{"pageNumber":"313","pageRowStart":"7800","pageSize":"25","recordCount":40783,"records":[{"id":70205570,"text":"70205570 - 2019 - A framework for quantifying resilience to forest disturbance","interactions":[],"lastModifiedDate":"2019-09-27T09:42:32","indexId":"70205570","displayToPublicDate":"2019-09-18T08:36:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A Framework for Quantifying Resilience to Forest Disturbance","title":"A framework for quantifying resilience to forest disturbance","docAbstract":"(Bradford) The concept of ecological resilience is an invaluable tool to assess the risk of state transitions and predict the impact of management on an ecosystem’s response to future disturbances. However, resilience is difficult to quantify and the factors contributing to resilience are often unknown in systems subject to multiple disturbances. We present a framework to assess the possibility of ponderosa pine and dry mixed conifer forests to be resilient to future disturbance by combining indicators of resistance to fire, insect, and drought disturbances using data from the Rio Tusas-Lower San Antonio landscape in northern New Mexico. On average, the dry mixed conifer forests received a higher score for potential resilience than the ponderosa pine (5.24 and 4.07, respectively, out of nine possible points). Canopy bulk density was the most important driver of the overall score in the dry mixed conifer type. In the ponderosa pine type, overall basal area and canopy bulk density were the strongest drivers of the overall score. These indicators have the greatest impact on the resilience score and provide the most effective targets for management to increase the possibility of resilience in these forest types. We validated the model in both forest types by comparing individual stands to an ‘ideal’ score for a stand that is within the historic range of variation (HRV) and confirmed that stands outside of HRV had a low possibility of resilience and stands that had received restoration-based treatments were more likely to be resilient. Our results provide evidence that the changes to forest structure and species composition that have occurred since the onset of fire exclusion have degraded the potential of these forest types to be resilient to future fire, insect, and drought-related disturbances. By modifying disturbances and resilience indicator thresholds this model can be applied to assess resilience across various regions and ecosystem types.","language":"English","publisher":"Frontiers","doi":"10.3389/ffgc.2019.00056","usgsCitation":"Bryant, T., Waring, K., Sanchez, M., and Bradford, J.B., 2019, A framework for quantifying resilience to forest disturbance: Frontiers in Forests and Global Change, v. 2, no. 56, 14 p., https://doi.org/10.3389/ffgc.2019.00056.","productDescription":"14 p.","ipdsId":"IP-101395","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":459779,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2019.00056","text":"Publisher Index Page"},{"id":367718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"56","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Bryant, Timothy","contributorId":219222,"corporation":false,"usgs":false,"family":"Bryant","given":"Timothy","email":"","affiliations":[{"id":39973,"text":"School of Forestry, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":771695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waring, Kristen","contributorId":219223,"corporation":false,"usgs":false,"family":"Waring","given":"Kristen","email":"","affiliations":[{"id":39973,"text":"School of Forestry, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":771696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanchez, Meador","contributorId":219224,"corporation":false,"usgs":false,"family":"Sanchez","given":"Meador","email":"","affiliations":[{"id":39973,"text":"School of Forestry, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":771697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":771694,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263729,"text":"70263729 - 2019 - Length-scale-dependent relationships between VS30 and topographic slopes in southern California","interactions":[],"lastModifiedDate":"2025-02-20T16:52:35.614349","indexId":"70263729","displayToPublicDate":"2019-09-17T10:44:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Length-scale-dependent relationships between VS30 and topographic slopes in southern California","title":"Length-scale-dependent relationships between VS30 and topographic slopes in southern California","docAbstract":"

In engineering seismology, the time‐averaged shear‐wave velocity (⁠VS⁠) of the upper 30 m of the crust (⁠VS30⁠) is the primary parameter used in ground‐motion models to predict seismic site effects. ⁠VS30⁠ is typically derived from in situ recordings of VS⁠, although proxy‐based approaches (using geologic and/or geomorphometric classifications) are provisionally adopted when measurement‐based ⁠VS30⁠ are sparse or not readily available. Despite the acceptance of proxy approaches, there are limited studies that examine the empirical relationships between ⁠VS30⁠ and topographic attributes measured from various length scales and different resolutions of the digital elevation model. In this study, we examine the relationships between compiled ⁠VS30⁠ measurements from 218 sites in southern California and topographic metrics of slope and relief measured over various length scales. We find that the correlations between topographic metrics and ⁠VS30⁠ are weak but statistically significant. The correlations are improved when topographic slopes and relief are measured over length scales longer than typical hillslopes and ⁠VS30⁠ sites are separated by different geologic groups. This is likely because ⁠VS30⁠⁠, especially on the rock sites, is better reflected in topographic metrics that capture large‐scale topographic relief, as well as landscape positions such as hilltops and valley bottoms.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120190076","usgsCitation":"Lin, J., Moon, S., Yong, A., Meng, L., and Davies, P., 2019, Length-scale-dependent relationships between VS30 and topographic slopes in southern California: Bulletin of the Seismological Society of America, v. 109, no. 6, p. 2614-2625, https://doi.org/10.1785/0120190076.","productDescription":"12 p.","startPage":"2614","endPage":"2625","ipdsId":"IP-104595","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119,\n 34.8\n ],\n [\n -119,\n 33.875\n ],\n [\n -116.25,\n 33.875\n ],\n [\n -116.25,\n 34.8\n ],\n [\n -119,\n 34.8\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"109","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Jessica","contributorId":351141,"corporation":false,"usgs":false,"family":"Lin","given":"Jessica","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":927974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moon, Seulgi 0000-0001-5207-1781","orcid":"https://orcid.org/0000-0001-5207-1781","contributorId":264625,"corporation":false,"usgs":false,"family":"Moon","given":"Seulgi","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":927975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yong, Alan 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":204730,"corporation":false,"usgs":true,"family":"Yong","given":"Alan","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meng, Lingseng","contributorId":351143,"corporation":false,"usgs":false,"family":"Meng","given":"Lingseng","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":927977,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davies, Paul","contributorId":351144,"corporation":false,"usgs":false,"family":"Davies","given":"Paul","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":927978,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205389,"text":"70205389 - 2019 - Sediment and organic carbon transport and deposition driven by internal tides along Monterey Canyon, offshore California","interactions":[],"lastModifiedDate":"2019-11-13T13:40:12","indexId":"70205389","displayToPublicDate":"2019-09-17T08:43:48","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1370,"text":"Deep-Sea Research Part I: Oceanographic Research Papers","active":true,"publicationSubtype":{"id":10}},"title":"Sediment and organic carbon transport and deposition driven by internal tides along Monterey Canyon, offshore California","docAbstract":"Submarine canyons provide globally important conduits for sediment and organic carbon transport into the deep-sea. Using a novel dataset from Monterey Canyon, offshore central California, that includes an extensive array of water column sampling devices, we address how fine-grained sediment and organic carbon are transported, mixed, fractionated, and buried along a submarine canyon. Anderson-type sediment traps were deployed 10 to 300 meters above the seafloor on moorings anchored between 278–1849 m water depths along the axial channel of Monterey Canyon during three consecutive 6-month deployments (2015–2017). Tidal currents within the canyon suspended and transported fine-grained sediment and organic carbon that were captured in sediment traps, which show apparent patterns and composition of sediment and organic carbon transport along the canyon. High sediment accumulation rates in traps increased up-canyon and near the seafloor with fine-scale (<1 cm) layering that was increasingly distinctive in CT scans. There was no along-canyon trend in the organic carbon composition (percent modern carbon and isotopic signatures) among trap locations, suggesting mixing. Organic carbon content (weight percent total organic carbon) and excess 210Pb activities (dpm/g) increased down-canyon, reflecting reduced flux of sediment and organic carbon into traps. Differing organic carbon signatures in traps compared with previous measurements of seabed deposits along Monterey Canyon suggest that canyon deposits may not reflect organic carbon available to organisms and transported through the canyon with internal tides. Organic carbon burial efficiency estimates from comparing core and trap samples are low (~26% or much less), suggesting that the modern upper Monterey Canyon may not be an effective sink for carbon in biogeochemical and CO2 cycling. Organic carbon isotopic signatures appear more marine in traps that sample from the water column than in cores that sample seafloor deposits, likely owing to the influence of sediment density flow events on deposits and preferential consumption of relatively fresh marine organic carbon on the seafloor that was largely prevented in preserved traps. Along-canyon sediment and organic carbon transport by internal tides likely occurs in many modern global submarine canyons, but canyon deposits and remaining organic carbon appear to preferentially reflect episodic sediment density flow events unrelated to internal tides. This study provides a quantified example and conceptual schematic for internal-tide-related sediment and organic carbon transport, mixing, and burial trends along a submarine canyon that are likely to have common global aspects.","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr.2019.103108","usgsCitation":"Maier, K.L., Rosenberger, K.J., Paull, C.K., Gwiazda, R., Gales, J., Lorenson, T., Barry, J., Talling, P.J., McGann, M., Xu, J., Lundsten, E.M., Anderson, K., Litvin, S., Parsons, D., Clare, M., Simmons, S., Sumner, E., and Cartigny, M.J., 2019, Sediment and organic carbon transport and deposition driven by internal tides along Monterey Canyon, offshore California: Deep-Sea Research Part I: Oceanographic Research Papers, v. 153, 103108, https://doi.org/10.1016/j.dsr.2019.103108.","productDescription":"103108","ipdsId":"IP-103300","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459788,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1016/j.dsr.2019.103108>).","text":"Publisher Index Page"},{"id":367448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Monterey Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.30804443359375,\n 36.50301312197295\n ],\n [\n -121.73950195312499,\n 36.50301312197295\n ],\n [\n -121.73950195312499,\n 37.070519031125826\n ],\n [\n -122.30804443359375,\n 37.070519031125826\n ],\n [\n -122.30804443359375,\n 36.50301312197295\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"153","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Maier, Katherine L. 0000-0003-2908-3340","orcid":"https://orcid.org/0000-0003-2908-3340","contributorId":206421,"corporation":false,"usgs":false,"family":"Maier","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":771004,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Kurt J. 0000-0002-5185-5776 krosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5185-5776","contributorId":140453,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Kurt","email":"krosenberger@usgs.gov","middleInitial":"J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":771003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":771005,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gwiazda, Roberto","contributorId":147193,"corporation":false,"usgs":false,"family":"Gwiazda","given":"Roberto","email":"","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":771006,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gales, Jenny","contributorId":216506,"corporation":false,"usgs":false,"family":"Gales","given":"Jenny","email":"","affiliations":[{"id":39461,"text":"University of Plymouth, UK","active":true,"usgs":false}],"preferred":false,"id":771007,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":771008,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barry, James P.","contributorId":140935,"corporation":false,"usgs":false,"family":"Barry","given":"James P.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":771009,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Talling, Peter J.","contributorId":195515,"corporation":false,"usgs":false,"family":"Talling","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":771010,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":771011,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Xu, Jingping","contributorId":195514,"corporation":false,"usgs":false,"family":"Xu","given":"Jingping","affiliations":[],"preferred":false,"id":771012,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lundsten, Eve M.","contributorId":147191,"corporation":false,"usgs":false,"family":"Lundsten","given":"Eve","email":"","middleInitial":"M.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":771013,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Anderson, Krystle","contributorId":147192,"corporation":false,"usgs":false,"family":"Anderson","given":"Krystle","email":"","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":771014,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Litvin, Steven","contributorId":219014,"corporation":false,"usgs":false,"family":"Litvin","given":"Steven","email":"","affiliations":[{"id":37324,"text":"Monterey Bay Aquarium Research Institute","active":true,"usgs":false}],"preferred":false,"id":771015,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Parsons, Daniel","contributorId":216508,"corporation":false,"usgs":false,"family":"Parsons","given":"Daniel","affiliations":[{"id":39462,"text":"University of Hull, UK","active":true,"usgs":false}],"preferred":false,"id":771016,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Clare, Michael","contributorId":213585,"corporation":false,"usgs":false,"family":"Clare","given":"Michael","email":"","affiliations":[{"id":38805,"text":"National Oceanography Centre, University of Southampton Waterfront Campus, European Way, Southampton, SO14 3ZH, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":771017,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Simmons, Stephen","contributorId":216507,"corporation":false,"usgs":false,"family":"Simmons","given":"Stephen","affiliations":[{"id":39462,"text":"University of Hull, UK","active":true,"usgs":false}],"preferred":false,"id":771018,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sumner, Esther J.","contributorId":147189,"corporation":false,"usgs":false,"family":"Sumner","given":"Esther J.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":771019,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Cartigny, Matthieu J.B.","contributorId":195513,"corporation":false,"usgs":false,"family":"Cartigny","given":"Matthieu","email":"","middleInitial":"J.B.","affiliations":[],"preferred":false,"id":771020,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70205435,"text":"70205435 - 2019 - Reporting the limits of detection and quantification for environmental DNA assays","interactions":[],"lastModifiedDate":"2020-08-04T13:44:26.547842","indexId":"70205435","displayToPublicDate":"2019-09-16T17:33:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5840,"text":"Environmental DNA","active":true,"publicationSubtype":{"id":10}},"title":"Reporting the limits of detection and quantification for environmental DNA assays","docAbstract":"

Background

Environmental DNA (eDNA) analysis is increasingly being used to detect the presence and relative abundance of rare species, especially invasive or imperiled aquatic species. The rapid progress in the eDNA field has resulted in numerous studies impacting conservation and management actions. However, standardization of eDNA methods and reporting across the field is yet to be fully established, with one area being the calculation and interpretation of assay limit of detection (LOD) and limit of quantification (LOQ).

Aims

Here, we propose establishing consistent methods for determining and reporting of LOD and LOQ for single‐species quantitative PCR (qPCR) eDNA studies.

Materials & Methods/ Results

We utilize datasets from multiple cooperating laboratories to demonstrate both a discrete threshold approach and a curve‐fitting modeling approach for determining LODs and LOQs for eDNA qPCR assays. We also provide details of an R script developed and applied for the modeling method.

Discussion/Conclusions

Ultimately, standardization of how LOD and LOQ are determined, interpreted, and reported for eDNA assays will allow for more informed interpretation of assay results, more meaningful interlaboratory comparisons of experiments, and enhanced capacity for assessing the relative technical quality and performance of different eDNA qPCR assays.

","language":"English","publisher":"Wiley","doi":"10.1002/edn3.29","usgsCitation":"Klymus, K.E., Merkes, C.M., Allison, M.J., Goldberg, C.S., Helbing, C.C., Hunter, M., Jackson, C., Lance, R.F., Mangan, A., Monroe, E.M., Piaggio, A.J., Stokdyk, J.P., Wilson, C.C., and Richter, C.A., 2019, Reporting the limits of detection and quantification for environmental DNA assays: Environmental DNA, v. 2, no. 3, p. 271-282, https://doi.org/10.1002/edn3.29.","productDescription":"12 p.","startPage":"271","endPage":"282","ipdsId":"IP-108387","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":459793,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/edn3.29","text":"Publisher Index Page"},{"id":437336,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AKHU1R","text":"USGS data release","linkHelpText":"Reporting the limits of detection (LOD) and quantification (LOQ) for environmental DNA assays: Data"},{"id":367527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":771153,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":771154,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allison, Michael J.","contributorId":219053,"corporation":false,"usgs":false,"family":"Allison","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":39951,"text":"Department of Biochemistry and Microbiology, University of Victoria, 3800 Finnerty Road, Victoria, British Columbia, V8P 5C2, Canada","active":true,"usgs":false}],"preferred":false,"id":771155,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldberg, Caren S.","contributorId":76879,"corporation":false,"usgs":false,"family":"Goldberg","given":"Caren","email":"","middleInitial":"S.","affiliations":[{"id":5132,"text":"Washington State University, Pullman","active":true,"usgs":false}],"preferred":false,"id":771156,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Helbing, Caren C.","contributorId":219054,"corporation":false,"usgs":false,"family":"Helbing","given":"Caren","email":"","middleInitial":"C.","affiliations":[{"id":39951,"text":"Department of Biochemistry and Microbiology, University of Victoria, 3800 Finnerty Road, Victoria, British Columbia, V8P 5C2, Canada","active":true,"usgs":false}],"preferred":false,"id":771157,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":207589,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":771158,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jackson, Craig 0000-0003-4023-0276 cjackson@usgs.gov","orcid":"https://orcid.org/0000-0003-4023-0276","contributorId":192276,"corporation":false,"usgs":true,"family":"Jackson","given":"Craig","email":"cjackson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":771159,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lance, Richard F.","contributorId":176872,"corporation":false,"usgs":false,"family":"Lance","given":"Richard","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":771160,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mangan, Anna M.","contributorId":219055,"corporation":false,"usgs":false,"family":"Mangan","given":"Anna M.","affiliations":[{"id":39952,"text":"Wildlife Genetics Lab, U. 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Department of Agriculture, Animal and Plant Health Inspection Service, Wildlife Services, National Wildlife Research Center, 4101 LaPorte Ave., Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":771161,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Monroe, Emy M.","contributorId":219056,"corporation":false,"usgs":false,"family":"Monroe","given":"Emy","email":"","middleInitial":"M.","affiliations":[{"id":39953,"text":"Whitney Genetics Laboratory, Midwest Fisheries Center, U.S. Fish and Wildlife Service, 555 Lester Ave., Onalaska, WI","active":true,"usgs":false}],"preferred":false,"id":771162,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Piaggio, Antoinette J.","contributorId":174782,"corporation":false,"usgs":false,"family":"Piaggio","given":"Antoinette","email":"","middleInitial":"J.","affiliations":[{"id":12434,"text":"USDA, Wildlife Services, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":771163,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771164,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wilson, Chris C.","contributorId":149385,"corporation":false,"usgs":false,"family":"Wilson","given":"Chris","email":"","middleInitial":"C.","affiliations":[{"id":17723,"text":"3Aquatic Research Section, Ontario Ministry of Natural Resources, Trent University","active":true,"usgs":false}],"preferred":false,"id":771165,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":771166,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70207301,"text":"70207301 - 2019 - Monitoring plans for Louisiana’s system-wide assessment and monitoring program (SWAMP). Version IV","interactions":[],"lastModifiedDate":"2019-12-16T12:34:16","indexId":"70207301","displayToPublicDate":"2019-09-16T11:54:07","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5883,"text":"Cooperator Report","active":true,"publicationSubtype":{"id":1}},"title":"Monitoring plans for Louisiana’s system-wide assessment and monitoring program (SWAMP). Version IV","docAbstract":"

The System-Wide Assessment and Monitoring Program (SWAMP) has been envisioned as a long-term monitoring program to ensure a comprehensive network of coastal data collection activities is in place to support the development, implementation, and adaptive management of the coastal protection and restoration program within coastal Louisiana. The Coastwide Reference Monitoring System (CRMS) and Barrier Island Comprehensive Monitoring (BICM) programs have been implemented under SWAMP, while other aspects of system dynamics, including offshore and inland water-body boundary conditions, nontidal freshwater habitats, riverine conditions, risk status, and protection performance, are not presently the subject of CPRA-coordinated (Coastal Protection and Restoration Authority) monitoring. In order to implement these additional aspects of SWAMP, CPRA partnered with The Water Institute of the Gulf and others to develop 1) a programmatic monitoring plan for evaluating the effectiveness of the coastal protection and restoration program on a coastwide scale, and 2) basinwide monitoring plans that will incorporate the elements of the programmatic plan with specific data collection activities designed to capture effects within the basin. Monitoring plans were developed for Barataria Basin, Pontchartrain Region (includes Breton Sound, Pontchartrain and Mississippi River Delta Basins), and the western basins (Calcasieu-Sabine, Mermentau, Teche-Vermilion, Atchafalaya, and Terrebonne) for both the natural and human systems using a process to identify the monitoring variables, objectives, and sampling design. The monitoring variables and objectives identified fall under the general categories of weather and climate, biotic integrity, water quality, hydrology, physical terrain, population and demographics, housing and community characteristics, economy and employment, ecosystem dependency, residential properties protection, and critical infrastructure and essential services protection. A rigorous statistical analysis, examination of modeling needs, and thorough reviews of previous planning and monitoring efforts were conducted to develop the sampling designs for the natural and human system monitoring plans. The plan relies heavily on the use of existing data, thus, coordination with other agencies (e.g., LDEQ , LDWF) and CPRA’s existing monitoring programs (e.g., BICM, CRMS) is critical to the plan’s success. Implementation of the plans will require development of quality control and quality assurance protocols, specific standardized operating procedures for each of the data collection efforts, a data management plan, and a reporting framework to contribute to decision making and reducing uncertainty in management actions.

","language":"English","publisher":"Water Institute of the Gulf","usgsCitation":"Hemmerling, S., Baustian, M., Bienn, H., Dausman, A., Grace, A., Grimley, L., McInnis, A., Vingiello, M., Vu, H., Sable, S., Gentile, B., Lafargue, P., Hijuelos, A., Piazza, S., Stagg, C., Raynie, R., Haywood, E., and Khalid, S., 2019, Monitoring plans for Louisiana’s system-wide assessment and monitoring program (SWAMP). Version IV: Cooperator Report, xiii, 235 p.","productDescription":"xiii, 235 p.","ipdsId":"IP-109812","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"links":[{"id":370311,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cims.coastal.louisiana.gov/RecordDetail.aspx?Root=0&sid=23567"},{"id":370312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.71337890625,\n 30.282788098216884\n ],\n [\n -93.702392578125,\n 30.12612436422458\n ],\n [\n -93.75732421875,\n 30.0262995822237\n ],\n [\n -93.91113281249999,\n 29.79298413547051\n ],\n [\n -93.8177490234375,\n 29.6880527498568\n ],\n [\n -93.22448730468749,\n 29.754839972510933\n ],\n [\n 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study","docAbstract":"Beaches and islands provide economic value to humans and critical habitat for breeding and foraging wildlife. These ecosystems, however, are being severely impacted by global climate change and sea level rise through increased erosion and frequency of inundation. The case study presented here aimed to document island loss in the Chesapeake Bay and Delmarva coastal bay region of the United States using image processing techniques within a GIS from 1986 to 2016. Satellite imagery from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) sensors were processed within ArcMap 10.5 to determine spatial and temporal trends in island and beach habitat. Calculation of unweighted Cohen’s Kappa showed that classified scenes were, on average, within the range of moderate agreement between the classified Landsat scenes and the validation imagery within Google Earth (0.539). Recommendations regarding existing beach habitat management and future supplementation were created based on these results. From 1986 to 2016, island area declined by over 1,200 hectares (ha) with agriculture/open field (all open vegetated spaces) declining by nearly 82% and beach, surprisingly, increasing nearly 2%. This study was the first to document Chesapeake Bay region-wide island loss beyond the mid-2000s. The accuracy of this study was limited slightly by the 30 m spatial resolution of the imagery used. This technique may be best suited for documenting trends on large islands and along the mainland coastline.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rsase.2019.100265","usgsCitation":"Marban, P., Mullinax, J.M., Resop, J.P., and Prosser, D.J., 2019, Assessing beach and island habitat loss in the Chesapeake Bay and Delmarva coastal bay region, USA, through processing of Landsat TM and OLI imagery: A case study: Remote Sensing Applications: Society and Environment, v. 16, 100265, 10 p., https://doi.org/10.1016/j.rsase.2019.100265.","productDescription":"100265, 10 p.","ipdsId":"IP-106458","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":370203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay, Delmarva Coastal Bays","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.0638427734375,\n 39.65222681530652\n ],\n [\n -76.6790771484375,\n 39.2832938689385\n ],\n [\n -77.14599609375,\n 38.272688535980976\n ],\n [\n -76.607666015625,\n 36.83127162140714\n ],\n [\n -75.9814453125,\n 36.787291466820015\n ],\n [\n -74.8553466796875,\n 38.40194908237822\n ],\n [\n -75.0640869140625,\n 38.84826438869913\n ],\n [\n -76.0638427734375,\n 39.65222681530652\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marban, Paul R.","contributorId":221168,"corporation":false,"usgs":false,"family":"Marban","given":"Paul R.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":777269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":777270,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Resop, Jonathan P.","contributorId":221169,"corporation":false,"usgs":false,"family":"Resop","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":777271,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":777268,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207519,"text":"70207519 - 2019 - The first 3D conductivity model of the contiguous US: Reflections on geologic structure and application to induction hazards","interactions":[],"lastModifiedDate":"2019-12-23T07:54:17","indexId":"70207519","displayToPublicDate":"2019-09-16T07:47:49","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"8","title":"The first 3D conductivity model of the contiguous US: Reflections on geologic structure and application to induction hazards","docAbstract":"Estimation of ground level geoelectric fields has been identified by the National Space Weather Action Plan as a key component of assessment and mitigation of space weather impacts on critical infrastructure. Estimates of spatially and temporally variable electric fields are used to generate statistically based hazard maps and show promise toward monitoring and responding to geomagnetic disturbances in near real‐time. One approach to geoelectric field estimation is to employ three‐dimensional (3D) Earth conductivity models. These data‐constrained conductivity models are the results of regional magnetotelluric inversions based primarily on NSF’s Earthscope USArray impedances, which to date cover ~60% of the contiguous United States. Here, we present the first‐ever composite conductivity model of the contiguous United States and describe its compilation from 3D regional conductivity models, a global mantle conductivity model, offshore bathymetry, and sediment thickness data. We discuss structures within the conductivity model and how they relate to the complex geologic tapestry of the continent. Finally, we discuss the utility of this synthesis model for estimation and mitigation of geomagnetically induced currents.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geomagnetically induced currents from the Sun to the power grid","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"American Geophysical Union","doi":"10.1002/9781119434412.ch8","usgsCitation":"Kelbert, A., Bedrosian, P.A., and Murphy, B., 2019, The first 3D conductivity model of the contiguous US: Reflections on geologic structure and application to induction hazards, chap. 8 of Geomagnetically induced currents from the Sun to the power grid, p. 127-151, https://doi.org/10.1002/9781119434412.ch8.","productDescription":"25 p.","startPage":"127","endPage":"151","ipdsId":"IP-100625","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":370629,"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 -118.47656249999999,\n 32.24997445586331\n ],\n [\n -95.2734375,\n 23.885837699862005\n ],\n [\n -85.78125,\n 29.84064389983441\n ],\n [\n -79.8046875,\n 23.563987128451217\n ],\n [\n -79.8046875,\n 29.22889003019423\n ],\n [\n -74.8828125,\n 34.016241889667015\n ],\n [\n -66.09375,\n 42.293564192170095\n ],\n [\n -67.8515625,\n 47.27922900257082\n ],\n [\n -81.9140625,\n 42.293564192170095\n ],\n [\n -83.671875,\n 46.800059446787316\n ],\n [\n -93.1640625,\n 48.922499263758255\n ],\n [\n -122.6953125,\n 50.064191736659104\n ],\n [\n -125.5078125,\n 48.922499263758255\n ],\n [\n -126.5625,\n 40.17887331434696\n ],\n [\n -118.47656249999999,\n 32.24997445586331\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-16","publicationStatus":"PW","contributors":{"authors":[{"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":778346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":778347,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Benjamin S. 0000-0001-7636-3711","orcid":"https://orcid.org/0000-0001-7636-3711","contributorId":221483,"corporation":false,"usgs":false,"family":"Murphy","given":"Benjamin S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":778348,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208839,"text":"70208839 - 2019 - Moving at the speed of flight: Dabbling duck-movement rates and the relationship with electronic tracking interval","interactions":[],"lastModifiedDate":"2020-03-03T07:37:16","indexId":"70208839","displayToPublicDate":"2019-09-16T07:33:37","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Moving at the speed of flight: Dabbling duck-movement rates and the relationship with electronic tracking interval","docAbstract":"Context. Effective wildlife management requires information on habitat and resource needs, which can be estimated with movement information and modelling energetics. One necessary component of avian models is flight speeds at multiple temporal scales. Technology has limited the ability to accurately assess flight speeds, leading to estimates of questionable accuracy, many of which have not been updated in almost a century.\n\nAims. We aimed to update flight speeds of ducks, and differentiate between migratory and non-migratory flight speeds, a detail that was unclear in previous estimates. We also analysed the difference in speeds of migratory and non-migratory flights, and quantified how data collected at different temporal intervals affected estimates of flight speed.\n\nMethods. We tracked six California dabbling duck species with high spatio-temporal resolution GPS–GSM transmitters, calculated speeds of different flight types, and modelled how estimates varied by flight and data interval (30 min to 6 h).\n\nKey results. Median migratory speeds were faster (but non-significant) for the larger mallard (Anas platyrhynchos; 82.5 km h–1), northern pintail (Anas acuta; 79.0 km h–1) and gadwall (Mareca strepera; 70.6 km h–1), than the smaller-bodied northern shoveler (Spatula clypeata; 65.7 km h–1), cinnamon teal (Spatula cyanoptera; 63.5 km h–1) and American wigeon (Mareca Americana; 52 km h–1). Migratory flights were faster than non-migratory flights for all species and speeds were consistently slower with an increasing data interval.\n\nImplications. The need to balance time and energy requirements may drive different speeds for migratory and non-migratory flights. Lower speeds at longer intervals are likely to be due to a greater proportion of ‘loafing’ time included in flighted segments, demonstrating that data acquired at different intervals provide a means to evaluate and estimate behaviours that influence speed estimation. Shorter-interval data should be the most accurate, but longer-interval data may be easier to collect over lengthier timeframes, so it may be expedient to trade-off a degree of accuracy in broad-scale studies for the larger dataset. Our updated flight speeds for dabbling duck species can be used to parameterise and validate energetics models, guide management decisions regarding optimal habitat distribution, and, ultimately, improve conservation management of wetlands for waterfowl.","language":"English","publisher":"CSIRO","doi":"10.1071/WR19028","usgsCitation":"McDuie, F., Casazza, M.L., Keiter, D.A., Overton, C.T., Herzog, M.P., Feldheim, C.L., and Ackerman, J., 2019, Moving at the speed of flight: Dabbling duck-movement rates and the relationship with electronic tracking interval: Wildlife Research, v. 46, no. 6, p. 533-543, https://doi.org/10.1071/WR19028.","productDescription":"11 p.","startPage":"533","endPage":"543","ipdsId":"IP-102666","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":459806,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr19028","text":"Publisher Index Page"},{"id":372828,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McDuie, Fiona 0000-0002-1948-5613","orcid":"https://orcid.org/0000-0002-1948-5613","contributorId":222936,"corporation":false,"usgs":true,"family":"McDuie","given":"Fiona","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keiter, David A.","contributorId":176521,"corporation":false,"usgs":false,"family":"Keiter","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":783586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Overton, Cory T. 0000-0002-5060-7447 coverton@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-7447","contributorId":3262,"corporation":false,"usgs":true,"family":"Overton","given":"Cory","email":"coverton@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783588,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Feldheim, Cliff L.","contributorId":206561,"corporation":false,"usgs":false,"family":"Feldheim","given":"Cliff","email":"","middleInitial":"L.","affiliations":[{"id":37342,"text":"California Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":783589,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322 jackerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":147078,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua T.","email":"jackerman@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":783590,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70205335,"text":"70205335 - 2019 - Characterizing large earthquakes before rupture is complete","interactions":[],"lastModifiedDate":"2019-09-17T07:50:15","indexId":"70205335","displayToPublicDate":"2019-09-16T00:24:28","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing large earthquakes before rupture is complete","docAbstract":"Whether large and very large earthquakes are distinguishable from each other early on in the rupture process has been a subject often debated over the past several decades. Studies have shown that the frequency content of radiated seismic energy in the first few seconds of an earthquake scales with the final magnitude of the event, implying determinism. Other studies have shown that the recordings of ground displacement from small-to-moderate sized earthquakes are indistinguishable, and thus earthquakes share a universal early rupture process. Regardless of how earthquakes start, however, at some point in the rupture process events of different sizes must be distinguishable from one another. If that difference occurs early - i.e., before the rupture duration of the smaller event - this implies that earthquakes demonstrate some level of determinism. Here we show through analysis of a large database of source time functions and near-source displacement records that after an initiation phase, ruptures of M7-9 earthquakes organize into a slip pulse, the kinematic properties of which scale with magnitude. As such, early in the rupture process - after about 10s - large and very large earthquakes demonstrate different properties and can thus be distinguished.","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.aav2032","usgsCitation":"Melgar, D., and Hayes, G.P., 2019, Characterizing large earthquakes before rupture is complete: Science Advances, v. 5, no. 5, eaav2032, https://doi.org/10.1126/sciadv.aav2032.","productDescription":"eaav2032","onlineOnly":"Y","ipdsId":"IP-105666","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":459808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.aav2032","text":"Publisher Index Page"},{"id":367424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Melgar, Diego","contributorId":193030,"corporation":false,"usgs":false,"family":"Melgar","given":"Diego","email":"","affiliations":[],"preferred":false,"id":770877,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770878,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205336,"text":"70205336 - 2019 - Global earthquake response with imaging geodesy: recent examples from the USGS NEIC","interactions":[],"lastModifiedDate":"2019-09-16T09:14:10","indexId":"70205336","displayToPublicDate":"2019-09-15T23:12:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Global earthquake response with imaging geodesy: recent examples from the USGS NEIC","docAbstract":"The U.S. Geological Survey National Earthquake Information Center leads real-time efforts to provide rapid and accurate assessments of the impacts of global earthquakes, including estimates of ground shaking, ground failure, and the resulting human impacts. These efforts primarily rely on analysis of the seismic wavefield to characterize the source of the earthquake, which in turn informs a suite of disaster response products such as ShakeMap and PAGER. In recent years, the proliferation of rapidly acquired and openly available in-situ and remotely sensed geodetic observations has opened new avenues for responding to earthquakes around the world in the days following significant events. Geodetic observations, particularly from interferometric synthetic aperture radar (InSAR) and satellite optical imagery, provide a means to robustly constrain the dimensions and spatial complexity of earthquakes beyond what is typically possible with seismic observations alone. Here, we document recent cases where geodetic observations contributed important information to earthquake response efforts – from informing and validating seismically-derived source models to independently constraining earthquake impact products – and the conditions under which geodetic observations improve earthquake response products. We use examples from the 2013 Mw7.7 Baluchistan, Pakistan, 2014 Mw6.0 Napa, California, 2015 Mw7.8 Gorkha, Nepal, and 2018 Mw7.5 Palu, Indonesia earthquakes to highlight the varying ways geodetic observations have contributed to earthquake response efforts at the NEIC. We additionally provide a synopsis of the workflows implemented for geodetic earthquake response. As remote sensing geodetic observations become increasingly available and the frequency of satellite acquisitions continues to increase, operational earthquake geodetic imaging stands to make critical contributions to natural disaster response efforts around the world.","language":"English","publisher":"MDPI","doi":"10.3390/rs11111357","usgsCitation":"Barnhart, W.D., Hayes, G.P., and Wald, D.J., 2019, Global earthquake response with imaging geodesy: recent examples from the USGS NEIC: Remote Sensing, v. 11, no. 11, 1357, 20 p., https://doi.org/10.3390/rs11111357.","productDescription":"1357, 20 p.","onlineOnly":"Y","ipdsId":"IP-108767","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":459810,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11111357","text":"Publisher Index Page"},{"id":367423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, William D. wbarnhart@usgs.gov","contributorId":5299,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770881,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205360,"text":"70205360 - 2019 - Characterization and evaluation of controls on post-fire streamflow response across western U.S. watersheds","interactions":[],"lastModifiedDate":"2019-09-16T09:14:57","indexId":"70205360","displayToPublicDate":"2019-09-15T23:03:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Characterization and evaluation of controls on post-fire streamflow response across western U.S. watersheds","docAbstract":"This research investigates the impact of wildfires on watershed flow regimes, specifically focusing on evaluation of fire events within specified hydroclimatic regions in the western United States, and evaluating the impact of climate and geophysical variables on response. Eighty-two watersheds were identified with at least 10 years of continuous pre-fire daily streamflow records and 5 years of continuous post-fire daily flow records. Percent change in annual runoff ratio, low flows, high flows, peak flows, number of zero flow days, baseflow index, and Richards–Baker flashiness index were calculated for each watershed using pre- and post-fire periods. Independent variables were identified for each watershed and fire event, including topographic, vegetation, climate, burn severity, percent area burned, and soils data. \n\nResults show that low flows, high flows, and peak flows increase in the first 2 years following a wildfire and decrease over time. Relative response was used to scale response variables with the respective percent area of watershed burned in order to compare regional differences in watershed response. To account for variability in precipitation events, runoff ratio was used to compare runoff directly to PRISM precipitation estimates. To account for regional differences in climate patterns, watersheds were divided into nine regions, or clusters, through k-means clustering using climate data, and regression models were produced for watersheds grouped by total area burned. Watersheds in Cluster 9 (eastern California, western Nevada, Oregon) demonstrate a small negative response to observed flow regimes after fire. Cluster 8 watersheds (coastal California) display the greatest flow responses, typically within the first year following wildfire. Most other watersheds show a positive mean relative response. In addition, simple regression models show low correlation between percent watershed burned and streamflow response, implying that other watershed factors strongly influence response. \n\nSpearman correlation identified NDVI, aridity index, percent of a watershed's precipitation that falls as rain, and slope as being positively correlated with post-fire streamflow response. This metric also suggested a negative correlation between response and the soil erodibility factor, watershed area, and percent low burn severity. Regression models identified only moderate burn severity and watershed area as being consistently positively/negatively correlated, respectively, with response. The random forest model identified only slope and percent area burned as significant watershed parameters controlling response. \n\nResults will help inform post-fire runoff management decisions by helping to identify expected changes to flow regimes, as well as facilitate parameterization for model application in burned watersheds.","language":"English","publisher":"Copernicus Publications","doi":"10.5194/hess-22-1221-2018","usgsCitation":"Saxe, S., Hogue, T.S., and Hay, L., 2019, Characterization and evaluation of controls on post-fire streamflow response across western U.S. watersheds: Hydrology and Earth System Sciences, v. 22, no. 2, p. 1221-1237, https://doi.org/10.5194/hess-22-1221-2018.","productDescription":"17 p.","startPage":"1221","endPage":"1237","numberOfPages":"17","ipdsId":"IP-090164","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":459812,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-22-1221-2018","text":"Publisher Index Page"},{"id":367422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"22","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Saxe, Samuel 0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":770931,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":770932,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren E. 0000-0003-3763-4595","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":211478,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":770930,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205904,"text":"70205904 - 2019 - A process-based model of pre-eruption seismicity patterns and its use for eruption forecasting at dormant stratovolcanoes","interactions":[],"lastModifiedDate":"2019-10-28T14:27:48","indexId":"70205904","displayToPublicDate":"2019-09-15T12:48:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"A process-based model of pre-eruption seismicity patterns and its use for eruption forecasting at dormant stratovolcanoes","docAbstract":"Using long-term data sets, the eruptive size, timing and style can typically be successfully forecast for dormant volcanoes using current monitoring data and knowledge of precursory eruptive patterns. The USAID-USGS Volcano Disaster Assistance Program (VDAP) utilizes seismic data over multiple eruption cycles and across volcano types to successfully forecast eruption size, style and onset times, as well as changes within ongoing eruptions. From millions of seismic event observations, we show that seismic data, a proxy for rate-dependent strain changes in the magmatic system, combined with a process-based conceptual geologic model enables eruption forecasting, especially for stratovolcanoes dormant >20 years. We show that this model often works for some open systems, ones that sufficiently reseal to plug the conduit, and also where a deep intrusion occurs after phreatic or phreatomagmatic explosions have begun. This model is divided into four seismicity and geologic stages. We describe each of the four stages of seismicity in detail and how they relate to the ongoing geologic process of that stage: Stage 1. Deep intrusion and seismicity occurring at 10 to 40 + km depth below the volcanic edifice; Stage 2. Distal VT seismicity induced at distances from 2 to >30 km laterally from the summit by magma intruding into and inflating the upper-crustal magma storage region; Stage 3. Vent-clearing seismicity associated with the initial vent creation and clearing phase of eruptive activity. Stage 4. Repetitive event seismicity associated with the final ascent of magma from 2 km to the surface. We present 36 examples of this seismic progression from 26 volcanoes. We believe that this progression generally correlates with the magma flux and the eventual explosivity and is invaluable for forecasting eruptions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2019.03.004","usgsCitation":"White, R.A., and McCausland, W.A., 2019, A process-based model of pre-eruption seismicity patterns and its use for eruption forecasting at dormant stratovolcanoes: Journal of Volcanology and Geothermal Research, v. 382, p. 267-297, https://doi.org/10.1016/j.jvolgeores.2019.03.004.","productDescription":"31 p.","startPage":"267","endPage":"297","ipdsId":"IP-082435","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":459815,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2019.03.004","text":"Publisher Index Page"},{"id":368169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"382","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Randall A. 0000-0003-4074-8577 rwhite@usgs.gov","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":1993,"corporation":false,"usgs":true,"family":"White","given":"Randall","email":"rwhite@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":772819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCausland, Wendy A. 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":204380,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":772820,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205521,"text":"70205521 - 2019 - Relations of dissolved-oxygen variability, selected field constituents, and metabolism estimates to land use and nutrients in high-gradient Boston Mountain streams, Arkansas","interactions":[],"lastModifiedDate":"2020-09-02T12:43:52.51217","indexId":"70205521","displayToPublicDate":"2019-09-14T12:26:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Relations of dissolved-oxygen variability, selected field constituents, and metabolism estimates to land use and nutrients in high-gradient Boston Mountain streams, Arkansas","docAbstract":"

Continuous monitoring data can be extremely useful for assessing water quality conditions particularly for variables that exhibit dynamic diel swings such as dissolved oxygen. As a means of evaluating dissolved oxygen criteria used by the Arkansas Department of Environmental Quality (ADEQ) for assessing this stream class, we compared continuous dissolved oxygen (DO) data collected at five small- to moderate-sized (watersheds 10-100 mi2), high-gradient streams in the Boston Mountains distributed across a land-use and nutrient condition gradient. The current DO criteria employed by ADEQ for Boston Mountains streams >10 mi2 consists of both an exceedance rate and a magnitude, in which, streams may be considered 'impaired' if greater than 10% of DO measurements during a period of record are < 6 mg/L. The 10% exceedance rate, however, is a commonly used “default” value that requires independent testing for different ecoregion stream classifications. Our findings for the five Boston Mountain streams fit a general pattern established for other aquatic systems (e.g. larger streams, low-gradient streams, and lakes) where increasing land-use intensity generally results in increased nutrient concentrations, which can lead to stream eutrophication and increased DO variability. DO concentrations were < 6 mg/L for fewer than 4% of measurements at the two sites identified “a priori” as least disturbed by nutrient and land-use indices, while concentrations at the three sites identified as moderately and most disturbed were < 6 mg/L for 20 to 33% of measurements. These findings demonstrate that the 10% exceedance rate currently employed by ADEQ was effective at identifying various degrees of DO impairment in Boston Mountain streams. Our analysis also demonstrated that continuous pH and specific conductance data and estimates of stream metabolism were helpful for associating DO variability to anthropogenic or natural origins. Considerations that were useful for examining these relationships and evaluating ADEQ’s DO criteria should be applicable to DO studies in other locations where stream and geologic characteristics are like those of the Boston Mountains.

","language":"English","publisher":"Springer","doi":"10.1007/s10661-019-7737-0","usgsCitation":"Justus, B., Driver, L., Green, J., and Wentz, N., 2019, Relations of dissolved-oxygen variability, selected field constituents, and metabolism estimates to land use and nutrients in high-gradient Boston Mountain streams, Arkansas: Environmental Monitoring and Assessment, v. 10, no. 191, 632, 18 p., https://doi.org/10.1007/s10661-019-7737-0.","productDescription":"632, 18 p.","ipdsId":"IP-082531","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":367635,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Big Creek stream, Illinois Bayou stream, South Fork Little Red stream, Town Branch stream, White River 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\"}}]}","volume":"10","issue":"191","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Justus, Billy 0000-0002-3458-9656 bjustus@usgs.gov","orcid":"https://orcid.org/0000-0002-3458-9656","contributorId":202148,"corporation":false,"usgs":true,"family":"Justus","given":"Billy","email":"bjustus@usgs.gov","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Driver, Lucas 0000-0003-2549-1849","orcid":"https://orcid.org/0000-0003-2549-1849","contributorId":219176,"corporation":false,"usgs":true,"family":"Driver","given":"Lucas","email":"","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771495,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, J.J.","contributorId":219175,"corporation":false,"usgs":false,"family":"Green","given":"J.J.","email":"","affiliations":[{"id":39966,"text":"Arkansas Dept Env. Quality","active":true,"usgs":false}],"preferred":false,"id":771494,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wentz, Nathan","contributorId":219177,"corporation":false,"usgs":false,"family":"Wentz","given":"Nathan","email":"","affiliations":[{"id":39967,"text":"Arkansas Dept of Env. Quality","active":true,"usgs":false}],"preferred":false,"id":771496,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205385,"text":"70205385 - 2019 - Physically based estimation of rainfall thresholds triggering shallow landslides in volcanic slopes of southern Italy","interactions":[],"lastModifiedDate":"2019-09-17T08:50:30","indexId":"70205385","displayToPublicDate":"2019-09-14T08:49:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Physically based estimation of rainfall thresholds triggering shallow landslides in volcanic slopes of southern Italy","docAbstract":"On the 4th and 5th of March 2005, about 100 rainfall-induced landslides occurred along volcanic slopes of Camaldoli Hill in Naples, Italy. These started as soil slips in the upper substratum of incoherent and welded volcaniclastic deposits, then evolved downslope according to debris avalanche and debris flow mechanisms. This specific case of slope instability on complex volcaniclastic deposits remains poorly characterized and understood, although similar shallow landsliding phenomena have largely been studied in other peri-volcanic areas of the Campania region underlain by carbonate bedrock. Considering the landslide hazard in this urbanized area, this study focused on quantitatively advancing the understanding of the predisposing factors and hydrological conditions contributing to the initial landslide triggering. Borehole drilling, trial pits, dynamic penetrometer tests, topographic surveys, and infiltration tests were conducted on a slope sector of Camaldoli Hill to develop a geological framework model. Undisturbed soil samples were collected for laboratory testing to further characterize hydraulic and geotechnical properties of the soil units identified. In situ soil pressure head monitoring probes were also installed. A numerical model of two-dimensional variably saturated subsurface water flow was parameterized for the monitored hillslope using field and laboratory data. Based on the observed soil pressure head dynamics, the model was calibrated by adjusting the evapotranspiration parameters. This physically based hydrologic model was combined with an infinite-slope stability analysis to reconstruct the critical unsaturated/saturated conditions leading to slope failure. This coupled hydromechanical numerical model was then used to determine intensity–duration (I-D) thresholds for landslide initiation over a range of plausible rainfall intensities and topographic slope angles for the region. The proposed approach can be conceived as a practicable method for defining a warning criterion in urbanized areas threatened by rainfall-induced shallow landslides, given the unavailability of a consistent inventory of past landslide events that prevents a rigorous empirical analysis.","language":"English","publisher":"MDPI","doi":"10.3390/w11091915","usgsCitation":"Fusco, F., De Vita, P., Mirus, B.B., Baum, R.L., Allocca, V., Tufano, R., and Calcaterra, D., 2019, Physically based estimation of rainfall thresholds triggering shallow landslides in volcanic slopes of southern Italy: Water, v. 11, no. 9, Article 1915, https://doi.org/10.3390/w11091915.","productDescription":"Article 1915","ipdsId":"IP-102857","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":459825,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w11091915","text":"Publisher Index Page"},{"id":367450,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[15.52038,38.23116],[15.16024,37.44405],[15.3099,37.13422],[15.09999,36.61999],[14.33523,36.99663],[13.82673,37.10453],[12.431,37.61295],[12.57094,38.12638],[13.74116,38.03497],[14.76125,38.14387],[15.52038,38.23116]]],[[[9.21001,41.20999],[9.80998,40.50001],[9.66952,39.17738],[9.21482,39.24047],[8.80694,38.90662],[8.4283,39.17185],[8.38825,40.37831],[8.16,40.95001],[8.70999,40.89998],[9.21001,41.20999]]],[[[12.37649,46.76756],[13.80648,46.50931],[13.69811,46.01678],[13.93763,45.59102],[13.14161,45.73669],[12.32858,45.38178],[12.38387,44.88537],[12.26145,44.60048],[12.58924,44.09137],[13.52691,43.58773],[14.02982,42.76101],[15.14257,41.95514],[15.92619,41.96132],[16.1699,41.74029],[15.88935,41.54108],[16.785,41.17961],[17.51917,40.87714],[18.37669,40.35562],[18.48025,40.16887],[18.29339,39.81077],[17.73838,40.27767],[16.8696,40.44223],[16.44874,39.7954],[17.17149,39.4247],[17.05284,38.90287],[16.63509,38.84357],[16.10096,37.9859],[15.68409,37.90885],[15.68796,38.21459],[15.89198,38.75094],[16.10933,38.96455],[15.71881,39.54407],[15.41361,40.04836],[14.9985,40.17295],[14.70327,40.60455],[14.06067,40.78635],[13.62799,41.18829],[12.88808,41.25309],[12.10668,41.70453],[11.19191,42.35543],[10.51195,42.93146],[10.20003,43.92001],[9.70249,44.03628],[8.88895,44.36634],[8.42856,44.23123],[7.85077,43.76715],[7.43518,43.69384],[7.5496,44.1279],[7.00756,44.25477],[6.74996,45.02852],[7.09665,45.3331],[6.80236,45.70858],[6.84359,45.99115],[7.27385,45.77695],[7.75599,45.82449],[8.31663,46.16364],[8.48995,46.00515],[8.96631,46.03693],[9.18288,46.44021],[9.92284,46.3149],[10.36338,46.48357],[10.4427,46.89355],[11.04856,46.75136],[11.16483,46.94158],[12.15309,47.11539],[12.37649,46.76756]]]]},\"properties\":{\"name\":\"Italy\"}}]}","volume":"11","issue":"9","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Fusco, F. 0000-0002-6271-2228","orcid":"https://orcid.org/0000-0002-6271-2228","contributorId":219005,"corporation":false,"usgs":false,"family":"Fusco","given":"F.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":770977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Vita, P.","contributorId":219006,"corporation":false,"usgs":false,"family":"De Vita","given":"P.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":770978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":770979,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allocca, V.","contributorId":149077,"corporation":false,"usgs":false,"family":"Allocca","given":"V.","email":"","affiliations":[{"id":17631,"text":"Department of Earth, Environment and Resources Sciences, University of Naples “Federico II”, Naples, Italy.","active":true,"usgs":false}],"preferred":false,"id":770981,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tufano, R.","contributorId":219007,"corporation":false,"usgs":false,"family":"Tufano","given":"R.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":770982,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Calcaterra, D. 0000-0002-3480-3667","orcid":"https://orcid.org/0000-0002-3480-3667","contributorId":219008,"corporation":false,"usgs":false,"family":"Calcaterra","given":"D.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":770983,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227523,"text":"70227523 - 2019 - Energetic status and bioelectrical impedance modeling of Arctic grayling Thymallus arcticus in interior Alaska Rivers","interactions":[],"lastModifiedDate":"2022-01-20T12:58:29.761524","indexId":"70227523","displayToPublicDate":"2019-09-14T06:53:17","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Energetic status and bioelectrical impedance modeling of Arctic grayling Thymallus arcticus in interior Alaska Rivers","docAbstract":"

The energetic status of fishes represents energy stored as protein and lipids and reflects the ability of an individual to reproduce, migrate, and transition through life stages, ultimately influencing survival. However, traditional measurement methods, while highly accurate, are time consuming, expensive, and lethal, and nonlethal methods such as condition factor may not adequately characterize energetic status. We collected 161 Arctic grayling (Thymallus arcticus) from four interior Alaska river basins with varying hydrologic regimes during early summer and autumn seasons, and used multiple regression and model selection to evaluate the efficacy of bioelectrical impedance analysis (BIA), a nonlethal condition assessment method, to predict percent dry mass and percent lipid content estimated from proximate analysis. We found that Arctic grayling energetic status varied across seasons, by sex, and fish from sites with spawning runs of Pacific salmon had higher energy content than those from sites without salmon, potentially due to the influence of salmon-derived food subsidies. Electrical measurements explained 82% and 80% of the variability in percent dry mass and percent total lipids, respectively, and top models showed high predictive performance (observed vs. predicted root mean squared error ≤2.2%). Overall, we found the BIA approach to provide rapid, precise, and non-lethal estimates of Arctic grayling body condition. Such an approach may be useful for future work to characterize Arctic grayling bioenergetics and monitor fish condition under a rapidly changing Arctic environment.


","language":"English","publisher":"Springer","doi":"10.1007/s10641-019-00910-6","usgsCitation":"Falke, J.A., Bailey, L.T., Fraley, K.M., Lunde, M.J., and Gryska, A.D., 2019, Energetic status and bioelectrical impedance modeling of Arctic grayling Thymallus arcticus in interior Alaska Rivers: Environmental Biology of Fishes, v. 102, p. 1337-1349, https://doi.org/10.1007/s10641-019-00910-6.","productDescription":"13 p.","startPage":"1337","endPage":"1349","ipdsId":"IP-098094","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":394567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -161.103515625,\n 62.32920841458002\n ],\n [\n -136.845703125,\n 62.32920841458002\n ],\n [\n -136.845703125,\n 67.85898479324493\n ],\n [\n -161.103515625,\n 67.85898479324493\n ],\n [\n -161.103515625,\n 62.32920841458002\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"102","noUsgsAuthors":false,"publicationDate":"2019-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831238,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Lauren T.","contributorId":271265,"corporation":false,"usgs":false,"family":"Bailey","given":"Lauren","email":"","middleInitial":"T.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":831239,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fraley, Kevin M.","contributorId":189243,"corporation":false,"usgs":false,"family":"Fraley","given":"Kevin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":831240,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lunde, Michael J.","contributorId":271266,"corporation":false,"usgs":false,"family":"Lunde","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":831241,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gryska, Andrew D.","contributorId":271267,"corporation":false,"usgs":false,"family":"Gryska","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":56329,"text":"akfg","active":true,"usgs":false}],"preferred":false,"id":831242,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205313,"text":"70205313 - 2019 - Putative mitochondrial sex determination in the Bivalvia: Insights from a hybrid transcriptome assembly in freshwater mussels","interactions":[],"lastModifiedDate":"2019-10-10T06:36:14","indexId":"70205313","displayToPublicDate":"2019-09-13T14:38:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5062,"text":"Frontiers in Genetics","onlineIssn":"1664-8021","active":true,"publicationSubtype":{"id":10}},"title":"Putative mitochondrial sex determination in the Bivalvia: Insights from a hybrid transcriptome assembly in freshwater mussels","docAbstract":"

Bivalves exhibit an astonishing diversity of sexual systems, with genetic and environmental determinants of sex, and possibly the only example of mitochondrial genes influencing sex determination pathways in animals. In contrast to all other animal species in which strict maternal inheritance (SMI) of mitochondria is the rule, bivalves possess a system known as doubly uniparental inheritance (DUI) of mitochondria in which maternal and paternal mitochondria (and their corresponding female-transmitted or F mtDNA and male-transmitted or M mtDNA genomes) are transmitted within a species. Species with DUI also possess sex-associated mtDNA-encoded proteins (in addition to the typical set of 13), which have been hypothesized to play a role in sex determination. In this study, we analyzed the sex-biased transcriptome in gonads of two closely-related freshwater mussel species with different reproductive and mitochondrial transmission modes: the gonochoric, DUI species, Utterbackia peninsularis, and the hermaphroditic, SMI species, Utterbackia imbecillis. Through comparative analysis with other DUI and non-DUI bivalve transcriptomes already available, we identify common male and female-specific genes, as well as SMI and DUI-related genes, that are probably involved in sex determination and mitochondrial inheritance in this animal group. Our results contribute to the understanding of what could be the first animal sex determination system involving the mitochondrial genome

","language":"English","publisher":"Frontiers Media","doi":"10.3389/fgene.2019.00840","usgsCitation":"Capt, C., Renaut, S., Stewart, D., Johnson, N., and Breton, S., 2019, Putative mitochondrial sex determination in the Bivalvia: Insights from a hybrid transcriptome assembly in freshwater mussels: Frontiers in Genetics, v. 10, 840, 13 p., https://doi.org/10.3389/fgene.2019.00840.","productDescription":"840, 13 p.","ipdsId":"IP-106802","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":459830,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fgene.2019.00840","text":"Publisher Index Page"},{"id":367418,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Suwanee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.97012329101562,\n 29.569351295613394\n ],\n [\n -82.92832374572754,\n 29.569351295613394\n ],\n [\n -82.92832374572754,\n 29.606819652257347\n ],\n [\n -82.97012329101562,\n 29.606819652257347\n ],\n [\n -82.97012329101562,\n 29.569351295613394\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Capt, Charlotte","contributorId":205385,"corporation":false,"usgs":false,"family":"Capt","given":"Charlotte","email":"","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":770832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renaut, Sebastien","contributorId":218987,"corporation":false,"usgs":false,"family":"Renaut","given":"Sebastien","email":"","affiliations":[{"id":37091,"text":"Université de Montréal","active":true,"usgs":false}],"preferred":false,"id":770834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Donald","contributorId":205389,"corporation":false,"usgs":false,"family":"Stewart","given":"Donald","affiliations":[{"id":37092,"text":"Acadia University","active":true,"usgs":false}],"preferred":false,"id":770835,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Nathan A. 0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":770831,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Breton, Sophie 0000-0002-8286-486X","orcid":"https://orcid.org/0000-0002-8286-486X","contributorId":196560,"corporation":false,"usgs":false,"family":"Breton","given":"Sophie","email":"","affiliations":[],"preferred":false,"id":770833,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204621,"text":"cir1457 - 2019 - National earthquake information center strategic plan, 2019–23","interactions":[],"lastModifiedDate":"2020-09-01T13:55:24.927546","indexId":"cir1457","displayToPublicDate":"2019-09-13T10:30:00","publicationYear":"2019","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":"1457","displayTitle":"National Earthquake Information Center Strategic Plan, 2019–23","title":"National earthquake information center strategic plan, 2019–23","docAbstract":"

Executive Summary

Damaging earthquakes occur regularly around the world; since the turn of the 20th century, hundreds of earthquakes have caused significant loss of life and (or) millions of dollars or more in economic losses. While most of these did not directly affect the United States and its Territories, by studying worldwide seismicity we can better understand how to mitigate the effects of earthquakes when they do occur within U.S. borders. Within the U.S. Government, this mandate falls on the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC), which has the statutory responsibility for monitoring and reporting on earthquakes domestically and globally.

The NEIC has been operating since 1966, and throughout its history has been recognized as a world leader for earthquake information. For much of this time, NEIC has been cooperating with a number of regional seismic networks (RSNs) which operate in areas of heightened seismicity in the United States. In 2000, the Advanced National Seismic System (ANSS) was founded as a cooperative umbrella for earthquake-related data collection, analysis, and dissemination in the United States, thereby promoting advanced interoperability between the NEIC and RSN partners. The NEIC also cooperates and coordinates with dozens of global seismic networks. At present (2019), NEIC acquires real-time waveform data from more than 2,000 seismic stations worldwide, contributed from more than 130 seismic networks.

Since 2006, the NEIC has operated on a 24-hour, 7-days per week (24/7) basis, and reports on about 30,000 earthquakes per year. Soon after the occurrence of a significant global earthquake, notifications are issued to government representatives, aid agencies, the press, and members of the general public by the Earthquake Notification Service (ENS), electronic feeds, and through the USGS Earthquake Hazards Program (EHP) website. Event-specific web pages provide detailed source parameter information outlining the location and magnitude of the earthquake, including more detailed source characteristics like moment magnitude and focal mechanisms and finite fault solutions. Further, NEIC produces a suite of real-time situational awareness products, including ShakeMap, ShakeCast, Did-You-Feel-It? (DYFI?), and Prompt Assessment of Global Earthquakes for Response (PAGER), to characterize the shaking resulting from the earthquake and the impact it is likely to have on nearby populations and infrastructure. All of these products are ultimately archived in the ANSS Comprehensive Catalog (ComCat), hosted and served by the NEIC.

The NEIC also pursues an active research program to improve its ability to characterize earthquakes and understand their hazards. These efforts are all aimed at mitigating the risks of earthquakes to humankind.

To maintain its prominent position in earthquake monitoring, the NEIC must continue to evolve, concurrently improving its operations and 24/7 robustness, streamlining services and infrastructure, and keeping pace with research and innovation in the field of seismology. This document outlines how the NEIC might best achieve such goals, by describing specific avenues and opportunities for development in the next five years (2019–23).

Several key areas of operational and research focus are identified in this plan as being of the highest importance. First, NEIC must finalize improvements to its regional monitoring capabilities, including the implementation of a variety of improved earthquake detection and association algorithms. One of the most exciting avenues of recent research expansion in earthquake monitoring has involved the use of machine learning; NEIC must explore the benefits of machine learning for improved earthquake detection and source characterization. NEIC also needs to address issues related to the timeliness of earthquake information, exploring the benefits of distributing information as it becomes available, rather than when certain quality criteria are met. To that end, the incorporation of real-time Global Positioning System (GPS) data into the NEIC operational workflow will help improve the speed and accuracy of information for moderate-to-large earthquakes. Finally, NEIC should explore how to further expand and improve the quality and content of the products served during earthquake response efforts, including the generation of new earthquake sequence-specific products, adding an evolutionary component to earthquake information, and continued improvements to earthquake impact products.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1457","usgsCitation":"Hayes, G.P., Earle, P.S., Benz, H.M., Wald, D.J., and Yeck, W.L., 2019, National Earthquake Information Center strategic plan, 2019–23: U.S. Geological Survey Circular 1457, 17 p., https://doi.org/10.3133/cir1457.","productDescription":"vi, 20 p.","onlineOnly":"N","ipdsId":"IP-107447","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true}],"links":[{"id":367395,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1457/coverthb2.jpg"},{"id":367396,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1457/circ1457.pdf","text":"Report","size":"19.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1457"}],"contact":"

Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS 966
Denver, CO 80225

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2019-09-13","noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hayes, Gavin P. 0000-0003-3323-0112 ghayes@usgs.gov","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":147556,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin","email":"ghayes@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earle, Paul S. 0000-0002-3500-017X pearle@usgs.gov","orcid":"https://orcid.org/0000-0002-3500-017X","contributorId":173551,"corporation":false,"usgs":true,"family":"Earle","given":"Paul","email":"pearle@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":770773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Benz, Harley M. 0000-0002-6860-2134 benz@usgs.gov","orcid":"https://orcid.org/0000-0002-6860-2134","contributorId":794,"corporation":false,"usgs":true,"family":"Benz","given":"Harley","email":"benz@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":767801,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":767802,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":767803,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202192,"text":"sir20175037 - 2019 - Methods for estimating regional coefficient of skewness for unregulated streams in New England, based on data through water year 2011","interactions":[],"lastModifiedDate":"2026-01-23T16:05:31.669203","indexId":"sir20175037","displayToPublicDate":"2019-09-13T10:26:37","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5037","displayTitle":"Methods for Estimating Regional Coefficient of Skewness for Unregulated Streams in New England, Based on Data Through Water Year 2011","title":"Methods for estimating regional coefficient of skewness for unregulated streams in New England, based on data through water year 2011","docAbstract":"

The magnitude of annual exceedance probability floods is greatly affected by the coefficient of skewness (skew) of the annual peak flows at a streamgage. Standard flood frequency methods recommend weighting the station skew with a regional skew to better represent regional and stable conditions. This study presents an updated analysis of a regional skew for New England developed using a robust Bayesian weighted and generalized least squares regression model. Nineteen explanatory variables for 153 streamgages were tested in the regression analysis, but none were statistically significant and, as a result, a constant model was selected to define the regional skew for New England. The constant model for the New England region has, in log units, a skew of 0.37, a model error variance of 0.13, and an average variance of prediction at a new site of 0.14. An assessment of the selected regional skew model was conducted using a Monte Carlo analysis. The Monte Carlo simulations reveal that the perceived pattern in the station skews among the 153 streamgages is an artifact of the sample variability and the covariance structure of the errors.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175037","usgsCitation":"Veilleux, A.G., Zariello, P.J., Hodgkins, G.A., Ahearn, E.A., Olson, S.A., and Cohn, T.A., 2019, Methods for estimating regional coefficient of skewness for unregulated streams in New England, based on data through water year 2011: U.S. Geological Survey Scientific Investigations Report 2017–5037, 29 p., https://doi.org/10.3133/sir20175037.","productDescription":"Report: iv, 29 p.; Data Release","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-071009","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":367392,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MC98OM","linkHelpText":"Annual peak-flow data and PeakFQ output files for selected streamflow gaging stations operated by the U.S. Geological Survey in the New England region that were used to estimate regional skewness of annual peak flows"},{"id":367390,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5037/sir20175037.pdf","text":"Report","size":"18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2017–5037"},{"id":367389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5037/coverthb.jpg"}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island, Vermont","otherGeospatial":"New England","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.90673828125,\n 44.84808025602074\n ],\n [\n -67.82958984375,\n 46.042735653846506\n ],\n [\n -67.78564453125,\n 47.07012182383309\n ],\n [\n -68.345947265625,\n 47.4057852900587\n ],\n [\n -68.93920898437499,\n 47.2270293988673\n ],\n [\n -69.027099609375,\n 47.44294999517949\n ],\n [\n -69.224853515625,\n 47.45780853075031\n ],\n [\n -69.98291015625,\n 46.77749276376827\n ],\n [\n -70.301513671875,\n 46.210249600187225\n ],\n [\n -70.400390625,\n 45.79816953017265\n ],\n [\n -70.86181640625,\n 45.413876460821086\n ],\n [\n -71.16943359375,\n 45.3444241045224\n ],\n [\n -71.575927734375,\n 45.01141864227728\n ],\n [\n -74.24560546875,\n 44.99588261816546\n ],\n [\n -74.256591796875,\n 40.53050177574321\n ],\n [\n -72.13623046875,\n 40.90520969727358\n ],\n [\n -70.499267578125,\n 41.86956082699455\n ],\n [\n -70.72998046875,\n 42.22851735620852\n ],\n [\n -70.850830078125,\n 42.48830197960227\n ],\n [\n -70.59814453125,\n 42.65012181368022\n ],\n [\n -70.77392578125,\n 42.94838139765314\n ],\n [\n -70.169677734375,\n 43.69965122967144\n ],\n [\n -69.6533203125,\n 43.75522505306928\n ],\n [\n -66.90673828125,\n 44.84808025602074\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director,
Integrated Modeling and Prediction Division
U.S. Geological Survey
MS 415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2019-09-13","noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Veilleux, Andrea G. 0000-0002-8742-4660 aveilleux@usgs.gov","orcid":"https://orcid.org/0000-0002-8742-4660","contributorId":203278,"corporation":false,"usgs":true,"family":"Veilleux","given":"Andrea","email":"aveilleux@usgs.gov","middleInitial":"G.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":757168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarriello, Phillip J. 0000-0001-9598-9904 pzarriel@usgs.gov","orcid":"https://orcid.org/0000-0001-9598-9904","contributorId":1868,"corporation":false,"usgs":true,"family":"Zarriello","given":"Phillip","email":"pzarriel@usgs.gov","middleInitial":"J.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757170,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ahearn, Elizabeth A. 0000-0002-5633-2640 eaahearn@usgs.gov","orcid":"https://orcid.org/0000-0002-5633-2640","contributorId":194658,"corporation":false,"usgs":true,"family":"Ahearn","given":"Elizabeth","email":"eaahearn@usgs.gov","middleInitial":"A.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true},{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true}],"preferred":false,"id":757171,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olson, Scott A. 0000-0002-1064-2125","orcid":"https://orcid.org/0000-0002-1064-2125","contributorId":210173,"corporation":false,"usgs":true,"family":"Olson","given":"Scott A.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":757172,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cohn, Timothy A. tacohn@usgs.gov","contributorId":213234,"corporation":false,"usgs":true,"family":"Cohn","given":"Timothy","email":"tacohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":757173,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215323,"text":"70215323 - 2019 - Using a mechanistic model to develop management strategies to cool Apache Trout streams under the threat of climate change","interactions":[],"lastModifiedDate":"2020-10-16T14:15:08.962952","indexId":"70215323","displayToPublicDate":"2019-09-13T09:10:51","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Using a mechanistic model to develop management strategies to cool Apache Trout streams under the threat of climate change","docAbstract":"

User‐friendly stream temperature models populated with on‐site data may help in developing strategies to manage temperatures of individual stream reaches that are subject to climate change. We used the field‐tested Stream Segment Temperature model (U.S. Geological Survey) to simulate how altering discharge, groundwater input, channel wetted width, and shade prevents the temperatures of White Mountain, Arizona, stream reaches from exceeding the thermal tolerance of Apache Trout Oncorhynchus apache, both under existing conditions and under a climate change scenario. Simulations suggested increasing shade, either through streamside planting of specific numbers and species of plants or by other means, would be most effective and feasible for cooling the stream reaches we studied. Ponderosa pine Pinus ponderosa and Douglas fir Pseudotsuga menziesii provided the most shade followed in order by Engelman spruce Picea engelmannii, Bebb's willow Salix bebbiana, Arizona alder Alnus oblongifolia, and finally coyote willow Salix exigua. Vegetation survival depends on the appropriateness of site conditions at present and under climate change, and planting in buffer strips minimizes additional water removal from the watershed through evapotranspiration. Alternative shading options, including thick sedge growth, shade cloth, or felled woody vegetation, may be considered when environmental conditions do not support plantings. Increasing groundwater input can cool streams, but additional sources are scarce in the region. Decreasing the width‐to‐depth ratio would succeed best on reaches with widths greater than 2.0 m. Increasing discharge from upstream may lower water temperature on reaches with an initial discharge greater than 0.5 m3/s. Existing models provide suggestions to cool stream reaches. Further development of accessible software packages that incorporate evaporation, fragmentation, and other projected climate change effects into their routines will provide additional tools to help manage climate change effects.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10337","usgsCitation":"Baker, J.P., and Bonar, S.A., 2019, Using a mechanistic model to develop management strategies to cool Apache Trout streams under the threat of climate change: North American Journal of Fisheries Management, v. 39, no. 5, p. 849-867, https://doi.org/10.1002/nafm.10337.","productDescription":"19 p.","startPage":"849","endPage":"867","ipdsId":"IP-098411","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":379466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"White Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.225830078125,\n 33.53681606773302\n ],\n [\n -109.05853271484374,\n 33.53681606773302\n ],\n [\n -109.05853271484374,\n 34.440893571391165\n ],\n [\n -110.225830078125,\n 34.440893571391165\n ],\n [\n -110.225830078125,\n 33.53681606773302\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2019-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Baker, Joy Price","contributorId":243199,"corporation":false,"usgs":false,"family":"Baker","given":"Joy","email":"","middleInitial":"Price","affiliations":[{"id":40855,"text":"UA","active":true,"usgs":false}],"preferred":false,"id":801718,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":801719,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205297,"text":"70205297 - 2019 - Photosynthetic and respiratory responses of two bog shrub species to whole ecosystem warming and elevated CO2 at the boreal-temperate ecotone","interactions":[],"lastModifiedDate":"2019-10-11T16:05:01","indexId":"70205297","displayToPublicDate":"2019-09-12T14:04:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Photosynthetic and respiratory responses of two bog shrub species to whole ecosystem warming and elevated CO2 at the boreal-temperate ecotone","title":"Photosynthetic and respiratory responses of two bog shrub species to whole ecosystem warming and elevated CO2 at the boreal-temperate ecotone","docAbstract":"

Peatlands within the boreal-temperate ecotone contain the majority of terrestrial carbon in this region, and there is concern over the fate of such carbon stores in the face of global environmental changes. The Spruce and Peatland Response Under Changing Environments (SPRUCE) facility aims to advance the understanding of how such peatlands may respond to such changes, using a combination of whole ecosystem warming (WEW; +0, 2.25, 4.5, 6.75, and 9°C) and elevated CO2 (eCO2; +500 ppm) treatments in an intact bog ecosystem. We examined photosynthetic and respiration responses in leaves of two ericaceous shrub species–leatherleaf [Chamaedaphne calyculata (L.) Moench] and bog Labrador tea [Rhododendron groenlandicum (Oeder) Kron & Judd]–to the first year of combined eCO2 and WEW treatments at SPRUCE. We surveyed the leaf N content per area (Narea), net photosynthesis (AST) and respiration (RD25) at 25°C and 400 ppm CO2 and net photosynthesis at mean growing conditions (AGR) of newly emerged, mature and overwintered leaves. We also measured leaf non-structural carbohydrate content (NSC) in mature leaves. The effects of WEW and eCO2 varied by season and species, highlighting the need to accommodate such variability in modeling this system. In mature leaves, we did not observe a response to either treatment of AST or RD25 in R. groenlandicum, but we did observe a 50% decrease in AST of C. calyculata with eCO2. In mature leaves under eCO2, neither species had increased AGR and both had increases in NSC, indicating acclimation of photosynthesis to eCO2 may be related to source-sink imbalances of carbohydrates. Thus, productivity gains of shrubs under eCO2 may be lower than previously predicted for this site by models not accounting for such acclimation. In newly emerged leaves, AST increased with WEW in R. groenlandicum, but not C. calyculata. Overwintered leaves exhibited a decrease in RD25 for R. groenlandicum and in AST for C. calyculata with increasing WEW, as well as an increase of AGR with eCO2 in both species. Responses in newly emerged and overwintered leaves may reflect physiological acclimation or phenological changes in response to treatments.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffgc.2019.00054","usgsCitation":"Ward, E., Warren, J.M., McLennan, D., Dusenge, M.E., Way, D.A., Wullschleger, S.D., and Hanson, P.J., 2019, Photosynthetic and respiratory responses of two bog shrub species to whole ecosystem warming and elevated CO2 at the boreal-temperate ecotone: Frontiers in Forests and Global Change, v. 2, 54, 14 p., https://doi.org/10.3389/ffgc.2019.00054.","productDescription":"54, 14 p.","ipdsId":"IP-106522","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":459841,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2019.00054","text":"Publisher Index Page"},{"id":367414,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Marcell Experimental Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.54206085205078,\n 47.397652013137176\n ],\n [\n -93.44146728515625,\n 47.397652013137176\n ],\n [\n -93.44146728515625,\n 47.46987800000272\n ],\n [\n -93.54206085205078,\n 47.46987800000272\n ],\n [\n -93.54206085205078,\n 47.397652013137176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":218962,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":770778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Warren, Jeffrey M .","contributorId":198318,"corporation":false,"usgs":false,"family":"Warren","given":"Jeffrey","email":"","middleInitial":"M .","affiliations":[],"preferred":false,"id":770779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLennan, David A","contributorId":218963,"corporation":false,"usgs":false,"family":"McLennan","given":"David A","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":770780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dusenge, Mirindi E","contributorId":218964,"corporation":false,"usgs":false,"family":"Dusenge","given":"Mirindi","email":"","middleInitial":"E","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":770781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Way, Danielle A.","contributorId":199465,"corporation":false,"usgs":false,"family":"Way","given":"Danielle","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":770782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wullschleger, Stan D.","contributorId":167343,"corporation":false,"usgs":false,"family":"Wullschleger","given":"Stan","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":770783,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanson, Paul J","contributorId":218965,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"J","affiliations":[{"id":37070,"text":"Oak Ridge National Laboratory","active":true,"usgs":false}],"preferred":false,"id":770784,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209084,"text":"70209084 - 2019 - Projected warming disrupts the synchrony of riparian seed dispersal and snowmelt streamflow","interactions":[],"lastModifiedDate":"2020-03-16T06:21:15","indexId":"70209084","displayToPublicDate":"2019-09-12T13:58:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2863,"text":"New Phytologist","active":true,"publicationSubtype":{"id":10}},"title":"Projected warming disrupts the synchrony of riparian seed dispersal and snowmelt streamflow","docAbstract":"

• Globally, spring phenology and abiotic processes are shifting earlier with warming. Differences in the magnitudes of these shifts may decouple the timing of plant resource requirements from resource availability. In riparian forests across the northern hemisphere, warming could decouple seed dispersal from snowmelt peak streamflow, thus reducing moisture and safe-sites for dominant tree recruitment.

• We combined field observations with climate, hydrology, and phenology models to simulate future change in synchrony of seed dispersal and snowmelt peaks in the upper South Platte River Basin, Colorado, for three Salicaceae species that dominate western USA riparian forests.

• Chilling requirements for overcoming winter endodormancy were strongest in Salix exigua, moderately supported for Populus deltoides, and indiscernible in Salix amygdaloides. Ensemble mean projected warming of 3.5ºC shifted snowmelt peaks 10-19 d earlier relative to S. exigua and P. deltoides dispersal, because decreased winter chilling combined with increased spring forcing limited change in their dispersal phenology. In contrast, warming shifted both snowmelt peaks and S. amygdaloides dispersal 21 d earlier, maintaining their synchrony.

• Decoupling of snowmelt from seed dispersal for Salicaceae with strong chilling requirements is likely to reduce resources critical for recruitment of these foundational riparian forests, although the magnitude of future decoupling remains uncertain.

","language":"English","publisher":"Wiley","doi":"10.1111/nph.16191","usgsCitation":"Perry, L.G., Shafroth, P.B., Hay, L., Markstrom, S.L., and Bock, A.R., 2019, Projected warming disrupts the synchrony of riparian seed dispersal and snowmelt streamflow: New Phytologist, v. 225, no. 2, p. 693-712, https://doi.org/10.1111/nph.16191.","productDescription":"20 p.","startPage":"693","endPage":"712","ipdsId":"IP-110069","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":459842,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nph.16191","text":"Publisher Index Page"},{"id":437340,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P994V9LI","text":"USGS data release","linkHelpText":"Riparian seed dispersal phenology and snowmelt streamflow timing in the upper South Platte River Basin, observed in 2010-2011 and simulated for 1962-2098"},{"id":373275,"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 -109.16015624999999,\n 37.020098201368114\n ],\n [\n -102.041015625,\n 37.020098201368114\n ],\n [\n -102.041015625,\n 40.74725696280421\n ],\n [\n -109.16015624999999,\n 40.74725696280421\n ],\n [\n -109.16015624999999,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"225","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Perry, Laura G.","contributorId":220048,"corporation":false,"usgs":false,"family":"Perry","given":"Laura","email":"","middleInitial":"G.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":784861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":784860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren","contributorId":209452,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","affiliations":[],"preferred":true,"id":784904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":784905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784906,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205025,"text":"fs20193048 - 2019 - Rare earth elements in coal and coal fly ash","interactions":[],"lastModifiedDate":"2019-09-13T09:41:29","indexId":"fs20193048","displayToPublicDate":"2019-09-12T10:23:54","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3048","displayTitle":"Rare Earth Elements in Coal and Coal Fly Ash","title":"Rare earth elements in coal and coal fly ash","docAbstract":"

The rare earth elements (REEs) are a group of 17 elements sharing similar chemical properties. They include yttrium (Y, atomic number 39), scandium (Sc, atomic number 21), and the 15 elements of the lanthanide series, atomic numbers 57 (lanthanum, La) to 71 (lutetium, Lu). Because promethium (Pm, atomic number 61) does not occur in the Earth’s crust and scandium typically has different geological occurrences from other REEs, they are not discussed further herein.

REEs are, on average, more abundant than precious metals (for example, gold, silver, and platinum), but because of their unique geochemical properties, they do not commonly form economically viable ore deposits. Nevertheless, REEs are increasingly required for a range of modern applications in defense and renewable energy technologies and in commercial products, primarily as magnets, batteries, and catalysts. The United States currently (2018) produces REEs from a single mine in California, accounting for just 9 percent of global production, whereas 70 percent of global REE production comes from China. For these reasons, REEs are considered a critical resource, and the U.S. Geological Survey (USGS) has an interest in helping to identify new sources of REEs for domestic production.

In 2017, coal use accounted for about 30 percent of the electric power generated in the United States. Fly ash, produced during the burning of coal, is a fine­-grained solid derived from noncombustible constituents of coal, such as clay minerals and quartz. When coal is burned, REEs are retained and enriched in the fly ash and, as a result, fly ash has long been considered a potential resource for REEs.

The United States has the world’s largest coal reserves and, even though gas-­fired power generation has increased significantly in the last decade, the United States continues to produce vast quantities of fly ash, about half of which is beneficially reused, primarily in construction materials. The remainder is stored, mostly in landfills and impound­ments. Thus, annual fly ash production, combined with fly ash already in stor­age, constitutes a large potential resource.

Research into how to utilize coal and coal fly ash as sources of REEs is ongo­ing. Viable recovery of REEs from coal and coal ash requires identification of coals and ashes with the highest REE concentrations and development of workable methods for REE extraction and recovery. Understanding how REEs occur within fly ash, described in this fact sheet, is one of the keys to developing possible methods for their recovery.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193048","usgsCitation":"Scott, C., and Kolker, A., 2019, Rare earth elements in coal and coal fly ash: U.S. Geological Survey Fact Sheet 2019-3048, 4 p., https://doi.org/10.3133/fs20193048.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-098987","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":367383,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3048/fs20193048.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2019-3048"},{"id":367382,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3048/coverthb.jpg"}],"contact":"

Eastern Energy Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
956 National Center
Reston, VA 20192
https://energy.usgs.gov/

","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2019-09-12","noUsgsAuthors":false,"publicationDate":"2019-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Clint 0000-0003-2778-2711 clintonscott@usgs.gov","orcid":"https://orcid.org/0000-0003-2778-2711","contributorId":5332,"corporation":false,"usgs":true,"family":"Scott","given":"Clint","email":"clintonscott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":769614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kolker, Allan 0000-0002-5768-4533 akolker@usgs.gov","orcid":"https://orcid.org/0000-0002-5768-4533","contributorId":643,"corporation":false,"usgs":true,"family":"Kolker","given":"Allan","email":"akolker@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":769615,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70207326,"text":"70207326 - 2019 - Evaluation of maternal penning to improve calf survival in the Chisana Caribou Herd","interactions":[],"lastModifiedDate":"2019-12-17T10:13:50","indexId":"70207326","displayToPublicDate":"2019-09-12T10:02:57","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3773,"text":"Wildlife Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of maternal penning to improve calf survival in the Chisana Caribou Herd","docAbstract":"

Predation is a major limiting factor for most small sedentary caribou (Rangifer tarandus) populations, particularly those that are threatened or endangered across the southern extent of the species’ range. Thus, reducing predation impacts is often a management goal for improving the status of small caribou populations, and lethal predator removal is the primary approach that has been applied. Given that predator control programs are often contentious, other management options that can garner broader public acceptance need to be considered.

Substantial calf losses to predation in the few weeks following birth are common for these small caribou populations. Therefore, we employed a novel experimental approach of maternal penning with the goal of reducing early calf mortality in the Chisana Caribou Herd, a declining population in southwest Yukon and adjacent Alaska thought to number around 300 individuals. Maternal penning entailed temporarily holding pregnant females on their native range in a large pen secure from predators from late March through the initial weeks of calf rearing to mid‐June. During 2003–2006, we conducted 4 annual penning trials with 17–50 pregnant females each year (n = 146 total), assessed survival of calves born in the pens, and evaluated survival and nutritional effects of penning for females that were held. We also investigated the herd's population dynamics during 2003–2008 to determine effects of maternal penning on calf recruitment and population growth. In addition to information gained during maternal penning, we determined natality and survival patterns via radiotelemetry, conducted autumn age‐sex composition surveys each year, and censused the population in mid‐October 2003, 2005, and 2007. Based on our penning trials and demographic investigations, we used simulation models to evaluate the effects of maternal penning relative to a population's inherent growth rate (finite rate of increase [λ] without maternal penning) and penning effort (proportion of calves born in penning) to provide perspective on utility of this approach for improving the status of small imperiled caribou populations.

Pregnant females held in maternal penning tolerated captivity well in that they exhibited positive nutritional responses to ad libitum feed we provided and higher survival than free‐ranging females (0.993 and 0.951 for penned and free‐ranging females, respectively). Survival of pen calves from birth to mid‐June was substantially higher than that of free‐ranging calves (\"urn:x-wiley:00840173:media:wmon1044:wmon1044-math-0001\" = 0.950 and 0.376, respectively). This initial period accounted for 76% of the annual calf mortality in the free‐ranging population. Pen‐born calves maintained their survival advantage over wild‐born calves to the end of their first year (\"urn:x-wiley:00840173:media:wmon1044:wmon1044-math-0002\" = 0.575 and 0.192, respectively) during years penning occurred.

Females in the Chisana Herd were highly productive with 57% producing their first offspring at 2 years of age, and annual natality rates averaging 0.842 calves/female ≥2 years old. Age‐specific natality rates exceeded 0.900 for 4–9‐year‐olds, then exhibited senescent decline to 0.467 by 19 years old. Annual survival of free‐ranging adult females and calves averaged 0.892 and 0.184, respectively, over all study years; both were reduced during 2004 because of poor winter survival. We noted reduced nutritional condition of caribou late that winter in that females we captured were lighter than in other years and produced lighter calves. We suspect that the reduced survival during winter 2004 and the observed nutritional characteristics resulted from adverse snow conditions in combination with effects of the extreme drought experienced the previous summer. Age‐specific survival of adult females was ≥0.900 through 10 years of age, then declined with age.

The Chisana Herd numbered 720 caribou in mid‐October 2003, or more than twice that estimated prior to initiating maternal penning, and increased to 766 caribou by mid‐October 2007. We calculated that penning added 54.2 yearling recruits, or 40% of calves released from penning. Based on the maternal penning results and the population's vital rates, we determined that the herd would have been stable during 2003–2007 at about 713 caribou without maternal penning; thus, the increase in herd size we observed resulted from maternal penning and was equivalent to the estimate of additional yearling recruits. The improvement in the population trend invoked by maternal penning was limited by the larger than expected population size and resulting low penning effort (\"urn:x-wiley:00840173:media:wmon1044:wmon1044-math-0003\" = 11% of calves born in pen).

Our simulations corroborated that maternal penning increased population size by the number of additional recruits provided, even at low penning effort, for inherently stable populations. As the inherent rate of increase dropped below λ = 1.000, more of the additional recruits from penning were needed to offset the downward population inertia, thus requiring increased penning effort to reach stability. For populations declining at λ < 0.890, stability could not be achieved with 100% penning effort given the vital rates in our models.

Maternal penning in its limited application to date has proven to be broadly popular as a nonlethal management action aimed at reducing initial calf mortality from predation in small caribou populations. However, based on the Chisana program and 3 subsequent efforts elsewhere, improvement in population trends have been modest at best and come at a high financial cost. Given the necessity of maximizing penning effort, maternal penning may have a role in addressing conservation challenges for some small caribou populations that are stable or slowly declining, but its application should be primarily driven by objective assessment of the likelihood of improving population trends rather than popularity relative to other management options.

","language":"English","publisher":"Wiley","doi":"10.1002/wmon.1044","usgsCitation":"Adams, L., Farnell, R.G., Oakley, M.P., Jung, T., Larocque, L., Lortie, G., McLelland, J., Reid, M., Roffler, G.H., and Russell, D., 2019, Evaluation of maternal penning to improve calf survival in the Chisana Caribou Herd: Wildlife Monographs, v. 204, no. 1, p. 5-46, https://doi.org/10.1002/wmon.1044.","productDescription":"42 p.","startPage":"5","endPage":"46","ipdsId":"IP-079916","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":459845,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wmon.1044","text":"Publisher Index Page"},{"id":370338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Wrangell‐St. Elias National Park and Preserve, Kluane Wildlife Sanctuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -144.05273437499997,\n 60.48970392643919\n ],\n [\n -137.21923828125,\n 60.48970392643919\n ],\n [\n -137.21923828125,\n 63.38167869302983\n ],\n [\n -144.05273437499997,\n 63.38167869302983\n ],\n [\n -144.05273437499997,\n 60.48970392643919\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"204","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-09-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Adams, Layne G. 0000-0001-6212-2896 ladams@usgs.gov","orcid":"https://orcid.org/0000-0001-6212-2896","contributorId":2776,"corporation":false,"usgs":true,"family":"Adams","given":"Layne G.","email":"ladams@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":777714,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farnell, Richard G.","contributorId":56870,"corporation":false,"usgs":false,"family":"Farnell","given":"Richard","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":777715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oakley, Michelle P.","contributorId":221305,"corporation":false,"usgs":false,"family":"Oakley","given":"Michelle","email":"","middleInitial":"P.","affiliations":[{"id":33063,"text":"Yukon Department of Environment","active":true,"usgs":false}],"preferred":false,"id":777716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jung, Thomas","contributorId":221306,"corporation":false,"usgs":false,"family":"Jung","given":"Thomas","affiliations":[{"id":33063,"text":"Yukon Department of Environment","active":true,"usgs":false}],"preferred":false,"id":777717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Larocque, Lorne","contributorId":221307,"corporation":false,"usgs":false,"family":"Larocque","given":"Lorne","email":"","affiliations":[{"id":33063,"text":"Yukon Department of Environment","active":true,"usgs":false}],"preferred":false,"id":777718,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lortie, Grant","contributorId":221308,"corporation":false,"usgs":false,"family":"Lortie","given":"Grant","email":"","affiliations":[],"preferred":false,"id":777719,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McLelland, Jamie","contributorId":221309,"corporation":false,"usgs":false,"family":"McLelland","given":"Jamie","email":"","affiliations":[{"id":33063,"text":"Yukon Department of Environment","active":true,"usgs":false}],"preferred":false,"id":777720,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reid, Mason","contributorId":51639,"corporation":false,"usgs":true,"family":"Reid","given":"Mason","affiliations":[],"preferred":false,"id":777721,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Roffler, Gretchen H. groffler@usgs.gov","contributorId":1946,"corporation":false,"usgs":true,"family":"Roffler","given":"Gretchen","email":"groffler@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":777722,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Russell, Don","contributorId":200378,"corporation":false,"usgs":false,"family":"Russell","given":"Don","email":"","affiliations":[],"preferred":false,"id":777723,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70206677,"text":"70206677 - 2019 - Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors","interactions":[],"lastModifiedDate":"2019-11-15T16:03:38","indexId":"70206677","displayToPublicDate":"2019-09-11T15:46:47","publicationYear":"2019","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":"Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors","docAbstract":"

Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long‐term encounter surveys with multi‐season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model‐based understanding about at‐risk species with new data. This scenario is exemplified by a bat monitoring program in the Pacific Northwestern United States in which results from 8 years of surveys from 2003 to 2010 require updating with new data from 2016 to 2018. The new data were collected after the arrival of bat white‐nose syndrome and expansion of wind power generation, stressors expected to cause population declines in at least two vulnerable species, little brown bat (Myotis lucifugus) and the hoary bat (Lasiurus cinereus). We used multi‐season occupancy models with empirically informed prior distributions drawn from previous occupancy results (2003–2010) to assess evidence of contemporary decline in these two species. Empirically informed priors provided the bridge across the two monitoring periods and increased precision of parameter posterior distributions, but did not alter inferences relative to use of vague priors. We found evidence of region‐wide summertime decline for the hoary bat (\"urn:x-wiley:20457758:media:ece35612:ece35612-math-0001\" = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat (\"urn:x-wiley:20457758:media:ece35612:ece35612-math-0002\" = 1.1 ± 0.10). White‐nose syndrome was documented in the region in 2016 and may not yet have caused regional impact to the little brown bat. However, our discovery of hoary bat decline is consistent with the hypothesis that the longer duration and greater geographic extent of the wind energy stressor (collision and barotrauma) have impacted the species. These hypotheses can be evaluated and updated over time within our framework of pre–post impact monitoring and modeling. Our approach provides the foundation for a strategic evidence‐based conservation system and contributes to a growing preponderance of evidence from multiple lines of inquiry that bat species are declining.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5612","usgsCitation":"Rodhouse, T.J., Rodriguez, R.M., Banner, K.M., Ormsbee, P.C., Barnett, J., and Irvine, K., 2019, Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors: Ecology and Evolution, v. 9, no. 19, p. 11078-11088, https://doi.org/10.1002/ece3.5612.","productDescription":"11 p.","startPage":"11078","endPage":"11088","ipdsId":"IP-107039","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":459850,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5612","text":"Publisher Index Page"},{"id":369257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.8486328125,\n 41.75492216766298\n ],\n [\n -116.806640625,\n 41.75492216766298\n ],\n [\n -116.806640625,\n 49.081062364320736\n ],\n [\n -124.8486328125,\n 49.081062364320736\n ],\n [\n -124.8486328125,\n 41.75492216766298\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"19","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Rodhouse, Thomas J.","contributorId":173361,"corporation":false,"usgs":false,"family":"Rodhouse","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":775348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rodriguez, Rogelio M.","contributorId":220628,"corporation":false,"usgs":false,"family":"Rodriguez","given":"Rogelio","email":"","middleInitial":"M.","affiliations":[{"id":40195,"text":"Oregon State University-Cascades Campus","active":true,"usgs":false}],"preferred":false,"id":775349,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Banner, Katharine M.","contributorId":220630,"corporation":false,"usgs":false,"family":"Banner","given":"Katharine","email":"","middleInitial":"M.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":775350,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ormsbee, Patricia C.","contributorId":173426,"corporation":false,"usgs":false,"family":"Ormsbee","given":"Patricia","email":"","middleInitial":"C.","affiliations":[{"id":27227,"text":"U.S. Forest Service, Willamette National Forest","active":true,"usgs":false}],"preferred":false,"id":775351,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnett, Jenny","contributorId":220629,"corporation":false,"usgs":false,"family":"Barnett","given":"Jenny","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":775352,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":220632,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":775347,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203470,"text":"pp1837B - 2019 - Evaluation of chemical and hydrologic processes in the eastern Snake River Plain Aquifer based on results from geochemical modeling, Idaho National Laboratory, eastern Idaho","interactions":[],"lastModifiedDate":"2023-04-14T16:58:11.822101","indexId":"pp1837B","displayToPublicDate":"2019-09-11T15:03:14","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1837-B","displayTitle":"Evaluation of Chemical and Hydrologic Processes in the Eastern Snake River Plain Aquifer Based on Results from Geochemical Modeling, Idaho National Laboratory, Eastern Idaho","title":"Evaluation of chemical and hydrologic processes in the eastern Snake River Plain Aquifer based on results from geochemical modeling, Idaho National Laboratory, eastern Idaho","docAbstract":"

Nuclear research activities at the U.S. Department of Energy (DOE) Idaho National Laboratory (INL) produced liquid and solid chemical and radiochemical wastes that were disposed to the subsurface resulting in detectable concentrations of some waste constituents in the eastern Snake River Plain (ESRP) aquifer. These waste constituents may affect the water quality of the aquifer and may pose risks to the eventual users of the aquifer water. To understand these risks to water quality the U.S. Geological Survey, in cooperation with the DOE, conducted geochemical mass-balance modeling of the ESRP aquifer to improve the understanding of chemical reactions, sources of recharge, mixing of water, and groundwater flow directions in the shallow (upper 250 feet) aquifer at the INL.

Modeling was conducted using the water chemistry of 127 water samples collected from sites at and near the INL. Water samples were collected between 1952 and 2017 with most of the samples collected during the mid-1990s. Geochemistry and isotopic data used in geochemical modeling consisted of dissolved oxygen, carbon dioxide, major ions, silica, aluminum, iron, and the stable isotope ratios of hydrogen, oxygen, and carbon.

Geochemical modeling results indicated that the primary chemical reactions in the aquifer were precipitation of calcite and dissolution of plagioclase (An60) and basalt volcanic glass. Secondary minerals other than calcite included calcium montmorillonite and goethite. Reverse cation exchange, consisting of sodium exchanging for calcium on clay minerals, occurred near site facilities where large amounts of sodium were released to the ESRP aquifer in wastewater discharge. Reverse cation exchange acted to retard the movement of wastewater-derived sodium in the aquifer.

Regional groundwater inflow was the primary source of recharge to the aquifer underlying the Northeast and Southeast INL Areas. Birch Creek (BC), the Big Lost River (BLR), and groundwater from BC valley provided recharge to the North INL Area, and the BLR and groundwater from BC and Little Lost River (LLR) valleys provided recharge to the Central INL Area. The BLR, groundwater from the BLR and LLR valleys and the Lost River Range, and precipitation provided recharge to the Northwest and Southwest INL Areas. The primary source of recharge west and southwest of the INL was groundwater inflow from BLR valley. Upwelling geothermal water was a small source of recharge at two wells. Aquifer recharge from surface water in the northern, central, and western parts of the INL indicated that the aquifer in these areas was a dynamic, open system, whereas the aquifer in the eastern part of the INL, which receives little recharge from surface water, was a relatively static and closed system.

Sources of recharge identified from isotope ratios and geochemical modeling (major ion concentrations) were nearly identical for the North, Northeast, Southeast, and Central INL Areas, which indicated that both methods probably accurately identified the sources of recharge in these areas. Conversely, isotope ratios indicated that the BLR and groundwater from the LLR valley provided most recharge to the western parts of the Northwest and Southwest INL Areas, whereas geochemical modeling results indicated a smaller area of recharge from the BLR and groundwater from the LLR valley, a larger area of recharge from the Lost River Range, and recharge of groundwater from the BLR valley that extended to the west INL boundary. The results from geochemical modeling probably were more accurate because major ion concentrations, but not isotope ratios, were available to characterize groundwater from the BLR valley and the Lost River Range. 

Sources of recharge identified with a groundwater flow model (using particle tracking) and geochemical modeling were similar for the Northeast and Southeast INL Areas. However, differences between the models were that the geochemical model represented (1) recharge of groundwater from the Lost River Range in the western part of the INL, whereas the flow model did not, (2) recharge of groundwater from the BC and BLR valleys extending farther south and east, respectively, than the flow model, and (3) more recharge from the BLR in the Southwest INL Area than the flow model.

Mixing of aquifer water beneath the INL included (1) mixing of regional groundwater and water from the BC valley in the Northeast and Southeast INL Areas and (2) mixing of surface water (primarily from the BLR) and groundwater across much of the North, Central, Northwest, and Southwest INL Areas. Localized recharge from precipitation mixed with groundwater in the Northwest and Southwest INL Areas, and localized upwelling geothermal water mixed with groundwater in the Central and Northeast INL Areas. Flow directions of regional groundwater were south in the eastern part of the INL and south-southwest at downgradient locations. Groundwater from the BC and LLR valleys initially flowed southeast before changing to south-southwest flow directions that paralleled regional groundwater, and groundwater from the BLR valley initially flowed south before changing to a southsouthwest direction.

Wastewater-contaminated groundwater flowed south from the Idaho Nuclear Technology and Engineering Center (INTEC) infiltration ponds in a narrow plume, with the percentage of wastewater in groundwater decreasing due to dilution, dispersion, and (or) degradation from about 60‒80 percent wastewater 0.7‒0.8 mile (mi) south of the INTEC infiltration ponds to about 1.4 percent wastewater about 15.5 mi south of the INTEC infiltration ponds. Wastewater contaminated groundwater flowed southeast and then southwest from the Naval Reactors Facility industrial waste ditch, with the percentage of wastewater in groundwater decreasing from about 100 percent wastewater adjacent to the waste ditch to about 2 percent wastewater about 0.6 mi south of the waste ditch.


","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1837B","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Rattray, G.W., 2019, Evaluation of chemical and hydrologic processes in the eastern Snake River Plain aquifer based on results from geochemical modeling, Idaho National Laboratory, eastern Idaho: U.S. Geological Survey Professional Paper 1837-B (DOE/ID-22248), 85 p., https://doi.org/10.3133/pp1837B.","productDescription":"viii, 85 p.","ipdsId":"IP-098993","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":415799,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837D","text":"PP 1837 Chapter D","description":"PP 1837 Chapter D"},{"id":415798,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837C","text":"PP 1837 Chapter C","description":"PP 1837 Chapter C"},{"id":415797,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1837A","text":"PP 1837 Chapter A","description":"PP 1837 Chapter A"},{"id":367371,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1837/b/pp1837b.pdf","text":"Report","size":"13.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1837B"},{"id":367370,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1837/b/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Idaho National Laboratory","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.16629028320312,\n 43.402054267905655\n ],\n [\n -111.87515258789062,\n 43.402054267905655\n ],\n [\n -111.87515258789062,\n 43.68872888432795\n ],\n [\n -112.16629028320312,\n 43.68872888432795\n ],\n [\n -112.16629028320312,\n 43.402054267905655\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702

","tableOfContents":"","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2019-09-11","noUsgsAuthors":false,"publicationDate":"2019-09-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Rattray, Gordon W. 0000-0002-1690-3218 grattray@usgs.gov","orcid":"https://orcid.org/0000-0002-1690-3218","contributorId":2521,"corporation":false,"usgs":true,"family":"Rattray","given":"Gordon","email":"grattray@usgs.gov","middleInitial":"W.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":762788,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}