Arid and semiarid ecosystems drive year-to-year variability in the strength of the terrestrial carbon (C) sink, yet there is uncertainty about how soil C gains and losses contribute to this variation. To address this knowledge gap, we embedded C-depleted soil mesocosms, containing litter or biocrust C inputs, within an in situ dryland ecosystem warming experiment. Over the course of one year, changes in microbial biomass and total soil organic C pools were monitored alongside hourly measurements of soil CO2 flux. We also developed a biogeochemical model to explore the mechanisms that gave rise to observed soil C dynamics. Field data and model simulations demonstrated that water exerted much stronger control on soil biogeochemistry than temperature, with precipitation events triggering large CO2 pulses and transport of litter- and biocrust-derived C into the soil profile. We expected leaching of organic matter would result in steady accumulation of C within the mineral soil over time. Instead, the size of the total organic C pool fluctuated throughout the year, largely in response to microbial growth: increases in the size of microbial biomass were negatively correlated with the quantity of C residing in the top 2 cm, where most biogeochemical changes were observed. Our data and models suggest that microbial responses to precipitation events trigger rapid metabolism of dissolved organic C inputs, which strongly limit accumulation of autotroph-derived C belowground. Accordingly, changes in the magnitude and/or frequency of precipitation events in this dryland ecosystem could have profound impacts on the strength of the soil C sink.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006492","usgsCitation":"Waring, B.G., Smith, K.R., Grote, E.E., Howell, A.J., Reibold, R.H., Tucker, C.L., and Reed, S., 2021, Climatic controls on soil carbon accumulation and loss in a dryland ecosystems: Journal of Geophysical Research, v. 126, no. 12, e2021JG006492, 13 p., https://doi.org/10.1029/2021JG006492.","productDescription":"e2021JG006492, 13 p.","ipdsId":"IP-133338","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":450184,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1978553","text":"External Repository"},{"id":393749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Castle Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.46605682373047,\n 38.58896696823242\n ],\n [\n -109.30744171142578,\n 38.58896696823242\n ],\n [\n -109.30744171142578,\n 38.718465403583835\n ],\n [\n -109.46605682373047,\n 38.718465403583835\n ],\n [\n -109.46605682373047,\n 38.58896696823242\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Waring, Bonnie G. 0000-0002-8457-5164","orcid":"https://orcid.org/0000-0002-8457-5164","contributorId":245284,"corporation":false,"usgs":false,"family":"Waring","given":"Bonnie","email":"","middleInitial":"G.","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":829892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Kenneth R","contributorId":270738,"corporation":false,"usgs":false,"family":"Smith","given":"Kenneth","email":"","middleInitial":"R","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":829893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grote, Edmund E. 0000-0002-9103-9482 ed_grote@usgs.gov","orcid":"https://orcid.org/0000-0002-9103-9482","contributorId":4271,"corporation":false,"usgs":true,"family":"Grote","given":"Edmund","email":"ed_grote@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howell, Armin J. 0000-0003-1243-0238 ahowell@usgs.gov","orcid":"https://orcid.org/0000-0003-1243-0238","contributorId":196798,"corporation":false,"usgs":true,"family":"Howell","given":"Armin","email":"ahowell@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reibold, Robin H. 0000-0002-3323-487X","orcid":"https://orcid.org/0000-0002-3323-487X","contributorId":207499,"corporation":false,"usgs":true,"family":"Reibold","given":"Robin","email":"","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829896,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tucker, Colin L","contributorId":270737,"corporation":false,"usgs":false,"family":"Tucker","given":"Colin","email":"","middleInitial":"L","affiliations":[{"id":56205,"text":"U.S. National Forest Service, Northern Research Station, Houghton, MI 49931","active":true,"usgs":false}],"preferred":false,"id":829897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":829898,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220289,"text":"70220289 - 2021 - Arctic Alaska Basin, Hanna Trough and Beaufortian Rifted Margin Composite Tectono-Sedimentary Elements","interactions":[],"lastModifiedDate":"2025-02-04T16:09:58.361136","indexId":"70220289","displayToPublicDate":"2021-11-17T10:04:25","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Arctic Alaska Basin, Hanna Trough and Beaufortian Rifted Margin Composite Tectono-Sedimentary Elements","docAbstract":"The Arctic Alaska Composite Tectono-Sedimentary Element (AA CTSE) as defined for this volume comprises Mississippian to Lower Cretaceous strata beneath the Alaska North Slope and the Beaufort and Chukchi Seas of the Arctic Ocean. The AA CTSE rests on Devonian and older sedimentary and metasedimentary rocks, considered economic basement for petroleum, and is overlain by Cretaceous to Cenozoic syntectonic strata deposited in the foreland of the Chukotka and Brooks Range orogens. The Mississippian – Triassic part of the AA CTSE is divided into a fold-and-thrust belt in the south and a relatively undeformed platform in the north. The Jurassic – Lower Cretaceous part of the AA CTSE is divided into synrift basins in the north and rift-shoulder deposits in the south. The AA CTSE includes oil-prone source rocks in the Triassic, Jurassic, and Cretaceous and proven reservoir rocks spanning the Mississippian to Lower Cretaceous. Much of the central part of the AA CTSE lies in the oil window whereas the northern and southern parts are mainly in the gas window. Known hydrocarbon accumulations in the AA CTSE total more than 30 billion barrels of oil equivalent and yet-to-find estimates suggest a similar volume remains to be discovered","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geological Society, London, Memoirs","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"The Geological Society of London","doi":"10.1144/m57-2018-26","usgsCitation":"Houseknecht, D.W., 2021, Arctic Alaska Basin, Hanna Trough and Beaufortian Rifted Margin Composite Tectono-Sedimentary Elements, chap. of Geological Society, London, Memoirs, v. 57, 18 p., https://doi.org/10.1144/m57-2018-26.","productDescription":"18 p.","ipdsId":"IP-097477","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":490072,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1144/m57-2018-26","text":"Publisher Index Page"},{"id":481671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -136.5744800240832,\n 73.48129004791153\n ],\n [\n -138.2471596101051,\n 75.08779606716953\n ],\n [\n -155.01532165240496,\n 76.6720069019496\n ],\n [\n -173.46045058586498,\n 72.51742405199943\n ],\n [\n -166.78189271016328,\n 68.39273635171199\n ],\n [\n -134.69076739237028,\n 68.41381738362409\n ],\n [\n -136.5744800240832,\n 73.48129004791153\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"57","noUsgsAuthors":false,"publicationDate":"2021-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Houseknecht, David W. 0000-0002-9633-6910 dhouse@usgs.gov","orcid":"https://orcid.org/0000-0002-9633-6910","contributorId":645,"corporation":false,"usgs":true,"family":"Houseknecht","given":"David","email":"dhouse@usgs.gov","middleInitial":"W.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815020,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70225532,"text":"sir20215109 - 2021 - Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","interactions":[],"lastModifiedDate":"2021-11-23T13:06:28.021637","indexId":"sir20215109","displayToPublicDate":"2021-11-17T07:15:00","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":"2021-5109","displayTitle":"Documentation and Mapping of Flooding From the January and March 2018 Nor’easters in Coastal New England","title":"Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England","docAbstract":"In January and March 2018, coastal Massachusetts experienced flooding from two separate nor’easters. To put the January and March floods into historical context, the USGS computed statistical stillwater elevations. Stillwater elevations recorded in January 2018 in Boston (9.66 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 2 and 1 percent (between a 50- and 100-year recurrence interval). Stillwater elevations recorded in March 2018 in Boston (9.17 feet relative to the North American Vertical Datum of 1988) have an annual exceedance probability of between 4 and 2 percent (between a 25- and 50-year recurrence interval). Flood maps show that the area inundated by the January storm is slightly more extensive than that of the March storm, reflecting the respective profiles of the two storms. On the basis of a limited dataset, the attenuation of peak water levels was estimated as a function of the hydraulic distance inland and the starting stillwater elevation computed for the flood within 0.6 foot of what was measured in the field. A simple one-dimensional model was calibrated using flood elevation data collected after the January flood, and the results of the model were validated using flood elevation data collected after the March flood to model the attenuation of the flood elevations as the storms move inland.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215109","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Lombard, P.J., Olson, S.A., Sturtevant, L.P., and Kalmon, R.D., 2021, Documentation and mapping of flooding from the January and March 2018 nor’easters in coastal New England: U.S. Geological Survey Scientific Investigations Report 2021–5109, 13 p., https://doi.org/10.3133/sir20215109.","productDescription":"Report: iv, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-125348","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":390667,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RINQ4B","text":"USGS data release","linkHelpText":"Data and shapefiles used to document the floods associated with the January and March 2018 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Ecological transformation creates many challenges for public natural resource management and requires managers to grapple with new relationships to change and new ways to manage it. In the context of unfamiliar trajectories of ecological change, a manager can resist, accept, or direct change, choices that make up the resist-accept-direct (RAD) framework. In this article, we provide a conceptual framework for how to think about this new decision space that managers must navigate. We identify internal factors (mental models) and external factors (social feasibility, institutional context, and scientific uncertainty) that shape management decisions. We then apply this conceptual framework to the RAD strategies (resist, accept, direct) to illuminate how internal and external factors shape those decisions. Finally, we conclude with a discussion of how this conceptual framework shapes our understanding of management decisions, especially how these decisions are not just ecological but also social, and the implications for research and management.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/biosci/biab086","usgsCitation":"Clifford, K.R., Cravens, A.E., and Knapp, C.N., 2021, Responding to ecological transformation: Mental models, external constraints, and manager decision-making: BioScience, v. 72, no. 1, p. 57-70, https://doi.org/10.1093/biosci/biab086.","productDescription":"14 p.","startPage":"57","endPage":"70","ipdsId":"IP-127232","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450187,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/biosci/biab086","text":"Publisher Index Page"},{"id":394010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Clifford, Katherine R. 0000-0002-1385-8765","orcid":"https://orcid.org/0000-0002-1385-8765","contributorId":259886,"corporation":false,"usgs":true,"family":"Clifford","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830319,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravens, Amanda E. 0000-0002-0271-7967 aecravens@usgs.gov","orcid":"https://orcid.org/0000-0002-0271-7967","contributorId":196752,"corporation":false,"usgs":true,"family":"Cravens","given":"Amanda","email":"aecravens@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":830320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knapp, Corrine N.","contributorId":270993,"corporation":false,"usgs":false,"family":"Knapp","given":"Corrine","email":"","middleInitial":"N.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":830321,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226753,"text":"70226753 - 2021 - Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests","interactions":[],"lastModifiedDate":"2021-12-09T12:35:17.454202","indexId":"70226753","displayToPublicDate":"2021-11-17T06:32:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for fine-scale forest structure is necessary to model snowpack mass and energy budgets in montane forests","docAbstract":"Accurately modeling the effects of variable forest structure and change on snow distribution and persistence is critical to water resource management. The resolution of many snow models is too coarse to represent heterogeneous canopy structure in forests, and therefore, most models simplify forest effects on snowpack mass and energy budgets. To quantify the loss of snowpack prediction from simplifications of forest canopy-mediated processes, we applied a high-resolution energy balance snowpack model at two forested sites at a fine (1 m2) and coarse (100 m2) spatial resolution. Simulating open and forested areas separately, as is done in many land surface models (LSMs), leads to biases between the coarse and fine-scale simulations because there is no representation of areas that are near (e.g., <15 m from) trees but with no overhead canopy, which are common in forests of low to medium tree density. Consistent with previous LSM intercomparisons, the coarser simulations predict greater under-canopy radiation (by 30%–80% at our sites), faster snow ablation (by almost 2×), and earlier snow disappearance (by 1–22 days). Many of these biases are reduced dramatically or eliminated when canopy edge environments are considered in the coarser simulations. Furthermore, remaining disagreement between the 100-m and 1-m models can be partially explained by using a combination of tree height, canopy cover, and canopy edginess (which together can explain 46%–96% of remaining model biases). The lack of information about canopy edges and other fine-scale forest structure characteristics in many current LSMs may limit their reliability for simulating forest disturbance.
In a scientifically-transformative project, South Africa implemented a decade-long field experiment to understand how fisheries may be affecting its most iconic seabird, the African penguin Spheniscus demersus. This unique effort prohibits the take of anchovy and sardine within relatively small areas around four African penguin breeding colonies, two in the Benguela upwelling ecosystem and two in the adjacent Agulhas region. For the Benguela, fisheries closures within the birds’ primary foraging range increased their breeding productivity and perhaps reduced parental foraging efforts, indicating that the fisheries are competing with the birds for food. Results were less clear for foraging behaviour in the Agulhas, but no data on breeding success were collected there. The African penguin is endangered, its population continues to decline, and fisheries closures have been demonstrated to improve demographic traits that contribute to population growth. Therefore, given the critical status of the species, fisheries closures should be maintained, at least at Dassen Island where the population has great capacity to expand and support other nearby colonies. Continuing or implementing corresponding fisheries closures in the Agulhas region is also warranted, as well as creating and testing the value of pelagic closed areas during the non-breeding season when the penguins disperse widely across these ecosystems. These management actions would increase penguin food supplies and may help to meet societal goals of halting the decline of the penguin population, as well as maintaining the economic and cultural services provided by fisheries and ecotourism.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icesjms/fsab231","usgsCitation":"Sydeman, W., Hunt, G.L., Pikitch, E., Parrish, J.K., Piatt, J.F., Boersma, P.D., Kaufman, L., Anderson, D.W., Thompson, S.A., and Sherley, R.B., 2021, South Africa's experimental fisheries closures and recovery of the endangered African penguin: ICES Journal of Marine Science, v. 78, no. 10, p. 3538-3543, https://doi.org/10.1093/icesjms/fsab231.","productDescription":"5 p.","startPage":"3538","endPage":"3543","ipdsId":"IP-129144","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":450191,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsab231","text":"Publisher Index Page"},{"id":398878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"South Africa","otherGeospatial":"Agulhas, Benguela, Dassen Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 18.17138671875,\n -35.7465122599185\n ],\n [\n 27.6416015625,\n -35.7465122599185\n ],\n [\n 27.6416015625,\n -33.41310221370828\n ],\n [\n 18.17138671875,\n -33.41310221370828\n ],\n [\n 18.17138671875,\n -35.7465122599185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sydeman, William J.","contributorId":172574,"corporation":false,"usgs":false,"family":"Sydeman","given":"William J.","affiliations":[],"preferred":false,"id":840711,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunt, George L. Jr.","contributorId":56953,"corporation":false,"usgs":true,"family":"Hunt","given":"George","suffix":"Jr.","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":840712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pikitch, E.K.","contributorId":152152,"corporation":false,"usgs":false,"family":"Pikitch","given":"E.K.","email":"","affiliations":[],"preferred":false,"id":840713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parrish, Julia K.","contributorId":220055,"corporation":false,"usgs":false,"family":"Parrish","given":"Julia","email":"","middleInitial":"K.","affiliations":[{"id":40123,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":840714,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":840715,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boersma, P Dee","contributorId":290290,"corporation":false,"usgs":false,"family":"Boersma","given":"P","email":"","middleInitial":"Dee","affiliations":[],"preferred":false,"id":840720,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaufman, Les","contributorId":260242,"corporation":false,"usgs":false,"family":"Kaufman","given":"Les","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":840716,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, Daniel W.","contributorId":74345,"corporation":false,"usgs":false,"family":"Anderson","given":"Daniel","email":"","middleInitial":"W.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":840717,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thompson, Sarah Ann","contributorId":198394,"corporation":false,"usgs":false,"family":"Thompson","given":"Sarah","email":"","middleInitial":"Ann","affiliations":[],"preferred":false,"id":840718,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sherley, Richard B.","contributorId":198407,"corporation":false,"usgs":false,"family":"Sherley","given":"Richard","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":840719,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70226145,"text":"sir20215121 - 2021 - Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18","interactions":[],"lastModifiedDate":"2021-11-19T21:06:31.076465","indexId":"sir20215121","displayToPublicDate":"2021-11-16T13:40:00","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":"2021-5121","displayTitle":"Cyanobacteria, Cyanotoxin Synthetase Gene, and Cyanotoxin Occurrence Among Selected Large River Sites of the Conterminous United States, 2017–18","title":"Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18","docAbstract":"The U.S. Geological Survey measured cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins at 11 river sites throughout the conterminous United States in a multiyear pilot study during 2017–19 through the National Water Quality Assessment Project to better understand the occurrence of cyanobacteria and cyanotoxins in large inland and coastal rivers. This report focuses on the first 2 years of data collection (2017 and 2018) and describes occurrence of anatoxin-, cylindrospermopsin-, microcystin-, and saxitoxin-producing cyanobacteria, cyanotoxin synthetase genes (anaC, cyrA, taxa specific mcyE, and sxtA), and cyanotoxins (anatoxins, cylindrospermopsins, microcystins, and saxitoxins). Study findings demonstrate that cyanobacteria, cyanotoxin synthetase genes, and cyanotoxins are present in large U.S rivers under ambient conditions and show that downstream transport and flushing likely affect relative abundance of potential cyanotoxin-producing cyanobacteria. Additionally, the results agree with existing literature that support the importance of water temperature, light, and nutrients—as moderated by hydrologic conditions—in shaping the structure of riverine cyanobacterial communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215121","programNote":"National Water Quality Program","usgsCitation":"Zuellig, R.E., Graham, J.L., Stelzer, E.A., Loftin, K.A., and Rosen, B.H., 2021, Cyanobacteria, cyanotoxin synthetase gene, and cyanotoxin occurrence among selected large river sites of the conterminous United States, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5121, 22 p., https://doi.org/10.3133/sir20215121.","productDescription":"Report: v, 22 p.; 4 Data Releases; Related Work","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122244","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science 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U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey produces research quality, applications ready, Level-2 Science Products derived from Landsat Collection 2 Level-1 data. These products are used to monitor, assess, and project changes in land use, land cover, and environmental conditions affecting the human condition, natural processes, and biological habitats. Landsat Collection 2 Level-2 Science Products are time-series observational data processed for consistency and continuity to measure effects of environmental change and serve as input into Landsat essential climate variable Level-3 Science Products.
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U.S. Geological Survey
47914 252nd Street
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Occupancy models are often used to analyze long-term monitoring data to better understand how and why species redistribute across dynamic landscapes while accounting for incomplete capture. However, this approach requires replicate detection/non-detection data at a sample unit and many long-term monitoring programs lack temporal replicate surveys. In such cases, it has been suggested that surveying subunits within a larger sample unit may be an efficient substitution (i.e., space-for-time substitution). Still, the efficacy of fitting occupancy models using a space-for-time substitution has not been fully explored and is likely context dependent. Herein, we fit occupancy models to Delta Smelt (Hypomesus transpacificus) and Longfin Smelt (Spirinchus thaleichthys) catch data collected by two different monitoring programs that use the same sampling gear in the San Francisco Bay-Delta, USA. We demonstrate how our inferences concerning the distribution of these species changes when using a space-for-time substitution. Specifically, we found the probability that a sample unit was occupied was much greater when using a space-for-time substitution, presumably due to the change in the spatial scale of our inferences. Furthermore, we observed that as the spatial scale of our inferences increased, our ability to detect environmental effects on system dynamics was obscured, which we suspect is related to the tradeoffs associated with spatial grain and extent. Overall, our findings highlight the importance of considering how the unique characteristics of monitoring programs influences inferences, which has broad implications for how to appropriately leverage existing long-term monitoring data to understand the distribution of species.
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U.S. Geological Survey
MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
A previously constructed numerical model simulating the regional groundwater flow system in the vicinity of the Wright-Patterson Air Force Base near Dayton, Ohio, was updated to incorporate current hydrologic stresses and conditions and improve the usefulness of the model for water-supply planning and protection. The original model, which simulated conditions from 1997 to 2001, was reconstructed with the most recently available U.S. Geological Survey groundwater modeling software and recalibrated to represent average groundwater flow conditions for the period of October 2018.
The steady-state, three-dimensional, three-layer MODFLOW model of the aquifer encompasses about 241 square miles in Montgomery, Greene, and Clark Counties. The Great Miami buried-valley aquifer consists of glacial sands and gravels in a buried bedrock valley. The shale bedrock in the area is poorly permeable, but the glacial deposits can yield as much as 2,000 gallons per minute to wells. As groundwater is the primary source of drinking water in the heavily populated study area, groundwater pumping from the buried-valley aquifer represents the largest time-varying stress in the groundwater flow model. The model simulated 228 pumped wells. Hydraulic conductivities in the model ranged from less than 1 foot per day to 450 feet per day. Simulated recharge rates ranged from 6 inches per year to 12.2 inches per year. Boundary conditions and aquifer properties were unchanged from the previous model. Model grid spacing and orientation also were not modified from the previous model.
Parameter estimation software was used to optimize model input parameters by matching simulated values to observed (estimated or measured) values. Calibrated parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, riverbed conductance, and recharge. Model calibration used measured water levels (hydraulic heads) from 124 observation wells, and streamflow gain/loss measurements from select reaches of the Mad River and its tributaries were compared with simulated streamflow gain/loss. Performance of the updated model is similar to previous studies. Eighty-one percent of simulated hydraulic heads were within 10 feet of the measured hydraulic heads, but comparison of the simulated streamflow gain/loss with the measured gain/loss indicates that streamflow gain/loss is not well represented by the updated model.
The particle tracking program MODPATH was used to calculate groundwater flow paths from recharge areas to selected existing and proposed groundwater withdrawal sites that service Wright-Patterson Air Force Base. Areas contributing groundwater to withdrawal sites were delineated based on 1-, 5-, and 10-year groundwater travel times. In addition, groundwater flow paths were calculated to simulate a groundwater release at eight sites near Wright-Patterson Air Force Base.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215115","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineering Center, Wright-Patterson Air Force Base","usgsCitation":"Riddle, A.D., 2021, Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5115, 36 p., https://doi.org/ 10.3133/ sir20215115.","onlineOnly":"Y","ipdsId":"IP-119316","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":391514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5115/sir20215115.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5115"},{"id":391515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FN1JK4","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used for the update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio"},{"id":391513,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5115/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
In the Arctic, warming air and ocean temperatures have resulted in substantial changes to sea ice, which is primary habitat for polar bears (Ursus maritimus). Reductions in extent, duration, and thickness have altered sea ice dynamics, which influences the ability of polar bears to reliably access marine mammal prey. Because nutritional condition is closely linked to population vital rates, a progressive decline in access to prey or an increase in the energetic cost of accessing prey has the potential to adversely affect polar bear population dynamics. We examined long-term (1983–2015) patterns of spring body condition (indexed using residual body mass) and maternal investment (i.e., litter mass of cubs-of-the-year and yearlings; COY and YRL) of polar bears from Alaska’s southern Beaufort Sea to evaluate potential relationships with regional- and circumpolar-scale sea ice conditions and atmospheric patterns. The length of the summer open-water (OW) season (i.e., the period of time the sea ice is mostly absent from the continental shelf) increased at a rate of 18 days decade-1 over the study period. However, the OW season duration was not a strong determinant of spring residual body mass or litter mass. Residual body mass of independent (i.e., subadults and adults) female bears varied relative to age class, reproductive status, and the strength of the prior winter’s Arctic Oscillation (i.e., a circumpolar-scale mode of climate variability driven by long-term atmospheric patterns). Spring residual mass of independent males varied with age class and variation in wind speed (i.e., regional-scale short-term atmospheric patterns) during the winter of the year preceding capture. Over the study period, mean annual body mass of adult females unaccompanied by COY declined by 4 kg/ decade-1, while no temporal trends were evident in the mean annual body mass of adult females with COY, adult males, and subadults. Litter mass of COY varied relative to capture date, maternal age class and mass, litter size, and year of capture. Litter mass of YRL varied with capture date, maternal age class and mass, litter size, variation in winter wind speed (the year of and year preceding capture), and the strength of the prior winter’s Arctic Oscillation. Mean annual litter mass of COY decreased at a rate of 2.6 kg decade-1 and declined 0.68 kg for every 10 kg reduction in maternal mass. No trend was evident in the mean annual litter mass of yearlings. These findings suggest a nuanced response of the southern Beaufort Sea polar bears to environmental change, where some demographic groups (e.g., adult males and subadults) are presently more resilient than others to changes in the Arctic marine ecosystem.
Droughts are disproportionately impacting global dryland regions where ecosystem health and function are tightly coupled to moisture availability. Drought severity is commonly estimated using algorithms such as the standardized precipitation-evapotranspiration index (SPEI), which can estimate climatic water balance impacts at various hydrologic scales by varying computational length. However, the performance of these metrics as indicators of soil moisture dynamics at ecologically relevant scales, across soil depths, and in consideration of broader scale ecohydrological processes, requires more attention. In this study, we tested components of climatic water balance, including SPEI and SPEI computation lengths, to recreate multi-decadal and periodic soil-moisture patterns across soil profiles at 866 sites in the western United States. Modeling results show that SPEI calculated over the prior 12-months was the most predictive computation length and could recreate changes in moisture availability within the soil profile over longer periods of time and for annual recharge of deeper soil moisture stores. SPEI was slightly less successful with recreating spring surface-soil moisture availability, which is key to dryland ecosystems dominated by winter precipitation. Meteorological drought indices like SPEI are intended to be convenient and generalized indicators of meteorological water deficit. However, the inconsistent ability of SPEI to recreate ecologically relevant patterns of soil moisture at regional scales suggests that process-based models, and the larger data requirements they involve, remain an important tool for dryland ecohydrology
The U.S. Geological Survey (USGS) Water-Use Program is responsible for compiling and disseminating the Nation's water-use data. Working in cooperation with local, State, and Federal agencies, the USGS has collected and published national water-use estimates every 5 years, beginning in 1950. These water-use data may vary because of actual changes in water use, because of changes in estimation methods, or because of errors. Comparison and interpretation of these data is difficult without first determining the factors that contribute to data variability. This report describes factors that may affect data quality and documents ways to investigate the variability of public supply, self-supplied domestic, irrigation, and thermoelectric water-use data for the 1985–2015 compilations.
The USGS produces national water-use estimates for various categories of water use for every county in the United States. Knowledge about the sources of data for county estimates is important because factors such as estimation methodology and reporting affect data uncertainty Determination of meaningful patterns and trends in the data are contingent on the use of consistent methodology throughout the period of interest. With the many ways that water-use data have been collected, assembled, and estimated, multiple factors likely contribute to data uncertainty, Data used to produce these estimates may be furnished from agencies that collect information from entities who report water use; gaps in reported data are typically estimated to achieve a comprehensive county estimate. For example, public supply and thermoelectric category data are based primarily on furnished site-specific data; whereas crop irrigation is often furnished or estimated at the county scale. Public supply deliveries for domestic use and self-supplied domestic withdrawals are most often estimated by USGS personnel using per capita use rate coefficients. Irrigation may be estimated using crop water requirements, application rates, or other soil water balance methods when furnished reported data are not available.
Rates, percentages, medians, and interquartile ranges were used to investigate variability in the water-use data among States, regions, and years. The purposes of these evaluations were to (1) identify extreme values that may reflect changes in information sources, estimation methods, or errors; (2) indicate areas of variable or consistent values that are unexpected; and (3) indicate areas where values change because of local climate or other factors. Where factors are identified that contribute to data variability, such as a change in methodology, additional work could determine uncertainty because of these factors.
These evaluations identified the availability of information that is needed to address data limitations. Factors such as estimation methodology affect data quality. Some updates to method codes assigned in 2015 and assignment of method codes to earlier compilation datasets for all categories would provide much needed metadata for users of the data. Improvements in data documentation describing sources of information and estimation methods and additional metadata information from agencies and entities that furnish water-use data, would enable a more complete understanding and depiction of water-use patterns and trends. Additional metadata are needed for users of the data to better understand the water-use data and interpret changes in water use across the United States and with time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215082","usgsCitation":"Luukkonen, C.L., Belitz, K., Sullivan, S.L., and Sargent, P., 2021, Factors affecting uncertainty of public supply, self-supplied domestic, irrigation, and thermoelectric water-use data, 1985–2015—Evaluation of information sources, estimation methods, and data variability: U.S. Geological Survey Scientific Investigations Report 2021–5082, 78 p., https://doi.org/10.3133/sir20215082.","productDescription":"Report: ix, 78 p.; Database; Data Release","onlineOnly":"Y","ipdsId":"IP-123556","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391626,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TA1DI9","text":"USGS data release","linkHelpText":"Public supply, self-supplied domestic, irrigation, and thermoelectric water-use data from 5-year compilation datasets from 1985 to 2015 used to assess data variability and uncertainty"},{"id":391625,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5082/sir20215082.pdf","text":"Report","size":"26.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5082"},{"id":391624,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5082/coverthb.jpg"},{"id":391627,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database, accessed July 29, 2021,"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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U.S. Geological Survey
5840 Enterprise Drive
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Medicine Lake Highlands/Fall River Springs Aquifer System, located in northeastern California, is home to some of the largest first-order springs in the United States. This work assesses the likely effects of projected climate change on spring flow. Four anticipated climate futures (GFDL A2, GFDL B1, CCSM4 rcp 8.5, CNRM rcp 8.5) for California, which predict a range of conditions (generally warming and transitioning from snow to rain with variable amounts of total precipitation), are postulated to affect groundwater recharge primarily by changing evapotranspiration. The linkages between climate variables and spring flow are evaluated using a water balance model that represents the physics of evapotranspiration and recharge, the Basin Characterization Model. Three of the four climate scenarios (GFDL A2, GFDL B1, CCSM4 rcp 8.5) project that by the year 2100, groundwater recharge (and consequently decreased spring flow) will decrease by 27%, 21%, and 9%, respectively. The fourth scenario (CNRM rcp 8.5) showed an increase in recharge of 32% due to a significant increase in precipitation (27%). Evapotranspiration increases due to a shift in the type of precipitation and a longer growing season. While the likelihood of each scenario is outside the scope of this work, unless total precipitation increases dramatically in the future, increased temperatures and decreasing precipitation will likely result in reduced spring flows, along with warmer water temperatures in downstream habitats.
The Anthropocene has yet to be defined in a way that is functional both to the international geological community and to the broader fields of environmental and social sciences. Formally defining the Anthropocene as a chronostratigraphical series and geochronological epoch with a precise global start date would drastically reduce the Anthropocene’s utility across disciplines. Instead, we propose the Anthropocene be defined as a geological event, thereby facilitating a robust geological definition linked with a scholarly framework more useful to and congruent with the many disciplines engaging with human-environment interactions. Unlike formal epochal definitions, geological events can recognize the spatial and temporal heterogeneity and diverse social and environmental processes that interact to produce anthropogenic global environmental changes. Consequently, an Anthropocene Event would incorporate a far broader range of transformative human cultural practices and would be more readily applicable across academic fields than an Anthropocene Epoch, while still enabling a robust stratigraphic characterization.
White-nose syndrome (WNS) has caused dramatic declines of several cave-hibernating bat species in North America since 2006, which has increased the activity of non-susceptible species in some geographic areas or during times of night formerly occupied by susceptible species—indicative of disease-mediated competitive release (DMCR). Yet, this pattern has not been evaluated across multiple bat assemblages simultaneously or across multiple years since WNS onset. We evaluated whether WNS altered spatial and temporal niche partitioning in bat assemblages at four locations in the eastern United States using long-term datasets of bat acoustic activity collected before and after WNS arrival. Activity of WNS-susceptible bat species decreased by 79–98% from pre-WNS levels across the four study locations, but only one of our four study sites provided strong evidence supporting the DMCR hypothesis in bats post-WNS. These results suggest that DMCR is likely dependent on the relative difference in activity by susceptible and non-susceptible species groups pre-WNS and the relative decline of susceptible species post-WNS allowing for competitive release, as well as the amount of time that had elapsed post-WNS. Our findings challenge the generality of WNS-mediated competitive release between susceptible and non-susceptible species and further highlight declining activity of some non-susceptible species, especially Lasiurus borealis, across three of four locations in the eastern United States. These results underscore the broader need for conservation efforts to address the multiple potential interacting drivers of bat declines on both WNS-susceptible and non-susceptible species.
The origin and parental magma for layered cumulates in the Lower Banded series (LBS) and the J-M Reef Pd-Pt deposit of the Stillwater Complex remains poorly constrained. We present whole-rock lithogeochemistry and mineral chemistry from LBS rocks collected from drill holes and surface samples from the Mountain View area of the complex that in total span nearly the entirety of the LBS stratigraphy. Excess S, Pt, and Pd in the noritic and gabbronoritic cumulates of the LBS indicate that small amounts of high tenor sulfide liquid generated at very low degrees of sulfide oversaturation were ubiquitous parts of the cumulate assemblage. We show that a simple two-stage thermodynamic model of assimilation-batch crystallization of a komatiitic parental magma in the lower crust, produces a close match to a common suite of fine-grained gabbronorite dikes and sills that intrude both the complex and its footwall. After fractionating ultramafic cumulates in the lower crust, the model contaminated komatiitic liquid produces upper crustal cumulates by batch crystallization en route to or at the level of the intrusion. The modeled rocks have compositions and mineral assemblages closely resembling pyroxenite of the Bronzitite zone and both norite and gabbronorite cumulates in the lower LBS. The trends from the Bronzitite zone through Norite zone I and Gabbronorite zone I can be understood as the result of deposition of crystals from successive batches of the same contaminated parental magma, with an upward trend toward greater amounts of cooling before the separation of crystals from liquid. The olivine-bearing suite of Olivine-bearing zone I, which includes the J-M Reef, can be modeled by partial remelting of the same norite and gabbronorite cumulates due to a temporarily increased flux of hot, moderately less contaminated LBS parental magma that infiltrated partially molten cumulates because its density exceeded that of the interstitial liquid. This model suggests that infiltration of hot Mg-rich parental liquid into moderately PGE-enriched footwall cumulates may be fundamental to the formation of the extremely high tenor sulfide mineralization in the J-M Reef ore zone, and perhaps other reef-type deposits worldwide. The same metal tenors that would require silicate/sulfide mass ratios (i.e., R-factors) of 105 to 106 in a single stage of equilibration would be attained during this second stage of interaction by the incremental infiltration and passage of LBS parental magma through previously sulfide saturated cumulate mush.
Climate change is leading to increased drought intensity and fire frequency, creating early-successional landscapes with novel disturbance–recovery dynamics. In the Klamath Mountains of northwestern California and southwestern Oregon, early-successional interactions between nitrogen (N)-fixing shrubs (Ceanothus spp.) and long-lived conifers (Douglas-fir) are especially important determinants of forest development. We sampled post-fire vegetation and soil biogeochemistry in 57 plots along gradients of time since fire (7–28 years) and climatic water deficit (aridity). We found that Ceanothus biomass increased, and Douglas-fir biomass decreased with increasing aridity. High aridity and Ceanothus biomass interacted with lower soil C:N more than either factor alone. Ceanothus biomass was initially high after fire and declined with time, suggesting a large initial pulse of N-fixation that could enhance N availability for establishing Douglas-fir. We conclude that future increases in aridity and wildfire frequency will likely limit post-fire Douglas-fir establishment, though Ceanothus may ameliorate some of these impacts through benefits to microclimate and soils. Results from this study contribute to our understanding of the effects of climate change and wildfires on interspecific interactions and forest dynamics. Management seeking to accelerate forest recovery after high-severity fire should emphasize early-successional conifer establishment while maintaining N-fixing shrubs to enhance soil fertility.
","language":"English","publisher":"MDPI","doi":"10.3390/f12111567","usgsCitation":"Cinoglu, D., Epstein, H., Tepley, A.J., Anderson-Teixeira, K.J., Thompson, J.R., and Perakis, S.S., 2021, Climatic aridity shapes post-fire interactions between Ceanothus spp. and Douglas-fir (Pseudotsuga menziesii) across the Klamath Mountains: Forests, v. 12, no. 11, 1567, 15 p., https://doi.org/10.3390/f12111567.","productDescription":"1567, 15 p.","ipdsId":"IP-133295","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":450213,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f12111567","text":"Publisher Index Page"},{"id":401970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Klamath Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.47509765625,\n 40.622291783092706\n ],\n [\n -122.431640625,\n 40.622291783092706\n ],\n [\n -122.431640625,\n 42.342305278572816\n ],\n [\n -124.47509765625,\n 42.342305278572816\n ],\n [\n -124.47509765625,\n 40.622291783092706\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Cinoglu, Damla","contributorId":292365,"corporation":false,"usgs":false,"family":"Cinoglu","given":"Damla","email":"","affiliations":[{"id":34217,"text":"UT Austin","active":true,"usgs":false}],"preferred":false,"id":844406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Epstein, Howard E","contributorId":292366,"corporation":false,"usgs":false,"family":"Epstein","given":"Howard E","affiliations":[{"id":62885,"text":"UVA","active":true,"usgs":false}],"preferred":false,"id":844407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tepley, Alan J.","contributorId":139993,"corporation":false,"usgs":false,"family":"Tepley","given":"Alan","email":"","middleInitial":"J.","affiliations":[{"id":13346,"text":"University of Colorado at Boulder, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":844408,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson-Teixeira, Kristina J. 0000-0001-8461-9713","orcid":"https://orcid.org/0000-0001-8461-9713","contributorId":150956,"corporation":false,"usgs":false,"family":"Anderson-Teixeira","given":"Kristina","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":844409,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Jonathan R.","contributorId":292368,"corporation":false,"usgs":false,"family":"Thompson","given":"Jonathan","email":"","middleInitial":"R.","affiliations":[{"id":37315,"text":"Harvard","active":true,"usgs":false}],"preferred":false,"id":844410,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":844411,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226157,"text":"70226157 - 2021 - Depths inferred from velocities estimated by remote sensing: A flow resistance equation-based approach to mapping multiple river attributes at the reach scale","interactions":[],"lastModifiedDate":"2021-11-15T12:13:19.787666","indexId":"70226157","displayToPublicDate":"2021-11-13T06:10:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Depths inferred from velocities estimated by remote sensing: A flow resistance equation-based approach to mapping multiple river attributes at the reach scale","docAbstract":"In support of nutrient reduction efforts, total phosphorus loads and yields were computed using turbidity-surrogate and LOAD ESTimator (LOADEST) models for the Cedar River at Palo, Iowa, for January 1, 2009, to December 15, 2020. Sample data were used to create a total phosphorus concentration turbidity-surrogate model. Total phosphorus loads also were computed from two streamflow-based LOADEST load models for the periods 2009–20 and 2016–20. The 2009–20 model was used for comparison with previously published loads at this site. The 2016–20 LOADEST model was used with the turbidity-surrogate model before sensor deployment and during periods of missing sensor data to obtain a more complete annual total phosphorus load. This report presents computed loads and methods needed to compute site-specific loads accurately and track annual progress toward nutrient reduction goals within the State.
A comparison of loads from Weighted Regressions on Time, Discharge, and Season; LOADEST; and surrogate models indicated substantial differences at this site among these methods. Changes in both monitoring approaches (high-frequency sensor and surrogate data) and changes in load-calculation methods present potential challenges in assessing trends, such as assessment of load reduction.
Annual total phosphorus loads for the Cedar River at Palo, Iowa, ranged from 1,370 to 2,360 U.S. short tons per year for 2018–20, based on the turbidity-surrogate model with gaps in sensor data filled with the 2016–20 LOADEST model. Annual total phosphorus yields for the Cedar River ranged from 0.67 to 1.16 pounds per acre per year for 2018–20. Although this load estimate is lower than previous estimates for the benchmark period of 2006–10, when normalized by streamflow, nearly all the apparent reduction can be attributed to differences in the load-calculation methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215127","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Garrett, J.D., 2021, Total phosphorus loadings for the Cedar River at Palo, Iowa, 2009–20: U.S. Geological Survey Scientific Investigations Report 2021–5127, 15 p., https://doi.org/10.3133/sir20215127.","productDescription":"Report vi, 15 p.: Database; Related Work","onlineOnly":"Y","ipdsId":"IP-127065","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391620,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5127/coverthb.jpg"},{"id":391621,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5127/sir20215127.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5127"},{"id":391622,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":391623,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20185090","text":"Transport of nitrogen and phosphorus in the Cedar River Basin, Iowa and Minnesota, 2000–15"}],"country":"United States","state":"Palo","otherGeospatial":"Cedar River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.83197021484375,\n 42.02889410108475\n ],\n [\n -91.71180725097655,\n 42.02889410108475\n ],\n [\n -91.71180725097655,\n 42.09312731992276\n ],\n [\n -91.83197021484375,\n 42.09312731992276\n ],\n [\n -91.83197021484375,\n 42.02889410108475\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
The Mohave tui chub (Siphateles bicolor mohavensis) was nearly extirpated from the Mojave River drainage in California by the mid-twentieth century and was listed as endangered in 1970. A source population of Mohave tui chub exists at MC Spring in Zzyzx, California, and has been used for several re-establishment efforts in previous decades. Two potential habitats in the Mojave National Preserve with perennial sources of water were identified by the National Park Service as candidates for additional Mohave tui chub re-establishment: West Pond and Rainbow Wells Pond. West Pond, an artificial pond at Zzyzx near MC Spring, contained a population of Mohave tui chub that died off in 1985 because of changes in water quality. The pond was rehabilitated in the past several years through re-excavation and by pumping fresh groundwater into the pond. Rainbow Wells Pond is an abandoned excavated mine site in the Cima Dome area. The bottom of the excavation intersects the water table, forming a pond. In cooperation with the National Park Service, the U.S. Geological Survey monitored water-quality conditions at West Pond and Rainbow Wells Pond for 1 year to characterize the suitability of spring habitat for re-establishment of Mohave tui chub populations. Data were also collected at three existing Mohave tui chub habitats in Mojave National Preserve to provide further information on the range of acceptable physical and chemical conditions. Initial water-quality results at West Pond indicate the pond has similar water quality as existing Mohave tui chub habitats. Initial water-quality results at Rainbow Wells Pond indicate the dissolved oxygen concentrations and springtime water temperatures are less than the long-term tolerable ranges for Mohave tui chub.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215106","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Earp, K.J., and Paul, A.P., 2021, Water and sediment chemistry of selected existing and potential habitats of the Mohave tui chub, Mojave National Preserve, California, 2018: U.S. Geological Survey Scientific Investigations Report 2021–5106, 26 p., https://doi.org/10.3133/sir20215106.","productDescription":"Report: v, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-099414","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":391580,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"National Water Information System"},{"id":391576,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5106/covrthb.jpg"},{"id":391577,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5106/sir20215106.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":391578,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5106/sir20215106.xml"},{"id":391579,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5106/images"}],"country":"United States","state":"California","otherGeospatial":"Mojave National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.2301025390625,\n 35.47409160773029\n ],\n [\n -115.36193847656249,\n 35.54116627999815\n ],\n [\n -115.59814453125001,\n 35.55457449014312\n ],\n [\n -115.806884765625,\n 35.567980458012094\n ],\n [\n -116.43859863281249,\n 35.38457160381764\n ],\n [\n -116.55944824218749,\n 35.074964853989556\n ],\n [\n -116.54296874999999,\n 34.79576153473033\n ],\n [\n -116.16943359374999,\n 34.56085936708384\n ],\n [\n -115.7080078125,\n 34.36611072883117\n ],\n [\n -115.224609375,\n 34.261756524459805\n ],\n [\n -114.72473144531251,\n 34.30260622622907\n ],\n [\n -114.58740234375,\n 34.58347505599177\n ],\n [\n -114.6368408203125,\n 34.84536693184101\n ],\n [\n -114.6533203125,\n 35.016500995886005\n ],\n [\n -115.2301025390625,\n 35.47409160773029\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
In temperate regions of the world, food resources are seasonally limited, which causes some wildlife species to seek out nutrient-rich resources to better meet their caloric needs. Animals that utilize high-quality resources may reap fitness benefits as they prepare for mating, migration, or hibernation. American black bears (Ursus americanus) are omnivores that consume both plant and animal food resources to meet macronutrient needs. Black bears capitalize on high-quality food resources, such as soft mast in summer and hard mast during autumn, but we know less about the importance of resource quality during spring. Therefore, we sought to understand the relationship between the spatiotemporal variation in the availability of food and resource selection of black bears during spring. We also aimed to infer potential changes in foraging tactics, from opportunistic foraging to more active selection. Although black bears are described as opportunistic omnivores, we hypothesized they select areas with high-quality forage when available. We instrumented 7 black bears with GPS collars in 2017 and 2018 and estimated fine-scale resource selection with integrated step-selection functions. We found evidence that black bear movements were influenced by forage quality of vegetative food resources. However, we failed to find evidence that black bears actively alter their movements to take advantage of seasonal neonate elk. Although black bears represent a substantial cause of mortality for neonate elk, we found that black bears likely feed on neonates encountered opportunistically while traveling between patches of high-quality forage. Few studies have shown evidence of an omnivorous species capitalizing on spatiotemporal variation in forage quality, yet our data suggest this may be an important strategy for species with diverse diets, particularly where resources are seasonally limited.
Axis deer (Axis axis) are invasive species that threaten native ecosystems and agriculture on Maui Island. To mitigate negative effects, it is necessary to understand current abundance, population trajectory, and how to most effectively reduce the population. Our objectives were to examine the population history of Maui axis deer, estimate observed population growth, and use species-specific demographic parameters in a VORTEX population viability analysis to examine removal scenarios that would most effectively reduce the population. Only nine deer were introduced in 1959, but recent estimates of >10,000 deer suggest population growth rates (r) ranging between 0.147 and 0.160 even though >11,200 have been removed by hunters and resource managers. In VORTEX simulations, we evaluated an initial population size of 6,000 females and 4,000 males, reflecting the probable 3F:2M sex ratio, with annual removal rates of 10%, 20%, and 30% over a 10-year period. A removal rate of 10% resulted in a positive growth rate of 0.103 ± 0.001. A 20% removal rate resulted in only a slightly negative growth, while a 30% removal rate resulted in –0.130 ± 0.004. By increasing the ratio of females removed to 4F:1M in the 30% harvest scenario, the decline nearly doubled, resulting in –0.223 ± 0.004. Effectively reducing axis deer will most likely require an annual removal of approximately 20–30% of the population and with a greater proportion of females to increase the population decline. Selective removal of males may not only be inefficient, but also counterproductive to population reduction goals.
Tropical mangrove forests have been described as “coastal kidneys,” promoting sediment deposition and filtering contaminants, including excess nutrients. Coastal areas throughout the world are experiencing increased human activities, resulting in altered geomorphology, hydrology, and nutrient inputs. To effectively manage and sustain coastal mangroves, it is important to understand nitrogen (N) storage and accumulation in systems where human activities are causing rapid changes in N inputs and cycling. We examined N storage and accumulation rates in recent (1970 – 2016) and historic (1930 – 1970) decades in the context of urbanization in the San Juan Bay Estuary (SJBE, Puerto Rico), using mangrove soil cores that were radiometrically dated. Local anthropogenic stressors can alter N storage rates in peri-urban mangrove systems either directly by increasing N soil fertility or indirectly by altering hydrology (e.g., dredging, filling, and canalization). Nitrogen accumulation rates were greater in recent decades than historic decades at Piñones Forest and Martin Peña East. Martin Peña East was characterized by high urbanization, and Piñones, by the least urbanization in the SJBE. The mangrove forest at Martin Peña East fringed a poorly drained canal and often received raw sewage inputs, with N accumulation rates ranging from 17.7 to 37.9 g m–2 y–1 in recent decades. The Piñones Forest was isolated and had low flushing, possibly exacerbated by river damming, with N accumulation rates ranging from 18.6 to 24.2 g m–2 y–1 in recent decades. Nearly all (96.3%) of the estuary-wide mangrove N (9.4 Mg ha–1) was stored in the soils with 7.1 Mg ha–1 sequestered during 1970–2017 (0–18 cm) and 2.3 Mg ha–1 during 1930–1970 (19–28 cm). Estuary-wide mangrove soil N accumulation rates were over twice as great in recent decades (0.18 ± 0.002 Mg ha–1y–1) than historically (0.08 ± 0.001 Mg ha–1y–1). Nitrogen accumulation rates in SJBE mangrove soils in recent times were twofold larger than the rate of human-consumed food N that is exported as wastewater (0.08 Mg ha–1 y–1), suggesting the potential for mangroves to sequester human-derived N. Conservation and effective management of mangrove forests and their surrounding watersheds in the Anthropocene are important for maintaining water quality in coastal communities throughout tropical regions.