Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.
","language":"English","publisher":"MDPI","doi":"10.3390/w13050713","usgsCitation":"Fusco, F., Mirus, B.B., Baum, R.L., Calcaterra, D., and De Vita, P., 2021, Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments: Water, v. 13, no. 5, 27 p., https://doi.org/10.3390/w13050713.","productDescription":"27 p.","ipdsId":"IP-120315","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13050713","text":"Publisher Index Page"},{"id":384490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Vesuvius","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.327545166015625,\n 40.75974059207392\n ],\n [\n 14.53765869140625,\n 40.75974059207392\n ],\n [\n 14.53765869140625,\n 40.90832339902113\n ],\n [\n 14.327545166015625,\n 40.90832339902113\n ],\n [\n 14.327545166015625,\n 40.75974059207392\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-05","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":812515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":812516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":812517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":812518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":812519,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218517,"text":"ofr20201145 - 2021 - Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","interactions":[],"lastModifiedDate":"2021-03-05T12:53:46.034292","indexId":"ofr20201145","displayToPublicDate":"2021-03-04T15:39:22","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1145","displayTitle":"Estimated Total Phosphorus Loads for Selected Sites on Great Lakes Tributaries, Water Years 2014–2018","title":"Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","docAbstract":"Monthly and annual total phosphorus loads were estimated for water years 2014 through 2018 for 23 streamgaged (gaged) sites on tributaries to the Great Lakes. Processing and regression methods described by Robertson and others (2018) were used with discrete and continuous data collected during water years 2011 and 2018 to update regression models for estimating instantaneous flux with the same form of equations as published by Robertson and others (2018). Monthly and water year average fluxes for all but two of the 23 gage sites were estimated using a weighted combination of results from surrogate models (which have streamflow, turbidity, and seasonal indicators as explanatory variables) and unit-value (UV)-flow models which have only UV streamflow and seasonal indicators as explanatory variables. Two of the gage sites had extensive periods of missing turbidity records, so average flux estimates for those stations were based solely on results from UV-flow models.
For most sites, estimated loads of total phosphorus were computed and summed for water years 2014–2018. The cumulative loads were used to compute yields and flow-weighted mean concentrations for water years 2014–2018. The estimated cumulative total phosphorus loads for water years 2014–2018 ranged from 112 to 11,500 metric tons. The Maumee River site (U.S. Geological Survey gage number 04193500) had the largest estimated cumulative load for water years 2014–2018 and the third largest estimated flow-weighted mean concentration. In fact, the estimated cumulative load at the Maumee River site was more than three times larger than the second largest estimated cumulative load.
Estimated average annual total phosphorus yields and flow-weighted mean concentrations for water years 2014–2018 ranged from 0.016 metric tons per square kilometer to 0.771 metric tons per square kilometer and 0.033 milligram per liter to 0.466 milligram per liter, respectively. The Cattaraugus Creek gage site (U.S. Geological Survey gage number 04213500) had the highest estimated average annual total phosphorus yield and flow-weighted mean concentration. The average annual total phosphorus yield at the Cattaraugus Creek gage site was almost twice as large as the second largest estimated yield.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201145","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Koltun, G.F., 2021, Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018: U.S. Geological Survey Open-File Report 2020–1145, 13 p., https://doi.org/10.3133/ofr20201145.","productDescription":"Report: v, 13 p.; 2 Appendixes; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122090","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":383717,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145//ofr20201145_appendix_2.csv","text":"Appendix 2","size":"64.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2020–1145 Appendix 2","linkHelpText":"— Estimated monthly total phosphorus loads at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383712,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1145/coverthb.jpg"},{"id":383713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145.pdf","text":"Report","size":"1.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1145"},{"id":383714,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_1.xlsx","text":"Appendix 1","size":"16.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1145 Appendix 1","linkHelpText":"— Estimated annual total phosphorus loads and flow-weighted mean concentrations at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383715,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_1.csv","text":"Appendix 1","size":"8.45 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2020–1145 Appendix 1","linkHelpText":"— Estimated annual total phosphorus loads and flow-weighted mean concentrations at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383716,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_2.xlsx","text":"Appendix 2","size":"66.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1145 Appendix 2","linkHelpText":"— Estimated monthly total phosphorus loads at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383718,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEW32M","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model archive—Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018"}],"country":"United States","state":"Illinois, Indiana, Michigan, 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U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229-1737
Simulating long-term, landscape level changes in forest composition requires estimates of stand age to initialize succession models. Detailed stand ages are rarely available, and even general information on stand history often is lacking. We used data from USDA Forest Service Forest Inventory and Analysis (FIA) database to estimate broad age classes for a forested landscape to simulate changes in landscape composition and structure relative to climate change at Fort Drum, a 43,000 ha U.S. Army installation in northwestern New York. Using simple linear regression, we developed relationships between tree diameter and age for FIA site trees from the host and adjacent ecoregions and applied those relationships to forest stands at Fort Drum. We observed that approximately half of the variation in age was explained by diameter breast height (DBH) across all species studied (r2 = 0.42 for sugar maple Acer saccharum to 0.63 for white ash Fraxinus americana). We then used age-diameter relationships from published research on northern hardwood species to calibrate results from the FIA-based analysis. With predicted stand age, we used tree species life histories and environmental conditions represented by ecological site types to parameterize a stochastic forest landscape model (LANDIS-II) to spatially and temporally model successional changes in forest communities at Fort Drum. Forest stands modeled over 100 years without significant disturbance appeared to reflect expected patterns of increasing dominance by shade-tolerant mesophytic tree species such as sugar maple, red maple (Acer rubrum), and eastern hemlock (Tsuga canadensis) where soil moisture was sufficient. On drier sandy soils, eastern white pine (Pinus strobus), red pine (P. resinosa), northern red oak (Quercus rubra), and white oak (Q. alba) continued to be important components throughout the modeling period with no net loss at the landscape scale. Our results suggest that despite abundant precipitation and relatively low evapotranspiration rates for the region, low soil water holding capacity and fertility may be limiting factors for the spread of mesophytic species on excessively drained soils in the region. Increasing atmospheric temperatures projected for the region could alter moisture regimes for many coarse-textured soils providing a possible mechanism for expansion of xerophytic tree species.
","language":"English","publisher":"Hindawi","doi":"10.1155/2021/6650821","usgsCitation":"Odom, R.H., and Ford, W., 2021, Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model: International Journal of Forestry Research, v. 2021, 6650821, 16 p., https://doi.org/10.1155/2021/6650821.","productDescription":"6650821, 16 p.","ipdsId":"IP-111053","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":453196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1155/2021/6650821","text":"Publisher Index Page"},{"id":393572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fort Drum","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.0089111328125,\n 43.95921358836687\n ],\n [\n -75.509033203125,\n 43.95921358836687\n ],\n [\n -75.509033203125,\n 44.209772586984485\n ],\n [\n -76.0089111328125,\n 44.209772586984485\n ],\n [\n -76.0089111328125,\n 43.95921358836687\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2021","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, Richard H.","contributorId":171659,"corporation":false,"usgs":false,"family":"Odom","given":"Richard","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":829633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":829632,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218590,"text":"ofr20201146 - 2021 - Practical field survey operations for flood insurance rate maps","interactions":[],"lastModifiedDate":"2021-03-05T12:41:18.272992","indexId":"ofr20201146","displayToPublicDate":"2021-03-04T08:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1146","displayTitle":"Practical Field Survey Operations for Flood Insurance Rate Maps","title":"Practical field survey operations for flood insurance rate maps","docAbstract":"The U.S. Geological Survey assists the Federal Emergency Management Agency in its mission to identify flood hazards and zones for risk premiums for communities nationwide, by creating flood insurance rate maps through updating hydraulic models that use river geometry data. The data collected consist of elevations of river channels, banks, and structures, such as bridges, dams, and weirs that can affect flow. To account for the model complexity of river structure hydraulics and the fidelity between river channel and structure geometry, two distinct standards for collecting geometry data are presented, both using global navigation satellite system real-time network surveying. This method is adapted from U.S. Geological Survey manuals and is foundational in hydraulic surveying for flood insurance rate maps.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201146","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Taylor, N.J., and Simeone, C.E., 2021, Practical field survey operations for flood insurance rate maps: U.S. Geological Survey Open-File Report 2020–1146, 8 p., https://doi.org/10.3133/ofr20201146.","productDescription":"iv, 8 p.","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114316","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":383741,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm11D1","text":"Techniques and Methods 11-D1","linkHelpText":"- Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey"},{"id":383723,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1146/coverthb.jpg"},{"id":383724,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1146/ofr20201146.pdf","text":"Report","size":"662 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1146"},{"id":383725,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm11D3","text":"Techniques and Methods 11-D3","linkHelpText":"- Procedures and Best Practices for Trigonometric Leveling in the U.S. Geological Survey"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
We concur with Azizi‐Rad et al. (2021) that it is vital to critically evaluate and compare different soil carbon models, and we welcome the opportunity to further describe the unique contribution of the PROMISE model (Waring et al. 2020) to this literature. The PROMISE framework does share many features with established biogeochemical models, as our original manuscript highlighted in Table 1, and our work builds upon model innovations developed by many different groups, including that of Azizi‐Rad and colleagues. Yet, the PROMISE framework is distinctive due to where it places mechanistic emphasis, and how these mechanisms are formalized in the mathematical model structure.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15580","usgsCitation":"Waring, B.G., Sulman, B.N., Reed, S., Smith, A.P., Averill, C., Creamer, C., Cusack, D.F., Hall, S.J., Jastrow, J.D., Jilling, A., Kemner, K.M., Kleber, M., Allen Liu, X., Pett-Ridge, J., and Schulz, M., 2021, Response to ‘Stochastic and deterministic interpretation of pool models’: Global Change Biology, v. 27, no. 11, p. e11-e12, https://doi.org/10.1111/gcb.15580.","productDescription":"2 p.","startPage":"e11","endPage":"e12","ipdsId":"IP-127051","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453205,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.15580","text":"Publisher Index Page"},{"id":384273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Waring, Bonnie G. 0000-0002-8457-5164","orcid":"https://orcid.org/0000-0002-8457-5164","contributorId":245284,"corporation":false,"usgs":false,"family":"Waring","given":"Bonnie","email":"","middleInitial":"G.","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":811560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sulman, Benjamin N. 0000-0002-3265-6691","orcid":"https://orcid.org/0000-0002-3265-6691","contributorId":209890,"corporation":false,"usgs":false,"family":"Sulman","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":811561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, A. Peyton","contributorId":245298,"corporation":false,"usgs":false,"family":"Smith","given":"A.","email":"","middleInitial":"Peyton","affiliations":[],"preferred":false,"id":811563,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Averill, Colin","contributorId":245299,"corporation":false,"usgs":false,"family":"Averill","given":"Colin","email":"","affiliations":[],"preferred":false,"id":811564,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Creamer, Courtney 0000-0001-8270-9387","orcid":"https://orcid.org/0000-0001-8270-9387","contributorId":201952,"corporation":false,"usgs":true,"family":"Creamer","given":"Courtney","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":811565,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cusack, Daniela F. 0000-0003-4681-7449","orcid":"https://orcid.org/0000-0003-4681-7449","contributorId":245300,"corporation":false,"usgs":false,"family":"Cusack","given":"Daniela","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":811566,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hall, Steven J. 0000-0002-7841-2019","orcid":"https://orcid.org/0000-0002-7841-2019","contributorId":244336,"corporation":false,"usgs":false,"family":"Hall","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":811567,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jastrow, Julie D.","contributorId":254970,"corporation":false,"usgs":false,"family":"Jastrow","given":"Julie","email":"","middleInitial":"D.","affiliations":[{"id":51371,"text":"Environmental Science Division, Argonne National Laboratory, Lemont IL 60439","active":true,"usgs":false}],"preferred":false,"id":811568,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jilling, Andrea","contributorId":254971,"corporation":false,"usgs":false,"family":"Jilling","given":"Andrea","email":"","affiliations":[{"id":51372,"text":"Department of Plant and Soil Sciences, Oklahoma State University, Stillwater OK 74078","active":true,"usgs":false}],"preferred":false,"id":811569,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kemner, Kenneth M.","contributorId":245301,"corporation":false,"usgs":false,"family":"Kemner","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":811570,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kleber, Markus","contributorId":254972,"corporation":false,"usgs":false,"family":"Kleber","given":"Markus","affiliations":[{"id":51374,"text":"Department of Crop and Soil Science, Oregon State University, Corvallis OR 97331","active":true,"usgs":false}],"preferred":false,"id":811571,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Allen Liu, Xiao-Jun","contributorId":254973,"corporation":false,"usgs":false,"family":"Allen Liu","given":"Xiao-Jun","affiliations":[{"id":51375,"text":"Department of Microbiology, University of Massachusetts, Amherst MA 01003","active":true,"usgs":false}],"preferred":false,"id":811572,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pett-Ridge, Jennifer","contributorId":254974,"corporation":false,"usgs":false,"family":"Pett-Ridge","given":"Jennifer","affiliations":[{"id":51376,"text":"Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore CA 94551","active":true,"usgs":false}],"preferred":false,"id":811573,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":811574,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70219458,"text":"70219458 - 2021 - Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation","interactions":[],"lastModifiedDate":"2021-04-08T12:47:24.059845","indexId":"70219458","displayToPublicDate":"2021-03-04T07:44:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation","docAbstract":"Surface deformation in California's Central Valley (CV) has long been linked to changes in groundwater storage. Recent advances in remote sensing have enabled the mapping of CV deformation and associated changes in groundwater resources at increasingly higher spatiotemporal resolution. Here, we use interferometric synthetic aperture radar (InSAR) from the Sentinel‐1 missions, augmented by continuous Global Positioning System (cGPS) positioning, to characterize the surface deformation of the San Joaquin Valley (SJV, southern two‐thirds of the CV) for consecutive dry (2016) and wet (2017) water years. We separate trends and seasonal oscillations in deformation time series and interpret them in the context of surface and groundwater hydrology. We find that subsidence rates in 2016 (mean −42.0 mm/yr; peak −345 mm/yr) are twice that in 2017 (mean −20.4 mm/yr; peak −177 mm/yr), consistent with increased groundwater pumping in 2016 to offset the loss of surface‐water deliveries. Locations of greatest subsidence migrated outwards from the valley axis in the wetter 2017 water year, possibly reflecting a surplus of surface‐water supplies in the lowest portions of the SJV. Patterns in the amplitude of seasonal deformation and the timing of peak seasonal uplift reveal entry points and potential pathways for groundwater recharge into the SJV and subsequent groundwater flow within the aquifer. This study provides novel insight into the SJV aquifer system that can be used to constrain groundwater flow and subsidence models, which has relevance to groundwater management in the context of California's 2014 Sustainable Groundwater Management Act (SGMA).
Reservoirs in arid regions often provide critical water storage but little is known about their greenhouse gas (GHG) footprint. While there is growing appreciation of the role reservoirs play as GHG sources, there is a lack of understanding of GHG emission dynamics from reservoirs in arid regions and implications for environmental policy. Here we present initial GHG emission measurements from Lake Powell, a large water storage reservoir in the desert southwest United States. We report CO2-eq emissions from the shallow (< 15 m) littoral regions of the reservoir that are higher than the global average areal emissions from reservoirs (9.4 vs. 5.8 g CO2-eq m−2 d−1) whereas fluxes from the main reservoir were two orders of magnitude lower (0.09 g CO2-eq m−2 d−1). We then compared our measurements to modeled CO2 + CH4 emissions from the reservoir using four global scale models. Factoring these emissions into hydropower production at Lake Powell yielded low GHG emissions per MWh−1 as compared to fossil-fuel based energy sources. With the exception of one model, the estimated hydropower emissions for Lake Powell ranged from 10−32 kg CO2-eq MWh−1, compared to ∼400−1000 kg CO2-eq MWh−1 for natural gas, oil, and coal. We also estimate that reduced littoral habitat under low water levels leads to ∼50% reduction in the CO2 equivalent emissions per MWh. The sensitivity of GHG emissions to reservoir water levels suggests that the interaction will be an important policy consideration in the design and operation of arid region systems.
Fire transforms, fragments and sometimes maintains forests, creating mosaics of burned and unburned patches. Highly mobile animals respond to resources in the landscape at a variety of spatial scales, yet we know little about their landscape-scale relationships with fire.
We aimed to identify drivers of bat richness in a landscape mosaic of forested and burned areas while identifying spatial scales at which bat richness was most strongly related to extent, configuration, and diversity measures of landscape-level habitat.
We used multi-species hierarchical occupancy modelling to relate bat richness to landscape variables at 10 spatial scales, based on acoustic data collected in the Sierra Nevada, United States. We also assessed redundancy among landscape variable type (extent, configuration, and diversity) and between focal patch types (forested and burned).
Bat richness was positively associated with heterogenous landscapes, shown by positive associations with pyrodiversity, extent and mean area of burned patches, burned and forested edge density and patch density and relationships were generally consistent across scales. Extent of forest cover and burned areas were highly correlated, but configuration and diversity of these patch types diverged.
Bat communities of our study area appear to be largely resilient to wildfire and adapted to more heterogenous forests and shorter-interval fire regimes that likely predominated before the fire suppression era.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10980-021-01204-y","usgsCitation":"Blakey, R.V., Webb, E.B., Kesler, D.C., Siegel, R.B., Corcoran, D., Cole, J.S., and Johnson, M., 2021, Extent, configuration and diversity of burned and forested areas predict bat richness in a fire-maintained forest: Landscape Ecology, v. 36, p. 1101-1115, https://doi.org/10.1007/s10980-021-01204-y.","productDescription":"15 p.","startPage":"1101","endPage":"1115","ipdsId":"IP-114645","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":453221,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-021-01204-y","text":"Publisher Index Page"},{"id":395689,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Plumas National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.53649902343749,\n 39.72197606377427\n ],\n [\n -120.00915527343749,\n 39.825413103424786\n ],\n [\n -120.00915527343749,\n 39.96449067924025\n ],\n [\n -120.30029296875,\n 40.212440718286466\n ],\n [\n -120.59967041015624,\n 40.371658891506094\n ],\n [\n -120.82489013671875,\n 40.348637376031725\n ],\n [\n -121.0748291015625,\n 40.24389506699777\n ],\n [\n -121.28082275390625,\n 39.953964380766394\n ],\n [\n -120.53649902343749,\n 39.72197606377427\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","noUsgsAuthors":false,"publicationDate":"2021-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Blakey, R. 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C.","contributorId":275326,"corporation":false,"usgs":false,"family":"Kesler","given":"D.","email":"","middleInitial":"C.","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":833997,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Siegel, R. B.","contributorId":216846,"corporation":false,"usgs":false,"family":"Siegel","given":"R.","email":"","middleInitial":"B.","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":833998,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Corcoran, D.","contributorId":275327,"corporation":false,"usgs":false,"family":"Corcoran","given":"D.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833999,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cole, J. S.","contributorId":275328,"corporation":false,"usgs":false,"family":"Cole","given":"J.","email":"","middleInitial":"S.","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":834000,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Matthew mjjohnson@usgs.gov","contributorId":257370,"corporation":false,"usgs":false,"family":"Johnson","given":"Matthew","email":"mjjohnson@usgs.gov","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":834001,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254661,"text":"70254661 - 2021 - Evidence of economical territory selection in a cooperative carnivore","interactions":[],"lastModifiedDate":"2024-06-06T13:48:49.302663","indexId":"70254661","displayToPublicDate":"2021-03-03T08:30:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of economical territory selection in a cooperative carnivore","docAbstract":"As an outcome of natural selection, animals are probably adapted to select territories economically by maximizing benefits and minimizing costs of territory ownership. Theory and empirical precedent indicate that a primary benefit of many territories is exclusive access to food resources, and primary costs of defending and using space are associated with competition, travel and mortality risk. A recently developed mechanistic model for economical territory selection provided numerous empirically testable predictions. We tested these predictions using location data from grey wolves (Canis lupus) in Montana, USA. As predicted, territories were smaller in areas with greater densities of prey, competitors and low-use roads, and for groups of greater size. Territory size increased before decreasing curvilinearly with greater terrain ruggedness and harvest mortalities. Our study provides evidence for the economical selection of territories as a causal mechanism underlying ecological patterns observed in a cooperative carnivore. Results demonstrate how a wide range of environmental and social conditions will influence economical behaviour and resulting space use. We expect similar responses would be observed in numerous territorial species. A mechanistic approach enables understanding how and why animals select particular territories. This knowledge can be used to enhance conservation efforts and more successfully predict effects of conservation actions.
","language":"English","publisher":"Royal Society Publishing","doi":"10.1098/rspb.2021.0108","usgsCitation":"Sells, S., Mitchell, M.S., Podruzny, K.M., Gude, J., Keever, A., Boyd, D.K., Smucker, T., Nelson, A.A., Parks, T.W., Lance, N.J., Ross, M.S., and Inman, R.M., 2021, Evidence of economical territory selection in a cooperative carnivore: Proceedings of the Royal Society B: Biological Sciences, v. 288, no. 1946, 20210108, 10 p., https://doi.org/10.1098/rspb.2021.0108.","productDescription":"20210108, 10 p.","ipdsId":"IP-117340","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453222,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2021.0108","text":"Publisher Index Page"},{"id":429567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","geographicExtents":"{\n \"type\": 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It is commonly inferred that powerful turbidity currents are triggered by major external events, such as storms, river floods, or earthquakes. However, basic models for turbidity current triggering remain poorly tested, with few studies accurately recording precise flow timing. Here, we analyse the most detailed series of measurements yet made of powerful (up to 7.2 m s−1) turbidity currents, within Monterey Canyon, offshore California. During 18-months of instrument deployment, fourteen turbidity currents were directly monitored. No consistent triggering mechanism was observed, though flows did cluster around enhanced seasonal sediment supply. We compare turbidity current timing at Monterey Canyon (a sandy canyon-head fed by longshore drift) to the only other systems where numerous (>10-100) flows have been measured precisely via direct monitoring; the Squamish Delta (a sandy fjord-head delta), and the Congo Canyon (connected to the mud-dominated mouth of the Congo River). A common seasonal pattern emerges, leading to a new model for preconditioning and triggering of turbidity currents initiating through slope failure in areas of sediment accumulation, such as canyon heads or river mouths. In this model, rapid or sustained sediment supply alone can produce elevated pore pressures, which may persist, thereby predisposing slopes to fail. Once preconditioned, a range of minor external perturbations, such as moderate storm-waves, result in local pore pressure variation, and thus become effective triggers. Major external triggers are therefore not always a prerequisite for triggering of powerful turbidity currents.
This study examines the use of organic petrology techniques to quantify the amount of coal and carbonaceous combustion by-products (i.e., coke, coal tar/pitch, cenospheres) in sediments taken from the Kinnickinnic River adjacent to the former site of the Milwaukee Solvay Coke and Gas Company. These materials are of concern as contaminants like polycyclic aromatic hydrocarbons (PAHs) are known to readily adsorb to coal and combustion byproducts. Kinnickinnic River sediment samples (n = 36) ranging in depth (1–11 ft.) were collected from eight core locations to quantify and characterize carbonaceous material in the sediments. To determine the amount (vol%) of organic particulates, U.S. Geological Survey (USGS) modified the existing ASTM D2799 using the following categories: coal, coke, coal tar/pitch, inertinite organics, plant material, cenospheres, and mineral matter. Coal fragments were subdivided by rank using vitrinite reflectance (Ro, %) and organic components were further subdivided into the size fractions of coarse (250–1000 μm), fine (63–250 μm), and very fine (<63 μm). Of the 36 samples analyzed, concentrations of coal, coke, and coal tar/pitch ranged from 0 to 18.2 vol%, 0 to 32.0 vol%, and 0 to 2.6 vol%, respectively, with the highest concentrations occurring near point sources (e.g. discharge pipe and coal unloading operations). Samples that were furthest upstream and downstream from the Solvay site exhibited a marked decrease in particulate organics, with exception of one upstream location which had 19.8 vol% coke. Overall, the modified ASTM method provided a means to quantify the abundance of carbonaceous material present in the sediments. Petrography and total PAH concentrations did not provide a clear correlation to organic matter type or size fraction but the samples with the highest vol% organic matter in each core generally corresponded to the sample with the highest bulk PAH content.
Livestock diseases have devastating consequences economically, socially and politically across the globe. In certain systems, pathogens remain viable after host death, which enables residual transmissions from infected carcasses. Rapid culling and carcass disposal are well-established strategies for stamping out an outbreak and limiting its impact; however, wait-times for these procedures, i.e. response delays, are typically farm-specific and time-varying due to logistical constraints. Failing to incorporate variable response delays in epidemiological models may understate outbreak projections and mislead management decisions. We revisited the 2001 foot-and-mouth epidemic in the United Kingdom and sought to understand how misrepresented response delays can influence model predictions. Survival analysis identified farm size and control demand as key factors that impeded timely culling and disposal activities on individual farms. Using these factors in the context of an existing policy to predict local variation in response times significantly affected predictions at the national scale. Models that assumed fixed, timely responses grossly underestimated epidemic severity and its long-term consequences. As a result, this study demonstrates how general inclusion of response dynamics and recognition of partial controllability of interventions can help inform management priorities during epidemics of livestock diseases.
Groundwater is a primary source of drinking water in northern Utah County. The groundwater system is recharged mainly from precipitation in the adjacent Wasatch Mountains and infiltration of streamflow. In 2004, groundwater withdrawals were estimated to be roughly 44,500 acre-feet per year. In 2016, groundwater withdrawals were estimated to be greater than 63,400 acre-feet per year. To prepare for anticipated future increases in groundwater withdrawals, local cities identified 16 locations as feasible for managed aquifer recharge. Using an updated version of an existing U.S. Geological Survey groundwater flow model of northern Utah County, the Groundwater-Management Process for MODFLOW-2005 was used to investigate optimal managed aquifer recharge scenarios with the objective of maintaining acceptable reductions in simulated discharge at 12 groundwater discharge areas and flowing wells along Utah Lake.
The Groundwater-Management Process is applied to a 50-year (2017–66) projection of groundwater conditions using average recharge conditions and a linear increase of approximately 750 acre-feet per year of municipal groundwater withdrawals. Two sets of discharge constraints were applied. The first scenario constrains discharge to greater than or equal to 80 percent of the 2016 simulated groundwater discharge along Utah Lake. The constraint was met with a total managed aquifer recharge rate of roughly 7,300 acre-feet per year during 2042–56, and 15,600 acre-feet per year during 2057–66. A second scenario constrains discharge to greater than or equal to 90 percent of the 2016 simulated discharge. This constraint can only be met at 8 of the 12 discharge areas along Utah Lake. This required a managed aquifer recharge rate of roughly 10,000 acre-feet per year during 2042–56 and 15,400 acre-feet per year during 2057–66. For both scenarios, the Groundwater-Management Process indicated that all managed aquifer recharge sites need to be used to meet discharges constraints. The discharge constraints were informally defined on the basis of the water rights hierarchy associated with Utah Lake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215010","collaboration":"Prepared in cooperation with the North Utah County Aquifer Council","usgsCitation":"Stolp, B.J., and Brooks, L.E., 2021, Groundwater management process simulations using an updated version of the three-dimensional numerical model of groundwater flow in northern Utah Valley, Utah County, Utah: U.S. Geological Survey Scientific Investigations Report 2021–5010, 28 p., https://doi.org/10.3133/sir20215010.","productDescription":"vi, 28 p","numberOfPages":"28","ipdsId":"IP-119330","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":383759,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5010/sir20215010.xml"},{"id":383731,"rank":3,"type":{"id":34,"text":"Image 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Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
The Grass Valley orogenic gold district in the Sierra Nevada foothills province, central California, is the largest historical gold producer of the North American Cordillera. Gold mineralization is associated with shallowly dipping north-south veins hosted by the 160 Ma Grass Valley granodiorite to the southwest of the Grass Valley fault and steeply dipping east-west veins in accreted oceanic rocks to the northeast of this major fault. Quartz veins from both vein types show well-preserved primary textural relationships. Using a combination of petrographic and microanalytical techniques, the paragenetic sequence of minerals within the veins and the compositions of ore minerals were determined to constrain the mechanisms of quartz vein formation and gold deposition. The veins are composed of early quartz that formed through cooling of hydrothermal fluids derived from a geopressured reservoir at depth. The early quartz shows growth zoning in optical cathodoluminescence and contains abundant growth bands of primary inclusions. The primary inclusion assemblages and myriads of crosscutting secondary fluid inclusions have been affected by postentrapment modification, suggesting that early quartz formation was postdated by pronounced pressure fluctuations. These pressure fluctuations, presumably involving changes from lithostatic to hydrostatic conditions, may be related to fault failure of the host structure as predicted by the fault-valve model. Fluid flow associated with pressure cycling took place along microfractures and grain boundaries resulting in extensive recrystallization of the early quartz. Deposition of pyrite, arsenopyrite, and first-generation gold from these hydrothermal fluids causing recrystallization of the early quartz occurred as a result of wall-rock sulfidation. The gold forms invisible gold in the compositionally zoned pyrite or micron-sized inclusions within pyrite growth zones. The latest growth zones in euhedral quartz crystals that formed in association with this stage of the paragenesis contain very rare primary fluid inclusions that have not been affected by postentrapment modification. The hydrothermal system transitioned entirely to hydrostatic conditions immediately after formation of the latest quartz, explaining the preservation of the primary fluid inclusions. The formation of minor quartz in open spaces was followed by the deposition of second-generation native gold and telluride minerals that are commonly associated with base metal sulfides. Ore formation at this stage of the paragenesis is attributed to the rapid decompression of hydrothermal fluids escaping from the geopressured part of the crust into the overlying hydrostatic realm. There is no fluid inclusion evidence that this pressure drop resulted in fluid immiscibility of the hydrothermal fluids. Fluid inclusion evidence suggests that the north-south veins formed at a paleodepth of ~8 km, whereas the east-west veins appear to have formed at ~10 to 11 km below surface, confirming previous inferences that the NE-dipping Grass Valley reverse fault accommodated a large displacement. The findings of the study at Grass Valley have significant implications for the model for orogenic gold deposits, as the reconstruction of the paragenetic relationships provides evidence for the occurrence of two discrete events of gold introduction that occurred at different conditions during the evolution of the hydrothermal system.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.4794","usgsCitation":"Taylor, R., Monecke, T., Reynolds, T.J., and Monecke, J., 2021, Paragenesis of an orogenic gold deposit: New insights on mineralizing processes at the Grass Valley District, California: Economic Geology, v. 116, no. 2, p. 323-356, https://doi.org/10.5382/econgeo.4794.","productDescription":"34 p.","startPage":"323","endPage":"356","ipdsId":"IP-112775","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":385250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Grass Valley district","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.16845703125,\n 36.99377838872517\n ],\n [\n -118.63037109375,\n 37.622933594900864\n ],\n [\n -120.05859375,\n 39.223742741391305\n ],\n [\n -120.201416015625,\n 40.85537053192494\n ],\n [\n -122.40966796874999,\n 40.95501133048621\n ],\n [\n -122.11303710937499,\n 39.62261494094297\n ],\n [\n -121.234130859375,\n 37.96152331396614\n ],\n [\n -120.16845703125,\n 36.99377838872517\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"116","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Ryan D. 0000-0002-8845-5290","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":201948,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":814576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monecke, Thomas","contributorId":210730,"corporation":false,"usgs":false,"family":"Monecke","given":"Thomas","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":814577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reynolds, T. James","contributorId":257560,"corporation":false,"usgs":false,"family":"Reynolds","given":"T.","email":"","middleInitial":"James","affiliations":[{"id":39908,"text":"FLUID INC.","active":true,"usgs":false}],"preferred":false,"id":814578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Monecke, Jochen","contributorId":237834,"corporation":false,"usgs":false,"family":"Monecke","given":"Jochen","email":"","affiliations":[{"id":47621,"text":"Institute of Theoretical Physics, TU Bergakademie Freiberg, Leipziger Strae 23, 09596 Freiberg, Germany","active":true,"usgs":false}],"preferred":false,"id":814579,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226917,"text":"70226917 - 2021 - Cloud water interception in Hawai‘i: Developing capacity to characterize the spatial patterns and effects on water and ecological processes responses in Hawai‘i","interactions":[],"lastModifiedDate":"2021-12-21T15:26:52.456702","indexId":"70226917","displayToPublicDate":"2021-03-01T09:21:03","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9958,"text":"Final Technical Report","active":true,"publicationSubtype":{"id":1}},"title":"Cloud water interception in Hawai‘i: Developing capacity to characterize the spatial patterns and effects on water and ecological processes responses in Hawai‘i","docAbstract":"Cloud-water interception (CWI) is the process by which fog or cloud water droplets are captured and accumulate on the leaves and branches of plants, some of which drips to the ground. Prior studies in Hawai'i indicate that CWI is highly variable and can contribute substantially to total precipitation. In this study, we monitored CWI and other processes at five mountain field sites on the Islands of Oʻahu, Maui, and Hawaiʻi to explore how CWI (1) varies with different climate and vegetation characteristics, (2) affects plant water use and growth, and (3) contributes to water resources.\nResults show that annual CWI varied from 158 to 910 mm, accounting for 3-34% of total water input at individual sites. This large variation was caused by differences in the quantity of cloud water, wind speed, and vegetation structure between sites. We developed a model to predict CWI using both climatic and forest canopy characteristics. On average, the model underestimated annual CWI by 18%, but reproduced the site differences relatively well. Plant water use decreased during periods of fog events mainly because of associated higher humidity. This new CWI model can be used to assess impacts of climate and land cover change on CWI and provide valuable information for resource management in Hawai‘i, which was not previously possible.\nAt one field site, we explored the impacts of fog water on hydrological and ecological processes. Fog effects on native plant growth were indirect, primarily buffering effects of solar radiation. Removal of grass allowed natural regeneration of seedlings but did not alter soil moisture values. A soil data-collection program was initiated to help evaluate the role CWI has in providing moisture for plants, reducing wildfire risk within the fog zone, and contributing to groundwater recharge to aquifers that supply drinking water and groundwater discharge to streams.","largerWorkTitle":"Pacific Island Climate Adaptation Science Center Final Technical Report","language":"English","publisher":"Climate Adaptation Science Centers","usgsCitation":"Tseng, H., Fortini, L., Mair, A., Kagawa-Viviani, A., Yelenik, S.G., Miyazawa, Y., Nullet, M.A., Kennedy, J., DeLay, J., Leopold, C., and Giambelluca, T., 2021, Cloud water interception in Hawai‘i: Developing capacity to characterize the spatial patterns and effects on water and ecological processes responses in Hawai‘i: Final Technical Report, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-132954","costCenters":[{"id":522,"text":"Pacific Islands Climate Science Center","active":true,"usgs":true},{"id":525,"text":"Pacific 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Alan 0000-0003-0302-6647 dmair@usgs.gov","orcid":"https://orcid.org/0000-0003-0302-6647","contributorId":4975,"corporation":false,"usgs":true,"family":"Mair","given":"Alan","email":"dmair@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagawa-Viviani, Aurora","contributorId":220317,"corporation":false,"usgs":false,"family":"Kagawa-Viviani","given":"Aurora","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":828778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":256836,"corporation":false,"usgs":false,"family":"Yelenik","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":51875,"text":"formerly U.S. Geological Survey; currently Rocky Mountain Research Station, U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":828779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miyazawa, Yoshiyuki","contributorId":214590,"corporation":false,"usgs":false,"family":"Miyazawa","given":"Yoshiyuki","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":828780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nullet, Michael A","contributorId":214588,"corporation":false,"usgs":false,"family":"Nullet","given":"Michael","email":"","middleInitial":"A","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":828781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kennedy, Joseph 0000-0002-6608-2366","orcid":"https://orcid.org/0000-0002-6608-2366","contributorId":203317,"corporation":false,"usgs":true,"family":"Kennedy","given":"Joseph","email":"","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeLay, John","contributorId":270226,"corporation":false,"usgs":false,"family":"DeLay","given":"John","affiliations":[{"id":56117,"text":"UH Honolulu Community College","active":true,"usgs":false}],"preferred":false,"id":828783,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Leopold, Christina 0000-0003-0499-3196","orcid":"https://orcid.org/0000-0003-0499-3196","contributorId":178961,"corporation":false,"usgs":false,"family":"Leopold","given":"Christina","affiliations":[],"preferred":false,"id":828784,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Giambelluca, Thomas 0000-0002-6798-3780","orcid":"https://orcid.org/0000-0002-6798-3780","contributorId":212176,"corporation":false,"usgs":false,"family":"Giambelluca","given":"Thomas","email":"","affiliations":[{"id":38449,"text":"University of Hawai‘i at Mānoa","active":true,"usgs":false}],"preferred":false,"id":828785,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70219047,"text":"70219047 - 2021 - Characterization of deep-sea coral and sponge communities in Greater Farallones National Marine Sanctuary: Point Arena South Essential Fish Habitat Conservation Area and New Amendment 28 Areas","interactions":[],"lastModifiedDate":"2021-03-22T14:00:40.139087","indexId":"70219047","displayToPublicDate":"2021-03-01T08:54:49","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7778,"text":"National Marine Sactuaries Conservation Series","active":true,"publicationSubtype":{"id":1}},"title":"Characterization of deep-sea coral and sponge communities in Greater Farallones National Marine Sanctuary: Point Arena South Essential Fish Habitat Conservation Area and New Amendment 28 Areas","docAbstract":"This report summarizes samples collected during a remotely operated vehicle (ROV) cruise conducted in October 2019 on board E/V Nautilus. Areas sampled in Greater Farallones National Marine Sanctuary included areas proposed for fisheries management zoning in the Point Arena South (PAS) Essential Fish Habitat Conservation Area (EFH). Dive planning targeted habitats and biological communities of corals, sponges, and fishes in relation to the new, 2020 configuration of PAS EFH (hereafter referred to as PAS), which includes areas once closed to commercial bottom trawling and now opened to bottom trawling, once opened to bottom trawling and now closed, or that remain closed to commercial bottom trawling. Particular interest was given to enumerating deep-sea corals and sponges (DSCS) in these areas as they are long-lived, slow-growing species that are vulnerable to impacts from bottom trawling. Fish species were also enumerated. These data provide the most recent assessment and characterization for a portion of these areas before the final ruling on Amendment 28 went into effect on January 1, 2020 (50 C.F.R. part 660).
A total of seven sponge specimens were collected on this mission, some of which could potentially be new species, such as the large yellow ‘plate’-shaped sponge and the ‘palm frond’ morphology of the predatory sponge Asbestopluma, documented on both dives. Six coral collections were made, including three types of red Swiftia sp. gorgonians (two had fan-shaped morphology and one had branched morphology) with different polyp colors. A high diversity of fishes, particularly groundfish, were documented across the entire PAS area.
The findings from this cruise will be provided to NOAA’s National Marine Fisheries Service to help them identify biologically complex areas of the seafloor that are most sensitive to bottom trawling and aid in the ongoing management of this designated essential fish habitat conservation zone. Habitat data from these surveys will be used to confirm substrate prediction models that can be used to predict DSCS habitats where there is a dearth of visual observations.
","language":"English","publisher":"NOAA","usgsCitation":"Graiff, K., Roletto, J., Tezak, S., Williams, G.E., and Cochrane, G.R., 2021, Characterization of deep-sea coral and sponge communities in Greater Farallones National Marine Sanctuary: Point Arena South Essential Fish Habitat Conservation Area and New Amendment 28 Areas: National Marine Sactuaries Conservation Series, iv, 42 p.","productDescription":"iv, 42 p.","ipdsId":"IP-122453","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":384541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Greater Farallones National Marine Sanctuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.34075927734375,\n 38.55997877925585\n ],\n [\n -123.67309570312499,\n 38.87392853923629\n ],\n [\n -123.75274658203126,\n 38.97008658346543\n ],\n [\n -124.00680541992188,\n 38.92522904714054\n ],\n [\n -123.71429443359375,\n 38.51271370850396\n ],\n [\n -123.34075927734375,\n 38.55997877925585\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Graiff, Kaitlin","contributorId":255549,"corporation":false,"usgs":false,"family":"Graiff","given":"Kaitlin","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":812559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roletto, Jan","contributorId":152297,"corporation":false,"usgs":false,"family":"Roletto","given":"Jan","email":"","affiliations":[{"id":18902,"text":"Gulf of the Farallones National Marine Sanctuary","active":true,"usgs":false}],"preferred":false,"id":812560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tezak, Sage","contributorId":255550,"corporation":false,"usgs":false,"family":"Tezak","given":"Sage","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":812561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Gary E.","contributorId":198924,"corporation":false,"usgs":false,"family":"Williams","given":"Gary","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":812562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cochrane, Guy R. 0000-0002-8094-4583 gcochrane@usgs.gov","orcid":"https://orcid.org/0000-0002-8094-4583","contributorId":2870,"corporation":false,"usgs":true,"family":"Cochrane","given":"Guy","email":"gcochrane@usgs.gov","middleInitial":"R.","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":812563,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218744,"text":"70218744 - 2021 - Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure","interactions":[],"lastModifiedDate":"2021-06-01T17:47:28.644424","indexId":"70218744","displayToPublicDate":"2021-03-01T08:25:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure","docAbstract":"Zequanox® is a biopesticide registered by the U.S. Environmental Protection Agency (USEPA) and the Canadian Pest Management Regulatory Agency for controlling dreissenid mussels with demonstrated selective toxicity. However, some research has indicated that Zequanox may impact the body condition and survival of some non-target species. We assessed avoidance behaviors of two species of cold-, cool-, and warm-water fishes to Zequanox at the maximum concentration allowed by the USEPA label (100 mg/L as active ingredient). Naïve, juvenile fish (n = 30 per species) were individually observed in a two-flume choice tank through which Zequanox-treated and untreated water simultaneously flowed in an unobstructed arena. Individual fish were observed during an untreated control period (20 min) and two Zequanox-exposure periods (20 min each). Treatment was alternated between arena sides to account for potential side bias in the test subjects. Positional data were collected and tabulated in real time with EthoVision® XT software. Zequanox concentrations and water quality properties (pH, dissolved oxygen, temperature, and specific conductance) were monitored during each trial. Analysis of treatment response was performed using a contrast within linear mixed-effects models. Our results indicate that Brook Trout, Lake Trout, and Bluegill avoided Zequanox-treated water, Yellow Perch were indifferent to Zequanox-treated water, and Lake Sturgeon and Fathead Minnow were attracted to Zequanox-treated water. These results combined with existing species sensitivity literature may help inform resource managers of potential treatment-related risks.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre - REABIC","doi":"10.3391/mbi.2021.12.1.07","usgsCitation":"Barbour, M., Luoma, J.A., Severson, T.J., Wise, J.K., and Bennie, B., 2021, Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure: Management of Biological Invasions, v. 12, no. 1, p. 96-107, https://doi.org/10.3391/mbi.2021.12.1.07.","productDescription":"12 p.","startPage":"96","endPage":"107","ipdsId":"IP-111883","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":453273,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2021.12.1.07","text":"Publisher Index Page"},{"id":436481,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BWGW8F","text":"USGS data release","linkHelpText":"Avoidance behavior of cold-, cool-, and warmwater fish exposed to Zequanox in a two-choice preference chamber, data release"},{"id":385080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barbour, Matthew 0000-0002-0095-9188 mbarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-9188","contributorId":195580,"corporation":false,"usgs":true,"family":"Barbour","given":"Matthew","email":"mbarbour@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Severson, Todd J. 0000-0001-5282-3779 tseverson@usgs.gov","orcid":"https://orcid.org/0000-0001-5282-3779","contributorId":4749,"corporation":false,"usgs":true,"family":"Severson","given":"Todd","email":"tseverson@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811582,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wise, Jeremy K. 0000-0003-0184-6959 jwise@usgs.gov","orcid":"https://orcid.org/0000-0003-0184-6959","contributorId":5009,"corporation":false,"usgs":true,"family":"Wise","given":"Jeremy","email":"jwise@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811583,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennie, Barbara","contributorId":257430,"corporation":false,"usgs":false,"family":"Bennie","given":"Barbara","email":"","affiliations":[],"preferred":false,"id":814234,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240429,"text":"70240429 - 2021 - The influence of species life history and distribution characteristics on species responses to habitat fragmentation in an urban landscape","interactions":[],"lastModifiedDate":"2023-02-07T14:18:23.937206","indexId":"70240429","displayToPublicDate":"2021-03-01T08:12:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"The influence of species life history and distribution characteristics on species responses to habitat fragmentation in an urban landscape","docAbstract":"The magmatic-hydrothermal conceptual model for Carlin-type gold deposit genesis calls upon deep-seated Eocene plutons as the primary source of gold-bearing fluids. However, geophysical surveys, geologic mapping, drilling, geochronology, isotopic tracers, and fluid inclusion chemistry have returned ambiguous evidence for the existence of such plutons. The high-grade Cortez Hills gold deposit in northern Nevada hosts shallow, Eocene syn- and postmineralization intrusions, offering an ideal site to investigate the existence of a deep-seated pluton beneath the district. Here, major and trace element analyses of quartz-hosted melt inclusions from four Eocene rhyolite dikes cropping out within the Cortez Hills pit and results from independent thermobarometers provide a window into the subsurface Eocene magmatic plumbing system to test the existence of a deep-seated source pluton. Dissolved volatile contents, melt inclusion entrapment pressures, and thermodynamic phase equilibria indicate that dike magmas were sourced from ~4- to ≥9-km depth from a polybaric magma reservoir residing as a physically and geochemically interconnected crystal mush with extractable or eruptible magma pockets. Magmas ascended adiabatically (nearly isothermally), exsolving fluids, evolving modestly by fractional crystallization, while trapping quartz-hosted melt inclusions steadily from depth to subvolcanic levels where they were emplaced. These data represent the first unequivocal evidence for a deep-seated magma reservoir from which fluid-saturated magma emanated and released magmatic fluids beneath the Cortez district during gold mineralization. However, further investigation into the specific metallogenic potential and metal budget of parental magmas and the partitioning of gold between silicate melt and aqueous fluids will be necessary to provide evidence that exsolved magmatic fluids may have been gold bearing.
The Intermountain West region of the United States extends from the eastern margin of the Sierra Nevada and Cascade Mountains in the west to the Rocky Mountains in the east. The region is characterized by dextral shear along the eastern margin of the Sierra Nevada and nearly east-west extension in the Basin and Range. This region experienced four significant earthquake sequences in the first half of 2020. The most significant mainshocks were the 18 March 2020 Mw 5.7 earthquake north of Magna, Utah (a suburb of Salt Lake City), the 31 March 2020 Mw 6.5 earthquake northwest of Stanley, Idaho, the 15 May 2020 Mw 6.5 earthquake in the Monte Cristo Range, northwest of Tonopah, Nevada, and the 24 June 2020 Mw 5.8 earthquake near Lone Pine, California. The 15 articles appearing in this focus section explore timely and important topics associated with these sequences, including kinematic rupture models, near-field ground motions, aftershock statistics, geologic observations, seismic hazard implications, and seismotectonics. It is noteworthy that the efforts to record and characterize these earthquake sequences took place during travel and work restrictions necessitated by the COVID-19 pandemic.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220210001","usgsCitation":"Gold, R.D., Bormann, J., and Koper, K.D., 2021, Preface to the Focus Section on the 2020 Intermountain West earthquakes: Seismological Research Letters, v. 92, no. 2A, 4 p., https://doi.org/10.1785/0220210001.","productDescription":"4 p.","ipdsId":"IP-125613","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":385240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"92","issue":"2A","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":814551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bormann, Jayne","contributorId":257546,"corporation":false,"usgs":false,"family":"Bormann","given":"Jayne","affiliations":[{"id":52053,"text":"Nevada Seismological Laboratory, University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":814552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koper, Keith D.","contributorId":175489,"corporation":false,"usgs":false,"family":"Koper","given":"Keith","email":"","middleInitial":"D.","affiliations":[{"id":27579,"text":"Swiss Federal Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":814553,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231690,"text":"70231690 - 2021 - Unmixing multiple metamorphic muscovite age populations with powder X-ray diffraction and 40Ar/39Ar analysis","interactions":[],"lastModifiedDate":"2022-05-20T11:39:03.870966","indexId":"70231690","displayToPublicDate":"2021-03-01T06:35:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":732,"text":"American Journal of Science","active":true,"publicationSubtype":{"id":10}},"title":"Unmixing multiple metamorphic muscovite age populations with powder X-ray diffraction and 40Ar/39Ar analysis","docAbstract":"A combination of modal estimates from powder X-ray diffraction (XRD) experiments and argon isotopic data shows that muscovite 40Ar/39Ar total gas age correlates with muscovite composition near the retrograde Bald Mountain shear zone (BMSZ) in Claremont, New Hampshire, and that the shear zone was active at ∼245 Ma. Petrologic study demonstrates that chemical disequilibrium is preserved in muscovite grains in these samples. The recognition of this preservation is critical to the interpretation of the 40Ar/39Ar step-heating experiments, which never produce age plateaus and yield spectra with steps that range in age by ∼20 Ma. Petrographic, compositional, and crystallographic data all indicate that the age spectra reflect dissolution of metastable Na-rich muscovite and precipitation of stable Na-poor muscovite associated with deformation in the BMSZ.Comparison of whole rock and muscovite concentrate XRD patterns from individual samples demonstrates that the mineral separation process can fractionate these muscovite populations. Therefore, four muscovite concentrates of varying magnetic susceptibility were prepared from a single hand sample, analyzed by XRD, and dated. These four splits define a mixing line that resolves end-member ages of 244.5 ± 4.2 Ma and 302.5 ± 12.5 Ma (1σ). Although the ages are imprecise, the petrologically supported conclusion that these schists preserve two discrete ages is distinct from an interpretation that the spectra reflect cooling through closure at ∼270 Ma, as might be concluded in the absence of petrologic characterization. The XRD results also demonstrate that, even well above anchizone conditions, petrologic information relevant to 40Ar/39Ar dating is observable in subtle variations in the crystallography of muscovite grains.
Strain performance evaluations are vital for developing successful fishery management and restoration strategies. Here, we utilized genotypes from 36 microsatellites to investigate hatchery strain contribution to collections of naturally produced lake trout (Salvelinus namaycush) sampled across Lake Michigan. Strain composition varied by area, with recoveries of Seneca Lake strain exceeding expectations based on stocking records in northern Lake Michigan but performing similarly to other strains in southern Lake Michigan. Interstrain hybrids were present at moderate frequencies similar to expectations based on simulations, suggesting that strains are interbreeding randomly. We hypothesize that the superior performance of the Seneca Lake strain in northern Lake Michigan is partially due to adaptive advantages that facilitate increased survival in areas with high mortality from sea lamprey (Petromyzon marinus) predation, such as northern Lake Michigan. However, when this selective pressure is lessened, the Seneca Lake strain performs similarly to other strains. Our study demonstrates that strain performance can vary across small spatial scales and illustrates the importance of conducting thorough strain evaluations to inform management and conservation.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2020-0072","usgsCitation":"Larson, W., Kornis, M., Turnquist, K., Bronte, C., Holey, M., S. Dale Hanson, Treska, T., and Stott, W., 2021, The genetic composition of wild recruits in a recovering lake trout population in Lake Michigan: Canadian Journal of Fisheries and Aquatic Sciences, v. 78, no. 3, p. 286-300, https://doi.org/10.1139/cjfas-2020-0072.","productDescription":"15 p.","startPage":"286","endPage":"300","ipdsId":"IP-117531","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.7667444904358,\n 45.40752242438859\n ],\n [\n -87.92143062832137,\n 44.177449306420996\n ],\n [\n -88.29028758124349,\n 42.940747823525065\n ],\n [\n -88.05113007530815,\n 41.699747315481886\n ],\n [\n -86.44116921626345,\n 41.59527769642992\n ],\n [\n -85.9936111378925,\n 42.94092212399947\n ],\n [\n -86.15568612025434,\n 44.30117229837185\n ],\n [\n -84.57035066906786,\n 45.309637339506565\n ],\n [\n -84.95481454600835,\n 46.27927767660145\n ],\n [\n -86.5065882598793,\n 46.102466591922905\n ],\n [\n -87.7667444904358,\n 45.40752242438859\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"78","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, Wesley A.","contributorId":349236,"corporation":false,"usgs":false,"family":"Larson","given":"Wesley A.","affiliations":[{"id":83462,"text":"NOAA, former CRU scientist","active":true,"usgs":false}],"preferred":false,"id":924161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kornis, Matthew S.","contributorId":349237,"corporation":false,"usgs":false,"family":"Kornis","given":"Matthew S.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":924162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Turnquist, Keith N.","contributorId":349238,"corporation":false,"usgs":false,"family":"Turnquist","given":"Keith N.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":924163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bronte, Charles R.","contributorId":349239,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles R.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":924164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holey, Mark E.","contributorId":349240,"corporation":false,"usgs":false,"family":"Holey","given":"Mark E.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":924165,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"S. Dale Hanson","contributorId":349241,"corporation":false,"usgs":false,"family":"S. Dale Hanson","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":924166,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Treska, Theodore J.","contributorId":349242,"corporation":false,"usgs":false,"family":"Treska","given":"Theodore J.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":924167,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":924168,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228552,"text":"70228552 - 2021 - Contrasting patterns of demography and population viability among gopher tortoise (Gopherus polyphemus) populations at the species’ northern range edge","interactions":[],"lastModifiedDate":"2022-02-14T20:15:35.294629","indexId":"70228552","displayToPublicDate":"2021-02-28T13:58:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Contrasting patterns of demography and population viability among gopher tortoise (Gopherus polyphemus ) populations at the species’ northern range edge","title":"Contrasting patterns of demography and population viability among gopher tortoise (Gopherus polyphemus) populations at the species’ northern range edge","docAbstract":"Population viability analyses are useful tools to predict abundance and extinction risk for imperiled species. In southeastern North America, the federally threatened gopher tortoise (Gopherus polyphemus) is a keystone species in the diverse and imperiled longleaf pine (Pinus palustris) ecosystem, and researchers have suggested that tortoise populations are declining and characterized by high extinction risk. We report results from a 30-year demographic study of gopher tortoises in southern Alabama (1991–2020), where 3 populations have been stable and 3 others have declined. To better understand the demographic vital rates associated with stable and declining tortoise populations, we used a multi-state hierarchical mark-recapture model to estimate sex- and stage-specific patterns of demographic vital rates at each population. We then built a predictive population model to project population dynamics and evaluate extinction risk in a population viability context. Population structure did not change significantly in stable populations, but juveniles became less abundant in declining populations over 30 years. Apparent survival varied by age, sex, and site; adults had higher survival than juveniles, but female survival was substantially lower in declining populations than in stable ones. Using simulations, we predicted that stable populations with high female survival would persist over the next 100 years but sites with lower female survival would decline, become male-biased, and be at high risk of extirpation. Stable populations were most sensitive to changes in apparent survival of adult females. Because local populations varied greatly in vital rates, our analysis improves upon previous demographic models for northern populations of gopher tortoises by accounting for population-level variation in demographic patterns and, counter to previous model predictions, suggests that small tortoise populations can persist when habitat is managed effectively. © 2021 The Wildlife Society.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21996","usgsCitation":"Folt, B., Goessling, J., Tucker, A., Guyer, C., Herman, S., Shelton-Nix, E., and McGowan, C.P., 2021, Contrasting patterns of demography and population viability among gopher tortoise (Gopherus polyphemus) populations at the species’ northern range edge: Journal of Wildlife Management, v. 85, no. 4, p. 617-630, https://doi.org/10.1002/jwmg.21996.","productDescription":"14 p.","startPage":"617","endPage":"630","ipdsId":"IP-118037","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395925,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Conecuh National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.73568725585938,\n 31.00115451727899\n ],\n [\n -86.53656005859375,\n 31.00115451727899\n ],\n [\n -86.53656005859375,\n 31.129374846459353\n ],\n [\n -86.73568725585938,\n 31.129374846459353\n ],\n [\n -86.73568725585938,\n 31.00115451727899\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Folt, Brian","contributorId":267702,"corporation":false,"usgs":false,"family":"Folt","given":"Brian","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goessling, J.M.","contributorId":276114,"corporation":false,"usgs":false,"family":"Goessling","given":"J.M.","email":"","affiliations":[{"id":56925,"text":"Eckerd College","active":true,"usgs":false}],"preferred":false,"id":834563,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, A. M.","contributorId":243202,"corporation":false,"usgs":false,"family":"Tucker","given":"A. M.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834564,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guyer, C.","contributorId":267706,"corporation":false,"usgs":false,"family":"Guyer","given":"C.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834565,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herman, S.","contributorId":276115,"corporation":false,"usgs":false,"family":"Herman","given":"S.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834566,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shelton-Nix, E.","contributorId":276116,"corporation":false,"usgs":false,"family":"Shelton-Nix","given":"E.","email":"","affiliations":[{"id":56927,"text":"Alabama Department of Conservation and Natural Resources","active":true,"usgs":false}],"preferred":false,"id":834567,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":834568,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}