As part of a U.S. Geological Survey water-quality study started in 2018, in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency, a publicly available software package called R–QWTREND was developed for analyzing trends in stream-water quality. The R–QWTREND package is a collection of functions written in R, an open source language and a general environment for statistical computing and graphics. The package uses a parametric time-series model to express logarithmically transformed concentration in terms of flow-related variability, trend, and serially correlated model errors. Flow-related variability captures natural variability in concentration on the basis of concurrent and antecedent streamflow. The trend identifies systematic changes in concentration in terms of potential step trends, piecewise monotonic trends, or user-specified trends. Maximum likelihood estimation is used to estimate model parameters and determine the best-fit trend model. This report describes the time-series model and statistical methodology behind R–QWTREND and provides formal documentation for installing and using the package.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201014","collaboration":"Prepared in cooperation with the International Joint Commission, North Dakota Department of Environmental Quality, and Minnesota Pollution Control Agency","usgsCitation":"Vecchia, A.V., and Nustad, R.A., 2020, Time-series model, statistical methods, and software documentation for R–QWTREND—An R package for analyzing trends in stream-water quality (ver. 1.2, March 2023): U.S. Geological Survey Open-File Report 2020–1014, 51 p., https://doi.org/10.3133/ofr20201014.","productDescription":"Report: viii, 52 p.; Appendix; Dataset","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109088","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":414697,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1014/versionHist.txt","text":"Version History","size":"2 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":374762,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System","linkHelpText":"USGS water data for the Nation"},{"id":414696,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1014/ofr20201014.pdf","text":"Report","size":"4.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–2014"},{"id":374759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1014/coverthb3.jpg"},{"id":374761,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1014/downloads/","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2020–2014 Appendix 1","linkHelpText":"R–QWTREND Software Package"}],"edition":"Version 1.0: May 13, 2020; Version 1.1: November 30, 2021; Version 1.2: March 28, 2023","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Sustainability of dryland ecosystems depends on the functionality of soil-vegetation feedbacks that affect ecosystem processes, such as nutrient cycling, water capture and retention, soil erosion and deposition, and plant establishment and reproduction. Useful, common indicators can provide information on soil and site stability, hydrologic function, and biotic integrity. Evaluation of rangeland health thus relies on describing the condition and sustainability of these individual, measurable, and observable indicators that are linked to important ecosystem processes. This report focuses on the ~200,000 acres of the Grand Canyon-Parashant National Monument that is administered by the National Park Service (NPS)—one of the largest NPS units where livestock grazing is a permitted land-use activity. Many ecosystems in the monument are characterized by a low degree of resilience to improper grazing because of low and variable precipitation. The monument is marked by a high degree of environmental heterogeneity, including a large elevation gradient, widely differing precipitation patterns, a diversity of geologic substrates, and unique combinations of plant species.
The objective of this report is to (1) increase our understanding of the underlying landscape, soil, and climate setting factors that affect Grand Canyon-Parashant National Monument dryland ecosystem structure and function (also referred to as land potential) and (2) characterize the condition of monument ecosystems in relation to management concepts, such as rangeland health.
Data were analyzed by elevation zone using both univariate and multivariate approaches. Survey results document the high level of diversity within the study area, including 15 unique soil taxa and 271 species of plants. We collected three new plant species for Grand Canyon-Parashant National Monument and 17 new species for the NPS portion of the monument. Results also document a strong association between rangeland health indicators and elevation, topographic setting, and soils. Soil factors found to explain important variation across plots include the amount of exposed bedrock, soil rockiness, soil texture (and associated hydrologic properties), and soil depth. We also found that dominant species turnover across elevation may represent species’ differences in adaptation to climates, including Larrea tridentata, Coleogyne ramosissima, and Artemisia spp. Bromus rubens is the most common invasive species of concern recorded in this study, but other common invasive species are Bromus tectorum, Erodium cicutarium, and Schismus arabicus. Correlations between an index of cattle use and indicators of rangeland health suggest that areas with high cattle use have increased bare ground, decreased ground cover, increased frequency of Schismus arabicus, decreased cover of Coleogyne ramosissima and Ephedra spp., and increased cover of Gutierrezia spp. The few strong correlations observed between indicators of vascular plant community cover or abundance and indicators of cattle activity support rangeland assessment and monitoring strategies that do not rely solely on plant-based indicators are needed.
This work supports management of dryland ecosystems, including Grand Canyon-Parashant National Monument, using concepts of land potential. We conclude the report with recommendations on improving existing land-potential-based classification systems, associated interpretations, and methods for moving forward with a Grand Canyon-Parashant National Monument rangeland monitoring program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201040","usgsCitation":"Duniway, M.C., and Palmquist, E.C., 2020, Assessment of rangeland ecosystem conditions in Grand Canyon-Parashant National Monument, Arizona: U.S. Geological Survey Open-File Report 2020–1040, 42 p., https://doi.org/10.3133/ofr20201040.","productDescription":"Report: viii, 42 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-106479","costCenters":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"links":[{"id":374803,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJSJHT","linkHelpText":"Rangeland Ecosystem Data, Grand Canyon - Parashant National Monument, AZ, USA"},{"id":374801,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1040/coverthb.jpg"},{"id":374802,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1040/ofr20201040.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon-Parashant National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.005126953125,\n 35.679609609368576\n ],\n [\n -111.57714843749999,\n 35.679609609368576\n ],\n [\n -111.57714843749999,\n 36.97622678464096\n ],\n [\n -114.005126953125,\n 36.97622678464096\n ],\n [\n -114.005126953125,\n 35.679609609368576\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
We developed an initial inventory of ground failure features from the November 30, 2018, magnitude 7.1 Anchorage earthquake. This inventory of 153 features is from ground-based observations soon after the earthquake (December 5–10) that include the presence or absence of liquefaction, landslides, and individual crack traces of lateral spreads and incipient landslides. This is not a complete inventory and simply shows general trends and examples of types and distribution of ground failures documented. Overflight observations (December 1–6) documented landslide and liquefaction presence or absence within the Chugach Mountains and along Cook Inlet in regions inaccessible to vehicles, which greatly expanded the geographic scope of this reconnaissance. Field-mapped ground-failure observations have been augmented with a set of 565 georeferenced and annotated images from both field and overflight reconnaissance cataloging additional ground-failure presence or absence from the Anchorage earthquake.
Tidal erosion, fresh snowfall, limited daylight, and adverse flying conditions contributed significantly to the uncertainty and incompleteness of ground-failure observations during this reconnaissance. Notably, substantial liquefaction features at the mouths of the Little Susitna River (December 1) and Ingram Creek (December 5) were absent during subsequent overlapping missions (December 6 and 9, respectively) because of tidal action. A large rockfall observed on December 1 on Rainbow Peak was not observed during a subsequent December 5 overflight because of snow cover, which suggests that the general lack of observations of landsliding within the Chugach Mountains is uncertain.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201043","usgsCitation":"Grant, A.R.R., Jibson, R.W., Witter, R.C., Allstadt, K.E., Thompson, E.M., and Bender, A.M., 2020, Ground failure triggered by shaking during the November 30, 2018, magnitude 7.1 Anchorage, Alaska, earthquake: U.S. Geological Survey Open-File Report 2020–1043, 21 p., https://doi.org/10.3133/ofr20201043.","productDescription":"Report: iv, 21 p.; Data Release","numberOfPages":"21","onlineOnly":"Y","ipdsId":"IP-106267","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":374798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1043/coverthb.jpg"},{"id":374799,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1043/ofr20201043.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374800,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99ONUNM","linkHelpText":"Field Reconnaissance of Ground Failure Triggered by Shaking during the 2018 M7.1 Anchorage, Alaska, Earthquake"}],"country":"United States","state":"Alaska","city":"Anchorage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.54541015625,\n 60.66241476534369\n ],\n [\n -148.82080078125,\n 60.66241476534369\n ],\n [\n -148.82080078125,\n 61.77312286453146\n ],\n [\n -151.54541015625,\n 61.77312286453146\n ],\n [\n -151.54541015625,\n 60.66241476534369\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center
U.S. Geological Survey
350 N. Akron Road
Moffett Field, CA 94035
Context: Funding for habitat-management programs to maintain population viability is critical for conservation of migratory species; however, such financial resources are limited and can vary greatly over time. The Prairie Pothole Region (PPR) of North America is an excellent system for examining spatiotemporal patterns of funding for waterfowl conservation, because this transboundary region is crucial for reproduction and migration of many duck species.
Aims: We examine large-scale spatiotemporal variation in funding for waterfowl habitat conservation in the PPR during 2007–2016. Specifically, we quantify major sources of funding and how funds were directed towards particular geographies within Canada and the USA. We further examine how sources and magnitude of funding changed over time and in relation to numbers of hunters.
Methods: We assembled data from multiple sources to quantify funding (in US$, 2016 values) from (1) USA states and non-government organisations (NGOs), (2) Canadian government and NGOs, and (3) major USA-based federal funding sources to the Canadian and US portions of the PPR between 2007 and 2016. We fit linear regressions to examine spatiotemporal variation in funding and in numbers of active waterfowl hunters in the USA.
Key results: Whereas annual funding for the Canadian portion was comparatively stable throughout the 10 years (range: US$25–41 million), funding for the US portion was dynamic and increased between the first (range: US$36–48 million) and second (range: US$43–117 million) 5-year intervals, despite concurrent declines in the number of active waterfowl hunters in the USA.
Conclusions: We discovered contrasting trends and dynamics in multiple streams of funding for habitat conservation on each side of the border bisecting the PPR. These findings and approaches warrant closer attention by wildlife professionals. Work is needed to analyse past and future funding for habitat conservation, which can then be used to refine plans for maintaining or recovering populations of migratory species.
Implications: Although funding for waterfowl habitat conservation in the PPR increased over the past decade, trends were inconsistent among subregions and uncertain for some major funding sources. Better understanding of the complexities in funding will help inform more efficient long-term planning efforts for conservation of waterfowl and other migratory species.
","language":"English","publisher":"CSIRO","doi":"10.1071/WR19100","usgsCitation":"Mattsson, B.J., Devries, J., Dubovsky, J.A., Semmens, D., Thogmartin, W.E., Derbridge, J.J., and Lopez-Hoffman, L., 2020, Sources and dynamics of international funding for waterfowl conservation in the Prairie Pothole Region of North America: Wildlife Research, v. 47, no. 4, p. 279-295, https://doi.org/10.1071/WR19100.","productDescription":"17 p.","startPage":"279","endPage":"295","ipdsId":"IP-101539","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":467290,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr19100","text":"Publisher Index Page"},{"id":462329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mattsson, Brady J.","contributorId":197269,"corporation":false,"usgs":false,"family":"Mattsson","given":"Brady","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":914175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Devries, Jim","contributorId":344571,"corporation":false,"usgs":false,"family":"Devries","given":"Jim","affiliations":[{"id":7182,"text":"Ducks Unlimited Canada","active":true,"usgs":false}],"preferred":false,"id":914176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dubovsky, James A.","contributorId":201247,"corporation":false,"usgs":false,"family":"Dubovsky","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":914177,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":914178,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":914179,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Derbridge, Jonathan J. 0000-0003-3074-3166","orcid":"https://orcid.org/0000-0003-3074-3166","contributorId":290285,"corporation":false,"usgs":false,"family":"Derbridge","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[{"id":62394,"text":"The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":914247,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lopez-Hoffman, Laura","contributorId":231064,"corporation":false,"usgs":false,"family":"Lopez-Hoffman","given":"Laura","affiliations":[{"id":28236,"text":"Univ of Arizona","active":true,"usgs":false}],"preferred":false,"id":914180,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70218691,"text":"70218691 - 2020 - Forecasting the combined effects of anticipated climate change and agricultural conservation practices on fish recruitment dynamics in Lake Erie","interactions":[],"lastModifiedDate":"2021-03-05T13:45:03.459109","indexId":"70218691","displayToPublicDate":"2020-05-13T07:31:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting the combined effects of anticipated climate change and agricultural conservation practices on fish recruitment dynamics in Lake Erie","docAbstract":"Geomorphological features potentially related to subsurface ice, such as scalloped depressions, expanded craters, pedestal craters, and banded terrain, are present in and around Hellas Planitia, Mars. We present a radar survey of the region using the Shallow Radar (SHARAD) instrument on board the Mars Reconnaissance Orbiter (MRO) to identify candidate subsurface reflectors that may be due to the presence of potentially ice-rich deposits. We found that the majority of radar returns are likely from off-nadir surface topography (“clutter”), arising from the rough topography of the region. There is no widespread radar return from any subsurface interfaces. However, we identify a group of six reflectors adjacent to each other on a plateau in Malea Patera in which we have higher confidence. Landforms associated with a likely ice-rich mantle are associated with the plateau, but the thickness of this mantle does not correspond to the expected depth of the reflectors. However, layers beneath the mantle and marginal pitting at the edge of the plateau are similar to those associated with pedestal craters, which may be ice rich and are a similar thickness to the expected depth of the reflectors. Malea Patera has been interpreted to be a volcanic caldera, so the reflectors may be associated with a volcanic deposit within the plateau, although the evidence for this is inconclusive. Because this radar detection is localized and its origin ambiguous, we cannot use it to make conclusions about the thickness of subsurface deposits in the Hellas region as a whole. The lack of widespread radar reflectors in this region, as compared to the northern mid-latitudes where extensive radar reflections have been mapped, may be due in part to higher surface roughness, which creates radar clutter that may obscure subsurface reflectors. However on the southern rim of the basin and south of the basin, the lack of reflectors may indicate that the possible ice-rich deposits observed geomorphologically in this region are too thin to be resolved by SHARAD, are dielectrically similar to the underlying unit, or have a gradual vertical transition in ice content that is not reflective for the radar. This would imply that recent climate processes may have favored widespread, thick ice deposition or preservation in the northern hemisphere as compared to the southern hemisphere.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2020.113847","usgsCitation":"Cook, C.W., Bramson, A.M., Byrne, S., Holt, J.W., Christoffersen, M.S., Viola, D., Dundas, C.M., and Goudge, T.A., 2020, Sparse subsurface radar reflectors in Hellas Planitia, Mars: Icarus, v. 348, 113847, 9 p., https://doi.org/10.1016/j.icarus.2020.113847.","productDescription":"113847, 9 p.","ipdsId":"IP-103954","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":377144,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Hellas Plaitia, Mars","volume":"348","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Claire W","contributorId":237037,"corporation":false,"usgs":false,"family":"Cook","given":"Claire","email":"","middleInitial":"W","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":795033,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bramson, Ali M 0000-0003-4903-0916","orcid":"https://orcid.org/0000-0003-4903-0916","contributorId":201618,"corporation":false,"usgs":false,"family":"Bramson","given":"Ali","email":"","middleInitial":"M","affiliations":[{"id":27205,"text":"U. Arizona","active":true,"usgs":false}],"preferred":false,"id":795034,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Byrne, Shane","contributorId":53513,"corporation":false,"usgs":false,"family":"Byrne","given":"Shane","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":795035,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holt, John W 0000-0003-1314-7848","orcid":"https://orcid.org/0000-0003-1314-7848","contributorId":237030,"corporation":false,"usgs":false,"family":"Holt","given":"John","email":"","middleInitial":"W","affiliations":[{"id":27205,"text":"U. Arizona","active":true,"usgs":false}],"preferred":false,"id":795036,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christoffersen, Michael S","contributorId":237038,"corporation":false,"usgs":false,"family":"Christoffersen","given":"Michael","email":"","middleInitial":"S","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":795037,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Viola, Donna","contributorId":127526,"corporation":false,"usgs":false,"family":"Viola","given":"Donna","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":795038,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dundas, Colin M. 0000-0003-2343-7224 cdundas@usgs.gov","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":2937,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin","email":"cdundas@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":795039,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Goudge, Timothy A","contributorId":229474,"corporation":false,"usgs":false,"family":"Goudge","given":"Timothy","email":"","middleInitial":"A","affiliations":[{"id":41655,"text":"U. Texas","active":true,"usgs":false}],"preferred":false,"id":795040,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70196740,"text":"70196740 - 2020 - Decision implementation and the double-loop process in adaptive management of horseshoe crab harvest in Delaware Bay","interactions":[],"lastModifiedDate":"2020-05-27T11:56:34.685044","indexId":"70196740","displayToPublicDate":"2020-05-12T13:25:44","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"24","title":"Decision implementation and the double-loop process in adaptive management of horseshoe crab harvest in Delaware Bay","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","usgsCitation":"McGowan, C.P., Smith, D., and Lyons, J.E., 2020, Decision implementation and the double-loop process in adaptive management of horseshoe crab harvest in Delaware Bay, chap. 24 of Structured decision making: Case studies in natural resource management, p. 258-268.","productDescription":"11 p.","startPage":"258","endPage":"268","ipdsId":"IP-082680","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":375035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Delaware Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.06134033203125,\n 38.7283759182398\n ],\n [\n -74.9212646484375,\n 38.91881851059804\n ],\n [\n -74.99267578125,\n 38.950865400919994\n ],\n [\n -74.9212646484375,\n 39.13432124527173\n ],\n [\n -74.94873046875,\n 39.16414104768742\n ],\n [\n -75.08331298828125,\n 39.196076813671695\n ],\n [\n -75.1300048828125,\n 39.16839998800286\n ],\n [\n -75.267333984375,\n 39.281167913914636\n ],\n [\n -75.56396484375,\n 39.47224533091448\n ],\n [\n -75.574951171875,\n 39.457402514270825\n ],\n [\n -75.45135498046875,\n 39.33642177141801\n ],\n [\n -75.39093017578125,\n 39.25352462727606\n ],\n [\n -75.3826904296875,\n 39.06611426153784\n ],\n [\n -75.29754638671875,\n 39.00637903337455\n ],\n [\n -75.3057861328125,\n 38.93163900447185\n ],\n [\n -75.16571044921875,\n 38.82045110711473\n ],\n [\n -75.0640869140625,\n 38.805470223177466\n ],\n [\n -75.06134033203125,\n 38.7283759182398\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":734198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, David","contributorId":204503,"corporation":false,"usgs":true,"family":"Smith","given":"David","affiliations":[],"preferred":true,"id":734199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyons, James E. 0000-0002-9810-8751 jelyons@usgs.gov","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":177546,"corporation":false,"usgs":true,"family":"Lyons","given":"James","email":"jelyons@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":734200,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213352,"text":"70213352 - 2020 - Effects of climate change on plague exposure pathways and resulting disease dynamics","interactions":[],"lastModifiedDate":"2021-02-03T19:40:21.028748","indexId":"70213352","displayToPublicDate":"2020-05-12T12:24:55","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"seriesNumber":"16 RC01-012","title":"Effects of climate change on plague exposure pathways and resulting disease dynamics","docAbstract":"Introduction and Objectives: Sylvatic plague, a zoonotic flea-borne disease, caused by the bacterium Yersinia pestis, is relevant to the Department of Defense (DOD), because prairie dogs and other susceptible rodents are present on military installations in several western states. Arthropod-borne diseases, like plague, are thought to be particularly sensitive to local climate conditions. Expected changes in temperature and humidity over the next several decades will likely increase the geographical expansion of plague outbreaks in wildlife. Through a combination of field and laboratory work, along with data-driven modeling, we evaluated the potential effects of climate change on plague exposure pathways in prairie dogs and associated rodents to provide guidance to DOD partners regarding the potential for future outbreaks. Briefly, our specific objectives were to determine the relation between local climate conditions and the prevalence of plague and other pathogens while assessing the ecological roles of specific rodent hosts and vector species in plague dynamics, evaluate flea intensity on rodent hosts and in burrows in relation to local climate conditions, and develop models to predict the effects of climate change on plague dynamics.
Technical Approach: Using data and samples collected during a large field study on the effectiveness of vaccination to manage plague in prairie dogs, we assessed rodent/flea assemblages, pathogen prevalence in fleas, and determined how local climate conditions influence flea development rates and relative abundance. Live animals (prairie dogs and some small rodents) were trapped to collect fleas and other samples on 46 prairie dog plots in 6 western states, many sites near DOD lands. At seven additional locations on a latitudinal gradient, fleas were collected from burrows several times per year to assess seasonality and effects of local climate conditions on flea abundance. These data were then used to develop predictive models that could be used to test specific hypotheses.
Results: We determined that flea developmental rates, on-host flea abundance, species composition of the flea community, and burrow temperatures varied across a latitudinal gradient. Rodent and flea community composition and abundance differed geographically and were highly specialized. Flea-switching between prairie dogs and short-lived rodents was rare. Flea development rates, on-host flea abundance, and burrow temperatures increased with increasing ambient temperature. Although relative humidity can affect flea development, burrow humidity was uniformly high (~85%) across sampling sites and seasons. A large increase in the number of fleas found on a prairie dog colony, coupled with a greater number of infested burrows, could have substantial effects on plague dynamics in the western United States as the climate warms. In addition to affecting flea load, climate change may also influence body condition of prairie dogs by reducing the amount of forage. This may result in animals being more tolerant of high flea loads (less engaged in grooming behavior) and more vulnerable to disease.
A potentiometric-surface map for spring 2018 was created for the Mississippi River Valley alluvial (MRVA) aquifer using available groundwater-altitude data from 1,126 wells completed in the MRVA aquifer and from the altitude of the top of the water surface in area rivers from 66 streamgages. Personnel from Arkansas Natural Resources Commission, Arkansas Department of Health, Arkansas Geological Survey, Illinois Department of Agriculture, Illinois State Water Survey, Louisiana Department of Natural Resources, Louisiana Department of Transportation and Development, Mississippi Department of Environmental Quality, Missouri Department of Natural Resources, Yazoo Mississippi Delta Joint Water Management District, U.S. Department of Agriculture-Natural Resources Conservation Service, and U.S. Geological Survey (USGS) routinely collect groundwater-level data from wells screened in the MRVA aquifer. The USGS and the U.S. Army Corps of Engineers routinely collect data on river stage and streamflow for the rivers overlying the MRVA aquifer area. The potentiometric-surface map for 2018 was created utilizing existing groundwater and surface-water altitudes to support investigations to characterize the MRVA aquifer as part of the USGS Water Availability and Use Science Program.
Sufficient data were available to map the potentiometric surface of the MRVA aquifer for spring 2018 for about 87 percent of the aquifer area. The potentiometric contours ranged from 10 to 340 feet above North American Vertical Datum of 1988. The regional direction of groundwater flow was generally to the south-southwest, except in areas of groundwater-altitude depressions, where groundwater flowed into the depression, and near rivers, where flow can be from aquifer to the river or from the river into the aquifer. There are large depressions in the potentiometric-surface map in the lower one-half of the Cache region and in most of the Grand Prairie and Delta regions.
Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","usgsCitation":"Converse, S.J., 2020, Introduction to multi-criteria decision analysis, chap. of Structured decision making: Case studies in natural resource management, p. 51-61.","productDescription":"11 p.","startPage":"51","endPage":"61","ipdsId":"IP-096316","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":408215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854422,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854423,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":854424,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":854425,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832853,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70227715,"text":"70227715 - 2020 - Addressing disease risk to develop a health program for bighorn sheep in Montana","interactions":[],"lastModifiedDate":"2022-10-21T16:11:14.266348","indexId":"70227715","displayToPublicDate":"2020-05-12T11:20:33","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"14","title":"Addressing disease risk to develop a health program for bighorn sheep in Montana","docAbstract":"No abstract available.
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N.","contributorId":171706,"corporation":false,"usgs":false,"family":"Sells","given":"Sarah","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":831979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":831874,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gude, Justin A.","contributorId":95780,"corporation":false,"usgs":true,"family":"Gude","given":"Justin A.","affiliations":[],"preferred":false,"id":831980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209977,"text":"ofr20201036 - 2020 - Water-table elevation maps for 2008 and 2016 and water-table elevation changes in the aquifer system underlying eastern Albuquerque, New Mexico","interactions":[],"lastModifiedDate":"2020-05-13T11:50:00.644538","indexId":"ofr20201036","displayToPublicDate":"2020-05-12T11:13:22","publicationYear":"2020","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":"2020-1036","displayTitle":"Water-Table Elevation Maps for 2008 and 2016 and Water-Table Elevation Changes in the Aquifer System Underlying Eastern Albuquerque, New Mexico","title":"Water-table elevation maps for 2008 and 2016 and water-table elevation changes in the aquifer system underlying eastern Albuquerque, New Mexico","docAbstract":"The addition of surface water from the San Juan-Chama Drinking Water Project to the Albuquerque water supply and the reduction in per capita water use has led to decreased groundwater withdrawals. This decrease in withdrawals has resulted in rising groundwater levels since 2008 in portions of the aquifer underlying Albuquerque. The wells used to assess the Kirtland Air Force Base Bulk Fuels Facility (KAFB BFF) ethylene dibromide (EDB) groundwater contamination were installed with well screens that crossed the water table in order to monitor and sample groundwater within the EDB plume. While replacement wells have been installed, an understanding of the water-table response to decreases in regional groundwater withdrawals is required to evaluate the monitoring well network. Water-table elevation maps of the aquifer underlying the Albuquerque metropolitan area east of the Rio Grande for 2008 and 2016 and a map of the change in elevations in this 8-year period provide an improved understanding of the water-table elevations and the changes that are occurring.
The water-table elevation contours for both 2008 and 2016 show that groundwater generally flows from the Rio Grande and from the mountain-front recharge in the southeast toward the center of the study area, a major groundwater pumping center. The water-table elevation increased in most of the study area from 2008 to 2016. The area of greatest increase in the water-table elevation covers most of the northeastern part of the study area, where there has historically been pumping-related drawdown and subsequent groundwater-level rises in the production zone of the Santa Fe Group aquifer system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201036","collaboration":"Prepared in cooperation with Air Force Civil Engineer Center","usgsCitation":"Flickinger, A.K., and Mitchell, A.C., 2020, Water-table elevation maps for 2008 and 2016 and water-table elevation changes in the aquifer system underlying eastern Albuquerque, New Mexico: U.S. Geological Survey Open-File Report 2020–1036, 9 p., https://doi.org/10.3133/ofr20201036.","productDescription":"Report: vi, 9 p.; Data Release; Interactive Map","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111755 ","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":374556,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://usgs.maps.arcgis.com/home/item.html?id=3b038837dfe347daa8691931182788f5","text":"Interactive map of the study area"},{"id":374553,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1036/coverthb.jpg"},{"id":374554,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1036/ofr20201036.pdf","text":"Report","size":"2.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1036"},{"id":374555,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OHR8Z2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-tables elevations and other well construction data for 2008 and 2016 in eastern Albuquerque, New Mexico"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.6607666015625,\n 34.836349990763864\n ],\n [\n -105.9521484375,\n 34.836349990763864\n ],\n [\n -105.9521484375,\n 35.27701633139884\n ],\n [\n -106.6607666015625,\n 35.27701633139884\n ],\n [\n -106.6607666015625,\n 34.836349990763864\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
A high degree of uncertainty exists for understanding and predicting coastal estuarine response to changing climate, land-use, and sea-level conditions, leaving geologic records as a best-proxy for constraining potential outcomes. With the majority of the world's population focused in coastal regions, understanding how local systems respond to global, regional, and even local pressures is key in developing mitigation, adaptation, and management plans. The geomorphology of Mobile Bay in southeast Alabama (USA) has evolved considerably (e.g., bayhead delta back-stepping) over the late Holocene in response to global and regional sea-level and climate change. Smaller-scale geomorphic changes (e.g., spit and beach ridge development) have also had a significant influence on the evolution of the estuary. Organic matter characteristics, inorganic sediment geochemistry, benthic microfossils, and pollen in a ~ 3500 cal yr BP sediment sequence recovered in a gravity core (20GC) from Bon Secour Bay, a small sub-bay in the southeast corner of Mobile Bay, record time-varying marine influence. Increases in marine influence during ~3500 to 2300 cal yr BP and 1930 to 1160 cal yr BP are defined as zones with high-density and pre-dominantly calcareous foraminiferal species, abundant sand (>10%) and more marine-like geochemical signatures, which contrast the low-density and pre-dominantly agglutinated foraminiferal and more terrestrially influenced estuarine muds observed in other intervals of the sedimentary record (2300–1930 and 1160–400 cal yr BP) and the modern bay. Hydrodynamic models constrained by geomorphic boundary conditions for the time ~ 3500 cal yr BP, consistent with the most prominent marine-influenced sediment, provide insight to potential coastal configuration that might have permitted such marine water intrusion into the bay. Of several scenarios evaluated, a breach in Morgan Peninsula produces tidal circulation within the basin supportive of persistent marine incursions in the bay between ~3500 to 2300 cal yr BP. The findings show that slight variations in coastal configuration can have broad-scale effects on bays and estuaries with consequences that may relate to water quality, vertebrate and invertebrate habitat, and coastal vulnerability to episodic events like (extra)tropical storms.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.margeo.2020.106218","usgsCitation":"Smith, C., Jones, M.C., Osterman, L., and Passeri, D., 2020, Using multiple environmental proxies and hydrodynamic modeling to investigate Late Holocene climate and coastal change within a large Gulf of Mexico estuarine system (Mobile Bay, Alabama, USA): Marine Geology, v. 427, 106218, 12 p., https://doi.org/10.1016/j.margeo.2020.106218.","productDescription":"106218, 12 p.","ipdsId":"IP-112885","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456795,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.margeo.2020.106218","text":"Publisher Index Page"},{"id":378618,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":436990,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WGJO0S","text":"USGS data release","linkHelpText":"Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results"}],"country":"United States","state":"Alabama, Mississippi","otherGeospatial":"Mobile Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.14306640625,\n 30.107117887092357\n ],\n [\n -87.60498046875,\n 30.107117887092357\n ],\n [\n -87.60498046875,\n 30.95876857077987\n ],\n [\n -89.14306640625,\n 30.95876857077987\n ],\n [\n -89.14306640625,\n 30.107117887092357\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"427","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799272,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Miriam C. 0000-0002-6650-7619 miriamjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":4056,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","email":"miriamjones@usgs.gov","middleInitial":"C.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":799273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Osterman, Lisa 0000-0002-8603-5217 osterman@usgs.gov","orcid":"https://orcid.org/0000-0002-8603-5217","contributorId":218441,"corporation":false,"usgs":true,"family":"Osterman","given":"Lisa","email":"osterman@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799274,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227760,"text":"70227760 - 2020 - An adaptive approach to vegetation management in native prairies of the northern Great Plains","interactions":[],"lastModifiedDate":"2022-04-08T15:03:23.468106","indexId":"70227760","displayToPublicDate":"2020-05-12T09:59:38","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"An adaptive approach to vegetation management in native prairies of the northern Great Plains","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","usgsCitation":"Moore, C.T., Gannon, J.J., Shaffer, T.L., and Dixon, C., 2020, An adaptive approach to vegetation management in native prairies of the northern Great Plains, chap. of Structured decision making: Case studies in natural resource management, p. 246-257.","productDescription":"11 p.","startPage":"246","endPage":"257","ipdsId":"IP-091282","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":398387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Clinton T. 0000-0002-6053-2880 cmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-6053-2880","contributorId":3643,"corporation":false,"usgs":true,"family":"Moore","given":"Clinton","email":"cmoore@usgs.gov","middleInitial":"T.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gannon, Jill J. 0000-0002-9271-1349","orcid":"https://orcid.org/0000-0002-9271-1349","contributorId":272533,"corporation":false,"usgs":true,"family":"Gannon","given":"Jill","email":"","middleInitial":"J.","affiliations":[],"preferred":true,"id":832061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shaffer, Terry L. 0000-0001-6950-8951 tshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-6950-8951","contributorId":3192,"corporation":false,"usgs":true,"family":"Shaffer","given":"Terry","email":"tshaffer@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":832062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dixon, Cami","contributorId":272534,"corporation":false,"usgs":false,"family":"Dixon","given":"Cami","affiliations":[{"id":56382,"text":"U.S. Fish and Wildlife Service, National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":832063,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211060,"text":"70211060 - 2020 - Introduction to structuring decisions","interactions":[],"lastModifiedDate":"2020-07-13T14:21:12.83395","indexId":"70211060","displayToPublicDate":"2020-05-12T09:19:24","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"2","title":"Introduction to structuring decisions","docAbstract":"Decision structuring, also known as decision framing, provides the foundation and roadmap for analyzing a decision. For decisions that warrant a systematic approach, structuring begins with identifying the problem for analysis, which sounds simple but can be deceptively difficult because decision problems are often ill-formed at the start. Many have worked on a problem, alone or with others, only to realize down the road that it’s the wrong problem, which Ron Howard calls an “error of the third kind”. How a decision is framed, e.g., narrowly or broadly, can have a profound effect on subsequent analysis and solution. Tools and templates are available to get started, but perhaps no technique is more essential that simply taking the time to ponder on what the problem is all about. Structuring is an iterative process, which allows complexity to be added as needed because not all decisions need the full Monty analysis. All of the case studies in this book have gone through decision structuring and most followed an iterative, prototyping process. 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The pattern of erosion exhibited by the ice-bonded permafrost bluffs in Arctic Alaska, however, is not well explained by these tools. Investigation of the oceanographic, thermal, and mechanical processes that are relevant to permafrost bluff failure along Arctic coastlines is needed. We conducted physics-based numerical simulations of mechanical response that focus on the impact of geometric and material variability on permafrost bluff stress states for a coastal setting in Arctic Alaska that is prone to toppling mode block failure. Our three-dimensional geomechanical boundary-value problems output static realizations of compressive and tensile stresses. We use these results to quantify variability in the loci of potential instability. We observe that niche dimension affects the location and magnitude of the simulated maximum tensile stress more strongly than the bluff height, ice wedge polygon size, ice wedge geometry, bulk density, Young’s Modulus, and Poisson’s Ratio. Our simulations indicate that variations in niche dimension can produce radically different potential failure areas and that even relatively shallow vertical cracks can concentrate displacement within ice-bonded permafrost bluffs. These findings suggest that stability assessment approaches, for which the geometry of the failure plane is delineated a priori, may not be ideal for coastlines similar to our study area and could hamper predictions of erosion rates and nearshore sediment/biogeochemical loading.","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00143","collaboration":"","usgsCitation":"Thomas, M.A., Mota, A., Jones, B., Choens, R.C., Frederick, J.M., and Bull, D.L., 2020, Geometric and material variability influences stress states relevant to coastal permafrost bluff failure: Frontiers in Earth Science, v. 143, no. 8, p. 1-13, https://doi.org/10.3389/feart.2020.00143.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-115840","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":456798,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00143","text":"Publisher Index Page"},{"id":374750,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"143","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":788998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mota, Alejandro","contributorId":208633,"corporation":false,"usgs":false,"family":"Mota","given":"Alejandro","email":"","affiliations":[{"id":37854,"text":"Sandia National Laboratories California, Livermore, California, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":788999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Benjamin M. 0000-0002-1517-4711","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":208625,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin M.","affiliations":[{"id":37848,"text":"Water and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":true,"id":789000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choens, R. Charles","contributorId":224660,"corporation":false,"usgs":false,"family":"Choens","given":"R.","email":"","middleInitial":"Charles","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":789001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frederick, Jennifer M. 0000-0003-2414-778X","orcid":"https://orcid.org/0000-0003-2414-778X","contributorId":208631,"corporation":false,"usgs":false,"family":"Frederick","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[{"id":37851,"text":"Sandia National Laboratories, Albuquerque, New Mexico, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":789002,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bull, Diana L.","contributorId":208628,"corporation":false,"usgs":false,"family":"Bull","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":37851,"text":"Sandia National Laboratories, Albuquerque, New Mexico, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":789003,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211059,"text":"70211059 - 2020 - Introduction to prediction and the value of information","interactions":[],"lastModifiedDate":"2020-07-13T14:16:24.071125","indexId":"70211059","displayToPublicDate":"2020-05-12T09:12:37","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"17","title":"Introduction to prediction and the value of information","docAbstract":"Predicting the consequences of alternative actions in terms of the objectives is central to decision making. Modeling in the broadest sense, from simple to complex and based on data or expert judgment, comprises the essential toolkit for making decision-relevant predictions. Gaps in knowledge and the resulting uncertainty can make predictive modeling challenging. Gathering information to address knowledge gaps, thereby reducing uncertainty, can improve predictions. However, within a decision analysis, the value of information gathering depends on the extent that reduced uncertainty will improve the decision’s outcome. Decision makers commonly confront the choice to proceed directly to a decision in the face of uncertainty or to delay and attempt to reduce the uncertainty significantly before making the decision. Value of information analysis can help make a smart choice. This chapter introduces the purpose, approaches, and tools for addressing knowledge gaps within decision analysis. The three case studies, which follow, illustrate some of the challenges and solutions encountered when addressing knowledge gaps within a decision analysis.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Smith, D.R., 2020, Introduction to prediction and the value of information, chap. 17 of Structured decision making: Case studies in natural resource management, p. 189-195.","productDescription":"7 p.","startPage":"189","endPage":"195","ipdsId":"IP-101561","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":376314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376312,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/title/structured-decision-making/table-of-contents"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":792631,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211040,"text":"70211040 - 2020 - Introduction to risk analysis","interactions":[],"lastModifiedDate":"2020-07-14T14:36:40.62455","indexId":"70211040","displayToPublicDate":"2020-05-12T09:11:18","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"13","title":"Introduction to risk analysis","docAbstract":"Many decisions are made in the face of uncertainty that either cannot or will not be reduced, and the challenge to the decision maker is how to manage the risk imposed by that uncertainty. This chapter will introduce the field of risk analysis, focusing on both the scientific tasks (estimating the probabilities and magnitudes of possible outcomes) and the policy-relevant value judgments needed (understanding the risk tolerances of the decision makers and stakeholders). The three case studies that follow demonstrate a range of approaches to risk management in a natural resource setting.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Runge, M.C., and Converse, S.J., 2020, Introduction to risk analysis, chap. 13 of Structured decision making: Case studies in natural resource management, p. 149-155.","productDescription":"7 p.","startPage":"149","endPage":"155","ipdsId":"IP-101847","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":376313,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376280,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/title/structured-decision-making/table-of-contents"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":792546,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211045,"text":"70211045 - 2020 - Allocating funds under the National Fish Habitat Action Plan","interactions":[],"lastModifiedDate":"2020-07-13T14:10:50.614263","indexId":"70211045","displayToPublicDate":"2020-05-12T09:08:47","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"Allocating funds under the National Fish Habitat Action Plan","docAbstract":"Each year, the Director of the U.S. Fish and Wildlife Service (Service), with advice from a Fisheries Management Team, allocates funding to support the National Fish Habitat Action Plan. The Service distributes the funds to Fish Habitat Partnerships (FHPs), who, in turn, undertake projects that “protect, restore, or enhance fish and aquatic habitats or otherwise directly support habitat-related priorities of Fish Habitat Partnerships.” Initially, this allocation was made based on a simple formula: larger FHPs received twice the allocation of smaller FHPs. But as the number of partnerships grew, and as funding grew at a slower rate, inequities developed among the FHPs. In 2012, the Service convened a structured decision making process to develop a more equitable, transparent, and strategic formula for annual funding allocation. The initial decision analysis, which focused on strategic aspects of the allocation, is described in this chapter. Deliberate consideration of decision analysis concepts brought about two advances: a focus on the fundamental long-term objective of maximizing the sustainability of aquatic species populations; and recognition that the benefits of the relatively small investment by the Service occur through leveraging contributions from management partners and increasing the efficiency of on-the-ground projects. Four allocation strategies were evaluated, using formal expert judgment methods, against an array of ecological and administrative objectives. The resulting consequence table was presented to Service managers to illustrate the considerations that underlie an allocation strategy. The insights of this initial decision analysis led to further internal discussions within the Service, and development of a fully articulated allocation method. In December 2013, the Director of the Service approved this new, competitive, performance-based method for allocating funds to FHPs, and it has been used since then to guide decision making. This case study illustrates the power of problem framing, the importance of articulating fundamental objectives, and the value of making transparent the hidden predictions at the heart of any decision.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Runge, M.C., 2020, Allocating funds under the National Fish Habitat Action Plan, chap. 3 of Structured decision making: Case studies in natural resource management, p. 23-35.","productDescription":"13 p.","startPage":"23","endPage":"35","ipdsId":"IP-100275","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":376311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376285,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/title/structured-decision-making/table-of-contents"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792591,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211050,"text":"70211050 - 2020 - Introduction to resource allocation","interactions":[],"lastModifiedDate":"2020-08-06T19:09:51.866535","indexId":"70211050","displayToPublicDate":"2020-05-12T08:45:02","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"9","title":"Introduction to resource allocation","docAbstract":"With ongoing habitat loss and degradation, ever-increasing threats to biodiversity, and limited funding for conservation and management, nearly every natural resource manager routinely faces difficult resource allocation problems. Funding and capacity for natural resource management rarely meet the need, and informed resource allocations are increasingly important. These decision problems include not only habitat and species management but also a wide variety of administrative decisions. Ranking projects or plans by benefit-cost ratio is an intuitive, heuristic approach to resource allocation but may be inefficient. We present a general resource allocation framework in which these decision problems can be stated mathematically, making it relatively easy to find solutions using mathematical programming such as linear programming. amenable to Linear programming and other constrained optimization routines, which can be implemented in common software applications and used with a wide variety of decision problems, including project prioritization and portfolio decisions. Constrained optimization has advantages over intuitive benefit-cost ratios and can accommodate single and multiple objective problems. We also introduce the three case studies in this section illustrating a variety of resource allocation problems: the first case study shows how to select cost-effective management actions for discrete management units such as wetlands or grassland patches; the second, how to use a patch dynamics model to allocation allocate resources for a reserve network that protects habitat for multiple species of conservation concern; and the third, how to use stochastic simulation to determine allocation of resources in space and time for invasive species management.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structured decision making: Case studies in natural resource management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Johns Hopkins University Press","usgsCitation":"Lyons, J., 2020, Introduction to resource allocation, chap. 9 of Structured decision making: Case studies in natural resource management, p. 99-107.","productDescription":"9 p.","startPage":"99","endPage":"107","ipdsId":"IP-107386","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":376295,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376293,"type":{"id":15,"text":"Index Page"},"url":"https://jhupbooks.press.jhu.edu/title/structured-decision-making/table-of-contents"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, James E. 0000-0002-9810-8751","orcid":"https://orcid.org/0000-0002-9810-8751","contributorId":228916,"corporation":false,"usgs":true,"family":"Lyons","given":"James E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792602,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70213096,"text":"70213096 - 2020 - Aseismic transient slip on the Gofar transform fault, East Pacific Rise","interactions":[],"lastModifiedDate":"2020-09-09T13:43:43.218066","indexId":"70213096","displayToPublicDate":"2020-05-12T08:40:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Aseismic transient slip on the Gofar transform fault, East Pacific Rise","docAbstract":"Oceanic transform faults display a unique combination of seismic and aseismic slip behavior, including a large globally averaged seismic deficit, and the local occurrence of repeating magnitude (M)