{"pageNumber":"252","pageRowStart":"6275","pageSize":"25","recordCount":41062,"records":[{"id":70240110,"text":"70240110 - 2021 - Extensive frost weathering across unglaciated North America during the Last Glacial Maximum","interactions":[],"lastModifiedDate":"2023-01-27T12:43:57.894865","indexId":"70240110","displayToPublicDate":"2021-01-19T06:39:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Extensive frost weathering across unglaciated North America during the Last Glacial Maximum","docAbstract":"

In unglaciated terrain, the imprint of past glacial periods is difficult to discern. The topographic signature of periglacial processes, such as solifluction lobes, may be erased or hidden by time and vegetation, and thus their import diminished. Belowground, periglacial weathering, particularly frost cracking, may have imparted a profound influence on weathering and erosion rates during past climate regimes. By combining a mechanical frost-weathering model with the full suite of Last Glacial Maximum climate simulations, we elucidate the meters-deep magnitude and continent-spanning expanse of frost weathering across unglaciated North America at ∼21 ka. The surprising extent of modeled frost weathering suggests, by proxy, the broad legacy of diverse periglacial processes. Complementing previous studies that championed the role of precipitation-driven changes in Critical Zone evolution, our results imply an additional strong temperature control on surficial process efficacy across much of modern North America, both during glacial periods and modern climes.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL090305","usgsCitation":"Marshall, J.J., Roering, J., Rempel, A.W., Shafer, S., and Bartlein, P.J., 2021, Extensive frost weathering across unglaciated North America during the Last Glacial Maximum: Geophysical Research Letters, v. 48, no. 5, e2020GL090305, 12 p., https://doi.org/10.1029/2020GL090305.","productDescription":"e2020GL090305, 12 p.","ipdsId":"IP-121558","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":453814,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl090305","text":"Publisher Index Page"},{"id":436556,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KC0L47","text":"USGS data release","linkHelpText":"PMIP3/CMIP5 lgm simulated temperature data for North America downscaled to a 10-km grid"},{"id":412398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Jill J 0000-0002-2388-2072","orcid":"https://orcid.org/0000-0002-2388-2072","contributorId":301809,"corporation":false,"usgs":false,"family":"Marshall","given":"Jill","email":"","middleInitial":"J","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":862610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":862611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rempel, Alan W.","contributorId":200642,"corporation":false,"usgs":false,"family":"Rempel","given":"Alan","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":862612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafer, Sarah 0000-0003-3739-2637 sshafer@usgs.gov","orcid":"https://orcid.org/0000-0003-3739-2637","contributorId":149866,"corporation":false,"usgs":true,"family":"Shafer","given":"Sarah","email":"sshafer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":862613,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bartlein, Patrick J. 0000-0001-7657-5685","orcid":"https://orcid.org/0000-0001-7657-5685","contributorId":211587,"corporation":false,"usgs":false,"family":"Bartlein","given":"Patrick","email":"","middleInitial":"J.","affiliations":[{"id":33397,"text":"U of Oregon","active":true,"usgs":false}],"preferred":false,"id":862614,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223444,"text":"70223444 - 2021 - Migration of injected wastewater with high levels of ammonia in a saline aquifer in south Florida","interactions":[],"lastModifiedDate":"2021-08-30T12:05:28.839956","indexId":"70223444","displayToPublicDate":"2021-01-18T10:31:01","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Migration of injected wastewater with high levels of ammonia in a saline aquifer in south Florida","docAbstract":"

Treated wastewater with high levels of ammonia has been injected, since March 1983 into the deep saline units of the Lower Floridan aquifer (LFA) from a treatment plant near the east coast of Miami-Dade County in southeastern Florida. Monitoring wells in the plant recorded ammonia concentrations above ambient levels at hydrogeologic units located about 1000 ft (304.8 m) above injection depths between 2500 and 2800 ft (762 and 853 m) below sea level. A solute-transport model was developed to assess the horizontal and vertical extent of the injected ammonia, with ammonia moving from the injected zone into the overlying units: the upper semiconfining unit, the uppermost permeable zone of the LFA, and the middle semiconfining units of the Avon Park Formation. Ammonia is assumed to be transported under the effects of local heterogeneity in a porous limestone aquifer with high-salinity ambient groundwater and via upward migration through quasi-vertical pathways. A flow model of the migration of the injected ammonia was calibrated with PEST using head, salinity, and ammonia concentration data measured from 1983 to 2013. Borehole geophysical data support the high permeability of the uppermost permeable zone in the LFA. Average simulated head, normalized salinity, and ammonia concentration residuals over all monitoring wells were −1.37 ft, 0.01, and −0.67 mg/L, respectively. Model results are consistent with undetectable ammonia concentrations in the Upper Floridan aquifer.

","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.13076","usgsCitation":"Sepulveda, N., and Lohmann, M., 2021, Migration of injected wastewater with high levels of ammonia in a saline aquifer in south Florida: Groundwater, v. 59, no. 4, p. 597-613, https://doi.org/10.1111/gwat.13076.","productDescription":"17 p.","startPage":"597","endPage":"613","ipdsId":"IP-107330","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":436559,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EWI8N0","text":"USGS data release","linkHelpText":"Data Sets for Simulation of Migration of Injected Wastewater with High Levels of Ammonia in a Saline Aquifer in South Florida, using SEAWAT v 4"},{"id":388589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.9088134765625,\n 25.175116531621764\n ],\n [\n -79.42291259765625,\n 25.175116531621764\n ],\n [\n -79.42291259765625,\n 26.04444515079636\n ],\n [\n -80.9088134765625,\n 26.04444515079636\n ],\n [\n -80.9088134765625,\n 25.175116531621764\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"59","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Nicasio 0000-0002-6333-1865 nsepul@usgs.gov","orcid":"https://orcid.org/0000-0002-6333-1865","contributorId":1454,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Nicasio","email":"nsepul@usgs.gov","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true}],"preferred":true,"id":822044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lohmann, Melinda A. 0000-0003-1472-159X","orcid":"https://orcid.org/0000-0003-1472-159X","contributorId":216660,"corporation":false,"usgs":true,"family":"Lohmann","given":"Melinda A.","affiliations":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"preferred":true,"id":822045,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224315,"text":"70224315 - 2021 - Adaptive monitoring in action: Reconsidering design-based estimators reveals underestimation of whitebark pine disease prevalence in the Greater Yellowstone Ecosystem","interactions":[],"lastModifiedDate":"2021-09-21T12:28:40.418885","indexId":"70224315","displayToPublicDate":"2021-01-18T07:25:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Adaptive monitoring in action: Reconsidering design-based estimators reveals underestimation of whitebark pine disease prevalence in the Greater Yellowstone Ecosystem","docAbstract":"
  1. Identifying and understanding status and trends in ecological indicators motivates continual monitoring over decades. Many programs rely on probability surveys and their companion design-based estimators for status assessments (e.g. Horvitz–Thompson). Design-based estimators do not easily extend to trend estimation nor situations with observation errors. Field-based monitoring efforts inevitably have turnover of field crew members which may affect consistency and accuracy of data collection over time. Additionally, design-based estimators ignore the complexities of spatial and temporal heterogeneity in an ecological indicator and how this variability may be linked to environmental or biological dynamics. We propose monitoring programs should re-evaluate their prescribed statistical methods, consider model-based approaches and adapt their sampling designs as needed to improve inferences.
  2. The Greater Yellowstone Ecosystem, home to two of the most iconic U.S. National Parks, has experienced significant declines in whitebark pine Pinus albicaulis communities due to forest pathogens, insect outbreaks, wildland fires and drought. Whitebark pine is a keystone species found in mountainous environments throughout the Western U.S. and Canada. We assessed the design-based ratio estimator originally recommended for estimating prevalence of white pine blister rust Cronartium ribicola. We compared the design-based estimator to a model-based approach that accounts for the sampling design, imperfect detection and allows for infection probabilities to vary over space and time.
  3. Ignoring observation errors led to lower estimated prevalence of white pine blister rust in the general population. Using model-based approaches, we found that the probability of infection has increased since 2004. However, overall prevalence likely has not changed because of the mountain pine beetle Dendroctonus ponderosae-induced shift towards smaller diameter trees that have a lower probability of infection compared to their larger cohorts.
  4. Synthesis and Applications. Using a design-based approach to detect change in ecological indicators falls short because of the inability to account for observation errors or to explore environmental or biological factors explaining temporal dynamics. Inherently understanding the mechanisms leading to changes in an ecological indicator over time informs potential management actions. Our assessment underscores the need for continued evaluation and updating of a monitoring program's sampling design and analytical procedures to maintain relevancy.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13837","usgsCitation":"Shanahan, E., Wright, W., and Irvine, K., 2021, Adaptive monitoring in action: Reconsidering design-based estimators reveals underestimation of whitebark pine disease prevalence in the Greater Yellowstone Ecosystem: Journal of Applied Ecology, v. 58, no. 5, p. 1079-1089, https://doi.org/10.1111/1365-2664.13837.","productDescription":"11 p.","startPage":"1079","endPage":"1089","ipdsId":"IP-118384","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":490077,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13837","text":"Publisher Index Page"},{"id":389531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Greater Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.03881835937499,\n 41.918628865183045\n ],\n [\n -108.424072265625,\n 41.918628865183045\n ],\n [\n -108.424072265625,\n 45.00365115687186\n ],\n [\n -111.03881835937499,\n 45.00365115687186\n ],\n [\n -111.03881835937499,\n 41.918628865183045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Shanahan, Erin","contributorId":265915,"corporation":false,"usgs":false,"family":"Shanahan","given":"Erin","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":823705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wright, Wilson 0000-0003-4276-3850","orcid":"https://orcid.org/0000-0003-4276-3850","contributorId":265916,"corporation":false,"usgs":false,"family":"Wright","given":"Wilson","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":823706,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Irvine, Kathryn 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":220632,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":823707,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218838,"text":"70218838 - 2021 - A geology and geodesy based model of dynamic earthquake rupture on the Rodgers Creek‐Hayward‐Calaveras Fault System, California","interactions":[],"lastModifiedDate":"2021-03-18T12:14:26.547237","indexId":"70218838","displayToPublicDate":"2021-01-17T07:15:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"A geology and geodesy based model of dynamic earthquake rupture on the Rodgers Creek‐Hayward‐Calaveras Fault System, California","docAbstract":"

The Hayward fault in California's San Francisco Bay area produces large earthquakes, with the last occurring in 1868. We examine how physics‐based dynamic rupture modeling can be used to numerically simulate large earthquakes on not only the Hayward fault, but also its connected companions to the north and south, the Rodgers Creek and Calaveras faults. Equipped with a wealth of images of this fault system, including those of its 3D geology and 3D geometry, in addition to inferences about its interseismic creep‐rate pattern and rock‐friction behavior, we use a finite‐element computer code to perform 3D dynamic earthquake rupture simulations. We find that the rock properties affect the locations and amount of slip produced in our simulated large earthquakes. Crucial factors that control rupture behavior in our modeling are the earthquake nucleation locations, the fault geometry, and the data that reveal where the fault system is creeping or locked. Our findings suggest that large Rodgers Creek‐Hayward‐Calaveras‐Northern Calaveras (RC‐H‐C‐NC) fault‐system earthquakes may result from dynamic rupture that starts in a locked part of the fault system, but is then stopped by the creeping parts, leading to high‐magnitude‐6 earthquakes; or, from dynamic rupture that starts in a locked part of the fault system, then cascades through some of the creeping parts, leading to magnitude‐7 earthquakes.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JB020577","usgsCitation":"Harris, R.A., Barall, M., Lockner, D.A., Moore, D.E., Ponce, D.A., Graymer, R., Funning, G.J., Morrow, C.A., Kyriakopoulos, C., and Eberhart-Phillips, D., 2021, A geology and geodesy based model of dynamic earthquake rupture on the Rodgers Creek‐Hayward‐Calaveras Fault System, California: JGR Solid Earth, v. 126, e2020JB020577, 28 p., https://doi.org/10.1029/2020JB020577.","productDescription":"e2020JB020577, 28 p.","ipdsId":"IP-120122","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":453828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jb020577","text":"Publisher Index Page"},{"id":384448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","otherGeospatial":"San Andres Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.98095703125,\n 37.18657859524883\n ],\n [\n -121.77246093750001,\n 37.18657859524883\n ],\n [\n -121.77246093750001,\n 38.38472766885085\n ],\n [\n -122.98095703125,\n 38.38472766885085\n ],\n [\n -122.98095703125,\n 37.18657859524883\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Harris, Ruth A. 0000-0002-9247-0768 harris@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-0768","contributorId":786,"corporation":false,"usgs":true,"family":"Harris","given":"Ruth","email":"harris@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":812382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barall, Michael 0000-0001-7724-8563","orcid":"https://orcid.org/0000-0001-7724-8563","contributorId":198670,"corporation":false,"usgs":false,"family":"Barall","given":"Michael","affiliations":[],"preferred":false,"id":812383,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockner, David A. 0000-0001-8630-6833 dlockner@usgs.gov","orcid":"https://orcid.org/0000-0001-8630-6833","contributorId":567,"corporation":false,"usgs":true,"family":"Lockner","given":"David","email":"dlockner@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":812384,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moore, Diane E. 0000-0002-8641-1075 dmoore@usgs.gov","orcid":"https://orcid.org/0000-0002-8641-1075","contributorId":2704,"corporation":false,"usgs":true,"family":"Moore","given":"Diane","email":"dmoore@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":812385,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ponce, David A. 0000-0003-4785-7354 ponce@usgs.gov","orcid":"https://orcid.org/0000-0003-4785-7354","contributorId":1049,"corporation":false,"usgs":true,"family":"Ponce","given":"David","email":"ponce@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":812386,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graymer, Russell 0000-0003-4910-5682","orcid":"https://orcid.org/0000-0003-4910-5682","contributorId":207816,"corporation":false,"usgs":true,"family":"Graymer","given":"Russell","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":812387,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Funning, Gareth J. 0000-0002-8247-0545","orcid":"https://orcid.org/0000-0002-8247-0545","contributorId":172418,"corporation":false,"usgs":false,"family":"Funning","given":"Gareth","email":"","middleInitial":"J.","affiliations":[{"id":6984,"text":"UC Riverside","active":true,"usgs":false}],"preferred":false,"id":812388,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morrow, Carolyn A. 0000-0003-3500-6181 cmorrow@usgs.gov","orcid":"https://orcid.org/0000-0003-3500-6181","contributorId":3206,"corporation":false,"usgs":true,"family":"Morrow","given":"Carolyn","email":"cmorrow@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":812389,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kyriakopoulos, Christodoulos 0000-0001-9283-2282","orcid":"https://orcid.org/0000-0001-9283-2282","contributorId":255461,"corporation":false,"usgs":false,"family":"Kyriakopoulos","given":"Christodoulos","email":"","affiliations":[{"id":17864,"text":"University of Memphis","active":true,"usgs":false}],"preferred":false,"id":812390,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Eberhart-Phillips, Donna 0000-0003-0392-8659","orcid":"https://orcid.org/0000-0003-0392-8659","contributorId":190650,"corporation":false,"usgs":false,"family":"Eberhart-Phillips","given":"Donna","email":"","affiliations":[],"preferred":false,"id":812391,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70217336,"text":"70217336 - 2021 - Habitat features predict carrying capacity of a recovering marine carnivore","interactions":[],"lastModifiedDate":"2021-01-18T17:12:30.706481","indexId":"70217336","displayToPublicDate":"2021-01-15T11:07:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Habitat features predict carrying capacity of a recovering marine carnivore","docAbstract":"

The recovery of large carnivore species from over‐exploitation can have socioecological effects; thus, reliable estimates of potential abundance and distribution represent a valuable tool for developing management objectives and recovery criteria. For sea otters (Enhydra lutris), as with many apex predators, equilibrium abundance is not constant across space but rather varies as a function of local habitat quality and resource dynamics, thereby complicating the extrapolation of carrying capacity (K) from one location to another. To overcome this challenge, we developed a state‐space model of density‐dependent population dynamics in southern sea otters (E. l. nereis), in which K is estimated as a continuously varying function of a suite of physical, biotic, and oceanographic variables, all described at fine spatial scales. We used a theta‐logistic process model that included environmental stochasticity and allowed for density‐independent mortality associated with shark bites. We used Bayesian methods to fit the model to time series of survey data, augmented by auxiliary data on cause of death in stranded otters. Our model results showed that the expected density at K for a given area can be predicted based on local bathymetry (depth and distance from shore), benthic substrate composition (rocky vs. soft sediments), presence of kelp canopy, net primary productivity, and whether or not the area is inside an estuary. In addition to density‐dependent reductions in growth, increased levels of shark‐bite mortality over the last decade have also acted to limit population expansion. We used the functional relationships between habitat variables and equilibrium density to project estimated values of K for the entire historical range of southern sea otters in California, USA, accounting for spatial variation in habitat quality. Our results suggest that California could eventually support 17,226 otters (95% CrI = 9,739–30,087). We also used the fitted model to compute candidate values of optimal sustainable population abundance (OSP) for all of California and for regions within California. We employed a simulation‐based approach to determine the abundance associated with the maximum net productivity level (MNPL) and propose that the upper quartile of the distribution of MNPL estimates (accounting for parameter uncertainty) represents an appropriate threshold value for OSP. Based on this analysis, we suggest a candidate value for OSP (for all of California) of 10,236, which represents 59.4% of projected K.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21985","usgsCitation":"Tinker, M., Yee, J.L., Laidre, K.L., Hatfield, B.B., Harris, M.D., Tomoleoni, J.A., Bell, T.W., Saarman, E., Carswell, L., and Miles, A.K., 2021, Habitat features predict carrying capacity of a recovering marine carnivore: Journal of Wildlife Management, v. 85, no. 2, p. 303-323, https://doi.org/10.1002/jwmg.21985.","productDescription":"21 p.","startPage":"303","endPage":"323","ipdsId":"IP-122195","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453835,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21985","text":"Publisher Index Page"},{"id":382279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.27880859375001,\n 33.815666308702774\n ],\n [\n -118.740234375,\n 34.125447565116126\n ],\n [\n -119.091796875,\n 34.23451236236987\n ],\n [\n -119.61914062499999,\n 34.50655662164561\n ],\n [\n -120.36621093749999,\n 34.56085936708384\n ],\n [\n -120.69580078125001,\n 35.31736632923788\n ],\n [\n -121.728515625,\n 36.474306755095235\n ],\n [\n -121.70654296874999,\n 36.94989178681327\n ],\n [\n -122.34374999999999,\n 37.43997405227057\n ],\n [\n -121.75048828124999,\n 37.54457732085582\n ],\n [\n -121.97021484374999,\n 37.96152331396614\n ],\n [\n -120.7177734375,\n 38.03078569382294\n ],\n [\n -121.28906250000001,\n 38.39333888832238\n ],\n [\n -122.58544921875,\n 38.238180119798635\n ],\n [\n -123.06884765625,\n 38.08268954483802\n ],\n [\n -122.82714843749999,\n 37.666429212090605\n ],\n [\n -122.54150390625,\n 37.125286284966805\n ],\n [\n -122.01416015625,\n 36.77409249464195\n ],\n [\n -122.1240234375,\n 36.43896124085945\n ],\n [\n -121.33300781249999,\n 35.51434313431818\n ],\n [\n -120.87158203125,\n 34.88593094075317\n ],\n [\n -120.78369140624999,\n 34.470335121217474\n ],\n [\n -120.12451171875,\n 33.687781758439364\n ],\n [\n -118.27880859375001,\n 33.815666308702774\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Tinker, M. Tim 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":207839,"corporation":false,"usgs":true,"family":"Tinker","given":"M. Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":false,"id":808385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808386,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Laidre, Kristin L.","contributorId":191798,"corporation":false,"usgs":false,"family":"Laidre","given":"Kristin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":808387,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hatfield, Brian B. 0000-0003-1432-2660 brian_hatfield@usgs.gov","orcid":"https://orcid.org/0000-0003-1432-2660","contributorId":147917,"corporation":false,"usgs":true,"family":"Hatfield","given":"Brian","email":"brian_hatfield@usgs.gov","middleInitial":"B.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808388,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harris, Michael D.","contributorId":127460,"corporation":false,"usgs":false,"family":"Harris","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":808389,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808390,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bell, Tom W.","contributorId":149016,"corporation":false,"usgs":false,"family":"Bell","given":"Tom","email":"","middleInitial":"W.","affiliations":[{"id":7168,"text":"UCSB","active":true,"usgs":false}],"preferred":false,"id":808391,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Saarman, Emily","contributorId":247807,"corporation":false,"usgs":false,"family":"Saarman","given":"Emily","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":808392,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Carswell, Lilian P.","contributorId":221789,"corporation":false,"usgs":false,"family":"Carswell","given":"Lilian P.","affiliations":[{"id":40429,"text":"USFWS - Ventura FWO","active":true,"usgs":false}],"preferred":false,"id":808393,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Miles, A. Keith 0000-0002-3108-808X keith_miles@usgs.gov","orcid":"https://orcid.org/0000-0002-3108-808X","contributorId":196,"corporation":false,"usgs":true,"family":"Miles","given":"A.","email":"keith_miles@usgs.gov","middleInitial":"Keith","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808394,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70227769,"text":"70227769 - 2021 - Using grazing to manage herbaceous structure for a heterogeneity-dependent bird","interactions":[],"lastModifiedDate":"2022-01-31T15:19:36.502867","indexId":"70227769","displayToPublicDate":"2021-01-15T09:12:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Using grazing to manage herbaceous structure for a heterogeneity-dependent bird","docAbstract":"

Grazing management recommendations often sacrifice the intrinsic heterogeneity of grasslands by prescribing uniform grazing distributions through smaller pastures, increased stocking densities, and reduced grazing periods. The lack of patch-burn grazing in semi-arid landscapes of the western Great Plains in North America requires alternative grazing management strategies to create and maintain heterogeneity of habitat structure (e.g., animal unit distribution, pasture configuration), but knowledge of their effects on grassland fauna is limited. The lesser prairie-chicken (Tympanuchus pallidicinctus), an imperiled, grassland-obligate, native to the southern Great Plains, is an excellent candidate for investigating effects of heterogeneity-based grazing management strategies because it requires diverse microhabitats among life-history stages in a semi-arid landscape. We evaluated influences of heterogeneity-based grazing management strategies on vegetation structure, habitat selection, and nest and adult survival of lesser prairie-chickens in western Kansas, USA. We captured and monitored 116 female lesser prairie-chickens marked with very high frequency (VHF) or global positioning system (GPS) transmitters and collected landscape-scale vegetation and grazing data during 2013–2015. Vegetation structure heterogeneity increased at stocking densities ≤0.26 animal units/ha, where use by nonbreeding female lesser prairie-chickens also increased. Probability of use for nonbreeding lesser prairie-chickens peaked at values of cattle forage use values near 37% and steadily decreased with use ≥40%. Probability of use was positively affected by increasing pasture area. A quadratic relationship existed between growing season deferment and probability of use. We found that 70% of nests were located in grazing units in which grazing pressure was <0.8 animal unit months/ha. Daily nest survival was negatively correlated with grazing pressure. We found no relationship between adult survival and grazing management strategies. Conservation in grasslands expressing flora community composition appropriate for lesser prairie-chickens can maintain appropriate habitat structure heterogeneity through the use of low to moderate stocking densities (<0.26 animal units/ha), greater pasture areas, and site-appropriate deferment periods. Alternative grazing management strategies (e.g., rest-rotation, season-long rest) may be appropriate in grasslands requiring greater heterogeneity or during intensive drought. Grazing management favoring habitat heterogeneity instead of uniform grazing distributions will likely be more conducive for preserving lesser prairie-chicken populations and grassland biodiversity.

","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.21984","usgsCitation":"Kraft, J.D., Haukos, D.A., Bain, M.R., Rice, M.B., Robinson, S., Sullins, D.S., Hagen, C., Pitman, J., Lautenbach, J., Plumb, R., and Lautenbach, J., 2021, Using grazing to manage herbaceous structure for a heterogeneity-dependent bird: Journal of Wildlife Management, v. 85, no. 2, p. 354-368, https://doi.org/10.1002/jwmg.21984.","productDescription":"15 p.","startPage":"354","endPage":"368","ipdsId":"IP-092108","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":453841,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/jwmg.21984","text":"External Repository"},{"id":395138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.953125,\n 37.01132594307015\n ],\n [\n -98.382568359375,\n 37.01132594307015\n ],\n [\n -98.382568359375,\n 39.29179704377487\n ],\n [\n -101.953125,\n 39.29179704377487\n ],\n [\n -101.953125,\n 37.01132594307015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraft, John D.","contributorId":172789,"corporation":false,"usgs":false,"family":"Kraft","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":832156,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bain, Matthew R.","contributorId":272571,"corporation":false,"usgs":false,"family":"Bain","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":33811,"text":"TNC","active":true,"usgs":false}],"preferred":false,"id":832157,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rice, Mindy B.","contributorId":214399,"corporation":false,"usgs":false,"family":"Rice","given":"Mindy","email":"","middleInitial":"B.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":832158,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Robinson, Samantha","contributorId":272573,"corporation":false,"usgs":false,"family":"Robinson","given":"Samantha","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832159,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sullins, Dan S.","contributorId":272574,"corporation":false,"usgs":false,"family":"Sullins","given":"Dan","email":"","middleInitial":"S.","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832160,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hagen, Christian A.","contributorId":272575,"corporation":false,"usgs":false,"family":"Hagen","given":"Christian A.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":832161,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pitman, James","contributorId":176512,"corporation":false,"usgs":false,"family":"Pitman","given":"James","affiliations":[],"preferred":false,"id":832162,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lautenbach, Joseph","contributorId":272577,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Joseph","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832163,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Plumb, Reid","contributorId":272578,"corporation":false,"usgs":false,"family":"Plumb","given":"Reid","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832164,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lautenbach, Jonathan","contributorId":272579,"corporation":false,"usgs":false,"family":"Lautenbach","given":"Jonathan","affiliations":[{"id":48533,"text":"ksu","active":true,"usgs":false}],"preferred":false,"id":832165,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70217305,"text":"70217305 - 2021 - Seed production patterns of surviving Sierra Nevada conifers show minimal change following drought","interactions":[],"lastModifiedDate":"2021-01-18T13:39:10.353799","indexId":"70217305","displayToPublicDate":"2021-01-15T07:37:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Seed production patterns of surviving Sierra Nevada conifers show minimal change following drought","docAbstract":"

Reproduction is a key component of ecological resilience in forest ecosystems, so understanding how seed production is influenced by extreme drought is key to understanding forest recovery trajectories. If trees respond to mortality-inducing drought by preferentially allocating resources for reproduction, the recovery of the stand to pre-drought conditions may be enhanced accordingly. We used a 20-year annual seed capture data set to investigate whether seed production by three tree genera commonly found in the Sierra Nevada (Abies, Pinus, and Calocedrus) was correlated with variation in local weather, which included an extreme drought spanning multiple years. We tested whether average seed production differed during the drought years, and whether annual seed counts could be explained by three weather variables: spring temperature, annual precipitation, and summer climatic water deficit (CWD). We fit models testing for four separate effects: (1) a priming year model (weather 1 year prior to reproductive bud initiation), (2) a bud initiation model (weather in the year of reproductive bud initiation), (3) a pollination year model (weather in the year of pollination), and (4) maturation year model (weather in the year of seed maturation). For genera with two-year reproductive cycles, the pollination and maturation models were combined. We found support for the summer CWD Abies maturation year model, which suggested higher seed outputs immediately following dry summer conditions. The spring temperature pollination year model was selected for Pinus, which suggested that seed output is higher following warm spring weather during pollination. The annual precipitation priming year model was selected for Calocedrus, which showed a negative association between seed production and wetter conditions two years prior to seed production. More parent tree basal area resulted in higher seed output for all genera, though the confidence intervals overlapped 0 for Calocedrus. Permutation tests sugested there was no systematic difference in mean seed production during the drought after accounting for live tree basal area, regardless of genus. These results highlight the variability in response across genera, and suggest that the influence of seed production on forest recovery following drought-related mortality may depend on affected species and the timing of the mortality event within the masting cycle. A greater understanding of species-level masting to drought stress is needed to more precisely predict community-level recovery following drought.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2020.118598","usgsCitation":"Wright, M., van Mantgem, P., Stephenson, N.L., Das, A., and Keeley, J., 2021, Seed production patterns of surviving Sierra Nevada conifers show minimal change following drought: Forest Ecology and Management, v. 480, 118598, 21 p., https://doi.org/10.1016/j.foreco.2020.118598.","productDescription":"118598, 21 p.","ipdsId":"IP-116685","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436562,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B425MF","text":"USGS data release","linkHelpText":"Seed source, not drought, determines patterns of seed production in Sierra Nevada conifers"},{"id":436561,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B425MF","text":"USGS data release","linkHelpText":"Seed source, not drought, determines patterns of seed production in Sierra Nevada conifers"},{"id":382253,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.12451171875,\n 36.03133177633189\n ],\n [\n -117.68554687499999,\n 36.03133177633189\n ],\n [\n -117.68554687499999,\n 38.58252615935333\n ],\n [\n -120.12451171875,\n 38.58252615935333\n ],\n [\n -120.12451171875,\n 36.03133177633189\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"480","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Micah C. 0000-0002-5324-1110","orcid":"https://orcid.org/0000-0002-5324-1110","contributorId":229071,"corporation":false,"usgs":true,"family":"Wright","given":"Micah","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":808318,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":808319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808320,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217188,"text":"ofr20201136 - 2021 - Development and application of surrogate models, calculated loads, and aquatic export of carbon based on specific conductance, Big Cypress National Preserve, south Florida, 2015–17","interactions":[],"lastModifiedDate":"2021-01-15T12:46:29.556276","indexId":"ofr20201136","displayToPublicDate":"2021-01-14T12:15:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1136","displayTitle":"Development and Application of Surrogate Models, Calculated Loads, and Aquatic Export of Carbon Based on Specific Conductance, Big Cypress National Preserve, South Florida, 2015–17","title":"Development and application of surrogate models, calculated loads, and aquatic export of carbon based on specific conductance, Big Cypress National Preserve, south Florida, 2015–17","docAbstract":"

Understanding the carbon transport within aquatic environments is crucial to quantifying global and local carbon budgets, yet limited empirical data currently (2021) exist. This report documents methodology and provides data for quantifying the aquatic export of carbon from a cypress swamp within Big Cypress National Preserve and is part of a larger carbon budget study. The U.S. Geological Survey operated two continuous monitoring stations, 022889001 and 022909471, that measured flow volume and water quality within the Big Cypress National Preserve in South Florida from September 2015 to October 2017. Station 022889001 represented the flow into the study area and station 022909471 represented the flow out of the study area. Site-specific regression models were developed by using continuously measured specific conductance and concomitant, discretely collected dissolved organic carbon, dissolved inorganic carbon, and particulate carbon samples to calculate total carbon (TC) concentrations at 15-minute intervals.

Calculated TC concentrations typically increased as flow was decreasing and decreased as flow was increasing. TC loads were calculated by multiplying concentrations and flow volume, and the difference between the load calculations for input/output locations of the swamp flow system was used to determine the aquatic carbon export from the study area.

Calculated monthly TC loads ranged from 0 metric tons in spring 2017 at both stations to 3,145 and 7,821 metric tons in September 2017 at 022889001 and 022909471, respectively. During 2016, the annual loads were 10,479 and 15,243 metric tons at 022889001 and 022909471, respectively. Calculated monthly aquatic TC exports from the study area ranged from −0.7 gram of carbon per square meter in May 2016 to 44.1 grams of carbon per square meter during September 2017. The carbon export from the study area varied monthly, increased as flow increased, and was greatly influenced by Hurricane Irma in September 2017. The aquatic TC export from the Sweetwater Strand study area was 42.0 grams of carbon per square meter per year in 2016, which is substantially (about 15 times) larger than the estimated overall mean riverine carbon export per square meter for the eastern United States; however, it was also less than the monthly export of carbon in September 2017. The monthly aquatic carbon export from the study area in September 2017 alone was greater than the aquatic carbon export from all of 2016, which is largely the result of the substantial increase in flow attributed to Hurricane Irma.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201136","collaboration":"Greater Everglades Priority Ecosystem Science Program","usgsCitation":"Booth, A.C., 2021, Development and application of surrogate models, calculated loads, and aquatic export of carbon based on specific conductance, Big Cypress National Preserve, South Florida, 2015–17: U.S. Geological Survey Open-File Report 2020–1136, 14 p., https://doi.org/10.3133/ofr20201136.","productDescription":"Report: v, 14 p.; Data Release; 2 Appendixes","onlineOnly":"Y","ipdsId":"IP-112929","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":382104,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix2.rtf","text":"Appendix 2","size":"960 kB","description":"OFR 2020-1136 Appendix 2 rtf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022909471: Loop Road Culverts Monroe Station to Florida Trail, Florida (rtf file)"},{"id":382062,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1136/coverthb.jpg"},{"id":382063,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1136/ofr20201136.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136"},{"id":382064,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EXZLJT","text":"USGS data release","linkHelpText":"Calculated carbon concentrations, loads, and export in Big Cypress National Preserve, South Florida, 2015-2017"},{"id":382101,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix1.pdf","text":"Appendix 1","size":"424 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136 Appendix 1 pdf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022889001: Tamiami Canal 11 Mile Road to Monroe Station, Florida"},{"id":382102,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix2.pdf","text":"Appendix 2","size":"356 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1136 Appendix 2 pdf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022909471: Loop Road Culverts Monroe Station to Florida Trail, Florida"},{"id":382103,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1136/appendix1.rtf","text":"Appendix 1","size":"2.91 MB","description":"OFR 2020-1136 Appendix 1 rtf file","linkHelpText":"Model Archive for Total Carbon Concentration at U.S. Geological Survey Station 022889001: Tamiami Canal 11 Mile Road to Monroe Station, Florida (rtf file)"}],"country":"United States","state":"Florida","otherGeospatial":"Big Cypress National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.22604370117186,\n 25.812254545273433\n ],\n [\n -80.8978271484375,\n 25.812254545273433\n ],\n [\n -80.8978271484375,\n 26.058016587844723\n ],\n [\n -81.22604370117186,\n 26.058016587844723\n ],\n [\n -81.22604370117186,\n 25.812254545273433\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559

","tableOfContents":"","publishedDate":"2021-01-14","noUsgsAuthors":false,"publicationDate":"2021-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Booth, Amanda 0000-0002-2666-2366 acbooth@usgs.gov","orcid":"https://orcid.org/0000-0002-2666-2366","contributorId":5432,"corporation":false,"usgs":true,"family":"Booth","given":"Amanda","email":"acbooth@usgs.gov","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807908,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70218755,"text":"70218755 - 2021 - The weight of cities: Urbanization effects on Earth’s subsurface","interactions":[],"lastModifiedDate":"2021-03-12T14:56:28.755208","indexId":"70218755","displayToPublicDate":"2021-01-14T08:55:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7751,"text":"AGU Advances","active":true,"publicationSubtype":{"id":10}},"title":"The weight of cities: Urbanization effects on Earth’s subsurface","docAbstract":"

Across the world, people increasingly choose to live in cities. By 2050, 70% of Earth's population will live in large urban areas. Upon considering a large city, questions arise such as, how much does that weigh? What are its effects on the landscape? Does it cause measurable subsidence? Here I calculate the weight of San Francisco Bay region urbanization, where 7.75 million people live at, or near the coast. It is difficult to account for everything that is in a city. I assume that most of the weight is buildings and their contents, which allows the use of base outline and height data to approximate their mass, which is cumulatively 1.6·1012 kg. I build a series of finite element models to study effects of pressure exerted by the weight distribution. Within the elastic realm, I look at compression, flexure, isostatic compensation, stress change, dilatation, and fluid flow changes. Within the nonlinear realm I show example calculations of primary and secondary settlement of soils under load. The combined modeled subsidence from building loads is at least 5–80 mm, with the largest contributions coming from nonlinear settlement and creep in soils. A general result is closing of pore space and redirection of pore fluids. While the calculated subsidence of the Bay Area is relatively small compared with other sources of elevation change such as pumping and recharge of aquifers, all sources of subsidence are concerning given an expected 200–300 mm sea level rise at San Francisco by the year 2050.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020AV000277","usgsCitation":"Parsons, T.E., 2021, The weight of cities: Urbanization effects on Earth’s subsurface: AGU Advances, v. 2, no. 1, e2020AV000277, 15 p., https://doi.org/10.1029/2020AV000277.","productDescription":"e2020AV000277, 15 p.","ipdsId":"IP-121590","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":487292,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020av000277","text":"Publisher Index Page"},{"id":384359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811689,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217247,"text":"ofr20201135 - 2021 - An assessment of the economic potential of lignite and leonardite resources in the Williston Basin, North Dakota","interactions":[],"lastModifiedDate":"2021-01-15T12:52:49.599044","indexId":"ofr20201135","displayToPublicDate":"2021-01-13T16:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1135","displayTitle":"An Assessment of the Economic Potential of Lignite and Leonardite Resources in the Williston Basin, North Dakota","title":"An assessment of the economic potential of lignite and leonardite resources in the Williston Basin, North Dakota","docAbstract":"

The Bureau of Land Management (BLM) requested assistance from the U.S. Geological Survey (USGS) to conduct an assessment study to identify areas that may have economic potential for the future extraction of lignite and leonardite resources in the Williston Basin in North Dakota. The study will be used by the BLM to assist with the preparation of a revised resource management plan for the Williston Basin, in accordance with BLM planning policies.

The assessment of the economic potential of lignite resources required the establishment of criteria defining an economic lignite deposit. In consultation with the BLM, criteria were established to delineate drill holes that contained economic lignite beds. The criteria established are a minimum lignite bed thickness, a minimum cumulative lignite thickness, a maximum cumulative stripping ratio, and a maximum overburden. Likewise, an assessment of the economic potential of leonardite deposits required the establishment of criteria delineating drill holes that contained economic leonardite deposits. The criteria established are a minimum leonardite bed thickness, a minimum cumulative leonardite thickness, and a maximum overburden.

The drill hole data utilized in this study were obtained from the National Coal Resources Data System database and from several coal companies. Data from more than 20,000 drill holes, both proprietary and nonproprietary, were used to compile areas of economic potential for lignite or leonardite.

Areas delineated as having lignite or leonardite resources with economic potential, based on the established criteria, were present in 24 counties in the western portion of North Dakota. Areas of economic potential were delineated using a visual best-fit method without croplines. Areas defined as having economic potential for certain lignite beds or leonardite deposits may extend beyond known croplines in this study.

Stratigraphically, the lignite and leonardite deposits in the Williston Basin in North Dakota are mostly found in the Paleocene Fort Union Formation. Thick (greater than 20 feet) and laterally extensive (greater than 5 square miles) lignite beds are present in the Fort Union Formation throughout the Sentinel Butte and Tongue River Members. Lignite beds are also present in the Ludlow Member of the Fort Union Formation, although they are not as numerous or thick as they are in the overlying Sentinel Butte and Tongue River Members. As a result of lateral facies changes and migrating fluvial channel complexes in the Fort Union Formation, lignite beds of varying thickness occupy different stratigraphic horizons vertically throughout the Williston Basin.

The calculation of volumes for lignite and leonardite resources was not part of the scope of this study requested by the BLM, but a future study by the USGS may involve a comprehensive assessment of lignite resources and reserves in the Williston Basin. This future study could combine geologic data compiled in this study with geologic data from a previously unpublished 2019 assessment study by the USGS in the Williston Basin in eastern Montana. This future USGS study could also include the calculation of volumes for lignite resources and reserves, based on economic models derived using analogs from active mining operations in the Williston Basin and available spot market or contract coal prices.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201135","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Shaffer, B.N., 2021, An assessment of the economic potential of lignite and leonardite resources in the Williston Basin, North Dakota: U.S. Geological Survey Open-File Report 2020–1135, 14 p., https://doi.org/10.3133/ofr20201135.","productDescription":"vi, 14 p.","onlineOnly":"Y","ipdsId":"IP-120360","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":436582,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93GGU6P","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Mercer and Oliver Counties, North Dakota"},{"id":436581,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93GGU6P","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Mercer and Oliver Counties, North Dakota"},{"id":436580,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NWIHEE","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in McLean County, North Dakota"},{"id":436579,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NWIHEE","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in McLean County, North Dakota"},{"id":436578,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94V9WV8","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Billings County, North Dakota"},{"id":436577,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94V9WV8","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Billings County, North Dakota"},{"id":436576,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90636SP","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Golden Valley County, North Dakota"},{"id":436575,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90636SP","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Golden Valley County, North Dakota"},{"id":436574,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FHHH4T","text":"USGS data release","linkHelpText":"Drill hole data for coal beds in the Paleocene Fort Union Formation in the Williston Basin in Dunn County, North Dakota"},{"id":382138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1135/coverthb.jpg"},{"id":382139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1135/ofr20201135.pdf","text":"Report","size":"6.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1135"}],"country":"United States","state":"North Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0625,\n 45.89000815866184\n ],\n [\n -99.931640625,\n 45.89000815866184\n ],\n [\n -99.931640625,\n 49.009050809382046\n ],\n [\n -104.0625,\n 49.009050809382046\n ],\n [\n -104.0625,\n 45.89000815866184\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046

","tableOfContents":"","publishedDate":"2021-01-14","noUsgsAuthors":false,"publicationDate":"2021-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Brian N. 0000-0002-8787-7504","orcid":"https://orcid.org/0000-0002-8787-7504","contributorId":203755,"corporation":false,"usgs":true,"family":"Shaffer","given":"Brian N.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":808140,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70217540,"text":"70217540 - 2021 - Linking modern pollen accumulation rates to biomass: Quantitative vegetation reconstruction in the western Klamath Mountains, NW California, USA","interactions":[],"lastModifiedDate":"2021-04-22T16:12:40.933615","indexId":"70217540","displayToPublicDate":"2021-01-13T15:35:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3562,"text":"The Holocene","active":true,"publicationSubtype":{"id":10}},"title":"Linking modern pollen accumulation rates to biomass: Quantitative vegetation reconstruction in the western Klamath Mountains, NW California, USA","docAbstract":"

Quantitative reconstructions of vegetation abundance from sediment-derived pollen systems provide unique insights into past ecological conditions. Recently, the use of pollen accumulation rates (PAR, grains cm−2 year−1) has shown promise as a bioproxy for plant abundance. However, successfully reconstructing region-specific vegetation dynamics using PAR requires that accurate assessments of pollen deposition processes be quantitatively linked to spatially-explicit measures of plant abundance. Our study addressed these methodological challenges. Modern PAR and vegetation data were obtained from seven lakes in the western Klamath Mountains, California. To determine how to best calibrate our PAR-biomass model, we first calculated the spatial area of vegetation where vegetation composition and patterning is recorded by changes in the pollen signal using two metrics. These metrics were an assemblage-level relevant source area of pollen (aRSAP) derived from extended R-value analysis (sensu Sugita, 1993) and a taxon-specific relevant source area of pollen (tRSAP) derived from PAR regression (sensu Jackson, 1990). To the best of our knowledge, aRSAP and tRSAP have not been directly compared. We found that the tRSAP estimated a smaller area for some taxa (e.g. a circular area with a 225 m radius for Pinus) than the aRSAP (a circular area with a 625 m radius). We fit linear models to relate PAR values from modern lake sediments with empirical, distance-weighted estimates of aboveground live biomass (AGLdw) for both the aRSAP and tRSAP distances. In both cases, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus, and TCT (Taxodiaceae, Cupressaceae, and Taxaceae families) – were statistically significant and reasonably precise estimators of contemporary AGLdw. However, predictions weighted by the distance defined by aRSAP tended to be more precise. The relative root-mean squared error for the aRSAP biomass estimates was 9% compared to 12% for tRSAP. Our results demonstrate that calibrated PAR-biomass relationships provide a robust method to infer changes in past plant biomass.

","language":"English","publisher":"SAGE Publishing","doi":"10.1177/0959683620988038","usgsCitation":"Knight, C.A., Baskaran, M., Bunting, M.J., Champagne, M.R., Potts, M.D., Wahl, D., Wanket, J., and Battles, J.J., 2021, Linking modern pollen accumulation rates to biomass: Quantitative vegetation reconstruction in the western Klamath Mountains, NW California, USA: The Holocene, v. 31, no. 5, p. 814-829, https://doi.org/10.1177/0959683620988038.","productDescription":"16 p.","startPage":"814","endPage":"829","ipdsId":"IP-122720","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":453850,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hull-repository.worktribe.com/output/3679635","text":"External Repository"},{"id":382454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Western Klamath Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.365234375,\n 40.052847601823984\n ],\n [\n -121.4208984375,\n 40.052847601823984\n ],\n [\n -121.4208984375,\n 42.220381783720605\n ],\n [\n -124.365234375,\n 42.220381783720605\n ],\n [\n -124.365234375,\n 40.052847601823984\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Knight, Clarke A. 0000-0003-0002-6959","orcid":"https://orcid.org/0000-0003-0002-6959","contributorId":248212,"corporation":false,"usgs":false,"family":"Knight","given":"Clarke","email":"","middleInitial":"A.","affiliations":[{"id":49825,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California 94720 USA","active":true,"usgs":false}],"preferred":false,"id":808617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baskaran, Mark","contributorId":87867,"corporation":false,"usgs":false,"family":"Baskaran","given":"Mark","email":"","affiliations":[{"id":7147,"text":"Wayne State University","active":true,"usgs":false}],"preferred":false,"id":808618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunting, M. Jane 0000-0002-3152-5745","orcid":"https://orcid.org/0000-0002-3152-5745","contributorId":248213,"corporation":false,"usgs":false,"family":"Bunting","given":"M.","email":"","middleInitial":"Jane","affiliations":[{"id":49826,"text":"Department of Geography, Geology and Environment, University of Hull, Cottingham Road, Hull, HU6 7RX UK","active":true,"usgs":false}],"preferred":false,"id":808619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Champagne, Marie Rhondelle 0000-0001-8236-3910","orcid":"https://orcid.org/0000-0001-8236-3910","contributorId":248214,"corporation":false,"usgs":true,"family":"Champagne","given":"Marie","email":"","middleInitial":"Rhondelle","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":808620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Potts, Matthew D. 0000-0001-7442-3944","orcid":"https://orcid.org/0000-0001-7442-3944","contributorId":248215,"corporation":false,"usgs":false,"family":"Potts","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":49825,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, California 94720 USA","active":true,"usgs":false}],"preferred":false,"id":808621,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wahl, David 0000-0002-0451-3554","orcid":"https://orcid.org/0000-0002-0451-3554","contributorId":206113,"corporation":false,"usgs":true,"family":"Wahl","given":"David","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":808622,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wanket, James","contributorId":248216,"corporation":false,"usgs":false,"family":"Wanket","given":"James","email":"","affiliations":[{"id":49829,"text":"Department of Geography, California State University, Sacramento, Sacramento, California 95819 USA","active":true,"usgs":false}],"preferred":false,"id":808623,"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":808624,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70217337,"text":"70217337 - 2021 - Assessing the impact of drought on arsenic exposure from private domestic wells in the conterminous United States","interactions":[],"lastModifiedDate":"2021-02-04T14:31:23.035113","indexId":"70217337","displayToPublicDate":"2021-01-13T11:02:40","publicationYear":"2021","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":"Assessing the impact of drought on arsenic exposure from private domestic wells in the conterminous United States","docAbstract":"

This study assesses the potential impact of drought on arsenic exposure from private domestic wells by using a previously developed statistical model that predicts the probability of elevated arsenic concentrations (>10 μg per liter) in water from domestic wells located in the conterminous United States (CONUS). The application of the model to simulate drought conditions used systematically reduced precipitation and recharge values. The drought conditions resulted in higher probabilities of elevated arsenic throughout most of the CONUS. While the increase in the probability of elevated arsenic was generally less than 10% at any one location, when considered over the entire CONUS, the increase has considerable public health implications. The population exposed to elevated arsenic from domestic wells was estimated to increase from approximately 2.7 million to 4.1 million people during drought. The model was also run using total annual precipitation and groundwater recharge values from the year 2012 when drought existed over a large extent of the CONUS. This simulation provided a method for comparing the duration of drought to changes in the predicted probability of high arsenic in domestic wells. These results suggest that the probability of exposure to arsenic concentrations greater than 10 μg per liter increases with increasing duration of drought. These findings indicate that drought has a potentially adverse impact on the arsenic hazard from domestic wells throughout the CONUS.

","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b05835","usgsCitation":"Lombard, M.A., Daniel, J., Jeddy, Z., Hay, L., and Ayotte, J.D., 2021, Assessing the impact of drought on arsenic exposure from private domestic wells in the conterminous United States: Environmental Science & Technology, v. 55, no. 3, p. 1822-1831, https://doi.org/10.1021/acs.est.9b05835.","productDescription":"10 p.","startPage":"1822","endPage":"1831","ipdsId":"IP-109293","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":453853,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.9b05835","text":"Publisher Index Page"},{"id":436586,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TU0R2T","text":"USGS data release","linkHelpText":"Datasets for assessing the impact of drought on arsenic exposure from private domestic wells in the 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Johnni","contributorId":247808,"corporation":false,"usgs":false,"family":"Daniel","given":"Johnni","email":"","affiliations":[{"id":17914,"text":"CDC","active":true,"usgs":false}],"preferred":false,"id":808396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeddy, Zuha","contributorId":247809,"corporation":false,"usgs":false,"family":"Jeddy","given":"Zuha","email":"","affiliations":[{"id":17914,"text":"CDC","active":true,"usgs":false}],"preferred":false,"id":808397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hay, Lauren 0000-0003-3763-4595","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":205020,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":808398,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 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Pinatubo in 1991","interactions":[],"lastModifiedDate":"2022-05-27T13:24:00.547192","indexId":"70231781","displayToPublicDate":"2021-01-13T08:17:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Formation of dense pyroclasts by sintering of ash particles during the preclimactic eruptions of Mt. Pinatubo in 1991","docAbstract":"

Dense, vitric, dacitic pyroclasts (dacite lithics) from the 1991 preclimactic explosions of Mt. Pinatubo were analyzed for their vesicular and crystal textures and dissolved H2O and CO2 contents. Micron-scale heterogeneities in groundmass glass volatile contents (0.9 wt% differences in H2O within 500 μm) are observed and argue that parts of the dacite lithics equilibrated at different depths before finally being constructed. Greater vesicularities and larger and greater number densities of vesicles are observed in groundmass glass around phenocrysts compared to groundmass glass away from phenocrysts, similar to textures produced in experiments that sintered bimodal distributions of particles. Furthermore, increasingly greater proportions of stretched and distorted vesicles are observed in lithics from the later explosions, which parallels the increasingly shorter reposes between explosions. Finally, micron-sized crystal fragments are ubiquitous in groundmass glass of all dacite lithics. The textures, together with the variable volatile contents, lead us to propose a model that the dacite lithics formed by rapid and repetitive sintering of ash particles derived from a variety of depths on the conduit walls above the fragmentation level. We speculate that sintering of conduit material produced impermeable layers that retarded gas flow through the conduit, causing pressure to build until the cap failed and the next explosion occurred.

","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-01427-y","usgsCitation":"Wang, Y., Gardner, J., and Hoblitt, R., 2021, Formation of dense pyroclasts by sintering of ash particles during the preclimactic eruptions of Mt. Pinatubo in 1991: Bulletin of Volcanology, v. 83, 6, 13 p., https://doi.org/10.1007/s00445-020-01427-y.","productDescription":"6, 13 p.","ipdsId":"IP-123722","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":401291,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","otherGeospatial":"Mount Pinatubo","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 120.28038024902344,\n 15.101957550324563\n ],\n [\n 120.41015624999999,\n 15.101957550324563\n ],\n [\n 120.41015624999999,\n 15.208662610868245\n ],\n [\n 120.28038024902344,\n 15.208662610868245\n ],\n [\n 120.28038024902344,\n 15.101957550324563\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"83","noUsgsAuthors":false,"publicationDate":"2021-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Yining","contributorId":292117,"corporation":false,"usgs":false,"family":"Wang","given":"Yining","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":843815,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gardner, James E.","contributorId":292118,"corporation":false,"usgs":false,"family":"Gardner","given":"James E.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":843816,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoblitt, Richard P. 0000-0001-5850-4760","orcid":"https://orcid.org/0000-0001-5850-4760","contributorId":292119,"corporation":false,"usgs":false,"family":"Hoblitt","given":"Richard P.","affiliations":[{"id":62834,"text":"USGS Volcano Science Center","active":true,"usgs":false}],"preferred":false,"id":843817,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263410,"text":"70263410 - 2021 - Coseismic fault slip and afterslip associated with the M5.7 March 18, 2020 Magna, Utah, earthquake","interactions":[],"lastModifiedDate":"2025-02-10T15:39:35.36594","indexId":"70263410","displayToPublicDate":"2021-01-13T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Coseismic fault slip and afterslip associated with the M5.7 March 18, 2020 Magna, Utah, earthquake","docAbstract":"

The 2020 Magna, Utah, earthquake produced observable crustal deformation over a ∼ 100 km2 area around the southeast margin of Great Salt Lake, but it did not produce any surface rupture. To obtain a detailed picture of the fault slip, we combine strong motion seismic waveforms with GPS static offsets and Interferometric Synthetic Aperture Radar (InSAR) observations to obtain kinematic and static slip models of the event. We sample the regional seismic wavefield with 3-component records from 68 stations of the University of Utah Seismograph Stations network. We find that coseismic slip and afterslip, with predominantly normal slip, distributed on a shallowly west-dipping plane, possibly augmented by afterslip on a steeply northeast-dipping plane, best fits the joint dataset. The west-dipping plane locates near previously inferred sources of interseismic creep at depth. Hence the earthquake may have occurred on the downdip ex-tension of the Wasatch fault and activated further slip (afterslip) at shallow depth east of the hypocenter. This inferred afterslip may have driven the vigorous aftershock activity that was concentrated east of the hypocenter.

","language":"English","publisher":"GeoScienceWorld","doi":"10.1785/0220200312","usgsCitation":"Pollitz, F., Wicks, C., and Svarc, J.L., 2021, Coseismic fault slip and afterslip associated with the M5.7 March 18, 2020 Magna, Utah, earthquake: Seismological Research Letters, v. 92, no. 2A, p. 741-754, https://doi.org/10.1785/0220200312.","productDescription":"14 p.","startPage":"741","endPage":"754","ipdsId":"IP-122052","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","city":"Magna","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.10921941420736,\n 40.723327593996174\n ],\n [\n -112.10921941420736,\n 40.684935353345196\n ],\n [\n -112.05066964819599,\n 40.684935353345196\n ],\n [\n -112.05066964819599,\n 40.723327593996174\n ],\n [\n -112.10921941420736,\n 40.723327593996174\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"92","issue":"2A","noUsgsAuthors":false,"publicationDate":"2021-01-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wicks, Charles 0000-0002-0809-1328","orcid":"https://orcid.org/0000-0002-0809-1328","contributorId":9023,"corporation":false,"usgs":true,"family":"Wicks","given":"Charles","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Svarc, Jerry L. 0000-0002-2802-4528","orcid":"https://orcid.org/0000-0002-2802-4528","contributorId":212736,"corporation":false,"usgs":true,"family":"Svarc","given":"Jerry","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926886,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217368,"text":"70217368 - 2021 - Three-dimensional distribution of residence time metrics in the glaciated United States using metamodels trained on general numerical models","interactions":[],"lastModifiedDate":"2024-09-16T22:32:11.340035","indexId":"70217368","displayToPublicDate":"2021-01-12T07:59:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Three-dimensional distribution of residence time metrics in the glaciated United States using metamodels trained on general numerical models","docAbstract":"

Residence time distribution (RTD) is a critically important characteristic of groundwater flow systems; however, it cannot be measured directly. RTD can be inferred from tracer data with analytical models (few parameters) or with numerical models (many parameters). The second approach permits more variation in system properties but is used less frequently than the first because large‐scale numerical models can be resource intensive. Using a novel automated approach, a set of 115 inexpensive general simulation models (GSMs) was used to create RTD metrics (fraction of young groundwater, defined as < 65 years old; mean travel time of young fraction; median travel time of old fraction; and mean path length). GSMs captured the general trends in measured tritium concentrations in 431 wells. Boosted Regression Tree metamodels were trained to predict these RTD metrics using available wall‐to‐wall hydrogeographic digital sets as explanatory features. The metamodels produced a three‐dimensional distribution of predictions throughout the glacial system that generally matched with the numerical model RTD metrics. In addition to the expected importance of aquifer thickness and recharge rate in predicting RTD metrics, two new data sets, Multi‐Order Hydrologic Position (MOHP) and hydrogeologic terrane were important predictors. These variables by themselves produced metamodels with Nash‐Sutcliffe efficiency close to the full metamodel. Metamodel predictions showed that the volume of young groundwater stored in the glaciated U.S. is about 6,000 km3, or about 0.5% of globally stored young groundwater.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027335","usgsCitation":"Starn, J., Kauffman, L.J., Carlson, C.S., Reddy, J., and Fienen, M., 2021, Three-dimensional distribution of residence time metrics in the glaciated United States using metamodels trained on general numerical models: Water Resources Research, v. 57, no. 2, ee2020WR027335, 17 p., https://doi.org/10.1029/2020WR027335.","productDescription":"ee2020WR027335, 17 p.","ipdsId":"IP-111637","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488991,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr027335","text":"Publisher Index Page"},{"id":436588,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BNWWCU","text":"USGS data release","linkHelpText":"Data for Three-dimensional distribution of groundwater residence time metrics in the glaciated United States using metamodels trained on general numerical simulation models"},{"id":382315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.75744400890645,\n 49.35633946833349\n ],\n [\n -125.75744400890645,\n 42.11912973645357\n ],\n [\n -67.66280273829909,\n 42.11912973645357\n ],\n [\n -67.66280273829909,\n 49.35633946833349\n ],\n [\n -125.75744400890645,\n 49.35633946833349\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"57","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Starn, J. Jeffrey 0000-0001-5909-0010 jjstarn@usgs.gov","orcid":"https://orcid.org/0000-0001-5909-0010","contributorId":1916,"corporation":false,"usgs":true,"family":"Starn","given":"J. Jeffrey","email":"jjstarn@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":808531,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kauffman, Leon J. 0000-0003-4564-0362","orcid":"https://orcid.org/0000-0003-4564-0362","contributorId":206428,"corporation":false,"usgs":true,"family":"Kauffman","given":"Leon","email":"","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808532,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carlson, Carl S. 0000-0001-7142-3519 cscarlso@usgs.gov","orcid":"https://orcid.org/0000-0001-7142-3519","contributorId":1694,"corporation":false,"usgs":true,"family":"Carlson","given":"Carl","email":"cscarlso@usgs.gov","middleInitial":"S.","affiliations":[{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808533,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reddy, James E. 0000-0002-6998-7267","orcid":"https://orcid.org/0000-0002-6998-7267","contributorId":206426,"corporation":false,"usgs":true,"family":"Reddy","given":"James E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808534,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808535,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217256,"text":"70217256 - 2021 - Historic population estimates for bottlenose dolphins (Tursiops truncatus) in Aragua, Venezuela indicate monitoring need","interactions":[],"lastModifiedDate":"2021-01-14T13:46:00.315075","indexId":"70217256","displayToPublicDate":"2021-01-12T07:41:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":869,"text":"Aquatic Mammals","active":true,"publicationSubtype":{"id":10}},"title":"Historic population estimates for bottlenose dolphins (Tursiops truncatus) in Aragua, Venezuela indicate monitoring need","docAbstract":"

This study reports historic capture-mark-recapture survival and abundance estimates of common bottlenose dolphins (Tursiops truncatus) based on photo-identification surveys of coastal Venezuela (along the Aragua coast between Turiamo Bay and Puerto Colombia). We used the most recent data available: dolphins identified by unique dorsal fin marks during wet and dry season surveys conducted from 2004 to 2008. Dolphin encounter histories were analyzed in the Closed Capture Robust Design framework, with the top model including random movement, constant survival, and capture-recapture probabilities that varied by secondary periods. Survival of marked adults was estimated at 0.99 (95% CI = 0.97 to 1.00). Population estimates for all adults (marked and unmarked) averaged 31 animals (SD = 13.8), and for all dolphins (all adults and calves), 41 animals (SD = 17.2). Coastal bottlenose dolphins face numerous threats, including ship strikes, oil spills, conflict with recreational and industrial fisheries, other negative human interactions, biotoxins, chemicals, noise, freshwater discharge, and coastal development. Further, small populations are, in general, at increased risk due to reduced resiliency and recovery potential when exposed to such threats and to expected environmental and demographic stochasticity. These historic estimates of abundance and survival are critical for establishing a reference state and indicate a need for ongoing monitoring of the small dolphin population while the Aragua coast is still, as of yet, relatively little impacted by humans. Should coastal development increase (as is the global trend) and/or environmental catastrophes (e.g., harmful algal blooms, hurricanes, and oil spills) occur, these historic estimates will be essential for assessing impacts and guiding management and conservation interventions. Our results show year-round dolphin presence and highlight the Venezuelan coastal–oceanic landscape as an area of both future research and conservation importance.

","language":"English","publisher":"Aquatic Mammals","doi":"10.1578/AM.47.1.2021.10","usgsCitation":"Cobarrubia-Russo, S., Barber-Meyer, S., Barreto, G.R., and Molero-Lizarraga, A., 2021, Historic population estimates for bottlenose dolphins (Tursiops truncatus) in Aragua, Venezuela indicate monitoring need: Aquatic Mammals, v. 1, no. 47, p. 10-20, https://doi.org/10.1578/AM.47.1.2021.10.","productDescription":"11 p.","startPage":"10","endPage":"20","ipdsId":"IP-118661","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":382151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Venezuela","state":"Aragua","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.291259765625,\n 10.541821094659076\n ],\n [\n -67.87353515625,\n 10.477008906900293\n ],\n [\n -67.818603515625,\n 10.3257278721883\n ],\n [\n -67.6318359375,\n 10.109486058403773\n ],\n [\n -67.445068359375,\n 10.001310360636928\n ],\n [\n -67.2802734375,\n 9.903921416774978\n ],\n [\n -67.03857421875,\n 9.709057068618208\n ],\n [\n -66.95068359374999,\n 9.611582210984674\n ],\n [\n -67.0166015625,\n 9.44906182688142\n ],\n [\n -66.73095703125,\n 9.308148692484803\n ],\n [\n -66.51123046875,\n 9.524914302345891\n ],\n [\n -66.544189453125,\n 10.055402736564236\n ],\n [\n -67.03857421875,\n 10.152746165571939\n ],\n [\n -67.291259765625,\n 10.541821094659076\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","issue":"47","noUsgsAuthors":false,"publicationDate":"2021-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Cobarrubia-Russo, Sergio 0000-0002-3351-1929","orcid":"https://orcid.org/0000-0002-3351-1929","contributorId":247716,"corporation":false,"usgs":false,"family":"Cobarrubia-Russo","given":"Sergio","email":"","affiliations":[{"id":49631,"text":"Laboratorio de Ecosistemas y Cambio Global, Centro de Ecología, Instituto Venezolano de Investigaciones Científicas","active":true,"usgs":false}],"preferred":false,"id":808175,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber-Meyer, Shannon 0000-0002-3048-2616","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":217939,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":808176,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barreto, Guillermo R.","contributorId":247743,"corporation":false,"usgs":false,"family":"Barreto","given":"Guillermo","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":808205,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Molero-Lizarraga, Alimar 0000-0003-1646-9818","orcid":"https://orcid.org/0000-0003-1646-9818","contributorId":247717,"corporation":false,"usgs":false,"family":"Molero-Lizarraga","given":"Alimar","email":"","affiliations":[{"id":49634,"text":"Unidad de Diversidad Biológica, Instituto Venezolano de Investigaciones Cientificas IVIC","active":true,"usgs":false}],"preferred":false,"id":808206,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217252,"text":"70217252 - 2021 - Exposure to domoic acid is an ecological driver of cardiac disease in southern sea otters","interactions":[],"lastModifiedDate":"2021-01-14T13:31:16.688143","indexId":"70217252","displayToPublicDate":"2021-01-12T07:28:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Exposure to domoic acid is an ecological driver of cardiac disease in southern sea otters","docAbstract":"

Harmful algal blooms produce toxins that bioaccumulate in the food web and adversely affect humans, animals, and entire marine ecosystems. Blooms of the diatom Pseudo-nitzschia can produce domoic acid (DA), a toxin that most commonly causes neurological disease in endothermic animals, with cardiovascular effects that were first recognized in southern sea otters. Over the last 20 years, DA toxicosis has caused significant morbidity and mortality in marine mammals and seabirds along the west coast of the USA. Identifying DA exposure has been limited to toxin detection in biological fluids using biochemical assays, yet measurement of systemic toxin levels is an unreliable indicator of exposure dose or timing. Furthermore, there is little information regarding repeated DA exposure in marine wildlife. Here, the association between long-term environmental DA exposure and fatal cardiac disease was investigated in a longitudinal study of 186 free-ranging sea otters in California from 2001 – 2017, highlighting the chronic health effects of a marine toxin. A novel Bayesian spatiotemporal approach was used to characterize environmental DA exposure by combining several DA surveillance datasets and integrating this with life history data from radio-tagged otters in a time-dependent survival model. In this study, a sea otter with high DA exposure had a 1.7-fold increased hazard of fatal cardiomyopathy compared to an otter with low exposure. Otters that consumed a high proportion of crab and clam had a 2.5- and 1.2-times greater hazard of death due to cardiomyopathy than otters that consumed low proportions. Increasing age is a well-established predictor of cardiac disease, but this study is the first to identify that DA exposure affects the risk of cardiomyopathy more substantially in prime-age adults than aged adults. A 4-year-old otter with high DA exposure had 2.3 times greater risk of fatal cardiomyopathy than an otter with low exposure, while a 10-year old otter with high DA exposure had just 1.2 times greater risk. High Toxoplasma gondii titers also increased the hazard of death due to heart disease 2.4-fold. Domoic acid exposure was most detrimental for prime-age adults, whose survival and reproduction are vital for population growth, suggesting that persistent DA exposure will likely impact long-term viability of this threatened species. These results offer insight into the pervasiveness of DA in the food web and raise awareness of under-recognized chronic health effects of DA for wildlife at a time when toxic blooms are on the rise.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2020.101973","usgsCitation":"Moriarty, M.E., Tinker, M., Miller, M., Tomoleoni, J.A., Staedler, M.M., Fujii, J.A., Batac, F.I., Dodd, E.M., Kudela, R.M., Zubkousky-White, V., and Johnson, C., 2021, Exposure to domoic acid is an ecological driver of cardiac disease in southern sea otters: Harmful Algae, v. 101, 101973, 12 p., https://doi.org/10.1016/j.hal.2020.101973.","productDescription":"101973, 12 p.","ipdsId":"IP-125410","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453868,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2020.101973","text":"Publisher Index Page"},{"id":382149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.56347656249999,\n 34.27083595165\n ],\n [\n -120.10253906249999,\n 34.27083595165\n ],\n [\n -120.10253906249999,\n 37.38761749978395\n ],\n [\n -122.56347656249999,\n 37.38761749978395\n ],\n [\n -122.56347656249999,\n 34.27083595165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"101","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moriarty, Megan E.","contributorId":247708,"corporation":false,"usgs":true,"family":"Moriarty","given":"Megan","email":"","middleInitial":"E.","affiliations":[{"id":49627,"text":"Karen C. Drayer Wildlife Health Center and EpiCenter for Disease Dynamics, One Health Institute, University of California Davis School of Veterinary Medicine, 1089 Veterinary Medicine Dr. VM3B, Davis, CA, United States","active":true,"usgs":false}],"preferred":true,"id":808157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinker, M. Tim 0000-0002-3314-839X","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":221787,"corporation":false,"usgs":false,"family":"Tinker","given":"M. Tim","affiliations":[{"id":40428,"text":"University of California, Santa Cruz; former USGS PI","active":true,"usgs":false}],"preferred":false,"id":808158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Melissa","contributorId":214302,"corporation":false,"usgs":false,"family":"Miller","given":"Melissa","affiliations":[{"id":39007,"text":"CA Dept of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":808159,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":808160,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staedler, Michelle M. 0000-0002-1101-6580","orcid":"https://orcid.org/0000-0002-1101-6580","contributorId":213742,"corporation":false,"usgs":false,"family":"Staedler","given":"Michelle","email":"","middleInitial":"M.","affiliations":[{"id":6953,"text":"Monterey Bay Aquarium","active":true,"usgs":false}],"preferred":false,"id":808161,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fujii, Jessica A. 0000-0003-4794-479X","orcid":"https://orcid.org/0000-0003-4794-479X","contributorId":196602,"corporation":false,"usgs":false,"family":"Fujii","given":"Jessica","email":"","middleInitial":"A.","affiliations":[],"preferred":true,"id":808162,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Batac, Francesca I.","contributorId":168467,"corporation":false,"usgs":false,"family":"Batac","given":"Francesca","email":"","middleInitial":"I.","affiliations":[{"id":13632,"text":"CDFW, Bishop, CA","active":true,"usgs":false}],"preferred":false,"id":808163,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dodd, Erin M.","contributorId":168468,"corporation":false,"usgs":false,"family":"Dodd","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":13632,"text":"CDFW, Bishop, CA","active":true,"usgs":false}],"preferred":false,"id":808164,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":808165,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zubkousky-White, Vanessa","contributorId":247709,"corporation":false,"usgs":false,"family":"Zubkousky-White","given":"Vanessa","email":"","affiliations":[{"id":49630,"text":"California Department of Public Health, Environmental Management Branch, 850 Marina Bay Pkwy, Richmond, CA, United States","active":true,"usgs":false}],"preferred":false,"id":808166,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Johnson, Christine K.","contributorId":23771,"corporation":false,"usgs":false,"family":"Johnson","given":"Christine K.","affiliations":[],"preferred":false,"id":808167,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70217220,"text":"70217220 - 2021 - Eroding Cascadia— Sediment and solute transport and landscape denudation in western Oregon and northwestern California","interactions":[],"lastModifiedDate":"2021-10-08T11:27:36.141752","indexId":"70217220","displayToPublicDate":"2021-01-11T07:43:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Eroding Cascadia— Sediment and solute transport and landscape denudation in western Oregon and northwestern California","docAbstract":"

Riverine measurements of sediment and solute transport give empirical basin-scale estimates of bed-load, suspended-sediment, and silicate-solute fluxes for 100,000 km2 of northwestern California and western Oregon. This spatially explicit sediment budget shows the multifaceted control of geology and physiography on the rates and processes of fluvial denudation. Bed-load transport is greatest for steep basins, particularly in areas underlain by the accreted Klamath terrane. Bed-load flux commonly decreases downstream as clasts convert to suspended load by breakage and attrition, particularly for softer rock types. Suspended load correlates strongly with lithology, basin slope, precipitation, and wildfire disturbance. It is highest in steep regions of soft rocks, and our estimates suggest that much of the suspended load is derived from bed-load comminution. Dissolution, measured by basin-scale silicate-solute yield, constitutes a third of regional landscape denudation. Solute yield correlates with precipitation and is proportionally greatest in low-gradient and wet basins and for high parts of the Cascade Range, where undissected Quaternary volcanic rocks soak in 2−3 m of annual precipitation. Combined, these estimates provide basin-scale erosion rates ranging from ∼50 t ∙ km−2 ∙ yr−1 (approximately equivalent to 0.02 mm ∙ yr−1) for low-gradient basins such as the Willamette River to ∼500 t ∙ km−2 ∙ yr−1 (∼0.2 mm ∙ yr−1) for steep coastal drainages. The denudation rates determined here from modern measurements are less than those estimated by longer-term geologic assessments, suggesting episodic disturbances such as fire, flood, seismic shaking, and climate change significantly add to long-term landscape denudation.

","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35710.1","usgsCitation":"O'Connor, J., Mangano, J., Wise, D., and Roering, J.R., 2021, Eroding Cascadia— Sediment and solute transport and landscape denudation in western Oregon and northwestern California: Geological Society of America Bulletin, v. 133, no. 9-10, p. 1851-1874, https://doi.org/10.1130/B35710.1.","productDescription":"24 p.","startPage":"1851","endPage":"1874","ipdsId":"IP-118050","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":382127,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","otherGeospatial":"Cascade range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.69482421875,\n 38.42777351132905\n ],\n [\n -121.640625,\n 38.42777351132905\n ],\n [\n -121.640625,\n 46.63435070293566\n ],\n [\n -124.69482421875,\n 46.63435070293566\n ],\n [\n -124.69482421875,\n 38.42777351132905\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","issue":"9-10","noUsgsAuthors":false,"publicationDate":"2021-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":808085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mangano, Joseph F. 0000-0003-4213-8406","orcid":"https://orcid.org/0000-0003-4213-8406","contributorId":247673,"corporation":false,"usgs":true,"family":"Mangano","given":"Joseph F.","affiliations":[{"id":5072,"text":"Office of Communication and Publishing","active":true,"usgs":true}],"preferred":true,"id":808086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wise, Daniel R. 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":210599,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel R.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roering, Joshua R.","contributorId":247674,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua","email":"","middleInitial":"R.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":808088,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236104,"text":"70236104 - 2021 - Globally prevalent land nitrogen memory amplifies water pollution following drought years","interactions":[],"lastModifiedDate":"2022-08-29T12:27:22.037665","indexId":"70236104","displayToPublicDate":"2021-01-11T07:26:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Globally prevalent land nitrogen memory amplifies water pollution following drought years","docAbstract":"

Enhanced riverine delivery of terrestrial nitrogen (N) has polluted many freshwater and coastal ecosystems, degrading drinking water and marine resources. An emerging view suggests a contribution of land N memory effects—impacts of antecedent dry conditions on land N accumulation that disproportionately increase subsequent river N loads. To date, however, such effects have only been explored for several relatively small rivers covering a few episodes. Here we introduce an index for quantifying land N memory effects and assess their prevalence using regional observations and global terrestrial-freshwater ecosystem model outputs. Model analyses imply that land N memory effects are globally prevalent but vary widely in strength. Strong effects reflect large soil dissolved inorganic N (DIN) surpluses by the end of dry years. During the subsequent wetter years, the surpluses are augmented by soil net mineralization pulses, which outpace plant uptake and soil denitrification, resulting in disproportionately increased soil leaching and eventual river loads. These mechanisms are most prominent in areas with high hydroclimate variability, warm climates, and ecosystem disturbances. In 48 of the 118 basins analyzed, strong memory effects produce 43% (21%–88%) higher DIN loads following drought years than following average years. Such a marked influence supports close consideration of prevalent land N memory effects in water-pollution management efforts.

","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/abd1a0","usgsCitation":"Lee, M., Stock, C., Shevliakova, E., Malyshev, S., and Milly, P.C., 2021, Globally prevalent land nitrogen memory amplifies water pollution following drought years: Environmental Research Letters, v. 16, 014049, 12 p., https://doi.org/10.1088/1748-9326/abd1a0.","productDescription":"014049, 12 p.","ipdsId":"IP-097531","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":453872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/abd1a0","text":"Publisher Index Page"},{"id":405787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2021-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Lee, Minjin","contributorId":177261,"corporation":false,"usgs":false,"family":"Lee","given":"Minjin","email":"","affiliations":[],"preferred":false,"id":850076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stock, Charles A.","contributorId":217586,"corporation":false,"usgs":false,"family":"Stock","given":"Charles A.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":850078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shevliakova, Elena","contributorId":201589,"corporation":false,"usgs":false,"family":"Shevliakova","given":"Elena","email":"","affiliations":[{"id":36211,"text":"GFDL/NOAA","active":true,"usgs":false}],"preferred":false,"id":850077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Malyshev, Sergey","contributorId":189177,"corporation":false,"usgs":false,"family":"Malyshev","given":"Sergey","affiliations":[],"preferred":false,"id":850080,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milly, Paul C. D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":176836,"corporation":false,"usgs":true,"family":"Milly","given":"Paul","email":"cmilly@usgs.gov","middleInitial":"C. D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":false,"id":850079,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229141,"text":"70229141 - 2021 - Regal fritillary (Speyeria idalia) sex ratio in tallgrass prairie: Effects of survey timing and management regime","interactions":[],"lastModifiedDate":"2022-03-01T13:25:40.088538","indexId":"70229141","displayToPublicDate":"2021-01-11T07:22:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5153,"text":"The American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Regal fritillary (Speyeria idalia) sex ratio in tallgrass prairie: Effects of survey timing and management regime","docAbstract":"

The regal fritillary, Speyeria idalia (Drury), was once a common inhabitant of North American grassland communities. Regal fritillary populations are commonly reported to have a male biased adult sex ratio (ASR) throughout their range. We assessed the observed ASR of regal fritillary throughout an annual flight period, investigated how the overall density of both sexes changed, and tested effects of prescribed fire, grazing and haying management treatments on male and female density. We found that regal fritillary exhibited an observed 2:1 male biased ASR across the entire emergence period. Our analysis also revealed that male density peaked earlier than female density in the flight period. Point estimates of density indicated sites that received prescribed burning at the moderate fire-return interval supported ≥1.3 times greater density of males and ≥5.6 times greater density of females versus sites burned with short and long fire-return intervals. Additionally, this effect was enhanced when combined with grazing which showed males were ≥1.9 times and females had ≥1.2 times greater point estimates of density in sites that were grazed and burned at a moderate fire-return interval versus other sites. The relatively stable status of regal fritillary within our study region suggests that a 2:1 male to female ASR may be considered the model composition of populations throughout their range. Likewise, the dynamic nature of the ASR throughout the flight period highlights the importance of conducting surveys across the flight period. Finally, these results corroborate an increasing number of research results that reveal common prairie management practices, such as prescribed fire can be applied within sites that contain regal fritillary and continue to support stable populations.

","language":"English","publisher":"BioOne","doi":"10.1637/0003-0031-185.1.57","usgsCitation":"McCullough, K., Haukos, D.A., and Albanese, G., 2021, Regal fritillary (Speyeria idalia) sex ratio in tallgrass prairie: Effects of survey timing and management regime: The American Midland Naturalist, v. 185, no. 1, p. 57-76, https://doi.org/10.1637/0003-0031-185.1.57.","productDescription":"20 p.","startPage":"57","endPage":"76","ipdsId":"IP-122840","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396595,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://bioone.org/journals/the-american-midland-naturalist/volume-185/issue-1/0003-0031-185.1.57/Regal-Fritillary-Speyeria-idalia-Sex-Ratio-in-Tallgrass-Prairie/10.1637/0003-0031-185.1.57.full"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.3388671875,\n 38.20365531807149\n ],\n [\n -95.20751953125,\n 38.20365531807149\n ],\n [\n -95.20751953125,\n 39.774769485295465\n ],\n [\n -97.3388671875,\n 39.774769485295465\n ],\n [\n -97.3388671875,\n 38.20365531807149\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"185","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCullough, Kelsey","contributorId":200244,"corporation":false,"usgs":false,"family":"McCullough","given":"Kelsey","email":"","affiliations":[],"preferred":false,"id":836760,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":836759,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Albanese, Gene","contributorId":287438,"corporation":false,"usgs":false,"family":"Albanese","given":"Gene","affiliations":[{"id":61586,"text":"mas","active":true,"usgs":false}],"preferred":false,"id":836761,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217195,"text":"70217195 - 2021 - Upland burning and grazing as strategies to offset climate-change effects on wetlands","interactions":[],"lastModifiedDate":"2021-04-08T14:28:20.228474","indexId":"70217195","displayToPublicDate":"2021-01-11T07:11:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Upland burning and grazing as strategies to offset climate-change effects on wetlands","docAbstract":"

Wetland ecosystems perform a multitude of services valued by society and provide critical habitat for migratory birds and other wildlife. Despite their importance, wetlands have been lost to different local, regional, and global drivers. Remaining wetlands are extremely sensitive to changing temperature and precipitation regimes. Management of grassland areas in wetland catchments may be an effective strategy for counteracting potentially negative impacts of climate change on wetlands. Our objective was to estimate the effects of climate changes on wetland hydrology, and to explore strategies for increasing surface-water inputs to wetlands. We coupled a field study with process-based simulation modeling of wetland-water levels. We found that climate change could decrease the number of wetlands that hold ponded water during the waterfowl breeding season by 14% under a hot wet scenario or 29% under a hot dry scenario if no upland-management actions were taken. Upland burning reduced pond losses to 9% (hot wet) and 26% (hot dry). Upland grazing resulted in the smallest loss of ponded wetlands, 6% loss under the hot-and-wet scenario and 22% loss under the hot-and-dry scenario. Overall, water inputs could be increased by either burning or grazing of upland vegetation thereby reducing pond losses during the waterfowl breeding season. While field results suggest that both grazing and burning can reduce the vegetative structure that could lead to increases in runoff in grassland catchments, our model simulations indicated that additional actions may be needed for managers to minimize future meteorologically driven water losses.


","language":"English","publisher":"Springer","doi":"10.1007/s11273-020-09778-1","usgsCitation":"McKenna, O.P., Renton, D.A., Mushet, D.M., and DeKeyser, E.S., 2021, Upland burning and grazing as strategies to offset climate-change effects on wetlands: Wetlands Ecology and Management, v. 29, https://doi.org/10.1007/s11273-020-09778-1.","productDescription":"16 p.","startPage":"208","ipdsId":"IP-112405","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":453876,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11273-020-09778-1","text":"Publisher Index Page"},{"id":382084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","edition":"193","noUsgsAuthors":false,"publicationDate":"2021-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":807933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renton, David A. drenton@usgs.gov","contributorId":247571,"corporation":false,"usgs":false,"family":"Renton","given":"David","email":"drenton@usgs.gov","middleInitial":"A.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":807934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":807935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeKeyser, Edward S.","contributorId":247572,"corporation":false,"usgs":false,"family":"DeKeyser","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":807936,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217204,"text":"70217204 - 2021 - Thermal constraints on energy balance, behaviour and spatial distribution of grizzly bears","interactions":[],"lastModifiedDate":"2021-02-17T21:48:01.347238","indexId":"70217204","displayToPublicDate":"2021-01-10T07:08:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1711,"text":"Functional Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Thermal constraints on energy balance, behaviour and spatial distribution of grizzly bears","docAbstract":"1. Heat dissipation limit theory posits that energy available for growth and reproduction in endotherms is limited by their ability to dissipate heat. In mammals, endogenous heat production increases markedly during gestation and lactation, and thus female mammals may be subject to greater thermal constraints on energy expenditure than males. Such constraints likely have important implications for behaviour and population performance in a warming climate.\n2. We used a mechanistic simulation model based on the first principles of heat and mass transfer to study thermal constraints on activity (both timing and intensity) of captive female grizzly bears Ursus arctos in current and future climate scenarios. We then quantified the relative importance of regulatory behaviours for maintaining heat balance using GPS telemetry locations of lactating versus non-lactating female bears from Yellowstone National Park, and assessed the degree to which costs of thermoregulation constrained the distribution of sampled bears in space and time.\n3. Lactating female bears benefitted considerably more from behavioural cooling mechanisms (e.g. partial submersion in cool water or bedding on cool substrate) than non-lactating females in our simulations; the availability of water for thermoregulation increased the number of hours during which lactating females could be active by up to 60% under current climatic conditions and by up to 43% in the future climate scenario. Moreover, even in the future climate scenario, lactating bears were able to achieve heat balance 24 hr/day by thermoregulating behaviourally when water was available to facilitate cooling.\n4. The most important predictor of female grizzly bear distribution in Yellowstone, regardless of reproductive status, was elevation. However, variables associated with the thermal environment were relatively more important for predicting the distribution of lactating than non-lactating female bears.\n5. Our results suggest that the costs of heat dissipation, which are modulated by climate, may impose constraints on the behaviour and energetics of large endotherms like grizzly bears, and that access to water for cooling will likely be an increasingly important driver of grizzly bear distribution in Yellowstone as the climate continues to warm.","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2435.13727","usgsCitation":"Rogers, S.A., Robbins, C.T., Mathewson, P.D., Carnahan, A.M., van Manen, F.T., Haroldson, M.A., Porter, W., Rogers, T.R., Soule, T., and Long, R.A., 2021, Thermal constraints on energy balance, behaviour and spatial distribution of grizzly bears: Functional Ecology, v. 35, no. 2, p. 398-410, https://doi.org/10.1111/1365-2435.13727.","productDescription":"13 p.","startPage":"398","endPage":"410","ipdsId":"IP-117651","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":453881,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2435.13727","text":"Publisher Index Page"},{"id":382083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.20361328125,\n 43.58834891179792\n ],\n [\n -108.91845703124999,\n 43.58834891179792\n ],\n [\n -108.91845703124999,\n 45.0502402697946\n ],\n [\n -111.20361328125,\n 45.0502402697946\n ],\n [\n -111.20361328125,\n 43.58834891179792\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-01-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Rogers, Savannah A.","contributorId":247592,"corporation":false,"usgs":false,"family":"Rogers","given":"Savannah","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":807984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robbins, Charlie T.","contributorId":247593,"corporation":false,"usgs":false,"family":"Robbins","given":"Charlie","email":"","middleInitial":"T.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":807985,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mathewson, Paul D.","contributorId":247594,"corporation":false,"usgs":false,"family":"Mathewson","given":"Paul","email":"","middleInitial":"D.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":807986,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carnahan, Anthony M.","contributorId":207641,"corporation":false,"usgs":false,"family":"Carnahan","given":"Anthony","email":"","middleInitial":"M.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":807987,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":807988,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":807989,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Porter, Warren P.","contributorId":247595,"corporation":false,"usgs":false,"family":"Porter","given":"Warren P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":807990,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rogers, Taylor R.","contributorId":247596,"corporation":false,"usgs":false,"family":"Rogers","given":"Taylor","email":"","middleInitial":"R.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":807991,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Soule, Terrence","contributorId":247597,"corporation":false,"usgs":false,"family":"Soule","given":"Terrence","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":807992,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Long, Ryan A.","contributorId":236989,"corporation":false,"usgs":false,"family":"Long","given":"Ryan","email":"","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":807993,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70217756,"text":"70217756 - 2021 - Uranium(VI) attenuation in a carbonate-bearing oxic alluvial aquifer","interactions":[],"lastModifiedDate":"2021-02-01T17:26:10.71364","indexId":"70217756","displayToPublicDate":"2021-01-08T11:23:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Uranium(VI) attenuation in a carbonate-bearing oxic alluvial aquifer","docAbstract":"

Uranium minerals are commonly found in soils and sediment across the United States at an average concentration of 2–4 mg/kg. Uranium occurs in the environment primarily in two forms, the oxidized, mostly soluble uranium(VI) form, or the reduced, sparingly soluble reduced uranium(IV) form. Here we describe subsurface geochemical conditions that result in low uranium concentrations in an alluvial aquifer with naturally occurring uranium in soils and sediments in the presence of complexing ligands under oxidizing conditions. Groundwater was saturated with respect to calcite and contained calcium (78–90 mg/L) with elevated levels of carbonate alkalinity (291–416 mg/L as HCO3−). X-ray adsorption near edge structure (XANES) spectroscopy identified that sediment-associated uranium was oxidized as a uranium(VI) form (85%). Calcite was the predominant mineral by mass in the ultrafine fraction in uranium-bearing sediments (>16 mg/kg). Groundwater geochemical modeling indicated calcite and/or a calcium-uranyl-carbonate mineral such as liebigite in equilibrium with groundwater. The δ13C (0.57‰ ± 0.15‰) was indicative of abiotic carbonate deposition. Thus, solid-phase uranium(VI) associated with carbonate is likely maintaining uranium(VI) groundwater levels below the maximum contaminant level (MCL; 30 µg/L), presenting a deposition mechanism for uranium attenuation rather than solely a means of mobilization.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.125089","usgsCitation":"Nolan, P., Bone, S., Campbell, K.M., Pannell, D., Healy, O., Stange, M., Bargar, J., and Weber, K., 2021, Uranium(VI) attenuation in a carbonate-bearing oxic alluvial aquifer: Journal of Hazardous Materials, v. 412, 125089, 11 p., https://doi.org/10.1016/j.jhazmat.2021.125089.","productDescription":"125089, 11 p.","ipdsId":"IP-124037","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":453886,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1807547","text":"Publisher Index Page"},{"id":436589,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98GDZFV","text":"USGS data release","linkHelpText":"X-ray diffraction data of sediment samples from Hastings, Nebraska"},{"id":382853,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","city":"Hastings","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.49929809570312,\n 40.49030405370212\n ],\n [\n -98.27133178710938,\n 40.49030405370212\n ],\n [\n -98.27133178710938,\n 40.65980593837852\n ],\n [\n -98.49929809570312,\n 40.65980593837852\n ],\n [\n -98.49929809570312,\n 40.49030405370212\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"412","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nolan, PJ","contributorId":248603,"corporation":false,"usgs":false,"family":"Nolan","given":"PJ","email":"","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":809493,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bone, S","contributorId":248604,"corporation":false,"usgs":false,"family":"Bone","given":"S","email":"","affiliations":[{"id":36408,"text":"SLAC National Accelerator Laboratory","active":true,"usgs":false}],"preferred":false,"id":809494,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell, Kate M. 0000-0002-8715-5544 kcampbell@usgs.gov","orcid":"https://orcid.org/0000-0002-8715-5544","contributorId":1441,"corporation":false,"usgs":true,"family":"Campbell","given":"Kate","email":"kcampbell@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":809495,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pannell, David","contributorId":217709,"corporation":false,"usgs":false,"family":"Pannell","given":"David","email":"","affiliations":[{"id":16662,"text":"University of Western Australia","active":true,"usgs":false}],"preferred":false,"id":809496,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Healy, O","contributorId":248605,"corporation":false,"usgs":false,"family":"Healy","given":"O","email":"","affiliations":[{"id":16602,"text":"University of Nebraska, Lincoln","active":true,"usgs":false}],"preferred":false,"id":809497,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stange, M","contributorId":248606,"corporation":false,"usgs":false,"family":"Stange","given":"M","email":"","affiliations":[{"id":49962,"text":"Hastings Utilities","active":true,"usgs":false}],"preferred":false,"id":809498,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bargar, J","contributorId":248607,"corporation":false,"usgs":false,"family":"Bargar","given":"J","affiliations":[{"id":36408,"text":"SLAC National Accelerator Laboratory","active":true,"usgs":false}],"preferred":false,"id":809499,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weber, KA","contributorId":248608,"corporation":false,"usgs":false,"family":"Weber","given":"KA","email":"","affiliations":[{"id":16602,"text":"University of Nebraska, Lincoln","active":true,"usgs":false}],"preferred":false,"id":809500,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70218764,"text":"70218764 - 2021 - Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska","interactions":[],"lastModifiedDate":"2021-03-12T14:36:45.473522","indexId":"70218764","displayToPublicDate":"2021-01-08T08:31:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7753,"text":"Frontiers in Earth Science","active":true,"publicationSubtype":{"id":10}},"title":"Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska","docAbstract":"

Accelerating erosion of the Alaska Beaufort Sea coast is increasing inputs of organic matter from land to the Arctic Ocean, and improved estimates of organic matter stocks in eroding coastal permafrost are needed to assess their mobilization rates under contemporary conditions. We collected three permafrost cores (4.5–7.5 m long) along a geomorphic gradient near Drew Point, Alaska, where recent erosion rates average 17.2 m year−1. Down-core patterns indicate that organic-rich soils and lacustrine sediments (12–45% total organic carbon; TOC) in the active layer and upper permafrost accumulated during the Holocene. Deeper permafrost (below 3 m elevation) mainly consists of Late Pleistocene marine sediments with lower organic matter content (∼1% TOC), lower C:N ratios, and higher δ13C values. Radiocarbon-based estimates of organic carbon accumulation rates were 11.3 ± 3.6 g TOC m−2 year−1 during the Holocene and 0.5 ± 0.1 g TOC m−2 year−1 during the Late Pleistocene (12–38 kyr BP). Within relict marine sediments, porewater salinities increased with depth. Elevated salinity near sea level (∼20–37 in thawed samples) inhibited freezing despite year-round temperatures below 0°C. We used organic matter stock estimates from the cores in combination with remote sensing time-series data to estimate carbon fluxes for a 9 km stretch of coastline near Drew Point. Erosional fluxes of TOC averaged 1,369 kg C m−1 year−1 during the 21st century (2002–2018), nearly doubling the average flux of the previous half-century (1955–2002). Our estimate of the 21st century erosional TOC flux year−1 from this 9 km coastline (12,318 metric tons C year−1) is similar to the annual TOC flux from the Kuparuk River, which drains a 8,107 km2 area east of Drew Point and ranks as the third largest river on the North Slope of Alaska. Total nitrogen fluxes via coastal erosion at Drew Point were also quantified, and were similar to those from the Kuparuk River. This study emphasizes that coastal erosion represents a significant pathway for carbon and nitrogen trapped in permafrost to enter modern biogeochemical cycles, where it may fuel food webs and greenhouse gas emissions in the marine environment.

","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.598933","usgsCitation":"Bristol, E.M., Connolly, C.T., Lorenson, T., Richmond, B., Ilgen, A.G., Choens, C.R., Bull, D.L., Kanevskiy, M.Z., Iwahana, G., Jones, B., and McClelland, J., 2021, Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska: Frontiers in Earth Science, v. 8, 598933, 13 p., https://doi.org/10.3389/feart.2020.598933.","productDescription":"598933, 13 p.","ipdsId":"IP-123906","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453895,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.598933","text":"Publisher Index Page"},{"id":384352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Drew Point","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.09326171875,\n 70.52123408593832\n ],\n [\n -151.072998046875,\n 70.52123408593832\n ],\n [\n -151.072998046875,\n 71.37812702610609\n ],\n [\n -158.09326171875,\n 71.37812702610609\n ],\n [\n -158.09326171875,\n 70.52123408593832\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Bristol, Emily M.","contributorId":255060,"corporation":false,"usgs":false,"family":"Bristol","given":"Emily","email":"","middleInitial":"M.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":811740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connolly, Craig T.","contributorId":255063,"corporation":false,"usgs":false,"family":"Connolly","given":"Craig","email":"","middleInitial":"T.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":811741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":811742,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richmond, Bruce M.","contributorId":255065,"corporation":false,"usgs":false,"family":"Richmond","given":"Bruce M.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":811743,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ilgen, Anastasia G.","contributorId":255069,"corporation":false,"usgs":false,"family":"Ilgen","given":"Anastasia","email":"","middleInitial":"G.","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":811744,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Choens, Charles R.","contributorId":255072,"corporation":false,"usgs":false,"family":"Choens","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":811745,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bull, Diana L.","contributorId":208628,"corporation":false,"usgs":false,"family":"Bull","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":37851,"text":"Sandia National Laboratories, Albuquerque, New Mexico, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":811746,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kanevskiy, Mikhail Z.","contributorId":199153,"corporation":false,"usgs":false,"family":"Kanevskiy","given":"Mikhail","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":811747,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Iwahana, Go 0000-0003-4628-1074","orcid":"https://orcid.org/0000-0003-4628-1074","contributorId":208638,"corporation":false,"usgs":false,"family":"Iwahana","given":"Go","email":"","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":811748,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jones, Benjamin M. 0000-0002-1517-4711","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":208625,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin M.","affiliations":[{"id":37848,"text":"Water and Environmental Research Center, University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":true,"id":811749,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McClelland, James W.","contributorId":255074,"corporation":false,"usgs":false,"family":"McClelland","given":"James W.","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":811750,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70222486,"text":"70222486 - 2021 - Using high sample rate lidar to measure debris-flow velocity and surface geometry","interactions":[],"lastModifiedDate":"2021-07-30T13:28:47.272777","indexId":"70222486","displayToPublicDate":"2021-01-08T08:27:26","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7559,"text":"Environmental and Engineering Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Using high sample rate lidar to measure debris-flow velocity and surface geometry","docAbstract":"

Debris flows evolve in both time and space in complex ways, commonly starting as coherent failures but then quickly developing structures such as roll waves and surges. These processes are readily observed but difficult to study or quantify because of the speed at which they evolve. Many methods for studying debris flows consist of point measurements (e.g., flow height or basal stresses), which are inherently limited in spatial coverage and cannot fully characterize the spatiotemporal evolution of a flow. In this study, we use terrestrial lidar to measure debris-flow profiles at high sampling rates to examine debris-flow movement with high temporal and spatial precision and accuracy. We acquired measurements during gate-release experiments at the U.S. Geological Survey debris-flow flume, a unique experimental facility where debris flows can be artificially generated at a large scale. A lidar scanner was used to record repeat topographic profiles of the moving debris flows along the length of the flume with a narrow swath width (∼1 mm) at a rate of 60 Hz. The high-resolution lidar profiles enabled us to quantify flow front velocity of the debris flows and provided an unprecedented record of the development and evolution of the flow structure with a sub-second time resolution. The findings of this study demonstrate how to obtain quantitative measurements of debris-flow movement. In addition, the data help us to quantitatively define the development of a saltating debris-flow front and roll waves behind the debris-flow front. Such measurements may help constrain future modeling efforts.

","language":"English","publisher":"Association of Environmental and Engineering Geologists","doi":"10.2113/EEG-D-20-00045","usgsCitation":"Rengers, F.K., Rapstine, T.D., Olsen, M., Allstadt, K.E., Iverson, R.M., Leshchinsky, B., Obryk, M., and Smith, J., 2021, Using high sample rate lidar to measure debris-flow velocity and surface geometry: Environmental and Engineering Geoscience, v. 27, no. 1, p. 113-126, https://doi.org/10.2113/EEG-D-20-00045.","productDescription":"14 p.","startPage":"113","endPage":"126","ipdsId":"IP-122361","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":436591,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OU3U4P","text":"USGS data release","linkHelpText":"Lidar data for gate release experiment at the USGS Debris-Flow Flume 24 and 25 May 2017"},{"id":387585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820187,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rapstine, Thomas D 0000-0001-5939-9587","orcid":"https://orcid.org/0000-0001-5939-9587","contributorId":224777,"corporation":false,"usgs":true,"family":"Rapstine","given":"Thomas","email":"","middleInitial":"D","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820188,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olsen, Michael","contributorId":215348,"corporation":false,"usgs":false,"family":"Olsen","given":"Michael","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":820189,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820190,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Iverson, Richard M. 0000-0002-7369-3819 riverson@usgs.gov","orcid":"https://orcid.org/0000-0002-7369-3819","contributorId":536,"corporation":false,"usgs":true,"family":"Iverson","given":"Richard","email":"riverson@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820191,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leshchinsky, Ben","contributorId":215350,"corporation":false,"usgs":false,"family":"Leshchinsky","given":"Ben","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":820192,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Obryk, Maciej K. 0000-0002-8182-8656","orcid":"https://orcid.org/0000-0002-8182-8656","contributorId":203477,"corporation":false,"usgs":true,"family":"Obryk","given":"Maciej","middleInitial":"K.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820193,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Joel B. 0000-0001-7219-7875","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":242670,"corporation":false,"usgs":false,"family":"Smith","given":"Joel B.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":820194,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}