This guide describes general protocols and provides templates for news media management at the U.S. Geological Survey (USGS) Cascades Volcano Observatory (CVO) and is intended for use by the CVO scientist-in-charge, communications staff, scientists, and guest communications colleagues. This public version, with CVO names and contact information removed, may be useful to other agencies developing their own protocols and templates. This guide evolved from a smaller document hastily assembled out of necessity during the complex and overwhelming news media interest in the 2004–2008 Mount St. Helens eruption. News media interest exceeded the need for life-saving crisis communication and foretold of the need for future multi-faceted and well-coordinated news media and social media responses during future volcanic events.
This guide accompanies the USGS Volcano Science Center’s (VSC’s) general guidelines and protocols for how communications staff at all VSC observatories will work together to respond to news media requests. The protocols and templates are applicable to (1) normal conditions when CVO has an opportunity to be proactive with its messages and to raise general awareness, (2) general responses to news media and TV documentary inquiries, (3) intense news media interest where the responsibility to communicate information and hazards rests primarily with staff at CVO, and (4) intense and overwhelming news media interest that requires a multiagency response. This guide reflects general protocols in effect at the time of publication. The information will be modified as conditions change. Although “news media” generally refers to traditional outlets such as TV, radio, and newspapers, the protocols used to engage these traditional outlets apply also when responding to bloggers, online news services, and social media comments.
Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
In 1943, Aldo Leopold observed that the real problem of wildlife management is not how to handle wildlife, but how to manage humans. As with any other aspect of wildlife management, social sciences can improve understanding the human dimensions of wildlife disease management (WDM). Human activities have accelerated the emergence of wildlife diseases, and human concerns about the ecological, social, and economic impacts of wildlife diseases and their management have led to diseases becoming headline-worthy public issues. This chapter provides guidance to help front-line professionals understand and address the public’s perspectives and behaviors relevant to WDM. This chapter focuses on practical needs of wildlife disease managers who have to consider and interact with specific stakeholders and the broader public. The chapter does not dive deeply into social science; instead it briefly reviews some concepts that are most relevant to WDM. The chapter also suggests where to look for assistance and additional resources for further reading. Following brief introductory comments, the chapter is organized around a simple model of the general process for WDM. It addresses three key areas where social science can assist in WDM—audience research to understand stakeholders; engaging stakeholders in wildlife disease management; and using risk communication about wildlife diseases and disease management to inspire risk-wise behavior.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Techniques in disease surveillance and investigation in Book 15: Field Manual of Wildlife Diseases","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm15C8","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service and National Park Service","usgsCitation":"Leong, K.M, and Decker, D.J., 2020, Human dimensions considerations in wildlife disease management: U.S. Geological Survey Techniques and Methods, book 15, chap. C8, 21 p., https://doi.org/10.3133/tm15C8.","productDescription":"iv, 21 p.","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-101578","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":373455,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/15/c08/coverthb3.jpg"},{"id":373456,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/15/c08/tm15c8.pdf","text":"Report","size":"1.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 15–C8"}],"publicComments":"This report is Chapter 8 of Section C: Techniques in disease surveillance and investigation in Book 15: Field Manual of Wildlife Diseases\n","contact":"Director, National Wildlife Health Center
U.S. Geological Survey
6006 Schroeder Road
Madison, WI 53711–6223
Numerous studies have documented the linkages between agricultural nitrogen loads and surface water degradation. In contrast, potential water quality improvements due to agricultural best management practices are difficult to detect because of the confounding effect of background nitrate removal rates, as well as the groundwater-driven delay between land surface action and stream response. To characterize background controls on nitrate removal in two agricultural catchments, we calibrated groundwater travel time distributions with subsurface environmental tracer data to quantify the lag time between historic agricultural inputs and measured baseflow nitrate. We then estimated spatially distributed loading to the water table from nitrate measurements at monitoring wells, using machine learning techniques to extrapolate the loading to unmonitored portions of the catchment to subsequently estimate catchment removal controls. Multiple models agree that in-stream processes remove as much as 75% of incoming loads for one subcatchment while removing <20% of incoming loads for the other. The use of a spatially variable loading field did not result in meaningfully different optimized parameter estimates or model performance when compared with spatially constant loading derived directly from a county-scale agricultural nitrogen budget. Although previous studies using individual well measurements have shown that subsurface denitrification due to contact with a reducing argillaceous confining unit plays an important role in nitrate removal, the catchment-scale contribution of this process is difficult to quantify given the available data. Nonetheless, the study provides a baseline characterization of nitrate transport timescales and removal mechanisms that will support future efforts to detect water quality benefits from ongoing best management practice implementation.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/jeq2.20049","usgsCitation":"Zell, W.O., Culver, T., Sanford, W.E., and Goodall, J.L., 2020, Quantifying background nitrate removal mechanisms in an agricultural watershed with contrasting subcatchment baseflow concentrations: Journal of Environmental Quality, v. 49, no. 2, p. 392-403, https://doi.org/10.1002/jeq2.20049.","productDescription":"12 p.","startPage":"392","endPage":"403","ipdsId":"IP-110824","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":437051,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VWY11M","text":"USGS data release","linkHelpText":"MODFLOW-2005 and MODPATH6 models used to simulate groundwater flow and nitrate transport in two tributaries to the Upper Chester River, Maryland"},{"id":430521,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Upper Chester study area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76,\n 39.333\n ],\n [\n -76,\n 39.25\n ],\n [\n -75.916667,\n 39.25\n ],\n [\n -75.916667,\n 39.333\n ],\n [\n -76,\n 39.333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Zell, Wesley O. 0000-0002-8782-6627","orcid":"https://orcid.org/0000-0002-8782-6627","contributorId":339721,"corporation":false,"usgs":true,"family":"Zell","given":"Wesley","email":"","middleInitial":"O.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culver, Teresa B","contributorId":339722,"corporation":false,"usgs":false,"family":"Culver","given":"Teresa B","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":904930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":904931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goodall, Jonathan L","contributorId":339724,"corporation":false,"usgs":false,"family":"Goodall","given":"Jonathan","email":"","middleInitial":"L","affiliations":[{"id":25492,"text":"University of Virginia","active":true,"usgs":false}],"preferred":false,"id":904932,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70209617,"text":"70209617 - 2020 - Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity","interactions":[],"lastModifiedDate":"2020-08-06T19:34:08.003257","indexId":"70209617","displayToPublicDate":"2020-03-25T08:07:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2419,"text":"Journal of Petroleum Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity","docAbstract":"In recent years, geologists and petroleum engineers have struggled to clearly identify the mechanisms that drive productivity in horizontal, hydraulically-fractured oil wells producing from the middle member of the Bakken formation. This paper fills a gap in the literature by showing how this play’s heterogeneity affects factors that drive well productivity. It is important because understanding the relative strength of productivity drivers and how predictors vary spatially facilitates best-practices for well site selection and well completion design. The paper describes an application of the Random Forest (RF) machine learning technique to identify these mechanisms and to evaluate their importance across 9 subareas of the North Dakota portion of the Bakken play. The study examined productivity of 7311 wells initiating production from 2010 through 2017. Well productivity varied considerably across the 9 subareas within the play, so it was not surprising that the dominant predictors, the initial 180-day water cut and the 30-day initial gas production, vary spatially to mirror local conditions that strongly affect well productivity. The relative importance of well completion predictor variables, that is, the numbers of fractures stages per well, volume of injected proppant per stage, volume of injected fluids per stage, and lateral length, varied considerably across the subareas. Statistical permutation tests are presented that generally confirm the importance rankings. Subarea Random Forest models explained from 50 percent to 82 percent of the variation in productivity test samples while the play-wide model explained 73 percent of the test sample well productivity. Weakness in the predictive ability of the Random Forest models are traced to the limited variability in the training data. Implications of the empirical findings regarding the Bakken play for operators and for research and government institutions are discussed in the concluding section.","language":"English","publisher":"Elsevier","doi":"10.1016/j.petrol.2020.107150","usgsCitation":"Attanasi, E., Freeman, P., and Coburn, T., 2020, Well predictive performance of play-wide and Subarea Random Forest models for Bakken productivity: Journal of Petroleum Science and Engineering, v. 191, 107150, 12 p., https://doi.org/10.1016/j.petrol.2020.107150.","productDescription":"107150, 12 p.","ipdsId":"IP-109805","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":457284,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.petrol.2020.107150","text":"Publisher Index Page"},{"id":374051,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80859375,\n 47.931066347509784\n ],\n [\n -107.2705078125,\n 46.5286346952717\n ],\n [\n -103.5791015625,\n 45.02695045318546\n ],\n [\n -101.689453125,\n 45.30580259943578\n ],\n [\n -99.84374999999999,\n 46.89023157359399\n ],\n [\n -98.701171875,\n 48.951366470947725\n ],\n [\n -108.10546875,\n 48.951366470947725\n ],\n [\n -109.072265625,\n 48.980216985374994\n ],\n [\n -108.80859375,\n 47.931066347509784\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":787187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":224150,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":787188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coburn, Tim","contributorId":224151,"corporation":false,"usgs":false,"family":"Coburn","given":"Tim","email":"","affiliations":[{"id":38022,"text":"University of Tulsa","active":true,"usgs":false}],"preferred":false,"id":787189,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209556,"text":"70209556 - 2020 - Sequential biodegradation of 1,2,4-trichlorobenzene at oxic-anoxic groundwater interfaces in model laboratory columns","interactions":[],"lastModifiedDate":"2020-08-06T19:17:57.204399","indexId":"70209556","displayToPublicDate":"2020-03-25T07:32:21","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2233,"text":"Journal of Contaminant Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Sequential biodegradation of 1,2,4-trichlorobenzene at oxic-anoxic groundwater interfaces in model laboratory columns","docAbstract":"Halogenated organic solvents such as chlorobenzenes (CBs) are frequent groundwater contaminants due to legacy spills. When contaminated anaerobic groundwater discharges into surface water through wetlands and other transition zones, aeration can occur from various physical and biological processes at shallow depths, resulting in oxic-anoxic interfaces (OAIs). This study investigated the potential for 1,2,4-trichlorobenzene (1,2,4-TCB) biodegradation at OAIs. A novel upflow column system was developed to create stable anaerobic and aerobic zones, simulating a natural groundwater OAI. Two columns containing (1) sand and (2) a mixture of wetland sediment and sand were operated continuously for 295 days with varied doses of 0.14-1.4 mM sodium lactate (NaLac) as a model electron donor. Both column matrices supported anaerobic reductive dechlorination and aerobic degradation of 1,2,4-TCB spatially separated between anaerobic and aerobic zones. Reductive dechlorination produced a mixture of di- and monochlorobenzene daughter products, with estimated zero-order dechlorination rates up to 31.3 µM/hr. Aerobic CB degradation, limited by available dissolved oxygen, occurred for 1,2,4-TCB and all dechlorinated daughter products. Initial reductive dechlorination did not enhance the overall observed extent or rate of subsequent aerobic CB degradation. Increasing NaLac dose increased the extent of reductive dechlorination, but suppressed aerobic CB degradation at 1.4 mM NaLac due to increased oxygen demand. 16S-rRNA sequencing of biofilm microbial communities revealed strong stratification of functional anaerobic and aerobic organisms between redox zones including the sole putative reductive dechlorinator detected in the columns, Dehalobacter. The sediment mixture column supported enhanced reductive dechlorination compared to the sand column at all tested NaLac doses and growth of Dehalobacter populations up to 4.1×108 copies/g (51% relative abundance), highlighting the potential benefit of sediments in reductive dechlorination processes. Results from these model systems suggest both substantial anaerobic and aerobic CB degradation can co-occur along the OAI at contaminated sites where bioavailable electron donors and oxygen are both present.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jconhyd.2020.103639","usgsCitation":"Chow, S.J., Lorah, M.M., Wadhawan, A.R., Durant, N.D., and Bouwer, E.J., 2020, Sequential biodegradation of 1,2,4-trichlorobenzene at oxic-anoxic groundwater interfaces in model laboratory columns: Journal of Contaminant Hydrology, v. 231, 103639, 13 p., https://doi.org/10.1016/j.jconhyd.2020.103639.","productDescription":"103639, 13 p.","ipdsId":"IP-111522","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":457286,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7217665","text":"External Repository"},{"id":373945,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"231","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chow, Steven J.","contributorId":224063,"corporation":false,"usgs":false,"family":"Chow","given":"Steven","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":786947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorah, Michelle M. 0000-0002-9236-587X","orcid":"https://orcid.org/0000-0002-9236-587X","contributorId":224040,"corporation":false,"usgs":true,"family":"Lorah","given":"Michelle","middleInitial":"M.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wadhawan, Amar R.","contributorId":224041,"corporation":false,"usgs":false,"family":"Wadhawan","given":"Amar","email":"","middleInitial":"R.","affiliations":[{"id":40822,"text":"Arcadis U.S. Inc.","active":true,"usgs":false}],"preferred":false,"id":786844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Durant, Neal D.","contributorId":224042,"corporation":false,"usgs":false,"family":"Durant","given":"Neal","email":"","middleInitial":"D.","affiliations":[{"id":36571,"text":"Geosyntec Consultants","active":true,"usgs":false}],"preferred":false,"id":786845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bouwer, Edward J.","contributorId":224043,"corporation":false,"usgs":false,"family":"Bouwer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":786846,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228430,"text":"70228430 - 2020 - Electrofishing encounter probability, survival, and dispersal of stocked age-0 Muskellunge in Wisconsin lakes","interactions":[],"lastModifiedDate":"2022-02-10T15:05:58.812086","indexId":"70228430","displayToPublicDate":"2020-03-20T08:58:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Electrofishing encounter probability, survival, and dispersal of stocked age-0 Muskellunge in Wisconsin lakes","docAbstract":"Boat electrofishing is often used to sample age-0 Muskellunge Esox masquinongy for indexing recruitment or evaluating stocking success. However, electrofishing samples typically result in low CPUE, prompting concerns regarding whether catch rates reflect actual abundance or whether boat electrofishing is generally ineffective for capturing age-0 Muskellunge (i.e., if fish are not being encountered by the gear). To address these concerns, we used radiotelemetry to evaluate the probability of encountering stocked age-0 Muskellunge (230–350 mm TL) during standardized fall electrofishing surveys in three Wisconsin lakes. Our approach also allowed us to evaluate short-term survival and dispersal from stocking locations. Despite limited dispersal (<2.5 km) from the stocking locations and relatively high short-term survival (75–94%) of radio-tagged fish, few age-0 Muskellunge were located within the path of the electrofishing boat (7–30%). Furthermore, the probability of encounter by boat electrofishing varied by as much as 6.3 times among lakes. Differences in encounter probability among lakes appeared to be related to lake basin and habitat characteristics. Overlays of electrofishing sampling effort and fish locations revealed that traditional shoreline electrofishing may not be an effective way of estimating age-0 Muskellunge CPUE. Modifications to electrofishing protocols, including increased effort in offshore areas and consideration of basin characteristics and habitat, may be needed to increase encounter probabilities and the utility of boat electrofishing for sampling age-0 Muskellunge.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10418","usgsCitation":"Dembkowski, D., Kerns, J.A., Easterly, E.G., and Isermann, D.A., 2020, Electrofishing encounter probability, survival, and dispersal of stocked age-0 Muskellunge in Wisconsin lakes: North American Journal of Fisheries Management, v. 40, no. 2, p. 383-393, https://doi.org/10.1002/nafm.10418.","productDescription":"11 p.","startPage":"383","endPage":"393","ipdsId":"IP-111289","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Stella Lake, Twin Valley Lake, Upper Gresham Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.21207427978516,\n 45.70557989372282\n ],\n [\n -89.17963027954102,\n 45.70557989372282\n ],\n [\n -89.17963027954102,\n 45.72236042939562\n ],\n [\n -89.21207427978516,\n 45.72236042939562\n ],\n [\n -89.21207427978516,\n 45.70557989372282\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.75199222564697,\n 46.06087385306044\n ],\n [\n -89.723,\n 46.06087385306044\n ],\n [\n -89.723,\n 46.07721993221842\n ],\n [\n -89.75199222564697,\n 46.07721993221842\n ],\n [\n -89.75199222564697,\n 46.06087385306044\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.10273933410645,\n 43.01569327500512\n ],\n [\n -90.08136749267578,\n 43.01569327500512\n ],\n [\n -90.08136749267578,\n 43.03708953184211\n ],\n [\n -90.10273933410645,\n 43.03708953184211\n ],\n [\n -90.10273933410645,\n 43.01569327500512\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-03-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Dembkowski, Daniel J.","contributorId":275781,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":834283,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kerns, Janice A.","contributorId":275782,"corporation":false,"usgs":false,"family":"Kerns","given":"Janice","email":"","middleInitial":"A.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":834284,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Easterly, Emma G.","contributorId":275785,"corporation":false,"usgs":false,"family":"Easterly","given":"Emma","email":"","middleInitial":"G.","affiliations":[{"id":33303,"text":"University of Wisconsin Stevens Point","active":true,"usgs":false}],"preferred":false,"id":834285,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834282,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210265,"text":"70210265 - 2020 - 40Ar/39Ar and U-Pb SIMS zircon ages of Ediacaran dikes from the Arabian-Nubian Shield of south Jordan","interactions":[],"lastModifiedDate":"2020-05-27T13:53:26.508555","indexId":"70210265","displayToPublicDate":"2020-03-20T08:50:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"40Ar/39Ar and U-Pb SIMS zircon ages of Ediacaran dikes from the Arabian-Nubian Shield of south Jordan","docAbstract":"A spectacular feature of the Arabian-Nubian Shield (ANS) is the abundance of well-exposed and extensive Neoproterozoic dike swarms of multiple generations. These dikes are generally categorized into metamorphosed and unmetamorphosed post-orogenic dike swarms. The unmetamorphosed dikes in the northern ANS can be grouped into an old and young generations. We dated three dikes from the old generation of the unmetamorphosed dikes: a composite dike with latite margins and rhyolite core (607 ± 6 Ma, U-Pb), a biotite rhyolite dike (600 ± 4 Ma, 40Ar/39Ar age of biotite) and an andesite dike (594 ± 3, 40Ar/39Ar age of amphibole). We propose that these dikes representing the old generation were emplaced at different episodes extending approximately between 607 and 590 Ma. Time and composition equivalent plutonic rocks are common in Jordan and the northern ANS. These dikes crosscut the late to post-collisional granitoids and display a subduction-related character as evidenced from the Nb-Ta anomaly, suggesting a transitional magmatic activity from the orogenic to extensional environment. This generation of dikes is absent in the alkali feldspar A-type granite dated at 586 ± 5 Ma in Jordan and equivalent rocks in the northern ANS, which are crosscut only by the dolerite dikes which has an approximate crystallization age of ~579 Ma (40Ar/39Ar whole rock total gas age). Their within-plate character is supported by the absence of the Nb-Ta anomaly and the high field strength elements tectonic discrimination plots. We propose that these dolerite dikes represent the last Neoproterozoic igneous activity in the northern ANS, i.e. the magmatic activity was terminated ~50 m.y. before the estimated age of the Ram Unconformity at ~530 Ma. This age is in agreement with a previously suggested model of mantle lithosphere delamination from below the northern ANS after a significant crust-mantle thickening caused by the East African Orogeny. The thickening triggered exceptionally rapid uplift, followed by erosional unroofing of the ANS rocks, some lateral extension, and post-orogenic magmatic activity. This was followed by thermal relaxation and subsidence and the gradual denudation, erosion, and peneplanation that gradually developed until the approximate age of the unconformity at ~530 Ma.","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2020.105714","usgsCitation":"Ghanem, H., McAleer, R.J., Jarrar, G.H., Al Hseinat, M., and Whitehouse, M., 2020, 40Ar/39Ar and U-Pb SIMS zircon ages of Ediacaran dikes from the Arabian-Nubian Shield of south Jordan: Precambrian Research, v. 434, 105714, 21 p., https://doi.org/10.1016/j.precamres.2020.105714.","productDescription":"105714, 21 p.","ipdsId":"IP-112209","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":375071,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Red Sea, Sinai Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 34.541015625,\n 29.99300228455108\n ],\n [\n 34.27734375,\n 31.12819929911196\n ],\n [\n 31.289062500000004,\n 31.57853542647338\n ],\n [\n 30.41015625,\n 28.07198030177986\n ],\n [\n 32.16796875,\n 16.88865978738161\n ],\n [\n 42.1875,\n 9.709057068618208\n ],\n [\n 48.427734375,\n 8.494104537551882\n ],\n [\n 48.515625,\n 14.349547837185362\n ],\n [\n 41.484375,\n 24.686952411999155\n ],\n [\n 34.541015625,\n 29.99300228455108\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"434","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ghanem, Hind","contributorId":189107,"corporation":false,"usgs":false,"family":"Ghanem","given":"Hind","email":"","affiliations":[],"preferred":false,"id":789842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":789843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jarrar, Ghaleb H. 0000-0003-3424-3337","orcid":"https://orcid.org/0000-0003-3424-3337","contributorId":224974,"corporation":false,"usgs":false,"family":"Jarrar","given":"Ghaleb","middleInitial":"H.","affiliations":[{"id":35514,"text":"University of Jordan","active":true,"usgs":false}],"preferred":false,"id":789844,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Al Hseinat, Mu’ayyad 0000-0003-3269-1144","orcid":"https://orcid.org/0000-0003-3269-1144","contributorId":224975,"corporation":false,"usgs":false,"family":"Al Hseinat","given":"Mu’ayyad","email":"","affiliations":[{"id":35514,"text":"University of Jordan","active":true,"usgs":false}],"preferred":false,"id":789845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whitehouse, Martin 0000-0003-2227-577X","orcid":"https://orcid.org/0000-0003-2227-577X","contributorId":224976,"corporation":false,"usgs":false,"family":"Whitehouse","given":"Martin","email":"","affiliations":[{"id":39794,"text":"Swedish Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":789846,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211822,"text":"70211822 - 2020 - Genetic family reconstruction characterizes Lake Sturgeon use of newly constructed spawning habitat and larval dispersal","interactions":[],"lastModifiedDate":"2020-08-10T13:10:20.527958","indexId":"70211822","displayToPublicDate":"2020-03-20T08:04:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Genetic family reconstruction characterizes Lake Sturgeon use of newly constructed spawning habitat and larval dispersal","docAbstract":"Since 2004, seven spawning reefs have been constructed in the St. Clair–Detroit River system to remediate lost spawning habitat and increase recruitment of Lake Sturgeon Acipenser fulvescens . Assessment of management actions by collecting and enumerating eggs and larvae provided evidence of spawning Lake Sturgeon and survival of eggs until larval dispersal at constructed reef sites. However, the number of spawners contributing sampled offspring (N s ), effective number of breeders (N b ), and extent of larval dispersal was unknown. Genetic reconstruction of familial relationships assigned eggs and larvae (n = 725) collected in 2015 and 2016 to full‐ and half‐sibling groups and estimated N s , N b , and genetic connectivity. We used a modified COLONY simulation module to simulate and convert 18 microsatellite loci (13 disomic and 5 polysomic) to 205 dominant present/absent markers to increase marker number and familial assignment accuracy in family reconstruction analysis. We assessed COLONY's ability to accurately infer familial relationships across small (n = 50), moderate (n = 125), and large (n = 750) larval sample sizes using two assumed allele frequency distributions for polysomic loci. We found that with fewer offspring sampled, COLONY underestimated N s and with large sample sizes overestimated N s . However, estimates were usually within 12–16% of the simulated true N s . Across reefs, estimates of N s were 151 in 2015 and 208 in 2016, and N b was similar (158 in 2015 and 198 in 2016). Evidence of full‐ and half‐sibling larvae collected at multiple locations indicated that individual Lake Sturgeon spawned at multiple locations within years and larvae dispersed considerable distances. Estimating N s , N b , larval dispersal, and inferred genetic connectivity between locations provides managers with population demographic parameters to assess habitat remediation projects. Continued monitoring, including genetic family reconstruction, may provide insight into the long‐term effects of constructed spawning habitat on recruitment and population‐level genetic diversity.
This geologic map depicts and briefly describes geologic units underlying Petroglyph National Monument and immediately adjacent areas in Bernalillo County, New Mexico. The Monument is underlain dominantly by Quaternary basalts of the Albuquerque Volcanoes volcanic field, a series of basin-filling volcanic flows and associated vents from a monogenetic volcanic highland along the eastern margin of the Llano de Albuquerque. This compilation builds on data of previously published geologic maps and reports but includes new interpretive synthesis of volcanic stratigraphy and a unified representation of Quaternary surficial deposits overlying volcanic deposits within the Monument and areas immediately adjacent. This geologic map emphasizes the distribution of Quaternary volcanic vent areas and lava flow deposits which were incompletely mapped on previous publications. Surficial deposits are simplified, but uniformly mapped and described in contrast to varying map unit distributions, names and descriptions presented in the references above. Underlying deposits of the upper Santa Fe Group are exposed in the western part of the map area and described briefly.
North-trending, syn- and post-eruption faulting is well preserved in the volcanic field and reflected in the subsurface models of aeromagnetic data. These faults are dominated by dip-slip displacement and are interpreted as extensional faults of the central Albuquerque Basin of the northern Rio Grande rift. Elongate distribution of vents for most of the volcanic deposits are spatially associated with the easternmost of these faults and are interpreted to reflect eruptions from fissures paralleling the regional extensional fault trends of the rift.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3447","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Thompson, R.A., Chan, C.F., Gilmer, A.K., and Shroba, R.R., 2020, Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico: U.S. Geological Survey Scientific Investigations Map 3447, scale 1:24,000, https://doi.org/10.3133/sim3447.","productDescription":"2 Sheets: 50.50 inches x 40.00 inches; Data Release; ReadMe","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102605","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":373216,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LW817K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Release for Geologic Map of Petroglyph National Monument and Vicinity, Bernalillo County, New Mexico"},{"id":373215,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3447/sim3447_georeferenced.pdf","text":"Sheet—Georeferenced geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3447"},{"id":399520,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109803.htm"},{"id":373213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3447/coverthb.jpg"},{"id":373222,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3447/ReadMe.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3447 Read Me"},{"id":373214,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3447/sim3447.pdf","text":"Sheet—Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3447"}],"scale":"24000","country":"United States","state":"New Mexico","county":"Bernalillo County","otherGeospatial":"Petroglyph National Monument and vicinity","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.79946899414062,\n 35.097439809364204\n ],\n [\n -106.68823242187499,\n 35.097439809364204\n ],\n [\n -106.68823242187499,\n 35.188961188789925\n ],\n [\n -106.79946899414062,\n 35.188961188789925\n ],\n [\n -106.79946899414062,\n 35.097439809364204\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Center Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
Acoustic telemetry, for tracking fish movement histories, is multidimensional capturing both spatial and temporal domains. Oftentimes, analyses of such data are limited to a single domain, one domain nested within the other, or ad hoc approaches that simultaneously consider both domains. Sequence analysis, on the other hand, offers a repeatable statistical framework that uses a sequence alignment algorithm to calculate pairwise dissimilarities among individual movement histories and then hierarchical agglomerative clustering to identify groups of fish with similar movement histories. The objective of this paper is to explore how acoustic telemetry data can be fit to this statistical framework and used to identify commonalities in the movement histories of acoustic-tagged sea lamprey during upstream migration through the St. Clair-Detroit River System.
Five significant clusters were identified among individual fish. Clusters represented differences in timing of movements (short vs long duration in the Detroit R. and Lake St. Clair); extent of upstream migration (ceased migration in Lake St. Clair, lower St. Clair R., or upper St. Clair R.), and occurrence of fallback (return to Lake St. Clair after ceasing migration in the St. Clair R.). Inferences about sea lamprey distribution and behavior from these results were similar to those reached in a previous analysis using ad-hoc analysis methods.
The repeatable statistical framework outlined here can be used to group sea lamprey movement histories based on shared sequence characteristics (i.e., chronological order of “states” occupied). Further, this framework is flexible and allows researchers to define a priori the movement aspect (e.g., order, timing, duration) that is important for identifying both common or previously undetected movement histories. As such, we do not view sequence analysis as a panacea but as a useful complement to other modelling approaches (i.e., exploratory tool for informing hypothesis development) or a stand-alone semi-quantitative method for generating a simplified, temporally and spatially structured view of complex acoustic telemetry data and hypothesis testing when observed patterns warrant further investigation.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-020-00195-y","usgsCitation":"Lowe, M.R., Holbrook, C., and Hondorp, D.W., 2020, Detecting commonality in multidimensional fish movement histories using sequence analysis: Animal Biotelemetry, v. 8, 10, 14 p., https://doi.org/10.1186/s40317-020-00195-y.","productDescription":"10, 14 p.","ipdsId":"IP-114379","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":457331,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-020-00195-y","text":"Publisher Index Page"},{"id":421938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan","otherGeospatial":"St. Clair River Detroit River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.33021246125514,\n 42.011299379305854\n ],\n [\n -82.17664800813024,\n 42.011299379305854\n ],\n [\n -82.17664800813024,\n 43.03151009761868\n ],\n [\n -83.33021246125514,\n 43.03151009761868\n ],\n [\n -83.33021246125514,\n 42.011299379305854\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Lowe, Michael R. 0000-0002-4645-9429","orcid":"https://orcid.org/0000-0002-4645-9429","contributorId":10539,"corporation":false,"usgs":true,"family":"Lowe","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":886255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886256,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886257,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70209081,"text":"sir20195077 - 2020 - Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona","interactions":[],"lastModifiedDate":"2022-04-22T21:15:48.594847","indexId":"sir20195077","displayToPublicDate":"2020-03-18T12:15:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5077","displayTitle":"Geochemical and Mineralogical Study of the Red Mountain Porphyry Copper-Molybdenum Deposit and Vicinity, Santa Cruz County, Arizona","title":"Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona","docAbstract":"The Red Mountain porphyry copper-molybdenum deposit (Cu-Mo deposit or PCD) is located in the northern part of the Patagonia Mountains, Santa Cruz County, Arizona. Extensive core drilling has delineated a large, deep-seated, structurally intact mineral system that extends from the present surface to depths of more than 1,765 meters. This system is hosted in a thick complex of predominantly felsic to andesitic volcanic rocks of the Cretaceous Period. This complex was intruded by scattered bodies of the Tertiary Period that are predominantly quartz monzonite porphyry; no major associated source intrusion has yet been found at depth.
A total of 818 samples of core were analyzed for as many as 44 elements. The abundances and distributions at depth of at least 17 of these elements (silver [Ag], arsenic [As], gold [Au], boron [B], bismuth [Bi], copper [Cu], mercury [Hg], potassium [K], molybdenum [Mo], lead [Pb], sulfur [S], antimony [Sb], tin [Sn], tellurium [Te], thallium [Tl], tungsten [W], and zinc [Zn]) are related mostly to events that generated the Red Mountain system. Many of these same samples were also analyzed by X-ray diffraction for a suite of minerals. The multielement and mineralogical analyses of the core samples provide important information about the concentrations, associations, and distributions of select elements and minerals, including zoning patterns that may not be apparent from visual examination of core samples. The distributions of selected elements and minerals in these samples reveal an unusually complete mineral system that extends from a typical PCD with potassic alteration at depth to peripheral zones of phyllic and advanced argillic alteration as well as a copper-rich supergene enriched zone and the remnants of a leached cap.
R-mode factor analysis was run with 34 elements for a set of samples from the deep part of the hypogene Cu-Mo deposit and another set from the part of the supergene zone with the highest copper enrichment. For the hypogene zone dataset, five factors are related to the PCD: (1) Ag, Cu, Mo, S, and Te; (2) As, B, Hg, and Sb; (3) Au and sodium (Na); (4) manganese (Mn), Pb, and Zn; and (5) K and Tl. For the supergene dataset, the deposit-related factors include (1) Cu, Mo, S, and Te; (2) Ag, As, Hg, Pb, Sb, and Tl; (3) Au and Na; and (4) K and rubidium (Rb). The changes in element associations between the two datasets indicate that some of these new associations are a result of formation of several suites of hypogene minerals in the deep part of the deposit and different hypogene mineral suites in the peripheral part of the deposit. Some changes may be because of the effects of supergene processes.
Zones containing deposit-related elements and minerals common to many PCDs are present at Red Mountain. These zones include a crude, inverted cup-shaped shell containing anomalous copper accompanied by high concentrations of Ag, Au, K, Mo, total S, sulfate S, Sb, Te, and Tl, as well as local concentrations of As, B, Hg, Pb, and Zn. Hydrothermal minerals spatially associated with the deep hypogene Cu-Mo deposit include chalcopyrite, molybdenite, pyrite, plagioclase, orthoclase, biotite, magnetite, calcite, quartz, and anhydrite.
Many of the hydrothermally deposited elements that are spatially related to the deposit are also concentrated in zones above the deep part of the deposit, including Ag, As, K, Pb, Sb, Te, Tl, and Zn. These elements are concentrated either (1) in generally wide, flat zones present in the upper part of the system or (2) in crudely arcuate peripheral zones found mainly in the middle part of the system and surrounding the deep part of the deposit. Near-surface, restricted hypogene anomalies are present for bismuth, mercury, tin, and tungsten.
The upper part of the deposit has been subjected to supergene enrichment and weathering. Deposit-related elements that remain anomalous in this area include Ag, As, Au, B, Bi, cobalt (Co), Cu, Hg, Mo, Pb, S, Sb, Sn, Te, Tl, uranium (U), and W. These positive concentrations indicate that, with the exception of copper and possibly mercury and uranium, these elements had relatively low chemical mobilities in the supergene enrichment and later weathering environments at Red Mountain. Most may have been deposited during one or more hypogene events and then redistributed locally during later events. Zinc is the only deposit-related element that has clearly been depleted as a result of supergene and (or) weathering events. Minerals that are common in the unweathered upper part of the system include chalcocite, pyrite, quartz, sericite, alunite, and pyrophyllite, as well as less common covellite, enargite, tennantite, tourmaline, barite, anglesite, and other sulfide or sulfate minerals.
Subsequent to formation of the Red Mountain Cu-Mo deposit and supergene enrichment, chemical weathering produced an area of pervasive hematite and other iron oxides in the near-surface part of the deposit to form a leached cap. These iron-rich minerals formed primarily as a result of the oxidation of pyrite. This event was accompanied by losses of cobalt, mercury, magnesium, and zinc, as well as destruction of sericite, plagioclase, pyrite, clay minerals, and pyrophyllite.
A total of 122 rock samples, 119 soil samples, and samples of three plant species (57 mesquite, 108 oak, and 68 juniper) were collected over and around Red Mountain. For the rock and soil samples, the distributions of anomalous Ag, As, Bi, Cu, Fe, Mo, Pb, Sb, Te, and Tl best delineated the exposed part of the deposit. The highest concentrations of many of these elements are centered on one or both of two main areas with exposures of quartz monzonite porphyry. The high concentrations of arsenic in the deposit area (as much as 390 parts per million (ppm) in rock and 1,500 ppm in soil) and of lead (as much as 2,370 ppm in rock and 1,490 ppm in soil) are particularly noteworthy.
The concentrations of various elements in the plant ash vary widely among the three species and are species dependent. Many of the deposit-related elements are either nonessential for plant growth or are considered toxic at certain concentration ranges. In spite of this, the distributions of potentially toxic Ag, As, Bi, Cd, Cu, Mo, Pb, Sb, selenium (Se), and Zn produce deposit-related anomalies for one or more of the three species.
Vegetation sampling offered no advantage over rock or soil sampling as an exploration tool. From an environmental standpoint, however, the plant analyses provide baseline data for both essential and nonessential elements that might be useful, for example, for selecting native plant species for revegetating mine waste areas.
The exposed part of the Red Mountain deposit has not been greatly disturbed as a result of mining and other activities. However, some of the rock, soil, and plant samples that were collected near the Harshaw Creek and Alum Gulch drainages, which are peripheral to Red Mountain, are also anomalous for various deposit-related elements. These anomalies are probably the result of dispersion of stream sediments contaminated with material from past mining.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195077","usgsCitation":"Chaffee, M.A., 2020, Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona: U.S. Geological Survey Scientific Investigations Report 2019–5077, 164 p., https://doi.org/10.3133/sir20195077.","productDescription":"Report: x, 164 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-085267","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":373304,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BS56JZ","text":"USGS data release","linkHelpText":"Data to accompany U.S. Geological Survey Scientific Investigations Report 2019-5077: Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona"},{"id":399536,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109795.htm"},{"id":373303,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5077/sir20195077.pdf","text":"Report","size":"22.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5077"},{"id":373302,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5077/coverthb.jpg"}],"country":"United States","state":"Arizona","county":"Santa Cruz County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-111.364,31.4234],[-111.3654,31.5211],[-111.2983,31.5216],[-111.2634,31.5218],[-111.1608,31.522],[-111.1595,31.5403],[-111.1616,31.5508],[-111.1612,31.6389],[-111.1614,31.7242],[-111.0036,31.7247],[-110.9557,31.7247],[-110.8906,31.7255],[-110.8712,31.7257],[-110.8518,31.7255],[-110.8523,31.731],[-110.7941,31.7309],[-110.7042,31.7308],[-110.6902,31.7306],[-110.6838,31.7305],[-110.6692,31.7308],[-110.6644,31.7303],[-110.617,31.7306],[-110.5341,31.7309],[-110.4485,31.7307],[-110.4485,31.702],[-110.4482,31.6883],[-110.4483,31.6536],[-110.448,31.6157],[-110.4561,31.6154],[-110.4558,31.6017],[-110.4555,31.5871],[-110.4562,31.4684],[-110.4561,31.3328],[-110.4611,31.3328],[-110.4888,31.3328],[-110.5574,31.3324],[-110.6259,31.3323],[-110.6645,31.3321],[-110.7229,31.3318],[-110.7915,31.3315],[-110.8238,31.3313],[-110.8261,31.3312],[-110.8351,31.3312],[-110.8659,31.3309],[-110.8787,31.3308],[-110.9721,31.3301],[-111.0496,31.3294],[-111.0664,31.3292],[-111.0728,31.3292],[-111.1604,31.3577],[-111.1676,31.3601],[-111.1705,31.361],[-111.1725,31.3617],[-111.1746,31.3624],[-111.2218,31.3778],[-111.2843,31.3978],[-111.364,31.4234]]]},\"properties\":{\"name\":\"Santa Cruz\",\"state\":\"AZ\"}}]}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
Due to extensive land conversion over the last century, much of the native prairie pothole ecosystem has been converted to agricultural or other human uses. The prairie pothole ecosystem is found in the northern plains of Iowa, Minnesota, South Dakota, North Dakota, and Montana. Because most of the land in this region is privately owned and used for agricultural production, most impacts to wildlife habitat are the result of decisions by individual landowners. Landowner trust in natural resource management agencies is important for agencies to effectively accomplish their mission. We measured the nature (competence and fairness) and level of trust that western Minnesota landowners have in the Minnesota Department of Natural Resources (MnDNR) and landowners’ wildlife value orientations (WVO). Landowners rated MnDNR slightly higher in competence than fairness; however, these two dimensions were strongly correlated. We developed a MnDNR trust scale (six items) and a three-cluster model dividing landowners along the MnDNR trust scale, which we named Negative (28%), Neutral (43%), and Positive (29%). We provide evidence supporting the salient values similarity (SVS) model that states people have trust in agencies holding similar values; landowners reporting greater importance for wildlife consideration when making land-use decisions also reported greater trust in the MnDNR. In addition, mutualist landowners had the highest trust in the MnDNR and utilitarian landowners the lowest level of trust, which is opposite of the trust relationship reported for the general public with state wildlife agencies. Based on the SVS model, our results suggest that mutualist landowners perceive greater congruence with MnDNR goals related to wildlife habitat compared to utilitarian landowners.
","language":"English","publisher":"Springer","doi":"10.1007/s10669-020-09766-z","usgsCitation":"Gigliotti, L.M., Sweikert, L., Cornicelli, L., and Fulton, D.C., 2020, Minnesota landowners’ trust in their department of natural resources, salient values similarity and wildlife value orientations: Environment Systems and Decisions, v. 40, p. 577-587, https://doi.org/10.1007/s10669-020-09766-z.","productDescription":"11 p.","startPage":"577","endPage":"587","ipdsId":"IP-103189","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Larry M. 0000-0002-1693-5113 lgigliotti@usgs.gov","orcid":"https://orcid.org/0000-0002-1693-5113","contributorId":3906,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Larry","email":"lgigliotti@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweikert, Lily A.","contributorId":264825,"corporation":false,"usgs":false,"family":"Sweikert","given":"Lily A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":822032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornicelli, Louis","contributorId":264826,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Louis","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822034,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210858,"text":"70210858 - 2020 - Deglacial temperature controls on no-analog community establishment in the Great Lakes Region","interactions":[],"lastModifiedDate":"2020-06-30T13:37:00.272631","indexId":"70210858","displayToPublicDate":"2020-03-18T08:30:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Deglacial temperature controls on no-analog community establishment in the Great Lakes Region","docAbstract":"Understanding the drivers of vegetation dynamics and no-analog communities in eastern North America is hampered by a scarcity of independent temperature indicators. We present a new branched glycerol dialkyl glycerol tetraether (brGDGT) temperature record from Bonnet Lake, Ohio (18 to 8 ka) and report uncertainty estimates based on Bayesian linear regression and bootstrapping. We also reanalyze a previously published brGDGT record from Silver Lake, Ohio, using improved chromatographic methods. All pollen- and brGDGT-based temperature reconstructions showed qualitatively similar deglacial trends but varying magnitudes. Separating 5- and 6- methyl brGDGTs resulted in substantially lower estimates of deglacial temperature variations (6.4 °C) than inferred from earlier brGDGT methods and pollen (11.8 °C, 12.0 °C respectively). Similar trends among proxies suggest good fidelity of brGDGTs to temperature, despite calibration uncertainties. At both sites, the rise and decline of no-analog communities closely track brGDGT-inferred temperatures, with a lag of 0 to 150 years. The timing of temperature and ecological events varies between Bonnet and Silver Lakes, likely due to age model uncertainties. Climate sensitivity analyses indicate a linear sensitivity of vegetation composition to temperature variations, albeit noisy and significant only with a 500-year bin. The formation of no-analog plant communities in the upper Midwest is closely linked to late-glacial warming, but other factors, such as temperature seasonality or end-Pleistocene megafaunal extinctions, remain viable.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106245","usgsCitation":"Fastovich, D., Russell, J.M., Jackson, S., and Williams, J.W., 2020, Deglacial temperature controls on no-analog community establishment in the Great Lakes Region: Quaternary Science Reviews, v. 234, 106245, 16 p., https://doi.org/10.1016/j.quascirev.2020.106245.","productDescription":"106245, 16 p.","ipdsId":"IP-107598","costCenters":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":457336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106245","text":"Publisher Index Page"},{"id":376014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.869140625,\n 37.71859032558816\n ],\n [\n -79.7607421875,\n 37.71859032558816\n ],\n [\n -79.7607421875,\n 41.902277040963696\n ],\n [\n -85.869140625,\n 41.902277040963696\n ],\n [\n -85.869140625,\n 37.71859032558816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"234","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fastovich, David","contributorId":225614,"corporation":false,"usgs":false,"family":"Fastovich","given":"David","email":"","affiliations":[],"preferred":false,"id":791886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, James M.","contributorId":174740,"corporation":false,"usgs":false,"family":"Russell","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":27506,"text":"Department of Earth, Environmental and Planetary Sciences, Brown University, Providence RI 02912 USA","active":true,"usgs":false}],"preferred":false,"id":791887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":791749,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, John W.","contributorId":16761,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":791888,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259110,"text":"70259110 - 2020 - Linking landscape-scale conservation to regional and continental outcomes for a migratory species","interactions":[],"lastModifiedDate":"2024-10-03T16:17:29.344521","indexId":"70259110","displayToPublicDate":"2020-03-18T07:03:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Linking landscape-scale conservation to regional and continental outcomes for a migratory species","docAbstract":"Land-use intensification on arable land is expanding and posing a threat to biodiversity and ecosystem services worldwide. We develop methods to link funding for avian breeding habitat conservation and management at landscape scales to equilibrium abundance of a migratory species at the continental scale. We apply this novel approach to a harvested bird valued by birders and hunters in North America, the northern pintail duck (Anas acuta), a species well below its population goal. Based on empirical observations from 2007–2016, habitat conservation investments for waterfowl cost $313 M and affected less than 2% of the pintail’s primary breeding area in the Prairie Pothole Region of Canada. Realistic scenarios for harvest and habitat conservation costing an estimated $588 M (2016 USD) led to predicted pintail population sizes less than 3 M when assuming average parameter values. Accounting for parameter uncertainty, converting 70–100% of these croplands to idle grassland (cost: $35.7B–50B) is required to achieve the continental population goal of 4 M individuals under the current harvest policy. Using our work as a starting point, we propose continued development of modeling approaches that link conservation funding, habitat delivery, and population response to better integrate conservation efforts and harvest management of economically important migratory species.
Many models in population ecology, including spatial capture–recapture (SCR) models, assume that individuals are distributed and detected independently of one another. In reality, this is rarely the case – both antagonistic and gregarious relationships lead to non-independent spatial configurations, with territorial exclusion at one end of the spectrum and group-living at the other. Previous simulation studies suggest that grouping has limited impact on the outcome of SCR analyses. However, group associations entail not only spatial clustering of activity centers but also coordinated space use by group members, potentially impacting both ecological and observation processes underlying SCR analysis. We simulated SCR scenarios with different strengths of aggregation (clustering of individuals into groups with shared activity centers) and cohesion (synchronization of detection patterns of members of a group). We then fit SCR models to the simulated data sets and evaluated the effect of aggregation and cohesion on parameter estimates. Low to moderate aggregation and cohesion did not impact the bias and precision of estimates of density and the scale parameter of the detection function. However, non-independence between individuals led to high levels of overdispersion. Overdispersion strongly decreased the coverage of confidence intervals around parameter estimates, thereby increasing the probability of erroneous predictions. Our results indicate that SCR models are robust to moderate levels of aggregation and cohesion. Nonetheless, spatial dependence between individuals can lead to false inference. We recommend that practitioners 1) test for the presence of overdispersion in SCR data caused by aggregation and cohesion, and, if necessary, 2) correct their variance estimates using the overdispersion factor ĉ . Approaches for doing both are described in this paper. We also urge the development of SCR models that incorporate spatial associations between individuals not only to account for overdispersion but also to obtain quantitative information about social aspects of study populations.
","language":"English","publisher":"BioOne","doi":"10.2981/wlb.00649","usgsCitation":"Bischof, R., Dupont, P., Milleret, C., Chipperfield, J., and Royle, J.A., 2020, Consequences of ignoring group association in spatial capture-recapture analysis: Wildlife Biology, v. 2020, no. 1, wlb.00649, 11 p., https://doi.org/10.2981/wlb.00649.","productDescription":"wlb.00649, 11 p.","ipdsId":"IP-113777","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00649","text":"Publisher Index Page"},{"id":377200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2020","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bischof, Richard","contributorId":237793,"corporation":false,"usgs":false,"family":"Bischof","given":"Richard","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dupont, Pierre","contributorId":237794,"corporation":false,"usgs":false,"family":"Dupont","given":"Pierre","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milleret, Cyril","contributorId":237795,"corporation":false,"usgs":false,"family":"Milleret","given":"Cyril","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipperfield, Joseph","contributorId":237796,"corporation":false,"usgs":false,"family":"Chipperfield","given":"Joseph","email":"","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795328,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209124,"text":"70209124 - 2020 - A pheromone antagonist liberates female sea lamprey from a sensory trap to enable reliable communication","interactions":[],"lastModifiedDate":"2021-12-09T15:23:54.081925","indexId":"70209124","displayToPublicDate":"2020-03-17T07:13:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"A pheromone antagonist liberates female sea lamprey from a sensory trap to enable reliable communication","docAbstract":"The evolution of male signals and female preferences remains a central question in the study of animal communication. The sensory trap model suggests males evolve signals that mimic cues used in nonsexual contexts and thus manipulate female behavior to generate mating opportunities. Much evidence supports the sensory trap model, but how females glean reliable information from both mimetic signals and their model cues remains unknown. We discovered a mechanism whereby a manipulative male signal guides reliable communication in sea lamprey (Petromyzon marinus). Migratory sea lamprey follow a larval cue into spawning streams; once sexually mature, males release a pheromone that mimics the larval cue and attracts females. Females conceivably benefit from the mimetic pheromone during mate search but must discriminate against the model cue to avoid orienting toward larvae in nearby nursery habitats. We tested the hypothesis that spawning females respond to petromyzonol sulfate (PZS) as a behavioral antagonist to avoid attraction to the larval cue while tracking the male pheromone despite each containing attractive 3-keto petromyzonol sulfate (3kPZS). We found 1) PZS inhibited electrophysiological responses to 3kPZS and abated preferences for 3kPZS when mixed at the same or greater concentrations, 2) larvae released more PZS than 3kPZS whereas males released more 3kPZS than PZS, and 3) mixtures of 3kPZS and PZS applied at ratios measured in larval and male odorants resulted in the discrimination observed between the natural odors. Our study elucidates how communication systems that arise via deception can facilitate reliable communication.
Landslides in Puerto Rico range from nuisances to deadly events. Centuries of agricultural and urban modification of the landscape have perturbed many already unstable hillsides on the tropical island. One of the main triggers of mass wasting on the island is the high-intensity rainfall that is associated with tropical atmospheric systems. Puerto Rico’s geographic position and rugged topography render millions of residents vulnerable to widespread landslide events. In this study, a high-resolution (5 meters), high-intensity rainfall-induced landslide susceptibility model was produced using the frequency-ratio method. Datasets utilized in the model included a complete-island landslide inventory created from imagery obtained after Hurricanes Irma and María impacted the island during September 2017, slope inclination, land-surface curvature, soil type, geologic terrane, mean annual precipitation, land use, soil moisture, and distance to roadways and streams. The final data product (plate 1) is a statistically viable representation of where landslides are likely to initiate during or soon after intense rainfall, with a robust receiver operating characteristic area-under-curve value of 0.87. The model output raster pixel values were binned into 100 equal-area quantiles and then classified into Low, Moderate, High, Very High, and Extremely High classes of susceptibility. The Extremely High susceptibility classification represents the most vulnerable 1 percent of the island, whereas Very High, High, Moderate, and Low classifications cover 9, 20, 30, and 40 percent of the island, respectively. The susceptibility map is intended to assist in planning future development, mitigation measures, and post-event emergency response; however, it is not a substitute for site-specific, slope-stability assessments performed by licensed geologists and engineers. Additionally, the map does not portray locations where landslide material may travel after mobilization, and which may be at extreme risk; nor does it necessarily portray where landslides may occur during earthquakes or mass wasting triggered by prolonged, relatively low-intensity rainfall.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201022","collaboration":"Prepared in cooperation with the University of Puerto Rico at Mayagüez","usgsCitation":"Hughes, K.S., and Schulz, W.H., 2020, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-File Report 2020–1022, 91 p., 1 plate, scale 1:150,000, https://doi.org/10.3133/ofr20201022.","productDescription":"Report: viii, 91 pages; 2 Sheets: 49.11 x 33.86 inches; Application Sites; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-116377","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":373245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_pamphlet.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1022 pamphlet","linkHelpText":"English 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Sediment from urban impervious surfaces has the potential to be an important vector for contaminants, particularly where stormwater culverts and other buried channels draining large impervious areas exit from underground pipes into open channels. To better understand urban sediment sources and their relation to fallout radionuclides, we collected samples of rainfall, urban sediment (pavement sediment, topsoil), streambank sediment, and fluvial sediment (suspended sediment and bed sediment) for 7Be, 210Pbex, and 137Cs analysis. The results indicate that each rainfall event tags pavement sediment with elevated activities of 7Be and 210Pbex such that runoff from impervious surfaces in the buried channel part of the stream network contains the highest activities. Pavement sediment, because it is characteristically a thin veneer, has a small mass to rainwater ratio resulting in a greater tagging of 7Be and 210Pbex activity than does topsoil on a per gram basis. An unmixing model indicated that suspended-sediment samples collected at the culvert outlet from the buried-channel network are from pavement sediment sources (45 ± 25%) with a smaller component of topsoil (22 ± 19%), and a component from streambanks (32 ± 35%) that we infer to be older channel material and subsoil eroded from within the culvert system. Downstream from the culvert, suspended sediment collected from the open-channel parts of the stream had 7Be and 210Pbex activities that were substantially reduced by the contribution of sediment from streambanks (57 ± 15%), with pavement contributions decreasing to 15 (±9%) and topsoil contributing 28 (±7%). The results highlight the utility of 7Be, 210Pbex, and 137Cs as tracers of urban sediment sources, resulting in a unique radionuclide signature for urban watersheds compared to other sediment-source settings.
Swimming exercise and dissolved oxygen (DO) are important parameters to consider when operating intensive salmonid aquaculture facilities. While previous research has focused on each of these two variables in rainbow trout Oncorhynchus mykiss, studies examining both variables in combination, and their potential interaction, are absent from the scientific literature. Both swimming exercise (usually measured in body lengths per second, or BL/s) and DO can be readily controlled in modern aquaculture systems; therefore, we sought to evaluate the effects of these variables, separately and combined, on several outcomes in rainbow trout including growth performance, fin health and survival. Rainbow trout fry (18 g) were stocked into 12 circular 0.5 m3 tanks, provided with either high (1.5–2 BL/s) or low (approximately 0.5 BL/s) swimming exercise and high (100% saturation) or low (70% saturation) DO, and grown to approximately 1 kg. By the conclusion of the study, higher DO was independently associated with significantly (p < .05) increased growth performance. Significant differences were not noted in other outcomes, namely feed conversion, condition factor and mortality, although caudal and right pectoral fin damage was associated with low oxygen and low swimming exercise treatments respectively. Cardiosomatic index was significantly higher among exercised fish. These results suggest that swimming exercise and DO at saturation during the culture of rainbow trout can be beneficial to producers through improved growth performance and cardiac health.
","language":"English","publisher":"Wiley","doi":"10.1111/are.14600","usgsCitation":"Waldrop, T., Summerfelt, S., Mazik, P.M., Kenney, P.B., and Good, C., 2020, The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss: Aquaculture Research, v. 51, no. 6, p. 2582-2589, https://doi.org/10.1111/are.14600.","productDescription":"8 p.","startPage":"2582","endPage":"2589","ipdsId":"IP-113465","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/are.14600","text":"Publisher Index Page"},{"id":397025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Waldrop, Thomas","contributorId":279449,"corporation":false,"usgs":false,"family":"Waldrop","given":"Thomas","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Summerfelt, Steven","contributorId":279450,"corporation":false,"usgs":false,"family":"Summerfelt","given":"Steven","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kenney, P. Brett","contributorId":279452,"corporation":false,"usgs":false,"family":"Kenney","given":"P.","email":"","middleInitial":"Brett","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Good, Christopher","contributorId":279454,"corporation":false,"usgs":false,"family":"Good","given":"Christopher","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834956,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}