The Eocene Green River Formation contains the largest oil shale deposits in the world and is a welldocumented example of a lacustrine depositional system. In addition, mineral resources associated with oil shale in the Piceance Basin nahcolite [NaHCO3] and dawsonite [NaAl(CO3)(OH)2)] are of current and potential economic value, respectively. Detailed geochemical analysis across the basin can aid in the understanding of the depositional environment, sedimentary processes, and water-chemistry evolution in this system. Quantitative geochemical data for Green River oil shale from the Piceance Basin of Colorado were collected by inductively coupled plasma optical emission spectroscopy and mass spectrometry as part of this study. The basin margin is represented by samples from exposures at Douglas Pass (Garfield County) and the basin center area is characterized by core samples from two drilled wells: the Shell 23X-2 and John Savage 24-1 (Rio Blanco County). Major elements and groups of elements are used as proxies for clastic influx (Si, Al, K, Ti), carbonate deposition (Ca, Mg), salinity (Na), paleo-productivity (P), and redox state (Fe, S), respectively. Minor and trace elements reinforce observations based on major elements, including Rb, Zr, Nb for clastic influx and Mn, Sr for carbonate. Trace elements are used to characterize redox conditions (As, Mo, U, V, Co, Ni, Cu, Zn) and salinity (Rb/K, B/Ga). Chemical distinctions between the basin margin and the basin center, in terms of these components and total organic carbon concentrations, support the model of a permanently stratified lake through most of the depositional interval. A primary purpose of the study was to conduct more extensive sampling to confirm conclusions of a previous reconnaissance study. Geochemical data from this study indicates elevated Na around the basin margin occurring earlier than in the deeper basin. Early in the history of Lake Uinta, the salinity may have been elevated first in the shallower marginal waters, due to increased evaporation, which then led to elevated salinity in the basin center through transport of saline density currents. Other indicators of salinity (Rb/K, B/Ga) do not track Na content in intervals where clay minerals are absent due to diagenetic alteration under hypersaline conditions but may be used to indicate the salinities at which authigenic Na-bearing minerals begin to form. Most Na-rich samples show high proportions of clastic constituents (Si, Al, K, Ti) compared to conventional carbonate constituents (Ca, Mg). Redox-sensitive period IV transition metal elements (V, Co, Ni, Cu, Zn) show only local occurrence of significant enrichment relative to average shale abundances. Analysis of Fe/Al ratios for this dataset suggests that the depletion of these elements may be related to source rocks depleted in mafic constituents, with apparent redox-related enrichments subdued by this effect. The basin margin samples reflect generally oxic bottom waters, with some intervals deposited under more reducing, possibly dysoxic to anoxic conditions. The basin center results indicate more reducing conditions, with Mo and U enrichment factors suggesting operation of a particulate shuttle mechanism that scavenged Mo on Fe/Mn-oxyhydroxides that redissolved at depth, with Mo precipitating along with sulfides and/or organic matter at or near the sediment/water interface.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Utah Geological Association","doi":"10.31711/ugap.v50i.114","usgsCitation":"Boak, J., Wu, T., and Birdwell, J.E., 2022, Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements, chap. of The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record, v. 50, p. 266-297, https://doi.org/10.31711/ugap.v50i.114.","productDescription":"32 p.","startPage":"266","endPage":"297","ipdsId":"IP-127516","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":446590,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31711/ugap.v50i.114","text":"Publisher Index Page"},{"id":435705,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q5VOQB","text":"USGS data release","linkHelpText":"Geochemical data for the Green River Formation in the Piceance Basin, Colorado: Major and trace element concentrations and total organic carbon content"},{"id":406448,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Green River Formation, Piceance Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.1439208984375,\n 39.48284540453334\n ],\n [\n -107.8692626953125,\n 39.64799732373418\n ],\n [\n -107.91320800781249,\n 40.027614437486655\n ],\n [\n -108.2647705078125,\n 40.17467622056341\n ],\n [\n -108.6492919921875,\n 40.069664523297774\n ],\n [\n -108.7811279296875,\n 39.88023492849342\n ],\n [\n -108.5394287109375,\n 39.6437675734185\n ],\n [\n -108.1439208984375,\n 39.48284540453334\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"editors":[{"text":"Hurst, C. J.","contributorId":206942,"corporation":false,"usgs":false,"family":"Hurst","given":"C.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":851360,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Boak, Jeremy 0000-0003-0251-434X","orcid":"https://orcid.org/0000-0003-0251-434X","contributorId":296328,"corporation":false,"usgs":false,"family":"Boak","given":"Jeremy","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":851288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Tengfei 0000-0003-2804-5537","orcid":"https://orcid.org/0000-0003-2804-5537","contributorId":296330,"corporation":false,"usgs":false,"family":"Wu","given":"Tengfei","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":851289,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":851290,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70251892,"text":"70251892 - 2022 - Changes in aquatic vegetation cover following lock closure on the Illinois Waterway from 2019 – 2021","interactions":[],"lastModifiedDate":"2024-03-05T15:06:25.784988","indexId":"70251892","displayToPublicDate":"2022-09-01T08:18:54","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17168,"text":"Completion Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"LTRMP-2019AER7","title":"Changes in aquatic vegetation cover following lock closure on the Illinois Waterway from 2019 – 2021","docAbstract":"Over the summer of 2020, the Illinois Waterway was closed to complete maintenance on lock chambers along the Illinois River. This closure restricted inter-pool vessel traffic along the river and potentially changed habitat characteristics for aquatic vegetation establishment and growth. To assess if patterns of vegetation establishment and growth changed during the closure, peak biomass imagery from 2019 (pre closure) and 2021 (post closure) were compared for a vegetation response. This assessment found locations where aquatic vegetation increased and locations where aquatic vegetation decreased. However, due to unforeseen limitations in vegetation and water sampling, a causal reason for observed changed in vegetation could not be established.","language":"English","publisher":"U.S. Army Corps of Engineers’ Upper Mississippi River Restoration Program","usgsCitation":"Strassman, A.C., 2022, Changes in aquatic vegetation cover following lock closure on the Illinois Waterway from 2019 – 2021: Completion Report LTRMP-2019AER7, 44 p.","productDescription":"44 p.","ipdsId":"IP-145368","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":426307,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/data_library/ltrmp_other/IWW_Closure_Veg_Change_Co-op_Report_Final_20221201.pdf"},{"id":426318,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.28788998955925,\n 42.108326944582615\n ],\n [\n -90.7915689421851,\n 42.108326944582615\n ],\n [\n -90.7915689421851,\n 38.76757015391274\n ],\n [\n -87.28788998955925,\n 38.76757015391274\n ],\n [\n -87.28788998955925,\n 42.108326944582615\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Strassman, Andrew C. 0000-0002-9792-7181 astrassman@usgs.gov","orcid":"https://orcid.org/0000-0002-9792-7181","contributorId":4575,"corporation":false,"usgs":true,"family":"Strassman","given":"Andrew","email":"astrassman@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":895948,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70238985,"text":"70238985 - 2022 - Potential cheatgrass abundance within lightly invaded areas of the Great Basin","interactions":[],"lastModifiedDate":"2022-12-20T14:13:06.35989","indexId":"70238985","displayToPublicDate":"2022-09-01T08:06:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Potential cheatgrass abundance within lightly invaded areas of the Great Basin","docAbstract":"Context
Anticipating where an invasive species could become abundant can help guide prevention and control efforts aimed at reducing invasion impacts. Information on potential abundance can be combined with information on the current status of an invasion to guide management towards currently uninvaded locations where the threat of invasion is high.
Objectives
We aimed to support management by developing predictive maps of potential cover for cheatgrass (Bromus tectorum), a problematic invader that can transform plant communities. We integrated our predictions of potential abundance with mapped estimates of current cover to quantify invasion potential within lightly invaded areas.
Methods
We used quantile regression to model cheatgrass abundance as a function of climate, weather, and disturbance, treating outputs as low to high invasion scenarios. We developed a species-specific set of covariates and validated model performance using spatially and temporally independent data.
Results
Potential cheatgrass abundance was higher in areas that had burned, at low elevations, and when fall germination conditions were more favorable. Our results highlight the extensive areas across the Great Basin where cheatgrass abundance could increase to levels that can alter fire behavior and cause other ecological impacts.
Conclusions
We predict potential cheatgrass abundance to quantify relative invasion risk. Our model results provide high and low scenarios of cheatgrass abundance to guide resource allocation and planning efforts across shrubland ecosystems of the Great Basin that remain relatively uninvaded. Combining information on an invasive species’ current and potential abundance can yield spatial predictions to guide resource allocation and management action.
Anthropogenic alterations have resulted in widespread degradation of stream conditions. To aid in stream restoration and management, baseline estimates of conditions and improved explanation of factors driving their degradation are needed. We used random forests to model biological conditions using a benthic macroinvertebrate index of biotic integrity for small, non-tidal streams (upstream area ≤200 km2) in the Chesapeake Bay watershed (CBW) of the mid-Atlantic coast of North America. We utilized several global and local model interpretation tools to improve average and site-specific model inferences, respectively. The model was used to predict condition for 95,867 individual catchments for eight periods (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019). Predicted conditions were classified as Poor, FairGood, or Uncertain to align with management needs and individual reach lengths and catchment areas were summed by condition class for the CBW for each period. Global permutation and local Shapley importance values indicated percent of forest, development, and agriculture in upstream catchments had strong impacts on predictions. Development and agriculture negatively influenced stream condition for model average (partial dependence [PD] and accumulated local effect [ALE] plots) and local (individual condition expectation and Shapley value plots) levels. Friedman's H-statistic indicated large overall interactions for these three land covers, and bivariate global plots (PD and ALE) supported interactions among agriculture and development. Total stream length and catchment area predicted in FairGood conditions decreased then increased over the 19-years (length/area: 66.6/65.4% in 2001, 66.3/65.2% in 2011, and 66.6/65.4% in 2019). Examination of individual catchment predictions between 2001 and 2019 showed those predicted to have the largest decreases in condition had large increases in development; whereas catchments predicted to exhibit the largest increases in condition showed moderate increases in forest cover. Use of global and local interpretative methods together with watershed-wide and individual catchment predictions support conservation practitioners that need to identify widespread and localized patterns, especially acknowledging that management actions typically take place at individual-reach scales.
ABSTRACT: Nuclear inclusion X (NIX), the etiological agent of bacterial gill disease in Pacific razor clams Siliqua patula, was associated with host mortality events in coastal Washington State, USA, during the mid-1980s. Ongoing observations of truncated razor clam size distributions in Kalaloch Beach, Washington, raised concerns that NIX continues to impact populations. We conducted a series of spatial and longitudinal NIX surveillances, examined archived razor clam gill tissue, and used population estimates from stock assessments to test whether (1) the prevalence and intensity of NIX infections is higher at Kalaloch Beach relative to nearby beaches, (2) infected gill tissue has features consistent with historical descriptions of NIX-associated histopathology, and (3) annual clam survival is inversely related to NIX infection prevalence and intensity. NIX prevalence exceeded 85% at all sampled locations, and infection intensity was the highest at Kalaloch Beach by 0.9-2.6 orders of magnitude. Kalaloch Beach clams revealed histopathology consistent with previous NIX epidemics, including enlarged and/or rupturing branchial epithelial cells, branchial necrosis, and high hemocyte densities. Estimated annual survival was 22% at Kalaloch Beach, and ranged between 57 and 99% at other study sites. NIX infection intensity (via quantitative PCR) was not significantly correlated with annual survival; however, annual survival was lowest at Kalaloch Beach, where infection intensities were highest, suggesting that clams can tolerate infections up to a lethal threshold. Collectively these data support the hypothesis that high NIX intensities are associated with host mortality. NIX-associated mortality appears to be more pronounced at Kalaloch Beach relative to other Washington beaches.
","language":"English","publisher":"Inter-Research","doi":"10.3354/dao03685","usgsCitation":"Groner, M., Hershberger, P., Fradkin, S.C., Conway, C.M., Hawthorn, A.C., and Purcell, M.K., 2022, Evaluating the effect of nuclear inclusion X (NIX) infections on Pacific razor clam populations: Diseases of Aquatic Organisms, v. 151, p. 1-9, https://doi.org/10.3354/dao03685.","productDescription":"9 p.","startPage":"1","endPage":"9","ipdsId":"IP-138482","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":435707,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IV2C3L","text":"USGS data release","linkHelpText":"Histological and molecular testing of nuclear inclusion X in Pacific Razor clams from select locations in Washington, USA"},{"id":407125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"151","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Groner, Maya L. 0000-0002-3381-6415","orcid":"https://orcid.org/0000-0002-3381-6415","contributorId":292708,"corporation":false,"usgs":false,"family":"Groner","given":"Maya","middleInitial":"L.","affiliations":[{"id":62985,"text":"Senior Research Scientist, Bigelow Laboratory for Ocean Sciences, 60 Bigelow Drive, East Boothbay, ME 04544","active":true,"usgs":false}],"preferred":false,"id":852437,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hershberger, Paul 0000-0002-2261-7760","orcid":"https://orcid.org/0000-0002-2261-7760","contributorId":203322,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":852438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fradkin, Steven C.","contributorId":168638,"corporation":false,"usgs":false,"family":"Fradkin","given":"Steven","email":"","middleInitial":"C.","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":852439,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conway, Carla M. 0000-0002-3851-3616 cmconway@usgs.gov","orcid":"https://orcid.org/0000-0002-3851-3616","contributorId":2946,"corporation":false,"usgs":true,"family":"Conway","given":"Carla","email":"cmconway@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":852440,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hawthorn, Aine C. 0000-0002-8029-1383","orcid":"https://orcid.org/0000-0002-8029-1383","contributorId":292709,"corporation":false,"usgs":true,"family":"Hawthorn","given":"Aine","email":"","middleInitial":"C.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":852441,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Purcell, Maureen K. 0000-0003-0154-8433 mpurcell@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8433","contributorId":168475,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen","email":"mpurcell@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":852442,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262281,"text":"70262281 - 2022 - Lake Sturgeon movement after trap and transfer around two dams on the Menominee River, Wisconsin-Michigan","interactions":[],"lastModifiedDate":"2025-01-21T15:17:43.018461","indexId":"70262281","displayToPublicDate":"2022-09-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Lake Sturgeon movement after trap and transfer around two dams on the Menominee River, Wisconsin-Michigan","docAbstract":"Fish behavior after passage or transfer around dams is a critical component in determining whether the goals of these efforts are achieved, but these behaviors are often poorly understood. An elevator was constructed in the lowermost hydroelectric dam on the Menominee River, Wisconsin–Michigan; it is the first elevator specifically designed to capture Lake Sturgeon Acipenser fulvescens for upstream transfer above two dams, providing access to high-quality spawning and early life habitat. Our objectives were to determine whether (1) Lake Sturgeon transferred upstream remained upstream for at least one spawning opportunity; (2) spawning opportunity, time to reach the next dam upstream, and residency in different segments of the river were related to sex, capture method (elevator versus electrofishing), and season of transfer; and (3) the probability of fish transitioning back downstream of the two dams varied among months. We evaluated posttransfer behaviors of 139 Lake Sturgeon that were captured in the elevator or by electrofishing, implanted with acoustic transmitters, transferred upstream (in spring or fall) from fall 2014 to spring 2017, and monitored until fall 2018 using 20–23 stationary acoustic receivers deployed throughout the river. Most Lake Sturgeon (91%) remained upstream for at least one spawning opportunity. The probability of remaining for one spawning opportunity was not related to sex, fish capture method, or season of transfer. Residency times within the two impoundments and time to reach the next dam upstream varied among individual fish. A multistate model indicated that monthly survival after upstream transfer was high and that Lake Sturgeon typically remained above both dams in late fall to early spring, with most downstream movements occurring in April and May. Our results indicate that Lake Sturgeon transferred upstream have the potential to contribute offspring that may help to bolster the Lake Sturgeon population in Lake Michigan, but additional research may help in determining whether these contributions occur.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10379","usgsCitation":"Isermann, D.A., Raabe, J., Easterly, E., Schulze, J., Porter, N., Dembkowski, D., Donofrio, M., Kramer, D., and Elliott, R., 2022, Lake Sturgeon movement after trap and transfer around two dams on the Menominee River, Wisconsin-Michigan: Transactions of the American Fisheries Society, v. 151, no. 5, p. 611-629, https://doi.org/10.1002/tafs.10379.","productDescription":"19 p.","startPage":"611","endPage":"629","ipdsId":"IP-137127","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","otherGeospatial":"Menominee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.52923848272457,\n 45.083121335445355\n ],\n [\n -87.52923848272457,\n 45.431368318822194\n ],\n [\n -87.97927795298747,\n 45.431368318822194\n ],\n [\n -87.97927795298747,\n 45.083121335445355\n ],\n [\n -87.52923848272457,\n 45.083121335445355\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Raabe, Joshua K.","contributorId":348735,"corporation":false,"usgs":false,"family":"Raabe","given":"Joshua K.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Easterly, Emma G.","contributorId":348736,"corporation":false,"usgs":false,"family":"Easterly","given":"Emma G.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schulze, Joshua C.","contributorId":348738,"corporation":false,"usgs":false,"family":"Schulze","given":"Joshua C.","affiliations":[{"id":83404,"text":"USDA Forest Service Region 1","active":true,"usgs":false}],"preferred":false,"id":923729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porter, Nicholas J.","contributorId":348741,"corporation":false,"usgs":false,"family":"Porter","given":"Nicholas J.","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":923730,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dembkowski, Daniel J.","contributorId":348743,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":923731,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Donofrio, Michael C.","contributorId":348744,"corporation":false,"usgs":false,"family":"Donofrio","given":"Michael C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923732,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kramer, Darren R.","contributorId":348745,"corporation":false,"usgs":false,"family":"Kramer","given":"Darren R.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":923733,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Elliott, Robert F.","contributorId":348746,"corporation":false,"usgs":false,"family":"Elliott","given":"Robert F.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":923734,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262305,"text":"70262305 - 2022 - Stream macroinvertebrate reintroductions: A cautionary approach for restored urban streams","interactions":[],"lastModifiedDate":"2025-01-17T15:49:41.674407","indexId":"70262305","displayToPublicDate":"2022-09-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Stream macroinvertebrate reintroductions: A cautionary approach for restored urban streams","docAbstract":"Macroinvertebrate assemblages often remain depauperate in physically restored urban streams despite efforts to improve habitat conditions and increase species abundance and diversity. The lack of biological recovery may be due to a lack of a natural, nearby source of colonists, and this has inspired researchers and practitioners to reintroduce macroinvertebrates in otherwise restored urban streams to jump start the recovery process. However, without standardized guidelines that describe reintroduction best practices, some reintroduction programs may create additional problems (e.g., disease spread, genetic homogenization, population loss). To reduce these risks and limit a potential waste of resources, a cautionary approach is warranted. In this paper we summarize current stream reintroduction knowledge and detail best practices for aquatic macroinvertebrate reintroduction in restored urban streams. We provide criteria that managers can use to determine whether reintroduction is appropriate and demonstrate how researchers can use reintroduction as a tool to test hypotheses regarding factors limiting recolonization. We provide guidance for how to set clear reintroduction goals, select donor sites, determine the number of organisms required, establish reintroduction frequency and timing, and overcome challenges associated with monitoring. This framework can help managers create more successful reintroduction programs that can benefit urban stream restoration.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
This synthesis focuses on the history of research on coral reefs within two U.S. National Park Service units in St. Croix, U.S. Virgin Islands: Buck Island Reef National Monument (from 1961 to 2022) and Salt River Bay National Historical Park and Ecological Preserve (from 1980 to 2022). Buck Island Reef National Monument (BUIS) is off the north shore of the island of St. Croix, in the U.S. Virgin Islands. Established in 1961 and expanded in 2001, it is under the jurisdiction of the National Park Service (NPS). Long-term monitoring programs maintained by the NPS and jointly by the University of the Virgin Islands (UVI) and the Virgin Islands Department of Planning and Natural Resources (VIDPN).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Natural Resource Report","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"National Park Service","doi":"10.36967/2294235","usgsCitation":"Rogers, C., 2022, A synthesis of coral reef research at Buck Island Reef National Monument and Salt River Bay National Historical Park and Ecological Preserve, St. Croix, U.S. Virgin Islands: 1961 to 2022, xiii, 62 p., https://doi.org/10.36967/2294235.","productDescription":"xiii, 62 p.","ipdsId":"IP-138166","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":407694,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"U.S. Virgin Islands, Buck Island Reef National Monument, Salt River Bay National Historical Park and Ecological Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.82894897460938,\n 18.267173900767588\n ],\n [\n -64.62982177734375,\n 18.267173900767588\n ],\n [\n -64.62982177734375,\n 18.389714457728893\n ],\n [\n -64.82894897460938,\n 18.389714457728893\n ],\n [\n -64.82894897460938,\n 18.267173900767588\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rogers, Caroline 0000-0001-9056-6961","orcid":"https://orcid.org/0000-0001-9056-6961","contributorId":223023,"corporation":false,"usgs":true,"family":"Rogers","given":"Caroline","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":853461,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70238156,"text":"70238156 - 2022 - Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska","interactions":[],"lastModifiedDate":"2022-11-15T13:07:15.04658","indexId":"70238156","displayToPublicDate":"2022-08-31T07:04:23","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska","docAbstract":"The United States National Hydrography Dataset (NHD) is a database of vector features representing the surface water features for the country. The NHD was originally compiled from hydrographic content on U.S. Geological Survey topographic maps but is being updated with higher quality feature representations through flow-routing techniques that derive hydrography from high-resolution elevation data. However, deriving hydrography through flow-routing methods is a complex process that needs to be tailored to different geographic conditions, which can lead to varying solutions. To address this problem, this paper evaluates automated deep learning and its transferability to extract hydrography from interferometric synthetic aperture radar (IfSAR) elevation data spanning a range of geographic conditions in Alaska.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceDate":"August 22-28, 2022","conferenceLocation":"Florence, Italy","language":"English","publisher":"International Society for Photogrammetry and Remote Sensing (ISPRS)","doi":"10.5194/isprs-archives-XLVIII-4-W1-2022-449-2022","usgsCitation":"Stanislawski, L., Shavers, E.J., Duffy, A., Thiem, P.T., Jaroenchai, N., Wang, S., Jiang, Z., Kronenfeld, B.J., and Buttenfield, B.P., 2022, Scaling-up deep learning predictions of hydrography from IfSAR data in Alaska, in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Florence, Italy, August 22-28, 2022, p. 449-456, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-449-2022.","productDescription":"8 p.","startPage":"449","endPage":"456","ipdsId":"IP-143118","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":446603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/isprs-archives-xlviii-4-w1-2022-449-2022","text":"Publisher Index Page"},{"id":409352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.48017274535448,\n 67.79791394109961\n ],\n [\n -157.48017274535448,\n 66.00582490875226\n ],\n [\n -152.2946258703545,\n 66.00582490875226\n ],\n [\n -152.2946258703545,\n 67.79791394109961\n ],\n [\n -157.48017274535448,\n 67.79791394109961\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":857001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":857002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duffy, Alexander 0000-0001-6036-0583","orcid":"https://orcid.org/0000-0001-6036-0583","contributorId":299070,"corporation":false,"usgs":false,"family":"Duffy","given":"Alexander","email":"","affiliations":[{"id":64752,"text":"University of Missouri Science & Technology","active":true,"usgs":false}],"preferred":false,"id":857003,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":857004,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jaroenchai, Nattapon","contributorId":267318,"corporation":false,"usgs":false,"family":"Jaroenchai","given":"Nattapon","email":"","affiliations":[{"id":38021,"text":"University of Illinois Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":857005,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Shaowen","contributorId":198966,"corporation":false,"usgs":false,"family":"Wang","given":"Shaowen","email":"","affiliations":[],"preferred":false,"id":857006,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Zhe","contributorId":267317,"corporation":false,"usgs":false,"family":"Jiang","given":"Zhe","email":"","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":857007,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":857008,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":857009,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236950,"text":"70236950 - 2022 - Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis","interactions":[],"lastModifiedDate":"2024-05-16T15:46:58.587708","indexId":"70236950","displayToPublicDate":"2022-08-31T06:58:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12603,"text":"Journal of the American Society of Agricultural and Biological Engineers","active":true,"publicationSubtype":{"id":10}},"title":"Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis","docAbstract":"The Upper Rio Grande Basin (URGB) is a critical international water resource under pressure from a myriad of climatic, ecological, infrastructural, water-use, and legal constraints. The objective of this study is to provide a comprehensive assessment of the spatial distribution and temporal trends of selected water-budget components (snow processes, evapotranspiration (ET), streamflow processes, and groundwater storage) using integrated analyses, such as watershed modeling and water availability and use data in the URGB over the past three decades. A spatially distributed snow evolution modeling system simulated snowpack processes over 34 years (1984–2017). It highlighted snow water equivalent declines from -35 to -77 mm/decade with widespread variability across elevation zones and land cover types. Gridded actual ET data from the SSEBop model were developed and tested for the URGB and demonstrated that all land-cover types had significant decreasing trends (1986-2015) ranging from -14 to -80 mm/decade. Conductivity-mass-balance (CMB) hydrograph separation results found that baseflow forms a large component of total streamflow, ranging from 29 to 69% (49% average) of total streamflow at 17 URGB sites upstream of Albuquerque, NM. Three of 4 graphical hydrograph separation methods in the U.S. Geological Survey Groundwater Toolbox were found to be inappropriate for estimating baseflow in the URGB; the most promising method, baseflow index (BFI) Standard, was optimized using CMB data and tested at three URGB sites, with resulting overestimation of 0 to 47%. Simulated changes in groundwater storage were extracted from historical and recent groundwater-flow models of select alluvial basins (San Luis, Española, Middle Rio Grande, and Tularosa-Hueco). In general, decreases in groundwater storage were observed from 1903 to 2013 except for the San Luis alluvial basin (Colorado), where periods of recovery are observed. The PRMS hydrologic model was successfully calibrated for 9 near-native subbasins (Nash-Sutcliffe efficiency 0.47 to 0.85) and parameters translated to the remaining subbasins; compared to simulated near-native flows (with minimal influence of reservoirs or diversions), observed Rio Grande streamgage flows demonstrated reductions of 40% or more for New Mexico and Texas areas of the basin. Significant decreasing trends (1980-2015) in precipitation, snowmelt rate, streamflow, and baseflow were observed at many of the 12 streamgage basins studied, which suggests that the decreasing trends for actual ET may be related to overall decreasing water availability in the basin, with negative implications for agricultural production and groundwater abstraction. Water security concerns arise from our findings of higher fraction precipitation as rain, slower snowmelt rates leading to decreasing streamflow production, and an increasing fraction of baseflow, all of which will affect the timing and magnitude of water available for human needs in the basin.
","language":"English","publisher":"American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org","doi":"10.13031/ja.14964","usgsCitation":"Douglas-Mankin, K., Rumsey, C., Sexstone, G., Ivahnenko, T.I., Houston, N., Chavarria, S., Senay, G.B., Foster, L.K., Thomas, J., Flickinger, A.K., Galanter, A.E., Moeser, C.D., Welborn, T.L., Pedraza, D.E., Lambert, P., and Johnson, M.S., 2022, Upper Rio Grande Basin water-resource status and trends: Focus area study review and synthesis: Journal of the American Society of Agricultural and Biological Engineers, v. 65, no. 4, p. 881-901, https://doi.org/10.13031/ja.14964.","productDescription":"21 p.","startPage":"881","endPage":"901","ipdsId":"IP-133533","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science 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]\n ]\n }\n }\n ]\n}","volume":"65","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":223378,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":852780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rumsey, Christine 0000-0001-7536-750X crumsey@usgs.gov","orcid":"https://orcid.org/0000-0001-7536-750X","contributorId":146240,"corporation":false,"usgs":true,"family":"Rumsey","given":"Christine","email":"crumsey@usgs.gov","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sexstone, Graham A. 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0000-0002-6071-4545","orcid":"https://orcid.org/0000-0002-6071-4545","contributorId":206533,"corporation":false,"usgs":true,"family":"Houston","given":"Natalie","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852784,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chavarria, Shaleene 0000-0001-8792-1010","orcid":"https://orcid.org/0000-0001-8792-1010","contributorId":222578,"corporation":false,"usgs":true,"family":"Chavarria","given":"Shaleene","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852785,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources 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0000-0002-8638-2569","orcid":"https://orcid.org/0000-0002-8638-2569","contributorId":223702,"corporation":false,"usgs":true,"family":"Flickinger","given":"Allison","email":"","middleInitial":"K.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852789,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Galanter, Amy E. 0000-0002-2960-0136","orcid":"https://orcid.org/0000-0002-2960-0136","contributorId":205393,"corporation":false,"usgs":true,"family":"Galanter","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852790,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852791,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Welborn, Toby L. 0000-0003-4839-2405 tlwelbor@usgs.gov","orcid":"https://orcid.org/0000-0003-4839-2405","contributorId":2295,"corporation":false,"usgs":true,"family":"Welborn","given":"Toby","email":"tlwelbor@usgs.gov","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852793,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pedraza, Diana E. 0000-0003-4483-8094","orcid":"https://orcid.org/0000-0003-4483-8094","contributorId":207782,"corporation":false,"usgs":true,"family":"Pedraza","given":"Diana","email":"","middleInitial":"E.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852796,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Lambert, Patrick M. 0000-0001-6808-2303","orcid":"https://orcid.org/0000-0001-6808-2303","contributorId":296913,"corporation":false,"usgs":false,"family":"Lambert","given":"Patrick M.","affiliations":[{"id":32931,"text":"USGS - Retired","active":true,"usgs":false}],"preferred":false,"id":852794,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Johnson, Michael Scott 0000-0003-2378-7144 johnsonm@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-7144","contributorId":296914,"corporation":false,"usgs":true,"family":"Johnson","given":"Michael","email":"johnsonm@usgs.gov","middleInitial":"Scott","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852795,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70236128,"text":"ofr20221072 - 2022 - ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022","interactions":[],"lastModifiedDate":"2022-09-27T12:21:24.033152","indexId":"ofr20221072","displayToPublicDate":"2022-08-31T06:56:32","publicationYear":"2022","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":"2022-1072","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 1, 2022","title":"ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022","docAbstract":"The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 1 (January–March), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221072","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Hasan, M.N., Tuli, F.T.Z., Shaw, J.L., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report—Quarter 1, 2022: U.S. Geological Survey Open-File Report 2022–1072, 39 p., https://doi.org/10.3133/ofr20221072.","productDescription":"Report: vii, 39 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-140787","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":405985,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221072/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405885,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":405884,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1072/images"},{"id":405883,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1072/ofr20221072.XML"},{"id":405880,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1072/coverthb.jpg"},{"id":405882,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1072/ofr20221072.pdf","text":"Report","size":"4.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1072"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Deepwater Horizon oil spill caused extensive injury to natural resources in the Gulf of Mexico, and Gavia immer (common loon) were negatively affected from the spill. The Open Ocean Trustee Implementation Group funded the project Restoration of Common Loons in Minnesota to restore common loons lost to the spill. In 2020–21, priority lakes in an eight-county region in north-central Minnesota were identified to focus project activities. In 2021, surveys on these lakes were started to monitor common loon territory occupancy, nest success, and chick survival. We surveyed 62 lakes and identified 110 common loon territories that will be included in the project. At least 1 nest attempt was observed in 78 of 110 territories, and a second nest attempt was observed in 23 territories. A third nest attempt was observed in one territory. Successful nesting was observed in 32 of 110 territories. We present no formal data analysis and plan to analyze the data after the collection of all field data in subsequent years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221074","usgsCitation":"Beatty, W.S., Fara, L.J., Houdek, S.C., Kenow, K.P., and Gray, B.R., 2022, Restoration of Gavia immer (common loon) in Minnesota—2021 annual report: U.S. Geological Survey Open-File Report 2022–1074, 7 p., https://doi.org/10.3133/ofr20221074.","productDescription":"vi, 7 p.","numberOfPages":"16","onlineOnly":"Y","ipdsId":"IP-139232","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":435709,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LA536E","text":"USGS data release","linkHelpText":"Summary of Detection Data for Breeding Common Loons in North-central Minnesota (2021-2022) (ver. 1.1, August 2024)"},{"id":405938,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221074/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":405361,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1074/ofr20221074.XML"},{"id":405360,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1074/ofr20221074.pdf","text":"Report","size":"2.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1074"},{"id":405359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1074/coverthb.jpg"},{"id":405362,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1074/images"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.78955078125,\n 46.27103747280261\n ],\n [\n -93.240966796875,\n 46.27103747280261\n ],\n [\n -93.240966796875,\n 48.821332549646634\n ],\n [\n -96.78955078125,\n 48.821332549646634\n ],\n [\n -96.78955078125,\n 46.27103747280261\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Surveys and monitoring for the coastal Cactus Wren (Campylorhynchus brunneicapillus) were completed in San Diego County between March 2015 and July 2019. A total of 383 plots were surveyed across 3 genetic clusters (Otay, Lake Jennings, and Sweetwater/Encanto). From 2015 to 2019, 317 plots were surveyed 8 times (twice per year in 2015, 2017–19). Additional plots were added in later years as wrens were discovered in new locations. We found differences in the proportion of plots occupied in the genetic clusters, with a lower proportion of plots occupied in the Otay cluster than in the Lake Jennings and Sweetwater/Encanto clusters in all years. Plot occupancy increased each year in the Otay and Sweetwater/Encanto clusters but not in the Lake Jennings cluster. The number of Cactus Wren territories increased from 2015 through 2018, and then decreased in 2019 in all three genetic clusters.
We monitored nesting activities for two populations of Cactus Wrens in southern San Diego County. The Otay population consisted of two sites within the Otay genetic cluster, and the San Diego population consisted of two sites within the Sweetwater/Encanto and Lake Jennings genetic clusters. Nest monitoring occurred at 10–13 territories per year in the Otay population and 14–18 territories in the San Diego population from 2015 through 2019. All territories were occupied by pairs except two territories in 2015, five in 2016, and two in 2019. Between 46 and 74 Cactus Wren nests were monitored each year, which totaled 295 monitored nests from 2015 to 2019. To evaluate the direct influence of precipitation on breeding success, bio-year precipitation (“precipitation”) was calculated from July 1 of the prior year through June 30 of the breeding season year. Overall apparent nest success was positively influenced by precipitation with the lowest apparent nest success of 50 percent in 2015 and the highest apparent nest success of 72 percent in 2017, corresponding to the second lowest and the highest precipitation years, respectively. Apparent nest success also was higher in the Otay population than in the San Diego population. The number of brood nests initiated per pair and the number of renesting attempts per pair also were higher in years with more precipitation. Other metrics of Cactus Wren nesting success and productivity were positively influenced by the amount of precipitation, including clutch size and egg hatching success. The percent of hatchlings that fledged was greater in the Otay population than in the San Diego population but was not influenced by precipitation. The number of fledglings per pair was higher in years with more precipitation and was greater in the Otay population than in the San Diego population. Predation was the predominant cause of nest failure in both populations.
Analysis of Cactus Wren daily nest survival rate indicated that there was a population, and possibly a precipitation effect on nest survival, with the daily survival rate for the Otay population significantly higher than for the San Diego population and weak increase in the daily survival rate with more precipitation.
A total of 629 Cactus Wrens were banded during the course of the study, 360 in the San Diego population and 269 in the Otay population. Between 2015 and 2019, we resighted 301 color-banded adult birds that ranged between 1 and 8 years old. One additional color-banded bird was resighted in San Pasqual Valley (as part of a separate study); this bird originated in the San Diego population and was excluded from our analyses.
Annual survival was higher for adult Cactus Wrens (ranging from 60 to 70 percent) than for first-year wrens (ranging from 20 to 28 percent) and varied by year. Annual survival was also weakly but positively correlated with precipitation. Annual survival was higher for first year and adult Cactus Wrens following years with increased precipitation. We found no evidence that survival differed by population.
Banding also allowed us to examine whether there were differences in movement of adult and first-year Cactus Wrens by year or by population. We found that average dispersal distance for first-year Cactus Wrens was 1.9 kilometers in the Otay population and 1.6 kilometers in the San Diego population and did not differ by population or year. Dispersal between populations was not common. We detected five instances of movement of first-year wrens between the San Diego and Otay populations. All movements into and out of the San Diego population were from or into territories in the Sweetwater area. We detected no movement between the Lake Jennings site and either of the Sweetwater or Otay sites; however, we did detect one wren that dispersed from Lake Jennings to the San Pasqual Valley population in 2019, which was a distance of 26.4 kilometers. Adult Cactus Wrens were site-faithful, with 87 percent of adults remaining on the same territory between breeding seasons. Precipitation may be a weak driver of movement for adult Cactus Wrens, with adults more likely to remain on the same territory following years of increased precipitation. There was no difference in adult movement between populations.
Arthropods were collected in pitfall traps and by vacuum in 23 Cactus Wren territories during 3 sampling periods in 2016 (early nesting, peak nesting, and late nesting). Arthropods of 19 orders and at least 128 families were collected. Analysis of 43 Cactus Wren fecal samples identified 10 arthropod orders that were present in more than 10 percent of fecal samples. The most abundant arthropod order collected was Hymenoptera; however, Cactus Wrens consumed arthropods in the order Hymenoptera significantly less than their availability, suggesting that this order was avoided. No other orders were significantly selected or avoided; however, selection indices of arthropod families identified that two families of arthropods (Isopoda Porcellionidae [woodlice] and Hymenoptera Formicidae [ants]) were avoided. After excluding the taxa that were avoided or not represented in fecal samples, 95 percent of Cactus Wren prey items were collected in pitfall traps and 5 percent were collected by vacuum. The most abundant prey orders captured were Diptera, Coleoptera, Hemiptera, Hymenoptera, and Aranea.
Analysis of the abundance of Cactus Wren prey items by vegetation type and sampling period indicated that vegetation type by itself was not a significant predictor of arthropod abundance but interacted with sampling period. Seasonal availability of arthropods was highest in the peak nesting period, followed by early and late nesting periods for California sagebrush (Artemisia californica), lemonadeberry (Rhus integrifolia), non-native grass, and bare ground, whereas availability increased from early to late nesting periods for blue elderberry (Sambucus mexicana spp. caerulea), cactus (Opuntia spp. and Cylindropuntia spp.), California buckwheat (Eriogonum fasciculatum), native bunch grasses, and black mustard (Brassica nigra). During the early nesting period, arthropods were most abundant in native bunch grasses and least abundant in lemonadeberry. During the peak nesting period, arthropods were most abundant in native bunch grasses and in areas of bare ground and were least abundant in cactus and blue elderberry. During late nesting, arthropods were most abundant in blue elderberry and non-native grass and least abundant in lemonadeberry and mustard.
Each year from 2015 to 2019, vegetation data were collected at the same 23 territories where arthropods were sampled: 9 territories in the Otay population and 14 territories in the San Diego population. Cactus, California buckwheat, and non-native grasses were detected within at least 60 percent of sampling points in the Otay population. Cactus, California sagebrush, California buckwheat, non-native grass, and black mustard each were detected within an average of 40 percent of sampling points in the San Diego population. No native bunch grass or lemonadeberry were recorded at the Lake Jennings site within the San Diego population. The cover of shrub species was relatively stable throughout the 5 years. Cover of herbaceous species and bare ground had greater annual variation than shrub species.
We found that vegetation cover varied widely among territories, with territory accounting for 69 percent of the variation in vegetation cover. Redundancy analysis allowed us to identify the vegetation types that accounted for the most variation. We used the top scores from the redundancy analysis to identify six vegetation types to be used in generalized linear mixed models analyzing the relationships between vegetation type, precipitation, and Cactus Wren breeding productivity. Three vegetation variables influenced the number of fledglings produced per pair. California sagebrush had a positive effect on the number of fledglings per pair whereas non-native grass and black mustard had a negative effect.
Breeding productivity, survival, and movements of adult and first-year Cactus Wrens indicated that the Otay population behaved similarly to, if not out-performed, the San Diego population during the span of our project, suggesting that the driving forces behind low numbers of Cactus Wrens in the Otay population before 2015 were no longer in effect. The Cactus Wren populations in Otay and San Diego reached a peak in 2018, which followed a year of high productivity and survivorship, both of which were correlated with high precipitation. This peak in population size was consistent with reproductive timing and productivity in other bird populations in semi-arid ecosystems that were linked to precipitation and arthropod abundance. We did not find a strong link among arthropod abundance, vegetation composition, and Cactus Wren breeding productivity, likely in part because arthropod abundance varied by vegetation type and sampling period, suggesting that different vegetation types provided important sources of prey at different periods of the breeding season. Arthropod abundance also may not represent arthropod availability when vegetation structure discourages the ground foraging behavior of species such as Cactus Wrens. Cover of non-native grass negatively influenced breeding productivity, although arthropods were abundant in non-native grass. Other factors that could have influenced differential breeding productivity between the Otay and San Diego populations were habitat restoration, control of annual herbaceous vegetation, human disturbance, lingering effects of wildfire, and nest predation. Overall, precipitation appeared to be a driver of Cactus Wren breeding productivity and possibly survival, potentially obscuring proximate effects of arthropod or vegetation composition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221044","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Lynn, S., Houston, A., and Kus, B.E., 2022, Distribution and demography of Coastal Cactus Wrens in Southern California, 2015–19: U.S. Geological Survey Open-File Report 2022-1044, 44 p., https://doi.org/10.3133/ofr20221044.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-136839","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":435711,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P143ZTB2","text":"USGS data release","linkHelpText":"Cactus Wren Invertebrate Diet Derived from Sequencing of Nestling Fecal Samples in San Diego County, California"},{"id":405951,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221044/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Open-File Report 2022-1044"},{"id":405841,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1044/images"},{"id":405840,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.xml"},{"id":405839,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1044/ofr20221044.pdf","text":"Report","size":"4 MB"},{"id":405838,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1044/covrthb.jpg"},{"id":405842,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FK5","text":"Surveys and Monitoring of Coastal Cactus Wren in Southern San Diego County","description":"Kus, B.E., and Lynn, S., 2022, Surveys and monitoring of Coastal Cactus Wren in southern San Diego County: U.S. Geological Survey data release, https://doi.org/10.5066/F76H4FK5."}],"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 -117.3065185546875,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.52365781569917\n ],\n [\n -116.630859375,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.983324091837474\n ],\n [\n -117.3065185546875,\n 32.52365781569917\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
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Extreme heat is one of the most important pathways illustrating the connection between climate and human health, and climate change is expected to exacerbate this public health issue. This study first used a case-crossover analysis to characterize the historical (1980–2018) association between summertime heat and non-traumatic mortality in Washington State. A separate analysis was conducted for each of the state’s ten climate divisions to produce distinct exposure–response curves expressing odds of mortality as a function of humidex. Stratified analyses were used to assess the impact of age, sex, race/ethnicity, and select causes of death, and the reported results are pooled across all divisions using meta-analysis. The historical heat–mortality relationship was combined with climate projections to estimate the impact of climate change on heat-related deaths in 2030, 2050, and 2080 under two warming scenarios. The odds ratio (OR) and 95% confidence intervals of mortality at the 99th percentile of humidex compared to the 50th percentile did not include the null value in four climate divisions (E Olympic Cascade Foothills, NE Olympic San Juan, Northeastern, and Puget Sound Lowlands). The statewide odds of mortality are 8% higher (6%, 10%) on 99th percentile days compared to 50th percentile days, driven primarily by an OR of 1.09 (1.06, 1.11) in the Puget Sound Lowlands. Risk is higher for women than men and for Blacks than Whites. Risk increases with age and for diabetic, circulatory, cardiovascular, ischemic, cerebrovascular, and respiratory deaths. The 95% confidence intervals of projected heat-attributable mortality did not overlap with zero in three climate divisions (E Olympic Cascade Foothills, NE Olympic San Juan, and Puget Sound Lowlands). In these three divisions, the average percent increase in heat-attributable mortality across both warming scenarios is 35%, 35%, and 603% in 2030, 2050, and 2080, respectively. This research is the most extensive study of heat-related mortality in Washington to date and can help inform public health initiatives aiming to improve present and future health outcomes in the state.
","language":"English","publisher":"MDPI","doi":"10.3390/atmos13091392","usgsCitation":"Arnold, L., Scheuerell, M.D., and Isaksen, T., 2022, Mortality associated with extreme heat in Washington State: The historical and projected public health burden: Atmosphere, v. 13, no. 9, 1392, 21 p., https://doi.org/10.3390/atmos13091392.","productDescription":"1392, 21 p.","ipdsId":"IP-144123","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":446610,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/atmos13091392","text":"Publisher Index Page"},{"id":430218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"13","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Arnold, Logan 0000-0001-8903-2735","orcid":"https://orcid.org/0000-0001-8903-2735","contributorId":339402,"corporation":false,"usgs":false,"family":"Arnold","given":"Logan","email":"","affiliations":[],"preferred":false,"id":903609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Isaksen, T.B.","contributorId":338790,"corporation":false,"usgs":false,"family":"Isaksen","given":"T.B.","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":903611,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238704,"text":"70238704 - 2022 - Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia","interactions":[],"lastModifiedDate":"2022-12-06T13:08:24.04841","indexId":"70238704","displayToPublicDate":"2022-08-30T07:03:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Spaceborne InSAR mapping of landslides and subsidence in rapidly deglaciating terrain, Glacier Bay National Park and Preserve and vicinity, Alaska and British Columbia","docAbstract":"The Glacier Bay area in southeastern Alaska and British Columbia, encompassing Glacier Bay National Park and Preserve, has experienced rapid glacier retreat since the end of the Little Ice Age in the mid-1800s. The impact that rapid deglaciation has had on the slope stability of valley walls and on the sedimentation of fans and deltas adjacent to fjords and inlets is an ongoing research topic. Using 3-year (2018–2020) Sentinel-1 datasets, and an automated time-series persistent scatterer interferometric synthetic aperture radar (PSInSAR) processing method, we detected landslides or delta subsidence at 27 sites within a vast 180 × 180 km remote region encompassing Glacier Bay proper. Most of the sites that we identified had not been previously identified. We categorized the hazard source areas that we identified into three general types:1) slow-moving landslides on steep rocky slopes not near (> 2 km away from) present-day glacier termini (e.g., Tidal Inlet landslide), 2) slow-moving landslides directly adjacent to (< 2 km away from), and associated with glacier thinning and retreat, and 3) subsidence of glacial outwash fan deltas. In categories 1 and 2, we observed 22 landslides moving at velocities ranging from 0.5 to 4 cm/yr. In category 3, we detected five fan deltas subsiding at velocities ranging from 0.5 to 6 cm/yr. Within our measurement error, these velocities were consistent during the monitoring period. Because acceleration was not observed, the issuance of warnings of imminent rapid failure is not warranted, however, continued remote monitoring is warranted. Our interferometric synthetic aperture radar (InSAR) results could be combined with other data sets including field observations, subaerial and submarine landslide inventories, bedrock fabric mapping from newly available light detection and ranging (lidar) data, and geologic maps to produce an inherent susceptibility map for landslides in bedrock and fan deltas. This map could be used to forecast susceptibility for both earthquake and climatically induced landslides.
Agricultural land-use conversion has fragmented prairie wetland habitats in the Prairie Pothole Region (PPR), an area with one of the most wetland dense regions in the world. This fragmentation can lead to negative consequences for wetland obligate organisms, heightening risk of local extinction and reducing evolutionary potential for populations to adapt to changing environments.
This study models biotic connectivity of prairie-pothole wetlands using landscape genetic analyses of the northern leopard frog (Rana pipiens) to (1) identify population structure and (2) determine landscape factors driving genetic differentiation and possibly leading to population fragmentation.
Frogs from 22 sites in the James River and Lake Oahe river basins in North Dakota were genotyped using Best-RAD sequencing at 2868 bi-allelic single nucleotide polymorphisms (SNPs). Population structure was assessed using STRUCTURE, DAPC, and fineSTRUCTURE. Circuitscape was used to model resistance values for ten landscape variables that could affect habitat connectivity.
STRUCTURE results suggested a panmictic population, but other more sensitive clustering methods identified six spatially organized clusters. Circuit theory-based landscape resistance analysis suggested land use, including cultivated crop agriculture, and topography were the primary influences on genetic differentiation.
While the R. pipiens populations appear to have high gene flow, we found a difference in the patterns of connectivity between the eastern portion of our study area which was dominated by cultivated crop agriculture, versus the western portion where topographic roughness played a greater role. This information can help identify amphibian dispersal corridors and prioritize lands for conservation or restoration.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01515-8","usgsCitation":"Waraniak, J.M., Mushet, D., and Stockwell, C.A., 2022, Over the hills and through the farms: Land use and topography influence genetic connectivity of northern leopard frog (Rana pipiens) in the Prairie Pothole Region: Landscape Ecology, v. 37, p. 2877-2893, https://doi.org/10.1007/s10980-022-01515-8.","productDescription":"17 p.","startPage":"2877","endPage":"2893","ipdsId":"IP-137156","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446618,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-022-01515-8","text":"Publisher Index Page"},{"id":406585,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.39257812499999,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 45.920587344733654\n ],\n [\n -96.85546875,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 48.574789910928864\n ],\n [\n -102.39257812499999,\n 45.920587344733654\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationDate":"2022-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Waraniak, Justin M.","contributorId":211882,"corporation":false,"usgs":false,"family":"Waraniak","given":"Justin","email":"","middleInitial":"M.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":851527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":851528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stockwell, Craig A.","contributorId":194252,"corporation":false,"usgs":false,"family":"Stockwell","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":851529,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70246958,"text":"70246958 - 2022 - Estimating the effect of tidal marsh restoration on housing prices: A hedonic analysis in the Nisqually National Wildlife Refuge, Washington, USA","interactions":[],"lastModifiedDate":"2023-07-20T11:42:22.401021","indexId":"70246958","displayToPublicDate":"2022-08-30T06:37:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2596,"text":"Land","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the effect of tidal marsh restoration on housing prices: A hedonic analysis in the Nisqually National Wildlife Refuge, Washington, USA","docAbstract":"Streamflow drought is a recurring challenge, and understanding spatiotemporal patterns of past droughts is needed to manage future water resources. We examined regional patterns in streamflow drought metrics and compared these metrics to low flow timing and magnitude using long-term daily records for 555 minimally disturbed watersheds. For each streamgage, we calculated streamflow drought duration (number of days) and deficit (flow volume below a specified threshold) for each climate year (April 1–March 31). We identified drought using five thresholds (2%–30%) and two approaches: variable thresholds with unique values for each day of the year, and a fixed threshold based on all period-of-record flows. We then analyzed drought trends using the Mann-Kendall test with persistence adjustment for 1921–2020, 1951–2020, and 1981–2020, and computed correlations between annual streamflow drought metrics and climate metrics using values from a monthly water balance model. Spatial patterns in drought metrics were consistent between variable and fixed approaches, though fixed threshold durations were typically longer and variable threshold deficits larger. High interannual variability in drought duration emerged in the central, interior west, and southwestern U.S., with high deficit variability in the interior west. Drought metrics were weakly correlated with low flow magnitude and timing, providing unique information. Drought duration and deficit increased in the southern and western U.S. for both 1951–2020 and 1981–2020, particularly using fixed thresholds, and paralleled trends in aridity. Projections of continued aridification for the southern and western U.S. may increase drought durations and deficits and intensify water availability impacts.
The National Gap Analysis Project created species habitat distribution models for all terrestrial vertebrates in the United States to support conservation assessments and explore patterns of species richness. Those models link species to specific habitats throughout the range of each species. For most vertebrates, there are not enough occurrence data to drive inductive, range-wide species habitat distribution models at high spatial and thematic resolution. However, it is possible to use occurrence data for model evaluation. The combination of citizen science, formal species survey work, and digitized specimen archives are making millions of observations available to the scientific community. Our challenge is to combine the mostly unstructured data into metrics that help us characterize and understand patterns of biodiversity. In this work, we propose two model-evaluation metrics. The first, a buffer proportion assessment, is based on the proportion of habitat in the range relative to the mean proportion of habitat around each of the species’ occurrence records. The second is a measure of the sensitivity (proportion of true presence) to buffer distances around occurrence records. The buffer proportion is a modification of model prevalence versus point prevalence metric, whereby comparison to a null model allows us to determine if the model performs better or worse than random.
In this report, we describe the workflow used to compile and filter the species occurrence records from online resources (for example, the Global Biodiversity Information Facility) and show results for a single species, Desmognathus quadramaculatus (black-bellied salamander). For the salamander, 222 occurrence points met our criteria for inclusion in the evaluation. We found the model performed better than random with a buffer proportion index of 1.745, indicating about 5 times as much habitat was found adjacent to known occurrence records than would be expected from randomly located sites throughout the range. Sensitivity increased with larger buffer distances and leveled off to around 0.7 between 1,000- and 2,000-meter buffer distances, indicating the model is likely best suited for scales exceeding 1,000 meters. We plan to report the buffer proportion assessment and sensitivity metrics along with the full species model reports to increase understanding of the model’s performance and to use the metrics to help prioritize revisions to the models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm2A19","collaboration":"Prepared in cooperation with North Carolina State University, North Carolina Cooperative Fish and Wildlife Research Unit, Department of Applied Ecology","usgsCitation":"Rubino, M.J., McKerrow, A.J., Tarr, N.M., and Williams, S.G., 2022, Methods for evaluating Gap Analysis Project habitat distribution maps with species occurrence data: U.S. Geological Survey Techniques and Methods 2-A19, 13 p., https://doi.org/10.3133/tm2A19.","productDescription":"Report: vi, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-124954","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":405205,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/02/a19/images"},{"id":405206,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/02/a19/tm2a19.xml"},{"id":405202,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a19/tm2a19.pdf","text":"Report","size":"1.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 2A-19"},{"id":405201,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a19/coverthb.jpg"},{"id":405204,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H1308B","text":"USGS data release","linkHelpText":"Black-bellied Salamander (Desmognathus quadramaculatus) aBESAx_CONUS_2001v1 Habitat Map"}],"country":"United States","state":"Alabama, Georgia, North Carolina, Tennessee, Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.9580078125,\n 36.73888412439431\n ],\n [\n -82.41943359375,\n 36.73888412439431\n ],\n [\n -83.1884765625,\n 36.43896124085945\n ],\n [\n -84.04541015625,\n 36.13787471840729\n ],\n [\n -84.92431640625,\n 35.99578538642032\n ],\n [\n -85.7373046875,\n 35.38904996691167\n ],\n [\n -86.24267578125,\n 35.11990857099681\n ],\n [\n -86.220703125,\n 34.50655662164561\n ],\n [\n -85.58349609375,\n 34.361576287484176\n ],\n [\n -84.17724609375,\n 34.79576153473033\n ],\n [\n -82.81494140625,\n 35.02999636902566\n ],\n [\n -82.08984375,\n 35.28150065789119\n ],\n [\n -81.10107421874999,\n 35.44277092585766\n ],\n [\n -80.26611328125,\n 36.2265501474709\n ],\n [\n -80.068359375,\n 36.77409249464195\n ],\n [\n -79.7607421875,\n 37.37015718405753\n ],\n [\n -79.453125,\n 37.71859032558816\n ],\n [\n -79.2333984375,\n 38.09998264736481\n ],\n [\n -79.25537109375,\n 38.59970036588819\n ],\n [\n -79.65087890624999,\n 38.54816542304656\n ],\n [\n -80.04638671875,\n 38.42777351132902\n ],\n [\n -80.5517578125,\n 38.39333888832238\n ],\n [\n -80.83740234375,\n 37.996162679728116\n ],\n [\n -81.18896484375,\n 37.405073750176925\n ],\n [\n -81.49658203125,\n 36.914764288955936\n ],\n [\n -81.9580078125,\n 36.73888412439431\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Core Science Analytics and Synthesis
U.S. Geological Survey
Box 25046, Mail Stop 302
Denver, CO 80225