by the SSN prediction models. SSN predictions along the mainstem identified ~80% of the 1-km reach scale temperature warming or cooling trends observed in the TIR profiles. We assessed whether landscape features (e.g., tributary junctions, valley confinement, geomorphic reach classifications) could explain the fine-scale thermal heterogeneity in the TIR profiles (after accounting for the reach-scale temperature variability predicted by the SSN model) by fitting SSN models using the TIR profile observation points. Only the distance to the nearest upstream tributary was identified as a statistically significant landscape feature for explaining some of the thermal variability in the TIR profile data. When combined, TIR data and SSN models provide a data-rich evaluation of stream temperature captured in TIR imagery and a spatially extensive prediction of the network thermal diversity from the outlet to the headwaters.
At present, the most reliable information for inferring storm-time ground electric fields along electrical transmission lines comes from coarsely sampled, national-scale magnetotelluric (MT) data sets, such as that provided by the EarthScope USArray program. An underlying assumption in the use of such data is that they adequately sample the spatial heterogeneity of the surface relationship between geomagnetic and geoelectric fields. Here, we assess the degree to which the density of MT data sampling affects geoelectric hazard assessments. For electrical transmission networks in each of four focus regions across the contiguous United States, we perform two parallel band-limited (101–103 s) hazard analyses: one using only USArray-style (∼70-km station spacing) MT data, and one incorporating denser (≪70-km station spacing) MT data. We find that the use of USArray-style MT sampling alone provides a useful first-order estimate of integrated geoelectric fields along electrical transmission lines. However, we also find that the use of higher density MT data can in some areas lead to order-of-magnitude differences in line-averaged electric field estimates at the level of individual transmission lines and can also yield significant differences in subregional hazard patterns. As we demonstrate using variogram plots, these differences reflect short-spatial-scale variability in Earth conductivity, which in turn reflects regional lithotectonic structure and history. We also provide a cautionary example in the use of electrical conductivity models to predict dense MT data; although valuable for hazard applications, models may only be able to reproduce surface geoelectric fields as captured by the MT data from which they were derived.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020SW002693","usgsCitation":"Murphy, B.S., Lucas, G., Love, J.J., Kelbert, A., Bedrosian, P.A., and Rigler, E.J., 2021, Magnetotelluric sampling and geoelectric hazard estimation: Are national-scale surveys sufficient?: AGU Space Weather, v. 19, no. 7, e2020SW002693, 24 p., https://doi.org/10.1029/2020SW002693.","productDescription":"e2020SW002693, 24 p.","ipdsId":"IP-128631","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":488915,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020sw002693","text":"Publisher Index Page"},{"id":387180,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.33203124999997,\n 39.70718665682654\n ],\n [\n -120.93749999999997,\n 39.70718665682654\n ],\n [\n -120.93749999999997,\n 46.37725420510028\n ],\n [\n -125.33203124999997,\n 46.37725420510028\n ],\n [\n -125.33203124999997,\n 39.70718665682654\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.140625,\n 37.23032838760387\n ],\n [\n -94.921875,\n 37.23032838760387\n ],\n [\n -94.921875,\n 41.64007838467894\n ],\n [\n -99.140625,\n 41.64007838467894\n ],\n [\n -99.140625,\n 37.23032838760387\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.833984375,\n 33.211116472416855\n ],\n [\n -86.748046875,\n 33.211116472416855\n ],\n [\n -86.748046875,\n 38.75408327579141\n ],\n [\n -94.833984375,\n 38.75408327579141\n ],\n [\n -94.833984375,\n 33.211116472416855\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.240234375,\n 44.276671273775186\n ],\n [\n -83.671875,\n 44.276671273775186\n ],\n [\n -83.671875,\n 49.03786794532644\n ],\n [\n -96.240234375,\n 49.03786794532644\n ],\n [\n -96.240234375,\n 44.276671273775186\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Benjamin Scott 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":242928,"corporation":false,"usgs":true,"family":"Murphy","given":"Benjamin","email":"","middleInitial":"Scott","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819286,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lucas, Greg M. 0000-0003-1331-1863","orcid":"https://orcid.org/0000-0003-1331-1863","contributorId":223556,"corporation":false,"usgs":false,"family":"Lucas","given":"Greg M.","affiliations":[{"id":6605,"text":"USGS","active":true,"usgs":false}],"preferred":false,"id":819287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819288,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":819289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":819290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rigler, E. 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