The study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA.
Groundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex, layered aquifer system. Machine-learning methods (boosted regression trees) were applied to data from 3000 to 5000 wells. Predicted pH and the probability of anoxic conditions, defined by three thresholds of dissolved oxygen (0.5, 1, and 2 mg/L), were mapped at the 1-km2 scale for each of 10 regional aquifer layers.
Maps depict the extent of acidic groundwater and oxic conditions in the shallow, unconfined surficial aquifer and in unconfined, recharge-proximal areas of underlying aquifers, in contrast to alkaline and anoxic groundwater elsewhere. Geographic patterns and influential predictors–including elevation, overlying confining-units thickness, and simulated groundwater age and flux–are consistent with prior understanding of the processes controlling pH and redox in the aquifer system. The model-based maps support robust estimates of aquifer proportions, either areal or volumetric, likely to contain groundwater of a specified quality or be vulnerable to specific pH- or redox-sensitive contaminants. The machine-learning methods were an effective tool to map groundwater quality at the regional scale.
Climate change is expected to result in the tropicalization of coastal wetlands in the northern Gulf of Mexico, as warming winters allow tropical mangrove forests to expand their distribution poleward at the expense of temperate salt marshes. Data limitations near mangrove range limits have hindered understanding of the effects of winter temperature extremes on mangrove distribution and structure. Here, we investigated the influence of extreme freeze events on the abundance, height and coverage of black mangroves (Avicennia germinans ) near their northern range limit in Louisiana.
Coastal Louisiana, USA.
We quantified the relationships between the frequency of extreme freeze events and A. germinans abundance, height and coverage using: (a) mangrove observation points recorded via aerial surveys from a fixed‐wing aircraft; (b) 30 years of temperature data; and (c) mangrove mortality and leaf damage temperature thresholds. We used freeze frequency data and mangrove–climate relationships to evaluate and spatially depict the risk of A. germinans freeze damage across Louisiana.
We identified strong negative relationships between the frequency of extreme freeze events and A. germinans abundance, height and coverage. Avicennia germinans is most abundant, tall and continuous along the south‐eastern outer coast of Louisiana, where the frequency of extreme freeze events is reduced (i.e., lower risk of mangrove freeze damage) by the buffering effects of comparatively warm Gulf of Mexico waters. Conversely, the risk of A. germinans freeze damage has historically been very high across Louisiana's Chenier Plain and within more inland wetlands in the Deltaic Plain.
Our analyses advance understanding of how the frequency of extreme freeze events controls the distribution, height and coverage of A. germinans near its northern range limit. In addition to informing climate‐smart coastal restoration efforts, our findings can be used to better anticipate and prepare for the tropicalization of temperate wetlands due to climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13119","usgsCitation":"Osland, M., Day, R., and Michot, T.C., 2020, Frequency of extreme freeze events controls the distribution and structure of black mangroves (Avicennia germinans) near their northern range limit in coastal Louisiana: Diversity and Distributions, v. 26, no. 10, p. 1366-1382, https://doi.org/10.1111/ddi.13119.","productDescription":"Article: 17 p.; Data Release","startPage":"1366","endPage":"1382","ipdsId":"IP-116815","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456209,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13119","text":"Publisher Index Page"},{"id":376104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":379388,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RC8EIE"}],"country":"United States","state":"Louisiana","otherGeospatial":"Coastal Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.779296875,\n 29.152161283318915\n ],\n [\n -92.46093749999999,\n 28.998531814051795\n ],\n [\n -90.615234375,\n 28.8831596093235\n ],\n [\n -89.07714843749999,\n 29.305561325527698\n ],\n [\n -89.384765625,\n 30.29701788337205\n ],\n [\n -89.82421875,\n 30.600093873550072\n ],\n [\n -91.62597656249999,\n 30.44867367928756\n ],\n [\n -93.6474609375,\n 30.259067203213018\n ],\n [\n -94.130859375,\n 30.031055426540206\n ],\n [\n -93.779296875,\n 29.152161283318915\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":214842,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":221895,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michot, Thomas C.","contributorId":228798,"corporation":false,"usgs":false,"family":"Michot","given":"Thomas","email":"","middleInitial":"C.","affiliations":[{"id":41511,"text":"USGS WARC (retired)","active":true,"usgs":false}],"preferred":false,"id":792081,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210838,"text":"70210838 - 2020 - Refining genetic boundaries for Agassiz’s desert tortoise (Gopherus agassizii) in the western Sonoran Desert: The influence of the Coachella Valley on gene flow among populations in southern California","interactions":[],"lastModifiedDate":"2020-10-12T16:52:30.666957","indexId":"70210838","displayToPublicDate":"2020-06-29T09:08:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5093,"text":"Frontiers of Biogeography","active":true,"publicationSubtype":{"id":10}},"title":"Refining genetic boundaries for Agassiz’s desert tortoise (Gopherus agassizii) in the western Sonoran Desert: The influence of the Coachella Valley on gene flow among populations in southern California","docAbstract":"Understanding the influence of geographic features on the evolutionary history and population structure of a species can assist wildlife managers in delimiting genetic units (GUs) for conservation and management. Landscape features including mountains, low elevation depressions, and even roads can influence connectivity and gene flow among Agassiz’s desert tortoise (Gopherus agassizii) populations. Substantial changes in the landscape of the American Southwest occurred during the last six million years (including the formation of the Gulf of California and the lower Colorado River), which shaped the distribution and genetic structuring of tortoise populations. The area northwest of the Gulf of California is occupied by the Salton Trough, including the Coachella Valley at its northern end. Much of this area is below sea level and unsuitable as tortoise habitat, thus forming a potential barrier for gene flow. We assessed genetic relationships among three tortoise populations separated by the Coachella Valley. Two adjacent populations were on the east side of the valley in the foothills of the Cottonwood and Orocopia mountains separated by Interstate 10. The third population, Mesa, was located about 87 km away in the foothills of the San Bernardino Mountains at the far northwestern tip of the valley. The Cottonwood and Orocopia localities showed genetic affiliation with the adjacent Colorado Desert GU immediately to the east, and the Mesa population exhibited affiliation with both the Southern Mojave and Colorado Desert GUs, despite having a greater geographic distance (0.5x–1.5x greater) to the Colorado Desert GU. The genetic affiliation with the Colorado Desert GU suggests that the boundary for that GU needs to be substantially extended to the west to include the desert tortoise populations around the Coachella Valley. Their inclusion in the Colorado Desert GU may benefit these often overlooked populations when recovery actions are considered.
","language":"English","publisher":"University of California","doi":"10.21425/F5FBG46888","usgsCitation":"Lovich, J.E., Edwards, T., Berry, K.H., Puffer, S., Cummings, K.L., R., E.J., Agha, M., Wood, R., Brundige, K.D., and Murphy, R.W., 2020, Refining genetic boundaries for Agassiz’s desert tortoise (Gopherus agassizii) in the western Sonoran Desert: The influence of the Coachella Valley on gene flow among populations in southern California: Frontiers of Biogeography, v. 12, no. 3, e46888, 14 p., https://doi.org/10.21425/F5FBG46888.","productDescription":"e46888, 14 p.","ipdsId":"IP-115804","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":456214,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21425/f5fbg46888","text":"Publisher Index Page"},{"id":375969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Coachella Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.6748046875,\n 33.47727218776036\n ],\n [\n -115.72723388671875,\n 33.47727218776036\n ],\n [\n -115.72723388671875,\n 34.02762404762424\n ],\n [\n -116.6748046875,\n 34.02762404762424\n ],\n [\n -116.6748046875,\n 33.47727218776036\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research 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0000-0003-4957-0963","orcid":"https://orcid.org/0000-0003-4957-0963","contributorId":193099,"corporation":false,"usgs":true,"family":"Puffer","given":"Shellie R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":791679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cummings, Kristy L. 0000-0002-8316-5059","orcid":"https://orcid.org/0000-0002-8316-5059","contributorId":202061,"corporation":false,"usgs":true,"family":"Cummings","given":"Kristy","email":"","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":791680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"R., Ennen Joshua","contributorId":195518,"corporation":false,"usgs":false,"family":"R.","given":"Ennen","email":"","middleInitial":"Joshua","affiliations":[],"preferred":false,"id":791681,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Agha, Mickey","contributorId":22235,"corporation":false,"usgs":false,"family":"Agha","given":"Mickey","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false},{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":791682,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wood, Rachel 0000-0001-7953-3173","orcid":"https://orcid.org/0000-0001-7953-3173","contributorId":225576,"corporation":false,"usgs":false,"family":"Wood","given":"Rachel","email":"","affiliations":[{"id":6681,"text":"Brigham Young University","active":true,"usgs":false}],"preferred":false,"id":791683,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brundige, Kathleen D.","contributorId":193101,"corporation":false,"usgs":false,"family":"Brundige","given":"Kathleen","email":"","middleInitial":"D.","affiliations":[{"id":33710,"text":"Coachella Valley Conservation Commission, 73-710 Fred Waring Drive, Suite 200, Palm Desert, CA 92260-2516, USA","active":true,"usgs":false}],"preferred":false,"id":791684,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Murphy, Robert W.","contributorId":147498,"corporation":false,"usgs":false,"family":"Murphy","given":"Robert","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":791685,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70210822,"text":"70210822 - 2020 - Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change","interactions":[],"lastModifiedDate":"2020-06-29T12:45:16.577045","indexId":"70210822","displayToPublicDate":"2020-06-26T08:36:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3165,"text":"Proceedings of the National Academy of Sciences of the United States of America","active":true,"publicationSubtype":{"id":10}},"title":"Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change","docAbstract":"Over the past half century, migratory birds in North America have shown divergent population trends relative to resident species, with the former declining rapidly and the latter increasing. The role that climate change has played in these observed trends is not well understood, despite significant warming over this period. We used 43 y of monitoring data to fit dynamic species distribution models and quantify the rate of latitudinal range shifts in 32 species of birds native to eastern North America. Since the early 1970s, species that remain in North America throughout the year, including both resident and migratory species, appear to have responded to climate change through both colonization of suitable area at the northern leading edge of their breeding distributions and adaption in place at the southern trailing edges. Neotropical migrants, in contrast, have shown the opposite pattern: contraction at their southern trailing edges and no measurable shifts in their northern leading edges. As a result, the latitudinal distributions of temperate-wintering species have increased while the latitudinal distributions of neotropical migrants have decreased. These results raise important questions about the mechanisms that determine range boundaries of neotropical migrants and suggest that these species may be particularly vulnerable to future climate change. Our results highlight the potential importance of climate change during the nonbreeding season in constraining the response of migratory species to temperature changes at both the trailing and leading edges of their breeding distributions. Future research on the interactions between breeding and nonbreeding climate change is urgently needed.","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2000299117","usgsCitation":"Clark Rushing, Royle, A., Ziolkowski, D., and Pardieck, K.L., 2020, Migratory behavior and winter geography drive differential range shifts of eastern birds in response to recent climate change: Proceedings of the National Academy of Sciences of the United States of America, v. 117, no. 23, p. 12897-12903, https://doi.org/10.1073/pnas.2000299117.","productDescription":"7 p.","startPage":"12897","endPage":"12903","ipdsId":"IP-115090","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":456252,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2000299117","text":"Publisher Index Page"},{"id":375949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Eastern North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.9609375,\n 58.44773280389084\n ],\n [\n -80.33203125,\n 41.77131167976407\n ],\n [\n -85.25390625,\n 30.14512718337613\n ],\n [\n -81.9140625,\n 24.367113562651262\n ],\n [\n -74.00390625,\n 38.95940879245423\n ],\n [\n -60.1171875,\n 45.583289756006316\n ],\n [\n -53.26171875,\n 47.39834920035926\n ],\n [\n -64.16015624999999,\n 59.977005492196\n ],\n [\n -69.9609375,\n 58.44773280389084\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"117","issue":"23","noUsgsAuthors":false,"publicationDate":"2020-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Clark Rushing","contributorId":225554,"corporation":false,"usgs":false,"family":"Clark Rushing","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":791593,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791594,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ziolkowski, David 0000-0002-2500-4417 dziolkowski@usgs.gov","orcid":"https://orcid.org/0000-0002-2500-4417","contributorId":195409,"corporation":false,"usgs":true,"family":"Ziolkowski","given":"David","email":"dziolkowski@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791595,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pardieck, Keith L. 0000-0003-2779-4392 kpardieck@usgs.gov","orcid":"https://orcid.org/0000-0003-2779-4392","contributorId":4104,"corporation":false,"usgs":true,"family":"Pardieck","given":"Keith","email":"kpardieck@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791596,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211989,"text":"70211989 - 2020 - 'Dust in the wind’ from source-to-sink: Analysis of the 14-15 April 2015 storm in Utah","interactions":[],"lastModifiedDate":"2020-08-13T13:06:22.067961","indexId":"70211989","displayToPublicDate":"2020-06-24T08:03:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":666,"text":"Aeolian Research","active":true,"publicationSubtype":{"id":10}},"title":"'Dust in the wind’ from source-to-sink: Analysis of the 14-15 April 2015 storm in Utah","docAbstract":"On 14–15 April 2015, an intense intermountain cyclone in the western USA caused high winds and a dust storm that degraded air quality in the eastern Great Basin, and deposited dust-on-snow (DOS) in the Wasatch Range near Salt Lake City, Utah. We analyzed the storm and documented its “source-to-sink” development to relate the frontal passage with dust mobilization, air quality changes, and dust deposition on montane snowpack near Alta, Utah. This case study is first to track a dust storm and measure the elemental composition and radiative properties of the resulting DOS as a single specific event layer in Wasatch montane snowpack; prior studies have assessed seasonally aggregated DOS deposits. Dust plumes on MODIS imagery indicate mobilization from known regional “hotspots” for aeolian activity, including clay- and silt-rich alluvium, modern playas, and disturbed areas within the Pleistocene Paleolake Bonneville Basin. This 2015 single event dust layer was 1–3 cm thick with a median dust size of 10.81–12.55 µm; its measured radiative properties are similar to aggregated dusts previously assessed in Wasatch snowpack. Dust from the 2015 DOS event is enriched in the elements As, Cd, Cu, and Mo by a 10× factor relative to average elemental concentrations in the upper continental crust; its heavy metals (Cu, Pb, As, Cd, Mo, Zn) are probably derived from regional mine operations. Tracking elemental fluxes from source-to-sink is important for resolving environmental impacts, and informing future analysis of single storm dust loading, ecosystem impacts, and quantity and quality of meltwater-fed drinking water.
Surface rupture in the 2019 Ridgecrest, California, earthquake sequence occurred along two orthogonal cross faults and includes dominantly left‐lateral and northeast‐striking rupture in the
Ecological occupancy modeling has historically relied on high-quality, low-quantity designed-survey data for estimation and prediction. In recent years, there has been a large increase in the amount of high-quantity, unknown-quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed-survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed-survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed-survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red-eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3165","usgsCitation":"Huberman, D.B., Reich, B.J., Pacifici, K., and Collazo, J.A., 2020, Estimating the drivers of species distributions with opportunistic data using mediation analysis: Ecosphere, v. 11, no. 6, e03165, 13 p., https://doi.org/10.1002/ecs2.3165.","productDescription":"e03165, 13 p.","ipdsId":"IP-113854","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3165","text":"Publisher Index Page"},{"id":396100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.03125,\n 24.84656534821976\n ],\n [\n -66.09375,\n 24.84656534821976\n ],\n [\n -66.09375,\n 49.26780455063753\n ],\n [\n -97.03125,\n 49.26780455063753\n ],\n [\n -97.03125,\n 24.84656534821976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Huberman, D. B.","contributorId":279615,"corporation":false,"usgs":false,"family":"Huberman","given":"D.","email":"","middleInitial":"B.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reich, B. J.","contributorId":279616,"corporation":false,"usgs":false,"family":"Reich","given":"B.","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pacifici, Krishna","contributorId":244494,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210937,"text":"70210937 - 2020 - Geochemical characterization of groundwater evolution south of Grand Canyon, Arizona (USA)","interactions":[],"lastModifiedDate":"2020-12-10T13:16:29.306011","indexId":"70210937","displayToPublicDate":"2020-06-18T09:00:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical characterization of groundwater evolution south of Grand Canyon, Arizona (USA)","docAbstract":"Better characterization of the geochemical evolution of groundwater south of Grand Canyon, Arizona (USA), is needed to understand natural conditions and assess potential effects from breccia-pipe uranium mining in the region. Geochemical signatures of groundwater at 28 sampling locations were evaluated; baseline concentrations for select trace elements (As, B, Ba, Cr, Li, Mo, Rb, Se, Sr, Th, Tl, U, V) were established, and anomalous chemistry characteristics were identified. Concentrations at some groundwater sites exceeded the USEPA drinking water standard for As of 10 μg/L (Red Canyon, Miners, JT, Havasu, and Warm Springs) and U of 30 μg/L (Salt Creek Spring). Four springs from the study area (Blue, Havasu, Fern, and Warm Springs) had unique chemistry, which may indicate a deep flow path or potential contribution of fluids from lower in the crust. Other springs in the study area were distinguished by major anion water type: sulfate, bicarbonate, and a mixture of the two. Water type distinctions were somewhat spatially segregated, with sulfate type present on the western side of the study area, bicarbonate type on the eastern side, and a mixture of the two interspersed between the endmember sites. Sulfate-type water from this study area had low strontium isotopic ratio (87Sr/86Sr) values. The location of spring discharge within single drainages of the Grand Canyon may influence chemistry, as groundwater discharging from bedrock was altered after flowing through alluvial material. Geochemical analysis of groundwater in Grand Canyon indicates the importance of continued monitoring and better understanding of short-term chemical fluctuations.","language":"English","publisher":"Springer","doi":"10.1007/s10040-020-02192-0","usgsCitation":"Beisner, K.R., Solder, J.E., Tillman, F.D., Anderson, J.R., and Antweiler, R.C., 2020, Geochemical characterization of groundwater evolution south of Grand Canyon, Arizona (USA): Hydrogeology Journal, v. 28, p. 1615-1633, https://doi.org/10.1007/s10040-020-02192-0.","productDescription":"19 p.","startPage":"1615","endPage":"1633","ipdsId":"IP-109494","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":456364,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-020-02192-0","text":"Publisher Index Page"},{"id":376147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.44232177734374,\n 35.902399875143615\n ],\n [\n -111.697998046875,\n 35.902399875143615\n ],\n [\n -111.697998046875,\n 36.25977754677541\n ],\n [\n -112.44232177734374,\n 36.25977754677541\n ],\n [\n -112.44232177734374,\n 35.902399875143615\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Solder, John E. 0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":201953,"corporation":false,"usgs":true,"family":"Solder","given":"John","email":"","middleInitial":"E.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792226,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Jessica R. 0000-0002-3286-7552 jranderson@usgs.gov","orcid":"https://orcid.org/0000-0002-3286-7552","contributorId":193158,"corporation":false,"usgs":true,"family":"Anderson","given":"Jessica","email":"jranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792227,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Antweiler, Ronald C. 0000-0001-5652-6034 antweil@usgs.gov","orcid":"https://orcid.org/0000-0001-5652-6034","contributorId":1481,"corporation":false,"usgs":true,"family":"Antweiler","given":"Ronald","email":"antweil@usgs.gov","middleInitial":"C.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":792228,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211183,"text":"70211183 - 2020 - Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements","interactions":[],"lastModifiedDate":"2020-07-16T17:45:55.970993","indexId":"70211183","displayToPublicDate":"2020-06-17T09:30:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements","docAbstract":"Starting on 3 May 2018, a series of eruptive fissures opened in Kīlauea Volcano’s lower East Rift Zone (LERZ). Over the course of the next 3 months, intense degassing accompanied lava effusion from these fissures. Here, we report on ground-based observations of the gas emissions associated with Kīlauea’s 2018 eruption. Visual observations combined with radiative transfer modeling show that ultraviolet light could not efficiently penetrate the gas and aerosol plume in the LERZ, complicating SO2 measurements by differential optical absorption spectroscopy (DOAS). By applying a statistical method that integrates a radiative transfer model with the DOAS retrievals, we were able to calculate sulfur dioxide (SO2) emission rates along with estimates of their uncertainty. We find that sustained SO2 emissions were highest in June and early July, when approximately 200 kt SO2 were emitted daily. At the 68% confidence interval, we estimate that 7.1–13.6 Mt SO2 were released from the LERZ during the entire May to September eruptive episode. Scaling our results with in situ measurements of plume composition, we calculate that 11–21 Mt H2O and 1.5–2.8 Mt CO2 were also emitted. The gas and aerosol emissions caused hazardous conditions in areas proximal to the active vents, but plume dispersion modeling shows that the eruption also significantly impacted air quality hundreds of kilometers downwind. Combined with petrologic studies of the erupted lavas, our measurements indicate that 1.1–2.3 km3 dense-rock equivalent of lava were erupted from the LERZ, which is approximately twice the concomitant collapse volume of the volcano’s summit.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-01390-8","usgsCitation":"Kern, C., Lerner, A., Elias, T., Nadeau, P.A., Holland, L., Kelly, P.J., Werner, C., Clor, L., and Cappos, M., 2020, Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements: Bulletin of Volcanology, v. 82, 55, 24 p., https://doi.org/10.1007/s00445-020-01390-8.","productDescription":"55, 24 p.","ipdsId":"IP-115197","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":436927,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LXBJF3","text":"USGS data release","linkHelpText":"Differential Optical Absorption Spectroscopy data acquired during the 2018 rift eruption of Kilauea Volcano"},{"id":376424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3082275390625,\n 19.379170499941292\n ],\n [\n -155.2333831787109,\n 19.379170499941292\n ],\n [\n -155.2333831787109,\n 19.449111649832837\n ],\n [\n -155.3082275390625,\n 19.449111649832837\n ],\n [\n -155.3082275390625,\n 19.379170499941292\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Kern, Christoph 0000-0002-8920-5701 ckern@usgs.gov","orcid":"https://orcid.org/0000-0002-8920-5701","contributorId":3387,"corporation":false,"usgs":true,"family":"Kern","given":"Christoph","email":"ckern@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science 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0000-0002-6732-3686","orcid":"https://orcid.org/0000-0002-6732-3686","contributorId":215616,"corporation":false,"usgs":true,"family":"Nadeau","given":"Patricia","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":792997,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holland, Lacey","contributorId":147879,"corporation":false,"usgs":false,"family":"Holland","given":"Lacey","email":"","affiliations":[{"id":16953,"text":"University of Utah, Atmospheric Sciences","active":true,"usgs":false}],"preferred":false,"id":792998,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":792999,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Werner, Cynthia 0000-0003-3311-6694","orcid":"https://orcid.org/0000-0003-3311-6694","contributorId":224428,"corporation":false,"usgs":false,"family":"Werner","given":"Cynthia","affiliations":[{"id":37768,"text":"USGS Contractor","active":true,"usgs":false}],"preferred":false,"id":793000,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Clor, Laura E. 0000-0003-2633-5100","orcid":"https://orcid.org/0000-0003-2633-5100","contributorId":209969,"corporation":false,"usgs":true,"family":"Clor","given":"Laura E.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793001,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cappos, Michael 0000-0001-9883-1475","orcid":"https://orcid.org/0000-0001-9883-1475","contributorId":215607,"corporation":false,"usgs":true,"family":"Cappos","given":"Michael","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793002,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70211077,"text":"70211077 - 2020 - Origin and geochemistry of formation waters from the lower Eagle Ford Group, Gulf Coast Basin, south central Texas","interactions":[],"lastModifiedDate":"2020-07-16T20:01:36.320547","indexId":"70211077","displayToPublicDate":"2020-06-16T10:46:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"title":"Origin and geochemistry of formation waters from the lower Eagle Ford Group, Gulf Coast Basin, south central Texas","docAbstract":"The lower Eagle Ford Group (LEFG) is one of the most productive continuous hydrocarbon plays in the United States but few associated produced waters data and minimal interpretation have been published. This effort focuses on results from compositional and isotopic data from 39 produced water samples collected from horizontal wells producing from the LEFG in south central Texas. The depth of the LEFG increases by approximately 1 km across the study area, from northwest (2.9 km) to southeast (3.9 km). Associated increases in calculated reservoir temperature (125-165 C), development of reservoir over-pressuring (400-800 bars total pressure), and increased thermal maturity (heavy oil to gas condensate) also occur along this trend. Produced water salinity starts at nearly 100 g/L in the shallowest samples and decreases linearly with depth to <35 g/L. Comparison of Br/Cl and ⁸⁷Sr/⁸⁶Sr data between LEFG produced waters and the Louann salt, suggests that halite recycling is the mechanism behind salinity greater than seawater. Decreasing salinity with depth and thermal maturity in the Gulf Coast Basin have previously been shown to be a result of release of inter-layer water during smectite to illite conversion. The produced water samples show increasing 18O and decreasing 2H with depth, which is attributed temperature-dependent isotope fractionation of O and H exchange between seawater and clays and calcite with increasing temperature. Multiple sources of data indicate that the waters in the LEFG are not connate, but rather entered the unit prior to smectite-illite conversion. Presence of allochthonous water in many major tight oil and shale gas plays in the U.S., including the LEFG, suggests there is unknown mechanism allowing for water advection into low permeability reservoirs.","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2020.119754","collaboration":"None","usgsCitation":"Engle, M.A., Doolan, C.A., Pitman, J., Varonka, M., Chenault, J., Orem, W.H., McMahon, P.B., and Jubb, A., 2020, Origin and geochemistry of formation waters from the lower Eagle Ford Group, Gulf Coast Basin, south central Texas: Chemical Geology, v. 550, 119754, 12 p., https://doi.org/10.1016/j.chemgeo.2020.119754.","productDescription":"119754, 12 p.","ipdsId":"IP-117920","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":456377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2020.119754","text":"Publisher Index Page"},{"id":376366,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"San Marcos Arch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.98681640625,\n 28.033197847676377\n ],\n [\n -96.064453125,\n 28.033197847676377\n ],\n [\n -96.064453125,\n 30.637912028341123\n ],\n [\n -98.98681640625,\n 30.637912028341123\n ],\n [\n -98.98681640625,\n 28.033197847676377\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"550","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Engle, Mark A 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0000-0002-5974-0762","orcid":"https://orcid.org/0000-0002-5974-0762","contributorId":222078,"corporation":false,"usgs":true,"family":"Chenault","given":"Jessica","email":"","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792750,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792701,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792751,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":792703,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70198567,"text":"sir20185104 - 2020 - Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources","interactions":[],"lastModifiedDate":"2020-06-16T14:09:59.229883","indexId":"sir20185104","displayToPublicDate":"2020-06-16T09:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5104","displayTitle":"Conceptual Framework and Approach for Conducting a Geoenvironmental Assessment of Undiscovered Uranium Resources","title":"Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources","docAbstract":"This report presents a novel conceptual framework and approach for conducting a geologically based environmental assessment, or geoenvironmental assessment, of undiscovered uranium resources within an area likely to contain uranium deposits. The framework is based on a source-to-receptor model that prioritizes the most likely contaminant sources, contaminant pathways, and affected environmental media for three common uranium extraction methods—open pit or underground mining with milling and in situ recovery (ISR). Data on regional geology, hydrology, and climate, as well as historical uranium mining and milling records are used to estimate the probable amounts of waste rock, tailings, wastewater, surface land disturbance, and subsurface aquifer disturbance for likely mining methods. Constituents of concern that might take the form of leachates, dust, radon, and sediments formed by chemical and physical weathering are also identified in the geoenvironmental assessment. Finally, areas where constituents of concern are likely to occur and persist in air, land, surface water, and groundwater are indicated by the potential for dispersion of dust by wind, accumulation of radon because of air stagnation, dispersion of sediments and wastewater by runoff, and infiltration of wastewater or leachates with consideration of the likely mobility of contaminants in surface water and groundwater. The geoenvironmental assessment output can be summarized in the following primary products: (1) a descriptive geoenvironmental model; (2) maps and statistics of variables that indicate the potential for constituents of concern to occur and persist in air, land, surface water, and groundwater within a tract that is geologically permissive for the occurrence of uranium; and (3) tables providing estimated or indicated quantities of waste rock, tailings, wastewater, dust, and radon emissions that could be associated with undiscovered uranium resources, if extracted, for each permissive tract. The uranium geoenvironmental assessment could help natural resource managers to prioritize and (or) identify (1) important potential contaminant pathways, (2) management practices required depending on the types of constituents that could be of concern, (3) areas for response in the event of accidental release, and (4) future directions for study. Furthermore, indicators of rock and water volumes potentially associated with an undiscovered uranium deposit may be evaluated to make quantitative comparisons of water required for uranium production or potential waste products generated during uranium extraction from areas permissive for uranium resource occurrence throughout the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185104","usgsCitation":"Gallegos, T.J., Walton-Day, K., and Seal, R.R., II, 2020, Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources: U.S. Geological Survey Scientific Investigations Report 2018–5104, 28 p., https://doi.org/10.3133/sir20185104.","productDescription":"vi, 28 p.","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070792","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science 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Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Acid deposition has declined across eastern North America and northern Europe due to reduced emissions of sulfur and nitrogen oxides. Ecosystem recovery has been slow with limited improvement in surface water chemistry. Delayed recovery has encouraged acid-neutralization strategies to accelerate recovery of impaired biological communities. Lime application has been shown to increase pH and dissolved organic carbon (DOC), which could also drive increased mobilization of mercury (Hg) to surface waters. A four-year study was conducted within Honnedaga Lake’s watershed in the Adirondack region of New York to compare the effects of watershed and direct channel lime additions on Hg in stream water and macroinvertebrates. All treatments sharply increased stream pH and DOC concentrations, but large differences in the duration of impacts were apparent. The watershed treatment resulted in multi-year increases in concentrations and loads of total Hg (150%; 390%), DOC (190%; 350%) and nutrients, whereas total Hg and DOC increased for short periods (72–96 h) after channel treatments. No response of Hg in macroinvertebrates was evident following the watershed treatment, but a potential short-term and spatially constrained increase occurred after the channel treatment. Our observations indicate that both treatment approaches mobilize Hg, but that direct channel liming mobilizes considerably less than watershed liming over any period longer than a few days. During the final study year, increased methyl Hg concentrations were observed across reference and treated streams, which may reflect an extended dry period, highlighting that climate variation may also affect Hg dynamics.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10646-020-02224-1","usgsCitation":"Millard, G., Riva-Murray, K., Burns, D., Montesdeoca, M.S., and Driscoll, C., 2020, The impact of lime additions on mercury dynamics in stream chemistry and macroinvertebrates: A comparison of watershed and direct stream addition management strategies: Ecotoxicology, v. 29, p. 1627-1643, https://doi.org/10.1007/s10646-020-02224-1.","productDescription":"17 p.","startPage":"1627","endPage":"1643","ipdsId":"IP-109979","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":436928,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C17PA0","text":"USGS data release","linkHelpText":"Methylmercury and associated data in macroinvertebrates from tributaries of Honnedaga Lake and from the Middle Branch Black River in New York."},{"id":378508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Honnedaga Lake watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.89448547363281,\n 43.469864270218416\n ],\n [\n -74.74754333496092,\n 43.469864270218416\n ],\n [\n -74.74754333496092,\n 43.56496912804994\n ],\n [\n -74.89448547363281,\n 43.56496912804994\n ],\n [\n -74.89448547363281,\n 43.469864270218416\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Millard, Geoffrey D.","contributorId":240873,"corporation":false,"usgs":false,"family":"Millard","given":"Geoffrey D.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riva-Murray, Karen 0000-0001-6683-2238 krmurray@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-2238","contributorId":168876,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montesdeoca, Mario S.","contributorId":240877,"corporation":false,"usgs":false,"family":"Montesdeoca","given":"Mario","email":"","middleInitial":"S.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799042,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Driscoll, Charles T.","contributorId":240874,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799040,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208302,"text":"sim3441 - 2020 - Selected geologic maps of the Kodiak batholith and other Paleocene intrusive rocks, Kodiak Island, Alaska","interactions":[],"lastModifiedDate":"2020-06-12T16:10:02.495395","indexId":"sim3441","displayToPublicDate":"2020-06-12T07:52:07","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3441","displayTitle":"Selected Geologic Maps of the Kodiak Batholith and Other Paleocene Intrusive Rocks, Kodiak Island, Alaska","title":"Selected geologic maps of the Kodiak batholith and other Paleocene intrusive rocks, Kodiak Island, Alaska","docAbstract":"Kodiak Island in southern Alaska is one of the premier examples globally for the study of forearc magmatism. This location contains two Paleocene intrusive belts that formed due to the subduction of a migrating spreading ridge and slab-window: the Kodiak batholith and the trenchward magmatic belt. These magmatic rocks are part of the Sanak-Baranof belt, which extends for greater than 2,100 km along the southern Alaskan margin and vary in age from 61 to 50 Ma west to east.
Trenchward-belt rocks, with an 40Ar/39Ar age of 60.2±0.9 Ma, intrude into the Paleocene Ghost Rocks Formation and are composed of granitoids, basaltic dikes, and small gabbroic plutons that lie along or southward of the Kalsin Bay Fault. Such intrusions were emplaced at shallow levels and have abundant evidence of incomplete intermingling of basaltic and granitic magmas. These textures indicate trenchward-belt intrusions that froze before complete assimilation, leaving behind features such as abundant locally stoped blocks, gabbroic pods within granitic intrusions, and microstructural evidence such as strongly embayed olivine and pyroxene phenocrysts in granitoid bodies.
The Kodiak batholith and satellite intrusions extend for over 110 km along the axis of Kodiak Island and vary in width from 2 to 6 km. These intrude into the Late Cretaceous Kodiak Formation. U-Pb ages on zircon from the intrusions range from 59.2±0.2 Ma in the southwest to 58.4±0.2 Ma near its northwest tip. We interpret these ages as tracking the location of a migrating triple junction and associated slab-window. The batholith is composed of granite and granodiorite, with lesser amounts of tonalite and diorite. The center of the Kodiak batholith contains high-inclusion zones with abundant residual host rock fragments that were carried up from 5–10 km below current exposure levels. These high-inclusion zones contain biotite aggregates, pure quartz clots, and large xenocrysts of sillimanite, kyanite, andalusite, and garnet. This is a higher-pressure mineral assemblage than exists in the batholith metamorphic aureole. Gravity observations and modeling are consistent with the high-inclusion zones extending downward for 5–10 km. The Kodiak batholith results from a migrating triple junction and slab-window that led to high degrees of partial melting within the Kodiak accretionary prism.
Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
Global assessments predict the impact of sea-level rise on salt marshes with present-day levels of sediment supply from rivers and the coastal ocean. However, these assessments do not consider that variations in marsh extent and the related reconfiguration of intertidal area affect local sediment dynamics, ultimately controlling the fate of the marshes themselves. We conducted a meta-analysis of six bays along the United States East Coast to show that a reduction in the current salt marsh area decreases the sediment availability in estuarine systems through changes in regional-scale hydrodynamics. This positive feedback between marsh disappearance and the ability of coastal bays to retain sediments reduces the trapping capacity of the whole tidal system and jeopardizes the survival of the remaining marshes. We show that on marsh platforms, the sediment deposition per unit area decreases exponentially with marsh loss. Marsh erosion enlarges tidal prism values and enhances the tendency toward ebb dominance, thus decreasing the overall sediment availability of the system. Our findings highlight that marsh deterioration reduces the sediment stock in back-barrier basins and therefore compromises the resilience of salt marshes.
Groundwater-level conditions, generalized groundwater potentiometric surfaces, and generalized flow directions at the decommissioned Naval Air Warfare Center in West Trenton, New Jersey, were evaluated for calendar year 2018. Groundwater levels measured continuously in five on-site wells and one nearby off-site well were plotted as hydrographs for January 1, 2018, through December 31, 2018. Groundwater levels measured in 110 wells on June 18, 2018, were contoured as generalized potentiometric surfaces on maps and sections. Generalized groundwater-flow directions inferred from the June 2018 data are shown in the maps and sections.
Groundwater levels in six monitoring wells fluctuated in response to seasonal changes, precipitation, and pumping from “pump-and-treat” (P&T) wells. Record high precipitation totals in November, combined with a shutdown of three P&T wells in November, resulted in annual high water levels in late November for five of the six wells monitored. Annual high groundwater levels that occur during the fall are uncharacteristic of the typical timing of annual high water levels, which usually occur in the spring following low evapotranspiration during the winter months, compared to annual low water levels, which usually occur in fall because of high evapotranspiration during the summer months. The annual high water levels occurred following a 3-day precipitation event totaling 3.50 inches from November 24-26, which also caused the largest 1-day water-level increase for five of the six wells in 2018.
The groundwater-level contour maps and sections include generalized flow directions. Given the heterogeneity of the site’s fractured rock aquifers, contours and associated groundwater-flow directions shown on the maps and sections should be considered as broad conceptualizations. A nearly vertical fault striking southwest to northeast separates the northwestern part of the site underlain by the Lockatong Formation from the southeastern part, which is underlain by the Stockton Formation. In the Lockatong Formation, general groundwater-flow directions were toward P&T wells. The P&T wells limited the flow of groundwater in the Lockatong Formation from the site into the adjacent areas and contained most groundwater contamination within the site. A groundwater divide bisected the site; groundwater in the western part generally flowed to P&T wells 8BR, 15BR, 20BR, 29BR, 56BR, 91BR, and BRP-2, and groundwater in the eastern part generally flowed to P&T well 48BR. A groundwater divide also was present in the Stockton Formation. Groundwater west of the divide in the Stockton Formation generally flowed toward P&T well 22BR, and groundwater east of the divide generally flowed south and southeast, away from the site. Saprolite and fill from land surface to depths of 25 feet below land surface exhibit similar properties to those of porous media, and water levels in surficial wells were contoured using a porous media aquifer approach. Water levels in these surficial wells indicate that groundwater in the saprolite and fill flowed predominantly toward Gold Run and, to a lesser extent, the West Ditch spring that drains to Gold Run. In addition, some shallow groundwater was captured by the cone of depression in the fractured bedrock and was attributed to P&T well 48BR.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201016","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Fiore, A.R., and Lacombe, P.J., 2020, Groundwater levels and generalized potentiometric surfaces, former Naval Air Warfare Center, West Trenton, New Jersey, 2018: U.S. Geological Survey Open-File Report 2020–1016, 28 p., https://doi.org/10.3133/ofr20201016.","productDescription":"Report: v, 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-104199","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":375290,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1016/ofr20201016.pdf","text":"Report","size":"10.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1016"},{"id":375285,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1016/coverthb.jpg"},{"id":375288,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98N1GWV","text":"USGS data release","linkHelpText":"Reported groundwater levels and groundwater pump-and-treat withdrawals, former Naval Air Warfare Center, West Trenton, New Jersey, 2018"}],"country":"United States","state":"New Jersey","city":"West Trenton","otherGeospatial":"Former Naval Air Warfare Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.81613278388977,\n 40.26694411855267\n ],\n [\n -74.80834364891052,\n 40.26694411855267\n ],\n [\n -74.80834364891052,\n 40.27319835024231\n ],\n [\n -74.81613278388977,\n 40.27319835024231\n ],\n [\n -74.81613278388977,\n 40.26694411855267\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
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Snake fungal disease (SFD), or ophidiomycosis, is caused by the fungus Ophidiomyces ophiodiicola (Allender et al. 2015; Lorch et al. 2015). SFD is widespread across wild populations in the eastern United States (Lorch et al. 2016) and is known to infect more than 30 species of snake in North America and Europe (Lorch et al. 2016; Franklinos et al. 2017). No known phylogenetic or ecological patterns have been observed in susceptibility among snake taxa, and it is presumed that all species are likely susceptible (Burbrink et al. 2017).
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Glorioso, B., Bartoszek, I.A., and Lorch, J.M., 2020, Low-level detection of SFD-causing Ophidiomyces on Burmese Pythons in southwest Florida, with confirmation of the pathogen on co-occurring native snakes: Herpetological Review, v. 51, no. 2, p. 245-247.","productDescription":"3 p.","startPage":"245","endPage":"247","ipdsId":"IP-113676","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":375468,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375438,"type":{"id":15,"text":"Index Page"},"url":"https://ssarherps.org/herpetological-review-pdfs/"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.6171875,\n 24.986058021167594\n ],\n [\n -80.4638671875,\n 24.986058021167594\n ],\n [\n -80.4638671875,\n 27.352252938063845\n ],\n [\n -82.6171875,\n 27.352252938063845\n ],\n [\n -82.6171875,\n 24.986058021167594\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glorioso, Brad 0000-0002-5400-7414","orcid":"https://orcid.org/0000-0002-5400-7414","contributorId":204397,"corporation":false,"usgs":true,"family":"Glorioso","given":"Brad","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":790540,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bartoszek, Ian A.","contributorId":138954,"corporation":false,"usgs":false,"family":"Bartoszek","given":"Ian","email":"","middleInitial":"A.","affiliations":[{"id":12592,"text":"Conservancy of Southwest Florida, Naples, FL","active":true,"usgs":false}],"preferred":false,"id":790541,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":790542,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211707,"text":"70211707 - 2020 - Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence","interactions":[],"lastModifiedDate":"2020-08-07T13:28:29.468565","indexId":"70211707","displayToPublicDate":"2020-06-09T08:24:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Repeatable source, path, and site effects from the 2019 Ridgecrest M7.1 earthquake sequence","docAbstract":"We use a large instrumental dataset from the 2019 Ridgecrest earthquake sequence (Rekoske et al., 2019, 2020) to examine repeatable source‐, path‐, and site‐specific ground motions. A mixed‐effects analysis is used to partition total residuals relative to the Boore et al. (2014; hereafter, BSSA14) ground‐motion model. We calculate the Arias intensity stress drop for the earthquakes and find strong correlation with our event terms, indicating that they are consistent with source processes. We look for physically meaningful trends in the partitioned residuals and test the ability of BSSA14 to capture the behavior we observe in the data.
We find that BSSA14 is a good match to the median observations for
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Cape May and Supawna Meadows National Wildlife Refuges in New Jersey. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of 13 marsh management units within the refuges and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $785,000, but that further expenditures may yield diminishing return on investment. Management actions in optimal portfolios at total costs less than $785,000 included applying sediment to the marsh surface (thin layer deposition) in seven marsh management units, controlling the invasive reed Phragmites australis in four marsh management units, remediating hydrologic alterations in two marsh management units, and planting native vegetation in one marsh management unit. The management benefits were derived from expected improvements in the capacity for marsh elevation to keep pace with sea-level rise, increases in numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds, and increased cover of native vegetation. The prototype presented here provides a framework for decision making at the Cape May and Supawna Meadows National Wildlife Refuges that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201055","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Braudis, B., and Hanlon, H., 2020, Optimization of tidal marsh management at the Cape May and Supawna Meadows National Wildlife Refuges, New Jersey, through use of structured decision making: U.S. Geological Survey Open-File Report 2020–1055, 41 p., https://doi.org/10.3133/ofr20201055.","productDescription":"vii, 41 p.","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101980","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":375304,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1055/ofr20201055.pdf","text":"Report","size":"3.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1055"},{"id":375303,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1055/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Cape May, Supawna Meadows National Wildlife Refuges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.377197265625,\n 39.690280594818034\n ],\n [\n -75.1025390625,\n 39.95185892663005\n ],\n [\n -75.41015624999999,\n 39.9602803542957\n ],\n [\n -75.618896484375,\n 39.58029027440865\n ],\n [\n -75.3662109375,\n 39.2407625100131\n ],\n [\n -75.0146484375,\n 38.788345355085625\n ],\n [\n -74.42138671875,\n 39.07037913108751\n ],\n [\n -74.410400390625,\n 39.605688178320804\n ],\n [\n -74.77294921875,\n 39.36827914916014\n ],\n [\n -75.16845703124999,\n 39.40224434029275\n ],\n [\n -75.377197265625,\n 39.690280594818034\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708–4039
The novel β-coronavirus, SARS-CoV-2, may pose a threat to North American bat populations if bats are exposed to the virus through interaction with humans, if the virus can subsequently infect bats and be transmitted among them, and if the virus causes morbidity or mortality in bats. Further, if SARS-CoV-2 became established in bat populations, it could possibly serve as a source for new infection in humans, domesticated animals, or other wild animals. Wildlife management agencies in the United States are concerned about these potential risks and have begun to issue guidance regarding work that brings humans into contact with bats, but decision making is difficult because of the high degree of uncertainty about many of the relevant processes that could lead to virus transmission and establishment. The risk assessment described in this report was undertaken to provide management agencies with an understanding of the likelihood that the various steps in the causal pathways would lead to SARS-CoV-2 infection of North American bats from people. This assessment focused on the active season for bats in the temperate zone of North America (April 15 through November 15), and used Myotis lucifugus (little brown bats) as a surrogate species. At the time of this work (April 2020), no empirical data about the effects of SARS-CoV-2 on North American bats were available, so a formal process of expert judgment was used to elicit estimates of the underlying parameters. Twelve experts in bat ecology, epidemiology, virology, and wildlife disease from the United States, United Kingdom, and Australia participated in the elicitation. A Monte Carlo simulation model was used to integrate the parameter estimates elicited from the experts and to predict the likelihood of exposure and infection in bats through a series of transmission pathways, with particular attention to capturing uncertainty in the predictions.
Given the current state of knowledge as expressed by the expert panel, the results of this assessment indicate that there is a non-negligible risk of transmission of SARS-CoV-2 from humans to bats. For example, if a research scientist were shedding SARS-CoV-2 virus while handling bats under the field protocols used in North America prior to the COVID-19 pandemic, the risk model indicates that 50 percent (uncertainty, 15–84 percent) of those bats could be exposed to virus, and 17 percent (uncertainty, 3–51 percent) could become infected. Use of personal protective equipment, especially a respirator, is expected to reduce the exposure risk. The expert panel estimated that exposure risk from research scientists could be reduced 94–96 percent (uncertainty, 86–99 percent) through proper use of appropriate N95 respirators (a type of mechanical filter worn over the nose and mouth), dedicated clothing (such as Tyvek coveralls), and gloves. Should any North American bats become infected with SARS-CoV-2, the expert panel estimated that there is an approximately 33-percent chance the virus could spread within a bat population.
This study, conducted by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, identified several critical uncertainties that could affect the estimate of risks associated with SARS-CoV-2 entering bat populations—notably, the underlying probability that a human would be shedding virus while working with bats, the likelihood of the virus replicating in bat tissue, and the likelihood of transmission of the virus within bat populations. Ongoing empirical work during May–October 2020 may shed light on these issues. Follow-up work is needed to better understand the probability of transmission of SARS-CoV-2 to bats from the general public; the manner in which the probabilities of exposure, infection, and transmission would differ during hibernation compared to the breeding season; and the likelihood of important effects, like morbidity and mortality in bats, the possibility of zoonosis from a North American bat reservoir, and effects of and on other wildlife.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201060","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Runge, M.C., Grant, E.H.C., Coleman, J.T.H., Reichard, J.D., Gibbs, S.E.J., Cryan, P.M., Olival, K.J., Walsh, D.P., Blehert, D.S., Hopkins, M.C., and Sleeman, J.M., 2020, Assessing the risks posed by SARS-CoV-2 in and via North American bats—Decision framing and rapid risk assessment: U.S. Geological Survey Open-File Report 2020–1060, 43 p., https://doi.org/10.3133/ofr20201060.","productDescription":"vi, 43 p.","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118911","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":375248,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1060/ofr20201060.pdf","text":"Report","size":"4.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1060"},{"id":375199,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1060/coverthb.jpg"}],"country":"Canada, Mexico, United States","otherGeospatial":"North America","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.1640625,\n 13.581920900545844\n ],\n [\n -84.72656249999999,\n 19.973348786110602\n ],\n [\n -75.9375,\n 27.371767300523047\n ],\n [\n -55.8984375,\n 45.336701909968134\n ],\n [\n -48.8671875,\n 45.336701909968134\n ],\n [\n -65.0390625,\n 61.77312286453146\n ],\n [\n -58.71093750000001,\n 67.47492238478702\n ],\n [\n -84.375,\n 74.1160468394894\n ],\n [\n -125.5078125,\n 75.32002523220804\n ],\n [\n -135.703125,\n 70.95969716686398\n ],\n [\n -156.4453125,\n 71.52490903732816\n ],\n [\n -167.34375,\n 68.9110048456202\n ],\n [\n -168.046875,\n 61.60639637138628\n ],\n [\n -166.2890625,\n 53.12040528310657\n ],\n [\n -146.95312499999997,\n 57.326521225217064\n ],\n [\n -138.515625,\n 56.559482483762245\n ],\n [\n -131.484375,\n 48.922499263758255\n ],\n [\n -127.96875,\n 40.17887331434696\n ],\n [\n -116.01562499999999,\n 24.84656534821976\n ],\n [\n -98.7890625,\n 13.239945499286312\n ],\n [\n -93.1640625,\n 13.581920900545844\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
This paper outlines the development and demonstration of a new tool, TOUGH2–ChemPlugin (T2CPI) for predicting rock–water–CO2 interaction following injection of supercritical CO2 into a heterogeneous carbonate system. Specifically, modeling capabilities of TOUGH2, which examines multiphase flow and supercritical CO2 behavior, were combined with the geochemical modeling capabilities of The Geochemist’s Workbench® (GWB), using ChemPluginTM. ChemPlugin is a self-linking re-entrant software object that, when coupled to a transport simulator, retains the flow and transport capabilities of the simulator but enables incorporation of reactive chemistry via GWB. To test and assess the capabilities of T2CPI, results from T2CPI simulations were compared to those of TOUGHREACT, using the same carbonate reservoir parameters (based on the Dollar Bay Formation of the South Florida Basin). Overall, results of simulations from TOUGHREACT and T2CPI were very similar for nearly all evaluated parameters. Dissimilarities between the two programs included qualitative differences in how TOUGHREACT and T2CPI predicted calcite dissolution and the subsequent spatial pattern of the porosity gain caused by how each handles evaporation of water near the injection point. The TOUGHREACT program is a proven, widely used tool for evaluating CO2–brine–rock interaction following supercritical CO2 injection. The T2CPI tool offers similar capabilities and strengths of TOUGHREACT, with the ability to read in and use databases for a wide range of activity coefficient types. This program also has abilities to use a wide range of kinetic constraints, define those kinetic constraints with scripts or compiled libraries, account for colloidal transport, and/or account for a wide range of surface sorption models.
","language":"English","publisher":"AAPG","doi":"10.1306/eg.08061919003","collaboration":"None","usgsCitation":"Roberts-Ashby, T., Berger, P.M., Cunningham, J.A., Kumar, R., and Blondes, M., 2020, Modeling geologic sequestration of carbon dioxide in a deep saline carbonate reservoir with TOUGH2–ChemPlugin, a new tool for reactive transport modeling: Environmental Geosciences, v. 27, no. 2, p. 103-116, https://doi.org/10.1306/eg.08061919003.","productDescription":"14 p.","startPage":"103","endPage":"116","ipdsId":"IP-098127","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375280,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dollar Bay Formation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.18298339843749,\n 24.297040469311558\n ],\n [\n -79.73876953125,\n 24.297040469311558\n ],\n [\n -79.73876953125,\n 27.81478637667891\n ],\n [\n -83.18298339843749,\n 27.81478637667891\n ],\n [\n -83.18298339843749,\n 24.297040469311558\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roberts-Ashby, Tina L. 0000-0003-2940-1740","orcid":"https://orcid.org/0000-0003-2940-1740","contributorId":205925,"corporation":false,"usgs":true,"family":"Roberts-Ashby","given":"Tina L.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":785375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berger, Peter M.","contributorId":223538,"corporation":false,"usgs":false,"family":"Berger","given":"Peter","email":"","middleInitial":"M.","affiliations":[{"id":40735,"text":"Illionois State Geological Survey","active":true,"usgs":false}],"preferred":false,"id":785376,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cunningham, Jeffrey A.","contributorId":223539,"corporation":false,"usgs":false,"family":"Cunningham","given":"Jeffrey","email":"","middleInitial":"A.","affiliations":[{"id":40736,"text":"Dept of Civil and Environmental Engineering, University of South Florida","active":true,"usgs":false}],"preferred":false,"id":785377,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kumar, Ram","contributorId":223540,"corporation":false,"usgs":false,"family":"Kumar","given":"Ram","email":"","affiliations":[{"id":40737,"text":"Dept. of Chemical and Biomedical Engineering, Univ. of South Florida","active":true,"usgs":false}],"preferred":false,"id":790254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":785378,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211253,"text":"70211253 - 2020 - Locality note for rubber boa","interactions":[],"lastModifiedDate":"2020-08-06T23:20:25.341027","indexId":"70211253","displayToPublicDate":"2020-06-01T18:19:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"title":"Locality note for rubber boa","docAbstract":"CHARINA BOTTAE BOTTAE (N. Rubber Boa), USA: CALIFORNIA: Monterey Co.: Landels-Hill Big Creek Reserve, east side of Hwy. 1, 80 km (50 miles) south of Carmel, Calif., (36.0719055 N 121.5991555 W) 19 June, 2009; (36.0703611 N 121.5982222 W) 06 July 2009; (36.9516666 N 121.5991944 W) 27 July 2009. In chronological order, photo vouchers MVZObs:Herp:26, MVZObs:Herp:27, MVZObs:Herp:28. Verified by Mitchell Mulks, formerly of 84 Redondo Ave. Suisun City, Calif., Michelle Koo, Staff Curator, Biodiversity Informatics & GIS and Researcher, MVZ, U.C., Berkeley, Calif. New southern extension of the species in the Santa Lucia Range of Monterey Co. approximately 48 km. (30 miles) south of previous range extension south of Carmel in Bixby Canyon (Burger, L.W., Herpetologica, Vol. 8. Part 1. March 22, 1952), and approximately 4 km. (2.5 miles) south of MVZ #229876 found 20 miles north of Nacimiento Road, at approximate coordinates of 36.10033 N 121.62026 W.","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Tomoleoni, J.A., and Hoyer, R.F., 2020, Locality note for rubber boa: Herpetological Review, v. 51, no. 2, 1 p.","productDescription":"1 p.","ipdsId":"IP-113927","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":377146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377145,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://ssarherps.org/herpetological-review-pdfs/"}],"country":"United States","state":"California","county":"Monterey County","city":"Carmel","otherGeospatial":"Santa Lucia Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.89743041992189,\n 36.49556152992\n ],\n [\n -121.83151245117186,\n 36.49556152992\n ],\n [\n -121.83151245117186,\n 36.54908666159689\n ],\n [\n -121.89743041992189,\n 36.54908666159689\n ],\n [\n -121.89743041992189,\n 36.49556152992\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":793427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoyer, Richard F","contributorId":229515,"corporation":false,"usgs":false,"family":"Hoyer","given":"Richard","email":"","middleInitial":"F","affiliations":[{"id":41662,"text":"Corvallis, OR","active":true,"usgs":false}],"preferred":false,"id":793428,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70212741,"text":"70212741 - 2020 - Climate change projected to reduce prescribed burning opportunities in the south-eastern United States","interactions":[],"lastModifiedDate":"2020-09-24T16:03:00.806638","indexId":"70212741","displayToPublicDate":"2020-06-01T11:22:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"Climate change projected to reduce prescribed burning opportunities in the south-eastern United States","docAbstract":"Prescribed burning is a critical tool for managing wildfire risks and meeting ecological objectives, but its safe and effective application requires that specific meteorological criteria (a ‘burn window’) are met. 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We contend that the practical constraints of rising temperatures on prescribed fire activities represent a significant future challenge and show that even meeting basic burn criteria (as defined today) will become increasingly difficult over time, which speaks to the need for adaptive management strategies to prepare for such changes.
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Station","active":true,"usgs":false}],"preferred":false,"id":797384,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hiers, J Kevin","contributorId":239606,"corporation":false,"usgs":false,"family":"Hiers","given":"J Kevin","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":797385,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211483,"text":"70211483 - 2020 - Examining the mechanisms of species responses to climate change: Are there biological thresholds?","interactions":[],"lastModifiedDate":"2020-07-30T16:23:57.002172","indexId":"70211483","displayToPublicDate":"2020-05-30T11:18:26","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Examining the mechanisms of species responses to climate change: Are there biological thresholds?","docAbstract":"Climate-change-driven shifts in distribution and abundance have been documented in many species. However, in order to better predict species responses, managers are seeking to understand the mechanisms that are driving these changes, including any thresholds that might soon be crossed. Leveraging the research that has already been supported by the Northeast Climate Adaptation Science Center and its partners, this project used the latest modeling techniques combined with robust field data to examine the impact of specific climate variables, land use change, and species interactions on the future distribution and abundance of species of conservation concern. Moreover, this project documented biological thresholds related to climate variability and change for critical species in the Northeastern and Midwestern U.S. Specifically, our objectives were to identify the primary drivers (climate change vs. urban growth) of species distribution changes in the Northeast; examine the nature of species landscape capability change over time to identify potential thresholds; determine how changing temperatures and snowpack characteristics will drive species interactions; analyze the sensitivity of tree and bird responses to the magnitude, variability, periodicity, and seasonality of temperature and precipitation under climate change in the eastern U.S.; and identify how discrete climate triggers such as extreme events will correlate with known biological thresholds. Major outcomes included 1) refining the understanding of the mechanisms that drive projected changes in the distribution of vulnerable populations; and 2) improving how these results are conveyed to stakeholders by identifying understandable responses in the form of thresholds.","language":"English","publisher":"Northeast Climate Adaptation Science Center","usgsCitation":"DeLuca, W., Bonnot, T.W., Siren, A., Horton, R.M., Griffin, C.R., and Morelli, T.L., 2020, Examining the mechanisms of species responses to climate change: Are there biological thresholds?, 34 p.","productDescription":"34 p.","ipdsId":"IP-117230","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":376907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":376805,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f8c648de4b0546c0c397b43/57b35c6de4b03bcb01039665"}],"country":"United States","state":"Connecticut, Delaware, Illinois, Indiana, Iowa, Kentucky, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire New Jersey, New York, Ohio, Pennsylvania, Rhode Island. 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K.","contributorId":236810,"corporation":false,"usgs":false,"family":"Siren","given":"Alexej P. K.","affiliations":[],"preferred":false,"id":794252,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Horton, Radley M.","contributorId":139267,"corporation":false,"usgs":false,"family":"Horton","given":"Radley","email":"","middleInitial":"M.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":794253,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Griffin, Curtice R.","contributorId":74634,"corporation":false,"usgs":true,"family":"Griffin","given":"Curtice","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":794254,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":794255,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211588,"text":"70211588 - 2020 - Trends in oyster populations in the northeastern Gulf of Mexico: An assessment of river discharge and fishing effects over time and space","interactions":[],"lastModifiedDate":"2023-08-31T17:33:18.95725","indexId":"70211588","displayToPublicDate":"2020-05-30T08:03:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Trends in oyster populations in the northeastern Gulf of Mexico: An assessment of river discharge and fishing effects over time and space","docAbstract":"Within the Big Bend region of the northeastern Gulf of Mexico, one of the least developed coastlines in the continental USA, intertidal and subtidal populations of eastern oyster Crassostrea virginica (hereafter referred to as “oyster”) are a critical ecosystem and important economic constituent. We assessed trends in intertidal oyster populations, river discharge, and commercial fishing activity in the Suwannee River estuary within the Big Bend region using fisheries‐independent data from irregular monitoring efforts and publicly available environmental data. We used generalized linear models to evaluate counts of oysters from line‐transect surveys over time and space. We assessed model performance using simulation to understand potential bias and then evaluated whether these counts were related to freshwater inputs from the Suwannee River and commercial oyster fishing effort and landings at different time lags. We found that intertidal oyster counts have declined over time and that most of these declines are found in inshore intertidal oyster bars, which are becoming degraded. We also found a significant relationship between oyster counts and a 1‐year lag on mean daily Suwannee River discharge, but including commercial fishery trips or landings did not improve model fit. It is unclear whether declines in intertidal oyster bars are offset by formation of new oyster reefs elsewhere. These results quantify rapid declines in intertidal oyster reefs in a region of coastline with high conservation value that can be used to inform ongoing and proposed restoration projects in the region.","language":"English","publisher":"Wiley","doi":"10.1002/mcf2.10117","usgsCitation":"Moore, J.F., Pine, W.E., Frederick, P., Becker, S., Moreno, M., Dodrill, M., Boone, M., Sturmer, L., and Yurek, S., 2020, Trends in oyster populations in the northeastern Gulf of Mexico: An assessment of river discharge and fishing effects over time and space: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 12, no. 3, p. 191-204, https://doi.org/10.1002/mcf2.10117.","productDescription":"14 p.","startPage":"191","endPage":"204","ipdsId":"IP-114295","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456589,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10117","text":"Publisher Index Page"},{"id":377005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.51943969726561,\n 28.9913248161703\n ],\n [\n -82.67898559570311,\n 28.9913248161703\n ],\n [\n -82.67898559570311,\n 29.684473609006847\n ],\n [\n -83.51943969726561,\n 29.684473609006847\n ],\n [\n -83.51943969726561,\n 28.9913248161703\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, J. 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