Atrazine is presently one of the most abundantly used herbicides in the United States, and a common contaminant of natural water bodies and drinking waters in high-use areas. Dysregulation of reproductive processes has been demonstrated in atrazine exposed fish, including alteration of key endocrine pathways on hypothalamic-pituitary-gonadal (HPG) axis. However, the potential for atrazine-induced transgenerational inheritance of reproductive effects in fish has not been investigated. The present study examined the effects of early developmental atrazine exposure on transgenerational reproductive dysregulation in Japanese medaka (Oryzias latipes). F0 medaka were exposed to atrazine (ATZ, 5 or 50 μg/L), 17α-ethinylestradiol (EE2, 0.002 or 0.05 μg/L), or solvent control during the first twelve days of development with no subsequent exposure over three generations. This exposure overlapped with the critical developmental window for embryonic germ cell development, gonadogenesis, and sex determination. Exposed males and females of the F0 generation were bred to produce an F1 generation, and this was continued until the F2 generation. Sperm count and motility were not affected in F0 males; however, both parameters were significantly reduced in the males from F2 Low EE2 (0.002 μg/L), Low ATZ (5 μg/L), and High ATZ (50 μg/L) lineages. Fecundity was unaffected by atrazine or EE2 in F0 through F2 generations; however, fertilization rate was decreased in low atrazine and EE2 exposure lineages in the F2 generation. There were significant transgenerational differences in expression of the genes involved in steroidogenesis and DNA methylation. These results suggest that although early life exposure to atrazine did not cause significant phenotypes in the directly exposed F0 generation, subsequent generations of fish were at greater risk of reproductive dysfunction.
Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\\or small farms with mixed signatures from different crop types and\\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R2 = 0.95 for South Korea and R2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281.
Little is known about prey use by the orangebelly darter, Etheostoma radiosum, and what is known has been described from relatively large river systems. We examined prey use by orangebelly darters from first- and second-order tributaries in the Lower Mountain Fork River of southeastern Oklahoma. Adult darters (n = 141) were captured from five tributaries in 2015, and stomach contents were examined to determine prey use. Aquatic isopods were the most frequently consumed organism. This differs notably from previous reports that insects, primarily dipterans, were the predominant prey for the species.
","language":"English","publisher":"BioOne","doi":"10.1894/0038-4909-63-2-146","usgsCitation":"Reed, M.L., Hoback, W., and Long, J.M., 2019, Winter and spring diet of the orangebelly darter, Etheostoma radiosum, among tributaries of the Lower Mountain Fork River: Southwestern Naturalist, v. 63, no. 2, p. 146-148, https://doi.org/10.1894/0038-4909-63-2-146.","productDescription":"3 p.","startPage":"146","endPage":"148","ipdsId":"IP-092269","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":382522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Lower Mountain Fork River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.72274780273438,\n 33.95589097690208\n ],\n [\n -94.53907012939453,\n 33.95589097690208\n ],\n [\n -94.53907012939453,\n 34.15499986715356\n ],\n [\n -94.72274780273438,\n 34.15499986715356\n ],\n [\n -94.72274780273438,\n 33.95589097690208\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"63","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, M. L.","contributorId":243520,"corporation":false,"usgs":false,"family":"Reed","given":"M.","email":"","middleInitial":"L.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":802463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoback, W. W.","contributorId":243288,"corporation":false,"usgs":false,"family":"Hoback","given":"W. W.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":802464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":802465,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203600,"text":"70203600 - 2019 - The importance of groundwater flow to the formation of modern thrombolitic microbialites","interactions":[],"lastModifiedDate":"2019-05-23T15:16:41","indexId":"70203600","displayToPublicDate":"2019-05-22T15:12:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1751,"text":"Geobiology","active":true,"publicationSubtype":{"id":10}},"title":"The importance of groundwater flow to the formation of modern thrombolitic microbialites","docAbstract":"Modern microbialites are often located within groundwater discharge zones, yet the role of groundwater in microbialite accretion has yet to be resolved. To understand relationships between groundwater, microbialites, and associated microbial communities, we quantified and characterized groundwater flow and chemistry in active thrombolitic microbialites in Lake Clifton, Western Australia, and compared these observations to inactive thrombolites and lakebed sediments. Groundwater flows upward through an interconnected network of pores within the microstructure of active thrombolites, discharging directly from thrombolite heads into the lake. This upwelling groundwater is fresher than lake water and is hypothesized to support microbial mat growth by reducing salinity and providing limiting nutrients in an osmotically stressful and oligotrophic habitat. This is in contrast to inactive thrombolites that show no evidence of microbial mat colonization and are infiltrated by hypersaline lake water. Groundwater discharge through active thrombolites contrasts with the surrounding lakebed, where hypersaline lake water flows downward through sandy sediments at very low rates. Based on an appreciation for the role of microorganisms in thrombolite accretion, our findings suggest conditions favorable to thrombolite formation still exist in certain locations of Lake Clifton despite increasing lake water salinity. This study is the first to characterize groundwater flow rates, paths, and chemistry within a microbialite‐forming environment and provides new insight into how groundwater can support microbial mats believed to contribute to microbialite formation in modern and ancient environments.
The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, collected groundwater samples from 5 production wells and 5 domestic wells in the Delaware River Basin, 8 production wells and 7 domestic wells in the Genesee River Basin, and 1 municipal well, 7 production wells, and 13 domestic wells in the St. Lawrence River Basin in New York. All samples were collected from May through November 2015 in an effort to characterize groundwater quality in these basins. The samples were collected and processed by using standard U.S. Geological Survey procedures and were analyzed for 148 physiochemical properties and constituents, including dissolved gases, major ions, nutrients, trace elements, pesticides, volatile organic compounds, radionuclides, and indicator bacteria.
The Delaware River Basin study area covers 2,360 square miles (mi2) in southeastern New York. Of the 10 wells sampled in the Delaware River Basin, 3 are completed in sand and gravel, and 7 are completed in bedrock. Groundwater in the Delaware River Basin was generally of good quality, although properties and concentrations of some constituents—pH, iron, manganese, aluminum, radon-222, and total coliform bacteria—sometimes equaled or exceeded primary, secondary, or proposed drinking-water standards. The constituent most frequently detected in concentrations exceeding drinking-water standards (10 of 10 samples) was radon-222.
The Genesee River Basin study area includes the entire 2,439 mi2 of the basin in western New York. Of the 15 wells sampled in the Genesee River Basin, 6 are completed in sand and gravel, and 9 are completed in bedrock. Groundwater in the Genesee River Basin was generally of good quality, although properties and concentrations of some constituents—chloride, sodium, dissolved solids, iron, manganese, aluminum, arsenic, radon-222, methane, total coliform bacteria, fecal coliform bacteria, and Escherichia coli bacteria—sometimes equaled or exceeded primary, secondary, or proposed drinking-water standards. The constituent most frequently detected in concentrations exceeding drinking-water standards (12 of 15 samples) was radon-222.
The St. Lawrence River Basin study area includes the entire 5,650 mi2 of the basin in northeastern New York. Of the 21 wells sampled in the St. Lawrence River Basin, 7 are completed in sand and gravel, and 14 are completed in bedrock. Groundwater in the St. Lawrence River Basin was generally of good quality, although properties and concentrations of some constituents—pH, chloride, sodium, dissolved solids, iron, manganese, sulfate, nitrate, radon-222, total coliform bacteria, fecal coliform bacteria, and Escherichia coli bacteria—sometimes equaled or exceeded primary, secondary, or proposed drinking-water standards. The constituent most frequently detected in concentrations exceeding drinking-water standards (14 of 21 samples) was radon-222.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191005","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation ","usgsCitation":"Scott, T.-M., Nystrom, E.A., and Reddy, J.E., 2019, Groundwater quality in the Delaware, Genesee, and St. Lawrence River Basins, New York, 2015: U.S. Geological Survey Open-File Report 2019–1005, 42 p., 2 app., https://doi.org/10.3133/ofr20191005. 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York\",\"nation\":\"USA \"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180
Sea-cliff bedstraw (Galium buxifolium [Rubiaceae]) is a delicate dioecious subshrub endemic to Santa Cruz and San Miguel Islands, in the northern California Channel Islands. It was listed as endangered in 1997 under the Federal Endangered Species Act, threatened by soil loss, habitat alteration, and herbivory from more than a century of ranching land use. At the time of listing, there were eight populations known from Santa Cruz Island and two from San Miguel Island, restricted to shaded, northfacing volcanic ocean bluffs. The recovery plan included criteria to be considered for delisting such as stabilizing or increasing populations on both islands and recovery actions such as searching for more populations, seed banking, researching seed germination and life history, and reversing factors causing decline.
We began studying sea-cliff bedstraw on Santa Cruz Island in the early 2000s to better understand factors related to decline. We surveyed historic populations and searched for new occurrences 2004–06, researched population size-class structure and floral biology 2005–06 at three sites, and tracked demography 2005–14 while treating the invasive periwinkle (Vinca major) with herbicide at a fourth site. We collected seeds for germination studies and long-term seed banking from all four study sites.
The historically known Santa Cruz Island sites were still extant on the sea-cliffs, and we located 14 occurrences not documented at the time of listing. Sea-cliff bedstraw floral morphology appears evenly balanced between mainly pistillate and staminate plants at the study sites, with other floral types present as well, in the populations we sampled. Seed germination trials showed about a 40 percent germination rate from fresh imbibed seeds. Life-history stage structure 2005–06 varied from purely reproductive plants at one site to nearly equal representation of vegetative and reproductive plants at a second site, with seedlings also present at the remaining two sites.
Demographic study of tagged plants at the long-term study plot 2005–14 showed an episodic pattern of high recruitment with low mortality once plants became established beyond the first summer dormant season. A flush of seedlings was seen after periwinkle reduction, indicating that the periwinkle may have been repressing seedling establishment. About 93 percent of the already-established plants tagged in 2005 and 2006 were still present 10 years later in 2014. Findings indicate that sea-cliff bedstraw individuals are long-lived and once established have good survival rates. Taken together, these results imply that population growth is driven more by germination and recruitment into the mature population than by mortality of established plants, at least since feral animal eradication from Santa Cruz Island. Therefore, conservation actions might be best focused on nurturing safe sites for seed germination and seedling survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191054","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"McEachern, K., Chess, K.A., Flagg, K., and Niessen, K.G., 2019, Sea-cliff bedstraw (Galium buxifolium) patterns and trends, 2005–14, on Santa Cruz and San Miguel Islands, Channel Islands National Park, California: U.S. Geological Survey Open-File Report 2019–1054, 23 p., https://doi.org/10.3133/ofr20191054.","productDescription":"Report: vi, 23 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-106549","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":364050,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94J36GC","linkHelpText":"Demographic Data and Location Information for the Endangered Gailum Buxifolium from 2005 to 2014 on Santa Cruz and San Miguel Islands, California"},{"id":364030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1054/ofr20191054.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1054"},{"id":364029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1054/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Channel Islands, San Miguel Island, Santa Cruz Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.53649902343749,\n 33.84760762988741\n ],\n [\n -119.41589355468749,\n 33.84760762988741\n ],\n [\n -119.41589355468749,\n 34.14363482031264\n ],\n [\n -120.53649902343749,\n 34.14363482031264\n ],\n [\n -120.53649902343749,\n 33.84760762988741\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Director, Geology, Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
The Fort Rock dome, in Yavapai County, Ariz., is a roughly circular geologic structure in plan view, 2.5 km in diameter, that is similar in many ways to an impact crater; however, it is a structural dome caused by a potassic mafic intrusion at depth, and the crater-like depression in its center is erosional in origin. The Aquarius Mountains, west of the Fort Rock dome, in Mohave County, contain a felsic volcanic center that erupted tuffs, non-welded ash-flow tuffs, and lahars following dome emplacement.
This report discusses the radiometric ages of samples of rock units from both the Fort Rock dome and the Aquarius Mountains eruptive center. The ages for all samples span a short interval of time from 22.3 to 21.7 m.y. (earliest Miocene). The individual sample ages are consistent with the stratigraphic order of the rock units in the area, and the short age span is consistent with the absence of any significant unconformities in the section.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191038","usgsCitation":"Fuis, G.S., Calvert, A., and Sullivan, K., 2019, Radiometric ages of volcanic rocks on the Fort Rock dome and in the Aquarius Mountains, Yavapai and Mohave counties, Arizona: U.S. Geological Survey Open-File Report 2019–1038, 18 p., https://doi.org/10.3133/ofr20191038.","productDescription":"iv, 18 p.","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-100507","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":364031,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1038/coverthb.jpg"},{"id":364032,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1038/ofr20191038.pdf","text":"Report","size":"12.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1038"}],"country":"United States","state":"Arizona","county":"Yavapai County, Mohave 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Science Center
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, California 94025
The dramatic loss of glacial mass in low latitudes is causing shifts in downstream water availability and use during the driest months of the year. The world’s largest concentration of tropical glaciers lies in the Cordillera Blanca range of Peru, where glacial runoff is declining and regional stresses are emerging over water resources. Throughout the Cordillera Blanca, groundwater inputs from alpine meadow–talus complexes, locally known as pampas, supply proglacial streams with up to 80% of their flow during the region’s dry season. Structural knowledge of the pampa aquifers is needed to estimate their drainable groundwater storage capacity and residence time, to elucidate the role and importance of alpine groundwater storage in the regional water budget of the Cordillera Blanca. To understand the structure of these proglacial aquifers, multiple near-surface geophysical methods were implemented in a proglacial valley near dense networks of spring-fed tributaries. Geophysical results and borehole logs suggest groundwater is stored in a confined aquifer composed of buried talus deposits overlain by lacustrine clay, while deeper portions of the unit, 10–15 m in depth, are relatively clay-free and more hydraulically conductive. Based on these findings and assumptions of aquifer porosity, the pampas of the Callejon de Huaylas may store from 0.006 to 0.02 km3 of groundwater. Furthermore, these findings suggest that the talus aquifers of the Cordillera Blanca were formed in proglacial lakes, followed by infilling with fine lacustrine sediments that confine lower units and allow for groundwater discharge to springs via macropores and preferential flow.
Whereas research in volcano geodesy seeks to push the boundaries of our knowledge of the physics of volcanoes, monitoring looks at changes in volcano behavior to predict when a volcanic crisis might develop. To be effective, geodetic monitoring must be done before, during, and after eruptions and must be integrated with other monitoring techniques. It requires the type of long-term commitment of time and resources that academic and industry scientists generally cannot make. A few, well-placed geodetic monitoring stations can make a huge difference to a country's ability to alert its people to an imminent volcanic eruption.
Monitoring strategies vary greatly depending on several factors such as the activity of the individual volcano, access, and available personnel and funding. Rapid advances in technology allow for more precise geodetic monitoring today than was imaginable when many of the existing volcano observatories were established. Today, deformation measurements at active volcanoes are usually made with continuous Global Positioning System (CGPS) stations, supplemented by Interferometric Synthetic Aperture Radar (InSAR) images. Neither method requires a continuous presence of personnel in the field, except for the installation and maintenance of the GPS stations; however subsequent data analysis can be highly complex.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference Module in Earth Systems and Environmental Sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-409548-9.10902-9","usgsCitation":"Battaglia, M., Alpala, J., Alpala, R., Angarita, M., Arcos, D., Eullides, L., Euillades, P., Mueller, C., and Narvaez, L., 2019, Monitoring volcanic deformation, chap. of Reference Module in Earth Systems and Environmental Sciences, https://doi.org/10.1016/B978-0-12-409548-9.10902-9.","ipdsId":"IP-103581","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":364027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Battaglia, Maurizio 0000-0003-4726-5287 mbattaglia@usgs.gov","orcid":"https://orcid.org/0000-0003-4726-5287","contributorId":204742,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":762901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alpala, Jorge","contributorId":139634,"corporation":false,"usgs":false,"family":"Alpala","given":"Jorge","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alpala, Rosa","contributorId":215654,"corporation":false,"usgs":false,"family":"Alpala","given":"Rosa","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Angarita, Mario","contributorId":215655,"corporation":false,"usgs":false,"family":"Angarita","given":"Mario","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":762903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arcos, Dario","contributorId":139636,"corporation":false,"usgs":false,"family":"Arcos","given":"Dario","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eullides, Leonardo","contributorId":215656,"corporation":false,"usgs":false,"family":"Eullides","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":762905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Euillades, Pablo","contributorId":215657,"corporation":false,"usgs":false,"family":"Euillades","given":"Pablo","email":"","affiliations":[],"preferred":false,"id":762906,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Cyrill","contributorId":215658,"corporation":false,"usgs":false,"family":"Mueller","given":"Cyrill","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":762907,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Narvaez, Lourdes","contributorId":215659,"corporation":false,"usgs":false,"family":"Narvaez","given":"Lourdes","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762908,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215202,"text":"70215202 - 2019 - From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows","interactions":[],"lastModifiedDate":"2020-10-13T22:52:05.481431","indexId":"70215202","displayToPublicDate":"2019-05-21T08:49:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows","docAbstract":"Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.
Waterfowl are the natural hosts of avian influenza virus (AIV), and through migration spread the virus worldwide. Most AIVs carried by wild waterfowl are low pathogenic strains; however, Goose/Guangdong/1996 lineage clade 2.3.4.4 H5 highly pathogenic (HP) AIV now appears to be endemic in wild birds in much of the Eastern Hemisphere. Most research efforts studying AIV pathogenicity in waterfowl thus far have been directed toward dabbling ducks. In order to better understand the role of diving ducks in AIV ecology, we previously characterized the pathogenesis of clade 2.3.4.4 H5 HPAIV in lesser scaup (Aythya affinis). In an effort to further elucidate AIV infection in diving ducks, the relative susceptibility and pathogenesis of two North American lineage H7 HPAIV isolates from the most recent outbreaks in the United States was investigated. Lesser scaup were inoculated with either A/turkey/IN/1403-1/2016 H7N8 or A/chicken/TN/17-007147-2/2017 H7N9 HPAIV by the intranasal route. The approximate 50% bird infectious dose (BID50) of the H7N8 isolate was determined to be 103 50% egg infectious doses (EID50), and the BID50 of the H7N9 isolate was determined to be <102 EID50, indicating some variation in adaptation between the two isolates. No mortality or clinical disease was observed in either group except for elevated body temperatures at 2 and 4 days postinoculation (DPI). Virus shedding was detected up to 14 DPI from both groups, and there was a trend for shedding to have a longer duration and at higher titer levels from the cloacal route. These results demonstrate that lesser scaup are susceptible to both H7 lineages of HPAIV, and similar to dabbling duck species, they shed virus for long periods relative to gallinaceous birds and don't present with clinical disease.
Simulation models are often used to identify hydrologic regimes suitable for different riparian or floodplain tree species. However, most existing models pay little attention to forest successional processes or other disturbances that may interact with the hydrologic regime of river systems to alter forest dynamics in space and time. In this study, we introduce a flood disturbance module to the LANDIS-II forest succession modelling framework to enable investigations into how inundation interacts with other disturbances and successional processes to alter floodplain forest cover and community dynamics. We illustrate the functionality of the model using a case study with multiple scenarios in the Upper Mississippi River floodplain, USA. We found that model predictions of total forest cover and the abundance of specific forest community types were generally related to uncertainty in the susceptibility of different species and age classes to inundation. By simulation year 100, increases or decreases in total forest cover and forest type distributions were roughly proportional to the initial differences in the susceptibility of species and age classes to inundation. The largest decrease in total forest cover was associated with a scenario that included disturbance by the emerald ash borer (Agrilus planipennis) and when using susceptibility parameters corresponding to the weakest flood tolerance. In contrast, changes in the composition of aboveground biomass were not sensitive to differences in susceptibility, and generally showed shifts toward later successional species with higher shade tolerance and longer lifespans for all scenarios. Our findings suggest that flood inundation interacts with other disturbances (e.g., insect outbreaks) and forest successional processes to alter forest abundance, distribution, and species composition in this system. Our modelling framework should allow for future studies that examine such interactions in other systems, and in the context of alternative hydrologic scenarios and other disturbance regimes.
Understanding threats to species persistence requires knowledge of where species currently occur. We explore methods for estimating two important facets of species distributions, namely where the range limit occurs and how species interactions structure distributions. Accurate understanding of range limits is crucial for predicting range dynamics and shifts in response to interspecific interactions and climate change. Additionally, species interactions are increasingly recognized as an important but not well‐understood predictor of range shifts. Our objective was to predict range limits and contact zones for two plethodontid salamanders, the highly range‐restricted Shenandoah salamander (Plethodon shenandoah) and the wide‐ranging red‐backed salamander (Plethodon cinereus). Using detection/non‐detection data, we assess four methodological decisions when estimating species’ distributions: (1) accounting for imperfect detection, (2) covariates to predict species occurrences, (3) accounting for species interactions, and (4) the inclusion of spatial autocorrelation. We found that Shenandoah salamander and red‐backed salamander co‐occurrence would have been underestimated and the range edge misidentified had we not accounted for incomplete detection. Covariates related to habitat were not sufficient to explain species’ range boundaries. Models that included spatial autocorrelation (i.e., a conditional autoregressive random effect) performed better than models that included just species interactions (i.e., detection and occurrence were conditional on the other species being present) and models that included both spatial autocorrelation and species interactions. Further, we found that the breadth of primary contact zones was typically 60–170 m, which is greater on average than previous estimates. In addition, we frequently observed secondary, disjunct contact zones along the range boundary. Understanding the extent to which species co‐occur and how the range boundaries are shaped is crucial to conservation efforts. Our work indicates that accounting for detection is crucial for accurately characterizing range edges and that spatial models may be especially effective in modeling distributions at the boundary.
In 2015, the U.S. Geological Survey (USGS) entered into a cooperative agreement with the North Carolina Department of Transportation (NCDOT) to develop a North Carolina-enhanced variation of the national Stochastic Empirical Loading and Dilution Model (SELDM) with available North Carolina-specific streamflow and water-quality data and to demonstrate use of the model by documenting selected simulation scenarios. The USGS developed the national SELDM in cooperation with the Federal Highway Administration (FHWA) to provide the tools and techniques necessary for performing stormwater-quality simulations. SELDM uses a stochastic mass-balance approach to estimate combinations of flows, concentrations, and loads of stormwater constituents from the site of interest (often a highway catchment; nonhighway areas, such as a large impervious area at a shopping center complex, also can be used) and the basin upstream from the stormwater outfall to assess the risk for adverse effects of runoff. SELDM also can be used to simulate the effectiveness of volume reduction, hydrograph extension, and water-quality concentration reductions by stormwater best management practices (BMPs), which are designed to help mitigate the effects of runoff on receiving water bodies.
Some of the statistical inputs needed for the North Carolina-enhanced SELDM were either calculated or augmented using local or regional data from North Carolina. Streamflow statistics used by SELDM were determined for 266 streamgages across North Carolina on the basis of data available through the 2015 water year. Recession ratio statistics used for triangular hydrographs were also developed for 30 streamgages across the State. The NCDOT identified previous research reports on highway-runoff and BMP studies in North Carolina for review of potential data addition to the national FHWA Highway-Runoff Database (HRDB). Following USGS review of these data, a total of 25,087 event mean concentration values and 1,140 storm events for 39 highway-runoff sites and 195 analytes were uploaded to the national HRDB from six North Carolina highway-runoff research reports and a recent USGS bridge deck runoff study. Using data for 27 streamgages in North Carolina, a total of 57 water-quality transport curves were developed for seven constituents for use in simulating water-quality conditions in the upstream basin. Performance data for three BMPs (bioretention, grass strip or swale, and wetland channel) from NCDOT research data were incorporated into the North Carolina-enhanced SELDM for volume-reduction statistics, including the effectiveness of treating four water-quality constituents (total suspended solids, total nitrogen, total phosphorus, nitrate plus nitrite) and turbidity.
Simulations using the North Carolina-enhanced SELDM are presented for two hypothetical upstream basins in the Piedmont ecoregion and one hypothetical highway site to demonstrate how simulations can be used to provide risk-based information about potential effects of stormwater runoff on downstream water quality and the potential for mitigating those risks by using BMPs. The first group of simulations explores the stochastic variability in dilution factors (the ratio of the highway runoff to the total downstream stormflow) for a hypothetical Piedmont rural creek having drainage areas ranging from 1 to 100 square miles. The second group of simulations examines dilution factors based on variations in precipitation, streamflow, and recession ratios for two hypothetical Piedmont upstream basins (rural and urban) where the drainage area was held constant at 25 square miles. These simulations indicate the sensitivity of results to variations in each of the three variables. The third group of simulations examines the effects of varied concentrations in the upstream basin on water-quality conditions downstream from the highway crossing. Variations in upstream water-quality conditions for three constituents (suspended sediment concentration, total nitrogen, and total phosphorus) are based on water-quality transport curves selected from among the 57 curves developed as part of this study to represent low-, medium-, and high-concentration statistics. Simulations completed for this third group also examine the potential effects of grass swale and bioretention BMP treatment on total nitrogen and total phosphorus concentrations in highway runoff. The BMP performance data from the NCDOT research reports were applied in this group of simulations.
The stochastic mass-balance approach used in SELDM analyses and simulations provides a strong tool for engineers and water-resource managers to use in exploring a wide range of possible hydrologic and water-quality inputs and their effects on downstream water quality. The results of this study can not only aid engineers and managers in planning for potential adverse effects of runoff at site-specific locations, they can also help the USGS and other Federal and State agencies with oversight responsibilities in stormwater-quality issues to continue gathering data on potential water-quality effects in receiving streams.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195031","collaboration":"Prepared in cooperation with the North Carolina Department of Transportation, Division of Highways, Hydraulics Unit and the U.S. Department of Transportation, Federal Highway Administration, Office of Project Development and Environmental Review","usgsCitation":"Weaver, J.C., Granato, G.E., and Fitzgerald, S.A., 2019, Assessing water quality from highway runoff at selected sites in North Carolina with the Stochastic Empirical Loading and Dilution Model (SELDM) (ver 1.1, July 2, 2019): U.S. Geological Survey Scientific Investigations Report 2019–5031, 99 p., https://doi.org/10.3133/sir20195031.","productDescription":"Report: x, 99 p.; Data Releases","numberOfPages":"113","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095625","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":476,"text":"North 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U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
Inversion of airborne electromagnetic (AEM) data is an under-determined inverse problem, in that infinitely many resistivity models exist that will be able to explain the observed data, within measurement errors. Therefore, additional information or constraints must be taken into account to solve the inverse problem. In deterministic approaches, the goal is to locate one optimal model that can be obtained by using some form of smoothness constraints implied through a number of regularization choices. This model, however, will not necessarily represent realistic geological features. Probabilistic methods offer an alternative in which the solution is not one model, but a collection of models, whose variability represents the uncertainty. The probabilistic approach can also rely on implicit model assumptions, representing prior information (a type of regularization information) that may or may not be consistent with the actual available information. Here, we present an approach for AEM inversion in which the prior model is explicitly chosen by a user, preferably selected based on actual prior information available and then integrated with AEM data using a general Monte Carlo based sampling approach. This approach leads to a new workflow to AEM inversion in which geological prior information is independently and explicitly chosen before inversion is carried out. The main benefit of this approach is that each model obtained will, by construction, be consistent with prior (geological) information as well as geophysical data. Through examples based on synthetic and real AEM data, we will demonstrate the methodology, not least that the choice of prior information cannot be avoided: Either it is done explicitly, or it will be chosen implicitly by the choice of method used to invert the AEM data.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggz230","usgsCitation":"Hansen, T.M., and Minsley, B.J., 2019, Inversion of airborne EM data with an explicit choice of prior model: Geophysical Journal International, v. 218, no. 2, p. 1348-1366, https://doi.org/10.1093/gji/ggz230.","productDescription":"17 p.","startPage":"1348","endPage":"1366","ipdsId":"IP-106050","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":467607,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/731bd10f-dcc4-4142-a377-2f42a561b2c9","text":"External Repository"},{"id":382276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"218","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Hansen, Thomas Mejer","contributorId":199735,"corporation":false,"usgs":false,"family":"Hansen","given":"Thomas","email":"","middleInitial":"Mejer","affiliations":[{"id":27198,"text":"Niels Bohr Institute, University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":808408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":808409,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205612,"text":"70205612 - 2019 - Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests","interactions":[],"lastModifiedDate":"2019-09-27T10:33:51","indexId":"70205612","displayToPublicDate":"2019-05-20T10:27:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests","docAbstract":"Salinity loading in the Upper Colorado River Basin (UCRB) costs local economies upwards of $300 million US dollars annually. Salinity source models have generally included coarse spatial data to represent non‐agriculture sources. We developed new predictive soil property and cover maps at 30 m resolution to improve source representation in salinity modeling. Salinity loading erosion risk indices were also created based on soil properties, remotely sensed bare ground exposure, and topographic factors to examine potential surface soil erosion drivers. These new maps and data from previous SPARROW models were related to recently updated records of salinity at 309 stream gauges in the UCRB using random forest regressions. Resulting salinity yield predictions indicate more diffuse salinity sources, with slightly higher yields in more arid portions of the UCRB, and less overall load coming from irrigated agricultural sources. Model simulations still indicate irrigation to be the major human source of salinity (661,000 Mg, or 12%), but also suggest that 75,000 Mg (1.4%) of annual salinity in the UCRB is coming from areas with excessive exposed bare ground in high elevation mountain areas. Model inputs allow for field scale screening of locations that could be targeted for salinity control projects. Results confirm recent studies indicating limited surface erosional influence on salinity loading in UCRB surface waters, but impacts of monsoonal runoff events are still not fully understood, particularly in drylands. The study highlights the utility of new predictive soil maps and machine learning for environmental modeling.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR024054","usgsCitation":"Nauman, T., Ely, C., Miller, M., and Duniway, M.C., 2019, Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests: Water Resources Research, v. 55, no. 6, p. 4954-4973, https://doi.org/10.1029/2018WR024054.","productDescription":"20 p.","startPage":"4954","endPage":"4973","ipdsId":"IP-099154","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":499841,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/678588abb83c4b249680e0982160eaf7","text":"External Repository"},{"id":437460,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QSFDJN","text":"USGS data release","linkHelpText":"Salinity yield modeling spatial data for the Upper Colorado River Basin, USA"},{"id":367766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.89697265625,\n 41.062786068733026\n ],\n [\n -108.687744140625,\n 42.17154633452751\n ],\n [\n -110.225830078125,\n 43.42100882994726\n ],\n [\n -110.687255859375,\n 43.0287452513488\n ],\n [\n -110.863037109375,\n 41.09591205639546\n ],\n [\n -111.456298828125,\n 39.985538414809746\n ],\n [\n -112.203369140625,\n 36.76529191711624\n ],\n [\n -111.09374999999999,\n 36.146746777814364\n ],\n [\n -108.643798828125,\n 35.43381992014202\n ],\n [\n -107.64404296875,\n 36.33282808737917\n ],\n [\n -106.754150390625,\n 37.142803443716836\n ],\n [\n -106.6552734375,\n 37.84015683604136\n ],\n [\n -106.80908203125,\n 38.324420427006544\n ],\n [\n -106.424560546875,\n 39.32579941789298\n ],\n [\n -106.424560546875,\n 40.25437660372649\n ],\n [\n -106.89697265625,\n 41.062786068733026\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":219281,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":771865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ely, Christopher P. 0000-0001-5276-5046","orcid":"https://orcid.org/0000-0001-5276-5046","contributorId":219282,"corporation":false,"usgs":true,"family":"Ely","given":"Christopher P.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":219283,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":771868,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203783,"text":"70203783 - 2019 - Context matters: Using reinforcement learning to develop human-readable, state-dependent outbreak response policies","interactions":[],"lastModifiedDate":"2019-06-13T08:03:32","indexId":"70203783","displayToPublicDate":"2019-05-20T10:26:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3048,"text":"Philosophical Transactions of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Context matters: Using reinforcement learning to develop human-readable, state-dependent outbreak response policies","docAbstract":"The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) has been used to develop machine-readable context-dependent solutions for complex problems with many possible realisations ranging from video-games to the game of Go. RL could be a valuable tool to generate context-dependent policies for outbreak response, though translating the resulting policies into simple rules that can be read and interpreted by human decision-makers remains a challenge. Here we illustrate the application of RL to the development of context-dependent outbreak response policies to minimise outbreaks of foot-and-mouth disease. We show that control based on the resulting context-dependent policies, which adapt interventions to the specific outbreak, result in smaller outbreaks than static policies. We further illustrate two approaches for translating the complex machine-readable policies into simple heuristics that can be evaluated by human decision-makers.","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rstb.2018.0277","usgsCitation":"Probert, W.J., Lakkur, S., Fonnesbeck, C.J., Shea, K., Runge, M.C., Tildesley, M.J., and Ferrari, M.J., 2019, Context matters: Using reinforcement learning to develop human-readable, state-dependent outbreak response policies: Philosophical Transactions of the Royal Society B: Biological Sciences, v. 374, no. 1776, p. 1-9, https://doi.org/10.1098/rstb.2018.0277.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-101594","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467608,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rstb.2018.0277","text":"Publisher Index Page"},{"id":364606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"374","issue":"1776","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Probert, William J. 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M.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":764114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lakkur, Sandya","contributorId":216184,"corporation":false,"usgs":false,"family":"Lakkur","given":"Sandya","email":"","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":764115,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fonnesbeck, Christopher J","contributorId":216185,"corporation":false,"usgs":false,"family":"Fonnesbeck","given":"Christopher","email":"","middleInitial":"J","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":764116,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shea, Katriona 0000-0002-7607-8248","orcid":"https://orcid.org/0000-0002-7607-8248","contributorId":193646,"corporation":false,"usgs":false,"family":"Shea","given":"Katriona","email":"","affiliations":[],"preferred":false,"id":764117,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":764113,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tildesley, Michael J.","contributorId":126971,"corporation":false,"usgs":false,"family":"Tildesley","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6620,"text":"University of Nottingham, School of Biology","active":true,"usgs":false}],"preferred":false,"id":764118,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ferrari, Matthew J. 0000-0001-5251-8168","orcid":"https://orcid.org/0000-0001-5251-8168","contributorId":216186,"corporation":false,"usgs":false,"family":"Ferrari","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":764119,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}