Early life stages of pallid sturgeon Scaphirhynchus albus are rarely collected, and thus information on their biology and ecology is extremely limited. We sampled 75 larval pallid sturgeon (25-110 mm) and 148 larval shovelnose sturgeon S. platorynchus (15-95 mm) by trawl from the upper Missouri River (USA) in 2019. Stomach contents were identified to compare food use and diet overlap between the 2 sturgeon species at the order, family, and genus levels of taxonomic prey identification. Analyses were conducted with sites pooled and with sites separated by the confluence of the Yellowstone River (upper and lower). Abundance of dominant prey in the gut (Diptera larvae) increased with fish length for both species, and regression slopes were similar. Diet overlap at pooled sites decreased from 0.94 to 0.49 when prey were identified to order and genus, respectively, and decreases in diet overlap at individual sites were more pronounced. Larval pallid sturgeon consumed a maximum of 11 unique taxa, whereas shovelnose sturgeon consumed 6 taxa that were not consumed by pallid sturgeon. These results indicate that larval diets are similar between species when evaluated at coarse taxonomic scales, but at fine taxonomic scales, notable differences exist. As information about the diets of larval pallid sturgeon captured from a riverine environment are scarce and the use of shovelnose sturgeon as an indicator of available suitable food and habitat and as a dietary surrogate for pallid sturgeon has been under consideration, our results suggest that caution be exercised in modeling efforts or management actions relating to surrogacy.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01205","usgsCitation":"Holley, C.T., Braaten, P., Poulton, B., Heist, E.J., Umland, L., and Haddix, T., 2022, Diet composition and overlap of larval pallid sturgeon and shovelnose sturgeon from the upper Missouri River, USA: Endangered Species Research, v. 49, p. 103-114, https://doi.org/10.3354/esr01205.","productDescription":"12 p.","startPage":"103","endPage":"114","ipdsId":"IP-137091","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":446034,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01205","text":"Publisher Index Page"},{"id":435644,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B6I9A2","text":"USGS data release","linkHelpText":"Invertebrates enumerated from the diets of larval pallid sturgeon (Scaphirhynchus albus) and shovelnose sturgeon (S. platorynchus) in the Upper Missouri River, Montana and North Dakota in 2019"},{"id":412491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Upper Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.41387315510876,\n 48.772256767913774\n ],\n [\n -106.41387315510876,\n 47.094926014436766\n ],\n [\n -101.86746408165837,\n 47.094926014436766\n ],\n [\n -101.86746408165837,\n 48.772256767913774\n ],\n [\n -106.41387315510876,\n 48.772256767913774\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holley, Colt Taylor 0000-0003-4172-4331","orcid":"https://orcid.org/0000-0003-4172-4331","contributorId":272272,"corporation":false,"usgs":true,"family":"Holley","given":"Colt","email":"","middleInitial":"Taylor","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":862820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braaten, Patrick 0000-0003-3362-420X pbraaten@usgs.gov","orcid":"https://orcid.org/0000-0003-3362-420X","contributorId":152682,"corporation":false,"usgs":true,"family":"Braaten","given":"Patrick","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":862821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poulton, Barry","contributorId":301852,"corporation":false,"usgs":false,"family":"Poulton","given":"Barry","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":862823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heist, Edward J.","contributorId":221082,"corporation":false,"usgs":false,"family":"Heist","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":40317,"text":"Southern Illinois University, Fisheries and Illinois Aquaculture Center","active":true,"usgs":false}],"preferred":false,"id":862824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Umland, Levi","contributorId":301851,"corporation":false,"usgs":false,"family":"Umland","given":"Levi","email":"","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":862822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haddix, Tyler M.","contributorId":268184,"corporation":false,"usgs":false,"family":"Haddix","given":"Tyler M.","affiliations":[{"id":55585,"text":"Montana Fish, Wildlife and Parks, P.O. Box 165, Fort Peck, Montana","active":true,"usgs":false}],"preferred":false,"id":862851,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262041,"text":"70262041 - 2022 - Agricultural land use shapes dispersal in white-tailed deer (Odocoileus virginianus)","interactions":[],"lastModifiedDate":"2025-01-10T15:07:46.307314","indexId":"70262041","displayToPublicDate":"2022-10-26T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Agricultural land use shapes dispersal in white-tailed deer (Odocoileus virginianus)","docAbstract":"Background
Dispersal is a fundamental process to animal population dynamics and gene flow. In white-tailed deer (WTD; Odocoileus virginianus), dispersal also presents an increasingly relevant risk for the spread of infectious diseases. Across their wide range, WTD dispersal is believed to be driven by a suite of landscape and host behavioral factors, but these can vary by region, season, and sex. Our objectives were to (1) identify dispersal events in Wisconsin WTD and determine drivers of dispersal rates and distances, and (2) determine how landscape features (e.g., rivers, roads) structure deer dispersal paths.
Methods
We developed an algorithmic approach to detect dispersal events from GPS collar data for 590 juvenile, yearling, and adult WTD. We used statistical models to identify host and landscape drivers of dispersal rates and distances, including the role of agricultural land use, the traversability of the landscape, and potential interactions between deer. We then performed a step selection analysis to determine how landscape features such as agricultural land use, elevation, rivers, and roads affected deer dispersal paths.
Results
Dispersal predominantly occurred in juvenile males, of which 64.2% dispersed, with dispersal events uncommon in other sex and age classes. Juvenile male dispersal probability was positively associated with the proportion of the natal range that was classified as agricultural land use, but only during the spring. Dispersal distances were typically short (median 5.77 km, range: 1.3–68.3 km), especially in the fall. Further, dispersal distances were positively associated with agricultural land use in potential dispersal paths but negatively associated with the number of proximate deer in the natal range. Lastly, we found that, during dispersal, juvenile males typically avoided agricultural land use but selected for areas near rivers and streams.
Conclusion
Land use—particularly agricultural—was a key driver of dispersal rates, distances, and paths in Wisconsin WTD. In addition, our results support the importance of deer social environments in shaping dispersal behavior. Our findings reinforce knowledge of dispersal ecology in WTD and how landscape factors—including major rivers, roads, and land-use patterns—structure host gene flow and potential pathogen transmission.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-022-00342-5","usgsCitation":"Gilbertson, M., Ketz, A., Hunsaker, M., Jarosinski, D., Ellarson, W., Walsh, D.P., Storm, D., and Turner, W.C., 2022, Agricultural land use shapes dispersal in white-tailed deer (Odocoileus virginianus): Movement Ecology, v. 10, 43, 18 p., https://doi.org/10.1186/s40462-022-00342-5.","productDescription":"43, 18 p.","ipdsId":"IP-139079","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467154,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-022-00342-5","text":"Publisher Index Page"},{"id":465983,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"southwestern Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.9914420863848,\n 43.07956941638602\n ],\n [\n -90.9914420863848,\n 42.580032684433775\n ],\n [\n -89.45335614888498,\n 42.580032684433775\n ],\n [\n -89.45335614888498,\n 43.07956941638602\n ],\n [\n -90.9914420863848,\n 43.07956941638602\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbertson, Marie L. J.","contributorId":347968,"corporation":false,"usgs":false,"family":"Gilbertson","given":"Marie L. J.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ketz, Alison","contributorId":347969,"corporation":false,"usgs":false,"family":"Ketz","given":"Alison","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunsaker, Matthew","contributorId":347970,"corporation":false,"usgs":false,"family":"Hunsaker","given":"Matthew","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922785,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jarosinski, Dana","contributorId":347971,"corporation":false,"usgs":false,"family":"Jarosinski","given":"Dana","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":922786,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellarson, Wesley","contributorId":347972,"corporation":false,"usgs":false,"family":"Ellarson","given":"Wesley","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922787,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":922788,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Storm, Daniel J.","contributorId":347976,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922789,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922790,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70241088,"text":"70241088 - 2022 - Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989-2018","interactions":[],"lastModifiedDate":"2023-03-09T15:29:50.90571","indexId":"70241088","displayToPublicDate":"2022-10-25T09:23:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5836,"text":"Journal of Hydrology X","onlineIssn":"2589-9155","active":true,"publicationSubtype":{"id":10}},"title":"Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989-2018","docAbstract":"The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year variability at a regional extent is a major gap in modeling capability. Herein, we expand and adapt the U.S. Geological Survey (USGS) PRObability of Streamflow PERmanence (PROSPER) model from its original implementation in the Pacific Northwest (PROSPERPNW) to the upper Missouri River basin (PROSPERUM), a geographical region that includes mountain and prairie ecosystems of the northern United States. PROSPERUM is an empirical model used to estimate the probability that a stream channel has year-round flow in response to climatic conditions (monthly and annual) and static physiographic predictor variables of the upstream basin. The structure and approach of PROSPERUM are generally consistent with the PROSPERPNW model but include improved spatial resolution (10 m) and a longer modeling period. Average model accuracy was 81 %. Drainage area, upstream proportion as wetlands, and upstream proportion as developed land cover were the most important predictor variables. The PROSPERUM model identifies decreases in streamflow permanence during climatically drier years, although there is variability in the magnitude across basins highlighting geographically varying sensitivity to drought. Variability in the response of perennial streams to drought conditions among basins in the study area was also observed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2022.100138","usgsCitation":"Sando, R., Jaeger, K.L., Farmer, W., Barnhart, T., McShane, R., Welborn, T.L., Kaiser, K.E., Hafen, K., Blasch, K.W., York, B.C., and Shallcross, A., 2022, Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989-2018: Journal of Hydrology X, v. 17, 100138, 22 p., https://doi.org/10.1016/j.hydroa.2022.100138.","productDescription":"100138, 22 p.","ipdsId":"IP-137870","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":446045,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2022.100138","text":"Publisher Index Page"},{"id":413911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"upper Missouri River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.06578880812711,\n 48.98607018582902\n ],\n [\n -114.41405308204301,\n 46.6259860103564\n ],\n [\n -114.45505714000817,\n 45.56604735512229\n ],\n [\n -114.1280129319525,\n 45.6810748968652\n ],\n [\n -113.43692249781836,\n 44.85861341197983\n ],\n [\n -112.98504047311934,\n 44.442264035596594\n ],\n [\n -111.79851411576917,\n 44.50526246095063\n ],\n [\n -111.2396387557593,\n 44.90171571207168\n ],\n [\n -110.61806405817302,\n 42.14074973473086\n ],\n [\n -105.77175988800175,\n 41.952647712608155\n ],\n [\n -104.56426824820389,\n 42.95942508508247\n ],\n [\n -103.25615692243574,\n 43.83191953044022\n ],\n [\n -101.00144324324455,\n 44.44211500891038\n ],\n [\n -100.09948505313812,\n 44.838575527202494\n ],\n [\n -99.6320641494822,\n 46.96241959544966\n ],\n [\n -99.99744313272754,\n 48.133167378584716\n ],\n [\n -102.2714095903581,\n 48.758327670163794\n ],\n [\n -107.84693201879426,\n 48.8300878096519\n ],\n [\n -115.06578880812711,\n 48.98607018582902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":865992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":865994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":202558,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865995,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McShane, Ryan R. 0000-0002-3128-0039","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":219009,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welborn, Toby L. 0000-0003-4839-2405 tlwelbor@usgs.gov","orcid":"https://orcid.org/0000-0003-4839-2405","contributorId":2295,"corporation":false,"usgs":true,"family":"Welborn","given":"Toby","email":"tlwelbor@usgs.gov","middleInitial":"L.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865997,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kaiser, Kendra E. 0000-0003-1773-6236","orcid":"https://orcid.org/0000-0003-1773-6236","contributorId":211475,"corporation":false,"usgs":false,"family":"Kaiser","given":"Kendra","email":"","middleInitial":"E.","affiliations":[{"id":38255,"text":"Boise State Unviersity","active":true,"usgs":false}],"preferred":false,"id":865998,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hafen, Konrad 0000-0002-1451-362X","orcid":"https://orcid.org/0000-0002-1451-362X","contributorId":215959,"corporation":false,"usgs":true,"family":"Hafen","given":"Konrad","email":"","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865999,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blasch, Kyle W. 0000-0002-0590-0724 kblasch@usgs.gov","orcid":"https://orcid.org/0000-0002-0590-0724","contributorId":1631,"corporation":false,"usgs":true,"family":"Blasch","given":"Kyle","email":"kblasch@usgs.gov","middleInitial":"W.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866046,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"York, Benjamin C. 0000-0002-3449-3574 byork@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-3574","contributorId":213613,"corporation":false,"usgs":true,"family":"York","given":"Benjamin","email":"byork@usgs.gov","middleInitial":"C.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":866047,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shallcross, Alden","contributorId":302945,"corporation":false,"usgs":false,"family":"Shallcross","given":"Alden","email":"","affiliations":[{"id":37086,"text":"U.S. Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":866048,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70240484,"text":"70240484 - 2022 - Behavior of potentially toxic elements from stoker-boiler fly ash in Interior Alaska: Paired batch leaching and solid-phase characterization","interactions":[],"lastModifiedDate":"2023-02-09T12:49:48.415207","indexId":"70240484","displayToPublicDate":"2022-10-23T06:46:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1564,"text":"Environmental Science and Pollution Research","active":true,"publicationSubtype":{"id":10}},"title":"Behavior of potentially toxic elements from stoker-boiler fly ash in Interior Alaska: Paired batch leaching and solid-phase characterization","docAbstract":"Despite significant investigation of fly ash spills and mineralogical controls on the release of potentially toxic elements (PTEs) from fly ash, interactions with the surficial environment remain relatively poorly understood. We conducted 90-day batch leaching studies with paired analysis of supernatant and solid-phase mineralogy to assess the elemental release and transformation of fly ash upon reaction with aquatic media (18 MΩ cm−1 water and simulated rainwater). The fly ash in this study, collected from the University of Alaska Fairbanks stoker-boiler power plant, is high in unburned carbon (~20% LOI) and highly enriched in several PTEs relative to the upper continental crust. Supernatant concentrations of oxyanion-forming elements (e.g., As, Se, Mo, Sb) remained relatively low and constant, suggesting equilibrium with the solid phase, possibly ettringite [Ca6Al2(SO4)3(OH)12•26H2O], which is known to incorporate and sorb oxyanion-forming PTEs and was identified by X-ray diffraction. Synthetic precipitation leaching procedure (SPLP) results failed to capture important temporal trends. Lead and Ba supernatant concentrations consistently exceeded drinking water standards, as well as others upon exposure to simulated physiological solutions. Seven-day experiments with dissolved organic matter-isolate solutions indicated that for certain elements, liberation was influenced by carbon concentration and/or the identity of the isolate. Overall, this paired approach can serve as a model for future studies, bridging existing gaps between batch leaching and single-element mineralogical, sorption, or speciation studies.
The coastal waters of Cuba are home to a small, endangered population of West Indian manatee, which would benefit from a comprehensive characterization of the population’s genetic variation. We conducted the first genetic assessment of Cuban manatees to determine the extent of the population's genetic structure and characterize the neutral genetic diversity among regions within the archipelago. We genotyped 49 manatees at 18 microsatellite loci, a subset of 27 samples on 1703 single nucleotide polymorphisms (SNPs), and sequenced 59 manatees at the mitochondrial control region. The Cuba manatee population had low nuclear (microsatellites HE = 0.44, and SNP HE = 0.29) and mitochondrial genetic diversity (h = 0.068 and π = 0.00025), and displayed moderate departures from random mating (microsatellite FIS = 0.12, SNP FIS = 0.10). Our results suggest that the western portion of the archipelago undergoes periodic exchange of alleles based on the evidence of shared ancestry and low but significant differentiation. The southeast Guantanamo Bay region and the western portion of the archipelago were more differentiated than southwest and northwest manatees. The genetic distinctiveness observed in the southeast supports its recognition as a demographically independent unit for natural resource management regardless of whether it is due to historical isolation or isolation by distance. Estimates of the regional effective population sizes, with the microsatellite and SNP datasets, were small (all Ne < 60). Subsequent analyses using additional samples could better examine how the observed structure is masking simple isolation by distance patterns or whether ecological or biogeographic forces shape genetic patterns.
Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113307","usgsCitation":"Enderlin, E., Elkin, C., Gendreau, M., Marshall, H., O'Neel, S., McNeil, C., Florentine, C., and Sass, L., 2022, Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions: Remote Sensing of Environment, v. 283, 113307, 17 p., https://doi.org/10.1016/j.rse.2022.113307.","productDescription":"113307, 17 p.","ipdsId":"IP-141547","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113307","text":"Publisher Index Page"},{"id":486323,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1WHK","text":"USGS data release","linkHelpText":"Point Raw Glaciological Data: Ablation Stake, Snow Pit, and Probed Snow Depth Data on USGS Benchmark Glaciers"},{"id":408693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Idaho","otherGeospatial":"Reynolds Creek Experimental Watershed, Wolverine Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.9169563546656,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.36296558422342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.84207609134586,\n 60.42699332401864\n ],\n [\n -148.8772978314237,\n 60.46763185035496\n ],\n [\n -148.92384084509808,\n 60.44126913255184\n ],\n [\n -148.95214402908923,\n 60.43009729404224\n ],\n [\n -148.9219539661653,\n 60.37666770702921\n ],\n [\n -148.9112616522131,\n 60.37542411458642\n ],\n [\n -148.84207609134586,\n 60.42699332401864\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":855759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elkin, Colten","contributorId":298508,"corporation":false,"usgs":false,"family":"Elkin","given":"Colten","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gendreau, Madeline","contributorId":298509,"corporation":false,"usgs":false,"family":"Gendreau","given":"Madeline","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, H. P.","contributorId":298510,"corporation":false,"usgs":false,"family":"Marshall","given":"H. P.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":855763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNeil, Christopher J. 0000-0003-4170-0428 cmcneil@usgs.gov","orcid":"https://orcid.org/0000-0003-4170-0428","contributorId":5803,"corporation":false,"usgs":true,"family":"McNeil","given":"Christopher J.","email":"cmcneil@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":855764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":855766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":855765,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70259706,"text":"70259706 - 2022 - Exploring declustering methodology for addressing geothermal exploration bias","interactions":[],"lastModifiedDate":"2024-10-21T12:29:48.729606","indexId":"70259706","displayToPublicDate":"2022-10-21T07:28:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Exploring declustering methodology for addressing geothermal exploration bias","docAbstract":"Geothermal resources assessments use data that are unevenly distributed in space, with more data collected in areas with known thermal features. To meet the assumptions for geostatistical modeling (e.g., variography and kriging) such as having a random sample representative of the population, declustering may be needed to correct for spatial sample bias. Several declustering methods exist and to understand how best to use these methods, we apply these to real data and samples of that data. The work described herein summarizes the application of cell-based declustering to shallow temperature data (~20 cm) collected in a survey across a thermal feature in the Lower Geyser Basin, Yellowstone National Park, Wyoming. The sample dataset is a regular grid (3-m spacing) of temperatures across a 72-m square area, providing a shallow, subsurface temperature dataset collected with minimal spatial bias (a few grid locations near a hot spring could not be sampled). To test the influence of sample clustering on geothermal estimates, this dense dataset is sub-sampled irregularly to evaluate bias on temperature estimation. Three sampling strategies were tested: a simple random sample, a stratified random sample, and a stratified biased random sample. The naive mean (before declustering) values for each dataset were compared to the post-declustering mean to evaluate the effectiveness of declustering on correcting the mean for spatial bias. For the limited number of sample datasets evaluated, we found that although cell-based declustering did partially correct the mean, some bias remained (i.e., the estimate was improved, but not fully corrected). It is possible that the procedure documented herein (applied here to only a few random samples) could be applied to many random samples, so that robust conclusions might be drawn (e.g., Is there always some remaining bias in declustered estimates? Does it depend on the number of sample points?). In particular, bias could be evaluated for persistency, and uncertainty could be evaluated.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Lindsey, C.R., Price, A.N., and Burns, E.R., 2022, Exploring declustering methodology for addressing geothermal exploration bias: Geothermal Resources Council Transactions, v. 46, p. 1109-1119.","productDescription":"11 p.","startPage":"1109","endPage":"1119","ipdsId":"IP-141054","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":463063,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034661"},{"id":463064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lindsey, Cary Ruth","contributorId":345373,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Price, Adam N. 0000-0002-7211-4758","orcid":"https://orcid.org/0000-0002-7211-4758","contributorId":295971,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":916396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916397,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241136,"text":"70241136 - 2022 - Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","interactions":[],"lastModifiedDate":"2023-03-13T12:07:56.782111","indexId":"70241136","displayToPublicDate":"2022-10-21T07:05:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","docAbstract":"Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long-term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines.
Previous research suggests that predation by piscivorous colonial waterbirds may negatively influence the survival of Lost River Suckers (LRS) Deltistes luxatus and Shortnose Suckers (SNS) Chasmistes brevirostris in the Upper Klamath Basin (UKB), USA. However, estimates of predation from past studies, which were based on suckers with PIT tags, represent minimum estimates of sucker mortality because analyses did not account for the proportion of tags that were consumed by birds and deposited beyond their breeding colony. To address this uncertainty, we fed PIT-tagged suckers to American white pelicans Pelecanus erythrorhynchos to estimate deposition probabilities. A hierarchical Bayesian model was then used to estimate predation rates (percentage of available tagged fish that were consumed) on juvenile suckers that were released as part of the Sucker Assisted Rearing Program (SARP) and on wild juvenile and adult LRS and SNS during 2009–2020. Pelican deposition probabilities were estimated to be 0.47 (95% credible interval = 0.36–0.60), indicating that for every 100 tags consumed, 47 tags on average were deposited on breeding colonies by birds. Deposition-corrected estimates of predation rates were approximately two times greater than those previously reported and ranged annually from 4.3% (95% credible interval = 2.9–6.7%) to 8.5% (6.3–12.7%) on SARP juvenile suckers, from 4.3% (0.9–13.2%) to 10.5% (3.8–24.5%) on wild juvenile suckers, and from 0.1% (<0.1–0.3%) to 7.2% (2.8–16.4%) on adult suckers, depending on species and location. Results suggest that predation by colonial waterbirds, although not the original cause of sucker declines, was a substantial source of sucker mortality in some years. Future studies should consider models that jointly estimate both predation and survival and models that include environmental factors that potentially influence sucker susceptibility to avian predators in the UKB.
The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 2 (April–June), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed throughout the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at the lower orbit of 697 kilometers on May 5, 2022, extending the science mission to allow for essential data to be acquired during the 2022 Northern Hemisphere fire and growing season. Additional information about the Landsat 7 orbit lowering is here:
https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers.
Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Recreational hunting has been the dominant game management and conservation mechanism in the United States for the past century. However, there are numerous modern-day issues that reduce the viability and efficacy of hunting-based management, such as fewer hunters, overabundant wildlife populations, limited access, and emerging infectious diseases in wildlife. Quantifying the drivers of recreational harvest by hunters could inform potential management actions to address these issues, but this is seldom comprehensively accomplished because data collection practices limit some analytical applications (e.g., differing spatial scales of harvest regulations and harvest data). Additionally, managing large-scale issues, such as infectious diseases, requires collaborations across management agencies, which is challenging or impossible if data are not standardized. Here we discuss modern issues with the prevailing wildlife management framework in the United States from an analytical point of view with a case study of white-tailed deer (Odocoileus virginianus) in the Midwest. We have four aims: (1) describe the interrelated processes that comprise hunting and suggest improvements to current data collections systems, (2) summarize data collection systems employed by state wildlife management agencies in the Midwestern United States and discuss potential for large-scale data standardization, (3) assess how aims 1 and 2 influence managing infectious diseases in hunted wildlife, and (4) suggest actionable steps to help guide data collection standards and management practices. To achieve these goals, Wisconsin Department of Natural Resources disseminated a questionnaire to state wildlife agencies (Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, Wisconsin), and we report and compare their harvest management structures, data collection practices, and responses to chronic wasting disease. We hope our “call to action” encourages re-evaluation, coordination, and improvement of harvest and management data collection practices with the goal of improving the analytical potential of these data. A deeper understanding of the strengths and deficiencies of our current management systems in relation to harvest and management data collection methods could benefit the future development of comprehensive and collaborative management and research initiatives (e.g., adaptive management) for wildlife and their diseases.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.943411","usgsCitation":"Brandell, E., Storm, D., Van Deelen, T., Walsh, D.P., and Turner, W.C., 2022, A call to action: Standardizing white-tailed deer harvest data in the Midwestern United States and implications for quantitative analysis and disease management: Frontiers in Ecology and Evolution, v. 19, 943411, 19 p., https://doi.org/10.3389/fevo.2022.943411.","productDescription":"943411, 19 p.","ipdsId":"IP-139769","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":467155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.943411","text":"Publisher Index Page"},{"id":466006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, 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\"}}]}","volume":"19","noUsgsAuthors":false,"publicationDate":"2022-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Brandell, Ellen E.","contributorId":347965,"corporation":false,"usgs":false,"family":"Brandell","given":"Ellen E.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storm, Daniel J.","contributorId":347966,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Deelen, Timothy R.","contributorId":347967,"corporation":false,"usgs":false,"family":"Van Deelen","given":"Timothy R.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":922781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922782,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263097,"text":"70263097 - 2022 - Editorial: Habitat and distribution models of marine and estuarine species: Advances for a sustainable future","interactions":[],"lastModifiedDate":"2025-01-29T16:14:43.221849","indexId":"70263097","displayToPublicDate":"2022-10-19T10:12:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Editorial: Habitat and distribution models of marine and estuarine species: Advances for a sustainable future","docAbstract":"No abstract available.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.1050548","usgsCitation":"Fabrizio, M., Henderson, M., Rose, K., and Petitgas, P., 2022, Editorial: Habitat and distribution models of marine and estuarine species: Advances for a sustainable future: Frontiers in Marine Science, v. 9, 1050548., 4 p., https://doi.org/10.3389/fmars.2022.1050548.","productDescription":"1050548., 4 p.","ipdsId":"IP-145479","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":489758,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.1050548","text":"Publisher Index Page"},{"id":481462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2022-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Fabrizio, Mary C.","contributorId":350223,"corporation":false,"usgs":false,"family":"Fabrizio","given":"Mary C.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":925507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rose, Kenneth","contributorId":350225,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925508,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petitgas, Pierre","contributorId":350227,"corporation":false,"usgs":false,"family":"Petitgas","given":"Pierre","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925509,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259709,"text":"70259709 - 2022 - A modern multicentennial record of radiocarbon variability from an exactly dated bivalve chronology at the Tree Nob site (Alaska Coastal Current)","interactions":[],"lastModifiedDate":"2024-10-19T13:12:35.431924","indexId":"70259709","displayToPublicDate":"2022-10-19T08:09:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3225,"text":"Radiocarbon","active":true,"publicationSubtype":{"id":10}},"title":"A modern multicentennial record of radiocarbon variability from an exactly dated bivalve chronology at the Tree Nob site (Alaska Coastal Current)","docAbstract":"Quantifying the marine radiocarbon reservoir effect, offsets (ΔR), and ΔR variability over time is critical to improving dating estimates of marine samples while also providing a proxy of water mass dynamics. In the northeastern Pacific, where no high-resolution time series of ΔR has yet been established, we sampled radiocarbon (14C) from exactly dated growth increments in a multicentennial chronology of the long-lived bivalve, Pacific geoduck (Paneopea generosa) at the Tree Nob site, coastal British Columbia, Canada. Samples were taken at approximately decadal time intervals from 1725 CE to 1920 CE and indicate average ΔR values of 256 ± 22 years (1σ) consistent with existing discrete estimates. Temporal variability in ΔR is small relative to analogous Atlantic records except for an unusually old-water event, 1802–1812. The correlation between ΔR and sea surface temperature (SST) reconstructed from geoduck increment width is weakly significant (r2 = .29, p = .03), indicating warm water is generally old, when the 1802–1812 interval is excluded. This interval contains the oldest (–2.1σ) anomaly, and that is coincident with the coldest (–2.7σ) anomalies of the temperature reconstruction. An additional 32 14C values spanning 1952–1980 were detrended using a northeastern Pacific bomb pulse curve. Significant positive correlations were identified between the detrended 14C data and annual El Niño Southern Oscillation (ENSO) and summer SST such that cooler conditions are associated with older water. Thus, 14C is generally relatively stable with weak, potentially inconsistent associations to climate variables, but capable of infrequent excursions as illustrated by the unusually cold, old-water 1802–1812 interval.
Hydrologic and geophysical components interact to produce streambed scour. This study investigates methods for improving the utility of estimates of hydrologic flow in streams and rivers used when evaluating potential pier scour over the design-life of highway bridges in Virginia. Recent studies of streambed composition identify potential bridge design cost savings when attributes of cohesive soil and weathered rock unique to certain streambeds are considered within the bridge planning design. To achieve potential cost savings, however, attributes and effects of scour forces caused by water movement across the streambed surface must be accurately described and estimated.
This study explores the potential for improving estimates of the hydrologic component, namely hydrologic flow, afforded by empirically based deterministic, probabilistic, and statistical modeling of flows using streamgage data from 10 selected sites in Virginia. Methods are described and tools are provided that may assist with estimating hydrological components of flow duration and potential cumulative stream power for bridge designs in specific settings, and calculation of comprehensive projections of anticipated individual bridge pier scour rates. Examples of hydrologic properties needed to determine the rates of streambed scour are described for sites spanning a range of basin sizes and locations in Virginia. Deterministic, probabilistic, and statistical modeling methods are demonstrated for estimating hydrological components of streambed scour over a bridge design lifespan. Eight tools provide examples of streamflow analysis using daily and instantaneous streamflow data collected at 10 study sites in Virginia. Tool 1 provides a generalized system dynamics model of streamflow and sediment motion that may be used to estimate hydrologic flow over time. Tool 2 illustrates at-a-station hydraulic geometry using methods pioneered by Leopold and others. Tool 3 provides a system dynamics model developed to test the use of Monte-Carlo sampling of instantaneous streamflow measurements to augment and increase precision of site-specific period-of-record daily-flow values useful for driving stream-power and streambed scour estimates. Tool 4 integrates deterministic modeling, maximum likelihood logistic regression, and Monte-Carlo sampling to identify probable hydrologic flows. Tool 5 provides instantaneous flow hydrologic envelope profiles, using measured instantaneous flow data integrated with measured daily-flow value data. Tool 6 provides precise estimates of hydrologic flow over entire data time-series suitable for driving scour simulation models. Tool 7 provides a threshold of flow and probability of time-under-load interactive calculator that allows selection of a desired bridge design lifespan, ranging from 1 to 250 years, and identification of a flow interval of interest. Tool 8 provides a flow-random sampling interactive tool, developed to facilitate easy access to large datasets of randomly sampled flow data measurements from unique locations for purposes of computing and testing future models of bridge pier scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225059","collaboration":"Prepared in cooperation with the Virginia Department of Transportation","usgsCitation":"Austin, S.H., 2022, Virginia Bridge Scour Pilot Study—Hydrological Tools: U.S. Geological Survey Scientific Investigations Report 2022–5059, 46 p., https://doi.org/10.3133/sir20225059.","productDescription":"Report: vii, 46 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-137495","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":408486,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957ABZN","text":"USGS data release","linkHelpText":"Virginia bridge scour pilot study streamflow data"},{"id":408487,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":408485,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.XML"},{"id":408484,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5059/images/"},{"id":408483,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225059/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5059"},{"id":408482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.pdf","text":"Report","size":"9.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5059"},{"id":408481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5059/coverthb.jpg"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.28857421875,\n 39.554883059924016\n ],\n [\n -80.39794921875,\n 38.18638677411551\n ],\n [\n -80.4638671875,\n 37.52715361723378\n ],\n [\n -77.49755859375,\n 37.59682400108367\n ],\n [\n -77.32177734375,\n 39.53793974517628\n ],\n [\n -78.28857421875,\n 39.554883059924016\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Planning stormwater projects requires estimates of current and future extreme precipitation depths for events with specified return periods and durations. In this study, precipitation data from four downscaled climate datasets are used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the period 2050–89 primarily on the basis of Representative Concentration Pathways 4.5 and 8.5 emission scenarios from the Coupled Model Intercomparison Project Phase 5. The four downscaled climate datasets are (1) the Coordinated Regional Downscaling Experiment (CORDEX) dataset, (2) the Localized Constructed Analogs (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, and (4) the Jupiter Intelligence Weather Research and Forecasting Model (JupiterWRF) dataset. Change factors—multiplicative changes in expected extreme precipitation magnitude from current to future period—were computed for grid cells from the downscaled climate datasets containing National Oceanic and Atmospheric Administration Atlas 14 stations in central and south Florida. Change factors for specific durations and return periods may be used to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the period 2050–89 on the basis of changes in extreme precipitation derived from downscaled climate datasets. Model culling was implemented to select downscaled climate models that best captured observed historical patterns of precipitation extremes in central and south Florida.
Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally greater than one and increasing with return period. In general, median change factors were higher for the south-central Florida climate region (1.05–1.55 depending on downscaled climate dataset, duration, and return period) than for the south Florida climate region (1–1.4 depending on downscaled climate dataset, duration, and return period) when considering best performing models for both areas, indicating a projected overall increase in future extreme precipitation events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225093","collaboration":"Prepared in cooperation with the South Florida Water Management District","usgsCitation":"Irizarry-Ortiz, M.M., Stamm, J.F., Maran, C., and Obeysekera, J., 2022, Development of projected depth-duration frequency curves (2050–89) for south Florida: U.S. Geological Survey Scientific Investigations Report 2022–5093, 114 p., https://doi.org/10.3133/sir20225093.","productDescription":"Report: xii, 114 p.; 1 Table; Data Release","numberOfPages":"130","onlineOnly":"Y","ipdsId":"IP-134493","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":408474,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P935WRTG","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida"},{"id":435653,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3LEIL","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida (ver 2.0, May 2024)"},{"id":408853,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225093/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408472,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.xlsx","text":"Table 1.1","size":"50.0 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":408471,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5093/images"},{"id":408470,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.XML"},{"id":408469,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.pdf","text":"Report","size":"23.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5093"},{"id":408468,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5093/coverthb.jpg"},{"id":408473,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.csv","text":"Table 1.1","size":"18.6 kB","linkFileType":{"id":7,"text":"csv"}}],"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 -83.111572265625,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 24.327076540018634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
In this report, we apply the stream salmonid simulator (S3) to coho salmon (Oncorhynchus kisutch) in the Klamath River Basin by extending the original model to account for life history and disease dynamics specific to coho salmon. This version of S3 includes tracking of three separate life-history strategies representing the different time periods and ages at which fish leave natal tributaries such as the Scott and Shasta Rivers (age-0 spring, age-0 fall, or age-1 smolt). Once fish leave their natal tributaries and enter the Klamath River, the deterministic life-stage-structured population model simulates daily growth, movement, and survival. We extend the model to include non-natal tributary dynamics, where spring age-0 fish entry to non-natal tributaries is simulated based on environmental conditions in the main-stem Klamath River. Fish that use non-natal tributaries then reenter the Klamath River during the winter or spring as smolts and actively migrate downstream. We also consider the life history strategy where fish rear in natal tributaries and enter the Klamath River as age-1 smolts. In addition to simulating different life history pathways that coho salmon may take, we model disease dynamics, incorporating new information on Ceratonova shasta related infection and mortality. We incorporate competitive interactions between juvenile coho and Chinook salmon (Oncorhynchus tshawytscha) by simulating density-dependent movement dynamics in response to Chinook salmon abundance.
Model simulations suggest that total abundance and survival to the ocean differed between life-history strategies. In general, spring age-0 fish that leave their natal tributaries in their first spring had lower survival compared with fish that remained in natal tributaries and out-migrated later. Spring age-0 fish also had higher disease related mortality, owing to their residence in the main-stem Klamath River overlapping with periods of elevated C. shasta spore concentrations. Age-0 fish leaving their natal tributaries in the fall had near-zero disease related mortality. Most non-natal tributary use occurred at upstream tributary locations and was variable between the brood years depending on passage timing and environmental conditions. The inclusion of Chinook salmon in simulations resulted in decreased abundance and survival of Coho salmon reaching the ocean. In addition, we developed an R package to facilitate use of and continued development of S3 as a tool to guide management of juvenile salmonid populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221071","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Bureau of Reclamation","usgsCitation":"Dodrill, M.J., Perry, R.W., Som, N.A., Manhard, C.V., and Alexander, J.D., 2022, Extending the Stream Salmonid Simulator to accommodate the life history of coho salmon (Oncorhynchus kisutch) in the Klamath River Basin, Northern California: U.S. Geological Survey Open-File Report 2022–1071, 70 p., https://doi.org/10.3133/ofr20221071.","productDescription":"viii, 70 p.","onlineOnly":"Y","ipdsId":"IP-129401","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":408507,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1071/ofr20221071.XML"},{"id":408506,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1071/images"},{"id":408505,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221071/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1071"},{"id":408503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1071/coverthb.jpg"},{"id":408504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1071/ofr20221071.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1071"}],"country":"United States","state":"California","otherGeospatial":"Klamath River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.16748046874999,\n 41.071069130806414\n ],\n [\n -121.915283203125,\n 41.071069130806414\n ],\n [\n -121.915283203125,\n 42.037054301883806\n ],\n [\n -124.16748046874999,\n 42.037054301883806\n ],\n [\n -124.16748046874999,\n 41.071069130806414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The horizontal‐to‐vertical spectral ratios from microtremor (mHVSR) data obtained at 196 seismic stations in California are used to evaluate three alternative microtremor‐based proxies for site amplification for use in ground‐motion models (GMMs): the site fundamental period (
Pyrethroids, a class of commonly used insecticides, are frequently detected in aquatic environments, including estuaries. The influence that salinity has on organism physiology and the partitioning of hydrophobic chemicals, such as pyrethroids, has driven interest in how toxicity changes in saltwater compared to freshwater. Early life exposures in fish to pyrethroids cause toxicity at environmentally relevant concentrations, which can alter behavior. Behavior is a highly sensitive endpoint that influences overall organism fitness and can be used to detect toxicity of environmentally relevant concentrations of aquatic pollutants. Inland Silversides (Menidia beryllina), a commonly used euryhaline model fish species, were exposed from 5 days post fertilization (~1-day pre-hatch) for 96 h to six pyrethroids: bifenthrin, cyfluthrin, cyhalothrin, cypermethrin, esfenvalerate and permethrin. Exposures were conducted at three salinities relevant to brackish, estuarine habitat (0.5, 2, and 6 PSU) and across 3 concentrations, either 0.1, 1, 10, and/or 100 ng/L, plus a control. After exposure, Inland Silversides underwent a behavioral assay in which larval fish were subjected to a dark and light cycle stimuli to determine behavioral toxicity. Assessment of total distanced moved and thigmotaxis (wall hugging), used to measure hyper/hypoactivity and anxiety like behavior, respectively, demonstrate that even at the lowest concentration of 0.1 ng/L pyrethroids can induce behavioral changes at all salinities. We found that toxicity decreased as salinity increased for all pyrethroids except permethrin. Additionally, we found evidence to suggest that the relationship between log KOW and thigmotaxis is altered between the lower and highest salinities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.159398","usgsCitation":"Hutton, S., Siddiqui, S., Pedersen, E., Markgraf, C., Segarra, A., Hladik, M.L., Connon, R., and Brander, S.M., 2022, Comparative behavioral ecotoxicology of Inland Silverside larvae exposed to pyrethroids across a salinity gradient: Science of the Total Environment, v. 857, no. Part 3, 159398, 12 p., https://doi.org/10.1016/j.scitotenv.2022.159398.","productDescription":"159398, 12 p.","ipdsId":"IP-143936","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":408606,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"857","issue":"Part 3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hutton, Sara","contributorId":298401,"corporation":false,"usgs":false,"family":"Hutton","given":"Sara","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siddiqui, Samreen","contributorId":298402,"corporation":false,"usgs":false,"family":"Siddiqui","given":"Samreen","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855526,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pedersen, Emily","contributorId":298404,"corporation":false,"usgs":false,"family":"Pedersen","given":"Emily","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855527,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markgraf, Christopher","contributorId":298406,"corporation":false,"usgs":false,"family":"Markgraf","given":"Christopher","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":855528,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Segarra, Amelie 0000-0002-0551-0013","orcid":"https://orcid.org/0000-0002-0551-0013","contributorId":251846,"corporation":false,"usgs":false,"family":"Segarra","given":"Amelie","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":855529,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":203857,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855530,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Connon, Richard E","contributorId":152478,"corporation":false,"usgs":false,"family":"Connon","given":"Richard E","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":855531,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brander, Susanne M.","contributorId":187546,"corporation":false,"usgs":false,"family":"Brander","given":"Susanne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":855532,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70237634,"text":"70237634 - 2022 - It’s time for focused in situ studies of planetary surface-atmosphere interactions","interactions":[],"lastModifiedDate":"2022-10-17T14:20:50.197661","indexId":"70237634","displayToPublicDate":"2022-10-17T09:12:39","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"It’s time for focused in situ studies of planetary surface-atmosphere interactions","docAbstract":"A critical gap in planetary observations has been in situ characterization of extra-terrestrial, present-day atmospheric and surface environments and activity. While some surface activity has been observed and some in situ meteorological measurements have been collected by auxiliary instruments on Mars, existing information is insufficient to conclusively characterize the natural processes via concurrent and high-resolution measurement of environmental drivers and activity. Thus, many atmospheric, aeolian, and other surface processes models – which are used to generate key constraints on science and exploration in many areas of planetary investigation—such as surface exposure/erosion estimates, landscape interpretation, and modeling dust storm development—remain untested under non-Earth conditions.\nAnalogous terrestrial processes are often studied intensively via numerical modeling that integrates empirical results from laboratory and/or field studies of process-response interactions between the atmosphere and relevant surface landforms. Incorporation of such in situ measurements into model development has significantly advanced our understanding of atmosphere-surface interactions and related geomorphic processes on Earth, and is poised to do so on other planets. However, to date, such testing and refinement have not been possible in other planetary environments, partially because investigations of this sort require new technologies, mission architectures, and operations designs (e.g., different from large rovers focused on geochemical investigations) to fully address the key gaps in our understanding while keeping cost and risk low.\nFortunately, technological developments in the areas of surface access, instrumentation, and onboard processing/memory now enable small spacecraft to accommodate meteorological and aeolian instrumentation that could collect the needed measurements to fill this critical gap while remaining within typical small spacecraft resource budgets. Furthermore, maturity of our understanding of the broader geologic and atmospheric context on Mars provides a ready framework for ingestion of discrete ground truth measurements into our understanding of the broader and multi-scale martian natural systems and processes. These advancements make addressing key science questions with novel mission concepts feasible, promising results that would significantly advance our understanding of extraterrestrial surface-atmosphere interactions. This summary follows from a community-generated white paper for the ongoing Planetary Science/Astrobiology Decadal Survey, small spacecraft concept development at JPL, and numerous JPL and community discussions.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2022 IEEE Aerospace Conference (AERO)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2022 IEEE Aerospace Conference","conferenceDate":"March 5-12, 2022","conferenceLocation":"Big Sky, Montana, United States","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/AERO53065.2022.9843357","usgsCitation":"Diniega, S., Barba, N., Giersch, L., Jackson, B., Soto, A., Banfield, D., Day, M.D., Doran, G., Dundas, C., Mischna, M., Rafkin, S., Smith, I.B., Sullivan, R., Swann, C., Titus, T.N., Walker, I.J., Widmer, J., Burr, D., Mandrake, L., Vriend, N., and Williams, K.E., 2022, It’s time for focused in situ studies of planetary surface-atmosphere interactions, in 2022 IEEE Aerospace Conference (AERO), Big Sky, Montana, United States, March 5-12, 2022, p. 1-19, https://doi.org/10.1109/AERO53065.2022.9843357.","productDescription":"19 p.","startPage":"1","endPage":"19","ipdsId":"IP-134187","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":408388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth, Mars","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diniega, Serina","contributorId":212017,"corporation":false,"usgs":false,"family":"Diniega","given":"Serina","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":854720,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barba, 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Alejandro","contributorId":237034,"corporation":false,"usgs":false,"family":"Soto","given":"Alejandro","email":"","affiliations":[{"id":41659,"text":"SWRI","active":true,"usgs":false}],"preferred":false,"id":854724,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Banfield, Don","contributorId":297953,"corporation":false,"usgs":false,"family":"Banfield","given":"Don","email":"","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":854728,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Day, Mackenzie D.","contributorId":203790,"corporation":false,"usgs":false,"family":"Day","given":"Mackenzie","email":"","middleInitial":"D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":854730,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Doran, Gary","contributorId":297954,"corporation":false,"usgs":false,"family":"Doran","given":"Gary","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":854731,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":854732,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mischna, Michael","contributorId":229492,"corporation":false,"usgs":false,"family":"Mischna","given":"Michael","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":854734,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rafkin, 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Jacob","contributorId":258308,"corporation":false,"usgs":false,"family":"Widmer","given":"Jacob","affiliations":[{"id":28165,"text":"No affiliation","active":true,"usgs":false}],"preferred":false,"id":854739,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Burr, Devon M.","contributorId":229491,"corporation":false,"usgs":false,"family":"Burr","given":"Devon M.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":854729,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Mandrake, Lukas","contributorId":297955,"corporation":false,"usgs":false,"family":"Mandrake","given":"Lukas","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":854733,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Vriend, Nathalie","contributorId":229495,"corporation":false,"usgs":false,"family":"Vriend","given":"Nathalie","email":"","affiliations":[{"id":27136,"text":"University of Cambridge","active":true,"usgs":false}],"preferred":false,"id":854737,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Williams, Kaj E. 0000-0003-1755-1872 kewilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-1755-1872","contributorId":196988,"corporation":false,"usgs":true,"family":"Williams","given":"Kaj","email":"kewilliams@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":854740,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70237138,"text":"pp1874 - 2022 - Lessons learned from wetlands research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021","interactions":[],"lastModifiedDate":"2026-03-31T21:17:44.416924","indexId":"pp1874","displayToPublicDate":"2022-10-17T08:45:31","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1874","displayTitle":"Lessons Learned from Wetlands Research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021","title":"Lessons learned from wetlands research at the Cottonwood Lake Study Area, Stutsman County, North Dakota, 1967–2021","docAbstract":"Depressional wetlands in the Prairie Pothole Region of North America have a long history of investigation owing to their importance in maintaining migratory-bird populations, especially waterfowl. One area of particularly intensive study is the Cottonwood Lake study area in Stutsman County, North Dakota. Studies at the Cottonwood Lake study area began in 1967 and continue through the present (2022). During this period of scientific discovery, meteorological conditions at the Cottonwood Lake study area varied greatly and included one of the most severe droughts of the 20th century and one of the wettest periods in the past 500 years.
Persistent wet conditions that began in 1993 have contributed to state changes in many of the study area’s larger wetlands to lake-like conditions, whereas the smaller wetlands returned to seasonally ponded conditions during relatively dry years interspersed within the longer-term wet period. Additionally, some nonwetland areas of the study area developed wetland plant, hydrology, and soil characteristics during the 1993-to-present (2022) wet period. The persistently high stages of water in the larger wetlands since 1993 contributed to a buildup of dissolved solids and increases in salinity with time following an initial decrease in salinity caused by the dilution of dissolved solids within a larger volume of water. During 2021, drought conditions similar to the 1988 to 1992 period may develop if conditions persist. However, meteorological changes during the past 30 years have persisted long enough to be considered a change in climate conditions at the study area and, if such wet conditions continue, would represent a change from conditions that occurred in the past two millennia.
During the period of study covered in this report (1967–2021), biotic communities responded in a variety of ways to subtle and marked changes in ponded-water depths, permanence, and salinity among the different wetland types in the study area. This report provides background information on the Cottonwood Lake study area and its context within the Prairie Pothole Region, documents techniques used to quantify environmental conditions and biotic communities, describes major trends that have been observed, presents significant findings as “lessons learned,” discusses recent modeling advances, and highlights key messages to managers. The Wetland Continuum concept was used as a framework to place research findings within an ecological context and to highlight the dynamic nature of prairie-pothole wetland ecosystems.
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U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Aftershocks do not uniformly surround a mainshock, and instead occur in spatial clusters. Spatially variable physical properties of the crust may influence the spatial distribution of aftershocks. I study four aftershock sequences in Southern California (1992 Landers, 1999 Hector Mine, 2010 El Mayor—Cucapah, and 2019 Ridgecrest) to investigate which physical properties are spatially correlated with aftershock occurrence. I find that aftershocks correlate with several properties, including measures of stress and stress change from the mainshock, fault structure, kinematics, seismic velocity, and heat flow. Aftershock spatial density exhibits an order of magnitude or more variation as a function of these properties. I determine simple empirical relations between each of the properties and the aftershock spatial density, and use these relations to construct new spatial models that describe aftershock locations. The new spatial models are a significant improvement over a simple base model, but do not fully capture the dense spatial clustering of aftershocks. Numerous spatially varying physical properties exhibit no (or poor) correlation with aftershock spatial density, including temperature, rock composition, and rheological properties that might be expected to control aftershock occurrence. These results suggest that while spatially variable physical properties appear to influence aftershock locations, more work is necessary in order to establish the connections between aftershock occurrence and the causative physical properties.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024727","usgsCitation":"Hardebeck, J.L., 2022, Physical properties of the crust influence aftershock locations: JGR Solid Earth, v. 10, no. 127, e2022JB024727, 23 p., https://doi.org/10.1029/2022JB024727.","productDescription":"e2022JB024727, 23 p.","ipdsId":"IP-134643","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":412026,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Baja California, California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.78554692822775,\n 36.37592047497546\n ],\n [\n -121.78554692822775,\n 32.070443661872034\n ],\n [\n -114.04108196964091,\n 32.070443661872034\n ],\n [\n -114.04108196964091,\n 36.37592047497546\n ],\n [\n -121.78554692822775,\n 36.37592047497546\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"127","noUsgsAuthors":false,"publicationDate":"2022-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":861758,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237635,"text":"70237635 - 2022 - Tectonic subsidence modeling of diachronous transition from backarc to retroarc basin development and uplift during Cordilleran orogenesis, Patagonian-Fuegian Andes","interactions":[],"lastModifiedDate":"2022-10-17T13:34:17.854179","indexId":"70237635","displayToPublicDate":"2022-10-17T08:24:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Tectonic subsidence modeling of diachronous transition from backarc to retroarc basin development and uplift during Cordilleran orogenesis, Patagonian-Fuegian Andes","docAbstract":"Backstripped tectonic basin subsidence histories are critical for interpreting phases of lithospheric deformation and paleoenvironmental change from the stratigraphic record. This study presents new subsidence modeling of the Rocas Verdes Backarc Basin (RVB) and Magallanes-Austral retroarc foreland basin (MAB) of southernmost South America to evaluate along-strike changes in tectonic subsidence related to the Late Jurassic through Miocene history of the Southern Andes. We compiled composite stratigraphic sections for seven basin localities that span 47°–54°S from published sedimentological records of paleoenvironment, paleobathymetry, and geochronology. Modeling results resolve regional trends in basin tectonic subsidence, uplift, and sedimentation rate that influenced the depositional environment during five broad phases of RVB-MAB development: (a) Late Jurassic tectonic subsidence and basin deepening associated with rift-related backarc extension that postdated regional diachronous rift-related magmatism. (b) Southward younging of Early to Late Cretaceous pronounced acceleration in tectonic subsidence interpreted as the initiation of flexural loading and development of the MAB foreland basin system. (c) Late Cretaceous (ca. 85–70 Ma) tectonic uplift within the central foredeep ∼49° to 52°S, coeval with a shift from slope to shelf deposition at these latitudes. (d) A protracted period of low-magnitude basin uplift and relative tectonic quiescence during the Paleogene, with the exception of southernmost localities; and (e) Synchronous latest Oligocene-early Miocene tectonic subsidence linked to basin deepening and transgression across the northern and central basin sectors. Backstripped tectonic subsidence analysis corroborates existing interpretations for orogenic development in the RVB-MAB and sheds new light on complex polyphase basin histories where extension precedes convergence.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021TC006891","usgsCitation":"VanderLeest, R.A., Fosdick, J.C., Malkowski, M., Romans, B.W., Ghiglione, M.C., Schwartz, T.M., and Sickmann, Z.T., 2022, Tectonic subsidence modeling of diachronous transition from backarc to retroarc basin development and uplift during Cordilleran orogenesis, Patagonian-Fuegian Andes: Tectonics, v. 41, no. 10, e2021TC006891, 29 p., https://doi.org/10.1029/2021TC006891.","productDescription":"e2021TC006891, 29 p.","ipdsId":"IP-129371","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":446107,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021tc006891","text":"External Repository"},{"id":408377,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Argentina, Chile","otherGeospatial":"Andes Mountains, Magallanes-Austral retroarc foreland basin, Patagonia, Rocas Verdes Backarc Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.76391601562499,\n -54.1881554810715\n ],\n [\n -68.719482421875,\n -53.82011176955965\n ],\n [\n -70.72998046875,\n -53.59250480903936\n ],\n [\n -72.158203125,\n -52.29504228453733\n ],\n [\n -72.037353515625,\n -50.90303283111256\n ],\n [\n -71.861572265625,\n -50.06419173665909\n ],\n [\n -71.905517578125,\n -49.48953847306649\n ],\n [\n -71.641845703125,\n -48.2100321223404\n ],\n [\n -71.56494140625,\n -47.10752278534248\n ],\n [\n -71.455078125,\n -46.62680639535518\n ],\n [\n -73.212890625,\n -46.82261666880492\n ],\n [\n -73.58642578125,\n -48.056053763981225\n ],\n [\n -73.970947265625,\n -49.2032427441791\n ],\n [\n -74.02587890625,\n -50.52041218671901\n ],\n [\n -73.8720703125,\n -51.48138289610098\n ],\n [\n -73.41064453125,\n -52.72963909783716\n ],\n [\n -73.212890625,\n -53.3767749750602\n ],\n [\n -71.575927734375,\n -54.15600109028492\n ],\n [\n -70.46630859375,\n -54.81967870427068\n ],\n [\n -66.148681640625,\n -54.98391819036322\n ],\n [\n -65.9619140625,\n -54.463652645044775\n ],\n [\n -66.76391601562499,\n -54.1881554810715\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-10-13","publicationStatus":"PW","contributors":{"authors":[{"text":"VanderLeest, Rebecca A.","contributorId":229447,"corporation":false,"usgs":false,"family":"VanderLeest","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":854741,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fosdick, Julie C.","contributorId":297956,"corporation":false,"usgs":false,"family":"Fosdick","given":"Julie","email":"","middleInitial":"C.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":854742,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Malkowski, Matthew A.","contributorId":221753,"corporation":false,"usgs":false,"family":"Malkowski","given":"Matthew A.","affiliations":[{"id":40415,"text":". Department of Geological Sciences, Stanford University, Stanford CA 94305","active":true,"usgs":false}],"preferred":false,"id":854743,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Romans, Brian W.","contributorId":297958,"corporation":false,"usgs":false,"family":"Romans","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":854744,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ghiglione, Matias C.","contributorId":297961,"corporation":false,"usgs":false,"family":"Ghiglione","given":"Matias","email":"","middleInitial":"C.","affiliations":[{"id":64468,"text":"CONICET-Universidad de Buenos Aires","active":true,"usgs":false}],"preferred":false,"id":854745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwartz, Theresa Maude 0000-0001-6606-4072","orcid":"https://orcid.org/0000-0001-6606-4072","contributorId":245180,"corporation":false,"usgs":true,"family":"Schwartz","given":"Theresa","email":"","middleInitial":"Maude","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":854746,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sickmann, Zachary T.","contributorId":292770,"corporation":false,"usgs":false,"family":"Sickmann","given":"Zachary","email":"","middleInitial":"T.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":854747,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}