Development in natural areas is a leading threat to biodiversity. Global conservationists have called for the expansion of protected areas to preserve wildlands that are free from buildings, and in the U.S., the ‘America the Beautiful’ initiative aims to protect 30% of land and water areas by 2030 (known as the ‘30x30’ target). Here, we determined opportunities and limitations for conservation in the conterminous U.S. by assessing the extent of buildings in wildland vegetation. We focused specifically on National Forest lands, as these contain numerous private inholdings where development may occur. Using a newly available building footprint dataset, we determined 1) whether buildings were present and 2) numbers of buildings within three distances of wildland vegetation (100, 250, and 500 m), representing varying magnitudes of ecological impact. Our findings revealed that 29% of wildland vegetation nationwide was within 500 m of a building, 15% was within 250 m, and 5% was within 100 m. National Forest lands were less affected by building disturbance, but a substantial proportion (12%) of wildland vegetation area was within 500 m of a building. Of National Forest lands that were within 500 m of an inholding, 76% was not yet in proximity to a building; consequently, ∼10% of National Forest lands (143,474 km2) are susceptible to impacts from future development on inholdings. We conclude that National Forest inholdings are therefore important opportunity areas for 30x30 conservation goals. Our assessments can inform where conservation efforts can limit impacts from present and future development on biodiversity.
The objectives of this study are to identify per- and polyfluoroalkyl substances (PFAS) in Pennsylvania surface waters, corresponding associations with potential sources of PFAS contamination (PSOC) and other parameters, and compare raw surface water concentrations to human and ecological benchmarks. Surface water samples from 161 streams were collected in September 2019 and were analyzed for 33 target PFAS and water chemistry. Land use and physical attributes in upstream catchments and geospatial counts of PSOC in local catchments are summarized. The hydrologic yield of the sum of 33 PFAS (∑PFAS) for each stream was computed by normalizing each site's load by the drainage area of the upstream catchment. Utilizing conditional inference tree analysis, the percentage of development (>7.58 %) was identified as a primary driver of the ∑PFAS hydrologic yields. When percentage of development was removed from analysis, ∑PFAS yields were closely related to surface water chemistry associated with landscape alteration (e.g., development or agricultural cropland), such as concentrations of total nitrogen, chloride, and ammonia, but also to count of water pollution control facilities (agricultural, industrial, stormwater, and/or municipal waste pollution abatement facilities). In oil and gas development regions, ∑PFAS yields were associated with combined sewage outfalls. Sites surrounded by ≥2 electronic manufacturing facilities had elevated ∑PFAS yields (median = 241 ng/s/km2). Study results are critical to guide future research, regulatory policy, best practices that will mitigate PFAS contamination, and the communication of human health and ecological risks associated with PFAS exposure from surface waters.
Coastal Louisiana is currently experiencing high rates of wetland loss and large-scale ecosystem restoration is being implemented. One of the largest and most novel restoration projects is a controlled sediment diversion, proposed to rebuild and sustain wetlands by diverting sediment- and nutrient-rich water from the Mississippi River. However, the impact of this proposed sediment diversion on the nutrient budget of the receiving basin is largely unknown. A water quality model was developed to investigate the impact of the planned Mid-Barataria Sediment Diversion on the nutrient budget of the Barataria Basin (herein referred to as ‘the Basin’). The model results indicate that the planned diversion will increase TN and TP pools by about 38% and 17%, respectively, even with TN and TP loadings that increase by >300%. Water quality model results suggest that the increase of nutrients in the basin will be mitigated by increased advection transport (i.e., decreased residence time from ~170 days to ~40 days, leading to greater flushing) and increased removal via assimilation, denitrification, and settling within the Basin. Advection transport resulted in higher TN removal in the Basin than other processes, such as uptake or denitrification. Approximately 25% of the additional TN loading and 30% of the additional TP loading were processed within the Basin through the assimilation of phytoplankton and wetland vegetation, denitrification, and burial in the sediment/soils. These nutrient budgets help to better understand how the planned large-scale sediment diversion project may change the future ecological conditions within the estuaries of coastal Louisiana and near-shore northern Gulf of Mexico.
","language":"English","publisher":"MDPI","doi":"10.3390/w15111974","usgsCitation":"Jung, H., Nuttle, W.K., Baustian, M.M., and Carruthers, T., 2023, Influence of increased freshwater inflow on nitrogen and phosphorus budgets in a dynamic subtropical estuary, Barataria Basin, Louisiana: Water, v. 15, no. 11, 1974, 26 p., https://doi.org/10.3390/w15111974.","productDescription":"1974, 26 p.","ipdsId":"IP-147358","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":443414,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w15111974","text":"Publisher Index Page"},{"id":417528,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Barataria Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.95930658584572,\n 28.576529058181706\n ],\n [\n -89.35817902592156,\n 29.01036697967102\n ],\n [\n -89.24031087691701,\n 29.18888336467873\n ],\n [\n -89.67249408993412,\n 29.421859677181203\n ],\n [\n -90.17539819235454,\n 29.835100651604293\n ],\n [\n -90.3954187371632,\n 29.739625500016345\n ],\n [\n -90.2186165136559,\n 29.182023077646647\n ],\n [\n -89.95930658584572,\n 28.576529058181706\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Jung, Hoonshin","contributorId":305843,"corporation":false,"usgs":false,"family":"Jung","given":"Hoonshin","email":"","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":873997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nuttle, William K.","contributorId":189603,"corporation":false,"usgs":false,"family":"Nuttle","given":"William","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":873998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baustian, Melissa Millman 0000-0003-2467-2533","orcid":"https://orcid.org/0000-0003-2467-2533","contributorId":304015,"corporation":false,"usgs":true,"family":"Baustian","given":"Melissa","email":"","middleInitial":"Millman","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":873999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carruthers, Tim J. 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Near Moapa, Nevada, in the Bureau of Land Management’s Muddy River Floodplain Restoration Project Area, a previously constructed levee on the east side of the river alters the natural hydrology and decreases connectivity between the river and its floodplain. The Bureau of Land Management is interested in restoring the project area to a more natural state and proposed removing the existing levee (at the time of this study in 2019) on the east bank of the river and replacing it with a new levee farther away from the river. The 50-, 20-, 10-, 4-, 2-, and 1-percent annual exceedance probability flood streamflows were estimated based on a flood-frequency analysis of a streamgage in the study area. River cross-sections were surveyed and combined with a digital elevation model of the floodplains to create a coupled one- and two-dimensional hydraulic model of the study area. The estimated flood streamflows were used as inputs in the hydraulic model to simulate how flood-inundation extents would change with the proposed restoration. Simulated inundation extents expand with increasing flood magnitudes, with nearly the entire valley inundated by the 2-percent flood streamflow. Within the project area, inundation extents with restoration increased on the east floodplain and decreased on the west floodplain for the 20-, 10-, 4-, 2-, and 1-percent flood streamflows. Outside the Muddy River Floodplain Restoration Project Area, inundation extents decreased with restoration east of the project area for the 20- and 10-percent flood streamflows, but changes in extent for larger streamflows were minor because most of the streamflow leaves the main river channel upstream of the restoration area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235033","usgsCitation":"Morris, C.M., and Childres, H.K., 2023, Flood-inundation maps for the Muddy River, near Moapa, Nevada: U.S. Geological Survey Scientific Investigations Report 2023–5033, 22 p., https://doi.org/10.3133/sir20235033.","productDescription":"Report: viii, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104618","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":416999,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5033/sir20235033.pdf","text":"Report","size":"23 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417002,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5033/images"},{"id":416997,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K68IWI","text":"Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada","description":"Morris, C.M., 2023, Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada: U.S. Geological Survey data release, at https://doi.org/10.5066/P9K68IWI."},{"id":417000,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5033/covrthb.jpg"},{"id":417001,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5033/sir20235033.xml"},{"id":417003,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235033/full"},{"id":500898,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114737.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nevada","city":"Moapa","otherGeospatial":"Muddy River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.716667,\n 36.723611\n ],\n [\n -114.716667,\n 36.675\n ],\n [\n -114.675,\n 36.675\n ],\n [\n -114.675,\n 36.723611\n ],\n [\n -114.716667,\n 36.723611\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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U.S. Geological Survey
2730 N. Deer Run Road
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In contrast to many other arid region rivers, streamflow in the South Platte River is heavily augmented by trans-basin water imports and irrigation return flows. Hydrological changes began in the 1880s, resulting in channel narrowing and the development of a continuous Populus-Salix forest by the mid-twentieth century. We assessed the composition, structure and regeneration status of the riparian forest and identified environmental variables affecting annual Populus deltoides tree growth. We sampled forest structure at four sites in 2015, and conducted dendroecological analysis at seven additional sites in 2019. The riparian forest was dominated by P. deltoides, which occurred at all sites, comprising 79% of total tree basal area and 62% of total tree density. Age structure data indicated ongoing though episodic recruitment of P. deltoides, at least over the past ~130 years. We tested 14 linear mixed effects models to describe the effect of climate and streamflow on individual tree growth (modeled as the log of BAI, n = 237 trees). The most parsimonious model selected with AICc explained 28.6% of BAI variability, and included hydrology and climate factors during the growing season (i.e., June–August streamflow, June–July PDSI), some aspects of off-season (i.e., previous November and March) streamflow, along with tree age and study site effects. The riparian forest developed in response to, and has been maintained by, current climate conditions and water management regimes. It may be negatively affected by future climate change and increased urban water demand in the basin.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4151","usgsCitation":"Christensen, C., Katz, G.L., Friedman, J.M., Redmond, M.D., and Norton, A.S., 2023, No evidence for cottonwood forest decline along a flow-augmented western U.S. river: River Research and Applications, v. 39, no. 8, p. 1602-1615, https://doi.org/10.1002/rra.4151.","productDescription":"14 p.","startPage":"1602","endPage":"1615","ipdsId":"IP-147312","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":443426,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rra.4151","text":"Publisher Index Page"},{"id":417328,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"South Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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Despite the importance of early warning systems for these hazards, existing real-time meteorological and oceanographic forecast systems at regional and national scales, until now, have lacked estimates of runup necessary to predict wave-driven overwash and erosion. To address this need, we present an approach that includes wave runup in an operational, national-scale modeling system. Using this system, we quantify the contribution of waves to potential dune erosion events along 4,700 km of U.S. Atlantic and Gulf of Mexico sandy coastlines for a one-year period. Dune erosion events were predicted to occur at over 80% of coastal locations, where waves dominated shoreline total water levels, representing 73% of the signal. This shows that models that neglect the wave component underestimate the hazard. This new, national-scale operational modeling system provides communities with timely, local-scale (0.5 km resolution) coastal hazard warnings for all wave conditions, allowing for rapid decision-making related to safety and emergency management. 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These services are under a growing threat as human-related pressure threatens freshwater biodiversity on multiple fronts. Here, Abigail Lynch and colleagues discuss nine fundamental ecosystem services that freshwater biodiversity provide to people. 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Michael","contributorId":304963,"corporation":false,"usgs":false,"family":"Johnson","given":"J. 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Mounting concerns focus on the accelerating pace of biodiversity loss and declining ecological function within freshwater ecosystems that continue to threaten these natural benefits. Here, we catalog nine fundamental ecosystem services that the biotic components of indigenous freshwater biodiversity provide to people, organized into three categories: material (food; health and genetic resources; material goods), non-material (culture; education and science; recreation), and regulating (catchment integrity; climate regulation; water purification and nutrient cycling). If freshwater biodiversity is protected, conserved, and restored in an integrated manner, as well as more broadly appreciated by humanity, it will continue to contribute to human well-being and our sustainable future via this wide range of services and associated nature-based solutions to our sustainable future.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1633","usgsCitation":"Lynch, A., Cooke, S., Arthington, A.H., Baigun, C., Bossenbroek, L., Dickens, C., Harrison, I., Kimirei, I., Langhans, S.D., Murchie, K.J., Olden, J., Ormerod, S.J., Owuor, M., Raghavan, R., Samways, M.J., Schinegger, R., Sharma, S., Tachamo-Shah, R., Tickner, D., Tweddle, D., Young, N., and Jahnig, S.C., 2023, People need freshwater biodiversity: WIREs Water, v. 10, no. 3, e1633, 31 p., https://doi.org/10.1002/wat2.1633.","productDescription":"e1633, 31 p.","ipdsId":"IP-140324","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":443444,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1633","text":"Publisher Index 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Washington","active":true,"usgs":false}],"preferred":false,"id":873316,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ormerod, Steve J.","contributorId":259328,"corporation":false,"usgs":false,"family":"Ormerod","given":"Steve","email":"","middleInitial":"J.","affiliations":[{"id":17940,"text":"Cardiff University","active":true,"usgs":false}],"preferred":false,"id":873317,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Owuor, Margaret","contributorId":305610,"corporation":false,"usgs":false,"family":"Owuor","given":"Margaret","email":"","affiliations":[{"id":25430,"text":"University of Bern","active":true,"usgs":false}],"preferred":false,"id":873318,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Raghavan, Rajeev","contributorId":250656,"corporation":false,"usgs":false,"family":"Raghavan","given":"Rajeev","email":"","affiliations":[{"id":50216,"text":"Kerala University of Fisheries and Ocean 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type","interactions":[],"lastModifiedDate":"2023-05-24T15:56:03.751044","indexId":"70243886","displayToPublicDate":"2023-05-20T10:50:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Salinity and total dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type","docAbstract":"The total concentration of dissolved constituents in water is routinely quantified by measurements of salinity or total dissolved solids (TDS). However, salinity and TDS are operationally defined by their analytical methods and are not equivalent for most waters. Furthermore, multiple methods are available to determine salinity and TDS, and these methods have inherent differences. TDS is defined as the mass of anhydrous residue remaining in a sample vessel after evaporation and subsequent oven drying at a defined temperature. Salinity is a measure of the mass of dissolved salts in a given mass of solution. In addition, there are approaches that quantify the total solute (TS) concentration, including gases. The purpose of this study is to develop a proxy method using specific conductance and major-ion water type to reliably predict salinity, TDS, and/or TS. Thus, we compared several methods to calculate salinity, TDS, and TS for 6391 surface water samples and conclude the following: TDS measurements are best suited for studies of anhydrous residue (e.g., evaporites); salinity determined by summing the speciated ion concentrations, termed S∑spec, is the most comprehensive method to represent the concentration of dissolved constituents in natural waters; and TS determinations are useful if dissolved CO2 and other gases are of interest. Thus, we utilized S∑spec to compare differences between the various salinity, TDS, and TS methods, and to develop a new proxy method to predict salinity based on specific conductance (SC) and major ion water type, termed SSC_WT. For the surface waters used in this study, the median difference between TDS and S∑Spec was between −19% and −24%, depending on the method. The median difference between SSC_WT and S∑Spec was −2.4% for the samples in this study. The SSC_WT approach is cost effective, rapid, and capable of providing reliable real-time salinity determinations at surface water sites where SC data are available and water type is known.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2023.105684","usgsCitation":"McCleskey, R., Cravotta, C., Miller, M., Tillman, F.D., Stackelberg, P.E., Knierim, K.J., and Wise, D., 2023, Salinity and total dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type: Applied Geochemistry, v. 154, 105684, 13 p., https://doi.org/10.1016/j.apgeochem.2023.105684.","productDescription":"105684, 13 p.","ipdsId":"IP-148865","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":443454,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2023.105684","text":"Publisher Index Page"},{"id":417398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"154","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":873614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":873615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873617,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":873618,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873619,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wise, Daniel 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","email":"","affiliations":[],"preferred":true,"id":873620,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243759,"text":"70243759 - 2023 - Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","interactions":[],"lastModifiedDate":"2023-05-19T12:54:11.017578","indexId":"70243759","displayToPublicDate":"2023-05-19T07:29:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","docAbstract":"Objective: The American Eel Anguilla rostrata historically was one of the most common fish species in Atlantic coast watersheds, but extensive dam construction and other factors caused a widespread population decline. One of the watersheds where American Eels have declined considerably is the Mohawk River in eastern and central New York. Recent attempts to characterize the distribution and abundance of American Eels in this watershed have been ineffective, and the extent to which a series of locks and dams on the Hudson River and lower Mohawk River limits use of the watershed is unclear.
Methods: We developed a model between environmental DNA (eDNA) quantity and American Eel abundance in the Hudson River watershed in which the DNA concentration in water samples explained up to 65% of the variability in eel density and 56% of the variability in eel biomass. We then used this relationship to interpret eDNA data collected twice from 36 sites across the Mohawk River watershed in 2021 and make inferences about the distribution and abundance of American Eels.
Result: American Eel DNA was detected almost exclusively in the downstream-most 4 km of the Mohawk River within a series of barriers. The concentration of DNA was reduced by approximately 80% across each successive upstream barrier before becoming too low to detect consistently. Our data suggest that eel population density was high in the Hudson River estuary and declined rapidly in the lower Mohawk River, and the species was nearly absent or undetectable in the Mohawk River and its tributaries upstream of the Crescent Dam and the Waterford Flight of Locks.
Conclusion: Barriers appear to be largely restricting American Eels from using over 99% of the Mohawk River watershed. Therefore, improvements in fish passage at dams and hydroelectric facilities in the region could help the American Eel to regain access to this part of its native range.
","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10404","usgsCitation":"George, S.D., Baldigo, B., Rees, C., Bartron, M.L., Wiley, J.J., Stich, D.S., Wells, S.M., and Winterhalter, D., 2023, Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system: Transactions of the American Fisheries Society, v. 152, no. 3, p. 310-326, https://doi.org/10.1002/tafs.10404.","productDescription":"17 p.","startPage":"310","endPage":"326","ipdsId":"IP-143996","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":443462,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10404","text":"Publisher Index Page"},{"id":417238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.46545959281143,\n 43.193210843337965\n ],\n [\n -75.36068780837059,\n 43.1807103892566\n ],\n [\n -75.25020119932465,\n 43.095917619682325\n ],\n [\n -75.11304540878423,\n 43.06391605114166\n ],\n [\n -75.0406576304439,\n 43.00264908923049\n ],\n [\n -74.84063876923905,\n 43.01240019708166\n ],\n [\n -74.66347920645833,\n 42.963629178932905\n ],\n [\n -74.58156672044112,\n 42.881327587792015\n ],\n [\n -74.45774552064795,\n 42.879931698082856\n ],\n [\n -74.25772665944312,\n 42.84083396491155\n ],\n [\n -74.11866592736799,\n 42.86317855831385\n ],\n [\n -74.03865838288596,\n 42.83524655344701\n ],\n [\n -74.02532379213918,\n 42.75417231574653\n ],\n [\n -74.00627437678641,\n 42.707996466290325\n ],\n [\n -73.81976366766865,\n 42.62025113498041\n ],\n [\n -73.79690436924501,\n 42.550123385716574\n ],\n [\n -73.8254784922747,\n 42.384309188935845\n ],\n [\n -73.81404884306264,\n 42.31351753996279\n ],\n [\n -73.93977498439143,\n 42.2035468271101\n ],\n [\n -73.97025404895584,\n 42.10469190424271\n ],\n [\n -74.00454299659106,\n 41.89801206244189\n ],\n [\n -73.91310580289745,\n 41.86681033353875\n ],\n [\n -73.88262673833307,\n 42.11317123319691\n ],\n [\n -73.72642153243986,\n 42.28251957771869\n ],\n [\n -73.74547094779265,\n 42.37828214920688\n ],\n [\n -73.71880176629864,\n 42.619854148073244\n ],\n [\n -73.61212504032306,\n 42.82697116015191\n ],\n [\n -73.66165352024026,\n 42.88841312774437\n ],\n [\n -73.94442157442768,\n 42.88448522793766\n ],\n [\n -74.22444798011406,\n 42.97932677531503\n ],\n [\n -74.37874824447162,\n 42.982114021563405\n ],\n [\n -74.51971391808242,\n 42.93192425763374\n ],\n [\n -74.65305982555196,\n 43.01415827143066\n ],\n [\n -74.86679009610465,\n 43.05871377504309\n ],\n [\n -75.00966071125109,\n 43.05314610631808\n ],\n [\n -75.22491910473742,\n 43.151897073297846\n ],\n [\n -75.36207489527783,\n 43.21717980329879\n ],\n [\n -75.47065656278859,\n 43.221344415722996\n ],\n [\n -75.46545959281143,\n 43.193210843337965\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"152","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rees, Christopher B.","contributorId":196308,"corporation":false,"usgs":false,"family":"Rees","given":"Christopher B.","affiliations":[],"preferred":false,"id":873175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartron, Meredith L.","contributorId":149109,"corporation":false,"usgs":false,"family":"Bartron","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false},{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false}],"preferred":false,"id":873176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiley, John J. Jr.","contributorId":305554,"corporation":false,"usgs":false,"family":"Wiley","given":"John","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":873177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":873178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wells, Scott M.","contributorId":305556,"corporation":false,"usgs":false,"family":"Wells","given":"Scott","email":"","middleInitial":"M.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":873179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Winterhalter, Dylan R. 0000-0003-1774-8034","orcid":"https://orcid.org/0000-0003-1774-8034","contributorId":251765,"corporation":false,"usgs":true,"family":"Winterhalter","given":"Dylan R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873180,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243768,"text":"70243768 - 2023 - Watershed carbon yield derived from gauge observations and river network connectivity in the United States","interactions":[],"lastModifiedDate":"2023-05-19T12:28:27.653939","indexId":"70243768","displayToPublicDate":"2023-05-19T07:04:33","publicationYear":"2023","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":"Watershed carbon yield derived from gauge observations and river network connectivity in the United States","docAbstract":"River networks play a critical role in the global carbon cycle. Although global/continental scale riverine carbon cycle studies demonstrate the significance of rivers and streams for linking land and coastal regions, the lack of spatially distributed riverine carbon load data represents a gap for quantifying riverine carbon net gain or net loss in different regions, understanding mechanisms and factors that influence the riverine carbon cycle, and testing simulations of aquatic carbon cycle models at fine scales. Here, we (1) derive the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) for over 1,000 hydrologic stations across the Conterminous United States (CONUS) and (2) use the river network connectivity information for over 80,000 catchment units within the National Hydrography Dataset Plus (NHDPlus) to estimate riverine POC and DOC net gain or net loss for watersheds controlled between upstream-downstream hydrologic stations. The new riverine carbon load and watershed net gain/loss represent a unique contribution to support future studies for better\nunderstanding and quantification of riverine carbon cycles.","language":"English","publisher":"Springer","doi":"10.1038/s41597-023-02162-7","usgsCitation":"Qiu, H., Zhang, X., Yang, A., Wickland, K., Stets, E.G., and Chen, M., 2023, Watershed carbon yield derived from gauge observations and river network connectivity in the United States: Scientific Data, v. 10, 278, 13 p., https://doi.org/10.1038/s41597-023-02162-7.","productDescription":"278, 13 p.","ipdsId":"IP-150043","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":443465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-023-02162-7","text":"Publisher Index 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2007–18","interactions":[],"lastModifiedDate":"2026-03-06T20:51:31.369117","indexId":"sir20235027","displayToPublicDate":"2023-05-18T10:56:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5027","displayTitle":"Evaluating Drivers of Hydrology, Water Quality, and Benthic Macroinvertebrates in Streams of Fairfax County, Virginia, 2007–18","title":"Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18","docAbstract":"In 2007, the U.S. Geological Survey partnered with Fairfax County, Virginia, to establish a long-term water-resources monitoring program to evaluate the hydrology, water quality, and ecology of Fairfax County streams and the watershed-scale effects of management practices. Fairfax County uses a variety of management practices, policies, and programs to protect and restore its water resources, but the effects of such strategies are not well understood. This report used streamflow, water-quality, and ecological monitoring data collected from 20 Fairfax County watersheds from 2007 through 2018 to assess the effects of management practices, landscape factors, and climatic conditions on observed nutrient, sediment, salinity, and benthic-macroinvertebrate community responses.
Urbanization, climatic variability, and an increase in management practices occurred within Fairfax County during the study period. Impervious cover, housing units, wastewater infrastructure, and (or) stormwater infrastructure increased in most study watersheds. Climatic conditions varied among study years; countywide estimates of average-annual air temperature differed by about 3 degrees Celsius, and total precipitation ranged from about 34 to 63 inches per year. The effects of the management practices, implemented to reduce nitrogen, phosphorus, and (or) sediment loads, are considered in this study. These management practices primarily consist of stormwater retrofits and stream restorations; however, stream restorations account for most of the financial investment and expected load reductions. Management practices were implemented in half of the study watersheds, and most practices were installed and reductions credited late in the study period.
Changes in hydrologic response during storm events were evaluated over the study period because many management practices that were implemented were designed to achieve nutrient and sediment reductions by slowing or intercepting runoff. The average number and length of storm events was mostly unchanged throughout the monitoring network. Four watersheds with 10 years of streamflow data showed a mixture of trends in stormflow peak, volume, and rate-of-change. Event-mean nutrient and sediment concentrations from these watersheds were evaluated during storm events and generally showed increases in total phosphorus (TP) and suspended sediment and reductions or no changes in total nitrogen (TN).
Landscape inputs of nitrogen and phosphorus and the percentage of inputs delivered to streams were estimated for the study watersheds. Estimated phosphorus from fertilizer and nitrogen from atmospheric deposition represented large nutrient inputs in most watersheds; amounts of other nonpoint sources varied based on land use. Estimated nitrogen inputs declined throughout Fairfax County and in most study watersheds from 2008 through 2018; in comparison, phosphorus input changes were relatively small. Most nonpoint-nutrient inputs were retained on the landscape and did not reach streams, with slightly more nitrogen retention than phosphorus, on average. Retention rates were lower for years with more precipitation and streamflow. After adjusting for streamflow, TN and TP loads were generally higher for years with more nutrient inputs. Calculated as a function of flow-adjusted loads, TP retention declined at most stations from 2009 through 2018, in comparison, TN retention was relatively unchanged.
Landscape and climatic conditions affected spatial differences and changes in Fairfax County stream conditions from 2009 through 2018. TN concentrations were higher and increases over time were larger in watersheds with elevated septic-system density. TP concentrations were higher in watersheds with more turfgrass; concentrations were lower, but had larger increases over time, in watersheds with deeper soils. Suspended-sediment concentrations were higher in watersheds with greater stream densities. Specific conductance was higher in watersheds with more developed land use and shallower soils. Benthic-macroinvertebrate index of biotic integrity (IBI) scores were lower in watersheds with high road density and had larger increases over time in bigger, more developed watersheds. Annual variability in TN and TP concentrations and benthic-macroinvertebrate IBI scores was affected by precipitation; annual variability in suspended sediment concentrations and specific conductance was affected by air temperature.
After accounting for influences from landscape and climatic conditions, expected management-practice effects were not consistently observed in monitored stream responses. These effects were assessed by comparing expected management-practice load reductions with the timing, direction, and magnitude of changes in storm-event hydrology, nutrient and sediment loads, median-annual water-quality conditions, and benthic-macroinvertebrate IBI scores. An important consideration for future investigations of management-practice effects is how to control for water-quality and ecological variability caused by geologic properties, the urban environment, precipitation, and (or) air temperature. The interpretation of management-practice effects in this report was likely influenced by a combination of factors, including (1) the amount, timing, and location of management-practice implementation; (2) unmeasured landscape and climatic factors; (3) uncertain management-practice expectations; (4) hydrologic variability; and (5) analytical assumptions. Through continued data-collection efforts, particularly after management practices have been completed, many of these factors may become less influential in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235027","isbn":"978-1-4113-4516-4","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Webber, J.S., Chanat, J.G., Porter, A.J., and Jastram, J.D., 2023, Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18: U.S. Geological Survey Scientific Investigations Report 2023–5027, 198 p., https://doi.org/10.3133/sir20235027.","productDescription":"Report: xv, 198 p.; Data Release","numberOfPages":"198","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-139637","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":417148,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FW7KLH","text":"USGS data release","linkHelpText":"Climate, landscape, and water-quality metrics 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
The central Salem River in New Jersey is subject to periods of water-quality impairment, marked by elevated concentrations of phosphorus and chlorophyll-a, and low concentrations of and large diurnal swings in concentrations of dissolved oxygen. These seasonal eutrophic conditions are controlling factors for water quality in lower reaches, where the river is more lacustrine than in upper reaches, as a result of downstream damming. This biological productivity is supported by nutrient wash-off from agricultural areas in the surrounding watershed. To investigate this impairment, flow measurement and water-quality sampling were conducted during 2007–08 in support of development of a one-dimensional surface-water-quality model that simulates nutrient cycling and transformation processes.
The U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, used the U.S. Environmental Protection Agency Water Quality Analysis Simulation Program (WASP) to develop a receiving-water-quality model of the central Salem River between Woodstown and Deepwater, New Jersey, from April 2007 to October 2008. The main-stem river and largest tributary were simulated. In the flow model, kinematic wave flow is used to simulate flow in upper reaches and ponded weir flow is used to simulate flow in lower reaches. The water-quality model makes use of a mass-balance equation to simulate the fate and transport of nutrients, phytoplankton chlorophyll-a, dissolved oxygen, and oxygen demands (an indicator rather than a substance) in the river. Model input included channel characteristics, boundary conditions for flow and water quality, environmental parameters, vertical dispersion coefficients, settling rates, and kinetic constants. Inputs were estimated where field data were lacking, notably for tributary flows and nutrient loads.
The model was calibrated to observed flow variables and concentrations of dissolved oxygen, chlorophyll-a, and nutrients at sampling locations, with emphasis on growing-season conditions. Calibration was achieved through graphical and statistical comparison of simulated results to observed data. Sensitivity analyses were performed, and model limitations and applicability were evaluated. Simulated results closely matched observed data in most cases, although some were overpredicted slightly. The most important causes of overprediction were estimated tributary flows for the flow model and estimated tributary watershed loads for the water-quality model. Calibration of dissolved-oxygen concentrations was closer, and predicted diurnal variations were consistent with high algal photosynthesis/respiration, although lack of continuous dissolved-oxygen data precluded verifying these predictions. A similar caveat applies to predicted diurnal variations in chlorophyll-a. Simulated limitations on algal growth were consistent with those based on observed data and indicated phosphorus was the main limiting nutrient, except during certain periods when nitrogen was limiting.
Two water-quality management scenarios were simulated with the model to assess the effect of point- and nonpoint-source nutrient reductions on water-quality conditions in the river. Scenarios involved (1) a return of watershed land use to predevelopment natural conditions and (2) an extreme reduction in nutrient input. Although the extreme-nutrient-reduction scenario yielded improvements in water quality, the natural-conditions scenario yielded the largest improvements as indicated by minimal violations of surface-water-quality standards or thresholds. However, years may be needed to attain the full benefit of these management scenarios as a result of accumulation of phosphorus and organic carbon in riverbed sediments in lacustrine reaches. The results of this study indicate that the quality of water in the central Salem River will improve if management policies that mitigate the effects of nutrient-loading practices in the watershed, particularly those related to agriculture, are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225047","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Spitz, F.J., and DePaul, V.T., 2023, Simulation of flow and eutrophication in the central Salem River, New Jersey: U.S. Geological Survey Scientific Investigations Report 2022–5047, 72 p., https://doi.org/10.3133/sir20225047.","productDescription":"Report: x, 72 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109225","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500449,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114735.htm","linkFileType":{"id":5,"text":"html"}},{"id":417027,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JPJ","text":"USGS data release","linkHelpText":"WASP model used to simulate flow and eutrophication in the central Salem River, New Jersey"},{"id":417026,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5047/images/"},{"id":417025,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.XML"},{"id":417024,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5047"},{"id":417023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5047"},{"id":417022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5047/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Central Salem River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.51261142665348,\n 39.66506027345514\n ],\n [\n -75.13011677160785,\n 39.493754929673486\n ],\n [\n -75.01123329774249,\n 39.637202213256444\n ],\n [\n -75.41569555121957,\n 39.76545497451639\n ],\n [\n -75.51261142665348,\n 39.66506027345514\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Lake whitefish Coregonus clupeaformis are a native coldwater species supporting important recreational and commercial fisheries in the Laurentian Great Lakes. Climate-related changes in water temperature may have important implications for the future sustainability of these fisheries. However, projecting future habitat availability is difficult because limited information is available on lake whitefish thermal ecology in the region. In this study, archival temperature loggers were implanted into 400 lake whitefish from northwestern Lake Michigan, including Green Bay, during October–November 2017. Loggers recorded temperature for 11 months at 4-hr intervals. Thirteen recovered temperature loggers were used in analyses. In winter (1 December–31 March), temperatures occupied by lake whitefish ranged from 0 to 8.0 °C, while in spring (1 April–31 May) temperatures ranged from 0 to 20.0 °C. In summer (1 June–15 September) and fall (16 September–7 November), lake whitefish occupied temperatures of 4–21.5 and 4–21.0 °C, respectively. Average temperatures in summer (10.8 °C) were within the previously proposed optimal temperature range (10–14 °C) and broad thermal niche (7–17 °C); however, 58% of observations were outside the optimal temperature range and 11% of observations were outside the broad thermal niche. Our results suggest that lake whitefish from northwestern Lake Michigan inhabit temperatures both above and below previously reported expected temperature ranges. This study provides initial insights on lake whitefish thermal ecology in Lake Michigan and can be used as a baseline for future work aimed at determining how lake whitefish habitat availability may change in the future.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.03.002","usgsCitation":"Reed, K., Izzo, L.K., Binder, T., Hayden, T., Dembkowski, D., Hansen, S., Caroffino, D., Vandergoot, C., Krueger, C., and Isermann, D.A., 2023, Initial insights on the thermal ecology of lake whitefish in northwestern Lake Michigan: Journal of Great Lakes Research, v. 49, no. 3, p. 757-766, https://doi.org/10.1016/j.jglr.2023.03.002.","productDescription":"10 p.","startPage":"757","endPage":"766","ipdsId":"IP-147146","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":443470,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2023.03.002","text":"Publisher Index Page"},{"id":432341,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.13743171358965,\n 45.614745689438536\n ],\n [\n -86.85031511100541,\n 46.103265453360024\n ],\n [\n -88.90338431195246,\n 43.9119981902781\n ],\n [\n -88.0470508747241,\n 43.978600122172\n ],\n [\n -86.13743171358965,\n 45.614745689438536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Kayla","contributorId":340827,"corporation":false,"usgs":false,"family":"Reed","given":"Kayla","email":"","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Izzo, Lisa K.","contributorId":189241,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":907593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binder, Tom","contributorId":166711,"corporation":false,"usgs":false,"family":"Binder","given":"Tom","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hayden, Todd","contributorId":340810,"corporation":false,"usgs":false,"family":"Hayden","given":"Todd","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dembkowski, Daniel","contributorId":340808,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel","affiliations":[{"id":17717,"text":"University of Wisconsin-Stevens Point","active":true,"usgs":false}],"preferred":false,"id":907596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hansen, Scott","contributorId":191464,"corporation":false,"usgs":false,"family":"Hansen","given":"Scott","affiliations":[],"preferred":false,"id":907597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Caroffino, David","contributorId":340835,"corporation":false,"usgs":false,"family":"Caroffino","given":"David","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907598,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vandergoot, Christopher","contributorId":340837,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907599,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krueger, Charles","contributorId":340820,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":907600,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907601,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70243717,"text":"70243717 - 2023 - Study design and methods of the Wells and Enteric disease Transmission (WET) Trial, a randomised controlled trial","interactions":[],"lastModifiedDate":"2023-05-18T14:09:36.87445","indexId":"70243717","displayToPublicDate":"2023-05-18T09:05:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14427,"text":"BMJ Open","active":true,"publicationSubtype":{"id":10}},"title":"Study design and methods of the Wells and Enteric disease Transmission (WET) Trial, a randomised controlled trial","docAbstract":"Introduction: The burden of disease attributed to drinking water from private wells is not well characterised. The Wells and Enteric disease Transmission trial is the first randomised controlled trial to estimate the burden of disease that can be attributed to the consumption of untreated private well water. To estimate the attributable incidence of gastrointestinal illness (GI) associated with private well water, we will test if the household treatment of well water by ultraviolet light (active UV device) versus sham (inactive UV device) decreases the incidence of GI in children under 5 years of age.
Methods and analysis: The trial will enrol (on a rolling basis) 908 families in Pennsylvania, USA, that rely on private wells and have a child 3 years old or younger. Participating families are randomised to either an active whole-house UV device or a sham device. During follow-up, families will respond to weekly text messages to report the presence of signs and symptoms of gastrointestinal or respiratory illness and will be directed to an illness questionnaire when signs/symptoms are present. These data will be used to compare the incidence of waterborne illness between the two study groups. A randomly selected subcohort submits untreated well water samples and biological specimens (stool and saliva) from the participating child in both the presence and absence of signs/symptoms. Samples are analysed for the presence of common waterborne pathogens (stool and water) or immunoconversion to these pathogens (saliva).
Ethics: Approval has been obtained from Temple University’s Institutional Review Board (Protocol 25665). The results of the trial will be published in peer-reviewed journals.
Trial registration number: NCT04826991.
A variety of methods have been developed to detect antimicrobial resistance (AMR) in different environments to better understand the evolution and dissemination of this public health threat. Comparisons of results generated using different AMR detection methods, such as quantitative PCR (qPCR) and whole-genome sequencing (WGS), are often imperfect, and few studies have analysed samples in parallel to evaluate differences. In this study, we compared bacterial culture and WGS to a culture-independent commercially available qPCR assay to evaluate the concordance between methods and the utility of each in answering research questions regarding the presence and epidemiology of AMR in wild bird habitats.
We first assessed AMR gene detection using qPCR in 45 bacterial isolates from which we had existing WGS data. We then analysed 52 wild bird faecal samples and 9 spatiotemporally collected water samples using culture-independent qPCR and WGS of phenotypically resistant indicator bacterial isolates.
Overall concordance was strong between qPCR and WGS of bacterial isolates, although concordance differed among antibiotic classes. Analysis of wild bird faecal and water samples revealed that more samples were determined to be positive for AMR via qPCR than via culture and WGS of bacterial isolates, although qPCR did not detect AMR genes in two samples from which phenotypically resistant isolates were found.
Both qPCR and culture followed by sequencing may be effective approaches for characterising AMR genes harboured by wild birds, although data streams produced using these different tools may have advantages and disadvantages that should be considered given the application and sample matrix.
Vertical profiles of temperature, salinity and dissolved oxygen in Crater Lake, a caldera lake in the Oregon Cascade Range that receives hydrothermal inputs of heat and salt, were simulated with a 1-dimensional model. Twelve Global Circulation Models and two Representative Concentration Pathways (RCPs) were used to develop boundary conditions from 1950 to 2099. The model simulated the ventilation of deep water initiated by reverse stratification and subsequent thermobaric instability. All models predicted a reduction in the frequency of deep ventilation events, from an ensemble median frequency of 5.4 winters decade−1 during 1950–2005 to 4.3 (RCP4.5) or 2.5 (RCP8.5) winters decade−1 during 2045–2099. Favorable conditions for thermobaric instability-induced mixing currently occur infrequently and will become rare in the future. The salinity gradient resulting from hydrothermal inputs presents an additional barrier to thermobaric instability that will continue through 2099. A redistribution of salt to the deep lake may prevent ventilation all the way to the bottom in the future. Hypolimnetic dissolved oxygen percent saturation remained above 75% within the 21st century, consistent with oligotrophy and very small oxygen demands. The rate of change in all variables accelerated approaching 2099, coincident with elimination of winter reverse stratification. Historically, about half of the hydrothermal heat added to Crater Lake has been vented to the atmosphere. In the RCP8.5 scenario, the hydrothermal heat will cease to be vented to the atmosphere by the end of the 21st century, and then the temperature of the deep waters will increase rapidly.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.03.014","usgsCitation":"Wood, T.M., Wherry, S., Piccolroaz, S., and Girdner, S.F., 2023, Future climate-induced changes in mixing and deep oxygen content of a caldera lake with hydrothermal heat and salt inputs: Journal of Great Lakes Research, v. 49, no. 3, p. 563-580, https://doi.org/10.1016/j.jglr.2023.03.014.","productDescription":"18 p.","startPage":"563","endPage":"580","ipdsId":"IP-150869","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":443494,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2023.03.014","text":"Publisher Index Page"},{"id":435329,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96NLDLX","text":"USGS data release","linkHelpText":"1-D Deep Ventilation (1DDV) model for Crater Lake, Oregon, 1950-2100"},{"id":417960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Crater Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.2356963721314,\n 43.03291163525574\n ],\n [\n -122.2356963721314,\n 42.848854312940176\n ],\n [\n -121.96965348050013,\n 42.848854312940176\n ],\n [\n -121.96965348050013,\n 43.03291163525574\n ],\n [\n -122.2356963721314,\n 43.03291163525574\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Tamara M. 0000-0001-6057-8080 tmwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6057-8080","contributorId":1164,"corporation":false,"usgs":true,"family":"Wood","given":"Tamara","email":"tmwood@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875049,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":875050,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Piccolroaz, Sebastiano","contributorId":297277,"corporation":false,"usgs":false,"family":"Piccolroaz","given":"Sebastiano","affiliations":[{"id":64342,"text":"University of Trento, Department of Civil, Environmental and Mechanical Engineering, Trento, Italy","active":true,"usgs":false}],"preferred":false,"id":875051,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Girdner, Scott F","contributorId":168526,"corporation":false,"usgs":false,"family":"Girdner","given":"Scott","email":"","middleInitial":"F","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":875052,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250988,"text":"70250988 - 2023 - Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes","interactions":[],"lastModifiedDate":"2024-01-18T11:54:48.157094","indexId":"70250988","displayToPublicDate":"2023-05-18T05:53:22","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes","docAbstract":"Small waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO2 and CH4. Here, we measured partial pressures of CO2 (pCO2) and CH4 (pCH4) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability of pCO2 and pCH4 in space and time. Summer means of pCO2 and pCH4 were inversely related to waterbody size and positively related to floating vegetative cover; pCO2 was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonal pCO2 by up to 26%, mean seasonal pCH4 could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability in pCH4 and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities.
Carbapenem-resistant Enterobacteriaceae (CRE) are a global threat to human health and are increasingly being isolated from nonclinical settings. OXA-48-producing Escherichia coli sequence type 38 (ST38) is the most frequently reported CRE type in wild birds and has been detected in gulls or storks in North America, Europe, Asia, and Africa. The epidemiology and evolution of CRE in wildlife and human niches, however, remains unclear. We compared wild bird origin E. coli ST38 genome sequences generated by our research group and publicly available genomic data derived from other hosts and environments to (i) understand the frequency of intercontinental dispersal of E. coli ST38 clones isolated from wild birds, (ii) more thoroughly measure the genomic relatedness of carbapenem-resistant isolates from gulls sampled in Turkey and Alaska, USA, using long-read whole-genome sequencing and assess the spatial dissemination of this clone among different hosts, and (iii) determine whether ST38 isolates from humans, environmental water, and wild birds have different core or accessory genomes (e.g., antimicrobial resistance genes, virulence genes, plasmids) which might elucidate bacterial or gene exchange among niches. Our results suggest that E. coli ST38 strains, including those resistant to carbapenems, are exchanged between humans and wild birds, rather than separately maintained populations within each niche. Furthermore, despite close genetic similarity among OXA-48-producing E. coli ST38 clones from gulls in Alaska and Turkey, intercontinental dispersal of ST38 clones among wild birds is uncommon. Interventions to mitigate the dissemination of antimicrobial resistance throughout the environment (e.g., as exemplified by the acquisition of carbapenem resistance by birds) may be warranted.
","language":"English","publisher":"ASM Journals","doi":"10.1128/aem.00319-23","usgsCitation":"Ahlstrom, C., Woksepp, H., Sandegren, L., Ramey, A.M., and Bonnedahl, J., 2023, Exchange of carbapenem-resistant Escherichia coli Sequence Type 38 intercontinentally and among wild bird, human, and environmental niches: Applied and Environmental Microbiology, v. 89, no. 6, e0031923, https://doi.org/10.1128/aem.00319-23.","productDescription":"e0031923","ipdsId":"IP-149692","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":443503,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10304903","text":"External Repository"},{"id":419396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":879254,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woksepp, Hanna","contributorId":207263,"corporation":false,"usgs":false,"family":"Woksepp","given":"Hanna","email":"","affiliations":[],"preferred":false,"id":879255,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sandegren, Linus","contributorId":279688,"corporation":false,"usgs":false,"family":"Sandegren","given":"Linus","email":"","affiliations":[{"id":57339,"text":"Department of Medical Biochemistry and Microbiology, Infection biology, antimicrobial resistance and immunology, Uppsala University","active":true,"usgs":false}],"preferred":false,"id":879256,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":879257,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonnedahl, Jonas","contributorId":181800,"corporation":false,"usgs":false,"family":"Bonnedahl","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":879258,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243683,"text":"70243683 - 2023 - Bioaccumulation kinetics of model pharmaceuticals in the freshwater unionid pondmussel, Sagittunio subrostratus","interactions":[],"lastModifiedDate":"2023-06-09T15:24:46.635923","indexId":"70243683","displayToPublicDate":"2023-05-17T08:49:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Bioaccumulation kinetics of model pharmaceuticals in the freshwater unionid pondmussel, Sagittunio subrostratus","title":"Bioaccumulation kinetics of model pharmaceuticals in the freshwater unionid pondmussel, Sagittunio subrostratus","docAbstract":"Bioaccumulation of ionizable pharmaceuticals has been increasingly studied, with most reported aquatic tissue concentrations in field or laboratory experiments being from fish. However, higher levels of antidepressants have been observed in bivalves compared with fish from effluent-dominated and dependent surface waters. Such observations may be important for biodiversity because approximately 70% of freshwater bivalves in North America are considered to be vulnerable to extinction. Because experimental bioaccumulation information for freshwater bivalves is lacking, we examined accumulation dynamics in the freshwater pondmussel, Sagittunio subrostratus, following exposure to a model weak acid, acetaminophen (mean (±SD) = 4.9 ± 1 µg L–1), and a model weak base, sertraline (mean (±SD) = 1.1 ± 1.1 µg L–1) during 14-day uptake and 7-day depuration experiments. Pharmaceutical concentrations were analyzed in water and tissue using isotope dilution liquid chromatography–tandem mass spectrometry. Mussels accumulated two orders of magnitude higher concentrations of sertraline (31.7 ± 9.4 µg g–1) compared to acetaminophen (0.3 ± 0.1 µg g–1). Ratio and kinetic-based bioaccumulation factors of 28,836.4 (L kg–1) and 34.9 (L kg–1) were calculated for sertraline and for acetaminophen at 65.3 (L kg–1) and 0.13 (L kg–1), respectively. However, after 14 days sertraline did not reach steady-state concentrations, although it was readily eliminated by S. subrostratus. Acetaminophen rapidly reached steady-state conditions but was not depurated over a 7-day period. Future bioaccumulation studies of ionizable pharmaceuticals in freshwater bivalves appear warranted. Environ Toxicol Chem 2023;00:1–7. © 2023 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5590","usgsCitation":"Burket, S., Sims, J.L., Dorman, R.A., Kemble, N.E., Brunson, E., Steevens, J.A., and Brooks, B.W., 2023, Bioaccumulation kinetics of model pharmaceuticals in the freshwater unionid pondmussel, Sagittunio subrostratus: Environmental Toxicology and Chemistry, v. 42, no. 6, p. 1183-1189, https://doi.org/10.1002/etc.5590.","productDescription":"7 p.","startPage":"1183","endPage":"1189","ipdsId":"IP-136216","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":499335,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5590","text":"Publisher Index Page"},{"id":435331,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HVMQJL","text":"USGS data release","linkHelpText":"Morphometric measurements from unionid Pondmussel (Ligumia subrostrata) and concentrations of four per- and polyfluoroalkyl substances (PFAS) in water and mussels collected from a 14-day accumulation and 7-day elimination study"},{"id":435330,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OUHJ8N","text":"USGS data release","linkHelpText":"Concentration of sertraline and acetaminophen in freshwater mussel (Sagittunio subrostratus) and water from an exposure bioassay"},{"id":417132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","county":"Boone County","otherGeospatial":"Lake Paragon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.214285899655,\n 38.856471915263995\n ],\n [\n -92.21393836929818,\n 38.85738644821262\n ],\n [\n -92.21326727619508,\n 38.85836629189188\n ],\n [\n -92.21301561628127,\n 38.85853426373845\n ],\n [\n -92.21300363247597,\n 38.858711566924285\n ],\n [\n -92.21312347053026,\n 38.85874889385451\n ],\n [\n -92.21278792397871,\n 38.85912216207768\n ],\n [\n -92.21269205353535,\n 38.859579412980565\n ],\n [\n -92.21283585920021,\n 38.85985002947703\n ],\n [\n -92.21317140575175,\n 38.85992468212277\n ],\n [\n -92.21335116283278,\n 38.8598686926461\n ],\n [\n -92.21345901708142,\n 38.85989668738969\n ],\n [\n -92.21386646646538,\n 38.85952342323205\n ],\n [\n -92.2140102721306,\n 38.85923414216251\n ],\n [\n -92.21423796443312,\n 38.85900085011963\n ],\n [\n -92.21499294417428,\n 38.85852493197967\n ],\n [\n -92.21517270125533,\n 38.85817965604173\n ],\n [\n -92.21585577816369,\n 38.85761041367235\n ],\n [\n -92.21593966480175,\n 38.85737711630321\n ],\n [\n -92.2160834704666,\n 38.85737711630321\n ],\n [\n -92.21628719515857,\n 38.85686385939647\n ],\n [\n -92.21625124374236,\n 38.85667721960269\n ],\n [\n -92.21640703321249,\n 38.85621061797582\n ],\n [\n -92.21664670932068,\n 38.85589332712095\n ],\n [\n -92.21614338949448,\n 38.85483205731646\n ],\n [\n -92.21580784294294,\n 38.854421437393256\n ],\n [\n -92.21558015064004,\n 38.85435611127798\n ],\n [\n -92.21512476603421,\n 38.85438410819165\n ],\n [\n -92.21462144620727,\n 38.854692073517924\n ],\n [\n -92.21435780248817,\n 38.85506536303453\n ],\n [\n -92.21439375390437,\n 38.85558796506638\n ],\n [\n -92.21435780248817,\n 38.85575594347523\n ],\n [\n -92.21441772151529,\n 38.855867928860704\n ],\n [\n -92.21442970532058,\n 38.85609189910329\n ],\n [\n -92.2142139968233,\n 38.85612922740793\n ],\n [\n -92.21413011018525,\n 38.85629720453841\n ],\n [\n -92.214285899655,\n 38.856471915263995\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-02-21","publicationStatus":"PW","contributors":{"editors":[{"text":"Brunson, Eric 0000-0001-6624-0902","orcid":"https://orcid.org/0000-0001-6624-0902","contributorId":201761,"corporation":false,"usgs":true,"family":"Brunson","given":"Eric","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":872908,"contributorType":{"id":2,"text":"Editors"},"rank":5}],"authors":[{"text":"Burket, S. 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The groundwater basin underlying the City of Albuquerque, New Mexico, USA.
Study Focus
The study focuses on changes in groundwater storage and how those changes relate to groundwater-level changes. Groundwater storage change was measured using repeat microgravity at 35 stations from 2016 to 2021. Usually, storage is monitored by converting groundwater-level changes to storage changes using the aquifer storage coefficient, a difficult property to measure. With gravity, storage change can be measured directly. The storage coefficient, or specific yield in an unconfined aquifer, was estimated using the gravity method at individual sites, and a map created showing how this property varies over the region.
New Hydrological Insights for the Region
For the first time, a map of specific yield was produced based on interpolated maps of gravity-derived storage change and groundwater-level change, allowing inference of this property over a broad region even without collocated monitoring wells and gravity stations. Gravity data indicate aquifer drawdown and recovery mainly occurs in the central part of the basin where pumping is greatest. This spatial distribution is not captured by the more widely spaced monitoring well network. Because the aquifer is recovering from historically greater-magnitude pumping, net-neutral storage change (inflow=outflow) occurs when pumping from the central part of the well field is about 1.54 × 107 cubic meters (12,500 acre-feet) per year.