Hierarchical cluster analysis (HCA) was applied to a geochemical dataset representing the Eocene Green River Formation in the Piceance Basin of Colorado to identify chemofacies in core and outcrop samples from the basin margin and the basin center. The input dataset consisted of inductively coupled plasma optical emission spectroscopy and mass spectrometry and total organic carbon (TOC) content analyses of 186 basin margin outcrop samples and 190 basin center core samples discussed in Part 1 of this study (this volume). TOC values and twenty-five major and trace elements were used as variables to define statistical clusters of samples for the overall dataset, for the two basin center cores, and for each separate core or outcrop dataset by HCA applying Euclidean distance and Ward’s method algorithms. For each dataset, five cluster-defined chemofacies were identified. The chemofacies for each dataset show chemical affinities with five informally defined rock types– mudstone, marlstone, carbonate-rich mudstone, siliciclastic-rich mudstone/siltstone/sandstone, and Na-rich (saline) mudstone, with each showing variations in TOC content and abundance of redox sensitive minor and trace elements. A close relationship between enrichment of redox sensitive elements, particularly As and Mo, and TOC is identified in the basin center. Whereas enrichment factors (relative to average shale) are relatively low for many Period IV (PIV) transition metals, as discussed in Part 1 of this study, their consistent coherence in enrichment or depletion in HCA-defined chemofacies demonstrates the expected relationship between redox state and organic richness. Enrichment in PIV transition metals also shows a correlation to enrichment in elements with affinity for siliciclastic sediment. Enrichment/depletion among several groups of redox indicators is not everywhere consistent, with some chemofacies showing, for example, enrichment of PIV transition metals and depletion of sulfur and arsenic. Early timing of saline conditions in the basin margin is clearly displayed in the chemofacies log display, consistent with observations based on geochemical interpretations of concentrations and elemental ratios discussed in Part 1. Overall, the chemofacies are consistent with major mineralogical units and lake history stages defined in previous work, but provide more detail on the fluctuations in lake chemistry that occurred during deposition of Green River oil shale in the Piceance Basin.
","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.115","usgsCitation":"Wu, T., Boak, J., and Birdwell, J.E., 2022, Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: II. Chemofacies from hierarchical cluster analysis, chap. of The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record, v. 50, p. 298-323, https://doi.org/10.31711/ugap.v50i.115.","productDescription":"26 p.","startPage":"298","endPage":"323","ipdsId":"IP-127905","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":446585,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31711/ugap.v50i.115","text":"Publisher Index Page"},{"id":406450,"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":{"authors":[{"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":851291,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":851292,"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":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":851293,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236510,"text":"70236510 - 2022 - Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah","interactions":[],"lastModifiedDate":"2022-09-09T13:39:43.82889","indexId":"70236510","displayToPublicDate":"2022-09-01T08:33:14","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah","docAbstract":"The 15- to 65-m-thick informal Uteland Butte member of the Eocene Green River Formation represents the first widespread transgression of Lake Uinta in the Uinta Basin, Utah. This study assesses the spatial and temporal variation of Uteland Butte member deposits along a 40-km transect in the southwestern margin of the Uinta Basin using detailed measured sections, organic and inorganic geochemical data, and outcrop gamma ray logs. Fourteen lithofacies are identified, which comprise seven facies associations linked to with lacustrine, palustrine, and deltaic depositional settings. Facies associations are traceable laterally across the study area, where five 4- to 12-m-thick depositional cycles are identified. Each shallowing upwards cycle is defined by a >1.5-m-thick basal package of organic-rich, argillaceous laminated mudstone, and is capped by thick packages of bedded carbonate. In the far western study area (Kyune Creek Canyon), thick deposits of organic-rich mudstone are present and represent the most distal outcrop section; time-equivalent strata in the eastern study area (Minnie Maud Creek Canyon) are relatively organic lean with higher silt and clay content, interpreted to represent proximal lake margin deposits influenced by a nearby delta. The outcrop belt is correlated to more distal cores and well logs across the western Uinta Basin. Similar lithological and petrophysical patterns across the western Uinta Basin are used to subdivide stratigraphy into nine laterally contiguous sub-units based on nomenclature from the oil-producing area of the central basin (from base to top: lower Uteland Butte, D Bench, D Shale, C Bench, C Shale, B Bench, B Shale, A Bench, and A Shale). Siliciclastic clay-rich and carbonaterich intervals are correlated across the region and indicate distinct siliciclastic- and carbonate-dominated lake phases during Uteland Butte member deposition. Climate is interpreted to be the dominant driver of these claycarbonate cycles, in which relatively humid periods resulted in increased fluvially derived siliciclastic sediment into the basin (clay-rich periods), and arid periods resulted in evaporative conditions with decreased fluvial sediment input that favor carbonate accumulation. Climatically driven depositional cycles within the Uteland Butte member reflect, to a smaller degree, the larger scale climatically driven depositional cycles observed at the member- and formation levels of Paleocene and Eocene Uinta Basin stratigraphy. Importantly, the Uteland Butte member clay-carbonate cycles showcase how relatively small-scale climate shifts can impact basin-scale lacustrine deposition.
","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.106","usgsCitation":"Gall, R.D., Birdwell, J.E., Brinkerhoff, R., and Vanden Berg, M.D., 2022, Geologic characterization and depositional history of the Uteland Butte member, Green River Formation, southwestern Uinta Basin, Utah, chap. of The lacustrine Green River Formation: Hydrocarbon potential and Eocene climate record, v. 50, p. 37-62, https://doi.org/10.31711/ugap.v50i.106.","productDescription":"26 p.","startPage":"37","endPage":"62","ipdsId":"IP-127906","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":446587,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31711/ugap.v50i.106","text":"Publisher Index Page"},{"id":435703,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X66RQ4","text":"USGS data release","linkHelpText":"Geochemical and spectroscopic data on outcrop samples from the informal Uteland Butte member of the Eocene Green River Formation in Uinta Basin, Utah"},{"id":406449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Uinta Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.786376953125,\n 39.757879992021756\n ],\n [\n -109.281005859375,\n 40.283716270542584\n ],\n [\n -109.49523925781249,\n 40.56389453066509\n ],\n [\n -110.2972412109375,\n 40.543026009955014\n ],\n [\n -110.8740234375,\n 40.35073056591789\n ],\n [\n -110.687255859375,\n 40.057052221322\n ],\n [\n -110.28076171875,\n 39.85915479295669\n ],\n [\n -109.8797607421875,\n 39.73253798438173\n ],\n [\n -109.786376953125,\n 39.757879992021756\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationDate":"2022-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gall, Ryan D.","contributorId":296324,"corporation":false,"usgs":false,"family":"Gall","given":"Ryan","email":"","middleInitial":"D.","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":851281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":851282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brinkerhoff, Riley","contributorId":296326,"corporation":false,"usgs":false,"family":"Brinkerhoff","given":"Riley","email":"","affiliations":[{"id":64017,"text":"Wasatch Energy Management","active":true,"usgs":false}],"preferred":false,"id":851283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vanden Berg, Michael D.","contributorId":177609,"corporation":false,"usgs":false,"family":"Vanden Berg","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":851284,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236514,"text":"70236514 - 2022 - Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements","interactions":[],"lastModifiedDate":"2022-09-09T13:32:07.905743","indexId":"70236514","displayToPublicDate":"2022-09-01T08:22:11","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Geochemical studies of the Green River Formation in the Piceance Basin, Colorado: I. Major, minor, and trace elements","docAbstract":"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":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","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":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.
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":70237577,"text":"70237577 - 2022 - Causality guided machine learning model on wetland CH4 emissions across global wetlands","interactions":[],"lastModifiedDate":"2022-10-14T13:48:15.777176","indexId":"70237577","displayToPublicDate":"2022-08-31T16:42:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Causality guided machine learning model on wetland CH4 emissions across global wetlands","title":"Causality guided machine learning model on wetland CH4 emissions across global wetlands","docAbstract":"Wetland CH4 emissions are among the most uncertain components of the global CH4 budget. The complex nature of wetland CH4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH4 emissions. In this study, we used the flux measurements of CH4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH4 emissions in all studied wetland types. Ecosystem respiration (CO2) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH4 emissions within earth system land models.
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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.
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0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ivahnenko, Tamara I. 0000-0002-1124-7688 ivahnenk@usgs.gov","orcid":"https://orcid.org/0000-0002-1124-7688","contributorId":2050,"corporation":false,"usgs":true,"family":"Ivahnenko","given":"Tamara","email":"ivahnenk@usgs.gov","middleInitial":"I.","affiliations":[{"id":5078,"text":"Southwest Regional Director's Office","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":false,"id":852783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Houston, Natalie 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 Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":852786,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852787,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thomas, Jonathan V. 0000-0003-0903-9713","orcid":"https://orcid.org/0000-0003-0903-9713","contributorId":217874,"corporation":false,"usgs":true,"family":"Thomas","given":"Jonathan V.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852788,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Flickinger, Allison K. 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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
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
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":"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
Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data.
Features:
The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.
","language":"English","publisher":"Chapman and Hall/CRC","doi":"10.1201/9781351018784","usgsCitation":"Qian, S.S., Dufour, M.R., and Alameddine, I., 2022, Bayesian applications in environmental and ecological studies with R and Stan, 415 p., https://doi.org/10.1201/9781351018784.","productDescription":"415 p.","ipdsId":"IP-134860","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":407695,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Qian, Song S.","contributorId":198934,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":842387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dufour, Mark Richard 0000-0001-6930-7666","orcid":"https://orcid.org/0000-0001-6930-7666","contributorId":291450,"corporation":false,"usgs":true,"family":"Dufour","given":"Mark","email":"","middleInitial":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":842388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alameddine, Ibrahim","contributorId":244836,"corporation":false,"usgs":false,"family":"Alameddine","given":"Ibrahim","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":842389,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235983,"text":"ofr20221067 - 2022 - Updates for Wake Atoll biosecurity management, biological control, survey, and management, and integrated pest management plans","interactions":[],"lastModifiedDate":"2026-03-30T20:22:47.148148","indexId":"ofr20221067","displayToPublicDate":"2022-08-26T10:52:23","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":"2022-1067","displayTitle":"Updates for Wake Atoll Biosecurity Management, Biological Control, Survey, and Management, and Integrated Pest Management Plans","title":"Updates for Wake Atoll biosecurity management, biological control, survey, and management, and integrated pest management plans","docAbstract":"Pests and invasive species have been defined as any organism that can have real or perceived adverse effects on operations, or the well-being of personnel, native plants, animals, their environment and ecosystem processes; attack or damage real property, supplies, equipment, or are otherwise undesirable (paraphrased from many sources including 53 Federal Register [FR] 15975, May 4, 1988, as amended at 78 FR 13507, February 28, 2013). Biosecurity programs and pest management plans can be developed and implemented with the goals of preventing the arrival of or eradication or control of pests and invasive species to reduce the potential for adverse effects. Such plans have been developed for Wake Atoll (U.S. Air Force, unpub. data 2017). Periodic plan reviews are an integral step for evaluating plan efficacy and updating plans with new information for improving plan effectiveness. This report summarizes an evaluation of past, current, and potential biosecurity and pest management for Wake with the intent this information can be used for updating existing plans. This document was prepared in cooperation with the U.S. Air Force (USAF) and surveys were performed for the 611th Civil Engineer Squadron Natural Resources Program ACES PROJECT no. YGFZ17002 under agreement number F2MUAA7116GW01 between the USAF and the U.S. Geological Survey’s Western Ecological Research Center (USGS-WERC).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221067","collaboration":"Prepared in cooperation with the U.S. Air Force","programNote":"Ecosystems Mission Area—Land Management Research and Species Management Research Programs","usgsCitation":"Hathaway, S.A., Jacobi, J.D., Peck, R., and Fisher, R.N., 2022, Updates for Wake Atoll biosecurity management, biological control, survey, and management, and integrated pest management plans: U.S. Geological Survey Open-File Report 2022-1067, 56 p., https://doi.org/10.3133/ofr20221067.","productDescription":"viii, 56 p.","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-136103","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"links":[{"id":405629,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1067/ofr20221067.xml"},{"id":405628,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1067/ofr20221067.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":405627,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1067/covrthb.jpg"},{"id":501820,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113419.htm","linkFileType":{"id":5,"text":"html"}},{"id":405630,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1067/images"}],"contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Market-based conservation makes use of economic incentives to promote actions that avoid, minimize, or compensate for detrimental effects on natural resources and the environment. Examples of market-based conservation mechanisms include aquatic-resource (such as, streams, wetlands, and estuaries) compensatory mitigation, conservation banking, habitat exchanges, and payments for ecosystem services. A critical component in the operation of these market-based conservation mechanisms is the methods (hereafter referred to as “quantification tools”) used to assess existing (sometimes referred to as “baseline”) or potential site conditions. Quantification tools are used to assign values to the benefits provided by preservation, restoration, or enhancement actions, as well as the negative effects of human activities (for example, infrastructure development, energy extraction, and anthropogenic disasters).
In 2018, the U.S. Geological Survey (USGS) published a database describing the attributes of 69 quantification tools developed for United States conservation markets. The database focused on tools used for species-based mitigation, payments for ecosystem services, and ecolabel programs (Chiavacci and Pindilli, 2020). Recently, the USGS, in collaboration with the U.S. Environmental Protection Agency, revised the original database by updating the existing tool information, adding newly developed tools, and broadening the scope to include tools developed for compensatory mitigation under the Clean Water Act Section 404 Regulatory Program.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223068","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Chiavacci, S.J., French, E.D., and Morgan, J.A., 2022, Database of biodiversity, habitat, and aquatic-resource quantification tools used in market-based conservation — 2022 update: U.S. Geological Survey Fact Sheet 2022–3068, 2 p., https://doi.org/10.3133/fs20223068.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-140564","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":405533,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223068/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3068"},{"id":406471,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79G5M3X","text":"USGS data release","linkHelpText":"Database of Biodiversity, Habitat, and Aquatic Resource Quantification Tools Used for Market-based Conservation in the United States (ver. 2.0, June 2022)"},{"id":405535,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3068/fs20223068.XML"},{"id":405534,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3068/images/"},{"id":405509,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3068/fs20223068.pdf","text":"Report","size":"558 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3068"},{"id":405508,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3068/coverthb3.jpg"}],"contact":"Science and Decisions Center
12201 Sunrise Valley Drive
Reston, VA 20192
A radium industry existed between about 1914 and 1920 in Denver, Colorado, with operations located along the South Platte River. Sites associated with that industry were contaminated with radium and uranium processing residues and were incorporated into clean-up efforts as Operating Units (OUs) of the Denver Radium Superfund Site. Concentrations of uranium exceeding the U.S. Environmental Protection Agency maximum contaminant level of 0.03 milligrams per liter for drinking water are present in shallow groundwater at OU8. However, previous studies have shown concentrations of dissolved uranium can be naturally high in shallow groundwater of the South Platte River valley compared to other rivers of the world. This report compares dissolved uranium concentrations measured by the U.S. Geological Survey across the South Platte River valley to data collected at the OU8 of the Denver Radium Superfund Site. The U.S. Geological Survey data represent 5 distinct urban or agricultural geographic areas and included 230 sampling events at 114 wells during 1993 to 2013. The OU8 data represent 13 wells and groundwater discharge locations sampled during the years 2017 and 2018. Dissolved uranium concentrations were statistically significantly greater for both years of the OU8 data compared to three datasets from shallow groundwater beneath urban areas in the Denver metropolitan area. However, compared to OU8, concentrations were significantly greater in shallow groundwater from an agricultural area of the South Platte River valley distant from Denver. Additionally, each of the urban area datasets contained some individual dissolved uranium concentrations greater than the greatest concentrations from the two OU8 datasets. Thus, naturally occurring concentrations of dissolved uranium in shallow groundwater that are greater than those observed at OU8 are not uncommon in the South Platte River valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20225085","collaboration":"Prepared in cooperation the U.S. Environmental Protection Agency","usgsCitation":"Bern, C.R., 2022, Examination of dissolved uranium concentrations in regional shallow groundwater relative to Operable Unit 8 of the Denver Radium Superfund Site: U.S. Geological Survey Scientific Investigations Report 2022–5085, 16 p., https://doi.org/10.3133/sir20225085.","productDescription":"Report: vi, 16 p.; Database","onlineOnly":"Y","ipdsId":"IP-135030","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":405601,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":405600,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://semspub.epa.gov/work/08/100005517.pdf","text":"U.S. Environmental Protection Agency [EPA], 2018b—","linkHelpText":"Fifth five-year review report for Denver radium superfund site, Denver County, Colorado"},{"id":405596,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5085/coverthb.jpg"},{"id":405598,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5085/ofr20225085.pdf","text":"Report","size":"2.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5085"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.07598876953125,\n 39.115144700901475\n ],\n [\n -103.75213623046875,\n 39.115144700901475\n ],\n [\n -103.75213623046875,\n 39.8992015115692\n ],\n [\n -105.07598876953125,\n 39.8992015115692\n ],\n [\n -105.07598876953125,\n 39.115144700901475\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
The Great Lakes Basin covers around 536,393 square kilometers, and the Great Lakes hold more than 5,400 cubic miles of water, accounting for more than 20 percent of the world’s fresh surface water supply. The Great Lakes provide a source of drinking water to tens of millions of people in Canada and the United States and support one of the most diverse ecosystems in the world. Increasing urbanization combined with aging infrastructure and more extreme storm events because of changing weather patterns creates stormwater management challenges for communities across the Great Lakes region. A variety of green infrastructure (GI) practices, designed to decrease runoff and improve water quality, have been implemented throughout the region in response to these challenges; however, implementation often remains limited to local efforts and with little coordination among various levels of government because of, at least in part, a lack of clear standards for stormwater, limited funding, and a general uncertainty in the type and expected performance of these practices. City planners, engineers, and political leaders often see GI investment as riskier than other alternatives despite studies that determined, in most cases, practices can either reduce or not affect costs.
This report summarizes selected published reports and data sources from studies done in Great Lakes states and compares the measured effects of various GI practices and their applicability in different settings around the Great Lakes. By summarizing selected published reports and data sources from studies done in Great Lakes states, this report provides foundational information for U.S. Geological Survey scientists and their local and national partners to assess the ability of GI to reduce stormwater runoff in Great Lakes urban areas. GI includes a variety of stormwater management techniques designed to mimic natural hydrologic processes like infiltration and evapotranspiration, which can decrease the volume of water running into sewers and streams. It can also improve water quality by trapping sediment, nutrients, and other contaminants. A variety of landscape practices can be incorporated into urban areas as GI, but the discussion here is limited to vegetated basins, vegetated channels, permeable pavement, urban tree canopy, and green roofs. Other types of GI, such as downspout disconnection, rainwater harvesting, and wet and dry detention basins were not included because hydrologic function and associated components are not widely monitored or evaluated in literature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1496","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Baker, N.T., Sullivan, D.J., Selbig, W.R., Haefner, R.J., Lampe, D.C., Bayless, R., and McHale, M.R., 2022, Green infrastructure in the Great Lakes—Assessment of performance, barriers, and unintended consequences: U.S. Geological Survey Circular 1496, 70 p., https://doi.org/10.3133/cir1496.","productDescription":"Report: ix, 70 p.; 1 Table","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-128488","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Although polar bears (Ursus maritimus) of the Southern Beaufort Sea (SBS) subpopulation have commonly created maternal dens on sea ice in the past, maternal dens on land have become increasingly prevalent as sea ice declines. This trend creates conditions for increased human–bear interactions associated with local communities and industrial activity. Maternal denning is a vulnerable period in the polar bear life cycle, and den disturbance could lead to den abandonment, cub mortality, and negative population impacts. We used published long-term data to parameterize a Bayesian hierarchical model of annual land den abundance during 2000–2015, in 4 regions of northern Alaska, USA, with current or potential future oil and gas activity. We also estimated long-term (1982–2015) shifts in the spatial distribution of land dens within and among regions using kernel density estimation and assessed the influence of local and regional sea ice and snow conditions on den site selection using a random forest resource selection function. Our objectives were to quantify current den distribution and abundance, test for distributional shifts over time, and investigate if those shifts could be attributed to environmental variables related to den habitat. We estimated that between 2000 and 2015, the SBS contained a median 123 dens in a typical year, of which 68 occurred on land. The region between the Colville and Canning rivers, where most current oil and gas activity occurred, also contained the largest fraction of land dens. Overall, land dens were disproportionately concentrated on barrier islands and on land within 30 km of the coast. The probability of dens occurring on land varied from 1982–1999 to 2000–2015 in all regions, and the overall distribution of land dens shifted west between those periods. This regional-scale change in den distribution was predictable based on spatial and temporal heterogeneity in snow and sea ice conditions within 50 km of individual den locations. Land denning is likely to become increasingly common with continued sea ice loss, and our results and modeling framework could be used to design additional mitigation strategies for reducing the risk of incidental take due to den disturbance.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.22302","usgsCitation":"Patil, V.P., Durner, G.M., Douglas, D.C., and Atwood, T.C., 2022, Modeling the spatial and temporal dynamics of land-based polar bear denning in Alaska: Journal of Wildlife Management, v. 86, no. 8, e22302, 22 p., https://doi.org/10.1002/jwmg.22302.","productDescription":"e22302, 22 p.","ipdsId":"IP-134179","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":446643,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22302","text":"Publisher Index Page"},{"id":435714,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZNG8JT","text":"USGS data release","linkHelpText":"Code for analysis of polar bear maternal den abundance and distribution in four regions of northern Alaska and Canada within the Southern Beaufort Sea subpopulation boundary (1982-2015)"},{"id":406139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canadian, United States","state":"Alaska","otherGeospatial":"Southern Beaufort Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -172.6171875,\n 67.7427590666639\n ],\n [\n -122.51953124999999,\n 67.7427590666639\n ],\n [\n -122.51953124999999,\n 77.5041191797399\n ],\n [\n -172.6171875,\n 77.5041191797399\n ],\n [\n -172.6171875,\n 67.7427590666639\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"86","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Patil, Vijay P. 0000-0002-9357-194X vpatil@usgs.gov","orcid":"https://orcid.org/0000-0002-9357-194X","contributorId":203676,"corporation":false,"usgs":true,"family":"Patil","given":"Vijay","email":"vpatil@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":850654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":850655,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":850656,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":850657,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236730,"text":"70236730 - 2022 - Can lava flow like water? Assessing applications of critical flow theory to channelized basaltic lava flows","interactions":[],"lastModifiedDate":"2022-09-16T14:39:42.900391","indexId":"70236730","displayToPublicDate":"2022-08-25T09:35:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6454,"text":"Journal of Geophysical Research - Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Can lava flow like water? Assessing applications of critical flow theory to channelized basaltic lava flows","docAbstract":"Flowing lava and water have dramatically different physical properties but can form similar hydraulic structures, including undular hydraulic jumps, or standing wave trains. In water flows, undular hydraulic jumps are evidence of critical flow (Froude number ∼1) and open-channel hydraulic theory provides a powerful tool for estimating flow depth and velocity. Monitoring these parameters in an active lava channel is inherently challenging, but essential for calculating lava discharge (effusion rate), a primary control on the rate of flow front advance and ultimate flow runout distance. We analyze undular hydraulic jumps in both water and lava flows to assess the conditions under which they form and, by extension, the potential use of critical flow theory to estimate, in real time, lava flow velocity, depth, and discharge. Experimental data for water flows show that these structures mark the transition from supercritical to subcritical flow. Undular hydraulic jumps in the near-vent lava channel of the 2018 lower East Rift Zone eruption of Kīlauea, Hawaiʻi also reflect critical flow conditions; their wavelengths scale with flow depth and velocity, consistent with hydraulic theory. Calculated lava effusion rates are similar to estimates made using more traditional approaches (Jeffreys', 1925, https://doi.org/10.1080/14786442508634662, equation based on lava viscosity, density, and channel slope) and with lava volumes derived from topographic-change mapping. From this we conclude that critical flow phenomena show great potential to track flow dynamics and inform hazard assessment for a wide range of geophysical fluids.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006666","usgsCitation":"Dietterich, H., Grant, G., Fasth, B., Major, J., and Cashman, K., 2022, Can lava flow like water? Assessing applications of critical flow theory to channelized basaltic lava flows: Journal of Geophysical Research - Earth Surface, v. 127, no. 9, e2022JF006666, 26 p., https://doi.org/10.1029/2022JF006666.","productDescription":"e2022JF006666, 26 p.","ipdsId":"IP-138706","costCenters":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":406841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Dietterich, Hannah R. 0000-0001-7898-4343","orcid":"https://orcid.org/0000-0001-7898-4343","contributorId":212771,"corporation":false,"usgs":true,"family":"Dietterich","given":"Hannah R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":852036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Gordon E.","contributorId":30881,"corporation":false,"usgs":false,"family":"Grant","given":"Gordon E.","affiliations":[{"id":12647,"text":"U.S. Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":852037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fasth, Becky","contributorId":296636,"corporation":false,"usgs":false,"family":"Fasth","given":"Becky","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":852038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Major, J. J. 0000-0003-2449-4466","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":29461,"corporation":false,"usgs":true,"family":"Major","given":"J. J.","affiliations":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":852039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cashman, Katharine V.","contributorId":40097,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine V.","affiliations":[],"preferred":false,"id":852040,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255280,"text":"70255280 - 2022 - Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake","interactions":[],"lastModifiedDate":"2024-06-17T13:49:07.864148","indexId":"70255280","displayToPublicDate":"2022-08-25T08:43:11","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":"Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake","docAbstract":"Wildlife distributions are often subdivided into discrete conservation units to aid in implementing management and conservation objectives. Habitat suitability models, resistance surfaces, and resistant kernels provide tools for delineating spatially explicit conservation units but guidelines for parameterizing resistant kernels are generally lacking.
We used the federally threatened eastern indigo snake (Drymarchon couperi) as a case study for calibrating resistant kernels using observed movement data and resistance surfaces to help delineate habitat-based conservation units.
We simulated eastern indigo snake movements under different resistance surface and resistant kernel parameterizations and selected the scenario that produced simulated movement distances that best approximated the maximum observed annual movement distance. We used our calibrated resistant kernel to model range-wide connectivity and compared delineated conservation units to Euclidean distance-based population units from the recent eastern indigo snake species status assessment (SSA).
We identified a total of 255 eastern indigo snake conservation units, with numerous large (2500–5000 ha of suitable habitat) conservation units across the eastern indigo snake distribution. There was substantial variation in the degree of overlap with the SSA population units likely reflecting the spatial heterogeneity in habitat suitability and landscape resistance.
Our calibration approach is widely applicable to other systems for parameterizing biologically meaningful resistant kernels. Our conservation units can be used to prioritize future eastern indigo snake conservation efforts, identify areas where more survey work is needed, or identify small, isolated populations with high extinction risks.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01510-z","usgsCitation":"Bauder, J.M., Chandler, H.C., Elmore, M., and Jenkins, C.L., 2022, Incorporating habitat suitability, landscape distance, and resistant kernels to estimate conservation units for an imperiled terrestrial snake: Landscape Ecology, v. 37, https://doi.org/10.1007/s10980-022-01510-z.","productDescription":"15 p.","startPage":"2533","ipdsId":"IP-137585","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467167,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/666095","text":"External Repository"},{"id":430270,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.6589463070196,\n 30.27164925540076\n ],\n [\n -87.4471664851995,\n 30.273991912380097\n ],\n [\n -87.243062061103,\n 30.25688691395483\n ],\n [\n -86.504722900934,\n 30.288280599826876\n ],\n [\n -85.8821985085937,\n 30.127920150613292\n ],\n [\n -85.45848132490356,\n 29.678163497359677\n ],\n [\n -85.18012336836138,\n 29.52298729685313\n ],\n [\n -84.431494016169,\n 29.8435460527122\n ],\n [\n -84.27785036727468,\n 29.98120848065058\n ],\n [\n -83.89226362456542,\n 29.960791456317352\n ],\n [\n -83.47317674842255,\n 29.509171366635385\n ],\n [\n -83.08833538532544,\n 29.1434267271204\n ],\n [\n -82.82094989668451,\n 29.0956993019621\n ],\n [\n -82.67858009554807,\n 28.666898644234394\n ],\n [\n -82.84220852464915,\n 27.892907277971844\n ],\n [\n -81.9139493773393,\n 26.026944742093463\n ],\n [\n -81.3608497502143,\n 25.677695288573233\n ],\n [\n -81.20240783558897,\n 25.110332177132477\n ],\n [\n -80.5049186418957,\n 25.163605849744997\n ],\n [\n -81.39546331741505,\n 24.792875468134326\n ],\n [\n -81.84017458395616,\n 24.611130518564238\n ],\n [\n -81.49095266413781,\n 24.52625187147568\n ],\n [\n -80.30964454840066,\n 25.01219197845387\n ],\n [\n -80.28167788112444,\n 25.441483917895624\n ],\n [\n -80.07107634394508,\n 25.88314239044695\n ],\n [\n -80.01314206035136,\n 26.51492387439309\n ],\n [\n -80.110778674335,\n 27.072566624838785\n ],\n [\n -80.54296836001978,\n 28.439325701479135\n ],\n [\n -80.92465488637467,\n 29.033168269780845\n ],\n [\n -81.40440957377771,\n 30.780806652102896\n ],\n [\n -80.97885233616921,\n 32.04045515787071\n ],\n [\n -81.28694625263563,\n 32.58791480990446\n ],\n [\n -81.99407467041935,\n 32.551271523349655\n ],\n [\n -87.68936380825409,\n 31.462681042444245\n ],\n [\n -87.8537521440345,\n 31.229156643229587\n ],\n [\n -87.97481408751028,\n 30.53533344322976\n ],\n [\n -87.6589463070196,\n 30.27164925540076\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","edition":"2519","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan Mathias 0000-0002-2055-5324","orcid":"https://orcid.org/0000-0002-2055-5324","contributorId":337814,"corporation":false,"usgs":true,"family":"Bauder","given":"Javan","email":"","middleInitial":"Mathias","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":904088,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chandler, H. C.","contributorId":339318,"corporation":false,"usgs":false,"family":"Chandler","given":"H.","email":"","middleInitial":"C.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":904089,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Elmore, M.","contributorId":339320,"corporation":false,"usgs":false,"family":"Elmore","given":"M.","email":"","affiliations":[{"id":81289,"text":"Georgia Ecological Services","active":true,"usgs":false}],"preferred":false,"id":904090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenkins, C. L.","contributorId":339321,"corporation":false,"usgs":false,"family":"Jenkins","given":"C.","email":"","middleInitial":"L.","affiliations":[{"id":13223,"text":"The Orianne Society","active":true,"usgs":false}],"preferred":false,"id":904091,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236031,"text":"70236031 - 2022 - Genome resequencing clarifies phylogeny and reveals patterns of selection in the toxicogenomics model Pimephales promelas","interactions":[],"lastModifiedDate":"2022-08-26T12:08:25.041307","indexId":"70236031","displayToPublicDate":"2022-08-25T07:05:53","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"Genome resequencing clarifies phylogeny and reveals patterns of selection in the toxicogenomics model Pimephales promelas","docAbstract":"The fathead minnow (Pimephales promelas) is a model species for toxicological research. A high-quality genome reference sequence is available, and genomic methods are increasingly used in toxicological studies of the species. However, phylogenetic relationships within the genus remain incompletely known and little population-genomic data are available for fathead minnow despite the potential effects of genetic background on toxicological responses. On the other hand, a wealth of extant samples is stored in museum collections that in principle allow fine-scale analysis of contemporary and historical genetic variation.
Here we use short-read shotgun resequencing to investigate sequence variation among and within Pimephales species. At the genus level, our objectives were to resolve phylogenetic relationships and identify genes with signatures of positive diversifying selection. At the species level, our objective was to evaluate the utility of archived-sample resequencing for detecting selective sweeps within fathead minnow, applied to a population introduced to the San Juan River of the southwestern United States sometime prior to 1950.
We recovered well-supported but discordant phylogenetic topologies for nuclear and mitochondrial sequences that we hypothesize arose from mitochondrial transfer among species. The nuclear tree supported bluntnose minnow (P. notatus) as sister to fathead minnow, with the slim minnow (P. tenellus) and bullhead minnow (P. vigilax) more closely related to each other. Using multiple methods, we identified 11 genes that have diversified under positive selection within the genus. Within the San Juan River population, we identified selective-sweep regions overlapping several sets of related genes, including both genes that encode the giant sarcomere protein titin and the two genes encoding the MTORC1 complex, a key metabolic regulator. We also observed elevated polymorphism and reduced differentation among populations (FST) in genomic regions containing certain immune-gene clusters, similar to what has been reported in other taxa. Collectively, our data clarify evolutionary relationships and selective pressures within the genus and establish museum archives as a fruitful resource for characterizing genomic variation. We anticipate that large-scale resequencing will enable the detection of genetic variants associated with environmental toxicants such as heavy metals, high salinity, estrogens, and agrichemicals, which could be exploited as efficient biomarkers of exposure in natural populations.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.13954","usgsCitation":"Klymus, K.E., Hrabik, R.A., Thompson, N., and Cornman, R.S., 2022, Genome resequencing clarifies phylogeny and reveals patterns of selection in the toxicogenomics model Pimephales promelas: PeerJ, v. 10, e13954, 34 p., https://doi.org/10.7717/peerj.13954.","productDescription":"e13954, 34 p.","ipdsId":"IP-138759","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":446657,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.13954","text":"Publisher Index Page"},{"id":435715,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XXEUNR","text":"USGS data release","linkHelpText":"Genomic variation in the genus Pimephales: raw sequence data and single-nucleotide polymorphisms"},{"id":405677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":849724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hrabik, Robert A.","contributorId":148008,"corporation":false,"usgs":false,"family":"Hrabik","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":849725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":849726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":849727,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239304,"text":"70239304 - 2022 - Methods for robust estimates of tree biomass from pollen accumulation rates: Quantifying paleoecological reconstruction uncertainty","interactions":[],"lastModifiedDate":"2023-01-09T12:40:19.984396","indexId":"70239304","displayToPublicDate":"2022-08-25T06:38:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Methods for robust estimates of tree biomass from pollen accumulation rates: Quantifying paleoecological reconstruction uncertainty","docAbstract":"Pollen accumulation rates (PAR, grains cm–2 year–1) have been shown to be a reliable but methodologically complex bioproxy for quantitative reconstruction of past tree abundance. In a prior study, we found that the PARs of major tree taxa – Pseudotsuga, Pinus, Notholithocarpus, and the pollen group TC (Taxaceae and Cupressaceae families) – were robust and precise estimators of contemporary tree biomass. This paper expands our earlier work. Here, we more fully evaluate the errors associated with biomass reconstructions to identify weaknesses and recommend improvements in PAR-based reconstructions of forest biomass. We account for uncertainty in our biomass proxy in a formal, coherent fashion. The greatest error was introduced by the age models, underscoring the need for improved statistical approaches to age-depth modeling. Documenting the uncertainty in pollen vegetation models should be standard practice in paleoecology. We also share insights gained from the delineation of the relevant source area of pollen, advances in Bayesian 210Pb modeling, the importance of site selection, and the use of independent data to corroborate biomass estimates. Lastly, we demonstrate our workflow with a new dataset of reconstructed tree biomass between 1850 and 2018 AD from lakes in the Klamath Mountains, California. Our biomass records followed a broad trend of low mean biomass in the ∼1850s followed by large contemporary increases, consistent with expectations of forest densification due to twentieth century fire suppression policies in the American West. More recent reconstructed tree biomass estimates also corresponded with silviculture treatments occurring within the relevant source area of pollen of our lake sites.