{"pageNumber":"253","pageRowStart":"6300","pageSize":"25","recordCount":46679,"records":[{"id":70211855,"text":"70211855 - 2020 - A spatial analysis of climate gentrification in Orleans Parish, Louisiana post-Hurricane Katrina","interactions":[],"lastModifiedDate":"2020-08-10T16:53:10.091613","indexId":"70211855","displayToPublicDate":"2020-03-12T11:41:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1561,"text":"Environmental Research","active":true,"publicationSubtype":{"id":10}},"title":"A spatial analysis of climate gentrification in Orleans Parish, Louisiana post-Hurricane Katrina","docAbstract":"Background
Hurricane Katrina made landfall in New Orleans, Louisiana as a Category 3 storm in August 2005. Storm surges, levee failures, and the low-lying nature of New Orleans led to widespread flooding, damage to over 70% of occupied housing, and evacuation of 80–90% of city residents. Only 57% of the city's black population has returned. Many residents complain of gentrification following rebuilding efforts. Climate gentrification is a recently described phenomenon whereby the effects of climate change, most notably rising sea levels and more frequent flooding and storm surges, alter housing values in a way that leads to gentrification.
Objective
To examine the climate gentrification following hurricane Katrina by (1) estimating the associations between flooding severity, ground elevation, and gentrification and (2) whether these relationships are modified by neighborhood level pre- and post-storm sociodemographic factors.
Methods
Lidar data collected in 2002 were used to determine elevation. Water gauge height of Lake Ponchartrain was used to estimate flood depth. Using census tracts as a proxy for neighborhoods, demographic, housing, and economic data from the 2000 decennial census and the 2010 and 2015 American Community Survey 5-year estimates US Census records were used to determine census tracts considered eligible for gentrification (median income < 2000 Orleans Parish median income). A gentrification index was created using tract changes in education level, population above the poverty limit, and median household income. Proportional odds ordinal logistic regression was used with product terms to test for effect measure modification by sociodemographic factors.
Results
Census tracts eligible for gentrification in 2000 were 80.2% black. Median census tract flood depth was significantly lower in areas eligible to undergo gentrification (0.70 m vs. 1.03 m). Residents of gentrification-eligible tracts in 2000 were significantly more likely to be black, less educated, lower income, unemployed, and rent their home rather than own. In 2015 in these same eligible tracts, areas that underwent gentrification became significantly whiter, more educated, higher income, less unemployed, and more likely to live in a multi-unit dwelling. Gentrification was inversely associated with flood depth and directly associated with ground elevation in eligible tracts. Marginal effect modification was detected by the effect of pre-storm black race on the relationships of flood depth and elevation with gentrification.
Conclusions
Gentrification was strongly associated with higher ground elevation in New Orleans. These results provide evidence to support the idea of climate gentrification described in other low-elevation major metropolitan areas like Miami, FL. High elevation, low-income, demographically transitional areas in particular – that is areas that more closely resemble high-income area demographics, may be vulnerable to future climate gentrification.
Water samples were collected at several wastewater treatment plants in southeast Florida, and water and sediment samples were collected along and around one outfall pipe, as well as along several transects extending both north and south of the respective outfall outlet. Two sets of samples were collected to address potential seasonal differences, including 38 in the wet season (June 2018) and 42 in the dry season (March 2019). Samples were screened for the presence/absence of 15 select antibiotic resistance gene targets using the polymerase chain reaction. A contrast between seasons was found, with a higher frequency of detections occurring in the wet season and fewer during the dry season. These data illustrate an anthropogenic influence on offshore microbial genetics and seasonal flux regarding associated health risks to recreational users and the regional ecosystem.
","language":"English","publisher":"MDPI","doi":"10.3390/antibiotics9030118","usgsCitation":"Griffin, D.W., Banks, K., Gregg, K., Shedler, S., and Walker, B., 2020, Antibiotic resistance in marine microbial communities proximal to a Florida sewage outfall system: Antibiotics, v. 9, no. 3, 118, 8 p., https://doi.org/10.3390/antibiotics9030118.","productDescription":"118, 8 p.","ipdsId":"IP-116104","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457424,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/antibiotics9030118","text":"Publisher Index Page"},{"id":437060,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98KQWDN","text":"USGS data release","linkHelpText":"Southeast Florida and Florida Keys: Antibiotic Resistance in Association with Ocean Outfalls and the Antibiotic Treatment of Diseased Corals"},{"id":406443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.74951171875,\n 25.34402602913433\n ],\n [\n -79.9365234375,\n 25.34402602913433\n ],\n [\n -79.9365234375,\n 27.117812842321225\n ],\n [\n -80.74951171875,\n 27.117812842321225\n ],\n [\n -80.74951171875,\n 25.34402602913433\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":851295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Banks, Kenneth","contributorId":240580,"corporation":false,"usgs":false,"family":"Banks","given":"Kenneth","email":"","affiliations":[{"id":48095,"text":"Broward County, Environmental Protection and Growth Management Department","active":true,"usgs":false}],"preferred":false,"id":851296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gregg, Kurtis","contributorId":240581,"corporation":false,"usgs":false,"family":"Gregg","given":"Kurtis","email":"","affiliations":[{"id":48096,"text":"ERT, Inc, NOAA Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":851297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shedler, Sarah","contributorId":218584,"corporation":false,"usgs":false,"family":"Shedler","given":"Sarah","email":"","affiliations":[],"preferred":false,"id":851298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walker, Brian","contributorId":240583,"corporation":false,"usgs":false,"family":"Walker","given":"Brian","affiliations":[{"id":48098,"text":"Halmos college of Natural Sciences and Oceanography, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":851299,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70243066,"text":"70243066 - 2020 - Critical land change information enhances the understanding of carbon balance in the United States","interactions":[],"lastModifiedDate":"2023-04-28T11:51:57.05294","indexId":"70243066","displayToPublicDate":"2020-03-11T06:48:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Critical land change information enhances the understanding of carbon balance in the United States","docAbstract":"Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.
The newly constructed tallest building in California, the 73-story Wilshire Grand in Los Angeles, California, is designed in conformance with performance-based design procedures. The building is designed with concrete core–shear walls, three outriggers with buckling restrained braces (BRBs) located along the height, and two three-story truss-belt structural systems. The building is equipped with a 36-channel accelerometric seismic monitoring array that recorded the recent Mw7.1 Ridgecrest earthquake of 5 July 2019, as well as the Mw6.4 Ridgecrest earthquake of 4 July 2019. In this article, only the Mw7.1 event of 5 July 2019 is studied because of a larger response of the subject building during that earthquake. The earthquake records of 5 July 2019 are specifically studied to determine its dynamic characteristics and building-specific behavior. The structure exhibits torsional behavior most likely due to abrupt asymmetrical changes in the thickness and size in-plan of the core–shear walls. The translational and torsional modes during the earthquake are not closely coupled, which does not lead to a beating effect even though there is an appearance of it in the records. Available ambient records are used only to identify modal frequencies of the building and compare them with those from the Mw7.1 event of 5 July 2019. Due to the relatively low amplitude of shaking during the earthquake, the drift ratios are too small to cause any damage. It is expected that during stronger shaking levels likely to be caused by future events, these characteristics may change and the effect of BRBs can be better assessed.
The National Hydrography Dataset Plus, Version 2.1 (NHDPlusV2.1) is an attribute-rich digital stream network for the conterminous United States, serving as a foundational infrastructure for reporting hydrologic information at both regional and national scales. SPAtially Referenced Regressions On Watershed attributes (SPARROW) is a process-based statistical model that relies on a digital hydrologic network like NHDPlusV2.1 to establish spatial relations between quantities of monitored contaminant loads and contaminant sources, accounting for the physical characteristics along flow paths affecting contaminant transport. The U.S. Geological Survey National Water Quality Assessment project adopted and modified the medium-resolution NHDPlusV2.1 network for use as the primary framework supporting SPARROW modeling. This report describes the enhancements made to improve the routing capabilities and the value-added attributes of NHDPlusV2.1 to support modeling and other hydrologic analyses. These enhancements include corrections to inconsistencies in network/routing information, filling in missing attribute values of associated characteristics, accounting of water use affecting flow, new variables useful for interpreting network data, revised flowline attributes such as slope and flow, and incorporation of ancillary spatial data into the network. The resulting dataset containing the enhancements to the network is named E2NHDPlusV2_US. Although the enhancements described in the report were developed for use in SPARROW modeling, the enhancements are expected to be useful for a wide variety of hydrologic studies within the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195127","usgsCitation":"Brakebill, J.W., Schwarz, G.E., and Wieczorek, M.E., 2020, An enhanced hydrologic stream network based on the NHDPlus medium resolution dataset: U.S. Geological Survey Scientific Investigations Report 2019–5127, 49 p., https://doi.org/10.3133/sir20195127.","productDescription":"Report: vii, 49 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098180","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":372768,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P986KZEM","text":"USGS data release","linkHelpText":"E2NHDPlusV2_us: Database of Ancillary Hydrologic Attributes and Modified Routing for NHDPlus Version 2.1 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National Water Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20191-0002
","tableOfContents":"- Foreword
- Abstract
- Introduction
- Material and Methods
- Validation
- Summary and Conclusions
- References Cited
- Appendix 1. Description of Addition and Removal Events Spreadsheet
- Appendix 2. Description of Methods Used to Update Streamflow Estimates
- Appendix 3. Description of Methods Used to Update Slope Estimates
- Appendix 4. Description of Attributes in E2NHDPlusV2_us
- Appendix 5. Description of Selected Ancillary Geospatial Dataset Variables Assigned to the Catchments and Flowlines of NHDPlusV2.1
","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2020-03-09","noUsgsAuthors":false,"publicationDate":"2020-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Brakebill, John W. 0000-0001-9235-6810 jwbrakeb@usgs.gov","orcid":"https://orcid.org/0000-0001-9235-6810","contributorId":1061,"corporation":false,"usgs":true,"family":"Brakebill","given":"John","email":"jwbrakeb@usgs.gov","middleInitial":"W.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":783476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wieczorek, Michael E. 0000-0003-0999-5457 mewieczo@usgs.gov","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":178736,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael E.","email":"mewieczo@usgs.gov","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783477,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70215559,"text":"70215559 - 2020 - Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California’s Lost Hills and Belridge oil fields","interactions":[],"lastModifiedDate":"2020-10-23T14:06:56.145727","indexId":"70215559","displayToPublicDate":"2020-03-10T09:01:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic categorical groundwater salinity mapping from airborne electromagnetic data adjacent to California’s Lost Hills and Belridge oil fields","docAbstract":"Growing water stress has led to emerging interest in protecting fresh and brackish groundwater as a potential supplement to water supplies and raised questions about factors that could affect the future quality of fresh and brackish aquifers. Limited well infrastructure, particularly in regions where elevated salinity has led to limited historical groundwater development, hinders traditional mapping of salinity distributions through groundwater sampling. This paper presents a quantitative salinity mapping approach of the upper 300 m using high‐resolution, regionally comprehensive resistivity models derived from Bayesian inversion of an airborne electromagnetic survey adjacent to the Lost Hills and Belridge oil fields in the southwestern San Joaquin Valley of California. Using local water quality observations as an interpretational foundation, a probabilistic approach yields maps of fresh, saline, and brackish groundwater while quantifying joint uncertainty inherited from the geophysical data and interpretational relations. Saline and fresh regions are mapped with relatively high confidence in many locations, while areas of lower confidence, particularly at depth, can be mapped as their most probable salinity category while reflecting the relative uncertainty in the interpretation. These maps identify a stratified salinity structure, where saline water commonly occurs in the surficial aquifer overlying fresher groundwater in the Tulare aquifer, separated by regional confining clay layers. Downgradient of unlined surface water diversions, recharge of imported surface water results in relatively fresh groundwater throughout the depth of investigation.
The principle sources of trace elements entering upper Tenmile Creek, Montana, during September 2011, four trace metals and the metalloid arsenic, were identified and quantified by combining and analyzing streamflow data determined from tracer injection with trace-element concentrations and related water-quality data determined from synoptic sampling. The study reach was along upper Tenmile Creek, beginning downstream from the city of Helena’s diversion and extending 5,020 feet downstream. Results from the 2011 study, completed by the U.S. Geological Survey in cooperation with the Montana Department of Environmental Quality, were compared to results from a similar study conducted in 1998 to assess the effectiveness of mine reclamation and remediation work to reduce trace-element loading to upper Tenmile Creek, which has been ongoing throughout the drainage basin.
Main-stem concentrations of most trace elements analyzed were generally greater in 1998 than in 2011. However, the State of Montana human-health criteria for total-recoverable cadmium and arsenic were exceeded in parts of upper Tenmile Creek, and concentrations of cadmium and zinc exceeded the acute aquatic-life criteria at all main-stem sites during both studies. Total-recoverable copper concentrations observed in 2011 exceeded the chronic aquatic-life criterion upstream from the Lee Mountain adit, whereas, in 1998, all sites exceeded the acute aquatic-life criteria.
Direct comparison of loads from the 1998 and 2011 tracer studies were complicated by the differences in hydrologic conditions. Streamflow in 1998 was about 10 percent of the 2011 streamflow. The Lee Mountain Mine and Susie Lode adit were identified as major contributors of trace elements to upper Tenmile Creek in both studies. However, trace-element loading from the Lee Mountain Mine area was substantially reduced between 1998 and 2011. Total-recoverable loads of all trace elements showed substantial loss in 1998 but increased in 2011 downstream from the Susie Lode adit to the end of the study reach. This reach was one of the primary sources of trace-element loading to upper Tenmile Creek in 2011. This difference indicated that the streambed may act as a sink or a source for trace elements, depending on hydrologic conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195126","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Cleasby, T., and Eldridge, S.L.C., 2020, Quantification of trace element loading in the upper Tenmile Creek drainage basin near Rimini, Montana, September 2011: U.S. Geological Survey Scientific Investigations Report 2019–5126, 40 p., https://doi.org/10.3133/sir20195126.","productDescription":"Report: vii, 40 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-043897","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":399607,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109755.htm"},{"id":372808,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkHelpText":"– USGS water data for the Nation"},{"id":372807,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5126/sir20195126.pdf","text":"Report","size":"6.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5126"},{"id":372806,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5126/coverthb.jpg"}],"country":"United States","state":"Montana","county":"Lewis and Clark County","city":"Rimini","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.2533,\n 46.4808\n ],\n [\n -112.2444,\n 46.4808\n ],\n [\n -112.2444,\n 46.5008\n ],\n [\n -112.2533,\n 46.5008\n ],\n [\n -112.2533,\n 46.4808\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
","tableOfContents":"- Acknowledgments
- Abstract
- Introduction
- Methods
- Quality Assurance/Quality Control
- Quantification of Trace-Element Loading
- Summary and Conclusions
- References Cited
","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-03-09","noUsgsAuthors":false,"publicationDate":"2020-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Cleasby, Tom 0000-0003-0694-1541 tcleasby@usgs.gov","orcid":"https://orcid.org/0000-0003-0694-1541","contributorId":1137,"corporation":false,"usgs":true,"family":"Cleasby","given":"Tom","email":"tcleasby@usgs.gov","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":false,"id":774563,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caldwell Eldridge, Sara L. 0000-0001-8838-8940 seldridge@usgs.gov","orcid":"https://orcid.org/0000-0001-8838-8940","contributorId":4981,"corporation":false,"usgs":true,"family":"Caldwell Eldridge","given":"Sara","email":"seldridge@usgs.gov","middleInitial":"L.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":774564,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70212483,"text":"70212483 - 2020 - A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales","interactions":[],"lastModifiedDate":"2020-08-17T14:59:53.151452","indexId":"70212483","displayToPublicDate":"2020-03-09T09:55:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales","docAbstract":"Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass (Pennisetum setaceum) and goutweed (Aegopodium podagraria). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.
The inclusion of passive interrogation antenna (PIA) detection data has promise to increase precision of population abundance estimates (![\"\"](\"https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq1.gif\")
). However, encounter probabilities are often higher for PIAs than for physical capture. If the difference is not accounted for, ![\"\"](\"https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq2.gif\")
may be biased. Using simulations, we estimated the magnitude of bias resulting from mixed capture and detection probabilities and evaluated potential solutions for removing the bias for closed capture models. Mixing physical capture and PIA detections (pdet) resulted in negative biases in ![\"\"](\"https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq3.gif\")
. However, using an individual covariate to model differences removed bias and improved precision. From a case study of fish making spawning migrations across a stream-wide PIA (pdet ≤ 0.9), the coefficient of variation (CV) of ![\"\"](\"https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq4.gif\")
declined 39%–82% when PIA data were included, and there was a dramatic reduction in time to detect a significant change in ![\"\"](\"https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq5.gif\")
. For a second case study, with modest pdet (≤0.2) using smaller PIAs, CV (![\"\"](\"https://cdnsciencepub.com/cms/10.1139/cjfas-2019-0326/asset/images/cjfas-2019-0326ieq6.gif\")
) declined 4%–18%. Our method is applicable for estimating abundance for any situation where data are collected with methods having different capture–detection probabilities.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0326","usgsCitation":"Conner, M.M., Budy, P., Wilkison, R.A., Mills, M., Speas, D., Mackinnon, P.D., and Mark C. Mckinstry, 2020, Estimating population abundance with a mixture of physical capture and passive PIT tag antenna detection data: Canadian Journal of Fisheries and Aquatic Sciences, v. 77, no. 7, p. 1163-1171, https://doi.org/10.1139/cjfas-2019-0326.","productDescription":"9 p.","startPage":"1163","endPage":"1171","ipdsId":"IP-110596","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2019-0326","text":"Publisher Index Page"},{"id":395707,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"77","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Conner, Mary M.","contributorId":275216,"corporation":false,"usgs":false,"family":"Conner","given":"Mary","email":"","middleInitial":"M.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833836,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilkison, Richard A.","contributorId":275217,"corporation":false,"usgs":false,"family":"Wilkison","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":56749,"text":"ipc","active":true,"usgs":false}],"preferred":false,"id":833838,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mills, Michael","contributorId":275218,"corporation":false,"usgs":false,"family":"Mills","given":"Michael","email":"","affiliations":[{"id":56750,"text":"uwc","active":true,"usgs":false}],"preferred":false,"id":833839,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Speas, David","contributorId":275219,"corporation":false,"usgs":false,"family":"Speas","given":"David","email":"","affiliations":[{"id":56751,"text":"ubr","active":true,"usgs":false}],"preferred":false,"id":833840,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mackinnon, Peter D.","contributorId":275220,"corporation":false,"usgs":false,"family":"Mackinnon","given":"Peter","email":"","middleInitial":"D.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833841,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mark C. Mckinstry","contributorId":275221,"corporation":false,"usgs":false,"family":"Mark C. Mckinstry","affiliations":[{"id":56751,"text":"ubr","active":true,"usgs":false}],"preferred":false,"id":833842,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70209001,"text":"70209001 - 2020 - Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife","interactions":[],"lastModifiedDate":"2020-03-10T19:28:47","indexId":"70209001","displayToPublicDate":"2020-03-06T19:21:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife","docAbstract":"Human activity influences wildlife. However, the ecological and conservation significances of these influences are difficult to predict and depend on their population‐level consequences. This difficulty arises partly because of information gaps, and partly because the data on stressors are usually collected in a count‐based manner (e.g., number of dead animals) that is difficult to translate into rate‐based estimates important to infer population‐level consequences (e.g., changes in mortality or population growth rates). However, ongoing methodological developments can provide information to make this transition. Here, we synthesize tools from multiple fields of study to propose an overarching, spatially explicit framework to assess population‐level consequences of anthropogenic stressors on terrestrial wildlife. A key component of this process is using ecological information from affected animals to upscale from count‐based field data on individuals to rate‐based demographic inference. The five steps to this framework are (1) framing the problem to identify species, populations, and assessment parameters; (2) field‐based measurement of the effect of the stressor on individuals; (3) characterizing the location and size of the populations of interest; (4) demographic modeling for those populations; and (5) assessing the significance of stressor‐induced changes in demographic rates. The tools required for each of these steps are well developed, and some have been used in conjunction with each other, but the entire group has not previously been unified together as we do in this framework. We detail these steps and then illustrate their application for two species affected by different anthropogenic stressors. In our examples, we use stable hydrogen isotope data to infer a catchment area describing the geographic origins of affected individuals, as the basis to estimate population size for that area. These examples reveal unexpectedly greater potential risks from stressors for the more common and widely distributed species. This work illustrates key strengths of the framework but also important areas for subsequent theoretical and technical development to make it still more broadly applicable.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3046","usgsCitation":"Katzner, T., Braham, M.A., Conkling, T., Diffendorfer, J., Duerr, A.E., Loss, S., Nelson, D.M., Vander Zanden, H.B., and Yee, J.L., 2020, Assessing population-level consequences of anthropogenic stressors for terrestrial wildlife: Ecosphere, v. 11, no. 3, e03046, 23 p., https://doi.org/10.1002/ecs2.3046.","productDescription":"e03046, 23 p.","ipdsId":"IP-108403","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457460,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3046","text":"Publisher Index Page"},{"id":373085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Guatemala, Haiti, Honduras, Jamaica, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.89453125,\n 17.97873309555617\n ],\n [\n -75.76171875,\n 21.779905342529645\n ],\n [\n -81.5625,\n 30.751277776257812\n ],\n [\n -74.53125,\n 35.17380831799959\n ],\n [\n -71.3671875,\n 39.90973623453719\n ],\n [\n -58.35937499999999,\n 45.460130637921004\n ],\n [\n -51.67968749999999,\n 46.6795944656402\n ],\n [\n -55.8984375,\n 53.74871079689897\n ],\n [\n -63.45703124999999,\n 58.81374171570782\n ],\n [\n -78.22265625,\n 52.696361078274485\n ],\n [\n -80.15625,\n 51.069016659603896\n ],\n [\n -82.265625,\n 53.64463782485651\n ],\n [\n -99.66796875,\n 58.35563036280964\n ],\n [\n 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]\n}","volume":"11","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":784469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braham, Melissa A.","contributorId":199740,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":34303,"text":"West Virginia University, Department of Geology & Geography","active":true,"usgs":false}],"preferred":false,"id":784470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conkling, Tara 0000-0003-1926-8106","orcid":"https://orcid.org/0000-0003-1926-8106","contributorId":217915,"corporation":false,"usgs":true,"family":"Conkling","given":"Tara","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":784471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":784472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duerr, Adam E.","contributorId":190590,"corporation":false,"usgs":false,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":784473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loss, Scott R.","contributorId":140471,"corporation":false,"usgs":false,"family":"Loss","given":"Scott R.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":784474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nelson, David M.","contributorId":175098,"corporation":false,"usgs":false,"family":"Nelson","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13479,"text":"University of Maryland Center for Environmental Science, Appalachian Laboratory, 301 Braddock Road, Frostburg, Maryland","active":true,"usgs":false}],"preferred":false,"id":784476,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vander Zanden, Hannah B.","contributorId":138885,"corporation":false,"usgs":false,"family":"Vander Zanden","given":"Hannah","email":"","middleInitial":"B.","affiliations":[{"id":12562,"text":"Department of Geology and Geophysics, University of Utah; Archie Carr Center for Sea Turtle Research, University of Florida","active":true,"usgs":false}],"preferred":false,"id":784475,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":784477,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70227752,"text":"70227752 - 2020 - A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin","interactions":[],"lastModifiedDate":"2022-04-15T16:17:24.763904","indexId":"70227752","displayToPublicDate":"2020-03-06T11:02:45","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9366,"text":"CCAST Case Study on Actionable Science","active":true,"publicationSubtype":{"id":1}},"title":"A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin","docAbstract":"Management of water resources in the transboundary Rio Grande/Río Bravo Basin (the Basin) presents challenges for state and Federal entities in the United States and Mexico making management decisions on shared water resources. Damming, channelization, water availability, and allocation are governed by water rights and water-sharing agreements. Data and information sharing are important aspects of transboundary cooperation, but differences in format, content, spatial and temporal resolution, and language hinder collaboration. In addition, data on the kinds and geographic distribution of water governance and management institutions across the Basin have not been consistently documented. Existing data disparities parallel the hydrological and social fragmentation of the Basin.
Seeking to underscore the interdependence between social and environmental processes in the Basin, anthropologists and modelers collaborated to develop a socio-environmental geodatabase. This geodatabase is a first step in modeling the social components of decision making and their connectivity to environmental processes across the Basin. The geodatabase is available in an open-access domain and contains geospatial data related to water and land governance, hydrology, water use and hydraulic infrastructure, socioeconomics, and the biophysical environment necessary to advance the understanding of basin dynamics. Having these data documented and compiled in a central location serves as a resource to help decision makers better understand upstream and downstream social-environmental characteristics. This knowledge is useful for developing sustainable water management policies in a region where water resources are increasingly under pressure from climatic, environmental, and human-related changes.
","language":"English","publisher":"Collaborative Conservation and Adaptation Strategy Toolbox (CCAST)","usgsCitation":"Villa, J., 2020, A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin: CCAST Case Study on Actionable Science, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-123961","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":398832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395033,"type":{"id":15,"text":"Index Page"},"url":"https://arcg.is/0bava9"}],"country":"Mexico, United States","state":"Chihuahua, New Mexico, Texas","otherGeospatial":"Rio Grande/Río Bravo basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.12493896484374,\n 31.421631960419596\n ],\n [\n -105.90545654296875,\n 31.421631960419596\n ],\n [\n -105.90545654296875,\n 32.58384932565662\n ],\n [\n -107.12493896484374,\n 32.58384932565662\n ],\n [\n -107.12493896484374,\n 31.421631960419596\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Villa, Jennifer 0000-0002-4774-7166","orcid":"https://orcid.org/0000-0002-4774-7166","contributorId":245824,"corporation":false,"usgs":true,"family":"Villa","given":"Jennifer","email":"","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832043,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70212831,"text":"70212831 - 2020 - Soil biogeochemical responses of a tropical forest to warming and hurricane disturbance","interactions":[],"lastModifiedDate":"2020-08-31T13:42:02.129691","indexId":"70212831","displayToPublicDate":"2020-03-06T08:40:51","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"6","title":"Soil biogeochemical responses of a tropical forest to warming and hurricane disturbance","docAbstract":"Tropical forests represent <15% of Earths terrestrial surface yet support >50% of the planets species and play a disproportionately large role in determining climate due to the vast amounts of carbon they store and exchange with the atmosphere. Currently, disturbance patterns in tropical ecosystems are changing due to factors such as increased land use pressure and an occurrence of hurricanes. At the same time, these regions are expected to experience unprecedented warming before 2100. Despite the importance of these ecosystems for forecasting the global consequences of multiple stressors, our understanding of how projected changes in climate and disturbance will affect the biogeochemical cycling of tropical forests remains in its infancy. Until now, no studies to our knowledge have evaluated forest recovery following hurricane disturbance within the context of concurrent climatic change. Here, we present soil biogeochemical results from a tropical forest field warming experiment in Puerto Rico where, a year after experimental warming began, Hurricanes Irma and Mara greatly altered the forest, allowing a unique opportunity to explore the interacting effects of hurricane disturbance and warming. We tracked post-hurricane forest recovery for a year without warming to assess legacy effects of prior warming on the disturbance response, and then reinitiated warming treatments to further evaluate interactions between forest recovery and warmer temperatures. The data showed that warming affected multiple aspects of soil biogeochemical cycling even in the first year of treatment, with particularly large positive effects on soil microbial biomass pools (e.g., increases of 54, 43, and 46% relative to the control plots were observed for microbial biomass carbon, nitrogen, and phosphorus, respectively after 6 months of warming). We also observed significant effects of the hurricanes on soil biogeochemical cycling, as well as interactive controls of warming and disturbance. Taken together, our results showed dynamic soil responses that suggest the future of biogeochemical cycling in this tropical wet forest will be strongly shaped by the directional effects of warming and the episodic effects of hurricanes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Advances in Ecological Research","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Science Direct","doi":"10.1016/bs.aecr.2020.01.007","usgsCitation":"Reed, S., Reibold, R.H., Cavaleri, M.A., Alonso-Rodriguez, A.M., Berberich, M.E., and Wood, T.E., 2020, Soil biogeochemical responses of a tropical forest to warming and hurricane disturbance, chap. 6 of Advances in Ecological Research, v. 62, p. 225-252, https://doi.org/10.1016/bs.aecr.2020.01.007.","productDescription":"28 p.","startPage":"225","endPage":"252","ipdsId":"IP-115219","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":378009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.2747802734375,\n 17.90556881196468\n ],\n [\n -65.58563232421875,\n 17.90556881196468\n ],\n [\n -65.58563232421875,\n 18.534304453676864\n ],\n [\n -67.2747802734375,\n 18.534304453676864\n ],\n [\n -67.2747802734375,\n 17.90556881196468\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reibold, Robin H. 0000-0002-3323-487X","orcid":"https://orcid.org/0000-0002-3323-487X","contributorId":207499,"corporation":false,"usgs":true,"family":"Reibold","given":"Robin","email":"","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":797593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cavaleri, Molly A.","contributorId":206282,"corporation":false,"usgs":false,"family":"Cavaleri","given":"Molly","email":"","middleInitial":"A.","affiliations":[{"id":34284,"text":"School of Forest Resources and Environmental Science, Michigan Technological University","active":true,"usgs":false}],"preferred":false,"id":797594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alonso-Rodriguez, Aura M.","contributorId":206281,"corporation":false,"usgs":false,"family":"Alonso-Rodriguez","given":"Aura","email":"","middleInitial":"M.","affiliations":[{"id":37300,"text":"International Institute of Tropical Forestry, USDA Forest Service, Sabana Field Research Station, Luquillo, Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":797595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berberich, Megan E.","contributorId":239684,"corporation":false,"usgs":false,"family":"Berberich","given":"Megan","email":"","middleInitial":"E.","affiliations":[{"id":47972,"text":"U.S. Forest Service, International Institute of Tropical forestry, 1201 Calle Ceiba, Jardín Botánico Sur, San Juan, PR 00926, USA","active":true,"usgs":false}],"preferred":false,"id":797596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wood, Tana E.","contributorId":202372,"corporation":false,"usgs":false,"family":"Wood","given":"Tana","email":"","middleInitial":"E.","affiliations":[{"id":36399,"text":"International Institute of Tropical Forestry, USDA Forest Service, Rio Piedras, PR","active":true,"usgs":false}],"preferred":false,"id":797597,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70223324,"text":"70223324 - 2020 - Geodetic measurements of slow slip events southeast of Parkfield, CA","interactions":[],"lastModifiedDate":"2021-08-23T23:00:40.643575","indexId":"70223324","displayToPublicDate":"2020-03-05T17:54:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Geodetic measurements of slow slip events southeast of Parkfield, CA","docAbstract":"Tremor and low-frequency earthquakes are presumed to be indicative of surrounding slow, aseismic slip that is often below geodetic detection thresholds. This study uses data from borehole seismometers and long-baseline laser strainmeters to observe both the seismic and geodetic signatures of episodic tremor and slip on the Parkfield region of the San Andreas Fault near Cholame, CA. The observed occurrence rates of both the tremors and co-located families of low-frequency earthquakes are not steady but instead exhibit quasiperiodic bursts of increased activity. We show that these periods of elevated seismic activity correlate with statistically significant stacked strain signals consisting of 44 slow-slip events. Modeled individual slow-slip events and their total summed moment, which are constrained by seismic signals and stacked strain, respectively, indicate that the individual moment magnitudes of these events range from ![\"urn:x-wiley:jgrb:media:jgrb54084:jgrb54084-math-0001\"](\"https://agupubs.onlinelibrary.wiley.com/cms/asset/d234dc98-bafb-4857-b071-66f198957b70/jgrb54084-math-0001.png\")
4.6–5.2. We find that the measured geodetic signal likely precedes the seismic signal by several hours, consistent with the aseismic slip preceding and driving the observed seismic tremor activity. We confirm that strike-slip faults, in addition to subduction zones, are capable of producing episodic tremor and slip.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB019059","usgsCitation":"Delbridge, B.G., Carmichael, J.D., Nadeau, R., Shelly, D.R., and Burgmann, R., 2020, Geodetic measurements of slow slip events southeast of Parkfield, CA: Journal of Geophysical Research, v. 125, no. 5, e2019JB019059, 20 p., https://doi.org/10.1029/2019JB019059.","productDescription":"e2019JB019059, 20 p.","ipdsId":"IP-117218","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":457485,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1630866","text":"External Repository"},{"id":388397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Parkfield slow-slip region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.640625,\n 35.15584570226544\n ],\n [\n -118.27880859374999,\n 35.15584570226544\n ],\n [\n -118.27880859374999,\n 36.66841891894786\n ],\n [\n -121.640625,\n 36.66841891894786\n ],\n [\n -121.640625,\n 35.15584570226544\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Delbridge, Brent G. 0000-0003-2808-8772","orcid":"https://orcid.org/0000-0003-2808-8772","contributorId":192986,"corporation":false,"usgs":false,"family":"Delbridge","given":"Brent","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":821739,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carmichael, Joshua D. 0000-0001-5752-5738","orcid":"https://orcid.org/0000-0001-5752-5738","contributorId":264608,"corporation":false,"usgs":false,"family":"Carmichael","given":"Joshua","email":"","middleInitial":"D.","affiliations":[{"id":54513,"text":"EES-17 (Geophysics), Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":821740,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nadeau, Robert M. 0000-0003-1255-0643","orcid":"https://orcid.org/0000-0003-1255-0643","contributorId":264609,"corporation":false,"usgs":false,"family":"Nadeau","given":"Robert M.","affiliations":[{"id":54514,"text":"Berkeley Seismological Laboratory, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":821741,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":821742,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burgmann, Roland 0000-0002-3560-044X","orcid":"https://orcid.org/0000-0002-3560-044X","contributorId":264610,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","email":"","affiliations":[{"id":54514,"text":"Berkeley Seismological Laboratory, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":821743,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70208904,"text":"tm2A16 - 2020 - Standard operating procedures for wild horse and burro double-observer aerial surveys","interactions":[],"lastModifiedDate":"2020-03-06T06:11:36","indexId":"tm2A16","displayToPublicDate":"2020-03-05T13:27:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2-A16","displayTitle":"Standard Operating Procedures for Wild Horse and Burro Double-Observer Aerial Surveys","title":"Standard operating procedures for wild horse and burro double-observer aerial surveys","docAbstract":"The U.S. Geological Survey has been collaborating with the Bureau of Land Management to develop statistically reliable methods for wild horse and burro aerial survey data collection and analysis for more than a decade. In cooperation with Colorado State University, the U.S. Geological Survey tested several methods in herds with known abundance, resulting in two scientifically defensible aerial survey and population estimation techniques. These methods are now being applied by the Bureau of Land Management across the western United States, enabling better management of wild horses and burros. The purpose of these Standard Operating Procedures (SOPs) is to provide detailed instructions to the Bureau of Land Management wild horse and burro specialists who need to fly aerial surveys for management.
This report provides multiple SOPs that are related to Equus caballus (wild horse) and Equus asinus (wild burro) double-observer aerial surveys, along with datasheets, pre-survey checklists, and a quick-guide to the methods. SOP 1 describes how to carry out wild horse and burro aerial surveys as an aviation crew member. SOP 2, SOP 3, and SOP 4 relate to data management, and are important for the wild horse and burro specialist or other lead staff who will be responsible for documenting and archiving records from the survey. SOP 5 details double-observer reporting via a data entry spreadsheet and provides reference for analyzing double observer data to obtain population estimates. SOP 6 presents general principles for preparing aerial survey flight lines. SOP 7 provides instructions for using abundance estimates from aerial surveys to project population size forward in time. The appendixes provide survey datasheets, pre-survey checklists, and a quick-guide to SOP 1.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A16","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Griffin, P.C., Ekernas, L.S., Schoenecker, K.A., and Lubow, B.C., 2020, Standard operating procedures for wild horse and burro double-observer aerial surveys: U.S. Geological Survey Techniques and Methods, book 2, chap. A16, 76 p., https://doi.org/10.3133/tm2A16","productDescription":"Report: viii, 76 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","ipdsId":"IP-099245","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437070,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P946MHTZ","text":"USGS data release","linkHelpText":"Wild horse aerial double observer survey analysis R script"},{"id":372919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a16/coverthb.jpg"},{"id":372920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a16/tm2a16.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 2–A16"},{"id":372921,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/P946MHTZ","text":"Software","description":"USGS Data Release","linkHelpText":"– R script to analyze simultaneous double observer wild horse and burro aerial surveys"}],"contact":"Center Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526–8118
","tableOfContents":"- Preface
- Acknowledgments
- Introduction
- Standard Operating Procedure 1—Conducting Aerial Surveys with the Simultaneous Double-Observer Method
- Standard Operating Procedure 2—Global Positioning System Use
- Standard Operating Procedure 3—File Folder Structure
- Standard Operating Procedure 4—Processing Digital Photographs
- Standard Operating Procedure 5—Reference for Analyzing Double-Observer Data
- Standard Operating Procedure 6—Preparing Flight Lines for Aerial Surveys
- Standard Operating Procedure 7—Principles for Projecting Population Size
- Appendix 1. List of Herd Codes, by State
- Appendix 2. Examples of Percent Concealing Vegetation, 0–80 percent
- Appendix 3. Blank Data Forms
- Appendix 4. Pre-Survey Checklists
- Appendix 5. Quick-Guide to Double-Observer Surveys
","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-03-05","noUsgsAuthors":false,"publicationDate":"2020-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Griffin, Paul C. 0000-0001-8412-5713","orcid":"https://orcid.org/0000-0001-8412-5713","contributorId":223035,"corporation":false,"usgs":false,"family":"Griffin","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":783888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ekernas, L. Stefan 0000-0002-9205-1985","orcid":"https://orcid.org/0000-0002-9205-1985","contributorId":223034,"corporation":false,"usgs":true,"family":"Ekernas","given":"L.","email":"","middleInitial":"Stefan","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":783887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":783890,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bruce C. Lubow","contributorId":223036,"corporation":false,"usgs":false,"family":"Bruce C. Lubow","affiliations":[],"preferred":false,"id":783889,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70206596,"text":"pp1863 - 2020 - Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole","interactions":[],"lastModifiedDate":"2022-04-22T19:10:54.810814","indexId":"pp1863","displayToPublicDate":"2020-03-05T09:14:28","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1863","displayTitle":"Groundwater Characterization and Effects of Pumping in the Death Valley Regional Groundwater Flow System, Nevada and California, with Special Reference to Devils Hole","title":"Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole","docAbstract":"Groundwater flow and development were characterized in four groundwater basins of the Death Valley regional flow system in Nevada and California with calibrated, groundwater-flow models. Natural groundwater discharges in the Furnace Creek, Lower Amargosa, and Saratoga Spring areas were defined and distributed consistently with a revised hydrogeologic framework. This simplified hydrogeologic framework was limited to four hydraulically unique, hydrogeologic units: (1) basin fill; (2) carbonate rocks; (3) volcanic rocks; and (4) low-permeability granitic and siliciclastic rocks. Hydrogeologic units and division of carbonate and volcanic rocks between shallow and deep were supported by results from 271 aquifer tests and specific-capacity estimates. Greater than 90 percent of field-estimated transmissivity occurred within 1,600 feet (ft) of the water table. Pumping in the study area from 1960 to 2010 averaged 46,000 acre-feet per year (acre-ft/yr), which is 80 percent of the predevelopment discharge. The central Amargosa Desert and Pahrump Valley were the two primary pumping centers and measurably affected water levels across 900 square miles in 2018.
Water levels in Devils Hole were a special focus because endangered Devils Hole pupfish (Cyprinodon diabolis) are affected by water-level declines. Pumping 42,100 acre-ft by Cappaert Enterprises, formerly Spring Meadows, Inc., caused a 2.3-ft water-level decline in Devils Hole, which temporarily reduced habitat of Devils Hole pupfish by 85 percent in 1972. If no pumping occurred, water levels in Devils Hole would have risen naturally about 1 ft between 1973 and 2018 from temporal variations in recharge. The 2.6-ft range of measured water-level changes in Devils Hole was simulated with a root-mean-square error of 0.2 ft during the 70-year period of record. Simulated water-level declines from pumping totaled 1.4 ft in 2018, with 25 and 34 percent attributed to pumping by Cappaert Enterprises and the central Amargosa Desert, respectively. Water levels in Devils Hole will decline at rates of 0.1–0.2 ft per decade if pumping from Ash Meadows groundwater basin and the central Amargosa Desert continue at current rates. Effects of future natural water-level fluctuations remain unknown.
Ash Meadows and Alkali Flat–Furnace Creek Ranch groundwater basins are hydraulically connected near well AD-4, about 5 miles south of the town of Amargosa Valley, Nevada. About 40 percent of the discharge from the Furnace Creek area is recharged in the Ash Meadows groundwater basin. Basin fill in the central Amargosa Desert hydraulically connects carbonate rocks east of well AD-4 with saturated carbonate rocks in the Funeral Range. About 7 percent of the 960,000 acre-ft pumped from Ash Meadows and Alkali Flat–Furnace Creek Ranch groundwater basins prior to 2019 was captured discharge from springs and phreatophytes. Greater than 40 percent of the 2,080,000 acre-ft pumped from Pahrump Valley between 1910 and 2019 was capture that primarily discharged from Bennetts and Manse Springs.
Simulated advective-flow distances and velocities from underground nuclear tests are within the range of advective transport calculations from tritium data and previous radionuclide transport investigations. Boundary conditions and flow rates from the regional model in this study are plausible for local-scale flow and radionuclide transport models. Simulated 165-year groundwater-flow paths do not extend into pumping areas and effects of regional pumping on advective transport are negligible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1863","collaboration":"Prepared in cooperation with the U.S. Department of Energy Office of Environmental Management, National Nuclear Security Administration, Nevada Site Office, under Interagency Agreement DE-EM0004969","usgsCitation":"Halford, K.J., and Jackson, T.R., 2020, Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole: U.S. Geological Survey Professional Paper 1863, 178 p., https://doi.org/10.3133/pp1863.","productDescription":"Report: xvi, 178 p.; Data Release","ipdsId":"IP-105994","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":372815,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HIYVG2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 model and supplementary data used to characterize groundwater flow and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole"},{"id":399508,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109738.htm"},{"id":372814,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1863/pp1863.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1863"},{"id":372813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1863/coverthb2.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley, Devils Hole","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 35.6464\n ],\n [\n -115.0611,\n 35.6464\n ],\n [\n -115.0611,\n 37.7214\n ],\n [\n -117,\n 37.7214\n ],\n [\n -117,\n 35.6464\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
","tableOfContents":"- Abstract
- Introduction
- Geology
- Interbasin Flow Between Groundwater Basins
- Predevelopment Groundwater Flow
- Groundwater Development
- Integrated Estimation of Recharge and Hydraulic-Property Distributions with Numerical Models
- Simulated Predevelopment Groundwater Flow
- Effects of Groundwater Development
- Potential Effects of Future Groundwater Development
- Groundwater-Basin Boundary Uncertainty
- Evaluation of Advective Flow from Corrective Action Units
- Model Limitations
- Summary
- Acknowledgments
- References Cited
","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"publishedDate":"2020-03-05","noUsgsAuthors":false,"publicationDate":"2020-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Halford, Keith J. 0000-0002-7322-1846 khalford@usgs.gov","orcid":"https://orcid.org/0000-0002-7322-1846","contributorId":1374,"corporation":false,"usgs":true,"family":"Halford","given":"Keith","email":"khalford@usgs.gov","middleInitial":"J.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jackson, Tracie R. 0000-0001-8553-0323 tjackson@usgs.gov","orcid":"https://orcid.org/0000-0001-8553-0323","contributorId":150591,"corporation":false,"usgs":true,"family":"Jackson","given":"Tracie","email":"tjackson@usgs.gov","middleInitial":"R.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":false,"id":775092,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70209460,"text":"70209460 - 2020 - Biogeography of fire regimes in western US conifer forests: A trait-based approach","interactions":[],"lastModifiedDate":"2020-04-09T13:15:04.84918","indexId":"70209460","displayToPublicDate":"2020-03-05T08:05:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1839,"text":"Global Ecology and Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Biogeography of fire regimes in western US conifer forests: A trait-based approach","docAbstract":"Aim\nFunctional traits are a critical link between species distributions and the ecosystem processes that structure those species’ niches. Concurrent increases in the availability of functional trait data and our ability to model species distributions present an opportunity to develop functional trait biogeography, i.e. the mapping of functional traits across space. Functional trait biogeography can improve process-based predictions about the resistance of certain species assemblages to changing environmental conditions across landscape scales. We illustrate this concept by developing the first trait-based, quantitative ranking of fire resistance (adult tree survival) in North American conifer species, and mapping that fire resistance across space. \nLocation and Time period\nWestern Continental United States, present-day.\nMajor taxa studied\n29 common conifer tree species.\nMethods\nWe compiled six traits for each species: three relating to tree morphology and three relating to litter flammability. We combined these traits into a single fire resistance score, and used community-weighted averaging to estimate the fire resistance scores of different forest communities, using interpolated species distribution and relative abundance data.\nResults \nSpecies associated with historically frequent fire have high fire resistance scores (e.g., Pinus ponderosa), reflected by thick bark, tall crowns, and flammable litter. Species associated with subalpine or arid conditions have low fire resistance scores (e.g., Picea engelmannii and Pinus edulis), reflected by thin bark, short stature, poor self-pruning and low litter flammability. A map of forest community fire resistance across the western US reveals agreement with independent assessments of historical fire regimes, while also identifying areas where community-wide species traits may be mismatched with historical fire regimes. \nMain conclusions\nQuantifying the functional traits that confer resistance to tree-killing fire provides a direct link between ecosystem disturbance and community resistance. Understanding this link is critical to evaluating long-term resilience of different forest types under dynamic fire regimes. Our work represents the first known spatial representation of fire-resistance traits at a regional scale, and as such provides a link between functional traits and biogeography relevant to a critical ecosystem process.","language":"English","publisher":"Wiley","doi":"10.1111/geb.13079","collaboration":"","usgsCitation":"Stevens, J., Kling, M.M., Schwilk, D.W., Varner, J.M., and Kane, J., 2020, Biogeography of fire regimes in western US conifer forests: A trait-based approach: Global Ecology and Biogeography, v. 29, no. 5, p. 944-955, https://doi.org/10.1111/geb.13079.","productDescription":"12 p.","startPage":"944","endPage":"955","ipdsId":"IP-114014","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437071,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97F5P7L","text":"USGS data release","linkHelpText":"Fire resistance trait data for 29 western North American conifer species"},{"id":373858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"","otherGeospatial":"Western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.5078125,\n 30.600093873550072\n ],\n [\n -103.53515625,\n 30.600093873550072\n ],\n [\n -103.53515625,\n 49.49667452747045\n ],\n [\n -125.5078125,\n 49.49667452747045\n ],\n [\n -125.5078125,\n 30.600093873550072\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Jens 0000-0002-2234-1960","orcid":"https://orcid.org/0000-0002-2234-1960","contributorId":222191,"corporation":false,"usgs":true,"family":"Stevens","given":"Jens","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":786562,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kling, Matthew M.","contributorId":223923,"corporation":false,"usgs":false,"family":"Kling","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":786630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schwilk, Dylan W.","contributorId":103883,"corporation":false,"usgs":true,"family":"Schwilk","given":"Dylan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":786631,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varner, J. Morgan","contributorId":197482,"corporation":false,"usgs":false,"family":"Varner","given":"J.","email":"","middleInitial":"Morgan","affiliations":[],"preferred":false,"id":786632,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kane, Jeffrey M.","contributorId":35169,"corporation":false,"usgs":true,"family":"Kane","given":"Jeffrey M.","affiliations":[],"preferred":false,"id":786633,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70249715,"text":"70249715 - 2020 - Fundamental hydraulics of cross sections in natural rivers: Preliminary analysis of a large data set of acoustic doppler flow measurements","interactions":[],"lastModifiedDate":"2023-10-25T12:14:01.11108","indexId":"70249715","displayToPublicDate":"2020-03-05T07:07:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11438,"text":"Water Resource Research","active":true,"publicationSubtype":{"id":10}},"title":"Fundamental hydraulics of cross sections in natural rivers: Preliminary analysis of a large data set of acoustic doppler flow measurements","docAbstract":"We have assembled a comprehensive and publicly accessible U.S. Geological Survey (USGS) streamflow measurement data set, called HYDRoSWOT, from a USGS National Water Information System archive of acoustic Doppler current profiler river discharge measurements collected from a wide range of rivers throughout the United States. The data set provides a wealth of information on the range of hydraulic characteristics of river cross sections in the United States. Preliminary exploration of the data set, filtered for quality control, indicates that rivers tend toward consistent and predictable forms as discharge increases. The ratio of maximum-to-mean depth is highly predictable and is remarkably consistent across all river sizes and discharges. Distributions of hydraulic characteristics provide a large-scale perspective on the general hydraulic characteristics of rivers. The data set affords the opportunity to analyze hydraulic relations for individual rivers as a function of stage, geomorphic setting, and energy environments and, combined with additional information contained in this data set, might yield predictive relations that could help constrain and parameterize river hydraulic models.
Despite consensus that impervious surfaces increase flooding, the magnitude of the increase remains uncertain. This uncertainty largely stems from the challenge of isolating the effect of changes in impervious cover separate from other factors that also affect flooding. To control for these factors, prior study designs rely on either temporal or spatial variation in impervious cover. We leverage both temporal and spatial variation in a panel data regression design to isolate the effect of impervious cover on floods. With 39 years of data from 280 U.S. streamgages, we estimate that a one percentage point increase in impervious basin cover causes a 3.3% increase in annual flood magnitude (95%CI: 1.9%, 4.7%) on average. Using 2,109 streamgages, some of which have upstream regulation and/or overlapping basins, we estimate a larger effect: 4.6% (CI: 3.5%, 5.6%). The approach introduced here can be extended to estimate the causal effects of other drivers of hydrologic change.
This study of water use associated with development of continuous oil and gas resources in the Williston Basin is intended to provide a preliminary model-based analysis of water use in major regions of production of continuous oil and gas resources in the United States. Direct, indirect, and ancillary water use associated with development of continuous oil and gas resources in the Williston Basin was estimated in North Dakota and Montana from 2007 to 2017. Water-use data were aggregated by county and year, which were the sampling units used in this analysis. Linear and quantile regression models of water use in relation to the number of oil and gas wells developed were fit for the direct, indirect, and ancillary water-use categories for each State. A 95-percent confidence interval for each parameter estimate from the linear regression models was computed as a measure of uncertainty. Additional information on uncertainty can be gained from modeling other distribution parameters, so quantile regression models of the 5th, 50th, and 95th percentiles also were fit. To assess uncertainty in the estimates from the regression models of direct, indirect, and ancillary water use, leave-one-out cross-validation was used. Model performance was evaluated with three goodness-of-fit metrics used to compare the estimates and observations of water use.
Mean annual direct and indirect water use for development of continuous oil and gas resources in North Dakota was estimated at 4,512 million gallons (Mgal) per year (Mgal/yr), with a 95-percent confidence interval of 4,021–5,152 Mgal/yr, and in Montana was estimated at 196 Mgal/yr, with a 95-percent confidence interval of 189–203 Mgal/yr. Ancillary water use (for domestic and public supply) had an estimated annual mean of 2,753 Mgal/yr in North Dakota and 396 Mgal/yr in Montana. The coefficient from the linear regression model of direct water use was 3.86 Mgal per well and hydraulic fracturing water use was 3.70 Mgal per well for North Dakota. The mean estimate of direct water use had a 95-percent confidence interval of 3.48–4.23 Mgal per well. For North Dakota, the coefficient from the linear regression model of indirect water use was 0.453 Mgal per well, with a 95-percent confidence interval of 0.415–0.492 Mgal per well. Direct and indirect water use had a mean estimate of about 4.31 Mgal per well in North Dakota. The mean estimate of ancillary water use (for domestic and public supply) in North Dakota was 2.03 Mgal per well, with a 95-percent confidence interval of 1.76–2.31 Mgal per well. For Montana, the linear regression model of hydraulic fracturing water use had a mean estimate of 2.04 Mgal per well. The 95-percent confidence interval for the mean estimate was 1.80–2.28 Mgal per well. Direct and indirect water use in Montana had a mean estimate of 2.49 Mgal per well. The mean estimate of ancillary water use (for domestic and public supply) in Montana was 2.43 Mgal per well, with a 95-percent confidence interval of 1.76–3.11 Mgal per well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205012","collaboration":"Water Availability and Use Science Program","usgsCitation":"McShane, R.R., Barnhart, T.B., Valder, J.F., Haines, S.S., Macek-Rowland, K.M., Carter, J.M., Delzer, G.C., and Thamke, J.N., 2020, Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17: U.S. Geological Survey Scientific Investigations Report 2020–5012, 26 p., https://doi.org/10.3133/sir20205012","productDescription":"Report: vii, 26 p.; 2 Appendixes; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112448","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":399633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109737.htm"},{"id":372867,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5012 Appendix 2","linkHelpText":"– Water-Use Estimates and Coefficients"},{"id":372866,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5012 Appendix 1","linkHelpText":"– R Scripts"},{"id":372864,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5012/coverthb2.jpg"},{"id":372868,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPKRLW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data to Estimate Water Use Associated with Continuous Oil and Gas Development, Williston Basin, United States, 1980-2017 (ver. 2.0, September 2019)"},{"id":372865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5012"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8333,\n 44.8333\n ],\n [\n -99,\n 44.8333\n ],\n [\n -99,\n 49\n ],\n [\n -106.8333,\n 49\n ],\n [\n -106.8333,\n 44.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
","tableOfContents":"- Acknowledgments
- Abstract
- Introduction
- Methods for Analyzing Water Use
- Results of Water-Use Analysis
- Comparisons to Water-Use Estimates from Other Studies
- Limitations of Water-Use Analysis for the Williston Basin
- Summary
- References Cited
- Appendix 1. R Scripts
- Appendix 2. Water-Use Estimates and Coefficients
","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-03-04","noUsgsAuthors":false,"publicationDate":"2020-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"McShane, Ryan R. 0000-0002-3128-0039","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":219009,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782093,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":219010,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore","email":"","middleInitial":"B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782094,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valder, Joshua F. 0000-0003-3733-8868","orcid":"https://orcid.org/0000-0003-3733-8868","contributorId":220912,"corporation":false,"usgs":true,"family":"Valder","given":"Joshua F.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782095,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haines, Seth S. 0000-0003-2611-8165 shaines@usgs.gov","orcid":"https://orcid.org/0000-0003-2611-8165","contributorId":1344,"corporation":false,"usgs":true,"family":"Haines","given":"Seth","email":"shaines@usgs.gov","middleInitial":"S.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782096,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Macek-Rowland, Kathleen M. 0000-0003-2526-6860","orcid":"https://orcid.org/0000-0003-2526-6860","contributorId":219012,"corporation":false,"usgs":true,"family":"Macek-Rowland","given":"Kathleen M. ","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782097,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Carter, Janet M. 0000-0002-6376-3473","orcid":"https://orcid.org/0000-0002-6376-3473","contributorId":40660,"corporation":false,"usgs":true,"family":"Carter","given":"Janet M.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782098,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delzer, Gregory C. 0000-0002-7077-4963","orcid":"https://orcid.org/0000-0002-7077-4963","contributorId":203448,"corporation":false,"usgs":true,"family":"Delzer","given":"Gregory","email":"","middleInitial":"C.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782099,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thamke, Joanna N. 0000-0002-6917-1946 jothamke@usgs.gov","orcid":"https://orcid.org/0000-0002-6917-1946","contributorId":1012,"corporation":false,"usgs":true,"family":"Thamke","given":"Joanna N.","email":"jothamke@usgs.gov","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true}],"preferred":true,"id":782100,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70227658,"text":"70227658 - 2020 - The changing sociocultural context of wildlife conservation","interactions":[],"lastModifiedDate":"2022-01-25T13:13:03.956979","indexId":"70227658","displayToPublicDate":"2020-03-04T07:09:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"The changing sociocultural context of wildlife conservation","docAbstract":"We introduced a multilevel model of value shift to describe the changing social context of wildlife conservation. Our model depicts how cultural-level processes driven by modernization (e.g., increased wealth, education, and urbanization) affect changes in individual-level cognition that prompt a shift from domination to mutualism wildlife values. Domination values promote beliefs that wildlife should be used primarily to benefit humans, whereas mutualism values adopt a view that wildlife are part of one's social network and worthy of care and compassion. Such shifts create emergent effects (e.g., new interest groups) and challenges to wildlife management organizations (e.g., increased conflict) and dramatically alter the sociopolitical context of conservation decisions. Although this model is likely applicable to many modernized countries, we tested it with data from a 2017–2018 nationwide survey (mail and email panel) of 43,949 residents in the United States. We conducted hierarchical linear modeling and correlational analysis to examine relationships. Modernization variables had strong state-level effects on domination and mutualism. Higher levels of education, income, and urbanization were associated with higher percentages of mutualists and lower percentages of traditionalists, who have strong domination values. Values affected attitudes toward wildlife management challenges; for example, states with higher proportions of mutualists were less supportive of lethal control of wolves (Canis lupus) and had lower percentages of active hunters, who represent the traditional clientele of state wildlife agencies in the United States. We contend that agencies will need to embrace new strategies to engage and represent a growing segment of the public with mutualism values. Our model merits testing for application in other countries.
The fire regime is a central framing concept in wildfire science and ecology and describes how a range of wildfire characteristics vary geographically over time. Understanding and mapping fire regimes is important for guiding appropriate management and risk reduction strategies and for informing research on drivers of global change and altered fire patterns. Most efforts to spatially delineate fire regimes have been conducted by identifying natural groupings of fire parameters based on available historical fire data. This can result in classes with similar fire characteristics but wide differences in ecosystem types. We took a different approach and defined fire regime ecoregions for California to better align with ecosystem types, without using fire as part of the definition. We used an unsupervised classification algorithm to segregate the state into spatial clusters based on distinctive biophysical and anthropogenic attributes that drive fire regimes – and then used historical fire data to evaluate the ecoregions. The fire regime ecoregion map corresponded well with the major land cover types of the state and provided clear separation of historical patterns in fire frequency and size, with lower variability in fire severity. This methodology could be used for mapping fire regimes in other regions with limited historical fire data or forecasting future fire regimes based on expected changes in biophysical characteristics.