{"pageNumber":"480","pageRowStart":"11975","pageSize":"25","recordCount":184553,"records":[{"id":70221867,"text":"70221867 - 2021 - Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups","interactions":[],"lastModifiedDate":"2022-04-13T20:17:41.090462","indexId":"70221867","displayToPublicDate":"2021-06-30T09:14:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups","docAbstract":"<p><span>Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1 &amp; 2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have &gt;17% of their extent in Status 1 &amp; 2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.&nbsp;</span></p>","language":"English","publisher":"MDPI","doi":"10.3390/f12070864","usgsCitation":"McKerrow, A., Davidson, A., Rubino, M., Faber-Langendoen, D., and Dockter, D., 2021, Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups: Forests, v. 12, no. 7, 864, 15 p., https://doi.org/10.3390/f12070864.","productDescription":"864, 15 p.","ipdsId":"IP-129762","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":451704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f12070864","text":"Publisher Index Page"},{"id":387107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":819084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davidson, Anne","contributorId":197967,"corporation":false,"usgs":false,"family":"Davidson","given":"Anne","email":"","affiliations":[],"preferred":false,"id":819085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rubino, Matthew J. 0000-0003-0651-3053","orcid":"https://orcid.org/0000-0003-0651-3053","contributorId":215500,"corporation":false,"usgs":false,"family":"Rubino","given":"Matthew J.","affiliations":[{"id":39268,"text":"North Carolina State University, NC Cooperative Fish & Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":819086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faber-Langendoen, Don","contributorId":260895,"corporation":false,"usgs":false,"family":"Faber-Langendoen","given":"Don","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":819087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216814,"corporation":false,"usgs":true,"family":"Dockter","given":"Daryn","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":819088,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70236252,"text":"70236252 - 2021 - The drying regimes of non-perennial rivers and streams","interactions":[],"lastModifiedDate":"2022-08-31T13:36:51.788646","indexId":"70236252","displayToPublicDate":"2021-06-30T08:34:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The drying regimes of non-perennial rivers and streams","docAbstract":"<p><span>The flow regime paradigm is central to the aquatic sciences, where flow drives critical functions in lotic systems. Non-perennial streams comprise the majority of global river length, thus we extended this paradigm to stream drying. Using 894 USGS gages, we isolated 25,207 drying events from 1979 to 2018, represented by a streamflow peak followed by no flow. We calculated hydrologic signatures for each drying event and using multivariate statistics, grouped events into drying regimes characterized by: (a) fast drying, (b) long no-flow duration, (c) prolonged drying following low antecedent flows, (d) drying without a distinctive hydrologic signature. 77% of gages had more than one drying regime at different times within the study period. Random forests revealed land cover/use are more important to how a river dries than climate or physiographic characteristics. Clustering stream drying behavior may allow practitioners to more systematically adapt water resource management practices to specific drying regimes or rivers.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL093298","usgsCitation":"Price, A.N., Jones, C.N., Hammond, J., Zimmer, M., and Zipper, S., 2021, The drying regimes of non-perennial rivers and streams: Geophysical Research Letters, v. 48, no. 14, e2021GL093298, 12 p., https://doi.org/10.1029/2021GL093298.","productDescription":"e2021GL093298, 12 p.","ipdsId":"IP-127641","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":405993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"14","noUsgsAuthors":false,"publicationDate":"2021-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Price, Adam N. 0000-0002-7211-4758","orcid":"https://orcid.org/0000-0002-7211-4758","contributorId":295971,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":850332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, C. Nathan 0000-0002-5804-0510","orcid":"https://orcid.org/0000-0002-5804-0510","contributorId":295972,"corporation":false,"usgs":false,"family":"Jones","given":"C.","email":"","middleInitial":"Nathan","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":850333,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850334,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zimmer, Margaret 0000-0001-8287-1923","orcid":"https://orcid.org/0000-0001-8287-1923","contributorId":225158,"corporation":false,"usgs":false,"family":"Zimmer","given":"Margaret","affiliations":[{"id":41054,"text":"Earth and Planetary Sciences, University of California, Santa Cruz, CA, 95064, USA","active":true,"usgs":false}],"preferred":false,"id":850335,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zipper, Samuel 0000-0002-8735-5757","orcid":"https://orcid.org/0000-0002-8735-5757","contributorId":225160,"corporation":false,"usgs":false,"family":"Zipper","given":"Samuel","email":"","affiliations":[{"id":41056,"text":"Kansas Geological Survey, University of Kansas, Lawrence KS 66047, USA","active":true,"usgs":false}],"preferred":false,"id":850336,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70221670,"text":"ofr20211060 - 2021 - Estimated water withdrawals and use in Puerto Rico, 2015","interactions":[],"lastModifiedDate":"2021-07-01T11:41:28.037511","indexId":"ofr20211060","displayToPublicDate":"2021-06-30T08:33:16","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1060","displayTitle":"Estimated Water Withdrawals and Use in Puerto Rico, 2015","title":"Estimated water withdrawals and use in Puerto Rico, 2015","docAbstract":"<p>Water withdrawals and use in Puerto Rico for 2015 were estimated at 2,372 million gallons per day (Mgal/d), which was 21 percent less than withdrawals and use for 2010. The 2015 total water withdrawal and use estimates were the lowest since 1990 and coincided with a substantial decline of 25 percent in saline-water withdrawals for thermoelectric-power cooling processes from 2010 to 2015. Freshwater withdrawals were 671 Mgal/d, or 28 percent of total water withdrawals, and saline-water withdrawals were 1,701 Mgal/d, or 72 percent of total withdrawals. Fresh surface-water withdrawals were estimated at 548 Mgal/d, 10 percent less than in 2010, whereas fresh groundwater withdrawals were estimated at 122 Mgal/d, 2 percent less than in 2010. Saline surface-water withdrawals were 25 percent less than in 2010.</p><p>Freshwater withdrawals were greatest for public-supply water and irrigation in 2015 and, combined, accounted for 98 percent of Puerto Rico’s total freshwater withdrawals. Withdrawals in 2015 for public-supply water (576 Mgal/d) were 14 percent lower and withdrawals for irrigation (78 Mgal/d) were 104 percent greater than in 2010, possibly because of drought conditions in agricultural counties along the south and southeast coasts in 2015. The sources for public-supply water withdrawals in 2015 included surface water (88 percent) and groundwater (12 percent). Withdrawals for other uses, which account for the remaining 2 percent of Puerto Rico’s total freshwater withdrawals, were lower in 2015 than in 2010; specifically, withdrawals for domestic self-supplied use decreased by 78 percent, industrial withdrawals decreased by 15 percent, and withdrawals for livestock decreased by 25 percent. Freshwater withdrawals for thermoelectric power and mining were greater in 2015 than in 2010, increasing by 23 percent and 5 percent, respectively.</p><p>The total population of Puerto Rico decreased by 7 percent from 2010 to 2015, from 3.73 million people in 2010 to 3.47 million people in 2015. The number of people who obtained potable water from public-supply water facilities in 2015 was about 3.47 million, or about 100 percent of the population of Puerto Rico.</p><p>Public-supply water deliveries for domestic use accounted for 338 Mgal/d in 2015, which is 47 percent greater than in 2010, indicating an increase in domestic per capita use from 62 to 98 gallons per person per day from 2010 to 2015. Domestic self-supplied withdrawals were estimated at 0.52 Mgal/d in 2015, for an estimated 4,708 people (less than 1 percent of Puerto Rico’s population). All domestic self-supplied withdrawals were assumed to be from groundwater sources.</p><p>Irrigation freshwater withdrawals were 78 Mgal/d in 2015 and accounted for 12 percent of the total freshwater withdrawals for all uses. Surface-water deliveries from irrigation districts accounted for 44 percent of total irrigation withdrawals, whereas groundwater withdrawals accounted for 56 percent. About 37,000 acres were irrigated in 2015, a decrease of 11 percent or about 4,000 acres compared to 2010. About 99 percent of the acreage was irrigated by micro-irrigation and sprinkler systems in 2015. About 65 percent of the irrigation withdrawals were accounted for by four municipalities: Santa Isabel, Salinas, Lajas, and Juana Díaz.</p><p>Altogether, freshwater withdrawals for livestock, industrial, mining, and thermoelectric power accounted for 2 percent (16.2 Mgal/d) of freshwater withdrawals for all uses, 9 percent less than in 2010. About 71 percent of the freshwater withdrawn for these categories was from groundwater sources.</p><p>In 2015, 50 percent of the total freshwater withdrawn in Puerto Rico was apportioned to six municipalities: Arecibo, Trujillo Alto, Toa Alta, Villalba, Aguada, and Mayagüez. Arecibo accounted for about 18 percent of the total freshwater withdrawals, predominantly for public-supply water use. Trujillo Alto, Toa Alta, Villalba, Aguada, and Mayagüez accounted for about 32 percent (213 Mgal/d) of the total freshwater withdrawals, which were predominantly for public-supply water uses. Withdrawals in some of these municipalities are subsequently distributed to other municipalities such as those in the San Juan metro area. The Puerto Rico Aqueduct and Sewer Authority water service area for the San Juan metro area (referred to as W–102) accounted for about 28 percent of the total water delivered from public-supply water facilities to domestic users, which includes about 34 percent of the total population of Puerto Rico.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211060","collaboration":"Prepared in cooperation with the Puerto Rico Aqueduct and Sewer Authority and the Puerto Rico Environmental Quality Board","usgsCitation":"Molina-Rivera, W.L., and Irizarry-Ortiz, M.M., 2021, Estimated water withdrawals and use in Puerto Rico, 2015: U.S. Geological Survey Open-File Report 2021–1060, 38 p., https://doi.org/10.3133/ofr20211060.","productDescription":"Report: vii, 38 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-096352","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":386796,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9POVNC6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Spatial and tabular datasets of water withdrawals and use in Puerto Rico, 2015"},{"id":386795,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1060/ofr20211060.pdf","text":"Report","size":"6.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1060"},{"id":386794,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1060/coverthb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -67.445068359375,\n              17.764381077782076\n            ],\n            [\n              -65.1873779296875,\n              17.764381077782076\n            ],\n            [\n              -65.1873779296875,\n              18.651449894396634\n            ],\n            [\n              -67.445068359375,\n              18.651449894396634\n            ],\n            [\n              -67.445068359375,\n              17.764381077782076\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a data-mce-href=\"https://www.usgs.gov/centers/car-fl-water\" href=\"https://www.usgs.gov/centers/car-fl-water\">Caribbean-Florida Water Science Center</a><br>U.S. Geological Survey<br>4446 Pet Lane, Suite 108<br>Lutz, FL 33559</p><p><a data-mce-href=\"../contact\" href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Purpose and Scope</li><li>Data Compilation Procedures</li><li>Total Water Withdrawals and Use</li><li>Trends in Water Withdrawals and Use, 1990–2015</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2021-06-30","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Molina-Rivera, Wanda L. 0000-0001-5856-283X","orcid":"https://orcid.org/0000-0001-5856-283X","contributorId":54190,"corporation":false,"usgs":true,"family":"Molina-Rivera","given":"Wanda","email":"","middleInitial":"L.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818397,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irizarry-Ortiz, Michelle M. 0000-0001-5338-8940","orcid":"https://orcid.org/0000-0001-5338-8940","contributorId":260660,"corporation":false,"usgs":true,"family":"Irizarry-Ortiz","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818398,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70222579,"text":"70222579 - 2021 - Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources","interactions":[],"lastModifiedDate":"2021-08-05T13:08:29.47498","indexId":"70222579","displayToPublicDate":"2021-06-30T08:03:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9134,"text":"The Sedimentary Record","active":true,"publicationSubtype":{"id":10}},"title":"Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources","docAbstract":"The San Gabriel and Canton faults represent early stages in the development of the San Andreas fault system. However, questions of timing of initiation and magnitude of slip on these structures remain unresolved, with published estimates ranging from 42-75 km and likely starting in the Miocene. This uncertainty in slip history reflects an absence of appropriate piercing points. We attempt to better constrain the slip history on these faults by quantifying the changing proportions of source terranes contributing sediment to the Ventura Basin, California, through the Cenozoic, including refining data for a key piercing point.\nVentura Basin sediments show an increase in detrital zircon U-Pb dates and mineral abundances associated with crystalline sources in the northern San Gabriel Mountains through time, which we interpret to record the basin’s northwest translation by dextral strike-slip faulting. In particular, an Oligocene unit mapped as part of the extra-regional Sespe Formation instead has greater affinity to the Vasquez Formation. Specifically, the presence of a unimodal population of ~1180 Ma zircon, high (57%) plagioclase content, and proximal alluvial fan facies indicate that the basin was adjacent to the San Gabriel anorthosite during deposition of the Vasquez Formation, requiring 35-60 km of slip on the San Gabriel-Canton fault system. Mixture modeling of detrital zircon data supported by automated mineralogy highlights the importance of this piercing point along the San Gabriel-Canton fault system and suggests that fault slip began during the late Oligocene to early Miocene, which is earlier than published models. These two lines of evidence disagree with recent models that estimate >60 km of offset, requiring a reappraisal of the slip history of an early strand of the San Andreas transform zone.","language":"English","publisher":"Society for Sedimentary Geology","doi":"10.2110/sedred.2021.2.3","usgsCitation":"Gilbert, C., Jobe, Z.R., Johnstone, S., and Sharman, G.R., 2021, Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources: The Sedimentary Record, v. 19, no. 2, p. 12-21, https://doi.org/10.2110/sedred.2021.2.3.","productDescription":"10 p.","startPage":"12","endPage":"21","ipdsId":"IP-126612","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":451706,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2110/sedred.2021.2.3","text":"Publisher Index Page"},{"id":387714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"California","city":"Ventura","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.38568115234374,\n              34.24813554589752\n            ],\n            [\n              -119.1851806640625,\n              34.24813554589752\n            ],\n            [\n              -119.1851806640625,\n              34.44315867450577\n            ],\n            [\n              -119.38568115234374,\n              34.44315867450577\n            ],\n            [\n              -119.38568115234374,\n              34.24813554589752\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"19","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, Clark","contributorId":261777,"corporation":false,"usgs":false,"family":"Gilbert","given":"Clark","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":820621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jobe, Zane R.","contributorId":207547,"corporation":false,"usgs":false,"family":"Jobe","given":"Zane","email":"","middleInitial":"R.","affiliations":[{"id":37560,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado 80401, USA","active":true,"usgs":false}],"preferred":false,"id":820622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnstone, Samuel 0000-0002-3945-2499","orcid":"https://orcid.org/0000-0002-3945-2499","contributorId":207545,"corporation":false,"usgs":true,"family":"Johnstone","given":"Samuel","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":820623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":820624,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70226983,"text":"70226983 - 2021 - Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients","interactions":[],"lastModifiedDate":"2021-12-23T13:11:01.148018","indexId":"70226983","displayToPublicDate":"2021-06-30T07:07:44","publicationYear":"2021","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":"Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients","docAbstract":"<div class=\"abstract-group\"><div class=\"article-section__content en main\"><p>Plant community response to climate change will be influenced by individual plant responses that emerge from competition for limiting resources that fluctuate through time and vary across space. Projecting these responses requires an approach that integrates environmental conditions and species interactions that result from future climatic variability. Dryland plant communities are being substantially affected by climate change because their structure and function are closely tied to precipitation and temperature, yet impacts vary substantially due to environmental heterogeneity, especially in topographically complex regions. Here, we quantified the effects of climate change on big sagebrush (<i>Artemisia tridentata</i><span>&nbsp;</span>Nutt.) plant communities that span 76&nbsp;million ha in the western United States. We used an individual-based plant simulation model that represents intra- and inter-specific competition for water availability, which is represented by a process-based soil water balance model. For dominant plant functional types, we quantified changes in biomass and characterized agreement among 52 future climate scenarios. We then used a multivariate matching algorithm to generate fine-scale interpolated surfaces of functional type biomass for our study area. Results suggest geographically divergent responses of big sagebrush to climate change (changes in biomass of −20% to +27%), declines in perennial C<sub>3</sub><span>&nbsp;</span>grass and perennial forb biomass in most sites, and widespread, consistent, and sometimes large increases in perennial C<sub>4</sub><span>&nbsp;</span>grasses. The largest declines in big sagebrush, perennial C<sub>3</sub><span>&nbsp;</span>grass and perennial forb biomass were simulated in warm, dry sites. In contrast, we simulated no change or increases in functional type biomass in cold, moist sites. There was high agreement among climate scenarios on climate change impacts to functional type biomass, except for big sagebrush. Collectively, these results suggest divergent responses to warming in moisture-limited versus temperature-limited sites and potential shifts in the relative importance of some of the dominant functional types that result from competition for limiting resources.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15776","usgsCitation":"Palmquist, K.A., Schlaepfer, D.R., Renne, R.R., Torbit, S., Doherty, K., Remington, T.E., Watson, G., Bradford, J., and Lauenroth, W.K., 2021, Divergent climate change effects on widespread dryland plant communities driven by climatic and ecohydrological gradients: Global Change Biology, v. 27, no. 20, p. 5169-5185, https://doi.org/10.1111/gcb.15776.","productDescription":"17 p.","startPage":"5169","endPage":"5185","ipdsId":"IP-126819","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":393346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.267578125,\n              35.88905007936091\n            ],\n            [\n              -104.23828125,\n              35.88905007936091\n            ],\n            [\n              -104.23828125,\n              48.922499263758255\n            ],\n            [\n              -119.267578125,\n              48.922499263758255\n            ],\n            [\n              -119.267578125,\n              35.88905007936091\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"27","issue":"20","noUsgsAuthors":false,"publicationDate":"2021-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Palmquist, Kyle A.","contributorId":169517,"corporation":false,"usgs":false,"family":"Palmquist","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. 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,{"id":70221766,"text":"70221766 - 2021 - Evaluation of regulatory action and surveillance as preventive risk-mitigation to an emerging global amphibian pathogen Batrachochytrium salamandrivorans (Bsal)","interactions":[],"lastModifiedDate":"2023-06-23T13:16:35.462105","indexId":"70221766","displayToPublicDate":"2021-06-30T07:03:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluation of regulatory action and surveillance as preventive risk-mitigation to an emerging global amphibian pathogen <i>Batrachochytrium salamandrivorans</i> (Bsal)","title":"Evaluation of regulatory action and surveillance as preventive risk-mitigation to an emerging global amphibian pathogen Batrachochytrium salamandrivorans (Bsal)","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0040\">The emerging amphibian pathogen<span>&nbsp;</span><i>Batrachochytrium salamandrivorans</i><span>&nbsp;(Bsal) is a severe threat to global urodelan (salamanders, newts, and related taxa) biodiversity. Bsal has not been detected, to date, in North America, but the risk is high because North America is one of the global hotspots for urodelan biodiversity. The North American and United States response to the discovery of Bsal in Europe was to take a risk-based approach to preventive management actions, including interim regulations on importation of captive salamanders and a large-scale surveillance effort. Risk-based approaches to decision-making can extend to&nbsp;adaptive management&nbsp;cycles by periodically incorporating new information that reduces uncertainty in an estimate of risk or to assess the effect of mitigation actions which reduce risk directly. Our objectives were to evaluate the effects of regulatory action on the introduction of Bsal to the&nbsp;U.S., quantify how a large-scale surveillance effort impacted consequence risk, and to combine other new information on species susceptibility to re-evaluate Bsal risk to the U.S. Import regulations effectively reduced import volume of targeted species, but new research on species susceptibility suggests the list of regulated species was incomplete regarding Bsal reservoir species. Not detecting Bsal in an intensive surveillance effort improved confidence that Bsal was not present, however, the overall risk-reduction impact was limited because of the expansive area of interest (conterminous United States) and limited time frame of sampling. Overall, the preventive actions in response to the Bsal threat did reduce Bsal risk in the U.S. and we present an updated risk assessment to provide information for adaptive decision-making.</span></p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2021.109222","usgsCitation":"Grear, D.A., Mosher, B.A., Richgels, K., and Campbell Grant, E.H., 2021, Evaluation of regulatory action and surveillance as preventive risk-mitigation to an emerging global amphibian pathogen Batrachochytrium salamandrivorans (Bsal): Biological Conservation, v. 260, 109222, 9 p.; Data release, https://doi.org/10.1016/j.biocon.2021.109222.","productDescription":"109222, 9 p.; Data release","ipdsId":"IP-125684","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":451710,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index 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A.","contributorId":189579,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":818669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richgels, Katherine 0000-0003-2834-9477 krichgels@usgs.gov","orcid":"https://orcid.org/0000-0003-2834-9477","contributorId":167016,"corporation":false,"usgs":true,"family":"Richgels","given":"Katherine","email":"krichgels@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":818670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":818671,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70221769,"text":"70221769 - 2021 - Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada","interactions":[],"lastModifiedDate":"2021-07-16T11:49:32.681303","indexId":"70221769","displayToPublicDate":"2021-06-30T06:52:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5804,"text":"Geothermal Energy – Science, Society and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid-flow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fluid-flow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with<span>&nbsp;</span><i>k</i>-means clustering (NMF<i>k</i>), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1186/s40517-021-00199-8","usgsCitation":"Siler, D.L., Pepin, J.D., Vesselinov, V.V., Mudunuru, M.K., and Ahmmed, B., 2021, Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada: Geothermal Energy – Science, Society and Technology, v. 9, 17, 17 p., https://doi.org/10.1186/s40517-021-00199-8.","productDescription":"17, 17 p.","ipdsId":"IP-125602","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":451713,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40517-021-00199-8","text":"Publisher Index Page"},{"id":386929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Brady geothermal field","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -119.70703125,\n              39.16414104768742\n            ],\n            [\n              -118.38867187499999,\n              39.16414104768742\n            ],\n            [\n              -118.38867187499999,\n              40.212440718286466\n            ],\n            [\n              -119.70703125,\n              40.212440718286466\n            ],\n            [\n              -119.70703125,\n              39.16414104768742\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":818672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vesselinov, Velimir V.","contributorId":260765,"corporation":false,"usgs":false,"family":"Vesselinov","given":"Velimir","email":"","middleInitial":"V.","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":818674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mudunuru, Maruti K.","contributorId":260766,"corporation":false,"usgs":false,"family":"Mudunuru","given":"Maruti","email":"","middleInitial":"K.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":818675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ahmmed, Bulbul","contributorId":260767,"corporation":false,"usgs":false,"family":"Ahmmed","given":"Bulbul","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":818676,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70264979,"text":"70264979 - 2021 - Monochromatic long-period seismicity prior to the 2012 earthquake swarm at Little Sitkin Volcano, Alaska","interactions":[],"lastModifiedDate":"2025-03-27T15:04:43.551792","indexId":"70264979","displayToPublicDate":"2021-06-30T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Monochromatic long-period seismicity prior to the 2012 earthquake swarm at Little Sitkin Volcano, Alaska","docAbstract":"<p><span>Detection of the earliest stages of unrest is one of the most challenging and yet critically needed aspects of volcano monitoring. We investigate a sequence of five unusual long-period (LP) earthquakes that occurred in the days prior to the onset of a months-long volcano-tectonic (VT) earthquake swarm beneath Little Sitkin volcano in the Aleutian Islands during late 2012. The long-period earthquakes had two distinctive characteristics: their signals were dominated by a monochromatic spectral peak at approximately 0.57&nbsp;Hz and they had impulsive P and S-wave arrivals on a seismometer located on Amchitka Island 80&nbsp;km to the southeast of the volcano. In each case, the monochromatic earthquakes ended with a higher-frequency event after approximately 2&nbsp;min of duration. We find evidence that the five monochromatic LP earthquakes resulted from the resonance of a tabular magma body at middle crustal depths (15&nbsp;km) on the western side of Little Sitkin. Based on the resonant frequency and quality factor of the monochromatic LP earthquakes, we infer the magma body to have a lateral extent of 500&nbsp;m and a thickness of 9&nbsp;m. We interpret that a magmatic intrusion excited the monochromatic LP earthquakes and subsequently increased the stress beneath the volcano, leading to the onset of the shallow (&lt;10&nbsp;km depth) VT swarm five days later.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2021.689651","usgsCitation":"Haney, M.M., Buurman, H., Holtkamp, S., and McNutt, S., 2021, Monochromatic long-period seismicity prior to the 2012 earthquake swarm at Little Sitkin Volcano, Alaska: Frontiers in Earth Science, v. 9, 689651, 13 p., https://doi.org/10.3389/feart.2021.689651.","productDescription":"689651, 13 p.","ipdsId":"IP-128471","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2021.689651","text":"Publisher Index Page"},{"id":483943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Little Sitkin Volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              178.42575763155247,\n              52.01044407625301\n            ],\n            [\n              178.42575763155247,\n              51.8937086668314\n            ],\n            [\n              178.60197822354405,\n              51.8937086668314\n            ],\n            [\n              178.60197822354405,\n              52.01044407625301\n            ],\n            [\n              178.42575763155247,\n              52.01044407625301\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Buurman, Helena","contributorId":352827,"corporation":false,"usgs":false,"family":"Buurman","given":"Helena","affiliations":[{"id":84294,"text":"University of Alaska-Fairbanks Geophysical Institute","active":true,"usgs":false}],"preferred":false,"id":932155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holtkamp, Stephen","contributorId":352828,"corporation":false,"usgs":false,"family":"Holtkamp","given":"Stephen","affiliations":[{"id":84294,"text":"University of Alaska-Fairbanks Geophysical Institute","active":true,"usgs":false}],"preferred":false,"id":932156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNutt, Stephen","contributorId":352829,"corporation":false,"usgs":false,"family":"McNutt","given":"Stephen","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":932157,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70221893,"text":"70221893 - 2021 - Determination of burn severity models ranging from regional to continental scales for the conterminous United States","interactions":[],"lastModifiedDate":"2021-07-13T18:43:48.420401","indexId":"70221893","displayToPublicDate":"2021-06-29T13:35:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Determination of burn severity models ranging from regional to continental scales for the conterminous United States","docAbstract":"<p><span>Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and&nbsp;remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive&nbsp;spectral reflectance&nbsp;indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple&nbsp;vegetation classification&nbsp;systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from&nbsp;Landsat&nbsp;data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model&nbsp;goodness of fit&nbsp;was moderate (R</span><sup>2</sup><span>&nbsp;=&nbsp;0.55,&nbsp;</span><i>P</i><span>&nbsp;&lt;&nbsp;0.001) for dNBR and poor (R</span><sup>2</sup><span>&nbsp;=&nbsp;0.30, P&nbsp;&lt;&nbsp;0.001) for NBR. Within landcover classifications, CBI was better fit by dNBR than NBR. Finer scale regional regression models including BPS (dNBR&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><msup is=&quot;true&quot;><mi is=&quot;true&quot;>R</mi><mn is=&quot;true&quot;>2</mn></msup><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#xAF;</mo></mover></math>\"><span class=\"MJX_Assistive_MathML\">R2¯</span></span></span><span>&nbsp;= 0.56 and 0.00–0.83 R</span><sup>2</sup><span>&nbsp;range; NBR&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-2-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><msup is=&quot;true&quot;><mi is=&quot;true&quot;>R</mi><mn is=&quot;true&quot;>2</mn></msup><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#xAF;</mo></mover></math>\"><span class=\"MJX_Assistive_MathML\">R(bar)<sup>2</sup></span></span></span><span>&nbsp;= 0.43 and 0.00–0.82 R</span><sup>2</sup><span>&nbsp;range) and NVC (dNBR&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-3-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><msup is=&quot;true&quot;><mi is=&quot;true&quot;>R</mi><mn is=&quot;true&quot;>2</mn></msup><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#xAF;</mo></mover></math>\"><span class=\"MJX_Assistive_MathML\">R(bar)<sup>2</sup></span></span></span><span>&nbsp;= 0.55 and 0.15–0.78 R</span><sup>2</sup><span>&nbsp;range; NBR&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-4-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><msup is=&quot;true&quot;><mi is=&quot;true&quot;>R</mi><mn is=&quot;true&quot;>2</mn></msup><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#xAF;</mo></mover></math>\"><span class=\"MJX_Assistive_MathML\">R(bar)<sup>2</sup></span></span></span><span>&nbsp;= 0.41 and 0.00–0.79 R</span><sup>2</sup><span>&nbsp;range) were on average the same or better than the CONUS models for dNBR and NBR, with the strongest fit models exhibiting R</span><sup>2</sup><span>&nbsp;≥&nbsp;0.70, whereas larger scale regional models&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-5-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mover accent=&quot;true&quot; is=&quot;true&quot;><msup is=&quot;true&quot;><mi is=&quot;true&quot;>R</mi><mn is=&quot;true&quot;>2</mn></msup><mo stretchy=&quot;true&quot; is=&quot;true&quot;>&amp;#xAF;</mo></mover></math>\"><span class=\"MJX_Assistive_MathML\">R(bar)<sup>2</sup></span></span></span><span>&nbsp;ranged from 0.28 to 0.5. However, variation in accuracy among landcover types indicate that dNBR and NBR regression models could be used to effectively estimate CBI for future fires in certain regions, while for other regions models may require additional field observations or alternative spectral transformations. Our decision tree schema can be used to help users determine which scale is likely to produce the most accurate results using our models. The CBI regression models developed here, paired with the decision tree, provide users with a simple method to estimate burn severity in units of CBI for any fire within CONUS with moderate to high levels of confidence and provide a template for further development of models with new data going forward.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112569","usgsCitation":"Picotte, J., Cansler, C.A., Kolden, C.A., Lutz, J.A., Key, C., Benson, N., and Robertson, K., 2021, Determination of burn severity models ranging from regional to continental scales for the conterminous United States: Remote Sensing of Environment, v. 263, 112569, 12 p., https://doi.org/10.1016/j.rse.2021.112569.","productDescription":"112569, 12 p.","ipdsId":"IP-105398","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":387160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"geometry\": {\n        \"type\": \"MultiPolygon\",\n        \"coordinates\": [\n          [\n            [\n              [\n                -94.81758,\n                49.38905\n              ],\n              [\n                -94.64,\n                48.84\n              ],\n              [\n                -94.32914,\n                48.67074\n              ],\n              [\n                -93.63087,\n                48.60926\n              ],\n              [\n                -92.61,\n                48.45\n              ],\n              [\n                -91.64,\n                48.14\n              ],\n              [\n        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,{"id":70221851,"text":"70221851 - 2021 - Hotter drought escalates tree cover declines in blue oak woodlands of California","interactions":[],"lastModifiedDate":"2021-07-12T17:53:18.42089","indexId":"70221851","displayToPublicDate":"2021-06-29T12:49:12","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7749,"text":"Frontiers in Climate","active":true,"publicationSubtype":{"id":10}},"title":"Hotter drought escalates tree cover declines in blue oak woodlands of California","docAbstract":"<p><span>California has, in recent years, become a hotspot of interannual climatic variability, recording devastating climate-related disturbances with severe effects on tree resources. Understanding the patterns of tree cover change associated with these events is vital for developing strategies to sustain critical habitats of endemic and threatened vegetation communities. We assessed patterns of tree cover change, especially the effects of the 2012–2016 drought within the distribution range of blue oak (</span><i>Quercus douglasii</i><span>), an endemic tree species to California with a narrow geographic extent. We utilized multiple, annual land-cover and land-surface change products from the U.S. Geological Survey (USGS) Land Change Monitoring, Assessment and Projection (LCMAP) project along with climate and wildfire datasets to monitor changes in tree cover state and condition and examine their relationships with interannual climate variability between 1985 and 2016. Here, we refer to a change in tree cover class without a land-cover change to another class as “conditional change.” The unusual drought of 2012–2016, accompanied by anomalously high temperatures and vapor pressure deficit, was associated with exceptional spikes in the amount of both fire and non-fire induced tree cover loss and tree cover conditional change, especially in 2015 and 2016. Approximately 1,266 km</span><sup>2</sup><span>&nbsp;of tree cover loss and 617 km</span><sup>2</sup><span>&nbsp;of tree cover conditional change were recorded during that drought. Tree cover loss through medium to high severity fires was especially large in exceptionally dry and hot years. Our study demonstrates the usefulness of the LCMAP products for monitoring the effects of climatic extremes and disturbance events on both thematic and conditional land-cover change over a multi-decadal period. Our results signify that blue oak woodlands may be vulnerable to extreme climate events and changing wildfire regimes. Here, we present early evidence that frequent droughts associated with climate warming may continue to affect tree cover in this region, while drought interaction with wildfires and the resulting feedbacks may have substantial influence as well. Consequently, efforts to conserve the blue oak woodlands, and potentially other vegetation communities in the Western United States, may benefit from consideration of climate risks as well as the potential for climate-fire and vegetation feedbacks.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2021.689945","usgsCitation":"Dwomoh, F.K., Brown, J.F., Tollerud, H.J., and Auch, R.F., 2021, Hotter drought escalates tree cover declines in blue oak woodlands of California: Frontiers in Climate, v. 3, 689945, 15 p., https://doi.org/10.3389/fclim.2021.689945.","productDescription":"689945, 15 p.","ipdsId":"IP-129607","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":451715,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2021.689945","text":"Publisher Index 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(Geography)","active":false,"usgs":true}],"preferred":true,"id":818997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tollerud, Heather J. 0000-0001-9507-4456","orcid":"https://orcid.org/0000-0001-9507-4456","contributorId":210820,"corporation":false,"usgs":true,"family":"Tollerud","given":"Heather","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":818998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":818999,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70223414,"text":"70223414 - 2021 - Paths to computational fluency for natural resource educators, researchers, and managers","interactions":[],"lastModifiedDate":"2021-08-26T16:21:15.327269","indexId":"70223414","displayToPublicDate":"2021-06-29T11:21:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9155,"text":"Natural Resource Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Paths to computational fluency for natural resource educators, researchers, and managers","docAbstract":"<p><span>Natural resource management and supporting research teams need computational fluency in the data and model-rich 21st century. Computational fluency describes the ability of practitioners and scientists to conduct research and represent natural systems within the computer's environment. Advancement in information synthesis for natural resource management requires more sophisticated computational approaches, as well as reproducible, reusable, extensible, and transferable methods. Despite this importance, many new and current natural resource practitioners lack computational fluency and no common set of recommended resources and practices exist for learning these skills. Broadly, attaining computational fluency entails moving beyond the simple use of computers to applying sound computational principles and methods and including computational experts (such as computer scientists) on research teams. Our path for computational fluency includes using open-source tools when possible; reproducible data management, statistics, and modeling; understanding and applying the benefits of basic computer programming to carry out more complex procedures; tracking code with version control; working in controlled computer environments; and using advanced computing resources.</span></p>","language":"English","publisher":"Wiley","doi":"10.1111/nrm.12318","usgsCitation":"Erickson, R.A., Burnett, J.L., Wiltermuth, M.T., Bulliner, E.A., and Hsu, L., 2021, Paths to computational fluency for natural resource educators, researchers, and managers: Natural Resource Modelling, v. 34, no. 3, e12318, 21 p., https://doi.org/10.1111/nrm.12318.","productDescription":"e12318, 21 p.","ipdsId":"IP-124147","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":489797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nrm.12318","text":"Publisher Index Page"},{"id":388550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burnett, Jessica Leigh 0000-0002-0896-5099","orcid":"https://orcid.org/0000-0002-0896-5099","contributorId":248195,"corporation":false,"usgs":true,"family":"Burnett","given":"Jessica","email":"","middleInitial":"Leigh","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":821997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":821998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bulliner, Edward A. 0000-0002-2774-9295 ebulliner@usgs.gov","orcid":"https://orcid.org/0000-0002-2774-9295","contributorId":4983,"corporation":false,"usgs":true,"family":"Bulliner","given":"Edward","email":"ebulliner@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":821999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hsu, Leslie 0000-0002-5353-807X lhsu@usgs.gov","orcid":"https://orcid.org/0000-0002-5353-807X","contributorId":191745,"corporation":false,"usgs":true,"family":"Hsu","given":"Leslie","email":"lhsu@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":822000,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70234172,"text":"70234172 - 2021 - Forest thinning in the seaward fringe speeds up surface elevation increment and carbon accumulation in managed mangrove forests","interactions":[],"lastModifiedDate":"2022-08-02T16:20:52.801677","indexId":"70234172","displayToPublicDate":"2021-06-29T11:18:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Forest thinning in the seaward fringe speeds up surface elevation increment and carbon accumulation in managed mangrove forests","docAbstract":"<ol class=\"\"><li>Mangroves are significant carbon (C) sinks and ecological engineers as they accumulate sediments and increase soil surface elevation. Thus, the forest management practice of thinning may not only alter forest structure, but also facilitate new biogeomorphological processes that affect soil development. Thinning may create additional opportunity for understorey species, such as the light-demanding<span>&nbsp;</span><i>Acanthus ilicifolius</i>, to become a more prominent vegetation component of mangroves, which may further alter soil surface elevation trajectories.</li><li>Forest structure and soil surface elevation change (SEC) were monitored along transects from the landward edge (upper intertidal) to seaward fringe before and after thinning of non-native<span>&nbsp;</span><i>Sonneratia apetala</i><span>&nbsp;</span>plantations along the seaward edge of a mangrove site in China. Soil C accumulation was also evaluated.</li><li><i>Acanthus</i><span>&nbsp;</span>individuals colonized the forest gaps created by<span>&nbsp;</span><i>Sonneratia</i><span>&nbsp;</span>thinning in the seaward forest edge of the intertidal zones. In the absence of other pioneer species or other mangrove propagules/seeds,<span>&nbsp;</span><i>Acanthus</i><span>&nbsp;</span>pioneers occupied the areas rapidly (from 33.6 to 72.6&nbsp;stems/m<sup>2</sup>), and were successful opportunists.</li><li>Newly colonizing<span>&nbsp;</span><i>Acanthus</i><span>&nbsp;</span>vegetation significantly increased SEC from 25.1&nbsp;mm/year before<span>&nbsp;</span><i>Sonneratia</i><span>&nbsp;</span>was thinned to 46.5&nbsp;mm/year after thinning-induced<span>&nbsp;</span><i>Acanthus</i><span>&nbsp;</span>occupation. Furthermore,<span>&nbsp;</span><i>Acanthus</i><span>&nbsp;</span>occupation enhanced soil C accumulation at the seaward edge to 49.9 MgC&nbsp;ha<sup>−1</sup>&nbsp;year<sup>−1</sup>; a rate twofold higher than in the<span>&nbsp;</span><i>Sonneratia</i><span>&nbsp;</span>plantation before thinning.</li><li><i>Synthesis and applications</i>. Prolific<span>&nbsp;</span><i>Acanthus</i><span>&nbsp;</span>growth formed small elevation mounds on the seaward forest edge, which was suspected as a positive effect induced by thinning<span>&nbsp;</span><i>Sonneratia</i><span>&nbsp;</span>plantations that further facilitated colonization of<span>&nbsp;</span><i>Acanthus</i><span>&nbsp;</span>clones. Thinning non-native<span>&nbsp;</span><i>Sonneratia</i><span>&nbsp;</span>disturbed the established zonation, altered SEC, and is facilitating further succession of these mangrove forests into un-occupied aquatic areas. Higher elevations created by<span>&nbsp;</span><i>Acanthus</i><span>&nbsp;</span>expansion (vertically and horizontally) may further promote a greater percentage of landward edge mangrove species to colonize at these more favourable intertidal elevations yielding at least short-term gains in soil carbon accumulation by altering ecosystem function. Silvicultural activity should consider unintended influences on sedimentation patterns both in situ and in adjacent habitats in tidal forests, where small elevation changes affect mangrove species habitat preferences.</li></ol>","language":"English","publisher":"Wiley","doi":"10.1111/1365-2664.13939","usgsCitation":"Chen, L., Lin, Q., Krauss, K., Zhang, Y., Cormier, N., and Yang, Q., 2021, Forest thinning in the seaward fringe speeds up surface elevation increment and carbon accumulation in managed mangrove forests: Journal of Applied Ecology, v. 58, no. 9, p. 1899-1909, https://doi.org/10.1111/1365-2664.13939.","productDescription":"11 p.","startPage":"1899","endPage":"1909","ipdsId":"IP-121804","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":404666,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Chen, Luzhen","contributorId":194706,"corporation":false,"usgs":false,"family":"Chen","given":"Luzhen","email":"","affiliations":[],"preferred":false,"id":848084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lin, Qiulian","contributorId":294476,"corporation":false,"usgs":false,"family":"Lin","given":"Qiulian","email":"","affiliations":[{"id":63579,"text":"Xiamen University","active":true,"usgs":false}],"preferred":false,"id":848085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":848086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Yun","contributorId":146700,"corporation":false,"usgs":false,"family":"Zhang","given":"Yun","email":"","affiliations":[],"preferred":false,"id":848087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cormier, Nicole 0000-0003-2453-9900","orcid":"https://orcid.org/0000-0003-2453-9900","contributorId":214726,"corporation":false,"usgs":false,"family":"Cormier","given":"Nicole","affiliations":[{"id":16788,"text":"Macquarie University","active":true,"usgs":false}],"preferred":false,"id":848088,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Qiong","contributorId":294477,"corporation":false,"usgs":false,"family":"Yang","given":"Qiong","email":"","affiliations":[{"id":63580,"text":"Guangdong Neilingding Futian National Nature Reserve","active":true,"usgs":false}],"preferred":false,"id":848089,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70221697,"text":"70221697 - 2021 - Trait-based filtering mediates the effects of realistic biodiversity losses on ecosystem functioning","interactions":[],"lastModifiedDate":"2021-06-29T15:23:31.609203","indexId":"70221697","displayToPublicDate":"2021-06-29T10:21:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Trait-based filtering mediates the effects of realistic biodiversity losses on ecosystem functioning","docAbstract":"Biodiversity losses are a major driver of global changes in ecosystem functioning. While most studies of the relationship between biodiversity and ecosystem functioning have examined randomized species losses, trait-based filtering associated with species-specific vulnerability to drivers of diversity loss can strongly influence how ecosystem functioning responds to declining biodiversity. Moreover, the responses of ecosystem functioning to diversity loss may be mediated by environmental variability interacting with the suite of traits remaining in depauperate communities. We do not yet understand how communities resulting from realistic diversity losses (filtered by response traits) influence ecosystem functioning (via effect traits of the remaining community), especially under variable environmental conditions. Here we directly test how realistic and randomized plant diversity losses influence productivity and invasion resistance across multiple years in a California grassland. Compared with communities based on randomized diversity losses, communities resulting from realistic (drought-driven) species losses had higher invasion resistance under climatic conditions that matched the trait-based filtering they experienced. However, productivity declined more with realistic than with randomized species losses across all years, regardless of climatic conditions. Functional response traits aligned with effect traits for productivity but not for invasion resistance. Our findings illustrate that the effects of biodiversity losses depend not only on the identities of lost species, but also on how the traits of remaining species interact with varying environmental conditions. Understanding the consequences of biodiversity change requires studies that evaluate trait-mediated effects of species losses and incorporate the increasingly variable climatic conditions that future communities are expected to experience.","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.2022757118","usgsCitation":"Wolf, A.A., Funk, J.L., Selmants, P., Morozumi, C.N., Hernandez, D.L., Pasari, J.R., and Zavaleta, E.S., 2021, Trait-based filtering mediates the effects of realistic biodiversity losses on ecosystem functioning: Proceedings of the National Academy of Sciences, v. 118, no. 26, e2022757118, 7 p., https://doi.org/10.1073/pnas.2022757118.","productDescription":"e2022757118, 7 p.","ipdsId":"IP-121860","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":451720,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8256034","text":"Publisher Index Page"},{"id":386869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"118","issue":"26","noUsgsAuthors":false,"publicationDate":"2021-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Wolf, Amelia A.","contributorId":190685,"corporation":false,"usgs":false,"family":"Wolf","given":"Amelia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":818457,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Jennifer L.","contributorId":260668,"corporation":false,"usgs":false,"family":"Funk","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":818458,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selmants, Paul 0000-0001-6211-3957 pselmants@usgs.gov","orcid":"https://orcid.org/0000-0001-6211-3957","contributorId":192591,"corporation":false,"usgs":true,"family":"Selmants","given":"Paul","email":"pselmants@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":818459,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morozumi, Connor N","contributorId":260670,"corporation":false,"usgs":false,"family":"Morozumi","given":"Connor","email":"","middleInitial":"N","affiliations":[{"id":40432,"text":"Emory University","active":true,"usgs":false}],"preferred":false,"id":818460,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hernandez, Daniel L.","contributorId":205330,"corporation":false,"usgs":false,"family":"Hernandez","given":"Daniel","email":"","middleInitial":"L.","affiliations":[{"id":33615,"text":"Carleton College","active":true,"usgs":false}],"preferred":false,"id":818461,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pasari, Jae R","contributorId":260672,"corporation":false,"usgs":false,"family":"Pasari","given":"Jae","email":"","middleInitial":"R","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":818462,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zavaleta, Erika S","contributorId":190686,"corporation":false,"usgs":false,"family":"Zavaleta","given":"Erika","email":"","middleInitial":"S","affiliations":[],"preferred":false,"id":818463,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70221692,"text":"ofr20211041 - 2021 - GIS-based identification of areas that have resource potential for lode gold in Alaska","interactions":[],"lastModifiedDate":"2022-05-18T16:22:14.173807","indexId":"ofr20211041","displayToPublicDate":"2021-06-29T09:43:20","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1041","displayTitle":"GIS-Based Identification of Areas that have Resource Potential for Lode Gold in Alaska","title":"GIS-based identification of areas that have resource potential for lode gold in Alaska","docAbstract":"<p>Several comprehensive, data-driven geographic information system (GIS) analyses were conducted to assess prospectivity for lode gold in Alaska. These analyses use available geospatial datasets of lithologic, geochemical, mineral occurrence, and geophysical data to build models for recognizing different types of gold deposits within physiographic units defined by stream drainage basins that are approximately 100 square kilometers in area. The analytical methods successfully delineated areas in the State that contain known lode gold deposits and occurrences, providing some measure of confidence in their ability to predict gold prospectivity in areas of unknown lode gold potential. The results of our analyses indicate high prospectivity in a few areas scattered around the State that are not known to contain lode gold deposits.</p><p>In addition to assessing the potential for lode gold deposits in Alaska, we designed analyses to distinguish different lode gold deposit types, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. These can primarily be differentiated using their unique trace element geochemical fingerprints and elemental enrichments, which reflect the characteristics of the geologic environment and chemistry of the ore-forming fluids. We identified multiple parameters that would discriminate the different types of gold deposits, but owing to the limits of available data, the compositional similarity of ore-forming fluids among some types of lode gold deposits, and overlapping geologic environments, distinguishing deposit types at the state scale in Alaska remains problematic. These limitations resulted in overlapping areas of prospectivity for different deposit types, highlighting the challenges for targeted gold exploration in Alaska. Adjustment of some scoring parameters and recharacterization at smaller scales to highlight individual mineral systems for application of prospectivity analyses may be helpful at a district scale. At a regional scale, the aerial overlap of individual deposit type analyses reinforces confidence in prospectivity for a lode gold resource in a drainage basin. Our analysis for undivided lode gold deposits will be the most practical analysis for landuse decisions in which delineation of areas that have confident potential for gold deposits in general is the primary goal.</p><p>Data-driven GIS analysis for lode gold potential in Alaska, although limited by the size and uneven coverage of available datasets, objectively indicates prospectivity in areas where exposure is good as well as in areas under cover. The results of our analyses show medium to high prospectivity in areas that surround known deposits, indicating an overall expansion of areas that have the potential to contain gold deposits. Exploration in these areas may help improve the balance between the volume of gold produced in placer districts statewide and the relatively low volume of identified lode resources that contribute to these placer deposits. The results of our analyses can help focus future investigations in areas that show prospectivity but are not known to contain gold deposits, as well as in areas where data are lacking and the geology is poorly understood, and acquisition of additional data may help better define and constrain gold prospectivity.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211041","collaboration":"Prepared in cooperation with the Alaska Division of Geological & Geophysical Surveys and the Bureau of Land Management","usgsCitation":"Karl, S.M., Kreiner, D.C., Case, G.N.D., Labay, K.A., Shew, N.B., Granitto, M., Wang, B., and Anderson, E.D., 2021, GIS-based identification of areas that have resource potential for lode gold in Alaska (ver. 1.1, October 2021): U.S. Geological Survey Open-File Report 2021–1041, 75 p., 9 plates, https://doi.org/10.3133/ofr20211041.","productDescription":"Report: x, 75 p.; 9 Plates: 11.00 x 17.00 inches or smaller; Data Release; 3 Appendixes","numberOfPages":"75","additionalOnlineFiles":"Y","ipdsId":"IP-099538","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":400764,"rank":18,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223008","text":"Fact Sheet 2022-3008","description":"Karl, S.M., Kreiner, D.C., Case, G.N.D., and Labay, K., 2022, Geospatial analysis delineates lode gold prospectivity in Alaska: U.S. Geological Survey Fact Sheet 2022–3008, 4 p., https://doi.org/10.3133/fs202230008","linkHelpText":"-  Geospatial Analyses Delineate Lode Gold Prospectivity in Alaska"},{"id":391052,"rank":17,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_appendix3.xlsx","text":"Appendix 3","size":"250 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Lithology-Keyword Search Terms for the “Geologic Map of Alaska”"},{"id":391050,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_appendix1.xlsx","text":"Appendix 1","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Statistical Calculations of Levels of Background Values for Sediment and Rock Geochemical Data"},{"id":391049,"rank":14,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1041/versionHist.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":386835,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CAM3F9","linkHelpText":"Data and results for GIS-based identification of areas that have resource potential for lode gold in Alaska"},{"id":386834,"rank":12,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate9.pdf","text":"Plate 9","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map Showing Overlap of Gold-bearing Porphyry-Epithermal Gold and Reduced Intrusion-related-Orogenic Gold Deposit Prospectivity Maps"},{"id":391051,"rank":16,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_appendix2.xlsx","text":"Appendix 2","size":"300 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Alaska Resource Data File (ARDF) Mineral-Deposit-Keyword-and-Scoring Templates"},{"id":386829,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate4.pdf","text":"Plate 4","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Estimated Resource Potential and Certainty for Reduced Intrusion-related Gold Deposits"},{"id":386823,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_v1.1.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386822,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1041/covrthb.jpg"},{"id":386828,"rank":6,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate3.pdf","text":"Plate 3","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Estimated Resource Potential and Certainty for Orogenic Gold Deposits"},{"id":386827,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate2.pdf","text":"Plate 2","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Estimated Resource Potential and Certainty for Lode Gold—Undivided Deposits and Alaska Resource Data File Localities"},{"id":386824,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plates.pdf","text":"Plates 1 through 9","size":"32 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Combined PDF of all 9 plates"},{"id":386826,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate1.pdf","text":"Plate 1","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Estimated Resource Potential and Certainty for Lode Gold—Undivided Deposits"},{"id":386830,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate5.pdf","text":"Plate 5","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Estimated Resource Potential and Certainty for Epithermal Gold Deposits"},{"id":386831,"rank":9,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate6.pdf","text":"Plate 6","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map Showing Overlap of Orogenic, Intrusion-related and Epithermal Gold Deposit Prospectivity 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1.0: Jun 2021; Version 1.1: October 2021","contact":"<p><a data-mce-href=\"https://www.usgs.gov/centers/asc/connect\" href=\"https://www.usgs.gov/centers/asc/connect\" target=\"_blank\" rel=\"noopener\">Director</a>,<br><a href=\"https://www.usgs.gov/centers/asc/\" target=\"_blank\" rel=\"noopener\" data-mce-href=\"https://www.usgs.gov/centers/asc/\">Alaska Science Center</a><br><a data-mce-href=\"https://usgs.gov\" href=\"https://usgs.gov\" target=\"_blank\" rel=\"noopener\">U.S. Geological Survey</a><br>4210 University Drive<br>Anchorage, Alaska 99508</p>","tableOfContents":"<ul><li>Acknowledgments&nbsp;&nbsp;</li><li>Abstract&nbsp;&nbsp;</li><li>Introduction&nbsp;&nbsp;</li><li>Data Types and Analytical Process&nbsp;&nbsp;</li><li>GIS-Based Methods&nbsp;&nbsp;</li><li>Chapter 1. Lode Gold Deposits—Undivided&nbsp;&nbsp;</li><li>Chapter 2. Orogenic Gold Deposits&nbsp;&nbsp;</li><li>Chapter 3. Reduced Intrusion-related Gold Deposits&nbsp;&nbsp;</li><li>Chapter 4. Epithermal Gold Deposits&nbsp;&nbsp;</li><li>Chapter 5. Discussion of Discrimination of Lode Gold Deposit Types&nbsp;&nbsp;</li><li>Chapter 6. Gold-bearing Porphyry and Epithermal Gold Deposits&nbsp;&nbsp;</li><li>Chapter 7. Reduced Intrusion-Related&nbsp;&nbsp;</li><li>Chapter 8. Discussion of Discrimination of Lode Gold Deposit Types Using Model Combinations&nbsp;&nbsp;</li><li>Summary and Conclusions&nbsp;&nbsp;</li><li>Data Resources&nbsp;&nbsp;</li><li>References Cited&nbsp;&nbsp;</li><li>Appendix 1. Statistical Calculations of Levels of Background Values for Sediment and Rock&nbsp;&nbsp;</li><li>Appendix 2. Lithology-Keyword Search Terms for the \"Geologic Map of Alaska\"</li></ul>","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-06-29","revisedDate":"2021-10-28","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Karl, Susan M. 0000-0003-1559-7826 skarl@usgs.gov","orcid":"https://orcid.org/0000-0003-1559-7826","contributorId":502,"corporation":false,"usgs":true,"family":"Karl","given":"Susan","email":"skarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreiner, Douglas C. 0000-0002-4405-1403","orcid":"https://orcid.org/0000-0002-4405-1403","contributorId":220474,"corporation":false,"usgs":true,"family":"Kreiner","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Case, George N.D. 0000-0001-9826-5661 gcase@usgs.gov","orcid":"https://orcid.org/0000-0001-9826-5661","contributorId":224941,"corporation":false,"usgs":true,"family":"Case","given":"George","email":"gcase@usgs.gov","middleInitial":"N.D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":818437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Labay, Keith A. 0000-0002-6763-3190 klabay@usgs.gov","orcid":"https://orcid.org/0000-0002-6763-3190","contributorId":217714,"corporation":false,"usgs":true,"family":"Labay","given":"Keith","email":"klabay@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shew, Nora B. 0000-0003-0025-7220 nshew@usgs.gov","orcid":"https://orcid.org/0000-0003-0025-7220","contributorId":3382,"corporation":false,"usgs":true,"family":"Shew","given":"Nora","email":"nshew@usgs.gov","middleInitial":"B.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Granitto, Matthew 0000-0003-3445-4863 granitto@usgs.gov","orcid":"https://orcid.org/0000-0003-3445-4863","contributorId":1224,"corporation":false,"usgs":true,"family":"Granitto","given":"Matthew","email":"granitto@usgs.gov","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818440,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Bronwen 0000-0003-1044-2227 bwang@usgs.gov","orcid":"https://orcid.org/0000-0003-1044-2227","contributorId":2351,"corporation":false,"usgs":true,"family":"Wang","given":"Bronwen","email":"bwang@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818441,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":1733,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":818442,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70223206,"text":"70223206 - 2021 - Afghanistan artisanal and small-scale mining sector","interactions":[],"lastModifiedDate":"2021-08-18T13:08:20.58771","indexId":"70223206","displayToPublicDate":"2021-06-29T08:07:01","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Afghanistan artisanal and small-scale mining sector","docAbstract":"For millennia, extractive activity in Afghanistan (officially the Islamic Republic of Afghanistan, or IRA) has been entirely artisanal or small-scale in scope. Various international governments, organizations, and companies have supported the growth of the sector, and the Government of the Islamic Republic of Afghanistan (GoIRA) views its mineral wealth as vital to the country’s stability and prosperity moving forward (Ministry of Mines and Petroleum 2019a). The development of the sector has been prioritized in multiple national plans, strategies, and roadmaps. Of particular importance is the formalization of the artisanal and small-scale mining (ASM) sector to improve working conditions and enable the government to earn royalties from mineral production.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"The Delve Country Profile: Afghanistan","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Delve","usgsCitation":"DeWitt, J.D., Sunder, S., and Boston, K.M., 2021, Afghanistan artisanal and small-scale mining sector, 52 p.","productDescription":"52 p.","ipdsId":"IP-129568","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":388098,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388076,"type":{"id":15,"text":"Index Page"},"url":"https://delvedatabase.org/resources/delve-country-profile-afghanistan"}],"country":"Afghanistan","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[61.21082,35.65007],[62.23065,35.27066],[62.98466,35.40404],[63.19354,35.85717],[63.9829,36.00796],[64.54648,36.31207],[64.74611,37.11182],[65.58895,37.30522],[65.74563,37.66116],[66.21738,37.39379],[66.51861,37.36278],[67.07578,37.35614],[67.83,37.14499],[68.13556,37.02312],[68.85945,37.34434],[69.19627,37.15114],[69.51879,37.609],[70.11658,37.58822],[70.27057,37.73516],[70.3763,38.1384],[70.80682,38.48628],[71.34813,38.25891],[71.2394,37.95327],[71.54192,37.90577],[71.44869,37.06564],[71.84464,36.73817],[72.19304,36.94829],[72.63689,37.04756],[73.26006,37.49526],[73.9487,37.42157],[74.98,37.41999],[75.15803,37.13303],[74.57589,37.02084],[74.06755,36.83618],[72.92002,36.72001],[71.84629,36.50994],[71.26235,36.07439],[71.49877,35.65056],[71.61308,35.1532],[71.11502,34.73313],[71.15677,34.34891],[70.8818,33.98886],[69.93054,34.02012],[70.32359,33.35853],[69.68715,33.1055],[69.26252,32.50194],[69.31776,31.90141],[68.92668,31.62019],[68.55693,31.71331],[67.79269,31.58293],[67.68339,31.30315],[66.93889,31.30491],[66.38146,30.7389],[66.34647,29.88794],[65.04686,29.47218],[64.35042,29.56003],[64.148,29.34082],[63.55026,29.46833],[62.54986,29.31857],[60.87425,29.82924],[61.78122,30.73585],[61.69931,31.37951],[60.94194,31.54807],[60.86365,32.18292],[60.53608,32.98127],[60.9637,33.52883],[60.52843,33.67645],[60.80319,34.4041],[61.21082,35.65007]]]},\"properties\":{\"name\":\"Afghanistan\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"DeWitt, Jessica D. 0000-0002-8281-8134 jdewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-8134","contributorId":5804,"corporation":false,"usgs":true,"family":"DeWitt","given":"Jessica","email":"jdewitt@usgs.gov","middleInitial":"D.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":821396,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sunder, Sindhuja 0000-0002-3978-3263","orcid":"https://orcid.org/0000-0002-3978-3263","contributorId":264350,"corporation":false,"usgs":false,"family":"Sunder","given":"Sindhuja","email":"","affiliations":[{"id":54446,"text":"Aperture Federal, LLC","active":true,"usgs":false}],"preferred":false,"id":821397,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boston, Kathleen M 0000-0003-1301-9651","orcid":"https://orcid.org/0000-0003-1301-9651","contributorId":264351,"corporation":false,"usgs":false,"family":"Boston","given":"Kathleen","email":"","middleInitial":"M","affiliations":[{"id":54446,"text":"Aperture Federal, LLC","active":true,"usgs":false}],"preferred":false,"id":821398,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70239000,"text":"70239000 - 2021 - Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends","interactions":[],"lastModifiedDate":"2022-12-20T13:42:34.83873","indexId":"70239000","displayToPublicDate":"2021-06-29T07:39:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3464,"text":"Spectrochimica Acta Part B: Atomic Spectroscopy","active":true,"publicationSubtype":{"id":10}},"title":"Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends","docAbstract":"<p><span>The ChemCam instrument on the&nbsp;</span><i>Curiosity</i><span>&nbsp;rover provides chemical compositions of Martian rocks and soils using remote laser-induced breakdown spectroscopy (LIBS). The elemental calibration is stable as a function of distance for Ti, Fe, Mg, and Ca. The calibration shows small, systematically increasing abundance trends as a function of distance for Al, Na, K, and to some extent, Si. The distance effect is known to be due to a dependence with distance on the relative strengths of atomic transition lines. Emission lines representing transitions from relatively low energy levels remain intense at longer distances while emission lines representing transitions from higher energy levels decrease in intensity more rapidly as a function of distance. The multivariate algorithms used to determine elemental compositions rely on a large number of emission lines in many cases, so rather than trying to correct all emission lines, a study was made of the predicted compositions as a function of distance, in order to determine an empirical correction. Abundance trends can be well approximated by a linear trend with distance within the ranges of abundances and distances observed up to ~6&nbsp;m. Data from 11 distinct geological members and data groups of the Murray formation in Gale crater, Mars, were used to form the model, selecting the members and data groups yielding the best statistics. The model was tested using data from several targets observed from two different distances, and using data from the Kimberley formation, the composition of which is significantly different from the Murray formation, showing that the model works on other compositions beyond those used to build the model. For long-distance observations up to ~6&nbsp;m, corrections can be made back to an equivalent composition at the median distance of ChemCam observations (2.6&nbsp;m). The model has been validated up to 6.2&nbsp;m, although ChemCam is able to observe bedrock targets to &gt;7&nbsp;m, and iron meteorites to distances of &gt;9&nbsp;m.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.sab.2021.106247","usgsCitation":"Wiens, R.C., Blazon-Brown, A.J., Melikechi, N., Frydenvang, J., Dehouck, E., Clegg, S.M., Delapp, D., Anderson, R.B., Cousin, A., and Maurice, S., 2021, Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends: Spectrochimica Acta Part B: Atomic Spectroscopy, v. 182, 106247, https://doi.org/10.1016/j.sab.2021.106247.","productDescription":"106247","ipdsId":"IP-127439","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":451723,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://insu.hal.science/insu-03672412","text":"Publisher Index Page"},{"id":410791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"182","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wiens, Roger C.","contributorId":140330,"corporation":false,"usgs":false,"family":"Wiens","given":"Roger","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":859635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazon-Brown, A. J.","contributorId":300205,"corporation":false,"usgs":false,"family":"Blazon-Brown","given":"A.","email":"","middleInitial":"J.","affiliations":[{"id":65042,"text":"LANL, U. Mass.","active":true,"usgs":false}],"preferred":false,"id":859636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Melikechi, N.","contributorId":300206,"corporation":false,"usgs":false,"family":"Melikechi","given":"N.","affiliations":[{"id":65043,"text":"U. Mass.","active":true,"usgs":false}],"preferred":false,"id":859637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frydenvang, J.","contributorId":181927,"corporation":false,"usgs":false,"family":"Frydenvang","given":"J.","affiliations":[],"preferred":false,"id":859638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dehouck, E.","contributorId":290073,"corporation":false,"usgs":false,"family":"Dehouck","given":"E.","affiliations":[{"id":62330,"text":"Univ. Lyon, Univ. Lyon 1, ENSL, CNRS","active":true,"usgs":false}],"preferred":false,"id":859639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clegg, S. M.","contributorId":300207,"corporation":false,"usgs":false,"family":"Clegg","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":27196,"text":"LANL","active":true,"usgs":false}],"preferred":false,"id":859640,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delapp, D.","contributorId":290074,"corporation":false,"usgs":false,"family":"Delapp","given":"D.","affiliations":[{"id":62306,"text":"Space and Planetary Exploration Team, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":859641,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":859642,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cousin, A.","contributorId":290035,"corporation":false,"usgs":false,"family":"Cousin","given":"A.","affiliations":[{"id":62314,"text":"Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":859643,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Maurice, S.","contributorId":296856,"corporation":false,"usgs":false,"family":"Maurice","given":"S.","affiliations":[{"id":64219,"text":"Institut de Recherche en Astrophysique et Planetologie, Universite de Toulouse 3 Paul Sabatier, CNRS, CNES","active":true,"usgs":false}],"preferred":false,"id":859644,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}}
,{"id":70221750,"text":"70221750 - 2021 - Increasing hydroperiod in a karst-depression wetland based on 165 years of simulated daily water levels","interactions":[],"lastModifiedDate":"2021-07-01T12:27:24.261014","indexId":"70221750","displayToPublicDate":"2021-06-29T07:25:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Increasing hydroperiod in a karst-depression wetland based on 165 years of simulated daily water levels","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>The hydrology of seasonally inundated depression wetlands can be highly sensitive to climatic fluctuations. Hydroperiod—the number of days per year that a wetland is inundated—is often of primary ecological importance in these systems and can vary interannually depending on climate conditions. In this study we re-examined an existing hydrologic model to simulate daily water levels in Sinking Pond, a 35-hectare seasonally inundated karst-depression wetland in Tennessee, USA. We recalibrated the model using 22 years of climate and water-level observations and used the recalibrated model to reconstruct (hindcast) daily water levels over a 165-year period from 1855 to 2019. A trend analysis of the climatic data and reconstructed water levels over the hindcasting period indicated substantial increases in pond hydroperiod over time, apparently related to increasing regional precipitation. Wetland hydroperiod increased on average by 5.9 days per decade between 1920 and 2019, with a breakpoint around the year 1970. Hydroperiod changes of this magnitude may have profound consequences for wetland ecology, such as a transition from a forested wetland to a mostly open-water pond at the Sinking Pond site. More broadly, this study illustrates the needs for robust hydrologic models of depression wetlands and for consideration of model transferability in time (i.e., hindcasting and forecasting) under non-stationary hydroclimatic conditions. As climate change is expected to influence water cycles, hydrologic processes, and wetland ecohydrology in the coming decades, hydrologic model projections may become increasingly important to detect, anticipate, and potentially mitigate ecological impacts in depression wetland ecosystems.</p></div></div>","language":"English","publisher":"Springer","doi":"10.1007/s13157-021-01474-x","usgsCitation":"Cartwright, J.M., and Wolfe, W., 2021, Increasing hydroperiod in a karst-depression wetland based on 165 years of simulated daily water levels: Wetlands, v. 41, 75, 18 p., https://doi.org/10.1007/s13157-021-01474-x.","productDescription":"75, 18 p.","ipdsId":"IP-122342","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":451725,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-021-01474-x","text":"Publisher Index Page"},{"id":386916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Arnold Air Force Base","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -86.09230041503906,\n              35.35601619488275\n            ],\n            [\n              -86.03187561035156,\n              35.35601619488275\n            ],\n            [\n              -86.03187561035156,\n              35.4019238757293\n            ],\n            [\n              -86.09230041503906,\n              35.4019238757293\n            ],\n            [\n              -86.09230041503906,\n              35.35601619488275\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"41","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfe, William J. 0000-0002-3292-051X","orcid":"https://orcid.org/0000-0002-3292-051X","contributorId":224729,"corporation":false,"usgs":false,"family":"Wolfe","given":"William J.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":818610,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70221736,"text":"70221736 - 2021 - Creep on the Sargent Fault over the past 50 yr from alignment arrays with implications for slip transfer between the Calaveras and San Andreas Faults, California","interactions":[],"lastModifiedDate":"2021-12-10T16:34:21.151746","indexId":"70221736","displayToPublicDate":"2021-06-29T06:57:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Creep on the Sargent Fault over the past 50 yr from alignment arrays with implications for slip transfer between the Calaveras and San Andreas Faults, California","docAbstract":"<p><span>The 55‐km‐long Sargent fault connects the creeping Calaveras fault with the locked San Andreas fault through the Santa Cruz Mountains west of Gilroy, California. The position of the Sargent fault between these two faults may have implications for slip transfer and strain accumulation between a creeping and locked fault. The detection and measurement of creep on the Sargent fault would indicate where interseismic strain is accumulating adjacent to these neighboring faults. In 1969, two alignment arrays separated by 3.7&nbsp;km were installed across the central section of the Sargent fault to investigate potential creep. These arrays were measured in 1970 and 1975, and comparison of these measurements yielded a creep rate of 3.4 ± 0.6&nbsp;mm/yr across two fault strands in the northern array; results from the southern array were never published. In 2019 and 2020, we resurveyed both arrays using a total station and analyzed the results to determine accumulated fault creep. Our results show that between 1970 and 2020, a period of 49.3&nbsp;yr, the northern array was dextrally offset 164 ± 25&nbsp;mm across the same two fault strands that were active in the 1970s, yielding an average creep rate of 3.3 ± 1.3&nbsp;mm/yr. Thus, it appears that the 5 and 50 yr creep rates at this site are similar. The southern array, which may not span the entire fault zone, was dextrally offset 84 ± 13&nbsp;mm across two fault strands between 1970 and 2019, yielding an average creep rate of 1.7 ± 0.8&nbsp;mm/yr over 48.9 yr. These recent surveys document continued creep on the Sargent fault, which may reduce seismic strain accumulation and therefore seismic hazard. However, continued aseismic slip on this fault may result in the redistribution of stress and strain to adjacent faults and should be an area of continued study.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120210041","usgsCitation":"Mongovin, D., and Philibosian, B.E., 2021, Creep on the Sargent Fault over the past 50 yr from alignment arrays with implications for slip transfer between the Calaveras and San Andreas Faults, California: Bulletin of the Seismological Society of America, v. 111, no. 6, p. 3189-3203, https://doi.org/10.1785/0120210041.","productDescription":"15 p.","startPage":"3189","endPage":"3203","ipdsId":"IP-120386","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":386886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United  States","state":"California","otherGeospatial":"Sargent Fault","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.29980468749999,\n              36.31512514748051\n            ],\n            [\n              -120.78369140624999,\n              36.31512514748051\n            ],\n            [\n              -120.78369140624999,\n              37.23032838760387\n            ],\n            [\n              -122.29980468749999,\n              37.23032838760387\n            ],\n            [\n              -122.29980468749999,\n              36.31512514748051\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"111","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Mongovin, Daniel 0000-0002-1623-2637","orcid":"https://orcid.org/0000-0002-1623-2637","contributorId":255012,"corporation":false,"usgs":false,"family":"Mongovin","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":818566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":818567,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70226817,"text":"70226817 - 2021 - Characterizing ground motion amplification by extensive flat sediments: The seismic response of the eastern U.S. Atlantic Coastal Plain strata","interactions":[],"lastModifiedDate":"2021-12-14T12:58:24.210836","indexId":"70226817","displayToPublicDate":"2021-06-29T06:56:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing ground motion amplification by extensive flat sediments: The seismic response of the eastern U.S. Atlantic Coastal Plain strata","docAbstract":"<div class=\"article-section-wrapper js-article-section js-content-section  \"><p>We examine the effects that Atlantic Coastal Plain (ACP) strata have on ground motions in the eastern and southeastern United States. The ACP strata consist of widespread, nearly flat‐lying sediments, the upper portions of which are unconsolidated or semiconsolidated. The ACP sediments are deposited primarily on crystalline basement rocks, creating large velocity and density contrasts with the underlying rocks. At 211 sites on ACP strata to thicknesses of 4000&nbsp;m, we compute spectral ratios relative to the average of four bedrock sites west or northwest of the strata. Sites consist of stations of Earthscope’s USArray Transportable Array (TA), and temporary deployments in the Southeast Suture of the Atlantic Margin Experiment (SESAME), Eastern North American Margin (ENAM) experiment, and the DCShake deployment in Washington, D.C. For the TA and SESAME stations, we use signals from 13 teleseisms and three regional earthquakes as input, combining the north and east components of motion after taking the Fourier transforms. We also include similarly processed site responses from the ENAM and DCShake arrays that were computed in earlier studies. Results show prominent, fundamental resonance peaks at frequencies determined by reverberations in the entire sediment column, and that often define the largest amplifications for each frequency. As frequencies increase, these resonance peaks migrate to thinner ACP strata and increase in amplitude. The peaks are well defined at frequencies below about 1&nbsp;Hz, but become narrower and less defined regionally at higher frequencies. We develop simple equations to characterize amplification versus ACP thickness, which we approximate by cosine and Gaussian curves with amplifications of 1 on bedrock and rising to the resonance peak, and then decreasing to an average amplification at thicknesses greater than twice the resonance peak. Comparisons with other site corrections for the central and eastern United States based on sediment thickness show similarities on thin ACP strata but divergence on thicker sediments. The results also demonstrate the effectiveness of using teleseismic arrivals to characterize the site responses of sedimentary sequences.</p></div>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200328","usgsCitation":"Pratt, T.L., and Schleicher, L.S., 2021, Characterizing ground motion amplification by extensive flat sediments: The seismic response of the eastern U.S. Atlantic Coastal Plain strata: Bulletin of the Seismological Society of America, v. 111, no. 4, p. 1795-1823, https://doi.org/10.1785/0120200328.","productDescription":"29 p.","startPage":"1795","endPage":"1823","ipdsId":"IP-128910","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":392847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -90.17578124999999,\n              24.84656534821976\n            ],\n            [\n              -74.00390625,\n              24.84656534821976\n            ],\n            [\n              -74.00390625,\n              39.50404070558415\n            ],\n            [\n              -90.17578124999999,\n              39.50404070558415\n            ],\n            [\n              -90.17578124999999,\n              24.84656534821976\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"111","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":828382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schleicher, Lisa Sue 0000-0001-6528-1753","orcid":"https://orcid.org/0000-0001-6528-1753","contributorId":264892,"corporation":false,"usgs":true,"family":"Schleicher","given":"Lisa","email":"","middleInitial":"Sue","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":828383,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70221689,"text":"sir20215054 - 2021 - Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico","interactions":[],"lastModifiedDate":"2021-06-29T14:33:40.229468","indexId":"sir20215054","displayToPublicDate":"2021-06-28T16:46:42","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5054","displayTitle":"Estimating Flow-Duration Statistics and Low-Flow Frequencies for Selected Streams and the Implementation of a StreamStats Web-Based Tool in Puerto Rico","title":"Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico","docAbstract":"<p>Daily mean streamflow data from 28 U.S. Geological Survey streamflow-gaging stations in Puerto Rico with 10 or more years of unregulated or minimally affected flow record through water year 2018 were used to develop regression equations for flow duration and annual <i>n</i>-day low-flow statistics. Ordinary least-squares and generalized least-squares regression techniques were used to develop regional regression equations for flow-duration statistics at the 99th, 98th, 95th, 90th, 80th, 70th, 60th, and 50th percent exceedance probabilities and annual <i>n</i>-day low-flow frequency statistics for the 1-, 7-, 14-, and 30-day mean low flows with the 2-year (0.5 nonexceedance probability), 5-year (0.2 nonexceedance probability), and 10-year (0.1 nonexceedance probability) recurrence intervals. A StreamStats web application was developed to estimate basin and climatic characteristics for the regional regression equation analysis. Basin and climatic characteristics determined to be significant explanatory variables in one or more regression equations included drainage area, mean total annual reference evapotranspiration, and minimum basin elevation. The adjusted coefficient of determination for the flow-duration regression equations ranged from 57.7 to 81.4 percent. The pseudo coefficient of determination for the annual <i>n</i>-day low-flow regression equations ranged from 64.6 to 70.7 percent. The StreamStats web application incorporates the flow duration, and annual <i>n</i>-day low-flow regression equations and can provide streamflow estimates for most ungaged sites in the island.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215054","collaboration":"Prepared in cooperation with the Puerto Rico Environmental Quality Board","usgsCitation":"Williams-Sether, T., 2021, Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico: U.S. Geological Survey Scientific Investigations Report 2021–5054, 18 p., https://doi.org/10.3133/sir20215054.","productDescription":"Report: v, 17 p.; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-118184","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386816,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5054/coverthb.jpg"},{"id":386817,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5054/sir20215054.pdf","text":"Report","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5054"},{"id":386819,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386818,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y2QVJ6","text":"USGS data release","linkHelpText":"Data files for the development of regression equations for flow-duration statistics and n-day low-flow frequencies for ungaged streams in Puerto Rico through water year 2018"}],"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              -67.3187255859375,\n              17.85590441431915\n            ],\n            [\n              -65.5828857421875,\n              17.85590441431915\n            ],\n            [\n              -65.5828857421875,\n              18.557739984085266\n            ],\n            [\n              -67.3187255859375,\n              18.557739984085266\n            ],\n            [\n              -67.3187255859375,\n              17.85590441431915\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a data-mce-href=\"mailto:%20dc_nd@usgs.gov\" href=\"mailto:%20dc_nd@usgs.gov\">Director</a>, <a data-mce-href=\"https://www.usgs.gov/centers/dakota-water\" href=\"https://www.usgs.gov/centers/dakota-water\">Dakota Water Science Center</a><br>U.S. Geological Survey<br>821 East Interstate Avenue, Bismarck, ND 58503<br>1608 Mountain View Road, Rapid City, SD 57702</p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Statistical Methods</li><li>Development of Regional Regression Equations</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-06-28","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams-Sether, Tara 0000-0001-6515-9416 tjsether@usgs.gov","orcid":"https://orcid.org/0000-0001-6515-9416","contributorId":152247,"corporation":false,"usgs":true,"family":"Williams-Sether","given":"Tara","email":"tjsether@usgs.gov","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818431,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70221888,"text":"70221888 - 2021 - Further adventures in Mars DTM quality: Smoothing errors, sharpening details","interactions":[],"lastModifiedDate":"2021-07-14T11:49:43.972121","indexId":"70221888","displayToPublicDate":"2021-06-28T14:01:34","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Further adventures in Mars DTM quality: Smoothing errors, sharpening details","docAbstract":"We have used high-precision, high-resolution digital terrain models (DTMs) of the NASA Mars Science Laboratory (MSL) and Mars 2020 rover landing sites based on mosaicked images from the Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (MRO HiRISE) camera as a reference data set to evaluate DTMs based on Mars Express High Resolution Stereo Camera (MEX HRSC) images. The Next Generation Automatic Terrain Extraction (NGATE) matcher in the SOCET SET/GXP † commercial photogrammetric system produces DTMs with relatively good (small) horizontal resolution but high error, and results are terrain dependent, with poorer resolution and smaller errors on smoother surfaces. Multiple approaches to smoothing the NGATE DTMs give very similar tradeoffs between resolution and error. Smoothing the NGATE DTMs with a single pass of an area-based matcher, which has been the standard approach to generating planetary DTMs at the U.S. Geological Survey (USGS) to date is probably near-optimal in terms of both combined resolution-error performance and local slope estimation, but smoothing with a 5x5 lowpass filter performs as well or better. DTMs from the HRSC team processing pipeline fall within this same trade space but are less sensitive to terrain roughness. DTMs produced with the Ames Stereo Pipeline also fall in this space at resolutions intermediate between NGATE and the team pipeline. Although DTM resolution and error each vary by a factor of two, their product is much more consistent, varying by <20% across multiple image sets and matching algorithms. Refinement of the stereo DTM by photoclinometry can yield significant quantitative improvement in resolution and some improvement in error (improving their product by as much as a factor of two), provided that albedo variations over distances smaller than the stereo DTM resolution are not too severe.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"International Archives of Photogrammetry, Remote Sensing, and Spatial Information Science","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"XXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission III","conferenceDate":"July 5-9, 2021","language":"English","publisher":"ISPRS","doi":"10.5194/isprs-archives-XLIII-B3-2021-659-2021","usgsCitation":"Kirk, R.L., Mayer, D., Redding, B.L., Galuszka, D.M., Fergason, R.L., Hare, T.M., and Gwinner, K., 2021, Further adventures in Mars DTM quality: Smoothing errors, sharpening details, <i>in</i> International Archives of Photogrammetry, Remote Sensing, and Spatial Information Science, v. 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,{"id":70221578,"text":"sir20215043 - 2021 - Approaches for assessing long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM)","interactions":[],"lastModifiedDate":"2021-06-29T14:36:28.595882","indexId":"sir20215043","displayToPublicDate":"2021-06-28T13:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5043","displayTitle":"Approaches for Assessing Long-Term Annual Yields of Highway and Urban Runoff in Selected Areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM)","title":"Approaches for assessing long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM)","docAbstract":"<p>The California Department of Transportation, commonly known as CalTrans, and other municipal separate storm sewer system permittees in California as well as other State departments of transportation nationwide need information about potential loads and yields (loads per unit area) of constituents of concern in stormwater runoff and discharges from stormwater best management practices (BMPs). Although its National Pollution Discharge Elimination System stormwater permit is focused on areas subject to total maximum daily load (TMDL) regulations, CalTrans builds and maintains BMPs to minimize the adverse effects of roadway runoff on receiving waters throughout the State. This report describes approaches used by the U.S. Geological Survey in cooperation with CalTrans for using the Stochastic Empirical Loading and Dilution Model (SELDM) to assess long-term annual yields of highway and urban runoff in selected areas of California. In this study, a series of regional and local yields were simulated to provide statewide planning-level estimates and more refined TMDL-specific yield values. SELDM was used to analyze 368 State roadway and urban runoff yields for 53 runoff quality constituents. The analyses included 222 random-seed analyses, 60 regional State roadway-runoff analyses, 24 regional urban roadway-runoff analyses, and 62 focused TMDL-area analyses.</p><p>This report describes approaches and statistics used to analyze available hydrologic and runoff quality data in all analyses. Results for all analyses are provided in the model archive, but only a selected subset of results are presented as examples in this report. State roadway runoff, urban runoff, and BMP discharge yields for total suspended solids, total nitrogen, total phosphorus, and total zinc were selected as examples because they are widespread constituents of concern with substantial amounts of State roadway and urban runoff monitoring data. In this report, a hypothetical basin was specified by using available geographic information to demonstrate use of the State roadway and urban runoff yields to estimate long-term annual stormwater loads from developed areas. Application of these yields to the hypothetical basin indicates that although State-roadway yields may be higher than urban-runoff yields for some constituents, State-roadway loads may be a small proportion of total stormwater loads because State roadways themselves are a small fraction of the total impervious area in such basins. Although application of results from this study may have considerable uncertainty for any particular stormwater outfall, the study does provide robust estimates to support basin-scale runoff-load analyses in California. These analyses also provide estimates for the 12 U.S. Environmental Protection Agency level III ecoregions that are completely or partially within the boundaries of the State of California.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215043","collaboration":"Prepared in cooperation with the California Department of Transportation","usgsCitation":"Granato, G.E., and Friesz, P.J., 2021, Approaches for assessing long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2021–5043, 37 p., https://doi.org/10.3133/sir20215043.","productDescription":"Report: vii, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124902","costCenters":[{"id":466,"text":"New England Water Science 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 \"}}]}","contact":"<p><a href=\"mailto:dc_ nweng@usgs.gov\" data-mce-href=\"mailto:dc_ nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Simulation Methods</li><li>Results</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-06-28","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":197631,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory","email":"ggranato@usgs.gov","middleInitial":"E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friesz, Paul J. 0000-0002-4660-2336 pfriesz@usgs.gov","orcid":"https://orcid.org/0000-0002-4660-2336","contributorId":1075,"corporation":false,"usgs":true,"family":"Friesz","given":"Paul","email":"pfriesz@usgs.gov","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818158,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70221490,"text":"ofr20201105 - 2021 - Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17","interactions":[],"lastModifiedDate":"2021-06-28T14:54:40.661083","indexId":"ofr20201105","displayToPublicDate":"2021-06-28T09:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1105","displayTitle":"Distribution of Chlorinated Volatile Organic Compounds and Per- and Polyfluoroalkyl Substances in Monitoring Wells at the Former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17","title":"Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17","docAbstract":"<p>A study was conducted by the U.S. Geological Survey in cooperation with the U.S. Navy (the Navy) to determine the status of volatile organic compounds (VOCs) and per- and polyfluoroalkyl substances (PFASs) in groundwater at the former Naval Air Warfare Center (NAWC) in West Trenton, New Jersey. Wells contaminated with VOCs were sampled in 2014, 2015, 2016, and 2017 as part of the Navy’s long-term monitoring program. The results for trichloroethene (TCE), cis-1,2-dichloroethene (cisDCE), and vinyl chloride (VC) were plotted in map view to determine whether the areal extent of the contamination had changed over the 4-year period. TCE, cisDCE, and VC concentrations were plotted along nine lines of section across the former NAWC site to determine whether the vertical distribution of VOCs had changed during 2014–17. TCE, cisDCE, and VC concentrations over time were plotted on graphs for each well to determine long-term trends and changes in VOC concentrations. Data from 1990 to 2017 were used, if available, to make these graphs.</p><p>Results show that the areas of VOC concentrations greater than or equal to 1 microgram per liter decreased slightly on the northwestern side and the northeastern side of the NAWC site from 2014 to 2017 under the influence of a pump-and-treat system, natural attenuation processes, and engineered bioaugmentation experiments ongoing at the site. The pump-and-treat system continued to hydraulically contain the VOC contamination and kept it from moving offsite to the south and west of NAWC. One well northeast of the NAWC site, 50BR, was found to have detectable TCE and cisDCE concentrations. These detections indicated that VOC contamination had migrated offsite and that the pump-and-treat system was not containing the VOC contamination on the eastern side of the facility. Detectable VOC concentrations were present in wells as deep as 200 and 221 feet on the eastern and western sides of the NAWC site. TCE concentrations in most wells were found to be stable or to have slowly decreased since the facility closed in 1999. Only 7 wells, including 3 pump-and-treat extraction wells, showed substantial increases in TCE concentration from 2014 to 2017. Continuing sources of TCE to the system are desorption of TCE from organic materials in the aquifer, back diffusion of TCE from the contaminated bedrock matrix, and dissolution of remaining dense nonaqueous phase TCE in the aquifer.</p><p>Wells at the former NAWC site were sampled for PFASs in 2015, 2016, and 2017. Perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) results were plotted in map and cross-section views to determine the areal and vertical extent of the PFAS contamination at the site. PFOS, PFOA, and PFNA concentrations greater than their established maximum contaminant levels were detected in 25, 24, and 21 of the 26 wells sampled, respectively, on the eastern side of NAWC in 2017. Vertically, the highest PFAS concentrations were present in shallow wells along the fence near the firehouse and along the railroad tracks where the aqueous film-forming foam discharge reportedly occurred back in 1990. PFAS concentrations were detected in one well (54BR) as deep as 200 feet on the eastern side of the NAWC site. PFASs were present in wells east of the railroad tracks, indicating that PFAS-contaminated groundwater had moved offsite. In a limited test of five wells, samples collected with regenerated cellulose dialysis membrane (RCDM) passive samplers contained PFAS concentrations equal to those in samples from low-flow purging.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201105","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Imbrigiotta, T.E., and Fiore, A.R., 2021, Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17: U.S. Geological Survey Open-File Report 2020–1105, 107 p., https://doi.org/10.3133/ofr20201105.","productDescription":"Report: xii, 107 p.; Data Release; 4 Appendixes","numberOfPages":"107","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-110205","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":386575,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.xlsx","text":"Appendix 2","size":"288 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 2. Volatile organic compounds, per- and polyfluoroalkyl substances, and 1,4-dioxane concentrations measured in samples from wells at the former Naval Air Warfare Center site, West Trenton, New Jersey, 1990–2017"},{"id":386577,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.csv","text":"Appendix 2 as CSV file","size":"187 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386576,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.csv","text":"Appendix 1 as CSV file","size":"22.9 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386573,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCAQ5N","text":"USGS data release","linkHelpText":"Concentrations of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in groundwater and surface water, former Naval Air Warfare Center, West Trenton, New Jersey"},{"id":386572,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105.pdf","text":"Report","size":"9.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1105"},{"id":386571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1105/coverthb.jpg"},{"id":386574,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.xlsx","text":"Appendix 1","size":"43.7 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 1. Descriptions of boreholes, well locations, and well construction at the former Naval Air Warfare Center, West Trenton, New Jersey"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -74.80979204177856,\n              40.26746805544402\n            ],\n            [\n              -74.80759263038635,\n              40.27155298671227\n            ],\n            [\n              -74.8130750656128,\n              40.27224060619094\n            ],\n            [\n              -74.81433033943176,\n              40.26832763061523\n            ],\n            [\n              -74.81412649154663,\n              40.268139343654944\n            ],\n            [\n              -74.80979204177856,\n              40.26746805544402\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/nj-water\" data-mce-href=\"https://www.usgs.gov/centers/nj-water\">New Jersey Water Science Center</a><br>U.S. Geological Survey<br>3450 Princeton Pike Ste 110<br>Lawrenceville, New Jersey, 08648</p><p><a href=\"https://pubs.er.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Background</li><li>Methods</li><li>Results and Discussion</li><li>Summary and Conclusions</li><li>References Cited</li><li>Appendix 1. Descriptions of boreholes, well locations, and well construction at the former Naval Air Warfare Center, West Trenton, New Jersey</li><li>Appendix 2. Volatile organic compounds, per- and polyfluoroalkyl substances, and 1,4-dioxane concentrations measured in samples from wells at the former Naval Air Warfare Center site, West Trenton, New Jersey, 1990–2017</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2021-06-28","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":152114,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","email":"timbrig@usgs.gov","middleInitial":"E.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fiore, Alex R. 0000-0002-0986-5225 afiore@usgs.gov","orcid":"https://orcid.org/0000-0002-0986-5225","contributorId":4977,"corporation":false,"usgs":true,"family":"Fiore","given":"Alex","email":"afiore@usgs.gov","middleInitial":"R.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817837,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70224971,"text":"70224971 - 2021 - A more representative community of ecologists","interactions":[],"lastModifiedDate":"2021-10-11T13:07:43.044079","indexId":"70224971","displayToPublicDate":"2021-06-28T08:05:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"A more representative community of ecologists","docAbstract":"<p>Ecologists play a crucial role in providing solutions to the challenges facing the world. For most of the history of the field, however, the science of ecology has been pursued by white men, and increasingly, by white women. This lack of diversity is untenable today, not only because it is socially unjust, but also because solving environmental problems requires diversity. Ecology as a science is an extremely rewarding and fun career choice for many, but how can the field recruit a more diverse workforce to do this important and gratifying work? Attracting and retaining future ecologists of color is the focus of this Forum.</p><p>How do young people choose a career in ecology and environmental sciences? For many ecologists, environmental scientists and managers, and future natural resource professionals, an early field experience provides a crucial introduction and gateway. A sojourn at a field station, an extended field trip, or field expedition has introduced many of today’s professionals to their fields, but at the same time, a growing literature documents problematic behavior, discrimination, and other forms of harassment that have been far too frequently reported, and little attention has been given to conscious or implicit exclusion of students from diverse backgrounds.</p><p>In their lead article, Bowser and Cid, two leading researchers and long-time champions of diversity in ecology, build on experience in long-running programs as well as the literature to diagnose challenges facing diverse youth, and present approaches to encourage, rather than discourage, further engagement. They describe affirmative and creative measures that foster a sense of inclusion and community among young scholars; this sense of belonging and empowerment allows many to advance to studies and careers in ecology and environmental science.</p><p>In the subsequent six papers, authors explore topics either responding to, or inspired by, the lead paper. They explore alternatives to a field experience as a gateway to a career in ecology or environmental sciences through, for example, data science or the social sciences. Other comment papers describe challenges faced in the next phases of an academic career, barriers faced by specific cultural groups and the approaches, challenges, and outcomes of programs aiming to increase the diversity of the environmental STEM workforce. All responses sound a clarion call for change to hear and value different voices and perspectives to be heard and valued. These include (1) structural and cultural change to our institutions and reward structures; (2) developing and nurturing personal relationships among students and their mentors, within teams and in internships; (3) making entry to ecology inviting and making advancement in ecology free from systemic barriers; and (4) broadening our vision of ecology, and the ways we learn about the world’s ecosystems.</p><p>The Ecological Society of America, the home for<span>&nbsp;</span><i>Ecological Applications</i>, is committed to the diversity, equity and inclusion needed to tackle environmental challenges in unity (https://www.esa.org/esablog/2020/09/24/time-for-action-esa-initiates-a-diversity-equity-inclusion-and-justice-deij-task-force/). The world needs all available talent and perspectives to meet environmental challenges today.<span>&nbsp;</span><i>Ecological Applications</i><span>&nbsp;</span>has long published occasional papers on the profession of ecology and never ones more important or timely than these. The editorial team is proud to provide an outlet for the voices of our field, in all its current, if inadequate, diversity and honored to host the passionate and committed views of our authors.</p>","language":"English","publisher":"Wiley","doi":"10.1002/eap.2353","usgsCitation":"Schimel, D.S., and Baron, J., 2021, A more representative community of ecologists: Ecological Applications, v. 31, no. 6, e02353, 1 p., https://doi.org/10.1002/eap.2353.","productDescription":"e02353, 1 p.","ipdsId":"IP-126262","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451732,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2353","text":"Publisher Index Page"},{"id":390383,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Schimel, David S","contributorId":267312,"corporation":false,"usgs":false,"family":"Schimel","given":"David","email":"","middleInitial":"S","affiliations":[{"id":55473,"text":"Jep Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":824949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baron, Jill S. 0000-0002-5902-6251","orcid":"https://orcid.org/0000-0002-5902-6251","contributorId":215101,"corporation":false,"usgs":true,"family":"Baron","given":"Jill S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":824948,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70223146,"text":"70223146 - 2021 - Fully accounting for nest age reduces bias when quantifying nest survival","interactions":[],"lastModifiedDate":"2024-04-17T18:34:37.961408","indexId":"70223146","displayToPublicDate":"2021-06-28T07:42:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Fully accounting for nest age reduces bias when quantifying nest survival","docAbstract":"<p class=\"chapter-para\">Accurately measuring nest survival is challenging because nests must be discovered to be monitored, but nests are typically not found on the first day of the nesting interval. Studies of nest survival therefore often monitor a sample that overrepresents older nests. To account for this sampling bias, a daily survival rate (DSR) is estimated and then used to calculate nest survival to the end of the interval. However, estimates of DSR (and thus nest survival) can still be biased if DSR changes with nest age and nests are not found at age 0. Including nest age as a covariate of DSR and carefully considering the method of estimating nest survival can prevent such biases, but many published studies have not fully accounted for changes in DSR with nest age. I used a simulation study to quantify biases in estimates of nest survival resulting from changes in DSR with nest age under a variety of scenarios. I tested four methods of estimating nest survival from the simulated datasets and evaluated the bias and variance of each estimate. Nest survival estimates were often strongly biased when DSR varied with age but DSR was assumed to be constant, as well as when the model included age as a covariate but calculated nest survival from DSR at the mean monitored nest age (the method typically used in previous studies). In contrast, biases were usually avoided when nest survival was calculated as the product of age-specific estimates of DSR across the full nesting interval. However, the unbiased estimates often showed large variance, especially when few nests were found at young ages. Future field studies can maximize the accuracy and precision of nest survival estimates by aiming to find nests at young ages, including age as a covariate in the DSR model, and calculating nest survival as the product of age-specific estimates of DSR when DSR changes with nest age.</p>","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duab030","usgsCitation":"Weiser, E.L., 2021, Fully accounting for nest age reduces bias when quantifying nest survival: Ornithological Applications, v. 123, no. 3, duab030, 23 p., https://doi.org/10.1093/ornithapp/duab030.","productDescription":"duab030, 23 p.","ipdsId":"IP-123574","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":451735,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duab030","text":"Publisher Index Page"},{"id":436286,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9THD5GK","text":"USGS data release","linkHelpText":"Nest Survival Bias Analysis"},{"id":387895,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":821108,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
]}