Stream and river ecosystems provide subsidies of emergent adult aquatic insects and other resources to terrestrial food webs, and this lotic–land subsidy has garnered much attention in recent research. Here, we critically examine a list of biotic and abiotic variables—including productivity, dominant taxa, geomorphology, and weather—that should be important in affecting the nature of these subsidy dynamics between lotic and terrestrial ecosystems, especially the pathway from emergent aquatic insects to terrestrial predators. We also explore how interactions between these variables can lead to otherwise unexpected patterns in the importance of aquatic subsidies to terrestrial food webs. Utilizing a match-mismatch framework developed previously, we identify how these variables and interactions may be affected by a broad suite of stressors in addition to contaminants: climate change, land-use conversion, damming and water abstraction, and species invasions and extinctions. These stressors may all act to modify and potentially exacerbate the effects of contaminants on subsidies. The available literature on many variables is sparse, despite strong theoretical underpinnings supporting their importance for lotic–land subsidies. Notably, these understudied variables include those related to physical geomorphology and the structure of the stream/river and floodplain/riparian zone as well as species-specific interactions between aquatic and terrestrial organisms. We suggest that more explicit characterization of these variables and more research directly linking broad-scale stressors to subsidy resource–consumer interactions can help provide a more mechanistic understanding to lotic–land subsidy dynamics within a changing environment.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_7","usgsCitation":"Muehlbauer, J., Larsen, S., Jonsson, M., and Emilson, E.J., 2020, Variables affecting resource subsidies from streams and rivers to land and their susceptibility to global change stressors, chap. of Contaminants and ecological subsidies, p. 129-155, https://doi.org/10.1007/978-3-030-49480-3_7.","productDescription":"27 p.","startPage":"129","endPage":"155","ipdsId":"IP-090826","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Muehlbauer, Jeffrey 0000-0003-1808-580X","orcid":"https://orcid.org/0000-0003-1808-580X","contributorId":221739,"corporation":false,"usgs":true,"family":"Muehlbauer","given":"Jeffrey","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":806231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larsen, Stefano","contributorId":169188,"corporation":false,"usgs":false,"family":"Larsen","given":"Stefano","email":"","affiliations":[{"id":13099,"text":"German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":806232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jonsson, Micael","contributorId":245462,"corporation":false,"usgs":false,"family":"Jonsson","given":"Micael","email":"","affiliations":[{"id":49198,"text":"Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden","active":true,"usgs":false}],"preferred":false,"id":806233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Emilson, Erik J.S.","contributorId":245463,"corporation":false,"usgs":false,"family":"Emilson","given":"Erik","email":"","middleInitial":"J.S.","affiliations":[{"id":49199,"text":"Natural Resources Canada, Canadian Forest ServiceGreat Lakes Forestry Centre, Sault Ste. Marie, Canada","active":true,"usgs":false}],"preferred":false,"id":806234,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216144,"text":"70216144 - 2020 - Introduction: Ecological subsidies as a framework for understanding contaminant fate, exposure, and effects at the land-water interface","interactions":[],"lastModifiedDate":"2020-11-06T14:28:58.413961","indexId":"70216144","displayToPublicDate":"2020-10-16T08:25:06","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Introduction: Ecological subsidies as a framework for understanding contaminant fate, exposure, and effects at the land-water interface","docAbstract":"Ecologists have long recognized that ecological subsidies (the flow of organic matter, nutrients, and organisms between ecosystems) can strongly affect ecosystem processes and community structure in the recipient ecosystem. Animal movements, organic matter flows, and food web dynamics between linked aquatic and terrestrial systems can also influence contaminant fate, exposure, and effects at the land-water interface. Here and in this book, we develop a broad framework that highlights two important ways that ecological subsidies and contaminants interact. Ecological subsidies from the donor system can drive exposure to recipient systems, and contaminant exposures in the donor system can control subsidies and contaminant fluxes to the recipient systems. In the case of prey movement between ecosystems, subsidies drive exposure when contaminants present in aquatic environments bioaccumulate in the tissues of prey organisms at levels that are relatively non-toxic to the prey themselves. Conversely, exposure in the aquatic system can limit subsidies when pollutants are relatively toxic to prey organisms themselves and the magnitude of the subsidy (i.e., biomass of aquatic insects emerging to the terrestrial environment) is reduced. These effects of contaminants on subsidies are shaped by other global stressors that are ubiquitous in aquatic-riparian ecosystems (e.g., climate and land use change, species extinction and invasion, and eutrophication). As our understanding of these ecological and toxicological processes advances, there are increasing opportunities to make landscape-scale predictions of contaminant and animal fluxes and to integrate this knowledge of aquatic-riparian linkages into managing contaminant risks. Through these efforts to integrate the fields of ecology and ecotoxicology on this subject, we expect to gain greater insight on the ecological effects of contaminants on linked ecosystems as well as the ways in which food web dynamics and ecosystem processes can themselves govern the fate, transport, and exposure to contaminants in the environment.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and Ecological Subsidies: The Land-Water Interface","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_1","usgsCitation":"Walters, D., Kraus, J.M., and Mills, M.A., 2020, Introduction: Ecological subsidies as a framework for understanding contaminant fate, exposure, and effects at the land-water interface, chap. of Contaminants and Ecological Subsidies: The Land-Water Interface, p. 1-14, https://doi.org/10.1007/978-3-030-49480-3_1.","productDescription":"14 p.","startPage":"1","endPage":"14","ipdsId":"IP-116208","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":380259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Walters, David 0000-0002-4237-2158 waltersd@usgs.gov","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":147135,"corporation":false,"usgs":true,"family":"Walters","given":"David","email":"waltersd@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mills, Marc A.","contributorId":141085,"corporation":false,"usgs":false,"family":"Mills","given":"Marc","email":"","middleInitial":"A.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":804229,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216156,"text":"70216156 - 2020 - Synthesis: A framework for predicting the dark side of ecological subsidies","interactions":[],"lastModifiedDate":"2020-11-06T14:20:09.778211","indexId":"70216156","displayToPublicDate":"2020-10-16T08:17:35","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Synthesis: A framework for predicting the dark side of ecological subsidies","docAbstract":"In this chapter, we synthesize the state of the science regarding ecological subsidies and contaminants at the land-water interface and suggest research and management approaches for linked freshwater-terrestrial ecosystems. Specifically, we focus on movements of animals with complex life histories and the detrital inputs associated with animal and plant matter delivered to freshwaters. We present a framework based on the physicochemical parameters of contaminants and how they shape the relationship between contaminant persistence within resource subsidies (“dark side” of subsidies) and movement of resource subsidies (“bright side” of subsidies) across ecosystem boundaries. This relationship between the “dark side” and “bright side” of subsidies defines an important parameter space that allows researchers and practitioners to predict the potential impacts of aquatic contaminants on resource subsidies and their interaction with other stressors on consumers. Ecological factors such as ecosystem productivity, community composition, and consumer prey preference shape the ecotoxicological outcomes of aquatic contamination on subsidies. Landscape factors such as lithology, hydrogeomorphology, hydroperiod, and land use underlie chemical, toxicological, and ecological patterns and provide the context within which effects of contaminants play out. Finally, effects of contaminants combine with effects of other global stressors on timing, quality, and quantity of subsidies that drive responses to contaminants at the land-water interface. Understanding the “dark side” of ecological subsidies requires expertise from multiple disciplines. We attempt to synthesize current knowledge from those disciplines and generate conceptual models that ecologists can use to guide future research in understanding cross-ecosystem subsidies and contaminant fate and effects.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_14","usgsCitation":"Kraus, J.M., Wessner, J., and Walters, D., 2020, Synthesis: A framework for predicting the dark side of ecological subsidies, chap. of Contaminants and ecological subsidies, p. 343-372, https://doi.org/10.1007/978-3-030-49480-3_14.","productDescription":"30 p.","startPage":"343","endPage":"372","ipdsId":"IP-114721","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":380257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wessner, Jeff","contributorId":244602,"corporation":false,"usgs":false,"family":"Wessner","given":"Jeff","email":"","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":804244,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205915,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804245,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216141,"text":"70216141 - 2020 - Cross-ecosystem linkages and trace metals at the land-water interface","interactions":[],"lastModifiedDate":"2020-11-06T14:16:25.956662","indexId":"70216141","displayToPublicDate":"2020-10-16T08:12:20","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Cross-ecosystem linkages and trace metals at the land-water interface","docAbstract":"At low concentrations, trace metals are critical for sustaining life on Earth. However, at high concentrations, they become a global contaminant with particularly strong effects on freshwater communities. These effects can propagate to terrestrial ecosystems in part by altering production and community structure of adult aquatic insect emergence and aquatic insect-mediated metal fluxes to terrestrial insectivores. Here we highlight mechanisms driving effects of trace metals on aquatic organisms in general, aquatic insects specifically, and insectivorous consumers at the land-water interface. Specifically, we focus on how trace metals impact and bioaccumulate in aquatic organisms and communities and how these changes propagate through aquatic food web interactions and insect metamorphosis to alter fluxes of aquatically derived prey and trace metals to terrestrial consumers. Ultimately, trace metals impact food webs at the land-water interface by altering aquatic insect prey composition and availability for aquatic insectivores and by reducing aquatic insect subsidies to terrestrial consumers, and not by increasing exposure to trace metals in prey. Exposure of terrestrial insectivores to trace metals in prey is decoupled from aqueous concentrations due to high rates of metal excretion during insect metamorphosis from aquatic larvae to terrestrial adult. These effects increase reliance of aquatic insectivores on terrestrial insect prey subsidies and/or lead to declines and behavioral changes in terrestrial insectivore populations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Contaminants and ecological subsidies","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-49480-3_5","usgsCitation":"Kraus, J.M., and Pomeranz, J., 2020, Cross-ecosystem linkages and trace metals at the land-water interface, chap. of Contaminants and ecological subsidies, p. 91-109, https://doi.org/10.1007/978-3-030-49480-3_5.","productDescription":"19 p.","startPage":"91","endPage":"109","ipdsId":"IP-109559","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":380256,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2020-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pomeranz, Justin F.","contributorId":149789,"corporation":false,"usgs":false,"family":"Pomeranz","given":"Justin F.","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":804226,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215275,"text":"ofr20201108 - 2020 - Aquifer transmissivity in Nassau, Queens, and Kings Counties, New York, estimated from specific-capacity tests at production wells","interactions":[],"lastModifiedDate":"2020-10-16T12:33:17.218141","indexId":"ofr20201108","displayToPublicDate":"2020-10-15T15:50:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1108","displayTitle":"Aquifer Transmissivity in Nassau, Queens, and Kings Counties, New York, Estimated From Specific-Capacity Tests at Production Wells","title":"Aquifer transmissivity in Nassau, Queens, and Kings Counties, New York, estimated from specific-capacity tests at production wells","docAbstract":"As part of a cooperative effort between the U.S. Geological Survey and the New York State Department of Environmental Conservation to evaluate the sustainability of Long Island’s sole-source aquifer system, the transmissivities of four aquifers were estimated from specific-capacity tests at 447 production wells in Nassau, Queens, and Kings Counties on Long Island, New York. The specific-capacity test data, which included pumping rate, pumping duration, and drawdown, were obtained from published and unpublished records of driller-reported acceptance tests collected at production wells screened in the upper glacial, Jameco, Magothy, or Lloyd aquifers. Pumping rates from the production wells during the tests generally were greater than 400 gallons per minute and ranged up to 1,800 gallons per minute. Pumping duration generally was 8 hours or more. Transmissivities were estimated from the specific-capacity data by the Cooper-Jacob approximation of the Theis equation. The transmissivity estimates are considered rough approximations because the aquifers do not meet the ideal assumptions of the method, well losses and partial penetration were not accounted for, and aquifer storage coefficients were not known but were only estimated from available data.
The transmissivities estimated from production wells screened in the upper glacial aquifer in the outwash plain south of the moraine generally were greater than those of the aquifer north of the moraine. The transmissivities estimated from the wells screened in the upper glacial aquifer south of the moraine typically ranged (as defined by the 10th and 90th percentiles) from 3,800 to 15,000 feet squared per day (ft2/d), with a median value of 7,300 ft2/d. The transmissivities estimated from the wells screened in the upper glacial aquifer north of the moraine typically ranged from 2,100 to 7,400 ft2/d, with a median value of 4,400 ft2/d. The Jameco aquifer generally had the highest estimated transmissivities of all the aquifers analyzed. The estimated transmissivities for the Jameco aquifer typically ranged from 5,500 to 43,000 ft2/d, with a median value of 16,000 ft2/d. The Magothy and Lloyd aquifers had similar estimated transmissivities. The transmissivities estimated for the Magothy aquifer typically ranged from 2,700 to 13,000 ft2/d, with a median of 7,100 ft2/d. The estimated transmissivities of the Lloyd typically ranged from 3,000 to 14,000 ft2/d, with a median of 7,200 ft2/d.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201108","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Williams, J.H., Woodley, M., and Finkelstein, J.S., 2020, Aquifer transmissivity in Nassau, Queens, and Kings Counties, New York, estimated from specific-capacity tests at production wells: U.S. Geological Survey Open-File Report 2020–1108, 7 p., https://doi.org/10.3133/ofr20201108.","productDescription":"Report: iv, 7 p.; Dataset; Application Site","numberOfPages":"7","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108170","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":379362,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1108/coverthb.jpg"},{"id":379363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1108/ofr20201108.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1108"},{"id":379365,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://ny.water.usgs.gov/maps/aq-test/","text":"Aquifer Test Locator","linkFileType":{"id":5,"text":"html"}},{"id":379364,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"National Water Information System database","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","county":"Nassau County, Queens County, Kings County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0972900390625,\n 40.43858586704331\n ],\n [\n -73.388671875,\n 40.43858586704331\n ],\n [\n -73.388671875,\n 41.000629848685385\n ],\n [\n -74.0972900390625,\n 41.000629848685385\n ],\n [\n -74.0972900390625,\n 40.43858586704331\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Despite low species diversity and primary production, trophic structure (e.g., top predator species, predator size) is surprisingly variable among Arctic lakes. We investigated trophic structure in lakes of arctic Alaska containing arctic char Salvelinus alpinus using stomach contents and stable isotope ratios in two geographically-close but hydrologically-distinct lake clusters to investigate how these fish may interact and compete for limited food resources. Aside from different lake connectivity patterns (‘leaky’ versus ‘closed’), differing fish communities (up to five versus only two species) between lake clusters allowed us to test trophic hypotheses including: (1) arctic char are more piscivorous, and thereby grow larger and obtain higher trophic positions, in the presence of other fish species; and, (2) between arctic char size classes, resource polymorphism is more prominent, and thereby trophic niches are narrower and overlap less, in the absence of other predators. Regardless of lake cluster, we observed little direct evidence of arctic char consuming other fishes, but char were larger (mean TL = 468 vs 264 mm) and trophic position was higher (mean TP = 4.0 vs 3.8 for large char) in lakes with other fishes. Further, char demonstrated less intraspecific overlap when other predators were present whereas niche overlap was up to 100% in closed, char only lakes. As hydrologic characteristics (e.g., lake connectivity, water temperatures) will change across the Arctic owing to climate change, our results provide insight regarding potential concomitant changes to fish interactions and increase our understanding of lake trophic structure to guide management and conservation goals.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-020-04776-9","usgsCitation":"Klobucar, S.L., and Budy, P., 2020, Trophic structure of apex fish communities in closed versus leaky lakes of arctic Alaska: Oecologia, v. 194, p. 491-504, https://doi.org/10.1007/s00442-020-04776-9.","productDescription":"14 p.","startPage":"491","endPage":"504","ipdsId":"IP-109849","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Brooks Range, Toolik Field Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -149.69558715820312,\n 68.3996855982224\n ],\n [\n -149.20257568359375,\n 68.3996855982224\n ],\n [\n -149.20257568359375,\n 68.64455609820665\n ],\n [\n -149.69558715820312,\n 68.64455609820665\n ],\n [\n -149.69558715820312,\n 68.3996855982224\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"194","noUsgsAuthors":false,"publicationDate":"2020-10-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Klobucar, Stephen L.","contributorId":274993,"corporation":false,"usgs":false,"family":"Klobucar","given":"Stephen","email":"","middleInitial":"L.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833549,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216540,"text":"70216540 - 2020 - Experimental warming changes phenology and shortens growing season of the dominant invasive plant Bromus tectorum (cheatgrass)","interactions":[],"lastModifiedDate":"2020-11-25T16:59:26.09685","indexId":"70216540","displayToPublicDate":"2020-10-15T10:53:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5725,"text":"Frontiers in Plant Science","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Experimental warming changes phenology and shortens growing season of the dominant invasive plant Bromus tectorum (cheatgrass)","title":"Experimental warming changes phenology and shortens growing season of the dominant invasive plant Bromus tectorum (cheatgrass)","docAbstract":"Bromus tectorum (cheatgrass) has successfully invaded and established throughout the western United States. Bromus tectorum grows early in the season and this early growth allows B. tectorum to outcompete native species, which has led to dramatic shifts in ecosystem function and plant community composition after B. tectorum invades. If the phenology of native species is unable to track changing climate as effectively as B. tectorum’s phenology then climate change may facilitate further invasion. To better understand how B. tectorum phenology will respond to future climate, we tracked the timing of B. tectorum germination, flowering, and senescence over a decade in three in situ climate manipulation experiments with treatments that increased temperatures (2°C and 4°C above ambient), altered precipitation regimes, or applied a combination of each. Linear mixed-effects models were used to analyze treatment effects on the timing of germination, flowering, senescence, and on the length of the vegetative growing season (time from germination to flowering) in each experiment. Altered precipitation treatments were only applied in early years of the study and neither precipitation treatments nor the treatments’ legacies significantly affected B. tectorum phenology. The timing of germination did not significantly vary between any warming treatments and their respective ambient plots. However, plots that were warmed had advances in the timing of B. tectorum flowering and senescence, as well as shorter vegetative growing seasons. The phenological advances caused by warming increased with increasing degrees of experimental warming. The greatest differences between warmed and ambient plots were seen in the length of the vegetative growing season, which was shortened by approximately 12 and 7 days in the +4°C and +2°C warming levels, respectively. The effects of experimental warming were small compared to the effects of interannual climate variation, suggesting that interactive controls and the timing of multiple climatic factors are important in determining B. tectorum phenology. Taken together, these results help elucidate how B. tectorum phenology may respond to future climate, increasing our predictive capacity for estimating when to time B. tectorum control efforts and how to more effectively manage this exotic annual grass.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fpls.2020.570001","usgsCitation":"Howell, A.J., Winkler, D.E., Phillips, M.L., McNellis, B., and Reed, S., 2020, Experimental warming changes phenology and shortens growing season of the dominant invasive plant Bromus tectorum (cheatgrass): Frontiers in Plant Science, v. 11, 570001, 15 p., https://doi.org/10.3389/fpls.2020.570001.","productDescription":"570001, 15 p.","ipdsId":"IP-122205","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455034,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fpls.2020.570001","text":"Publisher Index Page"},{"id":380789,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","city":"Moab","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 -109.69848632812499,\n 38.50948995925553\n ],\n [\n -109.33868408203125,\n 38.50948995925553\n ],\n [\n -109.33868408203125,\n 38.74123075381228\n ],\n [\n -109.69848632812499,\n 38.74123075381228\n ],\n [\n -109.69848632812499,\n 38.50948995925553\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-10-15","publicationStatus":"PW","contributors":{"authors":[{"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":805557,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805558,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phillips, Michala Lee 0000-0001-7005-8740","orcid":"https://orcid.org/0000-0001-7005-8740","contributorId":245186,"corporation":false,"usgs":true,"family":"Phillips","given":"Michala","email":"","middleInitial":"Lee","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805559,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNellis, Brandon","contributorId":245187,"corporation":false,"usgs":false,"family":"McNellis","given":"Brandon","affiliations":[{"id":49106,"text":"Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, Idaho, USA","active":true,"usgs":false}],"preferred":false,"id":805560,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":805561,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216544,"text":"70216544 - 2020 - Climate sensitivity of water use by riparian woodlands at landscape scales","interactions":[],"lastModifiedDate":"2020-12-14T16:56:03.212697","indexId":"70216544","displayToPublicDate":"2020-10-15T10:46:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Climate sensitivity of water use by riparian woodlands at landscape scales","docAbstract":"Semi‐arid riparian woodlands face threats from increasing extractive water demand and climate change in dryland landscapes worldwide. Improved landscape‐scale understanding of riparian woodland water use (evapotranspiration, ET) and its sensitivity to climate variables is needed to strategically manage water resources, as well as to create successful ecosystem conservation and restoration plans for potential climate futures. In this work, we assess the spatial and temporal variability of Cottonwood (Populus fremontii)‐Willow (Salix gooddingii) riparian gallery woodland ET and its relationships to vegetation structure and climate variables for 80 km of the San Pedro River corridor in southeastern Arizona, USA, between 2014 and 2019. We use a novel combination of publicly available remote sensing, climate and hydrological datasets: cloud‐based Landsat thermal remote sensing data products for ET (Google Earth Engine EEFlux), Landsat multispectral imagery and field data‐based calibrations to vegetation structure (leaf‐area index, LAI), and open‐source climate and hydrological data. We show that at landscape scales, daily ET rates (6–10 mm day−1) and growing season ET totals (400–1,400 mm) matched rates of published field data, and modelled reach‐scale average LAI (0.80–1.70) matched lower ranges of published field data. Over 6 years, the spatial variability of total growing season ET (CV = 0.18) exceeded that of temporal variability (CV = 0.10), indicating the importance of reach‐scale vegetation and hydrological conditions for controlling ET dynamics. Responses of ET to climate differed between perennial and intermittent‐flow stream reaches. At perennial‐flow reaches, ET correlated significantly with temperature, whilst at intermittent‐flow sites ET correlated significantly with rainfall and stream discharge. Amongst reaches studied in detail, we found positive but differing logarithmic relationships between LAI and ET. By documenting patterns of high spatial variability of ET at basin scales, these results underscore the importance of accurately accounting for differences in woodland vegetation structure and hydrological conditions for assessing water‐use requirements. Results also suggest that the climate sensitivity of ET may be used as a remote indicator of subsurface water resources relative to vegetation demand, and an indicator for informing conservation management priorities.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13942","usgsCitation":"Mayes, M., Caylor, K.K., Singer, M.B., Stella, J., Roberts, D., and Nagler, P.L., 2020, Climate sensitivity of water use by riparian woodlands at landscape scales: Hydrological Processes, v. 34, no. 25, p. 4884-4903, https://doi.org/10.1002/hyp.13942.","productDescription":"10 p.","startPage":"4884","endPage":"4903","ipdsId":"IP-120214","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455038,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orca.cardiff.ac.uk/id/eprint/135647/","text":"External Repository"},{"id":380788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"San Pedro River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58563232421875,\n 31.3348710339506\n ],\n [\n -109.79461669921875,\n 31.3348710339506\n ],\n [\n -109.79461669921875,\n 32.15468722002481\n ],\n [\n -110.58563232421875,\n 32.15468722002481\n ],\n [\n -110.58563232421875,\n 31.3348710339506\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"25","noUsgsAuthors":false,"publicationDate":"2020-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Mayes, Marc","contributorId":245241,"corporation":false,"usgs":false,"family":"Mayes","given":"Marc","email":"","affiliations":[],"preferred":false,"id":805665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caylor, Kelly K.","contributorId":245242,"corporation":false,"usgs":false,"family":"Caylor","given":"Kelly","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":805666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singer, Michael B.","contributorId":168369,"corporation":false,"usgs":false,"family":"Singer","given":"Michael","email":"","middleInitial":"B.","affiliations":[{"id":25268,"text":"University of St Andrews, UK","active":true,"usgs":false}],"preferred":false,"id":805667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stella, John C","contributorId":149423,"corporation":false,"usgs":false,"family":"Stella","given":"John C","affiliations":[{"id":17732,"text":"Professor, Dept of Forest & Natural Resources Mgmt, SUNY at ESF","active":true,"usgs":false}],"preferred":false,"id":805668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roberts, Dar","contributorId":13721,"corporation":false,"usgs":true,"family":"Roberts","given":"Dar","affiliations":[],"preferred":false,"id":805669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805569,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216886,"text":"70216886 - 2020 - Sediment connectivity: A framework for analyzing coastal sediment transport pathways","interactions":[],"lastModifiedDate":"2020-12-14T14:53:07.559848","indexId":"70216886","displayToPublicDate":"2020-10-15T08:48:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Sediment connectivity: A framework for analyzing coastal sediment transport pathways","docAbstract":"Connectivity provides a framework for analyzing coastal sediment transport pathways, building on conceptual advances in graph theory from other scientific disciplines. Connectivity schematizes sediment pathways as a directed graph (i.e., a set of nodes and links). This study presents a novel application of graph theory and connectivity metrics like modularity and centrality to coastal sediment dynamics, exemplified here using Ameland Inlet in the Netherlands. We divide the study site into geomorphic cells (i.e., nodes) and then quantify sediment transport between these cells (i.e., links) using a numerical model. The system of cells and fluxes between them is then schematized in a network described by an adjacency matrix. Network metrics like link density, asymmetry, and modularity quantify system‐wide connectivity. The degree, strength, and centrality of individual nodes identify key locations and pathways throughout the system. For instance, these metrics indicate that under strictly tidal forcing, sand originating near shore predominantly bypasses Ameland Inlet via the inlet channels, whereas sand on the deeper foreshore mainly bypasses the inlet via the outer delta shoals. Connectivity analysis can also inform practical management decisions about where to place sand nourishments, the fate of nourishment sand, or how to monitor locations vulnerable to perturbations. There are still open challenges associated with quantifying connectivity at varying space and time scales and the development of connectivity metrics specific to coastal systems. Nonetheless, connectivity provides a promising technique for predicting the response of our coasts to climate change and the human adaptations it provokes.
Vascular plants are important in the wetland methane cycle, but their effect on production, oxidation, and transport has high uncertainty, limiting our ability to predict emissions. In a permafrost‐thaw bog in Interior Alaska, we used plant manipulation treatments, field‐deployed planar optical oxygen sensors, direct measurements of methane oxidation, and microbial DNA analyses to disentangle mechanisms by which vascular vegetation affect methane emissions. Vegetation operated on top of baseline methane emissions, which varied with proximity to the thawing permafrost margin. Emissions from vegetated plots increased over the season, resulting in cumulative seasonal methane emissions that were 4.1–5.2 g m−2 season−1 greater than unvegetated plots. Mass balance calculations signify these greater emissions were due to increased methane production (3.0–3.5 g m−2 season−1) and decreased methane oxidation (1.1–1.6 g m−2 season−1). Minimal oxidation occurred along the plant‐transport pathway, and oxidation was suppressed outside the plant pathway. Our data indicate suppression of methane oxidation was stimulated by root exudates fueling competition among microbes for electron acceptors. This contention is supported by the fact that methane oxidation and relative abundance of methanotrophs decreased over the season in the presence of vegetation, but methane oxidation remained steady in unvegetated treatments; oxygen was not detected around plant roots but was detected around silicone tubes mimicking aerenchyma; and oxygen injection experiments suggested that oxygen consumption was faster in the presence of vascular vegetation. Root exudates are known to fuel methane production, and our work provides evidence they also decrease methane oxidation.
Opportunities for growth and survival of aquatic organisms are spatially and temporally variable as habitat conditions across watersheds respond to interacting climatic, geomorphic, and hydrologic conditions. As conservation efforts often focus on identifying and protecting critical habitats, it is important to understand how this spatial and temporal variation in habitat quality affects the production dynamics of populations. Here, we use microchemical records preserved in otoliths to reconstruct juvenile habitat‐use by sockeye salmon that survived to spawn in a single population on the Alaska Peninsula. Successful individuals demonstrated a diverse array of juvenile behavioral strategies both within and among years. Importantly, the dominant juvenile behavioral strategy used by successful individuals changed among years, suggesting shifts in the relative benefits of different rearing habitats. The growth benefits of remaining in a more productive rearing lake were greatest in warm years indicating environmental influence on relative habitat quality. However, we found no strong relationship between the amount of growth accumulated in the productive rearing lake and overall population productivity across years. These results highlight the dynamic nature of habitat conditions and the beneficial effect of maintaining connectivity between diverse habitats for population productivity. When short‐term studies are used to demonstrate the relative values of different habitats to species of conservation concern, there is a distinct risk of under‐valuing habitats that may be critically important under alternative environmental conditions. In particular, land‐use decisions that reduce the range of habitat options available to species may erode a population’s ability to withstand environmental change over the long term.
While there is growing demand for use of climate model projections to understand the potential impacts of future climate on resources, there is a lack of effective visuals that convey the range of possible climates across spatial scales and with uncertainties that potential users need to inform their impact assessments and studies. We use usability testing including eye tracking to explore how a group of resource professionals (foresters) interpret and understand a series of graphical representations of future climate change, housed within a web-based decision support system (DSS), that address limitations identified in other tools. We find that a three-map layout effectively communicates the spread of future climate projections spatially, that location-specific information is effectively communicated if depicted both spatially on a map and temporally on a time series plot, and that model error metrics may be useful for communicating uncertainty and in demonstrating the utility of these future climate datasets.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/WCAS-D-19-0152.1","usgsCitation":"Davis, C., Aldridge, H.D., Boyles, R., McNeal, K., Mauldin, L.C., and Atkins, R.M., 2020, Visually communicating future climate in a web environment: Weather, Climate, and Society, v. 12, no. 4, p. 877-896, https://doi.org/10.1175/WCAS-D-19-0152.1.","productDescription":"20 p.","startPage":"877","endPage":"896","ipdsId":"IP-107086","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":455049,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/wcas-d-19-0152.1","text":"Publisher Index Page"},{"id":379577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Corey","contributorId":221987,"corporation":false,"usgs":false,"family":"Davis","given":"Corey","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":802272,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldridge, Heather D","contributorId":221986,"corporation":false,"usgs":false,"family":"Aldridge","given":"Heather","email":"","middleInitial":"D","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":802273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyles, Ryan 0000-0001-9272-867X","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":221983,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":802274,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McNeal, Karen","contributorId":221985,"corporation":false,"usgs":false,"family":"McNeal","given":"Karen","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":802275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mauldin, Lindsay C.","contributorId":221984,"corporation":false,"usgs":false,"family":"Mauldin","given":"Lindsay","email":"","middleInitial":"C.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":802276,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Atkins, Rachel M.","contributorId":221988,"corporation":false,"usgs":false,"family":"Atkins","given":"Rachel","email":"","middleInitial":"M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":802277,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215394,"text":"70215394 - 2020 - Utica shale play oil and gas brines: Geochemistry and factors influencing wastewater management","interactions":[],"lastModifiedDate":"2020-11-13T20:26:03.750261","indexId":"70215394","displayToPublicDate":"2020-10-14T10:27:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Utica shale play oil and gas brines: Geochemistry and factors influencing wastewater management","docAbstract":"The Utica and Marcellus Shale Plays in the Appalachian Basin are the fourth and first largest natural gas producing plays in the United States, respectively. Hydrocarbon production generates large volumes of brine (“produced water”) that must be disposed of, treated, or reused. Though Marcellus brines have been studied extensively, there are few studies from the Utica Shale Play. This study presents new brine chemical analyses from 16 Utica Shale Play wells in Ohio and Pennsylvania. Results from Na–Cl–Br systematics and stable and radiogenic isotopes suggest that the Utica Shale Play brines are likely residual pore water concentrated beyond halite saturation during the formation of the Ordovician Beekmantown evaporative sequence. The narrow range of chemistry for the Utica Shale Play produced waters (e.g., total dissolved solids = 214–283 g/L) over both time and space implies a consistent composition for disposal and reuse planning. The amount of salt produced annually from the Utica Shale Play is equivalent to 3.4% of the annual U.S. halite production. Utica Shale Play brines have radium activities 580 times the EPA maximum contaminant level and are supersaturated with respect to barite, indicating the potential for surface and aqueous radium hazards if not properly disposed of.
Land plays a critical role in both economic and environmental accounting. As an asset, it occupies a unique position at the intersection of the System of National Accounts (SNA), the System of Environmental-Economic Accounting Central Framework (SEEA-CF), and (as a spatial unit) SEEA Experimental Ecosystem Accounting (SEEA-EEA), making land a natural starting point for developing natural capital accounts more generally. We develop a pilot set of national and subnational land accounts for the United States that are consistent with the SEEA-CF and SNA principles, quantified in both physical and monetary terms. The physical accounts utilize detailed land use (National Land Use Database) and land cover (National Land Cover Database) datasets, which provide insights into how land cover in the U.S. is changing over time. To provide aggregate estimates of land values, we use a hedonic approach that exploits fine-grain microdata (“big data” from Zillow) that contains detailed information from hundreds of millions of property transactions and their corresponding physical characteristics covering much of the U.S. Methodologically, we show that it is feasible to produce monetary accounts for land that can be directly linked to and integrated with physical land cover/use. Overall, U.S. land cover has shown declines in forests, cropland, and pasture with increases in barren, scrub/shrub, and developed classes, which are particularly concentrated in the U.S. Southeast. Nominal land values in the U.S. fell about 28% ($7 trillion) from the boom to bust periods in the prior decade, albeit with substantial regional variation, and have subsequently experienced a nearly full recovery in recent years. We estimate private land in the contiguous 48 states to be worth approximately $25.1 trillion in 2016.
Natural resource managers use barriers to deter the movement of aquatic invasive species. Research and development of new invasive species barriers is often evaluated in pond and field scales using high‐resolution telemetry data. Telemetry data sets can be a rich source of data about fish movement and behavior but can be difficult to analyze due to the size of these data sets as well as their irregular sampling intervals. Due to the challenges, most barrier studies only use summary endpoints, such as barrier passage counts or average (e.g., mean or median) fish distance from the barrier, to describe the data. To examine more fine‐scale fish movement patterns, we developed a first‐order Markov chain. We then used this model to help understand the impacts of a barrier on fish behavior. For our study system, we used data from a previous study examining how bighead and silver carp (two invasive fish species in the United States) responded to a carbon dioxide (CO2) barrier in a pond.
","language":"English","publisher":"Wiley","doi":"10.1111/nrm.12281","usgsCitation":"Borland, L.K., Mulcahy, C.J., Bennie, B., Baumann, D.D., Haro, R.J., Van Appledorn, M., Jankowski, K.J., Cupp, A.R., and Erickson, R.A., 2020, Using Markov chains to quantitatively assess movement patterns of invasive fishes impacted by a carbon dioxide barrier in outdoor ponds: Natural Resource Modeling, v. 33, no. 4, e12281, 16 p., https://doi.org/10.1111/nrm.12281.","productDescription":"e12281, 16 p.","ipdsId":"IP-106075","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":455059,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nrm.12281","text":"Publisher Index Page"},{"id":380400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Borland, Lauren K","contributorId":244789,"corporation":false,"usgs":false,"family":"Borland","given":"Lauren","email":"","middleInitial":"K","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":804592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mulcahy, Collin J","contributorId":244790,"corporation":false,"usgs":false,"family":"Mulcahy","given":"Collin","email":"","middleInitial":"J","affiliations":[{"id":48976,"text":"SUNY Cobleskill","active":true,"usgs":false}],"preferred":false,"id":804593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennie, Barb","contributorId":244792,"corporation":false,"usgs":false,"family":"Bennie","given":"Barb","email":"","affiliations":[{"id":48977,"text":"UW-La Crosse","active":true,"usgs":false}],"preferred":false,"id":804594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baumann, Douglas D","contributorId":244793,"corporation":false,"usgs":false,"family":"Baumann","given":"Douglas","email":"","middleInitial":"D","affiliations":[{"id":48977,"text":"UW-La Crosse","active":true,"usgs":false}],"preferred":false,"id":804595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haro, Roger J.","contributorId":139538,"corporation":false,"usgs":false,"family":"Haro","given":"Roger","email":"","middleInitial":"J.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":804596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804598,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cupp, Aaron R. 0000-0001-5995-2100 acupp@usgs.gov","orcid":"https://orcid.org/0000-0001-5995-2100","contributorId":5162,"corporation":false,"usgs":true,"family":"Cupp","given":"Aaron","email":"acupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":804599,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":804600,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70214666,"text":"ofr20201101 - 2020 - Geologic and mineral map (modified from the 1975 original map compilation by A.S. Shadchinev and others) and hyperspectral surface materials maps of the Ghorband, Salang, and Panjsher River Basins; Kapisa, Panjsher, Parwan, and Baghlan Provinces, Afghanistan","interactions":[],"lastModifiedDate":"2021-08-23T16:19:59.150981","indexId":"ofr20201101","displayToPublicDate":"2020-10-13T12:15:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1101","displayTitle":"Geologic and Mineral Map (Modified from the 1975 Original Map Compilation by A.S. Shadchinev and Others) and Hyperspectral Surface Materials Maps of the Ghorband, Salang, and Panjsher River Basins; Kapisa, Panjsher, Parwan, and Baghlan Provinces, Afghanistan","title":"Geologic and mineral map (modified from the 1975 original map compilation by A.S. Shadchinev and others) and hyperspectral surface materials maps of the Ghorband, Salang, and Panjsher River Basins; Kapisa, Panjsher, Parwan, and Baghlan Provinces, Afghanistan","docAbstract":"The geologic map and cross sections are a redrafted and modified version of the Geologic map and map of mineral resources of the basins of Ghorband, Salang, and Panjsher; located in the Kapisa, Panjsher, Parwan, and Baghlan Provinces, Afghanistan. The original map and cross sections are contained in an unpublished Soviet report no. 1162A (Shadchinev and others, 1975) prepared in cooperation with the Ministry of Mines and Industries of the Royal Government of Afghanistan, in Kabul during 1975, under contract no. 55–184/17500. This redrafted map consists of parts of quadrangle map sheets 503–F, 504–C, 504–D, 504–E, and 504–F shown on an index map that can be found on the original 1:100,000-scale map by Shadchinev and others (1975). The redrafted map and cross sections illustrate the mineral deposits and geologic structure of the Ghorband, Salang, and Panjsher River Basins. Because there were no location coordinates provided on the original Soviet map, the map was registered to drainage patterns identified by contours from the Global Digital Elevation Model (GDEM). The end result can only be considered a best fit for the map extend, and some features may not be positioned in their correct geographic location.
The redrafted geologic map and cross sections reproduce the topology of rock units, contacts, and faults of the original Soviet map and cross sections, and includes minor modifications based on our examination of the originals. Table 1, provided on both map sheets 1 and 2, shows mineral commodity locations also from the original Soviet map. However, because of the poor quality of the original map, some map features could not be identified and some may be misinterpreted. Further, we have attempted to translate the original Russian terminology and rock classifications into modern English geologic usage as literally as possible without changing any genetic or process-oriented implications in the original rock-unit descriptions. We also use the rock-unit age designations from the original maps, however, rock-unit colors and symbols differ from the colors and symbols shown on the original version. Unit colors were selected according to the color and pattern scheme of the Commission for the Geological Map of the World (http://www.ccgm.org). Unit symbols were assigned based on the geologic age and unit descriptions provided on the original Soviet map. Elevations on the cross sections are derived from the original topography and may not match the Global GDEM topography used on the redrafted geologic map of this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201101","collaboration":"Prepared in cooperation with the Afghan Geological Survey under the auspices of the U.S. Agency for International Development","usgsCitation":"Stettner, W.R., Koroleva, N.E., Masonic, L.M., and Shields, D.A., comps., 2020, Geologic and mineral map (modified from the 1975 original map compilation by A.S. Shadchinev and others) and hyperspectral surface materials maps of the Ghorband, Salang, and Panjsher River Basins; Kapisa, Panjsher, Parwan, and Baghlan Provinces, Afghanistan: U.S. Geological Survey Open-File Report 2020–1101, 2 sheets, scale 1:100,000, https://doi.org/10.3133/ofr20201101.","productDescription":"2 Sheets: 41.50 x 30.50 inches and 41.50 x 52.00 inches","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057774","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":379032,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1101/ofr20201101_sheet2.pdf","text":"Sheet 2","size":"203 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":378954,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1101/coverthb.jpg"},{"id":378955,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1101/ofr20201101_sheet1.pdf","text":"Sheet 1","size":"61.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1101"}],"scale":"100000","country":"Afghanistan","state":"Baghlan, Kapisa, Panjsher, Parwan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 67.21435546875,\n 34.32529192442733\n ],\n [\n 71.3671875,\n 34.32529192442733\n ],\n [\n 71.3671875,\n 36.35052700542763\n ],\n [\n 67.21435546875,\n 36.35052700542763\n ],\n [\n 67.21435546875,\n 34.32529192442733\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
Sheet 1
Sheet 2
Close-kin mark–recapture (CKMR) is a powerful new method for the assessment of fish and wildlife population dynamics. Unlike traditional mark–recapture techniques, the use of kinship as an identifying mark is robust to many forms of capture heterogeneity including variation in gear efficiency and tagging-based effects such as loss and differential mortality. In addition, close-kin methods can be applied to a wider range of sampling designs than traditional methods (e.g., single-occasion surveys and lethal capture), can provide retrospective historical abundance estimates, and can produce survival estimates from as few as two sampling occasions. We evaluated the ability of CKMR to provide estimates of abundance and adult survival and then compared results to those from traditional mark–recapture. This analysis incorporated data from a three-year study of lake resident brook trout (Salvelinus fontinalis) where individuals were both physically (PIT) tagged and genotyped for 44 de novo developed microsatellites with high throughput sequencing. Traditional mark–recapture estimates were derived using Pollock’s Robust Design, relying upon three primary open sampling occasions and four secondary closed occasions. We found that close-kin methods produced contemporary estimates of adult abundance and survival that were similar to those produced by traditional mark–recapture in both magnitude and precision. Furthermore, CKMR provided abundance estimates for multiple years prior to sampling and, when restricted to data from a single year, still produced reliable abundance estimates for at least one and as many as three years. Retrospective abundance estimates corresponded with those from a separate historical two-sample mark–recapture dataset. This study provides support for the use of CKMR as a robust and sampling-efficient alternative to traditional mark–recapture methods of assessing population parameters.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3259","usgsCitation":"Marcy-Quay, B., Sethi, S., Therkildsen, N.O., and Kraft, C., 2020, Expanding the feasibility of fish and wildlife assessments with close-kin mark–recapture: Ecosphere, v. 11, no. 10, e3259, 14 p., https://doi.org/10.1002/ecs2.3259.","productDescription":"e3259, 14 p.","ipdsId":"IP-115671","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":455063,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3259","text":"Publisher Index Page"},{"id":395777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Honnedaga Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.8139762878418,\n 43.5031182163569\n ],\n [\n -74.79663848876953,\n 43.514945729095245\n ],\n [\n -74.8029899597168,\n 43.52229007033024\n ],\n [\n -74.81002807617186,\n 43.52888676718944\n ],\n [\n -74.87508773803711,\n 43.53797160252612\n ],\n [\n -74.86993789672852,\n 43.530006889344705\n ],\n [\n -74.82891082763672,\n 43.52353478532976\n ],\n [\n -74.81552124023438,\n 43.51681301924114\n ],\n [\n -74.81809616088867,\n 43.50573291871012\n ],\n [\n -74.8139762878418,\n 43.5031182163569\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Marcy-Quay, Benjamin","contributorId":275703,"corporation":false,"usgs":false,"family":"Marcy-Quay","given":"Benjamin","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":834236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sethi, Suresh 0000-0002-0053-1827 ssethi@usgs.gov","orcid":"https://orcid.org/0000-0002-0053-1827","contributorId":191424,"corporation":false,"usgs":true,"family":"Sethi","given":"Suresh","email":"ssethi@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Therkildsen, Nina O.","contributorId":275704,"corporation":false,"usgs":false,"family":"Therkildsen","given":"Nina","email":"","middleInitial":"O.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":834237,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraft, Clifford E.","contributorId":275705,"corporation":false,"usgs":false,"family":"Kraft","given":"Clifford E.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":834238,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215391,"text":"70215391 - 2020 - Dynamics of marsh-derived sediments in lagoon-type estuaries","interactions":[],"lastModifiedDate":"2020-11-30T16:17:53.439963","indexId":"70215391","displayToPublicDate":"2020-10-13T09:59:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Dynamics of marsh-derived sediments in lagoon-type estuaries","docAbstract":"Salt marshes are valuable ecosystems that must trap sediments and accrete in order to counteract the deleterious effect of sea‐level rise. Previous studies have shown that the capacity of marshes to build up vertically depends on both autogenous and exogenous processes including eco‐geomorphic feedbacks and sediment supply from in‐land and coastal ocean. There have been numerous efforts to quantify the role played by the sediments coming from marsh edge erosion on the resistance of salt marshes to sea‐level rise. However, the majority of existing studies investigating the interplay between lateral and vertical dynamics use simplified modelling approaches and they do not consider that marsh retreat can affect the regional scale hydrodynamics and sediment retention in back‐barrier basins.
In this study, we evaluated the fate of the sediments originating from marsh lateral loss by using high‐resolution numerical model simulations of Jamaica Bay, a small lagoonal estuary located in New York City. Our findings show that up to 42% of the sediments released during marsh edge erosion deposits on the shallow areas of the basin and over the vegetated marsh platforms, contributing positively to the sediment budget of the remaining salt marshes. Furthermore, we demonstrate that with the present‐day sediment supply from the ocean the system cannot keep pace with sea‐level rise even accounting for the sediment liberated in the bay through marsh degradation. Our study highlights the relevance of multiple sediment sources for the maintenance of the marsh complex.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JF005751","usgsCitation":"Donatelli, C., Kalra, T., Fagherazzi, S., Zhang, X., and Leonardi, N., 2020, Dynamics of marsh-derived sediments in lagoon-type estuaries: Journal of Geophysical Research, v. 125, e2020JF005751, 15 p., https://doi.org/10.1029/2020JF005751.","productDescription":"e2020JF005751, 15 p.","ipdsId":"IP-122498","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jf005751","text":"Publisher Index Page"},{"id":379479,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Jamaica Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.93524169921875,\n 40.56911064456484\n ],\n [\n -73.72718811035156,\n 40.56911064456484\n ],\n [\n -73.72718811035156,\n 40.656680564044166\n ],\n [\n -73.93524169921875,\n 40.656680564044166\n ],\n [\n -73.93524169921875,\n 40.56911064456484\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationDate":"2020-11-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Donatelli, Carmine","contributorId":205614,"corporation":false,"usgs":false,"family":"Donatelli","given":"Carmine","email":"","affiliations":[{"id":37127,"text":"University of Liverpool, Liverpool UK","active":true,"usgs":false}],"preferred":false,"id":801968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":801969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagherazzi, Sergio","contributorId":207153,"corporation":false,"usgs":false,"family":"Fagherazzi","given":"Sergio","email":"","affiliations":[{"id":37465,"text":"Boston University, Earth and Environment, Boston, 02215, USA.","active":true,"usgs":false}],"preferred":false,"id":801970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhang, Xoaohe","contributorId":243292,"corporation":false,"usgs":false,"family":"Zhang","given":"Xoaohe","email":"","affiliations":[{"id":48675,"text":"Department of Geography and Planning, School of Environmental Sciences, Faculty of Science and Engineering, University of Liverpool, Roxby Building, Chatham St., Liverpool L69 7ZT, UK","active":true,"usgs":false}],"preferred":false,"id":801971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leonardi, Nicoletta","contributorId":174783,"corporation":false,"usgs":false,"family":"Leonardi","given":"Nicoletta","affiliations":[{"id":27508,"text":"Dept of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":801972,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70214669,"text":"sim3448 - 2020 - Surficial geologic map of the Spirit Mountain SE and part of the Spirit Mountain NE 7.5' quadrangles, Nevada and Arizona","interactions":[],"lastModifiedDate":"2025-09-08T18:48:50.963025","indexId":"sim3448","displayToPublicDate":"2020-10-13T08:37:14","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3448","displayTitle":"Surficial Geologic Map of the Spirit Mountain SE and part of the Spirit Mountain NE 7.5’ Quadrangles, Nevada and Arizona","title":"Surficial geologic map of the Spirit Mountain SE and part of the Spirit Mountain NE 7.5' quadrangles, Nevada and Arizona","docAbstract":"This geologic map includes a trove of stratigraphic and geomorphic information that chronicles the inception and evolution of the lower Colorado River. The map area is located near the south end of the Lake Mead National Recreation Area about 80 km (50 mi) downstream from Hoover Dam. It spans parts of northwestern Arizona and southern Nevada near the south end of Cottonwood Valley. The map includes the Spirit Mountain SE 7.5' quadrangle and the southern part of the Spirit Mountain NE 7.5' quadrangle. The map area contains well-exposed Neogene and Quaternary strata and associated geomorphic features that record and are critical in dating the arrival of the Colorado River in the early Pliocene and the subsequent history of the river and its landscape through the Holocene. The valley is bounded on the west by the Newberry Mountains (Nevada) and on the east by the Black Mountains (Arizona) and includes part of Lake Mohave, a reservoir created by the completion of Davis Dam in 1951. This map does not include the geology of the reservoir floor and focuses only on surficial deposits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3448","collaboration":"Prepared in cooperation with Nevada Bureau of Mines and Geology; Arizona Geological Survey; University of Nevada, Las Vegas, Department of Geoscience; University of Oklahoma School of Geosciences; and National Park Service","usgsCitation":"House, P.K., Crow, R.S., Pearthree, P.A., Brock-Hon, A.L., Schwing, J., Thacker, J.O., and Gootee, B.F., 2020, Surficial geologic map of the Spirit Mountain SE and part of the Spirit Mountain NE 7.5' quadrangles, Nevada and Arizona: U.S. Geological Survey Scientific Investigations Map 3448, pamphlet 30 p., scale 1:24,000, https://doi.org/10.3133/sim3448.","productDescription":"Pamphlet: iv, 30 p.; 1 Sheet: 38.50 x 43.00 inches; Database; Metadata; Readme file","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-094912","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":379010,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3448/sim3448_metadata.xml","size":"18 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3448 metadata xml"},{"id":379009,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3448/sim3448_metadata.txt","size":"16 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3448 metadata txt"},{"id":379008,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3448/sim3448_pamphlet.pdf","text":"Pamphlet","size":"18.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3448 Pamphlet"},{"id":379007,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3448/sim3448.pdf","text":"Map","size":"22.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3448"},{"id":495225,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_110667.htm","linkFileType":{"id":5,"text":"html"}},{"id":379011,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3448/sim3448_readme.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3448 readme"},{"id":379012,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3448/sim3448_database.zip","size":"73.8 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3448 database (.zip)"},{"id":379006,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3448/coverthb.jpg"}],"country":"United States","state":"Arizona, Nevada","otherGeospatial":"Spirit Mountain quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.61212158203124,\n 35.21420969483077\n ],\n [\n -114.33197021484375,\n 35.21420969483077\n ],\n [\n -114.33197021484375,\n 35.641673184600585\n ],\n [\n -114.61212158203124,\n 35.641673184600585\n ],\n [\n -114.61212158203124,\n 35.21420969483077\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001-1600
Analyses of suspended sediment transport provide valuable insight into the role that sediment supply plays in causing geomorphic change. The sediment supply within a river system evolves depending on the discharge, flood frequency and duration, changes in sediment input, and ecohydraulic conditions that modify sediment transport processes. Changes in supply can be evaluated through analyses of coupled changes in suspended sediment concentration and grain size. The concentration of sand in transport in the Green and Colorado Rivers is most strongly controlled by discharge and the bed sand grain size distribution. Since the 1950s, sand loads have decreased in response to declines in peak discharge in the Green River and coarsening of the bed sand in the Colorado River. However, changes in the bed sand grain size distribution are associated with large changes in suspended sand concentration in both rivers; concentration varies by a factor of ~3 in the Green River and a factor of ~8 in the Colorado River, depending on the bed sand grain size distribution. Analyses of hysteresis in suspended sediment measurements show that sediment depletion during annual floods is most strongly controlled by flood duration, with peak discharge being nearly equally important in the Green River. Despite channel narrowing in both rivers, periods of bed sand coarsening and sediment depletion during annual floods indicate that these rivers are not necessarily in sediment surplus. Channel narrowing appears to be strongly controlled by short‐term declines in flood magnitude and the ecohydraulic effects of vegetation and may not be indicative of the long‐term sediment budget.
Soil respiration is a primary component of the terrestrial carbon cycle. However, predicting the response of soil respiration to climate change remains a challenge due to the complex interactions between environmental drivers, especially plant phenology, temperature, and soil moisture. In this study, we use a 1‐D diffusion‐reaction model to calculate depth‐resolved CO2 production rates from soil CO2 concentrations and surface efflux observations in a subalpine meadow in the East River watershed, CO. Modeled rates are compared to in situ soil temperature and moisture conditions and MODIS satellite enhanced vegetation index (EVI) representing plant phenology across three hydrologically distinct growing seasons from 2016–2018. While soil respiration correlated with temperature on diel timescales (p < 0.05), seasonal variability was dominated by soil moisture and plant phenology (p < 0.05). We observed significant respiration increases in response to precipitation events; however, magnitude and duration were significantly higher in 2017 than 2016 despite similar wetting characteristics. Based on MODIS EVI, we suggest that the respiration response to rainfall is controlled by plant phenology, which in turn reflects the capacity of plants to respond to precipitation via increased photosynthesis and autotrophic respiration, behavior that is not captured in typical soil respiration pulse models. Projected changes in montane climate such as earlier snowmelt and prolonged fore‐summer drought may decrease soil respiration fluxes by decreasing the overlap between peak productivity and the summer monsoon. Finally, we observed significant late season CO2 fluxes from the deep subsoil (>165 cm) that support growing evidence for the importance of subsoil processes in driving integrated respiration fluxes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JG005924","usgsCitation":"Winnick, M., Lawrence, C.R., McCormick, M., Druhan, J., and Maher, K., 2020, Soil respiration response to rainfall modulated by plant phenology in a montane meadow, East River, Colorado, USA: Journal of Geophysical Research Biogeosciences, v. 125, no. 10, e2020JG005924, 20 p., https://doi.org/10.1029/2020JG005924.","productDescription":"e2020JG005924, 20 p.","ipdsId":"IP-108485","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":455072,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1664387","text":"External Repository"},{"id":381100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"East River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.061767578125,\n 38.50626606567193\n ],\n [\n -106.82968139648436,\n 38.50626606567193\n ],\n [\n -106.82968139648436,\n 38.922023851268925\n ],\n [\n -107.061767578125,\n 38.922023851268925\n ],\n [\n -107.061767578125,\n 38.50626606567193\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Winnick, Mathew","contributorId":245458,"corporation":false,"usgs":false,"family":"Winnick","given":"Mathew","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":806219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawrence, Corey R. 0000-0001-6143-7781","orcid":"https://orcid.org/0000-0001-6143-7781","contributorId":202390,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","email":"","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":806220,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCormick, Maeve","contributorId":245459,"corporation":false,"usgs":false,"family":"McCormick","given":"Maeve","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":806221,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Druhan, Jennifer","contributorId":245460,"corporation":false,"usgs":false,"family":"Druhan","given":"Jennifer","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":806222,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maher, Kate","contributorId":245461,"corporation":false,"usgs":false,"family":"Maher","given":"Kate","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":806223,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216568,"text":"70216568 - 2020 - Compounding effects of white pine blister rust, mountain pine beetle, and fire threaten four white pine species","interactions":[],"lastModifiedDate":"2020-11-25T14:59:30.635993","indexId":"70216568","displayToPublicDate":"2020-10-12T08:40:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Compounding effects of white pine blister rust, mountain pine beetle, and fire threaten four white pine species","docAbstract":"Invasive pathogens and bark beetles have caused precipitous declines of various tree species around the globe. Here, we characterized long‐term patterns of mountain pine beetle (Dendroctonus ponderosae; MPB) attacks and white pine blister rust, an infectious tree disease caused by the pathogen, Cronartium ribicola. We focused on four dominant white pine host species in Sequoia and Kings Canyon National Parks (SEKI), including sugar pine (Pinus lambertiana), western white pine (P. monticola), whitebark pine (P. albicaulis), and foxtail pine (P. balfouriana). Between 2013 and 2017, we resurveyed 152 long‐term monitoring plots that were first surveyed and established between 1995 and 1999. Overall extent (plots with at least one infected tree) of white pine blister rust (blister rust) increased from 20% to 33%. However, the infection rate across all species decreased from 5.3% to 4.2%. Blister rust dynamics varied greatly by species, as infection rate decreased from 19.1% to 6.4% in sugar pine, but increased in western white pine from 3.0% to 8.7%. For the first time, blister rust was recorded in whitebark pine, but not foxtail pine plots. MPB attacks were highest in sugar pines and decreased in the higher elevation white pine species, whitebark and foxtail pine. Both blister rust and MPB were important factors associated with elevated mortality in sugar pines. We did not, however, find a relationship between previous fires and blister rust occurrence. In addition, multiple mortality agents, including blister rust, fire, and MPB, contributed to major declines in sugar pine and western white pine; recruitment rates were much lower than mortality rates for both species. Our results highlighted that sugar pine has been declining much faster in SEKI than previously documented. If blister rust and MPB trends persist, western white pine may follow similar patterns of decline in the future. Given current spread patterns, blister rust will likely continue to increase in higher elevations, threatening subalpine white pines in the southern Sierra Nevada. More frequent long‐term monitoring efforts could inform ongoing restoration and policy focused on threats to these highly valuable and diverse white pines.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3263","usgsCitation":"Dudney, J.C., Nesmith, J.C., Cahill, M., Cribbs, J.E., Duriscoe, D.M., Das, A., Stephenson, N.L., and Battles, J.J., 2020, Compounding effects of white pine blister rust, mountain pine beetle, and fire threaten four white pine species: Ecosphere, v. 11, no. 10, e03263, 20 p., https://doi.org/10.1002/ecs2.3263.","productDescription":"e03263, 20 p.","ipdsId":"IP-119965","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455074,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3263","text":"Publisher Index Page"},{"id":380779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequioia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.31427001953125,\n 35.52552053465406\n ],\n [\n -117.7679443359375,\n 35.52552053465406\n ],\n [\n -117.7679443359375,\n 37.07271048132943\n ],\n [\n -119.31427001953125,\n 37.07271048132943\n ],\n [\n -119.31427001953125,\n 35.52552053465406\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Dudney, Joan C","contributorId":245215,"corporation":false,"usgs":false,"family":"Dudney","given":"Joan","email":"","middleInitial":"C","affiliations":[{"id":33770,"text":"University of California at Berkeley","active":true,"usgs":false}],"preferred":false,"id":805640,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nesmith, Jonathan C B","contributorId":245216,"corporation":false,"usgs":false,"family":"Nesmith","given":"Jonathan","email":"","middleInitial":"C B","affiliations":[{"id":49124,"text":"National Park Service, Sierra Nevada Network Inventory & Monitoring Program","active":true,"usgs":false}],"preferred":false,"id":805641,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cahill, Matthew","contributorId":245219,"corporation":false,"usgs":false,"family":"Cahill","given":"Matthew","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":805642,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cribbs, Jennifer E","contributorId":245220,"corporation":false,"usgs":false,"family":"Cribbs","given":"Jennifer","email":"","middleInitial":"E","affiliations":[{"id":49124,"text":"National Park Service, Sierra Nevada Network Inventory & Monitoring Program","active":true,"usgs":false}],"preferred":false,"id":805643,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Duriscoe, Dan M","contributorId":245221,"corporation":false,"usgs":false,"family":"Duriscoe","given":"Dan","email":"","middleInitial":"M","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":805644,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Das, Adrian 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":201236,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805645,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805646,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Battles, John J.","contributorId":102006,"corporation":false,"usgs":false,"family":"Battles","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":805647,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70218020,"text":"70218020 - 2020 - The role of pre-magmatic rifting in shaping a volcanic continental margin: An example from the Eastern North American Margin","interactions":[],"lastModifiedDate":"2021-02-12T13:31:09.605389","indexId":"70218020","displayToPublicDate":"2020-10-12T07:27:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7585,"text":"Journal of Geophysical Research-- Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"The role of pre-magmatic rifting in shaping a volcanic continental margin: An example from the Eastern North American Margin","docAbstract":"Both magmatic and tectonic processes contribute to the formation of volcanic continental margins. Such margins are thought to undergo extension across a narrow zone of lithospheric thinning (~100 km). New observations based on existing and reprocessed data from the Eastern North American Margin contradict this hypothesis. With ~64,000 km of 2‐D seismic data tied to 40 wells combined with published refraction, deep reflection, receiver function, and onshore drilling efforts, we quantified along‐strike variations in the distribution of rift structures, magmatism, crustal thickness, and early post‐rift sedimentation under the shelf of Baltimore Canyon Trough (BCT), Long Island Platform, and Georges Bank Basin (GBB). Results indicate that BCT is narrow (80–120 km) with a sharp basement hinge and few rift basins. The seaward dipping reflectors (SDR) there extend ~50 km seaward of the hinge line. In contrast, the GBB is wide (~200 km), has many syn‐rift structures, and the SDR there extend ~200 km seaward of the hinge line. Early post‐rift depocenters at the GBB coincide with thinner crust suggesting “uniform” thinning of the entire lithosphere. Models for the formation of volcanic margins do not explain the wide structure of the GBB. We argue that crustal thinning of the BCT was closely associated with late syn‐rift magmatism, whereas the broad thinning of the GBB segment predated magmatism. Correlation of these variations to crustal terranes of different compositions suggests that the inherited rheology determined the premagmatic response of the lithosphere to extension.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB019576","usgsCitation":"Lang, G., ten Brink, U., Hutchinson, D., Mountain, G., and Schattner, U., 2020, The role of pre-magmatic rifting in shaping a volcanic continental margin: An example from the Eastern North American Margin: Journal of Geophysical Research-- Solid Earth, v. 125, no. 11, e2020JB019576, 33 p., https://doi.org/10.1029/2020JB019576.","productDescription":"e2020JB019576, 33 p.","ipdsId":"IP-121253","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455078,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020jb019576","text":"External Repository"},{"id":383254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Lang, G. 0000-0002-6505-5163","orcid":"https://orcid.org/0000-0002-6505-5163","contributorId":250704,"corporation":false,"usgs":false,"family":"Lang","given":"G.","email":"","affiliations":[{"id":50227,"text":"Dr. Moses Strauss Department of Marine Geosciences, Charney School of Marine Sciences, University of Haifa, Mt. Carmel, Haifa, 31905, Israel","active":true,"usgs":false}],"preferred":false,"id":810237,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"ten Brink, Uri S. 0000-0001-6858-3001 utenbrink@usgs.gov","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":127560,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri S.","email":"utenbrink@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":810238,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hutchinson, Deborah 0000-0002-2544-5466 dhutchinson@usgs.gov","orcid":"https://orcid.org/0000-0002-2544-5466","contributorId":174836,"corporation":false,"usgs":true,"family":"Hutchinson","given":"Deborah","email":"dhutchinson@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":810239,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mountain, G.S. 0000-0001-5221-0278","orcid":"https://orcid.org/0000-0001-5221-0278","contributorId":250705,"corporation":false,"usgs":false,"family":"Mountain","given":"G.S.","affiliations":[{"id":50229,"text":"Department of Earth and Planetary Sciences, Rutgers, The State University of New Jersey, 610 Taylor Road, Piscataway, New Jersey","active":true,"usgs":false}],"preferred":false,"id":810240,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schattner, U. 0000-0002-4453-4552","orcid":"https://orcid.org/0000-0002-4453-4552","contributorId":174637,"corporation":false,"usgs":false,"family":"Schattner","given":"U.","affiliations":[{"id":27488,"text":"Dr. Mosses Straus Dept of Marine Geosciences, Leon H. Charney School of Marine Sciences, University of Haifa","active":true,"usgs":false}],"preferred":false,"id":810241,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}