Subsurface drainage is used to efficiently drain saturated soils to support productive agriculture in poorly drained terrains. Although subsurface drainage alters the water balance for agricultural fields, its effect on groundwater resources and groundwater recharge is poorly understood. In Minnesota, subsurface drainage has begun to increase in southeastern Minnesota, even though this part of the State is underlain by permeable karstic bedrock aquifers with only a thin layer of glacial sediments separating these aquifers from land surface.
To gain a better understanding of groundwater recharge effects from subsurface drainage, the U.S. Geological Survey (USGS), in cooperation with the Legislative-Citizen Commission on Minnesota Resources, led a 2-year hydrologic study to investigate this connection for two agricultural fields in southeastern Minnesota with subsurface drainage. A total of three monitoring plots were used between the two agricultural fields: two monitoring plots that included an actively drained area with peripheral, undrained areas, and a third monitoring plot without any subsurface drainage. Multiple piezometer transects were set up across the three monitoring plots to characterize the unsaturated zone and shallow water-table flow using pressure transducers and soil moisture probes. From these piezometers, groundwater recharge rates were derived using two different methods: the RISE Water-Table Fluctuation (WTF) method and the DRAINMOD model. In addition to these two methods, the USGS Soil-Water-Balance (SWB) model was used to estimate potential recharge rates for three different monitoring plots.
In addition to deriving groundwater recharge rates, the hydrologic budget was analyzed to interpret the water-table surface elevation and soil volumetric water content time series. At one of the two drained plots, the transects exhibited varying water-table surface elevation patterns. Frequent backflow from the adjacent ditch caused subsurface drainage flow to slow down or stop drainage through the main collector drain and cause pipe pressurization, so the closest transect appeared to be mostly controlled by the drain pressurization, whereas the farthest transect was more efficiently drained. Both of the drained monitoring plots had an elevation gradient parallel to the pattern tiles, sloping downward towards the collector drain that aggregated the parallel lines into a single drain. Because the transects were set at different gradients in the field, some of the water-table surface elevation differences were also attributed to lateral flow towards the lowest parts of the field.
Three methods were used to derive potential groundwater recharge rates: the RISE WTF method, the USGS SWB model, and DRAINMOD-derived deep seepage rates. Potential groundwater recharge rates, using the RISE WTF method, across all piezometers were 1.55 and 1.94 inches per year, respectively, for water years 2017 and 2018. More differentiation of potential recharge rates between different piezometer types occurred for water year 2018. Although the difference was slightly more than 1 inch between the drained and nondrained piezometers for water year 2018, this difference was statistically significant based on a t-test with a p-value of 0.036 (α=0.05). When looking at recharge based on distance from the drain, the subsurface drain did not affect potential recharge, although other factors such as variability in screen depths, well construction, and specific yield variability cannot be eliminated. The SWB model was also used to estimate potential recharge rates for water years 2017–18, with rates between 2.44 and 5.92 inches per year for the two drained sites, generally higher than the RISE WTF estimates. DRAINMOD-derived potential recharge rates were generally the highest of the three methods, with potential recharge rates varying from 2.07 to 9.49 inches per year.
Overall, there was a lack of agreement between the three methods. These results were not remarkable, considering the fundamental differences in the methodology for each method. However, all methods did show a fundamental difference between piezometers within the drained area and piezometers outside the drained area, including the third undrained monitoring plot. The drained areas show a lower overall potential groundwater recharge compared to the nondrained areas for all three estimates. The difference for the 2018 recharge estimates was slightly higher than 1 inch for the RISE WTF method, the difference was almost double for the nine sites for the DRAINMOD model, and the difference between the drain and undrained plots was even more significant for the SWB model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205006","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources","usgsCitation":"Smith, E.A., and Berg, A.M., 2020, Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18: U.S. Geological Survey Scientific Investigations Report 2020–5006, 57 p., https://doi.org/10.3133/sir20205006.","productDescription":"Report: ix, 54 p.; 5 Appendixes; 3 Data Releases; Dataset","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112919","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":372354,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendixes.xlsx","text":"Appendix 1 and 2","size":"3.55 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5006 Appendixes"},{"id":372353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006.pdf","text":"Report","size":"4.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5006"},{"id":372352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5006/coverthb.jpg"},{"id":372355,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table1.1.csv","text":"Appendix 1.1","size":"1.55 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 1.1"},{"id":372356,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table1.2.csv","text":"Appendix 1.2","size":"1.66 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 1.2"},{"id":372357,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table2.1.csv","text":"Appendix 2.1","size":"13.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 2.1"},{"id":372358,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5006/sir20205006_appendix_table2.2.csv","text":"Appendix 2.2","size":"13.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5006 Appendix 2.2"},{"id":372359,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P987N30U","text":"USGS data release","linkHelpText":"DRAINMOD simulations for two agricultural drainage sites in western Fillmore County, southeastern Minnesota"},{"id":372360,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90N4AWG","text":"USGS data release","linkHelpText":"Soil-Water Balance model datasets used to estimate recharge for southeastern Minnesota, 2014–2018"},{"id":372361,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94LMOPP","text":"USGS data release","linkHelpText":"Potential groundwater recharge estimates based on a groundwater rise analysis technique for two agricultural sites in southeastern Minnesota, 2016–2018"},{"id":372362,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS dataset","linkHelpText":"– USGS groundwater data for Minnesota in USGS water data for the Nation"},{"id":399628,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109687.htm"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.4167,\n 43.595\n ],\n [\n -92.45,\n 43.595\n ],\n [\n -92.45,\n 43.5444\n ],\n [\n -92.4167,\n 43.5444\n ],\n [\n -92.4167,\n 43.595\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911
A rapid batch extraction method was evaluated to estimate potential for total dissolved solids (TDS) release by 65 samples of rock from coal and gas-bearing strata of the Appalachian Basin in eastern USA. Three different extractant solutions were considered: deionized water (DI), DI equilibrated with 10% CO2 atmosphere (DI + CO2), or 30% H2O2 under 10% CO2 (H2O2+CO2). In all extractions, 10 g of pulverized rock (<0.5-mm) were mixed with 20 mL of extractant solution and shaken for 4 h at 50 rpm and 20–22 °C. The 65 rock samples were classified as coal (n=3), overburden (n = 17), coal refuse that had weathered in the field (n = 14), unleached coal refuse that had oxidized during indoor storage (n = 20), gas-bearing shale (n = 10), and pyrite (n = 1). Extracts were analyzed for specific conductance (SC), TDS, pH, and major and trace elements, and subsequently speciated to determine ionic contributions to SC. The pH of extractant blanks decreased in the order DI (6.0), DI + CO2 (5.1), and H2O2+CO2 (2.6). The DI extractant was effective for mobilizing soluble SO4 and Cl salts. The DI + CO2 extractant increased weathering of carbonates and resulted in equivalent or greater TDS than the DI leach of the same material. The H2O2+CO2 extractant increased weathering of sulfides (and carbonates) and resulted in the greatest TDS production and lowest pH values. Of the 65 samples, 19 had leachate chemistry data from previous column experiments and 35 were paired to 10 field sites with leachate chemistry data. When accounting for the water-to-rock ratio, TDS from DI and DI + CO2 extractions were correlated to TDS from column experiments while TDS from H2O2+CO2 extractions was not. In contrast to column experiments, field SC was better correlated to SC measured from H2O2+CO2 extractions than DI extractions. The field SC and SC from H2O2+CO2 extractions were statistically indistinguishable for 7 of 9 paired data sets while SC from DI extractions underestimated field SC in 5 of 9 cases. Upscaling comparisons suggest that (1) weathering reactions in the field are more aggressive than DI water or synthetic rainwater extractants used in batch or column tests, and (2) a batch extraction method utilizing 30% H2O2 (which is mildly acidic without CO2 enrichment) could be effective for identifying rocks that will release high amounts of TDS.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2020.104540","usgsCitation":"Castillo-Meza, L.E., Cravotta, C., Tasker, T.L., Warner, N.R., Daniels, W.L., Orndorff, Z.W., Bergstresser, T., Douglass, A., Kimble, G., Streczywilk, J., Barton, C., Thompson, A., and Burgos, W.D., 2020, Batch extraction method to estimate total dissolved solids (TDS) release from coal refuse and overburden: Applied Geochemistry, v. 115, 104540, 16 p., https://doi.org/10.1016/j.apgeochem.2020.104540.","productDescription":"104540, 16 p.","ipdsId":"IP-106585","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":467297,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/102448","text":"External Repository"},{"id":377359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"115","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Castillo-Meza, L. E.","contributorId":237999,"corporation":false,"usgs":false,"family":"Castillo-Meza","given":"L.","email":"","middleInitial":"E.","affiliations":[{"id":47676,"text":"Department of Civil and Environmental Engineering, The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":795778,"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":795779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tasker, T. 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W.","contributorId":238003,"corporation":false,"usgs":false,"family":"Orndorff","given":"Z.","email":"","middleInitial":"W.","affiliations":[{"id":47677,"text":"Department of Crop and Soil Science, Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":795783,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bergstresser, T.","contributorId":238004,"corporation":false,"usgs":false,"family":"Bergstresser","given":"T.","email":"","affiliations":[{"id":47678,"text":"Geochemical Testing Laboratory","active":true,"usgs":false}],"preferred":false,"id":795784,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Douglass, A.","contributorId":238005,"corporation":false,"usgs":false,"family":"Douglass","given":"A.","email":"","affiliations":[{"id":47678,"text":"Geochemical Testing Laboratory","active":true,"usgs":false}],"preferred":false,"id":795785,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kimble, G.","contributorId":238006,"corporation":false,"usgs":false,"family":"Kimble","given":"G.","email":"","affiliations":[{"id":47678,"text":"Geochemical Testing Laboratory","active":true,"usgs":false}],"preferred":false,"id":795786,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Streczywilk, J.","contributorId":238007,"corporation":false,"usgs":false,"family":"Streczywilk","given":"J.","email":"","affiliations":[{"id":47678,"text":"Geochemical Testing Laboratory","active":true,"usgs":false}],"preferred":false,"id":795787,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Barton, C.","contributorId":238008,"corporation":false,"usgs":false,"family":"Barton","given":"C.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":795788,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Thompson, A","contributorId":238009,"corporation":false,"usgs":false,"family":"Thompson","given":"A","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":795789,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Burgos, W. 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In lizards, competition and predation are major forces influencing distribution and abundance, but there is also increasing evidence pointing at the influence of habitat structure and prey abundance. Our work explored the latter further by quantifying the effects of vegetation and prey abundance on occupancy and abundance (i.e., estimated probability of detecting more than two individuals) of two sympatrically occurring species in the northern karst belt of Puerto Rico. We hypothesized that Anolis cristatellus would occur in trunk–ground substrates and Anolis krugi on grass–bush substrates according to their ecomorphological classification. We also hypothesized that prey abundance, a component of habitat quality, would have a positive and strong effect on occupancy and abundance. Anolis cristatellus exhibited high occupancy rates (>0.80), influenced by mid-story tree size. A. cristatellus abundance fluctuated over time, with highest probability of detecting two or more individuals in January–March and July–September when prey abundance transitioned from low to high levels. Occupancy of A. krugi was positively influenced by sapling density and prey abundance. Prey abundance exerted a stronger influence on occupancy, but its influence on abundance was negative and strong. Biological interactions and the type of understory substrates may explain the negative relationship. Our study supported predicted relationships between ecomorphology and habitat, but also showed that higher prey abundance may not always translate to higher local abundance. We shed light on these interactions, knowledge needed to advance anole conservation in the advent of land use and climate change.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","doi":"10.1670/19-009","usgsCitation":"Vega-Castillo, S.J., Collazo, J.A., Puente-Rolón, A., and Cuevas, E., 2020, Influence of habitat structure and prey abundance on cccupancy and abundance of two anole ecomorphs, Anolis cristatellus and Anolis krugi, in secondary karst forests in northern Puerto Rico: Journal of Herpetology, v. 54, no. 1, p. 107-117, https://doi.org/10.1670/19-009.","productDescription":"11 p.","startPage":"107","endPage":"117","ipdsId":"IP-102474","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":395456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.8463134765625,\n 18.278910262696105\n ],\n [\n -66.5386962890625,\n 18.278910262696105\n ],\n [\n -66.5386962890625,\n 18.48742375381096\n ],\n [\n -66.8463134765625,\n 18.48742375381096\n ],\n [\n -66.8463134765625,\n 18.278910262696105\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Vega-Castillo, S. J.","contributorId":274615,"corporation":false,"usgs":false,"family":"Vega-Castillo","given":"S.","email":"","middleInitial":"J.","affiliations":[{"id":38462,"text":"University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":833162,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833163,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Puente-Rolón, A. R.","contributorId":274616,"corporation":false,"usgs":false,"family":"Puente-Rolón","given":"A. 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For the GoMAMN region of the Gulf of Mexico, the Landbird Working Group identified 19 species from 12 families as priorities for monitoring (Table 3.1). In addition, all species that stopover within the GoMAMN region during migration (i.e., passage migrants) are of concern, as are the habitats they use. The 19 priority species use a wide range of habitat types and include species that spend some (e.g., breeding, wintering, migration seasons) or all (e.g., residents) of their annual cycle in the GoMAMN region. The GoMAMN Landbird Working Group organized the priority landbirds into five groups based on a combination of habitat and season—forest breeding, forest wintering, grassland breeding, grassland wintering, and passage migrants—realizing that there would be overlap of habitats and seasons for some species. For example, Swainson's Warbler (Limnothlypis swainsonii) breeds in and migrates through forested habitat in the Gulf of Mexico region and Northern Bobwhite (Colinus virginianus) uses both prairie grasslands and evergreen forest (i.e., open pine savannas) (Table 3.1). For some species, such as Painted Bunting (Passerina ciris) and Common Ground-Dove (Columbina passerina), which often use scrub/shrub vegetation, the habitat-based designations above may be overly simplistic. Although it occurs along higher, drier fringes of palustrine and estuarine emergent marsh habitat, Sedge Wren (Cistothorus platensis) is included here as a landbird (rather than a marsh bird) because it is most commonly found during the winter along the Gulf coast in upland evergreen forest (i.e., wet pine savanna) habitat and grassland habitats. Selection of the five groups was predicated on the assumption that management efforts would be similar for species using these habitats in a given season, and that monitoring methods would be habitat and season specific.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Strategic Bird Monitoring Guidelines for the Northern Gulf of Mexico, Mississippi Agricultural and Forestry Experiment Station Research Bulletin 1228","language":"English","publisher":"Mississippi State University, Mississippi Agricultural and Forestry Experiment Station (MAFES)","usgsCitation":"Zenzal, T.J., Vermillion, W.G., Ferrato, J.R., Randall, L.A., Dobbs, R.C., and Baldwin, H., 2020, GoMAMN Strategic Bird Monitoring Guidelines: Landbirds, chap. 3 of Strategic Bird Monitoring Guidelines for the Northern Gulf of Mexico, Mississippi Agricultural and Forestry Experiment Station Research Bulletin 1228, p. 25-70.","productDescription":"46 p.","startPage":"25","endPage":"70","numberOfPages":"46","ipdsId":"IP-096258","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":381116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381107,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://gomamn.org/strategic-bird-monitoring-guidelines","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zenzal, Theodore J. Jr. 0000-0001-7342-1373","orcid":"https://orcid.org/0000-0001-7342-1373","contributorId":224399,"corporation":false,"usgs":true,"family":"Zenzal","given":"Theodore","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":806348,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vermillion, William G.","contributorId":36042,"corporation":false,"usgs":true,"family":"Vermillion","given":"William","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":806349,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ferrato, Jacqueline R.","contributorId":245516,"corporation":false,"usgs":false,"family":"Ferrato","given":"Jacqueline","email":"","middleInitial":"R.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":806350,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Randall, Lori A. 0000-0003-0100-994X randalll@usgs.gov","orcid":"https://orcid.org/0000-0003-0100-994X","contributorId":2678,"corporation":false,"usgs":true,"family":"Randall","given":"Lori","email":"randalll@usgs.gov","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":806351,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobbs, Robert Christopher 0000-0002-9079-7249","orcid":"https://orcid.org/0000-0002-9079-7249","contributorId":245518,"corporation":false,"usgs":true,"family":"Dobbs","given":"Robert","email":"","middleInitial":"Christopher","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":806352,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baldwin, Heather 0000-0003-1939-5439 baldwinh@usgs.gov","orcid":"https://orcid.org/0000-0003-1939-5439","contributorId":5635,"corporation":false,"usgs":true,"family":"Baldwin","given":"Heather","email":"baldwinh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":806353,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210737,"text":"70210737 - 2020 - Serosurvey of coyotes (Canis latrans), foxes (Vulpes vulpes, Urocyon cinereoargenteus) and raccoons (Procyon lotor) for exposure to influenza A viruses in the USA","interactions":[],"lastModifiedDate":"2020-10-13T22:37:04.934489","indexId":"70210737","displayToPublicDate":"2020-02-14T09:54:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3768,"text":"Wildlife Disease","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Serosurvey of coyotes (Canis latrans), foxes (Vulpes vulpes, Urocyon cinereoargenteus) and raccoons (Procyon lotor) for exposure to influenza A viruses in the USA","title":"Serosurvey of coyotes (Canis latrans), foxes (Vulpes vulpes, Urocyon cinereoargenteus) and raccoons (Procyon lotor) for exposure to influenza A viruses in the USA","docAbstract":"We tested coyote (Canis latrans), fox (Urocyon cinereoargenteus, Vulpes vulpes), and raccoon (Procyon lotor) sera for influenza A virus (IAV) exposure. We found 2/139 samples (1 coyote, 1 raccoon) had IAV antibodies and hemagglutination inhibition assays revealed the antibodies to the 2009/2010 H1N1 human pandemic virus or to the 2007 human seasonal H1N1 virus.
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deprivation","interactions":[],"lastModifiedDate":"2021-07-30T12:43:09.104431","indexId":"70222501","displayToPublicDate":"2020-02-14T07:41:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2041,"text":"International Journal of Environmental Research and Public Health","active":true,"publicationSubtype":{"id":10}},"title":"Tissue distribution and immunomodulation in channel catfish (Ictalurus punctatus) following dietary exposure to polychlorinated biphenyl Aroclors and food deprivation","docAbstract":"This study investigated collaborative groundwater‐flow modeling and scenario analysis in the Little Plover River basin, Wisconsin, USA where an unconfined aquifer supplies groundwater for agricultural irrigation, industrial processing, municipal water supply, and stream baseflow. We recruited stakeholders with diverse interests to identify, prioritize, and evaluate scenarios defined as management changes to the landscape. Using a groundwater flow model, we simulated the top 10 stakeholder‐ranked scenarios under historically informed dry, average, and wet weather conditions and evaluated the ability of scenarios to meet government‐defined stream flow performance measures. Results show that multiple changes to the landscape are necessary to maintain optimum stream flow, particularly during dry years. Yet, when landscape changes from three scenarios—transferring water from the local waste water treatment plant to basin headwaters, moving municipal wells further from the river and downstream, and converting 240 acre (97 ha) of irrigated land to unirrigated land—were simulated in combination, the probability of meeting or exceeding optimum flows rose to 75, 65, and 34% at upper, mid, and lower stream gages, respectively, in dry climate conditions. Discussions with stakeholders reveal that the collaborative model and scenario analysis process resulted in social learning that built upon the existing complex and dynamic institutional landscape. The approach provided a forum for solution‐based discussions, and the model served as an important mediation tool for the development and evaluation of community‐defined scenarios in a high conflict environment. Today, stakeholders continue to work collaboratively to overcome challenges and implement voluntary solutions in the basin.
Cisco (Coregonus artedi) and lake whitefish (Coregonus clupeaformis) are native fish species of management concern in the Laurentian Great Lakes that often overlap in spawning locations and timing. Thus, species-level inference from in situ sampling requires methods to differentiate their eggs. Genetic barcoding and hatching eggs to visually identify larvae are used but can be time and cost intensive. Observations in published literature indicate that lake whitefish eggs may be larger than cisco eggs in the Great Lakes, but this has not yet been substantiated. Samples from shared spawning grounds are unlikely to contain similarly sized or colored eggs from other species. Thus, we assessed whether lake whitefish and cisco eggs could be separated based on size alone. Fertilized, hardened eggs were collected in situ during spawning at Elk Rapids, Lake Michigan and Chaumont Bay, Lake Ontario and preserved in ethanol. Individual eggs were measured and genetically identified. Mean diameter for cisco (2.45 mm, SD = 0.22, n = 444) was smaller than for lake whitefish (3.21 mm, SD = 0.20, n = 99). We used classification trees to identify a species-separating size threshold of 2.88 mm (95% bootstrap CI = [2.877, 2.976]), which classified eggs with an accuracy rate of 96%. Differences between species across other samples from the same locations were mostly consistent with the threshold size, but we suggest validation if applying this method to other populations. Separation of cisco and lake whitefish eggs by diameter can be accurate, efficient, and especially suitable for large sample sizes.
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0000-0002-3792-9345","orcid":"https://orcid.org/0000-0002-3792-9345","contributorId":242991,"corporation":false,"usgs":true,"family":"Chalupnicki","given":"Marc","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":833969,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dey, Kristopher","contributorId":275305,"corporation":false,"usgs":false,"family":"Dey","given":"Kristopher","email":"","affiliations":[{"id":33110,"text":"Little Traverse Bay Bands of Odawa Indians","active":true,"usgs":false}],"preferred":false,"id":833970,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Herbert, Matthew","contributorId":275306,"corporation":false,"usgs":false,"family":"Herbert","given":"Matthew","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":833971,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70207411,"text":"cir1463 - 2020 - Cooperative Fish and Wildlife Research Units program—2019 year in review","interactions":[],"lastModifiedDate":"2020-02-14T06:16:40","indexId":"cir1463","displayToPublicDate":"2020-02-13T11:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1463","displayTitle":"Cooperative Fish and Wildlife Research Units Program—2019 Year in Review","title":"Cooperative Fish and Wildlife Research Units program—2019 year in review","docAbstract":"Dear Cooperators:
Members of the Cooperative Research Units are pleased to provide you with the “2019 Year in Review” report for the Cooperative Fish and Wildlife Research Units (CRUs). You will first note that this report looks a little different than those published in the past few years, as we opted for a shorter, more concise format this year. Inside you will find brief descriptions of just a few highlighted activities of unit scientists, students, and cooperators in support of our joint mission. Because of the shorter format, we are not able to include activities from every unit or State, but rest assured that we continue to value the great work that all of you do across the country and around the world.
In fiscal year 2019, the CRU program was very productive despite challenging conditions, including budget uncertainty, a month-long furlough, and hiring delays. John Organ, Chief of the CRU program, retired in January 2019. The process to replace John was delayed several times, but as I write this, the position has been announced on the Federal Government recruitment site. I am hopeful that by the time you read this, we will have a new permanent chief. Congress provided an increase of $1 million in our allocation for the express purpose of filling some of the vacancies in our scientific workforce. Since receiving that increase, the management team has been working to fill vacancies.
The program is fortunate to have excellent research scientists, dedicated leadership, and an outstanding administrative staff. However, our accomplishments depend on the tremendous support from all of you. We look forward to a productive 2020.
John D. Thompson
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U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 303
Reston, VA 20192
Dear friends,
I invite you to take a look at U.S. Geological Survey Circular 1463, “Cooperative Fish and Wildlife Research Units Program—2019 Year in Review,” now available at https://doi.org/10.3133/cir1463. In this report, you will find details about the Cooperative Fish and Wildlife Research Units (CRU) program concerning fish and wildlife science, students, staffing, vacancies, research funding, outreach and training, awards, accolades, and professional services. You will also see examples of unit projects with information on how results have been or are being applied by our cooperators.
Throughout the year, keep up with our research projects at https://www1.usgs.gov/coopunits/Headquarters/.
Regards,
John D. Thompson
Cooperative Fish and Wildlife Research Units Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 303
Reston, VA 20192
Hellbenders Cryptobranchus alleganiensis are critically imperiled amphibians throughout the eastern USA. Rock-lifting is widely used to monitor hellbenders but can severely disturb habitat. We asked whether artificial shelter occupancy (the proportion of occupied shelters in an array) would function as a proxy for hellbender abundance and thereby serve as a viable alternative to rock-lifting. We hypothesized that shelter occupancy would vary spatially in response to hellbender density, natural shelter density, or both, and would vary temporally with hellbender seasonal activity patterns and time since shelter deployment. We established shelter arrays (n = 30 shelters each) in 6 stream reaches and monitored them monthly for up to 2 yr. We used Bayesian mixed logistic regression and model ranking criteria to assess support for hypotheses concerning drivers of shelter occupancy. In all reaches, shelter occupancy was highest from June-August each year and was higher in Year 2 relative to Year 1. Our best-supported model indicated that the extent of boulder and bedrock (hereafter, natural shelter) in a reach mediated the relationship between hellbender abundance and shelter occupancy. More explicitly, shelter occupancy was positively correlated with abundance when natural shelter covered <20% of a reach, but uncorrelated with abundance when natural shelter was more abundant. While shelter occupancy should not be used to infer variation in hellbender relative abundance when substrate composition varies among reaches, we showed that artificial shelters can function as valuable monitoring tools when reaches meet certain criteria, though regular shelter maintenance is critical.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01014","usgsCitation":"Bodinof Jachowski, C.M., Ross, B., and Hopkins, W., 2020, Evaluating artificial shelter arrays as a minimally invasive monitoring tool for the hellbender Cryptobranchus alleganiensis: Endangered Species Research, v. 41, p. 167-181, https://doi.org/10.3354/esr01014.","productDescription":"15 p.","startPage":"167","endPage":"181","ipdsId":"IP-107334","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457730,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01014","text":"Publisher Index Page"},{"id":395435,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"upper Tennessee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.50732421875,\n 36.56260003738545\n ],\n [\n -80.540771484375,\n 36.56260003738545\n ],\n [\n -80.540771484375,\n 37.26530995561875\n ],\n [\n -82.50732421875,\n 37.26530995561875\n ],\n [\n -82.50732421875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bodinof Jachowski, C. M.","contributorId":274670,"corporation":false,"usgs":false,"family":"Bodinof Jachowski","given":"C.","email":"","middleInitial":"M.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":833211,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833212,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hopkins, W.A.","contributorId":274671,"corporation":false,"usgs":false,"family":"Hopkins","given":"W.A.","email":"","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":833213,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215612,"text":"70215612 - 2020 - OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features","interactions":[],"lastModifiedDate":"2020-10-26T14:47:58.968907","indexId":"70215612","displayToPublicDate":"2020-02-13T09:43:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5923,"text":"SoftwareX","active":true,"publicationSubtype":{"id":10}},"title":"OpenCLC: An open-source software tool for similarity assessment of linear hydrographic features","docAbstract":"The National Hydrography Dataset (NHD) is a foundational geospatial data source in the United States that enables extensive and diverse environmental research and supports decision-making in numerous contexts. However, the NHD requires regular validation and update given possible inconsistent initial collection and hydrographic changes. Furthermore, systems or tools that use NHD data must manage regular updates that occur within the high-resolution version of the NHD (NHD HR). This research contributes to filling this gap by establishing an open-source software tool named OpenCLC, which automatically identifies matching and mismatching line features between two sets of hydrographic flowlines. Aside from identifying differences among two version of NHD lines, results can be applied to improve the quality of NHD HR content. OpenCLC significantly outperforms the best available commercial off-the-shelf software in computational scalability, and it is made widely available as part of the CyberGIS Toolkit to benefit broad environmental and geospatial science communities.
Aim
Human development and agriculture can have transformative and homogenizing effects on natural systems, shifting the composition of ecological communities towards non-native and native species that tolerate or thrive under human-dominated conditions. These impacts cannot be fully captured by summarizing species presence, as they include dramatic changes to patterns of species abundance. However, how human land use patterns and species invasions intersect to shape patterns of abundance and dominance within ecological communities is poorly understood even in well-known taxa.
Location
Conterminous United States.
Time period
2010–2012.
Major taxa studied
Passeriformes.
Methods
We analyse continental-scale monitoring data to study the proportional abundance of non-native and native synanthropic species within passerine bird communities. Synanthropic species are those that benefit from an association with humans. We estimate how the amount and configuration of human development and agriculture relate to the degree to which human-associated species dominate passerine communities across the continent.
Results
Human-associated species comprised the majority of detected passerine individuals across two-thirds of bird surveys. Non-native and synanthropic species responded differently to land cover and reached highest relative abundance in different portions of the continent. The proportional abundance of synanthropic birds increased rapidly with development, but was not related to the configuration of land cover. The proportion of non-native individuals was higher when intensively-used land cover was more aggregated.
Main conclusions
Even low amounts of intensively-used lands were associated with a dramatic reshaping of passerine communities, with consequences for patterns of relative abundance across the continent.
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Accordingly, there is a recognized need for new methods and tools to help quantify the dynamic interaction between wild bird hosts and commercial poultry. Using satellite-marked waterfowl, we applied Bayesian joint hierarchical modeling to concurrently model species distributions, residency times, migration timing, and disease occurrence probability under an integrated animal movement and disease distribution modeling framework. Our results indicate that migratory waterfowl are positively related to AI occurrence over North America such that as waterfowl occurrence probability or residence time increase at a given location, so too does the chance of a commercial poultry AI outbreak. Analyses also suggest that AI occurrence probability is greatest during our observed waterfowl northward migration, and less during the southward migration. Methodologically, we found that when modeling disparate facets of disease systems at the wildlife-agriculture interface, it is essential that multiscale spatial patterns be addressed to avoid mistakenly inferring a disease process or disease-environment relationship from a pattern evaluated at the improper spatial scale. 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Service","active":true,"usgs":false}],"preferred":false,"id":782262,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prosser, Diann J. 0000-0002-5251-1799 dprosser@usgs.gov","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":2389,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","email":"dprosser@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":782255,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223794,"text":"70223794 - 2020 - Quantifying harvestable fish and crustacean production and associated economic values provided by oyster reefs","interactions":[],"lastModifiedDate":"2021-09-08T12:48:28.21585","indexId":"70223794","displayToPublicDate":"2020-02-13T07:46:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2926,"text":"Ocean and Coastal Management","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying harvestable fish and crustacean production and associated economic values provided by oyster reefs","docAbstract":"Quantifying ecosystem services can provide information to justify conservation and restoration decisions so as to allocate limited resources effectively. Consequently, decision makers and public typically ask for simple and understandable information with confidence regarding the availability of the services and the probable economic value. Here, we compiled published information on density enhancement and species life-history information to quantify fish and crustacean production and its uncertainty associated with the current extent of oyster (Crassostrea virginica) reefs in Mobile Bay, Alabama. We applied Alabama fishing size limits as a cutoff to exclude the production of non-harvestable size individuals. Fishery landing (2005–2015) and Willingness-To-Pay information were used to quantify the economic benefit of the harvestable production enhancement (commercial and recreational production). Sixteen species were found to be production-enhanced in the bay with a mean of 354 ± 182 g m−2 year−1, of which 170 ± 112 g m−2 year−1 was economically quantifiable based on their harvestable production and landing information. The mean economic value was $509,000 year−1 in direct economic value for commercial fishers and $19.59 million year−1 estimated by the willingnesstopay value from recreational anglers. The results demonstrated a substantial positive economic benefit of ecosystem services from oyster reefs associated with fishery production in Mobile Bay, Alabama. The method could be applied elsewhere to estimate the economic return from the investment of conserving and restoring of similar structured habitats.
The surface trace tool comprises a Python script written for ArcGIS that will determine the line of intersection between a planar feature and a surface. Specifically, this tool was designed for geologic applications where geologic planar-feature orientations are reported as strike and dip, and the intersecting surface is the ground. The tool output will show how planar geologic layers intersect with topography.
Determining where geologic features crop out on the surface can be used to guide new geologic mapping as well as reviewing existing geologic mapping. This tool was developed to aid in more efficient mapping of an unknown area. These unknown areas may be missing data, either owing to a lack of suitable outcrops or being difficult to traverse, and data about the areas may be extrapolated using this tool and surrounding data to determine where planar features might appear on the ground.
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
The North Branch Au Sable River, located in the northern lower peninsula of Michigan near Lovells, Michigan, has historically been known for its brook trout (Salvelinus fontinalis) and its status as a blue ribbon trout stream; however, within the past few decades, there has been a decline in fish population. The objectives of this study were to assess if concentrations of organic chemicals were present in quantities in the North Branch Au Sable River that may potentially harm aquatic species and to establish current baseline concentrations of organic chemicals against which future data can be compared. Passive sampling technology was used to collect information on the concentration, occurrence, transport, and fate of organic chemicals; these samplers absorb dissolved organic chemicals in the river over several weeks, as the timing and intensity of pesticide applications and the frequency of storm events and irrigation can cause fluctuations in organic chemical loading to surface waters. The chemical classes investigated as part of this study included pesticides (both legacy [organochlorine] and current use), polychlorinated biphenyls, polybrominated diphenyl ethers (PBDEs), and polycyclic aromatic hydrocarbons (PAHs).
Passive samplers, including semipermeable membrane devices and polar organic chemical integrative samplers, were deployed at four locations along the North Branch Au Sable River, near Lovells, Mich., in June 2018 for a total of 28 days. Several organic chemicals were detected in the North Branch Au Sable River at low concentrations. Organic chemicals were detected at every sampling location on the North Branch Au Sable River; however, not all chemicals were detected at every location. The highest number of organic chemicals were detected at the most downstream sampling site (North Branch Au Sable River at Kellogg's Bridge), and the lowest number of organic chemicals were detected at the next site upstream (North Branch Au Sable River at Twin Bridge Road). The organic contaminants most frequently detected at all sampling locations include the legacy pesticides pentachloroanisole, trans-chlordane, p,p'-dichlorodiphenyldichloroethylene, and p,p'-dichlorodiphenyltrichloroethane; the PBDE PBDE-28; and the PAHs 2-methylphenanthrene and perylene.
Organic chemical concentrations detected on the North Branch Au Sable River were below almost all water-quality benchmarks included in this report. However, low concentrations of organic chemicals may still pose a risk to aquatic organisms and throughout the trophic hierarchy because of low-dose additive and synergistic mixture effects, transgenerational effects, and a lack of established water-quality benchmarks for many organic chemicals. This report provides data on the current (2018) state of the North Branch Au Sable River and provided a baseline of organic contaminant data against which future data on the North Branch Au Sable River can be evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205002","collaboration":"Prepared for the Mason-Griffith Founders Chapter of Trout Unlimited in cooperation with Lovells Township, Michigan","usgsCitation":"Brennan, A.K., and Alvarez, D.A., 2020, Evaluation of legacy and emerging organic chemicals using passive sampling devices on the North Branch Au Sable River near Lovells, Michigan, June 2018: U.S. Geological Survey Scientific Investigations Report 2020–5002, 21 p., https://doi.org/10.3133/sir20205002.","productDescription":"vi, 21 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-112993","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399625,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109682.htm"},{"id":372284,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5002/sir20205002.pdf","text":"Report","size":"1.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5002"},{"id":372283,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5002/coverthb.jpg"}],"country":"United States","state":"Michigan","county":"Crawford County","city":"Lovells","otherGeospatial":"North Branch Au Sable River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.6833,\n 45\n ],\n [\n -84.25,\n 45\n ],\n [\n -84.25,\n 44.65\n ],\n [\n -84.6833,\n 44.65\n ],\n [\n -84.6833,\n 45\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911–4107