The sagebrush (Artemisia spp.) ecosystem extends across 251,473 square miles over portions of 13 western States. Affected by multiple stressors, including interactions among fire, invasive plants, and human land uses, this ecosystem has experienced significant loss, fragmentation, and degradation of landscapes once dominated by sagebrush. In turn, wildlife populations have declined following these deleterious conditions. Federal, State, local, and Tribal agencies, nongovernmental organizations, and industry have been galvanized by declining wildlife populations to implement management actions to confront the impacts of these stressors and ensure the long-term availability of the sagebrush ecosystem for the broad range of uses critical to stakeholders in the Western United States.
The sagebrush ecosystem provides habitat for more than 350 species of plants and animals that are dependent on sagebrush for all or part of their annual life history. The greater sage-grouse (Centrocercus urophasianus) stands out as an iconic species of this ecosystem. Sage-grouse populations occur in 11 States, and 2 Canadian Provinces and require relatively large expanses of sagebrush-dominated habitat to meet all their seasonal habitat needs. Recent management actions to conserve and maintain the sagebrush ecosystem have focused on the protection and restoration of sage-grouse habitat; however, each of the 350 species has a unique life history and differing area requirements (for example, large areas for mule deer [Odocoileus hemionus] and small areas for pygmy rabbit [Brachylagus idahoensis]), and some species, such as migratory birds, rely on various parts of the sagebrush ecosystem but only for part of the year (for example, Brewer’s sparrow [Spizella breweri]).
The U.S. Geological Survey (USGS) has a broad research program focused on the sagebrush ecosystem, wildlife species within the ecosystem, and the species’ response to stressors and management actions. The program provides a foundation of scientific information for use in major land and resource management decisions in the sagebrush ecosystem. By providing the science to inform these decisions, the USGS is assisting land and resource managers at the Federal, State, Tribal, and local levels working towards the goal of sustainable wildlife populations and restored landscapes. This information can inform planning and management conducted by nongovernmental organizations as well.
USGS research is tailored specifically to inform adaptive management, improve strategies for maintaining existing areas of intact sagebrush, and restoring degraded landscapes. Examples of research support for partners include providing information for actions such as the preclusion of the need to list the greater sage-grouse under the Endangered Species Act and recent revisions to Bureau of Land Management and U.S. Department of Agriculture Forest Service resource management plans and land use. The USGS continues to provide foundational science to inform science-based decisions within the U.S. Department of the Interior and other Federal, State, and local agencies and their continued conservation, management, and restoration of the sagebrush ecosystem to help support local economies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1470","usgsCitation":"Hanser, S.E., and Wiechman, L.A., eds., 2020, U.S. Geological Survey sagebrush ecosystem research annual report for 2020: U.S. Geological Survey Circular 1470, 94 p., https://doi.org/10.3133/cir1470.","productDescription":"vi, 94 p.","numberOfPages":"94","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119993","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":378250,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1470/coverthb.gif"},{"id":378249,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1470/cir1470.pdf","text":"Report","size":"11.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1470"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 48.69096039092549\n ],\n [\n -121.904296875,\n 45.583289756006316\n ],\n [\n -121.37695312499999,\n 41.376808565702355\n ],\n [\n -120.32226562500001,\n 38.41055825094609\n ],\n [\n -117.7734375,\n 35.460669951495305\n ],\n [\n -116.54296874999999,\n 33.65120829920497\n ],\n [\n -114.9609375,\n 34.379712580462204\n ],\n [\n -115.31249999999999,\n 36.1733569352216\n ],\n [\n -114.60937499999999,\n 36.66841891894786\n ],\n [\n -113.818359375,\n 35.53222622770337\n ],\n [\n -113.115234375,\n 34.08906131584994\n ],\n [\n -111.181640625,\n 33.43144133557529\n ],\n [\n -108.369140625,\n 33.43144133557529\n ],\n [\n -106.61132812499999,\n 33.94335994657882\n ],\n [\n -104.765625,\n 38.685509760012\n ],\n [\n -104.765625,\n 41.96765920367816\n ],\n [\n -104.853515625,\n 43.068887774169625\n ],\n [\n -103.71093749999999,\n 43.45291889355465\n ],\n [\n -103.18359375,\n 44.213709909702054\n ],\n [\n -103.0078125,\n 45.02695045318546\n ],\n [\n -102.568359375,\n 46.255846818480315\n ],\n [\n -102.3046875,\n 47.517200697839414\n ],\n [\n -102.568359375,\n 48.574789910928864\n ],\n [\n -105.46875,\n 48.86471476180277\n ],\n [\n -108.28125,\n 48.574789910928864\n ],\n [\n -111.4453125,\n 48.574789910928864\n ],\n [\n -113.203125,\n 47.21956811231547\n ],\n [\n -115.31249999999999,\n 46.619261036171515\n ],\n [\n -117.333984375,\n 46.800059446787316\n ],\n [\n -117.7734375,\n 47.87214396888731\n ],\n [\n -118.47656249999999,\n 48.86471476180277\n ],\n [\n -119.970703125,\n 49.15296965617042\n ],\n [\n -120.234375,\n 48.69096039092549\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Sage-Grouse and Sagebrush Ecosystem Program
U.S. Geological Survey
2150 Centre Ave, Building B
Fort Collins, Colorado
Endocrine disrupting chemicals (EDCs), such as bisphenol A (BPA) and 17α-ethinylestradiol (EE2), can have far reaching health effects, including transgenerational abnormalities in offspring that never directly contacted either chemical. We previously reported reduced fertilization rates and embryo survival at F2 and F3 generations caused by 7-day embryonic exposure (F0) to 100 μg/L BPA or 0.05 μg/L EE2 in medaka. Crossbreeding of fish in F2 generation indicated subfertility in males. To further understand the mechanisms underlying BPA or EE2-induced adult onset and transgenerational reproductive defects in males, the present study examined the expression of genes regulating the brain–pituitary–testis (BPT) axis in the same F0 and F2 generation male medaka. Embryonic exposure to BPA or EE2 led to hyperactivation of brain and pituitary genes, which are actively involved in reproduction in adulthood of the F0 generation male fish, and some of these F0 effects continued to the F2 generation (transgenerational effects). Particularly, the F2 generation inherited the hyperactivated state of expression for kisspeptin (kiss1 and kiss2) and their receptors (kiss1r and kiss2r), and gnrh and gnrh receptors. At F2 generation, expression of DNA methyltransferase 1 (dnmt1) decreased in brain of the BPA treatment lineage, while EE2 treatment lineage showed increased dnmt3bb expression. Global hypomethylation pattern was observed in the testis of both F0 and F2 generation fish. Taken together, these results demonstrated that BPA or EE2-induced transgenerational reproductive impairment in the F2 generation was associated with alterations of reproductive gene expression in brain and testis and global DNA methylation in testis.
The Norwegian Geochemical Standard North Sea Oil-1 was analyzed by gas chromatography triple quadrupole mass spectrometry (GC-QQQ-MS) on two instruments using independently developed analytical methods. Biomarker ratios determined by GC-QQQ-MS were compared to each other and to previously reported values determined by gas chromatography single quadrupole mass spectrometry (GC-Q-MS) or flame ionization detection (GC-FID). Hopane, sterane, and tricyclic ratio values determined by GC-QQQ-MS in multiple reaction monitoring (MRM) mode are comparable to each other, but their comparability to reported values measured by GC-Q-MS in selected ion monitoring (SIM) mode depends in part on whether the compounds in the ratio have similar or dissimilar mass spectral responses. For example, sterane and hopane stereoisomer thermal maturity ratios measured by GC-QQQ-MS in MRM mode agree with previously reported GC-Q-MS SIM values, but an offset is observed for ratios of rearranged hopanes or steranes to their non-rearranged counterparts. Triaromatic steroid, monoaromatic steroid, phenanthrene, and methylphenanthrene ratios measured by GC-QQQ-MS are comparable to each other and to reported GC-Q-MS SIM values. The carbon preference index is comparable across GC-QQQ-MS measurements and reported GC-FID values, while comparability is more variable for other ratios based on pristane, phytane, and/or n-alkanes. Comparability of variably acquired biomarker data could be enhanced in the future by developing instrument- and ratio-specific correction factors or by modifying GC-QQQ-MS parameters and MRM transitions to more closely reproduce previously reported values.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2020.104124","usgsCitation":"French, K.L., Leider, A., and Hallmann, C., 2020, Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS: Organic Geochemistry, v. 150, 104124, 4 p., https://doi.org/10.1016/j.orggeochem.2020.104124.","productDescription":"104124, 4 p.","ipdsId":"IP-119234","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":455295,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.orggeochem.2020.104124","text":"Publisher Index Page"},{"id":436788,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99ZMFJ5","text":"USGS data release","linkHelpText":"Data Release for "Comparability and reproducibility of biomarker ratio values measured by GC-QQQ-MS""},{"id":379343,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"150","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"French, Katherine L. 0000-0002-0153-8035","orcid":"https://orcid.org/0000-0002-0153-8035","contributorId":205462,"corporation":false,"usgs":true,"family":"French","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":801281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leider, Arne","contributorId":242996,"corporation":false,"usgs":false,"family":"Leider","given":"Arne","email":"","affiliations":[{"id":48601,"text":"Max-Planck-Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":801282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hallmann, Christian","contributorId":242997,"corporation":false,"usgs":false,"family":"Hallmann","given":"Christian","email":"","affiliations":[{"id":48601,"text":"Max-Planck-Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":801283,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231640,"text":"70231640 - 2020 - Palaeotsunamis in the Sino-Pacific region","interactions":[],"lastModifiedDate":"2022-05-17T11:43:24.846924","indexId":"70231640","displayToPublicDate":"2020-09-17T06:41:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1431,"text":"Earth-Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Palaeotsunamis in the Sino-Pacific region","docAbstract":"Palaeotsunami research in the Sino-Pacific region has increased markedly following the 2011 Tōhoku-oki tsunami. Recent studies encompass a variety of potential sources and cover a full range of research activities from detailed studies at individual sites through to region-wide data collation for the purposes of database development. We synthesise palaeotsunami data from around the region drawing on key examples to highlight the progress made since 2011. We focus on a wide range of spatial and temporal scales, from region-wide to local events, from multi-millennial site records to estimates of magnitude and frequency along national coastlines. The review is based on sub-regions but in reviewing the combined records highlights common events and anomalies. In doing so we identify future research opportunities and notable findings arising from our review.
Water-quality conditions (including temperature) in the Willamette River and many of its adjacent off-channel features, such as alcoves and side channels, were monitored between river miles 67 (near Salem, Oregon) and 168 (near Eugene, Oregon) during the summers of 2015 and 2016. One or more parameters (water temperature, dissolved oxygen, pH, specific conductance, and [or] water depth) were continuously measured at sites in the main channel (9 sites in 2015; 5 sites in 2016) and select off-channel features (20 features in 2015; 22 features in 2016). This study was initiated in reaction to the unusually warm, dry weather and resulting low streamflows that occurred in the Pacific Northwest in 2015 and the need for flow managers to understand the effects of streamflow on water-quality conditions in off-channel features of the Willamette River. Field monitoring was focused on documenting water-quality conditions during low summer streamflows and during fluctuations in streamflow, including when side channels became alcoves and reconnected to become side channels again.
Water in the main channel of the Willamette River upstream from river mile 50 near Newberg typically is well mixed during summer, with warm water temperatures (greater than 18 degrees Celsius) and high dissolved-oxygen concentrations (often greater than 7.7 milligrams per liter). During low summer flows, a diverse suite of off-channel features exists adjacent to the main channel of the Willamette River. Despite temporal and spatial variability within individual features, comparison of continuous water-temperature data between the main channel and off-channel features indicated that some off-channel features were consistently cooler than the main channel, some were consistently warmer than the main channel, and others frequently fluctuated between warmer or cooler than the main channel. Site-specific characteristics including upstream connection, depth, and presence or absence of aquatic or riparian vegetation were factors that seemed to affect the water quality of a feature.
Results from this study showed a relation between the geomorphology, hydrology, ecology, and water quality of an off-channel feature. Data confirmed that many features that can be classified as cold-water refuges based on water-temperature standards also contained low concentrations of dissolved oxygen that may not be suitable for sensitive fish species. A simplified site classification scheme is proposed that links water-quality conditions in measured off-channel features with site-specific characteristics and summer streamflows. The site classification scheme was extended to create a theoretical process matrix that relates measured water-quality conditions to a list of the processes and site-specific characteristics that could create those conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205068","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Smith, C.D., Mangano, J.F., and Rounds, S.A., 2020, Temperature and water-quality diversity and the effects of surface-water connection in off-channel features of the Willamette River, Oregon, 2015–16: U.S. Geological Survey Scientific Investigations Report 2020–5068, 70 p., https://doi.org/10.3133/sir20205068.","productDescription":"Report: viii, 70 p.; 3 Data Releases","onlineOnly":"Y","ipdsId":"IP-102289","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":378475,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F73T9FPK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Continuous temperature measurements to assess upstream connection of off-channel features of the middle and upper Willamette River, Oregon, summer, 2016"},{"id":378473,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ315D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Point measurements of temperature and water quality in main-channel and off-channel features of the Willamette River, 2015 -16"},{"id":378472,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5068/sir20205068.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5068"},{"id":378471,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5068/coverthb.jpg"},{"id":378474,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77M06DV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water surface elevations recorded by submerged water level loggers in off-channel features of the middle and upper Willamette River, Oregon, summer, 2016"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46435546875,\n 44.133333\n ],\n [\n -122.43713378906249,\n 44.133333\n ],\n [\n -122.43713378906249,\n 45.216667\n ],\n [\n -123.46435546875,\n 45.216667\n ],\n [\n -123.46435546875,\n 44.133333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Throughout the 2018 eruption of Kīlauea volcano (Hawai‘i), episodic collapses of a portion of the volcano’s summit caldera produced repeated
Plants and animals undergo certain recurring life-cycle events, such as migrations between summer and winter habitats or the annual blooming of plants. Known as phenology, the timing of these events is very sensitive to changes in climate (and changes in one species’ phenology can impact entire food webs and ecosystems). Shifts in phenology have been described as a “fingerprint” of the temporal and spatial responses of wildlife to climate change impacts. Thus, phenology provides one of the strongest indicators of the adaptive capacity of organisms (or the ability of organisms to cope with future environmental conditions).
In this study, researchers are exploring how the timing and occurrence of a number of highly migratory marine animals is changing due to a series of climatic and ecological shifts. First, using existing long-term historical data series, they will determine the direction and magnitude of how migration, abundance, or other phenological factors have changed for marine mammals, sea turtles, and fishes that migrate into the Gulf of Maine on a seasonal basis. Because marine animals are inherently difficult to detect, the team will apply dynamic occupancy models to evaluate seasonal migration patterns and habitat use across multiple habitats in the Gulf of Maine region. The project team will also synthesize regional information on a key, ecologically-important prey fish, sandlance, whose timing and abundance is a strong predictor of the occurrence and behavior of predator species targeted in this study as well as a range of other regional fish and wildlife of conservation and management concern. Results from this component of the project will identify coastal fish and wildlife species that are relatively more or less able to adapt and thus potentially vulnerable to climate change; determine the likely primary drivers of those changes; and identify data gaps and future monitoring needs. Ultimately, this information will be available and useful for regional coastal management and adaptation decisions that will allow managers to effectively plan for the future.
In a second component of the project, researchers will focus specifically on changes in migration patterns of the endangered North Atlantic right whale. While shifts in the distribution and time of recurring life events are adaptive responses that may help species cope with climate impacts, they can also lead to changes in how species interact with humans. The North Atlantic right whale is one of the most endangered whale species on the planet. In the North Atlantic Ocean, ship strikes and entanglements with commercial fishing gear represent fatal threats to right whales. Recent reports suggest that North Atlantic right whale migration patterns have changed. Many researchers posit that shifts in migration are responsible for recent increases in the overlap between right whales and human activities, especially fishing. To help understand how changes in right whale movements and behaviors may overlap with ship traffic, and thus put the animals at risk of encountering vessels, we will combine right whale habitat models with ship traffic maps. The end result will be a set of maps identifying risk levels.
Mercury (Hg) is a naturally occurring element that bonds with organic matter and, when converted to methylmercury, is a potent neurotoxicant. Here we estimate potential future releases of Hg from thawing permafrost for low and high greenhouse gas emissions scenarios using a mechanistic model. By 2200, the high emissions scenario shows annual permafrost Hg emissions to the atmosphere comparable to current global anthropogenic emissions. By 2100, simulated Hg concentrations in the Yukon River increase by 14% for the low emissions scenario, but double for the high emissions scenario. Fish Hg concentrations do not exceed United States Environmental Protection Agency guidelines for the low emissions scenario by 2300, but for the high emissions scenario, fish in the Yukon River exceed EPA guidelines by 2050. Our results indicate minimal impacts to Hg concentrations in water and fish for the low emissions scenario and high impacts for the high emissions scenario.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-020-18398-5","usgsCitation":"Schaefer, K., Elshorbany, Y., Jafarov, E., Schuster, P.F., Striegl, R.G., Wickland, K.P., and Sunderland, E.M., 2020, Potential impacts of mercury released from thawing permafrost: Nature-Communications, v. 11, 4650, 6 p., https://doi.org/10.1038/s41467-020-18398-5.","productDescription":"4650, 6 p.","ipdsId":"IP-115389","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455300,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-18398-5","text":"Publisher Index Page"},{"id":378509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -163.564453125,\n 60.19615576604439\n ],\n [\n -158.466796875,\n 61.52269494598361\n ],\n [\n -153.544921875,\n 63.89873081524394\n ],\n [\n -142.294921875,\n 61.56457388515458\n ],\n [\n -139.658203125,\n 59.88893689676585\n ],\n [\n -137.900390625,\n 60.1524422143808\n ],\n [\n -136.7578125,\n 61.52269494598361\n ],\n [\n -136.0986328125,\n 64.47279382008166\n ],\n [\n -145.3271484375,\n 67.90861918215302\n ],\n [\n -147.744140625,\n 67.97463396204759\n ],\n [\n -153.72070312499997,\n 67.45808150845772\n ],\n [\n -156.88476562499997,\n 66.7745857647255\n ],\n [\n -160.400390625,\n 64.92354174306496\n ],\n [\n -160.83984375,\n 63.64625919492172\n ],\n [\n -163.16894531249997,\n 62.85514553774182\n ],\n [\n -163.916015625,\n 63.37183226679281\n ],\n [\n -165.6298828125,\n 62.28836509824845\n ],\n [\n -163.564453125,\n 60.19615576604439\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaefer, Kevin 0000-0002-5444-9917","orcid":"https://orcid.org/0000-0002-5444-9917","contributorId":202096,"corporation":false,"usgs":false,"family":"Schaefer","given":"Kevin","email":"","affiliations":[{"id":36340,"text":"National Snow and National Snow and Ice Data Center, Cooperative Institute for Research, Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":799032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elshorbany, Yasin","contributorId":240870,"corporation":false,"usgs":false,"family":"Elshorbany","given":"Yasin","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":799033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jafarov, Elchin","contributorId":195182,"corporation":false,"usgs":false,"family":"Jafarov","given":"Elchin","affiliations":[],"preferred":false,"id":799034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schuster, Paul F. 0000-0002-8314-1372 pschuste@usgs.gov","orcid":"https://orcid.org/0000-0002-8314-1372","contributorId":1360,"corporation":false,"usgs":true,"family":"Schuster","given":"Paul","email":"pschuste@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":799035,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":799036,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":799037,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sunderland, Elsie M.","contributorId":65376,"corporation":false,"usgs":true,"family":"Sunderland","given":"Elsie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":799090,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215541,"text":"70215541 - 2020 - Position-specific distribution of hydrogen isotopes in natural propane: Effects of thermal cracking, equilibration and biodegradation","interactions":[],"lastModifiedDate":"2020-10-22T14:41:08.677038","indexId":"70215541","displayToPublicDate":"2020-09-16T09:36:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1759,"text":"Geochimica et Cosmochimica Acta","active":true,"publicationSubtype":{"id":10}},"title":"Position-specific distribution of hydrogen isotopes in natural propane: Effects of thermal cracking, equilibration and biodegradation","docAbstract":"Intramolecular isotope distributions, including isotope clumping and position specific fractionation, can provide proxies for the formation temperature and formation and destruction pathways of molecules. In this study, we explore the position-specific hydrogen isotope distribution in propane. We analyzed propane samples from 10 different petroleum systems with high-resolution molecular mass spectrometry. Our results show that the hydrogen isotope fractionation between central and terminal positions of natural propanes ranges from −102‰ to +205‰, a much larger range than that expected for thermodynamic equilibrium at their source and reservoir temperatures (36–63‰). Based on these findings, we propose that the hydrogen isotope structure of catagenic propane is largely controlled by irreversible processes, expressing kinetic isotope effects (KIEs). Kinetic control on hydrogen isotope composition of the products of thermal cracking is supported by a hydrous pyrolysis experiment using the Woodford Shale as substrate, in which we observed isotopic disequilibrium in the early stage of pyrolysis. We make a more general prediction of KIE signatures associated with kerogen cracking by simulating this chemistry in a kinetic Monte Carlo model for different types of kerogens. In contrast, unconventional shale fluids or hot conventional reservoirs contain propane with an isotopic structure close to equilibrium, presumably reflecting internal and/or heterogeneous exchange during high temperature storage (ca. 100–150 °C). In relatively cold (<100 °C) conventional gas accumulations, propane can discharge from its source to a colder reservoir, rapidly enough to preserve disequilibrium signatures even if the source rock thermal maturity is high. These findings imply that long times at elevated temperatures are required to equilibrate the hydrogen isotopic structure of propane in natural gas host rocks and reservoirs. We further defined the kinetics of propane equilibration through hydrogen isotope exchange experiments under hydrous conditions; these experiments show that hydrogen in propane is exchangeable over laboratory timescales when exposed to clay minerals such as kaolinite. This implies rather rapid transfer of propane from sources to cold reservoirs in some of the conventional petroleum systems. Propane is also susceptible to microbial degradation in both oxic and anoxic environments. Biodegradation of propane in the Hadrian and Diana Hoover oil fields (Gulf of Mexico) results in strong increases in central-terminal hydrogen isotope fractionation. This reflects preferential attack on the central position, consistent with previous studies.
The ecosystems along the border between the United States and Mexico are at increasing risk to wildfire due to interactions among climate, land-use, and fuel loads. A wide range of fuel treatments have been implemented to mitigate wildfire and its threats to valued resources, yet we have little information about treatment effectiveness. To fill critical knowledge gaps, we reviewed wildfire risk and fuel treatment studies that were conducted near the US-Mexico border and published in the peer-reviewed literature between 1986 and 2019. The number of studies has grown during this time in warm desert to forest ecosystems on primarily federal lands. The most common study topics included fire effects on native species, the role of invasive species and woody encroachment on wildfire risk, historical fire regimes, and remote sensing and modeling to study wildfire risk across the landscape. A majority of fuel treatment studies focused on prescribed burns, and fuel treatments collectively had mixed effects on mitigating future wildfire risk and threats to ecosystems depending on vegetation and fire characteristics. The diversity of ecosystems and land ownership along the US-Mexico border present unique challenges for understanding and managing wildfire risk, and also create opportunities for collaboration and cross-site studies to promote knowledge across broad environmental gradients.
Power consumption coefficients (PCCs) and dedicated flowmeter records for irrigation wells in three Utah groundwater basins were analyzed to develop a method to better characterize the accuracy of annual groundwater withdrawal estimates. The PCC method has been used by the U.S. Geological Survey in Utah since 1963 as a way to estimate groundwater withdrawal. As a result, most irrigation wells in Utah have historic records consisting of multiple PCCs. Over time, numerous wells have been retrofitted with dedicated flowmeters to more accurately describe groundwater use for irrigation. The combination of historical PCCs and flowmeter data was examined to classify wells as simple, complex, or borderline. The PCCs for each well were statistically analyzed for each period of record to determine the PCC coefficient of variation (CV). Variance, standard deviation, and CV also were calculated for each well, yielding similar results. The CV was selected as the best statistical method for classifying wells. Through field verification and examination of records, CV thresholds were established, allowing wells to be classified as simple, complex, or borderline. This well classification provides information on the uncertainty and best methods for quantifying annual groundwater withdrawals from irrigation wells in a basin.
Annual irrigation groundwater withdrawals in Tooele, Parowan, and Goshen Valleys were calculated by using various combinations of historical PCC records and data from dedicated flowmeters. Differences between annual groundwater withdrawal using the most recent measurements, and historic minimum, maximum, mean, and median PCCs were compared. The smallest percent difference between annual groundwater withdrawal calculated using the most recently measured PCCs, which is the current method for calculating withdrawal in most basins, in Tooele and Parowan Valleys, was 7 and 9 percent respectively, using historical median and mean.
In Goshen Valley, most wells have dedicated flowmeters, and there is a subset of wells that have 2016 power usage data, historical PCC records, and 2016 reported dedicated flowmeter withdrawal. Using this subset of irrigation wells, the smallest percent different between withdrawal from dedicated flowmeters and withdrawal calculated by using other methods was 5 percent (using withdrawal calculated with historical mean PCCs for each well). Annual groundwater withdrawal calculated using the most recently measured PCCs was 9-percent less than dedicated flowmeter reported withdrawal. So, if withdrawal from dedicated flowmeters is as close to reality as possible, then in the case of Goshen Valley, using historical mean PCCs to calculate withdrawal is closer to reality than using the most recently measured PCCs to calculate withdrawal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201106","collaboration":"Water Availability and Use Science ProgramDirector, Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
The coastal Pacific Northwest USA hosts thousands of deep-seated landslides. Historic landslides have primarily been triggered by rainfall, but the region is also prone to large earthquakes on the 1100-km-long Cascadia Subduction Zone megathrust. Little is known about the number of landslides triggered by these earthquakes because the last magnitude 9 rupture occurred in 1700 CE. Here, we map 9938 deep-seated bedrock landslides in the Oregon Coast Range and use surface roughness dating to estimate that past earthquakes triggered fewer than half of the landslides in the past 1000 years. We find landslide frequency increases with mean annual precipitation but not with modeled peak ground acceleration or proximity to the megathrust. Our results agree with findings about other recent subduction zone earthquakes where relatively few deep-seated landslides were mapped and suggest that despite proximity to the megathrust, most deep-seated landslides in the Oregon Coast Range were triggered by rainfall.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.aba6790","usgsCitation":"LaHusen, S., Duvall, A.R., Booth, A.M., Grant, A.R., Mishkin, B.A., Montgomery, D., Struble, W., Roering, J., and Wartman, J., 2020, Rainfall triggers more deep-seated landslides than Cascadia earthquakes in the Oregon Coast Range, USA: Science Advances, v. 6, no. 38, eaba6790, 11 p., https://doi.org/10.1126/sciadv.aba6790.","productDescription":"eaba6790, 11 p.","ipdsId":"IP-114882","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":455305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.aba6790","text":"Publisher Index Page"},{"id":378896,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.51904296875,\n 42.05745022024682\n ],\n [\n -123.3984375,\n 42.05745022024682\n ],\n [\n -123.3984375,\n 44.33956524809713\n ],\n [\n -124.51904296875,\n 44.33956524809713\n ],\n [\n -124.51904296875,\n 42.05745022024682\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LaHusen, Sean R 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":241904,"corporation":false,"usgs":false,"family":"LaHusen","given":"Sean R","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duvall, Alison R 0000-0002-7760-7236","orcid":"https://orcid.org/0000-0002-7760-7236","contributorId":241905,"corporation":false,"usgs":false,"family":"Duvall","given":"Alison","email":"","middleInitial":"R","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Booth, Adam M. 0000-0002-7339-0594","orcid":"https://orcid.org/0000-0002-7339-0594","contributorId":241907,"corporation":false,"usgs":false,"family":"Booth","given":"Adam","email":"","middleInitial":"M.","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":800162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":800163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mishkin, Benjamin A","contributorId":241909,"corporation":false,"usgs":false,"family":"Mishkin","given":"Benjamin","email":"","middleInitial":"A","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Montgomery, David R.","contributorId":241911,"corporation":false,"usgs":false,"family":"Montgomery","given":"David R.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Struble, William 0000-0002-8163-5088","orcid":"https://orcid.org/0000-0002-8163-5088","contributorId":241913,"corporation":false,"usgs":false,"family":"Struble","given":"William","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":800166,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":800167,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wartman, Joseph 0000-0001-7659-7198","orcid":"https://orcid.org/0000-0001-7659-7198","contributorId":241918,"corporation":false,"usgs":false,"family":"Wartman","given":"Joseph","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":800168,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70216424,"text":"70216424 - 2020 - Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2","interactions":[],"lastModifiedDate":"2020-11-17T13:53:10.508922","indexId":"70216424","displayToPublicDate":"2020-09-16T07:46:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2","docAbstract":"Motion‐activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter‐out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the “species model,” and one that determines if an image is empty or if it contains an animal, the “empty‐animal model.” Our species model and empty‐animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out‐of‐sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36%–91% across all out‐of‐sample datasets) and the empty‐animal model achieved an accuracy of 91%–94% on out‐of‐sample datasets from different continents. Our software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty‐animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths.
Retention, processing, and transport of solutes and particulates in stream corridors are influenced by the travel time of streamflow through stream channels, which varies dynamically with discharge. The effects of streamflow variability across sites and over time cannot be addressed by time-averaged models if parameters are based solely on the characteristics of mean streamflow. We develop methods to account for the effects of streamflow variability on travel time and compare our estimates to flow-weighted (“effective”) travel time at 100 streams in the southeastern United States. Velocity time series were generated for each stream from multiple-year (median 15.5 years), high-frequency (15 min interval) records of instantaneous streamflow and field measurements of velocity and inverted to produce time series of specific travel time [T/L]. The effective travel times for streams are 60–90% of the specific travel time of mean streamflow because a large fraction of the total streamflow volume is discharged during higher flows with higher velocities. We find that adjusting the specific travel time of mean streamflow at a site by a factor of 0.81 generally accounts for the effect of a skewed streamflow distribution, but at-site estimates of the coefficient of variation of streamflow are necessary to resolve differences in streamflow variability between streams or changes in variability over time. For example, the effective travel time of urban streams is less than the effective travel of forested streams in the southeastern United States as a result of increased streamflow variability in urban streams. Effective travel time accounts for both the variation in velocity with streamflow and the large fraction of streamflow discharged during high flows in most streams and provides time-averaged models with limited capability to account for effects of streamflow variability that otherwise they lack. This capability is needed for continental-scale modeling where streamflow variability is not uniform because of heterogeneous surficial geology, hydro-climatology, and vegetation and for applications where streamflow variability is not stationary as a response to climate change or hydrologic alteration.
Rapid ʻŌhiʻa Death (ROD) is a deadly disease that is threatening the native Hawaiian keystone tree species, ʻōhiʻa lehua (Metrosideros polymorpha Gaudich). Ambrosia beetles (Curculionidae: Scolytinae) and their frass are hypothesized to play a major role in the spread of ROD, although their ecological niches and frass production within trees and across the landscape are not well understood. We characterized the beetle communities and associated frass production from bolts (tree stem sections) representative of entire individual ʻōhiʻa trees from multiple locations across Hawaiʻi Island by rearing beetles and testing their frass for viable ROD-causing fungi. Additionally, we estimated frass production for three beetle species by weighing their frass over time. We found that Xyleborinus saxesenii (Ratzburg), Xyleborus affinis Eichhoff, Xyleborus ferrugineus (Fabricius), Xyleborus perforans (Wollaston), and Xyleborus simillimus Perkins were commonly found on ROD-infected ʻōhiʻa and each produced frass containing viable Ceratocystis propagules. The Hawaiʻi Island endemic beetle and the only native ambrosia beetle associated with ʻōhiʻa, X. simillimus, was limited to high elevations and appeared to utilize similar tree heights or niche dimensions as the invasive X. ferrugineus. Viable Ceratocystis propagules expelled in frass were found throughout entire tree bole sections as high as 13 m. Additionally, we found that X. ferrugineus produced over 4× more frass than X. simillimus. Our results indicate the ambrosia beetle community and their frass play an important role in the ROD pathosystem. This information may help with the development and implementation of management strategies to control the spread of the disease.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ee/nvaa108","usgsCitation":"Roy, K., Jaenecke, K., and Peck, R., 2020, Ambrosia beetle (Coleoptera: Curculionidae) communities and frass production in ʻŌhiʻa (Myrtales: Myrtaceae) infected with Ceratocystis (Microascales: Ceratocystidaceae) fungi responsible for Rapid ʻŌhiʻa Death: Environmental Entomology, v. 49, no. 6, p. 1345-1354, https://doi.org/10.1093/ee/nvaa108.","productDescription":"10 p.","startPage":"1345","endPage":"1354","ipdsId":"IP-119817","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":455315,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ee/nvaa108","text":"Publisher Index Page"},{"id":436789,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RJKOO6","text":"USGS data release","linkHelpText":"Hawai'i Island Rapid 'Ohi'a Death Ambrosia Beetle Communities and Frass 2018-2019"},{"id":381607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Hawai'i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.07177734375,\n 18.8335153964335\n ],\n [\n -154.75341796875,\n 18.8335153964335\n ],\n [\n -154.75341796875,\n 20.46818922264095\n ],\n [\n -156.07177734375,\n 20.46818922264095\n ],\n [\n -156.07177734375,\n 18.8335153964335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-09-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Roy, Kylle 0000-0002-7993-9031","orcid":"https://orcid.org/0000-0002-7993-9031","contributorId":213271,"corporation":false,"usgs":true,"family":"Roy","given":"Kylle","email":"","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":807196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaenecke, Kelly 0000-0002-7124-4788","orcid":"https://orcid.org/0000-0002-7124-4788","contributorId":211063,"corporation":false,"usgs":false,"family":"Jaenecke","given":"Kelly","email":"","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":807197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peck, Robert W. 0000-0002-8739-9493","orcid":"https://orcid.org/0000-0002-8739-9493","contributorId":193088,"corporation":false,"usgs":false,"family":"Peck","given":"Robert W.","affiliations":[],"preferred":false,"id":807198,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70213246,"text":"ofr20201107 - 2020 - Distribution and abundance of Aquila chrysaetos (golden eagles) in East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan area, California","interactions":[],"lastModifiedDate":"2020-09-17T14:06:01.343734","indexId":"ofr20201107","displayToPublicDate":"2020-09-16T06:43:43","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":"2020-1107","displayTitle":"Distribution and Abundance of Aquila chrysaetos (Golden Eagles) in the East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan Area, California","title":"Distribution and abundance of Aquila chrysaetos (golden eagles) in East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan area, California","docAbstract":"The East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan (HCP/NCCP) Preserve System was designed to protect and enhance ecological diversity and function in eastern Contra Costa County, California. Aquila chrysaetos (golden eagle) is a special-status species expected to benefit from biological goals of the HCP/NCCP. As part of a broader study, we estimated site-occupancy, abundance, and reproduction of golden eagles in the HCP/NCCP inventory area in 2019. We completed 99 surveys and recorded a total of 50 detections of territorial pairs of eagles at 20 (67 percent) of 30 sites (13.9-square-kilometer [km2] plots). Detection probability of territorial pairs was highest in January and February (≥0.75) and lowest in mid-June to late July (<0.50). After correcting for imperfect detection, the expected probability of site-occupancy was 0.69 (standard error [SE] = 0.09), and mean expected abundance was 0.76 pairs per site (SE = 0.16), or 27.4 pairs per 500 km2. We found evidence of successful nesting (≥1 young fledged) for 3 (14 percent) of 22 pairs of eagles monitored in 2019. Our study design and baseline results should be useful for future monitoring and conservation of golden eagles in the HCP/NCCP area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201107","collaboration":"Prepared in cooperation with East Contra Costa County Habitat Conservancy Science and Research Grant Program, East Bay Regional Parks District, Save Mount Diablo’s Mary Bowerman Science and Research Grant Program, and NextEra Energy","usgsCitation":"Wiens, J.D., Kolar, P.S., and Bell, D.A., 2020, Distribution and abundance of Aquila chrysaetos (golden eagles) in East Contra Costa County Habitat Conservation Plan/Natural Community Conservation Plan area, California: U.S. Geological Survey Open-File Report 2020-1107, 11 p., https://doi.org/10.3133/ofr20201107.","productDescription":"iv, 11 p.","onlineOnly":"Y","ipdsId":"IP-119617","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":378434,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1107/coverthb.jpg"},{"id":378435,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1107/ofr20201107.pdf","text":"Report","size":"3.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1107"}],"country":"United States","state":"California","county":"Contra Costa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.75048828124999,\n 37.37015718405753\n ],\n [\n -120.83312988281249,\n 37.37015718405753\n ],\n [\n -120.83312988281249,\n 38.08701320402273\n ],\n [\n -121.75048828124999,\n 38.08701320402273\n ],\n [\n -121.75048828124999,\n 37.37015718405753\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Tsunami risk management requires strategies that can address multiple sources with different recurrence intervals, wave-arrival times, and inundation extents. Probabilistic tsunami hazard analysis (PTHA) provides a structured way to integrate multiple sources, including the uncertainties due to the natural variability and limited knowledge of sources. PTHA-based products relate to specific average return periods (ARP) and while there has been considerable attention paid to ARP choice for building codes, guidance on ARP choice to support evacuation planning and related land use is lacking. We use the State of California (USA) coastal communities as a case study to explore the use of geospatial analysis and pedestrian-evacuation modeling for comparing the societal implications of tsunamis based on evacuation areas that reflect inundation from 475-year, 975-year, and 2475-year ARPs. Results demonstrate that changes in PTHA ARP had a substantial effect on the number of tax-lot parcels in PTHA evacuation areas, but not on the primary land use of these parcels or which communities had the largest number of exposed parcels. Composite PTHA maps provided high-level insights on hazard exposure and identified dominant sources; however, disaggregated PTHA outputs that reflect single source parameters (e.g., wave-arrival time) were necessary to quantify evacuation potential from local and distant tsunamis. Framing changes in ARP assumption based on changes in the number, land-use type, and potential evacuation challenges of parcels in evacuation areas can provide valuable insight on the real-world implications of which ARP to use in land use or evacuation planning.
Spatially distributed populations often rely on large-scale processes for long-term population stability. These processes are driven by individuals moving across the landscape through long-distance dispersal movements. However, as landscapes are continually altered by anthropogenic development, increased fragmentation and avoidance behavior can affect landscape permeability and limit dispersal. Lesser prairie-chickens (Tympanuchus pallidicinctus) are a species of concern that have lost significant portions (>90%) of their historic distribution in the Southern Great Plains of the United States and are currently being impacted by continued anthropogenic development. Using GPS telemetry locations of 346 lesser prairie-chickens across their entire geographic distribution, we identified 184 different long-distance movements that drive population connectivity. We used empirical cumulative distribution functions to create a selection–avoidance–neutral curve and estimated the spatial scale of response to anthropogenic features (i.e., towers and windmills, large transmission and smaller distribution powerlines, oil wells, roads, and fences) during these movements. In addition, we tested for behavioral differences between movement types (e.g., exploratory loops vs. long-distance movements between home ranges) and for regional differences in response among study areas. We found that during long-distance movements, lesser prairie-chickens generally avoided all anthropogenic feature types we tested despite some variation in the reported response distance among study areas. However, they avoided the tallest features (i.e., towers and windmills and transmission powerlines) at much greater distances in comparison with the shorter features in our analysis. Our results show that long-distance movements are likely affected by responses to functional landscape fragmentation through increased development of anthropogenic features in important connectivity zones. As our estimated response distances during long-distance movements varied in comparison with previously reported response distances during other behavioral states (e.g., breeding or nesting), using long-distance or dispersal specific movement data may be more appropriate when asking questions related to connectivity across the landscape.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3202","usgsCitation":"Peterson, J.M., Earl, J.E., Fuhlendorf, S.D., Elmore, D., Haukos, D.A., Tanner, A.M., and Carleton, S., 2020, Estimating response distances of lesser prairie-chickens to anthropogenic features during long-distance movements: Ecosphere, v. 11, no. 9, e03202, 15 p., https://doi.org/10.1002/ecs2.3202.","productDescription":"e03202, 15 p.","ipdsId":"IP-101823","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455320,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3202","text":"Publisher Index Page"},{"id":388350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, New Mexico, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.94140625,\n 32.32427558887655\n ],\n [\n -98.349609375,\n 32.32427558887655\n ],\n [\n -98.349609375,\n 40.245991504199026\n ],\n [\n -104.94140625,\n 40.245991504199026\n ],\n [\n -104.94140625,\n 32.32427558887655\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Peterson, Jacob M.","contributorId":264585,"corporation":false,"usgs":false,"family":"Peterson","given":"Jacob","email":"","middleInitial":"M.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Earl, Julia E.","contributorId":264586,"corporation":false,"usgs":false,"family":"Earl","given":"Julia","email":"","middleInitial":"E.","affiliations":[{"id":54510,"text":"ltu","active":true,"usgs":false}],"preferred":false,"id":821704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuhlendorf, Samuel D.","contributorId":264587,"corporation":false,"usgs":false,"family":"Fuhlendorf","given":"Samuel","email":"","middleInitial":"D.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elmore, Dwayne","contributorId":264588,"corporation":false,"usgs":false,"family":"Elmore","given":"Dwayne","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821706,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":821702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tanner, Ashley M.","contributorId":264589,"corporation":false,"usgs":false,"family":"Tanner","given":"Ashley","email":"","middleInitial":"M.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821707,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carleton, Scott A.","contributorId":264590,"corporation":false,"usgs":false,"family":"Carleton","given":"Scott A.","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":821708,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215144,"text":"70215144 - 2020 - Modeling the spatial dynamics of marsh ponds in New England salt marshes","interactions":[],"lastModifiedDate":"2020-10-08T12:34:21.986773","indexId":"70215144","displayToPublicDate":"2020-09-15T07:24:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the spatial dynamics of marsh ponds in New England salt marshes","docAbstract":"Ponds are common features on salt marshes, yet it is unclear how they affect large-scale marsh evolution. We developed a spatially explicit model that combines cellular automata for pond formation, expansion, and drainage, and partial differential equations for elevation dynamics. We use the mesotidal Barnstable marsh (MA, USA) as a case study, for which we measured pond expansion rate by remote sensing analysis over a 41-year time span. We estimated pond formation rate by comparing observed and modeled pond size distribution, and predicted pond deepening by comparing modeled and measured pond depth. The Barnstable marsh is currently in the pond recovery regime, i.e.,every pond revegetates and recovers the necessary elevation to support plant growth after re-connecting to the channel network. This pond dynamic creates an equivalent (i.e.,spatially and temporally averaged over the whole marsh) 0.5–2 mm/yr elevation loss that needs to be supplemented by excess vertical accretion. We explore how the pond regime would change with decreased sediment supply and increased relative sea-level rise (RSLR) rate, focusing on the case in which the vegetated marsh keeps pace with RSLR. When the RSLR rate remains below the minimum unvegetated deposition rate, the pond dynamics is nearly unaltered and ponds always occupy ~10% of the marsh area. However, when RSLR rate exceeds this threshold, the ponds in the marsh interior – which receive the least amount of suspended sediment – do not recover after drainage. These ponds transition to mudflats and permanently occupy up to 30% of the marsh area depending on RSLR rate. For marshes with a small tidal range, such as the microtidal Sage Lot Pond marsh on the opposite side of the peninsula from Barnstable marsh, high RSLR rates could bring every portion of the marsh into the pond runaway regime, with the whole marsh eventually converting into mudflats. In this regime, the existing marsh would disappear within centuries to millennia depending on the RSLR rate. Because of the spatial and temporal components of marsh evolution, a single RSLR threshold value applied across the entire marsh landscape provides a limited description of the marsh vulnerability to RSLR.
We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse in situ data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983–2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41597-020-00643-7","usgsCitation":"Verdin, A., Funk, C., Peterson, P., Landsfeld, M., Tuholske, C., and Grace, K., 2020, Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set: Scientific Data, v. 7, 303, 14 p., https://doi.org/10.1038/s41597-020-00643-7.","productDescription":"303, 14 p.","ipdsId":"IP-118171","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":455325,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-020-00643-7","text":"Publisher Index Page"},{"id":421817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Verdin, Andrew","contributorId":145812,"corporation":false,"usgs":false,"family":"Verdin","given":"Andrew","affiliations":[{"id":6713,"text":"University of Colorado, Boulder CO","active":true,"usgs":false}],"preferred":false,"id":885585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Pete","contributorId":192379,"corporation":false,"usgs":false,"family":"Peterson","given":"Pete","affiliations":[],"preferred":false,"id":885587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landsfeld, Martin","contributorId":192380,"corporation":false,"usgs":false,"family":"Landsfeld","given":"Martin","affiliations":[],"preferred":false,"id":885588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tuholske, Cascade","contributorId":330685,"corporation":false,"usgs":false,"family":"Tuholske","given":"Cascade","email":"","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":885589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Grace, Kathryn","contributorId":145815,"corporation":false,"usgs":false,"family":"Grace","given":"Kathryn","email":"","affiliations":[{"id":7215,"text":"University of Utah Dept. of Geography","active":true,"usgs":false}],"preferred":false,"id":885590,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70213199,"text":"70213199 - 2020 - Land-use change and future water demand in California’s central coast","interactions":[],"lastModifiedDate":"2020-09-15T12:17:33.92982","indexId":"70213199","displayToPublicDate":"2020-09-14T07:07:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Land-use change and future water demand in California’s central coast","docAbstract":"Understanding future land-use related water demand is important for planners and resource managers in identifying potential shortages and crafting mitigation strategies. This is especially the case for regions dependent on limited local groundwater supplies. For the groundwater dependent Central Coast of California, we developed two scenarios of future land use and water demand based on sampling from a historical land change record: a business-as-usual scenario (BAU; 1992–2016) and a recent-modern scenario (RM; 2002–2016). We modeled the scenarios in the stochastic, empirically based, spatially explicit LUCAS state-and-transition simulation model at a high resolution (270-m) for the years 2001–2100 across 10 Monte Carlo simulations, applying current land zoning restrictions. Under the BAU scenario, regional water demand increased by an estimated ~222.7 Mm3 by 2100, driven by the continuation of perennial cropland expansion as well as higher than modern urbanization rates. Since 2000, mandates have been in place restricting new development unless adequate water resources could be identified. Despite these restrictions, water demand dramatically increased in the RM scenario by 310.6 Mm3 by century’s end, driven by the projected continuation of dramatic orchard and vineyard expansion trends. Overall, increased perennial cropland leads to a near doubling to tripling perennial water demand by 2100. Our scenario projections can provide water managers and policy makers with information on diverging land use and water use futures based on observed land change and water use trends, helping to better inform land and resource management decisions.
","language":"English","publisher":"MDPI","doi":"10.3390/land9090322","usgsCitation":"Wilson, T., Van Schmidt, N.D., and Langridge, R., 2020, Land-use change and future water demand in California’s central coast: Land, v. 9, no. 322, p. 322-343, https://doi.org/10.3390/land9090322.","productDescription":"21 p.","startPage":"322","endPage":"343","ipdsId":"IP-112033","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455329,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land9090322","text":"Publisher Index Page"},{"id":378385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Santa Cruz, San Benito, Monterey, San Luis Obispo, & Santa Barbara","otherGeospatial":"central California coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.838623046875,\n 35.585851593232356\n ],\n [\n -121.06109619140625,\n 35.505400093441324\n ],\n [\n -120.89904785156251,\n 35.22094130403182\n ],\n [\n -120.66558837890626,\n 34.89043681762452\n ],\n [\n -120.63812255859375,\n 34.76417891445512\n ],\n [\n -120.58319091796874,\n 34.646766246519114\n ],\n [\n -120.22613525390624,\n 34.80252766591687\n ],\n [\n -120.0640869140625,\n 34.872411827691025\n ],\n [\n -120.838623046875,\n 35.585851593232356\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"322","noUsgsAuthors":false,"publicationDate":"2020-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":798599,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Schmidt, Nathan D. 0000-0002-5973-7934","orcid":"https://orcid.org/0000-0002-5973-7934","contributorId":240648,"corporation":false,"usgs":false,"family":"Van Schmidt","given":"Nathan","middleInitial":"D.","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":798600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langridge, Ruth 0000-0001-5320-8882","orcid":"https://orcid.org/0000-0001-5320-8882","contributorId":240649,"corporation":false,"usgs":false,"family":"Langridge","given":"Ruth","email":"","affiliations":[{"id":32898,"text":"U.C. Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":798601,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212635,"text":"70212635 - 2020 - Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States","interactions":[],"lastModifiedDate":"2023-11-08T16:13:16.263836","indexId":"70212635","displayToPublicDate":"2020-09-13T13:38:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6465,"text":"Journal of American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States","docAbstract":"High salinity limits groundwater use in parts of the Mississippi embayment. Machine learning was used to create spatially continuous and three‐dimensional predictions of salinity across drinking‐water aquifers in the embayment. Boosted regression tree (BRT) models, a type of machine learning, were used to predict specific conductance (SC) and chloride (Cl), and total dissolved solids (TDS) was calculated from a correlation with SC. Explanatory variables for BRT models included well location and construction, surficial variables (e.g., soils and land use), and variables extracted from a groundwater‐flow model, including simulated groundwater ages. BRT model fits (r2) were 0.74 (SC and Cl) and 0.62 (TDS). BRT models provided spatially continuous salinity predictions across surficial and deeper aquifers where discrete water‐quality samples were missing. Uncertainty was smaller where salinity was lower, and models tended to underpredict in areas of highest salinity. Despite this, BRT models were able to capture areas of documented high salinity that exceed the TDS secondary maximum contaminant level for drinking water of 500 mg/L. Variables that served as surrogates for position along groundwater flowpaths were the most important predictors, indicating that much of the control on dissolved solids is related to rock‐water interaction as residence time increases. BRT models additionally support hypotheses of both surficial and deep sources of salinity.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12879","usgsCitation":"Knierim, K.J., Kingsbury, J.A., Haugh, C., and Ransom, K.M., 2020, Using boosted regression tree models to predict salinity in Mississippi embayment aquifers, central United States: Journal of American Water Resources Association, v. 56, no. 6, https://doi.org/10.1111/1752-1688.12879.","productDescription":"20 p.","startPage":"1029","ipdsId":"IP-111775","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":455333,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12879","text":"Publisher Index Page"},{"id":436791,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WBFR1T","text":"USGS data release","linkHelpText":"Machine-learning model predictions and groundwater-quality rasters of specific conductance, total dissolved solids, and chloride in aquifers of the Mississippi embayment"},{"id":382516,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Embayment","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.3408203125,\n 36.98500309285596\n ],\n [\n -90.52734374999999,\n 36.73888412439431\n ],\n [\n -92.3291015625,\n 34.66935854524543\n ],\n [\n -93.779296875,\n 32.32427558887655\n ],\n [\n -92.548828125,\n 31.240985378021307\n ],\n [\n -90.52734374999999,\n 32.509761735919426\n ],\n [\n -88.857421875,\n 32.10118973232094\n ],\n [\n -87.2314453125,\n 30.789036751261136\n ],\n [\n -86.923828125,\n 31.690781806136822\n ],\n [\n -87.275390625,\n 32.879587173066305\n ],\n [\n -88.9453125,\n 33.87041555094183\n ],\n [\n -89.2529296875,\n 35.17380831799959\n ],\n [\n -88.6376953125,\n 36.59788913307022\n ],\n [\n -88.76953125,\n 36.914764288955936\n ],\n [\n -89.3408203125,\n 36.98500309285596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"6","edition":"1010","noUsgsAuthors":false,"publicationDate":"2020-09-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haugh, Connor J. 0000-0002-5204-8271","orcid":"https://orcid.org/0000-0002-5204-8271","contributorId":219945,"corporation":false,"usgs":true,"family":"Haugh","given":"Connor J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ransom, Katherine Marie 0000-0001-6195-7699","orcid":"https://orcid.org/0000-0001-6195-7699","contributorId":239552,"corporation":false,"usgs":true,"family":"Ransom","given":"Katherine","email":"","middleInitial":"Marie","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":797185,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70214081,"text":"70214081 - 2020 - The roles of storminess and sea level rise in decadal barrier island evolution","interactions":[],"lastModifiedDate":"2020-09-22T15:15:12.947787","indexId":"70214081","displayToPublicDate":"2020-09-13T10:09:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"The roles of storminess and sea level rise in decadal barrier island evolution","docAbstract":"Models of alongshore sediment transport during quiescent conditions, storm‐driven barrier island morphology, and poststorm dune recovery are integrated to assess decadal barrier island evolution under scenarios of increased sea levels and variability in storminess (intensity and frequency). Model results indicate barrier island response regimes of keeping pace, narrowing, flattening, deflation (narrowing and flattening), and aggradation. Under lower storminess scenarios, more areas of the island experienced narrowing due to collision. Under higher storminess scenarios, more areas experienced flattening due to overwash and inundation. Both increased sea levels and increased storminess resulted in breaching when the majority of the island was not keeping pace and deflation was the dominant regime due to increased overtopping. Under the highest storminess scenario, the island was unable to recover elevation after storms and drowned in just 10 years.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089370","usgsCitation":"Passeri, D., Dalyander, P., Long, J.W., Mickey, R.C., Jenkins, R., Thompson, D.M., Plant, N.G., Godsey, E., and Gonzalez, V., 2020, The roles of storminess and sea level rise in decadal barrier island evolution: Geophysical Research Letters, v. 47, no. 18, e2020GL089370, 8 p., https://doi.org/10.1029/2020GL089370.","productDescription":"e2020GL089370, 8 p.","ipdsId":"IP-121601","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":378665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.35273742675781,\n 30.207454473209072\n ],\n [\n -88.06571960449219,\n 30.207454473209072\n ],\n [\n -88.06571960449219,\n 30.29523927312319\n ],\n [\n -88.35273742675781,\n 30.29523927312319\n ],\n [\n -88.35273742675781,\n 30.207454473209072\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"18","noUsgsAuthors":false,"publicationDate":"2020-09-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Passeri, Davina 0000-0002-9760-3195 dpasseri@usgs.gov","orcid":"https://orcid.org/0000-0002-9760-3195","contributorId":166889,"corporation":false,"usgs":true,"family":"Passeri","given":"Davina","email":"dpasseri@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dalyander, P. Soupy 0000-0001-9583-0872","orcid":"https://orcid.org/0000-0001-9583-0872","contributorId":221891,"corporation":false,"usgs":false,"family":"Dalyander","given":"P. Soupy","affiliations":[{"id":40456,"text":"St. Petersburg Coastal and Marine Science Center (Former Employee)","active":true,"usgs":false}],"preferred":false,"id":799390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Long, Joseph W. 0000-0003-2912-1992","orcid":"https://orcid.org/0000-0003-2912-1992","contributorId":219235,"corporation":false,"usgs":false,"family":"Long","given":"Joseph","email":"","middleInitial":"W.","affiliations":[{"id":32398,"text":"University of North Carolina Wilmington","active":true,"usgs":false}],"preferred":false,"id":799391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jenkins, Robert L. III 0000-0003-2078-4618","orcid":"https://orcid.org/0000-0003-2078-4618","contributorId":202181,"corporation":false,"usgs":true,"family":"Jenkins","given":"Robert L.","suffix":"III","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799394,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Plant, Nathaniel G. 0000-0002-5703-5672 nplant@usgs.gov","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":3503,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","email":"nplant@usgs.gov","middleInitial":"G.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":799395,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Godsey, Elizabeth","contributorId":177095,"corporation":false,"usgs":false,"family":"Godsey","given":"Elizabeth","affiliations":[],"preferred":false,"id":799396,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gonzalez, Victor","contributorId":173702,"corporation":false,"usgs":false,"family":"Gonzalez","given":"Victor","affiliations":[],"preferred":false,"id":799397,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ]}