Regulatory entities, such as the Minnesota Department of Health, monitor public water systems for conformance with Federal and State monitoring requirements and water-quality standards. Although some contaminants have Federal and (or) State regulations and guidance values, many contaminants, such as pesticides and pharmaceuticals, are unregulated in that only non-enforceable health-based guidance values have been assigned to them. Furthermore, because these contaminants are not regulated, commonly only limited resources are available to public water systems or regulatory entities to monitor them in drinking water. Focused screening efforts on contaminants that are frequently detected in the environment can provide information to help monitoring entities prioritize their sampling efforts.
Here we assess the use of enzyme-linked immunosorbent assay (ELISA) method, a rapid, inexpensive screening method, as an alternative to more expensive methods to analyze source and finished drinking-water samples collected from public water systems throughout Minnesota for three commonly detected pesticides (atrazine, imidacloprid, and pyrethroids) and three commonly detected pharmaceuticals (caffeine, carbamazepine, and sulfamethoxazole). The ELISA results were compared to results provided by more advanced mass-spectrometry analytical methods at the U.S. Geological Survey National Water Quality Laboratory (NWQL) and SGS AXYS Analytical Services Ltd. (AXYS).
Overall, these datasets are highly censored (>80 percent) and contain multiple reporting limits within and between laboratories. To discern agreement between paired contaminant group results (target contaminant plus immunologically similar contaminants) by ELISA and the advanced analytical methods at NWQL and AXYS, presence-absence agreement analysis was coupled with false negative and false positive analysis. Analysis of presence-absence agreement shows that ELISA has generally good agreement (77.9 to 100 percent) with both NWQL and AXYS for all unregulated contaminant groups. Imidicloprid, pyrethroids, and caffeine contaminant groups have relatively low false positivity rates (16, 6, and 5 percent, respectively) when analyzed by ELISA, which indicates the ELISA method, for these contaminant groups, could be experiencing low-level interference attributed to the detection of immunologically similar contaminants. Similarly, sulfamethoxazole has a low false positivity rate (0.8 percent), which indicates ELISA is likely not overestimating results for this contaminant group. Analyses for carbamazepine and sulfamethoxazole by ELISA resulted in low false negativity rates (1.6 and 0.8 percent, respectively), which indicates the ELISA method is likely not underestimating the results for this contaminant group. Conversely, the atrazine contaminant group has a high false negativity rate (84 percent), which indicates the method has a strong negative bias and that ELISA underestimates results for this contaminant. These qualitative results indicate that the ELISA method could potentially serve as a reliable and cost-effective screening method to help drinking water monitoring entities prioritize sampling efforts for analyzing carbamazepine and sulfamethoxazole in source and finished drinking-water samples collected from public water systems. At the same time, although ELISA did not prove to be a good screening method for atrazine, evaluation of ELISA results indicated that its use for screening imidacloprid, pyrethroids, and caffeine could be beneficial for water testing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225066","collaboration":"Prepared in cooperation with the Minnesota Department of Health","usgsCitation":"Krall, A.L., Elliott, S.M., de Lambert, J.R., and Robertson, S.W., 2022, Comparison of the results of enzyme-linked immunosorbent assay (ELISA) to mass-spectrometry based analytical methods for six unregulated contaminants in source water and finished drinking-water samples: U.S. Geological Survey Scientific Investigations Report 2022–5066, 29 p., https://doi.org/10.3133/sir20225066.","productDescription":"Report: viii, 29 p.; Data Release","numberOfPages":"42","onlineOnly":"Y","ipdsId":"IP-129231","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":405279,"rank":6,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
We examined how dietary factors recorded by C and N influence Hg uptake in 347 individuals of yellowfin tuna (Thunnus albacares), an important subsistence resource from the Galápagos Marine Reserve (GMR) and the Ecuadorian mainland coast (EMC) in 2015-2016. We found no differences in total Hg (THg) measured in red muscle between the two regions and no seasonal differences, likely due to the age of the fish and slow elimination rates of Hg. Our THg concentrations are comparable to other studies in the Pacific (0.06–2.88 mg/kg wet weight), but a subset of individuals exhibited the highest mercury concentrations yet reported in yellowfin tuna. Mercury isotope values differed between Δ199Hg and δ202Hg in both regions (Δ199Hg = 2.86±0.04‰ vs. Δ199Hg = 2.33 ± 0.07‰), likely related to shifting food webs and differing photochemical processing of Hg prior to entry into the food web. There were significantly lower values of both δ15N and δ13C in tuna from GMR (δ15N: 8.5–14.2‰, δ13C: -18.5–-16.1‰) compared to those from the EMC (δ15N: 8.3–14.4‰, δ13C: -19.4–-11.9‰), of which δ13C values suggest spatially-constrained movements of tunas. Results from the pooled analysis, without considering region, indicated that variations in δ13C and δ15N values tracked changes of Hg stable isotopes. Our data indicate that the individual tuna from this study were resident fish of each region and heavily influenced by upwellings related to the Eastern Pacific Oxygen Minimum Zone and the Humboldt Current System. C, N, and Hg isotopes reflect foraging behavior mainly on epipelagic prey in shallow waters and that food web shifts drive Hg variations between these populations of tuna.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5458","usgsCitation":"Munoz-Abril, L., Valle, C.A., Alava, J.J., Janssen, S., Sunderland, E.M., Rubianes-Landazuri, F., and Emslie, S.D., 2022, Elevated mercury concentrations and isotope signatures (N, C, Hg) in yellowfin tuna (Thunnus albacares) from the Galápagos Marine Reserve and waters off Ecuador: Environmental Toxicology and Chemistry, v. 41, no. 11, p. 2732-2744, https://doi.org/10.1002/etc.5458.","productDescription":"13 p.","startPage":"2732","endPage":"2744","ipdsId":"IP-142672","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":407411,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Galápagos Marine Reserve, Pacific Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92,\n -2.4821334037305633\n ],\n [\n -89,\n -2.4821334037305633\n ],\n [\n -89,\n 2.1308562777325313\n ],\n [\n -92,\n 2.1308562777325313\n ],\n [\n -92,\n -2.4821334037305633\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.3642578125,\n -4\n ],\n [\n -80,\n -4\n ],\n [\n -80,\n 1\n ],\n [\n -83.3642578125,\n 1\n ],\n [\n -83.3642578125,\n -4\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-08-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Munoz-Abril, Laia","contributorId":296972,"corporation":false,"usgs":false,"family":"Munoz-Abril","given":"Laia","email":"","affiliations":[{"id":64260,"text":"Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Diego de Robles y Vía Interoceánica, Quito, Ecuador.","active":true,"usgs":false}],"preferred":false,"id":853018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valle, Carlos A","contributorId":296973,"corporation":false,"usgs":false,"family":"Valle","given":"Carlos","email":"","middleInitial":"A","affiliations":[{"id":64260,"text":"Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Diego de Robles y Vía Interoceánica, Quito, Ecuador.","active":true,"usgs":false}],"preferred":false,"id":853019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alava, Juan Jose","contributorId":296974,"corporation":false,"usgs":false,"family":"Alava","given":"Juan","email":"","middleInitial":"Jose","affiliations":[{"id":64261,"text":"Institute for the Oceans and Fisheries, University of British Columbia, 2202 Main Mall, Vancouver, BC V6T 1Z4, Canada","active":true,"usgs":false}],"preferred":false,"id":853020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sunderland, Elsie M.","contributorId":151016,"corporation":false,"usgs":false,"family":"Sunderland","given":"Elsie","email":"","middleInitial":"M.","affiliations":[{"id":18166,"text":"Harvard University, Cambridge, M","active":true,"usgs":false}],"preferred":false,"id":853022,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rubianes-Landazuri, Francisco","contributorId":296975,"corporation":false,"usgs":false,"family":"Rubianes-Landazuri","given":"Francisco","email":"","affiliations":[{"id":64260,"text":"Colegio de Ciencias Biológicas y Ambientales, Universidad San Francisco de Quito, Diego de Robles y Vía Interoceánica, Quito, Ecuador.","active":true,"usgs":false}],"preferred":false,"id":853023,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Emslie, Steven D","contributorId":296976,"corporation":false,"usgs":false,"family":"Emslie","given":"Steven","email":"","middleInitial":"D","affiliations":[{"id":64262,"text":"Department of Biology and Marine Biology, University of North Carolina, 601 S. College Rd., Wilmington, NC 28403, United States.","active":true,"usgs":false}],"preferred":false,"id":853024,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255119,"text":"70255119 - 2022 - Field testing a high-frequency acoustic attenuation system for measuring fine suspended sediments and algal movements","interactions":[],"lastModifiedDate":"2024-06-12T15:02:48.224979","indexId":"70255119","displayToPublicDate":"2022-08-17T10:00:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17816,"text":"Applied Acoustics","active":true,"publicationSubtype":{"id":10}},"title":"Field testing a high-frequency acoustic attenuation system for measuring fine suspended sediments and algal movements","docAbstract":"Acoustic measurements of suspended sediment have the potential to allow remote, autonomous monitoring of sediment movements at much higher temporal resolution than traditional manual sampling methods. Although suspended sands present a challenging measurement problem due to their logarithmic distribution with depth, fine clay sediments are distributed evenly throughout a stream cross section, making them amenable to point measurements. In order to improve measurement capabilities for fine sediments in stream channels, The National Center for Physical Acoustics at The University of Mississippi has developed a remote, autonomous acoustic system to monitor fine sediments transported in streams. The system was tested on the Middle Rio Grande near San Acacia, New Mexico, and in Goodwin Creek in Panola County, Mississippi. The acoustic instruments were compared to sediment concentrations from physical samples in both deployments. Diurnal patterns were found in the acoustic signals from the Middle Rio Grande, and a follow up experiment at The University of Mississippi Biological Field Station was used to investigate the potential effects of algal biomass on acoustic attenuation measurements. The results showed diurnal patterns in attenuation were associated with patterns in light, temperature, and dissolved oxygen. These results combined with information from the literature indicate diel movement of algal colonies in the water column of some water bodies may interfere with high-frequency acoustic measurements in natural environments and that acoustic methods have the potential to allow ecological researchers to evaluate mass algal movements in the field. Results from Goodwin Creek demonstrate that the acoustic system is able to provide measurements of sediment concentration with high temporal resolution that track well with expected sediment transport patterns in response to discharge hydrographs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apacoust.2022.108980","usgsCitation":"Carpenter, W.O., Goodwiller, B.T., Wren, D.G., Taylor, J.J., AuBuchon, J., and Brown, J., 2022, Field testing a high-frequency acoustic attenuation system for measuring fine suspended sediments and algal movements: Applied Acoustics, v. 198, 108980, https://doi.org/10.1016/j.apacoust.2022.108980.","productDescription":"108980","ipdsId":"IP-141566","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446755,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apacoust.2022.108980","text":"Publisher Index Page"},{"id":430013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"198","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carpenter, Wayne O.","contributorId":338687,"corporation":false,"usgs":false,"family":"Carpenter","given":"Wayne","email":"","middleInitial":"O.","affiliations":[{"id":36508,"text":"University of Mississippi","active":true,"usgs":false}],"preferred":false,"id":903457,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goodwiller, Bradley T.","contributorId":338690,"corporation":false,"usgs":false,"family":"Goodwiller","given":"Bradley","email":"","middleInitial":"T.","affiliations":[{"id":36508,"text":"University of Mississippi","active":true,"usgs":false}],"preferred":false,"id":903458,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wren, Daniel G.","contributorId":338693,"corporation":false,"usgs":false,"family":"Wren","given":"Daniel","email":"","middleInitial":"G.","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":903459,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Jason J.","contributorId":202410,"corporation":false,"usgs":false,"family":"Taylor","given":"Jason","email":"","middleInitial":"J.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":903460,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"AuBuchon, Jonathan","contributorId":256772,"corporation":false,"usgs":false,"family":"AuBuchon","given":"Jonathan","email":"","affiliations":[{"id":51859,"text":"Albuquerque District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":903461,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Jeb E. 0000-0001-7671-2379","orcid":"https://orcid.org/0000-0001-7671-2379","contributorId":225088,"corporation":false,"usgs":true,"family":"Brown","given":"Jeb E.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":903462,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236648,"text":"70236648 - 2022 - Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds","interactions":[],"lastModifiedDate":"2023-01-19T19:24:00.787879","indexId":"70236648","displayToPublicDate":"2022-08-17T09:52:04","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds","docAbstract":"Roadway deicing agents, including rock salt and brine containing NaCl, have had a profound impact on the water quality and aquatic health of rivers and streams in urbanized areas with temperate climates. Yet, few studies evaluate impacts to watersheds characterized by relatively low impervious surface cover (ISC; < 15 %). Here, we use long-term (1997-2019), monthly streamwater quality data combined with daily streamflow for six exurban and suburban watersheds in southeastern Pennsylvania to examine the relations among chloride (Cl−) concentrations and ISC. Both flow-normalized Cl− concentrations and ISC increased over time in each of the six watersheds, consistent with changes in watershed management (e.g., ISC, road salt application, etc.). The watersheds that experienced the greatest changes in percent ISC (e.g., agriculture replaced by residential and commercial development) experienced the greatest changes in flow-normalized Cl− concentrations. We also utilized a comprehensive mass-balance model (2011–2018) that indicated Cl− inputs exceeded the outputs for the study watersheds. Road salt applied to state roads, non-state roads, and other impervious surfaces accounted for the majority of Cl− inputs to the six watersheds. Furthermore, increasing Cl− concentrations during baseflow conditions confirm impacts to shallow groundwater. Although flow-normalized Cl− concentrations are below the U.S. Environmental Protection Agency's chronic threshold value for impacts to aquatic organisms, year-round exceedances may result before the end of this century based on current trends. Though reduced Cl− loading to streams may be achieved by limiting the expansion of impervious surfaces in exurban and suburban watersheds, changes in baseflow concentrations are likely to be gradual because of the accumulated Cl− in groundwater.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157933","usgsCitation":"Rossi, M., Kremer, P., Cravotta, C., Scheirer, K.E., and Goldsmith, S.T., 2022, Long-term impacts of impervious surface cover change and roadway deicing agent application on chloride concentrations in exurban and suburban watersheds: Science of the Total Environment, v. 851, no. Part 2, 157933, 13 p., https://doi.org/10.1016/j.scitotenv.2022.157933.","productDescription":"157933, 13 p.","ipdsId":"IP-139821","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":446757,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.157933","text":"Publisher Index Page"},{"id":406679,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Berks County, Bucks County, Chester County, Delaware County, Lehigh County, Montgomery County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.6,\n 39.812755695478124\n ],\n [\n -74.9542236328125,\n 39.812755695478124\n ],\n [\n -74.9542236328125,\n 40.2\n ],\n [\n -75.6,\n 40.2\n ],\n [\n -75.6,\n 39.812755695478124\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"851","issue":"Part 2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rossi, Marissa L. 0000-0003-2341-0312","orcid":"https://orcid.org/0000-0003-2341-0312","contributorId":296518,"corporation":false,"usgs":false,"family":"Rossi","given":"Marissa L.","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":851695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kremer, Peleg","contributorId":296521,"corporation":false,"usgs":false,"family":"Kremer","given":"Peleg","email":"","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":851696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scheirer, Krista E.","contributorId":296524,"corporation":false,"usgs":false,"family":"Scheirer","given":"Krista","email":"","middleInitial":"E.","affiliations":[{"id":64093,"text":"Aqua Pennsylvania","active":true,"usgs":false}],"preferred":false,"id":851698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldsmith, Steven T.","contributorId":193458,"corporation":false,"usgs":false,"family":"Goldsmith","given":"Steven","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":851699,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237670,"text":"70237670 - 2022 - Temporal coherence patterns of prairie pothole wetlands indicate the importance of landscape linkages and wetland heterogeneity in maintaining biodiversity","interactions":[],"lastModifiedDate":"2022-10-18T15:42:41.119981","indexId":"70237670","displayToPublicDate":"2022-08-16T10:29:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Temporal coherence patterns of prairie pothole wetlands indicate the importance of landscape linkages and wetland heterogeneity in maintaining biodiversity","docAbstract":"Wetland ecosystems are diverse, productive habitats that are essential reservoirs of biodiversity. Not only are they home to numerous wetland-specialist species, but they also provide food, water, and shelter that support terrestrial wildlife populations. However, like observed patterns of biodiversity loss, wetland habitats have experienced widespread loss and degradation. In order to conserve and restore wetlands, and thereby the biodiversity they support, it is important to understand how biodiversity in wetland habitats is maintained. Habitat heterogeneity and connectivity are thought to be predominate drivers of wetland biodiversity. We quantified temporal coherence (i.e., spatial synchrony) of wetland invertebrate communities using intra-class correlations among 16 wetlands sampled continuously over 24 years to better understand the relative influences wetland heterogeneity (i.e., internal processes specific to individual wetlands and spatial connectivity and external processes occurring on the landscape) on wetland biodiversity. We found that while wetlands with different ponded-water regimes (temporarily ponded or permanently ponded) often hosted different invertebrate communities, temporal shifts in invertebrate composition were synchronous. We also found the relative importance of internal versus external forces in determining community assembly vary depending on a wetland’s hydrologic function and climate influences. Our results confirm that heterogeneity and spatial connectivity of wetland landscapes are important drivers of wetland biodiversity.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.897872","usgsCitation":"McLean, K., Mushet, D., and Sweetman, J.N., 2022, Temporal coherence patterns of prairie pothole wetlands indicate the importance of landscape linkages and wetland heterogeneity in maintaining biodiversity: Frontiers in Ecology and Evolution, v. 10, 897872, 16 p., https://doi.org/10.3389/fevo.2022.897872.","productDescription":"897872, 16 p.","ipdsId":"IP-123627","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.897872","text":"Publisher Index Page"},{"id":408491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Stutsman County","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.1056,\n 47.0944\n ],\n [\n -99.088889,\n 47.0944\n ],\n [\n -99.088889,\n 47.1027\n ],\n [\n -99.1056,\n 47.1027\n ],\n [\n -99.1056,\n 47.0944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-16","publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248468,"corporation":false,"usgs":true,"family":"Mushet","given":"David M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":854924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetman, Jon N. 0000-0002-9849-7355","orcid":"https://orcid.org/0000-0002-9849-7355","contributorId":221489,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","email":"","middleInitial":"N.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":854925,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235698,"text":"sim3489 - 2022 - Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:16:53.878209","indexId":"sim3489","displayToPublicDate":"2022-08-15T12:33:16","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3489","displayTitle":"Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars","title":"Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars","docAbstract":"The landscape in Mars Transverse Mercator (MTM) −10022 and −15022 quadrangles (lat −7.5° N. to −17.5° N. between long 335° E. and 340° E.) in Margaritifer Terra preserves a record of sedimentary and alluvial deposits, volcanic and tectonic structures, and erosional landforms that record a long and complex geologic and geomorphic history. MTM −10022 and −15022 quadrangles primarily encompass Morava Valles, the terminus of the Samara-Himera and Paraná-Loire valley networks, the broad catchment informally named Margaritifer basin, and Margaritifer Chaos. Morava Valles is the lowermost reach of the northward draining mesoscale outflow system that consists of Uzboi Vallis, Ladon Valles, and Morava Valles, was sourced from flow out of Argyre basin, and incises across and between the ancient Ladon and Holden impact basins. The broad-scale topography and surface relief within the map, including the topographic low occupied by Margaritifer basin, were largely shaped during the Noachian by the formation of the Holden, Ladon, and Ares impact basins and the Chryse trough. Multiple processes modified the ancient surface until the Late Noachian and resulted in the formation of the terra unit that forms the widely exposed surface. Later resurfacing associated with likely sedimentary and volcanic processes modified predominantly lower elevation surfaces and basins during the Late Noachian into at least the Hesperian. Sedimentary processes during the Late Noachian were dominated by fluvial incision of the Samara-Himera and Paraná-Loire valley networks and discharge related to the dissection of Morava Valles that drained Ladon basin. The history of geomorphic activity within Margaritifer basin was more complex and was likely dominated by the evolution of Morava Valles relative to the formation of the valley networks. The floor of Margaritifer basin preserves likely lacustrine plains related to sedimentation in water ponded during early discharge from Morava Valles, which were later embayed by volcanic plains. Crater densities and cross-cutting relations indicate Margaritifer basin evolved over a relatively short period of geologic time. The timing of the last drainage out of Morava Valles is not well constrained but could have occurred during the Hesperian. Structural collapse and the formation of the Margaritifer Chaos and other chaotic terrain formed by the release of subsurface water that may have been related to volcanic activity along the southern margin of Margaritifer basin. Final geomorphic events within the map region include the formation of Late Hesperian to perhaps Amazonian alluvial fans within some craters and isolated mass wasting on steep slopes. A final, variable veneer associated with locally occurring impacts and redistribution of fine-grained material by eolian processes resulted in the landscape observed today.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3489","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Wilson, S.A., Grant, J.A., and Williams, K.K., 2022, Geologic map of MTM −10022 and −15022 quadrangles, Morava Valles and Margaritifer basin, Mars: U.S. Geological Survey Scientific Investigations Map 3489, pamphlet 11 p., 1 sheet, scale 1:500,000, https://doi.org/10.3133/sim3489.","productDescription":"Report: iv, 11 p.; 1 Sheet: 45.73 x 54.01 inches; 2 Databases; Metadata; Read Me","numberOfPages":"11","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-118399","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":435729,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJZDWR","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3489 Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405424,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9JJZDWR","text":"Interactive map","linkHelpText":"- Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405141,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_supdata.zip","text":"Supplemental Data","size":"500 MB","linkFileType":{"id":6,"text":"zip"}},{"id":405140,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_sheet.pdf","text":"Map sheet","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Geologic Map of MTM −10022 and −15022 Quadrangles, Morava Valles and Margaritifer Basin, Mars"},{"id":405138,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_metadata.xml","size":"7 KB","linkFileType":{"id":8,"text":"xml"}},{"id":405137,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_pamphlet.pdf","text":"Pamphlet","size":"700 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":405136,"rank":2,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_gis.zip","text":"GIS Files","size":"55 MB","linkFileType":{"id":6,"text":"zip"}},{"id":405135,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3489/covrthb.jpg"},{"id":405139,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3489/sim3489_readme.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}}],"otherGeospatial":"Margaritifer basin, Mars, Morava Valles basin","contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
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All pathogenic organisms are exposed to abiotic influences such as the microclimates and chemical constituents of their environments. Even those pathogens that exist primarily within their hosts or vectors can be influenced directly or indirectly. Yersinia pestis, the flea-borne bacterium causing plague, is influenced by climate and its survival in soil suggests a potentially strong influence of soil chemistry. We summarize a series of controlled studies conducted over four decades in Russia by Dr. Evgeny Rotshild and his colleagues that investigated correlations between trace metals in soils, plants, and insects, and the detection of plague in free-ranging small mammals. Trace metal concentrations in plots where plague was detected were up to 20-fold higher or lower compared to associated control plots, and these differences were >2-fold in 22 of 38 comparisons. The results were statistically supported in eight studies involving seven host species in three families and two orders of small mammals. Plague tended to be positively associated with manganese and cobalt, and the plague association was negative for copper, zinc, and molybdenum. In additional studies, these investigators detected similar connections between pasturellosis and concentrations of some chemical elements. A One Health narrative should recognize that the chemistry of soil and water may facilitate or impede epidemics in humans and epizootics in non-human animals.
","language":"English","publisher":"MDPI","doi":"10.3390/ijerph19169979","usgsCitation":"Kosoy, M., and Biggins, D.E., 2022, Plague and trace metals in natural systems: International Journal of Environmental Research and Public Health., v. 19, no. 16, 9979, 16 p., https://doi.org/10.3390/ijerph19169979.","productDescription":"9979, 16 p.","ipdsId":"IP-139292","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446795,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/ijerph19169979","text":"Publisher Index Page"},{"id":420659,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Kazakhstan, Mongolia, Russia, Uzbekistan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 89.61243249363457,\n 48.466692874478724\n ],\n [\n 97.55135950387046,\n 47.62550521415008\n ],\n [\n 100.05863199276087,\n 45.61730314876178\n ],\n [\n 107.59703605851308,\n 45.61644272490017\n ],\n [\n 117.91939263544919,\n 50.016697469514526\n ],\n [\n 114.52594957787551,\n 53.226451135161994\n ],\n [\n 104.46048981621641,\n 53.650228892772446\n ],\n [\n 93.26601227091152,\n 53.58743226668929\n ],\n [\n 86.19871833540293,\n 50.60502492758772\n ],\n [\n 89.61243249363457,\n 48.466692874478724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 131.52445743282328,\n 42.959534327424876\n ],\n [\n 133.9963905892347,\n 42.029488763129535\n ],\n [\n 140.3914092267985,\n 47.95819400683317\n ],\n [\n 134.99148683585338,\n 48.88738814243581\n ],\n [\n 133.08363821719092,\n 44.63086350828141\n ],\n [\n 131.70164687710013,\n 44.96600756264411\n ],\n [\n 131.52445743282328,\n 42.959534327424876\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 53.655114406350464,\n 45.481667787108734\n ],\n [\n 57.50335847359082,\n 46.82513492063478\n ],\n [\n 58.23209315029882,\n 50.60820394065402\n ],\n [\n 54.78716384083083,\n 50.839955137287575\n ],\n [\n 51.03936589302583,\n 51.86712489459745\n ],\n [\n 46.9304799930151,\n 50.38992855031401\n ],\n [\n 46.094105965124754,\n 48.256122327868354\n ],\n [\n 48.57953872080762,\n 45.92189885652772\n ],\n [\n 51.235842842518906,\n 46.87566419536793\n ],\n [\n 52.97267495391941,\n 46.82532952286226\n ],\n [\n 53.655114406350464,\n 45.481667787108734\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 61.93106513607361,\n 43.52165246683586\n ],\n [\n 60.98567078825775,\n 41.00558370742854\n ],\n [\n 62.64289594949247,\n 39.713860066117434\n ],\n [\n 64.25179698173943,\n 39.272687744425355\n ],\n [\n 66.9157819290491,\n 40.774219802330464\n ],\n [\n 65.46901711769192,\n 43.7560452565958\n ],\n [\n 61.93106513607361,\n 43.52165246683586\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"16","noUsgsAuthors":false,"publicationDate":"2022-08-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Kosoy, Michael","contributorId":329584,"corporation":false,"usgs":false,"family":"Kosoy","given":"Michael","email":"","affiliations":[{"id":78666,"text":"KB One Health, LLC","active":true,"usgs":false}],"preferred":false,"id":882651,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":882652,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70236930,"text":"70236930 - 2022 - In hot water? Patterns of macroinvertebrate abundance in Arctic thaw ponds and relationships with environmental variables","interactions":[],"lastModifiedDate":"2022-09-22T11:52:56.913627","indexId":"70236930","displayToPublicDate":"2022-08-12T06:50:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"In hot water? Patterns of macroinvertebrate abundance in Arctic thaw ponds and relationships with environmental variables","docAbstract":"The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering South Oyster Bay, a shallow embayment on the southern shore of Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bay and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Analysis revealed that the most substantial source of fecal contamination to South Oyster Bay was stormwater, particularly during the summer months. The highest frequency of fecal coliform detections in source sites were under wet summer conditions, and the highest fecal coliform concentrations were under wet summer conditions at the Cedar Creek near Bay Place and Unqua Lake Culvert sites (more than 16,000 most probable number per 100 milliliters each). The human-associated Bacteroides marker was the most frequently detected microbial source tracking marker in South Oyster Bay (50 percent positive detections). The human marker was detected at least twice in all surface water source and receptor sites, except for the Massapequa Lake East Culvert source site that did not have any positive human marker detections. Canine contamination was prolific at source sites but was associated with low fecal coliform concentrations in the winter months. All detections of the canine-associated Bacteroides marker were in samples collected during the winter season and were associated with fecal coliform concentrations below the reporting limit, indicating that birds are not a persistent source of fecal coliform to South Oyster Bay. The absence of fecal coliform and human markers in groundwater samples collected throughout the larger study area indicates that water from cesspools or septic tanks do not contribute fecal coliform to the bay. Further, microbial source tracking markers were not detected in the sandy sediment collected at Zachs Bay. Based a classification scheme developed to convey the degree of fecal contamination to stakeholders and resource managers, the Cedar Creek near Bay Place and Unqua Lake Culvert sites were identified as locations that contribute substantial fecal contamination to South Oyster Bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225082","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2022, Using microbial source tracking to identify fecal contamination sources in South Oyster Bay on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022–5082, 15 p., https://doi.org/10.3133/sir20225082.","productDescription":"Report: vi, 15 p.; Dataset","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130129","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":404967,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225082/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5082"},{"id":405037,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York"},{"id":404966,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5082/images/"},{"id":404965,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5082/sir20225082.XML"},{"id":404964,"rank":3,"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":404963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5082/sir20225082.pdf","text":"Report","size":"2.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5082"},{"id":404962,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5082/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island, South Oyster Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.50883483886719,\n 40.58606020705239\n ],\n [\n -73.37596893310547,\n 40.58606020705239\n ],\n [\n -73.37596893310547,\n 40.70016219564594\n ],\n [\n -73.50883483886719,\n 40.70016219564594\n ],\n [\n -73.50883483886719,\n 40.58606020705239\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
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Troy, NY 12180–8349
We released nearly 1.0 million 1-day post-hatch (dph) and 5-dph pallid sturgeon (Scaphirhynchus albus) free embryos in the Missouri River on 1 July 2019 and sequentially captured survivors at multiple sites through a 240-km river reach to quantify daily growth and survival rates during the early life stages. Genetic analysis was used to assign captured fish to released family lots and known ages. Growth rate was similar (0.74–0.75 mm day−1) between the 1- and 5-dph age groups during the 3–4-day dispersal period when water temperature averaged 16.8 °C. Daily survival rate was 0.64 during 1–4 dph for the original 1-dph age group and 0.80 during 5–7 dph for the original 5-dph age group. Total survival during free embryo dispersal (hatch to 9 dph) was estimated as 0.0437. The transition from dispersing as free embryos to settling as benthic larvae was verified for fish originally released as 5 dph. Growth of settled larvae was quantified with a Gompertz model through 75 dph (9 September; 112 mm) when water temperature was 18.8–21.0 °C in the rearing areas. Settled larvae had an estimated daily survival rate of 0.96, and estimated total survival during 9–75 dph was 0.0714. This study provides the first empirical survival estimates for pallid sturgeon early life stages in natural settings and is one of few studies reporting similar information for other sturgeon species. Applications of this work extend to pallid sturgeon restoration programs where population models are being developed to predict recruitment potential and population responses to river management alternatives.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-022-01294-w","usgsCitation":"Braaten, P., Holm, R., Powell, J.A., Heist, E., Buhman, A.C., Holley, C.T., Delonay, A.J., Haddix, T., Wilson, R., and Jacobson, R., 2022, Growth and survival rates of dispersing free embryos and settled larvae of pallid sturgeon (Scaphirhynchus albus) in the Missouri River, Montana and North Dakota: Environmental Biology of Fishes, v. 105, no. 8, p. 993-1014, https://doi.org/10.1007/s10641-022-01294-w.","productDescription":"12 p.; Data Release","startPage":"993","endPage":"1014","ipdsId":"IP-135304","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":446810,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10641-022-01294-w","text":"Publisher Index Page"},{"id":435731,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N2MFV8","text":"USGS data release","linkHelpText":"Pallid sturgeon free embryo drift and dispersal experiment data from the Upper Missouri River, Montana and North Dakota, 2019"},{"id":426965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.89968504532749,\n 48.5\n ],\n [\n -106.89968504532749,\n 47.35255729260322\n ],\n [\n -103.54636278007621,\n 47.35255729260322\n ],\n [\n -103.54636278007621,\n 48.5\n ],\n [\n -106.89968504532749,\n 48.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"105","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"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":897178,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holm, R.J.","contributorId":334977,"corporation":false,"usgs":false,"family":"Holm","given":"R.J.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":897180,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powell, J. A.","contributorId":69916,"corporation":false,"usgs":false,"family":"Powell","given":"J.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":897181,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heist, E.J.","contributorId":334978,"corporation":false,"usgs":false,"family":"Heist","given":"E.J.","affiliations":[{"id":13212,"text":"Southern Illinois University","active":true,"usgs":false}],"preferred":false,"id":897182,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buhman, Amy C.","contributorId":334979,"corporation":false,"usgs":false,"family":"Buhman","given":"Amy","email":"","middleInitial":"C.","affiliations":[{"id":13212,"text":"Southern Illinois University","active":true,"usgs":false}],"preferred":false,"id":897183,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":897179,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"DeLonay, Aaron J. 0000-0002-3752-2799 adelonay@usgs.gov","orcid":"https://orcid.org/0000-0002-3752-2799","contributorId":2725,"corporation":false,"usgs":true,"family":"DeLonay","given":"Aaron","email":"adelonay@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897184,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haddix, T.M.","contributorId":334982,"corporation":false,"usgs":false,"family":"Haddix","given":"T.M.","affiliations":[{"id":39047,"text":"Montana Fish, Wildlife, and Parks","active":true,"usgs":false}],"preferred":false,"id":897186,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, R.H.","contributorId":334984,"corporation":false,"usgs":false,"family":"Wilson","given":"R.H.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":897187,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jacobson, R. B. 0000-0002-8368-2064","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":92614,"corporation":false,"usgs":true,"family":"Jacobson","given":"R. B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":897185,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70235722,"text":"70235722 - 2022 - Tracking geomorphic changes after suburban development with a high density of green stormwater infrastructure practices in Montgomery County, Maryland","interactions":[],"lastModifiedDate":"2022-08-16T11:50:42.860448","indexId":"70235722","displayToPublicDate":"2022-08-11T06:46:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Tracking geomorphic changes after suburban development with a high density of green stormwater infrastructure practices in Montgomery County, Maryland","docAbstract":"Stream morphology is affected by changes on the surrounding landscape. Understanding the effects of urbanization on stream morphology is a critical factor for land managers to maintain and improve vulnerable stream corridors in urbanizing landscapes. Stormwater practices are used in urban landscapes to manage runoff volumes and peak flows, potentially mitigating alterations to the flow regime that drive changes in channel morphology. However, there remains a paucity of long-term studies assessing watershed-scale relationships between urbanization and effects on stream morphology where green stormwater infrastructure exists in high densities. This paper evaluates the geomorphic changes across four headwater catchments in the Chesapeake Bay Watershed over the course of >10 yr and relates these changes to urban development. Annual cross-sectional surveys conducted from 2002 to 2019 in one forested catchment, one agricultural catchment, and two treatment catchments were used to understand the relationship between urbanization and changes in stream morphology. Six cross-sectional geomorphic metrics were calculated and compared with development timelines and high flow events. A channel evolution model was then used to understand the status of morphologic stability at sites within the study. Results suggest downstream environments in developing areas are more impacted during early phases of suburban construction. Channel change during construction could be a result of sediment and erosion control efforts' limitations on preventing and controlling overland sediment mobilization or of increased discharge causing widening and thus bank-derived sediment to move to the streambed. Results demonstrate that geomorphic metrics are highly variable within a small area and are not always accurate representations of broader landscape changes but rather of the more localized environment at a specific stream segment. Despite a high density of stormwater management facilities in urban catchments, substantial alterations to cross sections were found at multiple locations in each catchment including the controls.
This study utilizes instruments from the Curiosity rover payload to develop an integrated paleoenvironmental and compositional reconstruction for the 65-m thick interval of stratigraphy comprising the Hartmann's Valley and Karasburg members of the Murray formation, Gale crater, Mars. The stratigraphy consists of cross-stratified sandstone (Facies 1), planar-laminated sandstone (Facies 2), and planar-laminated mudstone (Facies 3). Facies 1 is composed of sandstone showing truncated sets of concave-curvilinear laminae stacked into cosets. Sets are estimated to be meter-to sub-meter-scale, consistent with low-height dunes. Thin stratigraphic intervals of Facies 1 and stacking patterns with Facies 2 and 3 support a wet aeolian dune interpretation. Meter-thick packages of planar-laminated sandstone (Facies 2) are interpreted to represent interfingering dune-interdune strata. Facies 3 consists of meter-thick packages of planar-laminated mudstone interpreted to represent lacustrine deposition with persistent standing water. Integration of geochemistry with each facies reveals some compositional control based on the depositional process. Models for source rock composition from Alpha Particle X-Ray Spectrometer measurements show that facies derived from a basaltic source. Alteration indices and geochemical trends provide evidence that moderate chemical weathering occurred before compositional changes due to diagenesis. Differences in wt% FeO(T) and TiO2 between facies are minimal, though trends point to sediment sorting in transport. Comparisons to terrestrial basaltic sedimentary systems indicate that the Hartmann's Valley and Karasburg facies reflect deposition in an environment where diverse subaqueous and subaerial facies persisted adjacent to a long-lived body of water.
The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.
The Ulleung Basin Gas Hydrate field expeditions in 2007 (UBGH1) and 2010 (UBGH2) sought to assess the Basin's gas hydrate resource potential. Coring operations in both expeditions recovered evidence of gas hydrate, primarily as fracture-filling (or vein type) morphologies in mainly silt-sized, fine-grained sediment, but also as pore-occupying hydrate in the coarser-grained layers of interbedded sand and fine-grained systems. A commonality across many of these occurrences is the presence of diatoms in the fine-grained sediment. Here we tested fine-grained sediment (median grain size <12.5 μm) associated with hydrate occurrences at four UBGH2 sites (UBGH2-2-2, UBGH2-3, UBGH2-6 and UBGH2-11) to investigate potential impacts of diatoms on efforts to extract methane from hydrate, or to tap hydrocarbon reservoirs beneath hydrate-bearing sediment. Two key considerations are: the extent to which diatoms control sediment mechanical properties, and the extent to which pore-water freshening, which occurs as gas hydrate breaks down during resource extraction, alters the diatom control on sediment mechanical properties. We conducted experiments to measure sediment index properties, sedimentation behavior and compressibility to address these considerations. We relied on scanning electron microscope (SEM) imagery and X-ray powder diffraction (XRD) to characterize the sediment mineralogy. Our high-level findings are that at the ∼20–45% (by volume) diatom concentrations observed at these UBGH2 sites, sediment compressibility increases with diatom content, but diatoms only appear to increase porosity and permeability at the highest diatom concentration (∼45%). Our measurements suggest in situ compression indices of 0.35–0.55 and permeabilities on the order of 0.01milliDarcies (1 × 10−17 m2) can be anticipated at these sites. Importantly, these properties are not expected to vary significantly upon pore water freshening that accompanies gas hydrate dissociation during production.
Recently, the subsoils of ephemeral stream (arroyos) floodplains in the northern Chihuahuan Desert were discovered to contain large naturally occurring NO3− reservoirs (floodplain: ~38,000 kg NO3-N/ha; background: ~60 kg NO3-N/ha). These reservoirs may be mobilized through land use change or natural stream channel migration which makes differentiating between anthropogenic and natural groundwater NO3− sources challenging. In this study, the fate and sources of NO3− were investigated in an area with multiple NO3− sources such as accidental sewer line releases and sewage lagoons as well as natural reservoirs of subsoil NO3−. To differentiate sources, this study used a large suite of geochemical tools including δ15N[NO3], δ18O[NO3], δ15N[N2], δ13C[DIC], 14C, tritium (3H), dissolved gas concentrations, major ion chemistry, and contaminants of emerging concern (CEC) including artificial sweeteners. NO3− at sites with the highest concentrations (25 to 229 mg/L NO3-N) were determined to be largely sourced from naturally occurring subsoil NO3− based on δ15N[NO3] (<8 ‰) and mass ratios of Cl−/Br− (〈100) and NO3−/Cl− (>1.5). Anthropogenic NO3− was deciphered using mass ratios of Cl−/Br− (>120) and NO3−/Cl− (<1), δ15N[NO3] (>8 ‰), and CEC detections. Nitrogen isotope analyses indicated that denitrification is fairly limited in the field area. CEC were detected at 67 % of sites including 3H dead sites (<1 pCi/L) with low percent modern carbon-14 (PMC; <30 %). Local supply wells are 3H dead with low PMC; as 3H does not re-equilibrate and 14C is very slow to re-equilibrate during recirculation through infrastructure, sites with low PMC, 3H < 1 pCi/L, and CEC detections were interpreted as locations with substantial anthropogenic groundwater recharge. Neotame was used to identify locations of very recent (<15 years before present) or ongoing wastewater influxes to the aquifer. This work shows the important influence of naturally occurring subsoil NO3− reservoirs on groundwater in arid regions and the major contribution of artificial recharge.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.157345","usgsCitation":"Linhoff, B.S., 2022, Deciphering natural and anthropogenic nitrate and recharge sources in arid region groundwater: Science of the Total Environment, v. 848, 157345, 16 p., https://doi.org/10.1016/j.scitotenv.2022.157345.","productDescription":"157345, 16 p.","ipdsId":"IP-137249","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":446835,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.157345","text":"Publisher Index Page"},{"id":405070,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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Mexico\",\"nation\":\"USA \"}}]}","volume":"848","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Linhoff, Benjamin S. 0000-0002-9478-7558","orcid":"https://orcid.org/0000-0002-9478-7558","contributorId":215020,"corporation":false,"usgs":true,"family":"Linhoff","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":848738,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70240246,"text":"70240246 - 2022 - PCB exposure is associated with reduction of endosymbionts in riparian spider microbiomes","interactions":[],"lastModifiedDate":"2023-02-02T13:23:51.078019","indexId":"70240246","displayToPublicDate":"2022-08-10T07:22:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"PCB exposure is associated with reduction of endosymbionts in riparian spider microbiomes","docAbstract":"Microbial communities, including endosymbionts, play diverse and critical roles in host biology and reproduction, but contaminant exposure may cause an imbalance in the microbiome composition with subsequent impacts on host health. Here, we examined whether there was a significant alteration of the microbiome community within two taxa of riparian spiders (Tetragnathidae and Araneidae) from a site with historical polychlorinated biphenyl (PCB) contamination in southern Ontario, Canada. Riparian spiders specialize in the predation of adult aquatic insects and, as such, their contaminant levels closely track those of nearby aquatic ecosystems. DNA from whole spiders from sites with either low or high PCB contamination was extracted, and spider microbiota profiled by partial 16S rRNA gene amplicon sequencing. The most prevalent shift in microbial communities we observed was a large reduction in endosymbionts in spiders at the high PCB site. The abundance of endosymbionts at the high PCB site was 63 % and 98 % lower for tetragnathids and araneids, respectively, than at the low PCB site. Overall, this has potential implications for spider reproductive success and food webs, as riparian spiders are critical gatekeepers of energy and material fluxes at the land-water interface.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.156726","usgsCitation":"Perrotta, B.G., Kidd, K.A., and Walters, D., 2022, PCB exposure is associated with reduction of endosymbionts in riparian spider microbiomes: Science of the Total Environment, v. 842, 156726, 8 p., https://doi.org/10.1016/j.scitotenv.2022.156726.","productDescription":"156726, 8 p.","ipdsId":"IP-139490","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":489720,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.156726","text":"Publisher Index Page"},{"id":412613,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"842","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Perrotta, Brittany G. 0000-0003-2669-3047","orcid":"https://orcid.org/0000-0003-2669-3047","contributorId":301929,"corporation":false,"usgs":true,"family":"Perrotta","given":"Brittany","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863077,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kidd, Karen A.","contributorId":201554,"corporation":false,"usgs":false,"family":"Kidd","given":"Karen","email":"","middleInitial":"A.","affiliations":[{"id":25502,"text":"McMaster University","active":true,"usgs":false}],"preferred":false,"id":863078,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205921,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863079,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235712,"text":"70235712 - 2022 - Global dataset of species-specific inland recreational fisheries harvest for consumption","interactions":[],"lastModifiedDate":"2022-08-16T12:10:25.836446","indexId":"70235712","displayToPublicDate":"2022-08-10T07:06:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Global dataset of species-specific inland recreational fisheries harvest for consumption","docAbstract":"Inland recreational fisheries, found in lakes, rivers, and other landlocked waters, are important to livelihoods, nutrition, leisure, and other societal ecosystem services worldwide. Although recreationally-caught fish are frequently harvested and consumed by fishers, their contribution to food and nutrition has not been adequately quantified due to lack of data, poor monitoring, and under-reporting, especially in developing countries. Beyond limited global harvest estimates, few have explored species-specific harvest patterns, although this variability has implications for fisheries management and food security. Given the continued growth of the recreational fishery sector, understanding inland recreational fish harvest and consumption rates represents a critical knowledge gap. Based on a comprehensive literature search and expert knowledge review, we quantified multiple aspects of global inland recreational fisheries for 81 countries spanning ~192 species. For each country, we assembled recreational fishing participation rate and estimated species-specific harvest and consumption rate. This dataset provides a foundation for future assessments, including understanding nutritional and economic contributions of inland recreational fisheries.
We investigated the cumulative effects of predation by piscivorous colonial waterbirds on the survival of multiple salmonid (Oncorhynchus spp.) populations listed under the U.S. Endangered Species Act (ESA) and determined what proportion of all sources of fish mortality (1 –survival) were due to birds in the Columbia River basin, USA. Anadromous juvenile salmonids (smolts) were exposed to predation by Caspian terns (Hydroprogne caspia), double-crested cormorants (Nannopterum auritum), California gulls (Larus californicus), and ring-billed gulls (L. delawarensis), birds known to consume both live and dead fish. Avian consumption and survival probabilities (proportion of available fish consumed or alive) were estimated for steelhead trout (O. mykiss), yearling Chinook salmon (O. tshawytscha), sub-yearling Chinook salmon, and sockeye salmon (O. nerka) during out-migration from the lower Snake River to the Pacific Ocean during an 11-year study period (2008–2018). Results indicated that probabilities of avian consumption varied greatly across salmonid populations, bird species, colony location, river reach, and year. Cumulative consumption probabilities (consumption by birds from all colonies combined) were consistently the highest for steelhead, with annual estimates ranging from 0.22 (95% credible interval = 0.20–0.26) to 0.51 (0.43–0.60) of available smolts. The cumulative effects of avian consumption were significantly lower for yearling and sub-yearling Chinook salmon, with consumption probabilities ranging annually from 0.04 (0.02–0.07) to 0.10 (0.07–0.15) and from 0.06 (0.3–0.09) to 0.15 (0.10–0.23), respectively. Avian consumption probabilities for sockeye salmon smolts was generally higher than for Chinook salmon smolts, but lower than for steelhead smolts, ranging annually from 0.08 (0.03–0.22) to 0.25 (0.14–0.44). Although annual consumption probabilities for birds from certain colonies were more than 0.20 of available smolts, probabilities from other colonies were less than 0.01 of available smolts, indicating that not all colonies of birds posed a substantial risk to smolt mortality. Consumption probabilities were lowest for small colonies and for colonies located a considerable distance from the Snake and Columbia rivers. Total mortality attributed to avian consumption was relatively small for Chinook salmon (less than 10%) but was the single greatest source of mortality for steelhead (greater than 50%) in all years evaluated. Results suggest that the potential benefits to salmonid populations of managing birds to reduce smolt mortality would vary widely depending on the salmonid population, the species of bird, and the size and location of the breeding colony.
The increase in fires at the wildland–urban interface has raised concerns about the potential environmental impact of ash remaining after burning. Here, we examined the concentrations and speciation of iron-bearing nanoparticles in wildland–urban interface ash. Total iron concentrations in ash varied between 4 and 66 mg g−1. Synchrotron X-ray absorption near-edge structure (XANES) spectroscopy of bulk ash samples was used to quantify the relative abundance of major Fe phases, which were corroborated by transmission electron microscopy measurements. Maghemite (γ-(Fe3+)2O3) and magnetite (γ-Fe2+(Fe3+)2O4) were detected in most ashes and accounted for 0–90 and 0–81% of the spectral weight, respectively. Ferrihydrite (amorphous Fe(III)–hydroxide, (Fe3+)5HO8·4H2O), goethite (α-Fe3+OOH), and hematite (α-Fe3+2O3) were identified less frequently in ashes than maghemite and magnetite and accounted for 0–65, 0–54, and 0–50% of spectral weight, respectively. Other iron phases identified in ashes include wüstite (Fe2+O), zerovalent iron, FeS, FeCl2, FeCl3, FeSO4, Fe2(SO4)3, and Fe(NO3)3. Our findings demonstrate the impact of fires at the wildland–urban interface on iron speciation; that is, fires can convert iron oxides (e.g., maghemite, hematite, and goethite) to reduced iron phases such as magnetite, wüstite, and zerovalent iron. Magnetite concentrations (e.g., up to 25 mg g−1) decreased from black to gray to white ashes. Based on transmission electron microscopy (TEM) analyses, most of the magnetite nanoparticles were less than 500 nm in size, although larger particles were identified. Magnetite nanoparticles have been linked to neurodegenerative diseases as well as climate change. This study provides important information for understanding the potential environmental impacts of fires at the wildland–urban interface, which are currently poorly understood.
","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D2EN00439A","usgsCitation":"Baalousha, M., Desmau, M., Singerling, S., Webster, J.P., Matiasek, S., Stern, M.A., and Alpers, C.N., 2022, Discovery and potential ramifications of reduced iron-bearing nanoparticles — Magnetite, wüstite, and zero-valent iron — In wildland–urban interface fire ashes: Environmental Science Nano, v. 9, no. 11, p. 4136-4149, https://doi.org/10.1039/D2EN00439A.","productDescription":"14 p.","startPage":"4136","endPage":"4149","ipdsId":"IP-140231","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":446849,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/115415","text":"External Repository"},{"id":406373,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baalousha, Mohammed 0000-0001-7491-4954","orcid":"https://orcid.org/0000-0001-7491-4954","contributorId":255450,"corporation":false,"usgs":false,"family":"Baalousha","given":"Mohammed","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":851141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Desmau, Morgane 0000-0002-7828-183X","orcid":"https://orcid.org/0000-0002-7828-183X","contributorId":296283,"corporation":false,"usgs":false,"family":"Desmau","given":"Morgane","email":"","affiliations":[{"id":64009,"text":"Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany","active":true,"usgs":false}],"preferred":false,"id":851142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Singerling, Sheryl A. 0000-0001-8639-5039","orcid":"https://orcid.org/0000-0001-8639-5039","contributorId":296284,"corporation":false,"usgs":false,"family":"Singerling","given":"Sheryl A.","affiliations":[{"id":64010,"text":"Virginia Polytechnic Institute and State University, Blacksburg, Virginia","active":true,"usgs":false}],"preferred":false,"id":851143,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Webster, Jackson P.","contributorId":248454,"corporation":false,"usgs":false,"family":"Webster","given":"Jackson","email":"","middleInitial":"P.","affiliations":[{"id":49915,"text":"California State University Chico","active":true,"usgs":false}],"preferred":false,"id":851144,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Matiasek, Sandrine J. 0000-0003-0272-0354","orcid":"https://orcid.org/0000-0003-0272-0354","contributorId":210031,"corporation":false,"usgs":false,"family":"Matiasek","given":"Sandrine","middleInitial":"J.","affiliations":[{"id":38054,"text":"Department of Geological and Environmental Sciences, California State University Chico, 400 W 1st St, Chico, CA 95929, USA","active":true,"usgs":false}],"preferred":false,"id":851145,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851146,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Alpers, Charles N. 0000-0001-6945-7365 cnalpers@usgs.gov","orcid":"https://orcid.org/0000-0001-6945-7365","contributorId":411,"corporation":false,"usgs":true,"family":"Alpers","given":"Charles","email":"cnalpers@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":851147,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70238066,"text":"70238066 - 2022 - Projecting flood frequency curves under near-term climate change","interactions":[],"lastModifiedDate":"2022-11-08T12:38:08.023903","indexId":"70238066","displayToPublicDate":"2022-08-09T06:35:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Projecting flood frequency curves under near-term climate change","docAbstract":"Flood-frequency curves, critical for water infrastructure design, are typically developed based on a stationary climate assumption. However, climate changes are expected to violate this assumption. Here, we propose a new, climate-informed methodology for estimating flood-frequency curves under non-stationary future climate conditions. The methodology develops an asynchronous, semiparametric local-likelihood regression (ASLLR) model that relates moments of annual maximum flood to climate variables using the generalized linear model. We estimate the first two marginal moments (MM) – the mean and variance – of the underlying log-Pearson Type-3 distribution from the ASLLR with the monthly rainfall and temperature as predictors. The proposed methodology, ASLLR-MM, is applied to 40 U.S. Geological Survey streamgages covering 18 water resources regions across the conterminous United States. A correction based on the aridity index was applied on the estimated variance, after which the ASLLR-MM approach was evaluated with both historical (1951–2005) and projected (2006–2035, under RCP4.5 and RCP8.5) monthly precipitation and temperature from eight Global Circulation Models (GCMs) consisting of 39 ensemble members. The estimated flood-frequency quantiles resulting from the ASLLR-MM and GCM members compare well with the flood-frequency quantiles estimated using the historical period of observed climate and flood information for humid basins, whereas the uncertainty in model estimates is higher in arid basins. Considering additional atmospheric and land-surface conditions and a multi-level model structure that includes other basins in a region could further improve the model performance in arid basins.
The U.S. Geological Survey Benchmark Glacier Project combines decades of direct glaciological data with remote sensing data to advance the quantitative understanding of glacier-climate interactions. The global loss of glaciers, and consequent implications for water resources, sea level rise, and ecosystem function underscores the importance of U.S. Geological Survey glaciology research to facilitate adaptive strategies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223050","usgsCitation":"Florentine, C., and McKeon, L.A., 2022, U.S. Geological Survey Benchmark Glacier Project: U.S. Geological Survey Fact Sheet 2022-3050, 2 p., https://doi.org/10.3133/fs20223050.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-135923","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":404997,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223050/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3050"},{"id":404895,"rank":5,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/programs/climate-research-and-development-program/science/glaciers-and-climate-project#data","text":"Glaciers and Climate Project"},{"id":404894,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3050/images"},{"id":404893,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3050/fs20223050.xml"},{"id":404892,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3050/fs20223050.pdf","text":"Report","size":"4.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3050"},{"id":404891,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3050/coverthb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.54150390625,\n 48.16608541901253\n ],\n [\n -120.12451171875,\n 48.16608541901253\n ],\n [\n -120.12451171875,\n 49.095452162534826\n ],\n [\n -122.54150390625,\n 49.095452162534826\n ],\n [\n -122.54150390625,\n 48.16608541901253\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.89501953124999,\n 47.945786463687185\n ],\n [\n -113.13720703125,\n 47.945786463687185\n ],\n [\n -113.13720703125,\n 48.980216985374994\n ],\n [\n -114.89501953124999,\n 48.980216985374994\n ],\n [\n -114.89501953124999,\n 47.945786463687185\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -134.5166015625,\n 58.286395482881034\n ],\n [\n -132.0556640625,\n 58.286395482881034\n ],\n [\n -132.0556640625,\n 59.64554025144323\n ],\n [\n -134.5166015625,\n 59.64554025144323\n ],\n [\n -134.5166015625,\n 58.286395482881034\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150.35888671875,\n 60.4788788301667\n ],\n [\n -147.65625,\n 60.4788788301667\n ],\n [\n -147.65625,\n 62.01121819833755\n ],\n [\n -150.35888671875,\n 62.01121819833755\n ],\n [\n -150.35888671875,\n 60.4788788301667\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -144.66796875,\n 63.03503931552975\n ],\n [\n -141.15234374999997,\n 63.03503931552975\n ],\n [\n -141.15234374999997,\n 64.47279382008166\n ],\n [\n -144.66796875,\n 64.47279382008166\n ],\n [\n -144.66796875,\n 63.03503931552975\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Rocky Mountain Science Center
U.S. Geological Survey
2327 University Way, Suite 2
Bozeman, MT 59715
Early detection of dreissenid mussels (Dreissena polymorpha and D. rostriformis bugensis) is crucial to mitigating the economic and environmental impacts of an infestation. Plankton tow sampling is a common method used for early detection of dreissenid mussels, but little is known about the sampling intensity required for a high probability of early detection using the method. We used implicit dynamic occupancy models to estimate plankton tow detection probabilities of dreissenid mussels from a long-term data set containing plankton tow samples collected across central and western United States. We fit models using a) the entire data set, including water bodies with unknown occupancy status in addition to heavily infested water bodies, b) a data subset that included water bodies with paired water temperature data, and c) a data subset that included water bodies with lower dreissenid densities. For the entire data set, we found that estimated detection probabilities varied by water body size and ranged from approximately 0.10 to 0.86. For the water temperature subset, we observed the same pattern between detection probability and water body size as we did for the full data but additionally found that the estimated detection probabilities were much higher when water temperatures were above 12 °C. For the lower dreissenid density subset, we found that the estimated probability of detecting dreissenid mussels with a single aggregated plankton tow sample was near zero. Given these estimates, we conclude that the number of aggregated plankton tow samples taken per water body in the data is far fewer than the number needed to ensure a high probability of detecting dreissenid mussels, especially if they are at low densities. We summarize the analyses with a discussion of plankton tow sampling protocol changes needed to improve estimates of dreissenid detection probabilities.
","language":"English","publisher":"REABIC","doi":"10.3391/mbi.2022.13.4.05","usgsCitation":"Winder, M., Sepulveda, A., and Hoegh, A., 2022, An initial assessment of plankton tow detection probabilities for dreissenid mussels in the western United States: Management of Biological Invasions, v. 13, no. 4, p. 659-678, https://doi.org/10.3391/mbi.2022.13.4.05.","productDescription":"20 p.","startPage":"659","endPage":"678","ipdsId":"IP-137748","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446857,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2022.13.4.05","text":"Publisher Index Page"},{"id":405680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.5703125,\n 34.016241889667015\n ],\n [\n -94.5703125,\n 37.09023980307208\n ],\n [\n -94.482421875,\n 39.639537564366684\n ],\n [\n -95.888671875,\n 40.84706035607122\n ],\n [\n -96.591796875,\n 42.94033923363181\n ],\n [\n -97.20703125,\n 49.15296965617042\n ],\n [\n -123.04687499999999,\n 49.15296965617042\n ],\n [\n -123.3984375,\n 48.16608541901253\n ],\n [\n -124.8046875,\n 48.22467264956519\n ],\n [\n -124.541015625,\n 40.245991504199026\n ],\n [\n -123.57421875,\n 38.34165619279595\n ],\n [\n -121.9921875,\n 35.60371874069731\n ],\n [\n -119.00390625,\n 33.358061612778876\n ],\n [\n -116.630859375,\n 32.69486597787505\n ],\n [\n -110.302734375,\n 31.203404950917395\n ],\n [\n -108.19335937499999,\n 31.42866311735861\n ],\n [\n -106.5234375,\n 31.80289258670676\n ],\n [\n -103.0078125,\n 32.39851580247402\n ],\n [\n -103.0078125,\n 36.38591277287651\n ],\n [\n -99.931640625,\n 36.4566360115962\n ],\n [\n -99.755859375,\n 34.30714385628804\n ],\n [\n -94.5703125,\n 34.016241889667015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Winder, Meaghan","contributorId":295487,"corporation":false,"usgs":false,"family":"Winder","given":"Meaghan","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":849583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":849584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hoegh, Andrew","contributorId":265906,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":849585,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70239346,"text":"70239346 - 2022 - Electrical imaging for hydrogeology","interactions":[],"lastModifiedDate":"2023-01-10T14:46:39.606987","indexId":"70239346","displayToPublicDate":"2022-08-08T08:29:37","publicationYear":"2022","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"Electrical imaging for hydrogeology","docAbstract":"Geophysical methods offer hydrogeologists unprecedented access to understanding subsurface parameters and processes. In this book, we outline the theory and application of electrical imaging methods, which inject current into the ground and measure the resultant potentials. These data are sensitive to rock type, grain size, porosity, pore fluid electrical conductivity, saturation, and temperature. Here, we describe the physical basis for electrical imaging, parallels between electrical flow equations and the groundwater flow equation, practical considerations for field investigations, data processing and inverse modeling of field data, and how to QA/QC data. We additionally cover two case studies, including a 2-D waterborne survey and a 4-D dataset from a biostimulation experiment.
","language":"English","publisher":"The Groundwater Project","usgsCitation":"Singha, K., Johnson, T.C., Day-Lewis, F., and Slater, L., 2022, Electrical imaging for hydrogeology, xi, 74 p.","productDescription":"xi, 74 p.","ipdsId":"IP-127811","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":411626,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411625,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://gw-project.org/books/electrical-imaging-for-hydrogeology/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":861207,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Timothy C.","contributorId":199842,"corporation":false,"usgs":false,"family":"Johnson","given":"Timothy","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":861209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":861208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Slater, Lee D.","contributorId":255454,"corporation":false,"usgs":false,"family":"Slater","given":"Lee D.","affiliations":[{"id":39626,"text":"Rutgers University Newark","active":true,"usgs":false}],"preferred":false,"id":861210,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237293,"text":"70237293 - 2022 - Divergent gene expression profiles in Alaskan sea otters: An indicator of chronic domoic acid exposure?","interactions":[],"lastModifiedDate":"2023-01-10T13:58:06.163068","indexId":"70237293","displayToPublicDate":"2022-08-08T08:17:10","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12618,"text":"Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Divergent gene expression profiles in Alaskan sea otters: An indicator of chronic domoic acid exposure?","docAbstract":"An opportunistic investigation into ecosystem instability in Kachemak Bay (KBay), Alaska, has led us to investigate exposure to toxic algae in sea otters. We used gene expression to explore the physiological health of sea otters sampled in KBay in May 2019. We found altered levels of gene transcripts in comparison with reference sea otters from clinically normal, oil-exposed, and nutritionally challenged populations sampled over the past decade. KBay sea otters were markedly divergent from the other groups for five genes, which indicated the involvement of neurological, cardiac, immune, and detoxification systems. Further, analyses of urine and fecal samples detected domoic acid in the KBay sea otters. In combination, these results may point to chronic, low-level exposure to an algal toxin, such as domoic acid. With a warming climate, the frequency and severity of harmful algal blooms in marine environments is anticipated to increase, and novel molecular technologies to detect sublethal or chronic exposure to algal toxins will help provide an early warning of threats to the stability of populations and ecosystems.
","language":"English","publisher":"MDPI","doi":"10.3390/oceans3030027","usgsCitation":"Bowen, L., Knowles, S., Lefebvre, K., St Martin, M., Murray, M., Kloecker, K.A., Monson, D., Weitzman, B., Ballachey, B., Coletti, H., Waters-Dynes, S.C., and Cummings, C., 2022, Divergent gene expression profiles in Alaskan sea otters: An indicator of chronic domoic acid exposure?: Oceans, v. 3, no. 3, p. 401-418, https://doi.org/10.3390/oceans3030027.","productDescription":"18 p.","startPage":"401","endPage":"418","ipdsId":"IP-143428","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446860,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/oceans3030027","text":"Publisher Index Page"},{"id":408024,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kachemak Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.87774658203125,\n 59.38638116673929\n ],\n [\n -151.42730712890622,\n 59.48135847840536\n ],\n [\n -151.17462158203125,\n 59.55380845543803\n ],\n [\n -150.86700439453122,\n 59.818589695379316\n ],\n [\n -151.0455322265625,\n 59.825493056630116\n ],\n [\n -151.52069091796875,\n 59.655254704087724\n ],\n [\n -151.86676025390625,\n 59.47298884774133\n ],\n [\n -151.87774658203125,\n 59.38638116673929\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"3","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-08-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Bowen, Lizabeth 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swaters@usgs.gov","orcid":"https://orcid.org/0000-0002-9707-4684","contributorId":5826,"corporation":false,"usgs":true,"family":"Waters-Dynes","given":"Shannon","email":"swaters@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":854011,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cummings, C","contributorId":297392,"corporation":false,"usgs":false,"family":"Cummings","given":"C","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":854012,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70236949,"text":"70236949 - 2022 - Multi-decadal simulation of marsh topography evolution under sea level rise and episodic sediment loads","interactions":[],"lastModifiedDate":"2022-09-22T11:45:10.036468","indexId":"70236949","displayToPublicDate":"2022-08-08T06:42:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Multi-decadal simulation of marsh topography evolution under sea level rise and episodic sediment loads","docAbstract":"Coastal marsh within Mediterranean climate zones is exposed to episodic watershed runoff and sediment loads that occur during storm events. Simulating future marsh accretion under sea level rise calls for attention to: (a) physical processes acting over the time scale of storm events and (b) biophysical processes acting over time scales longer than storm events. Using the upper Newport Bay in Southern California as a case study, we examine the influence of event-scale processes on simulated change in marsh topography by comparing: (a) a biophysical model that integrates with an annual time step and neglects event-scale processes (BP-Annual), (b) a physical model that resolves event-scale processes but neglects biophysical interactions (P-Event), and (c) a biophysical model that resolves event-scale physical processes and biophysical processes at annual and longer time scales (BP-Event). A calibrated BP-Event model shows that large (>20-year return period) episodic storm events are major drivers of marsh accretion, depositing up to 30 cm of sediment in one event. Greater deposition is predicted near fluvial sources and tidal channels and less on marshes further from fluvial sources and tidal channels. In contrast, the BP-Annual model poorly resolves spatial structure in marsh accretion as a consequence of neglecting event-scale processes. Furthermore, the P-Event model significantly overestimates marsh accretion as a consequence of neglecting marsh surface compaction driven by annual scale biophysical processes. Differences between BP-Event and BP-Annual models translate up to 20 cm per century in marsh surface elevation.