Persistence of aquatic fauna depends on the conditions and connectivity of surface water and groundwater. In light of altered baseflows and both current and future predicted increases in stream temperatures, it is important to assess current thermal conditions, examine thermal responses of aquatic fauna, and evaluate water-management practices. Our study objectives were to determine (1) how changes in baseflow levels in the Kiamichi River influence hyporheic exchange, which correspondingly influences temperature at the reach scale; (2) temperature tolerances of stream fishes as a means for predicting how habitat complexity influences stream-fish populations; and (3) assess how dam releases influence the downstream temperature and dissolved oxygen regime during the low-flow period. We quantified hyporheic exchange at four reaches and, as expected, found higher groundwater exchange via transient storage occurred at the upstream sites. The net groundwater flux estimation was negative for the majority of reaches indicating that surface water is lost to groundwater during summer (i.e., losing), baseflow conditions. We determined critical thermal maximum (CTMax) for 17 stream fishes and thermal tolerances ranged 32-38°C. We determined the average thermal tolerance for two habitat fish guilds to calculate changes in thermal stress due to hypothetical reservoir release scenarios. We developed a process-based Water Quality Analysis Simulation Program model to predict downstream temperature conditions over 74-km of river in response to reservoir releases that corresponded to discharges of 0.00 (control), 0.34, 0.59, 0.76, 1.13, and 1.50 m3/s. Based on the dissolved oxygen conditions observed in 2015 and 2017 and biological oxygen demand sampling results, reservoir releases did not directly reduce dissolved oxygen concentrations in the Kiamichi River (though dissolved oxygen concentrations are limited to current water-release strategies by the managing agency). We simulated three scenarios using three water-release temperatures: 27.64°C, 26.00°C and 24.07°C that corresponded to average reservoir temperatures at gate locations on the dam. We compared the predicted temperature time series with CTMax of two fish-habitat guilds to quantify the cumulative time when stream fishes experienced severe thermal stress downstream from Sardis Reservoir. According to our simulations, reservoir releases would be capable of regulating downstream water temperature during the summer baseflow period. The 0.00 m3/s scenario resulted in 130 h of thermal stress for benthic fishes, and 73 h for mid-column fishes. As expected, thermal relief increased with increasing release magnitude and decreasing release water temperature. The 0.34 m3/s release scenario reduced thermal stress (range is simulations from the top and bottom gate) by 11-18% for mid-column fishes and 8-12% for benthic fishes with an effective distance (where the cumulative time above CTMax was reduced by half) of 1-2 km for both guilds. The 0.59 m3/s release scenario reduced thermal stress by 18-25% for mid-column fishes and 12-20% for benthic fishes with effective distances of 4-8 km and 2-7 km, respectively. Three releases representing pre-dam flow magnitudes (0.76, 1.13 and 1.50 m3/s released from top gate) reduced thermal stress up to 46% for mid-column fishes and 41% for benthic fishes with an effective distance of 13-16 km, respectively. Lastly, we quantified temperature-induced stress via whole-body cortisol concentration of six stream fishes in response to prolonged thermal exposure at two temperatures (27°C and 32°C). We found no difference in cortisol levels between temperatures for any of the six species, indicating acclimation to elevated temperatures during the test period. However, Highland Stoneroller Campostoma spadiceum expressed cortisol concentrations greater than typical basal levels at both temperatures, suggesting stress from factors other than temperature (i.e., captivity). Our results suggest different reservoir-release options could improve downstream thermal-fish habitat during the summer baseflow period.
","language":"English","doi":"10.3996/css49046075","usgsCitation":"Brewer, S., Fox, G., Zhou, Y., and Alexander, J., 2020, Understanding the impacts of surface-groundwater conditions on stream fishes under altered baseflow conditions: Cooperator Science Series CSS-136-2020, 113 p., https://doi.org/10.3996/css49046075.","productDescription":"113 p.","ipdsId":"IP-106826","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494942,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-09-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Brewer, Shannon K. 0000-0002-1537-3921","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":340552,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":947039,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, G.","contributorId":273105,"corporation":false,"usgs":false,"family":"Fox","given":"G.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":947040,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Y.","contributorId":360419,"corporation":false,"usgs":false,"family":"Zhou","given":"Y.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":947041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, J.","contributorId":305320,"corporation":false,"usgs":false,"family":"Alexander","given":"J.","email":"","affiliations":[],"preferred":false,"id":947042,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70244010,"text":"70244010 - 2020 - Using advanced population genomics to better understand the relationship between offshore and spawning habitat use for Atlantic Sturgeon","interactions":[],"lastModifiedDate":"2023-05-31T14:05:38.468729","indexId":"70244010","displayToPublicDate":"2020-01-01T08:58:09","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5709,"text":"OCS Study","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"2020-062","title":"Using advanced population genomics to better understand the relationship between offshore and spawning habitat use for Atlantic Sturgeon","docAbstract":"Atlantic Sturgeon (Acipenser oxyrinchus oxyrinchus) are a large-bodied anadromous fish that historically supported important fisheries along the east coast of the United States. Following years of overharvest and habitat degradation, populations experienced severe declines. In 2012, the National Marine Fisheries Service listed Atlantic Sturgeon under the Endangered Species Act (ESA; 61 FR 4722). Their listing named five Distinct Population Segments (DPSs), predicated on genetic groups composed of geographically proximate populations.
Federal management of Atlantic Sturgeon presents challenges, as sturgeon from each of the five DPSs mix extensively in coastal and marine habitats yet take and recovery progress must be evaluated separately for each unit. Genetic assignment testing based on mitochondrial and microsatellite markers allows individuals to be assigned back to their natal river and DPS. However, this approach is not perfect and some individuals may be incorrectly assigned. Recent advances in genomics offer the potential of a higher resolution approach to genetic assignment testing, and thus may reduce uncertainty associated with assignment testing. In addition, genomics allows a greater number of markers to be examined from across a broader portion of the sturgeon genome, thus may provide an enhanced perspective of population structure for the species, and potentially allow other previously intractable questions to be addressed (Bernatchez et al. 2017, Supple and Shapiro 2018).
We used next-generation sequencing to develop a draft genome for Atlantic Sturgeon and identify single nucleotide polymorphisms (SNPs) that could be used to resolve the natal river and DPS of individual Atlantic Sturgeon. We identified 1,210 candidate SNPs within the nuclear genome as well as 49 SNPs within the mitochondrial genome. After filtering and review, we selected 161 nuclear SNPs and 39 mitochondrial SNPs for further testing and evaluation. We used genotyping-in-thousands by sequencing (GT-seq) to simultaneously sequence nuclear SNP loci, mitochondrial SNP loci, and the existing panel of twelve microsatellite loci. This effort required a pilot sequencing run on a single sturgeon sample to test marker amplification and refine primer strengths, followed by a series of sequencing runs to generate baseline data for 288 individuals representing nine populations of Atlantic Sturgeon in four DPSs.
Using baseline data from the nine populations, we ran a series of genomic analyses to characterize diversity within and among populations, providing a benchmark for this species using the new SNP markers. Allelic richness was similar for all populations, although there was a general trend of more northern population containing greater levels of allelic richness. Interestingly, we observed linkage disequilibrium among many pairs of loci within many populations. This might be the result of physical linkage but could also suggest these populations are recovering from genetic bottlenecks and/or are effectively small, leading to specific haplotypes to be favored by chance. Pairwise differentiation among populations varied among the populations (FST range: 0.010-0.098) and was significantly correlated (r = 0.771; P < 0.001) to pairwise FST observed using microsatellite markers). Population clustering and ordination techniques using the new genomic data both support an overall population structure that is similar to the current DPS management units (which were developed primarily based on microsatellite genetic data). Overall, this suggests that existing microsatellite markers and the panel of SNP markers developed in this study provide similar information about the populations structure and ecology of Atlantic Sturgeon. Given the observed differences in allele frequencies among populations, our genomic baseline supports previous assertations that Atlantic Sturgeon show natal homing, despite mixing extensively in marine waters during non-breeding periods. Lower levels of differentiation between populations in the South Atlantic DPS suggest that populations in this region may have greater levels of gene flow relative to their more northerly conspecifics, which has also previously been suggested based on microsatellite data. The observed differentiation among populations provides the necessary foundation for determining the natal river and DPS of Atlantic Sturgeon using assignment testing.
We tested the utility of our new genomic baseline for resolving the population and DPS of Atlantic Sturgeon. Our nuclear SNP markers showed utility for identifying the origin of unknown Atlantic Sturgeon samples, as 86.5% were assigned to the correct DPS and 66.3% were assigned to the correct natal river. However, since this study was funded the Conservation Genetics and Genomics Laboratory at Leetown Science Center has made significant improvements to their microsatellite genetic baseline, which now performs more effectively than our new genomic approach (the genetic baseline includes 12 populations and 5 DPSs, and correctly assigns 95.8% of individuals to DPS and 84.9% of individuals to their natal population using 12 microsatellite loci). We conducted an ad hoc exploration of how additional microsatellite or nuclear SNP loci may further improve the accuracy of assignment testing. We found that additional microsatellite markers are likely to result in greater improvements in assignment efficiency than additional nuclear SNPs. However, a much larger number of SNP loci (which if identified could be sequenced using other methods that are now available; e.g., the RAD-capture approach published by Ali et al. 2016) could produce assignment efficiencies that are greater than what is currently feasible using microsatellites. In the absence of further research and development of additional SNP markers for Atlantic Sturgeon (possibly using an approach other than GT-seq), the existing microsatellite loci are the most effective means available to determine the natal river and DPS of Atlantic Sturgeon encountered in offshore waters.
Because our new genomic markers were less effective than the existing panel of 12 microsatellite markers, we chose to use the existing microsatellite markers to assign Atlantic Sturgeon captured in another BOEM-funded study (cooperative agreement M16AC00003; Monitoring endangered Atlantic Sturgeon and commercial finfish habitat use offshore New York) following consultation with our project officer. Using this approach, we genotyped and assigned 186 Atlantic Sturgeon captured in coastal waters off the Rockaway Peninsula, New York. The vast majority of these sturgeon were assigned to the New York Bight DPS (94.62%), and most appear to belong to the Hudson River population (87.10%) with smaller contributions from the Delaware River population (7.53%). Smaller contributions (2.15%) were observed from six other populations, including those from the James, York, Kennebec, Ogeechee, and Edisto rivers. Although most of the fish we assigned were assigned to the nearest spawning rivers (Hudson and Delaware), the contributions from distant rivers is consistent with the propensity of this species to move long distances and form mixed stock aggregations along the continental shelf. This finding indicates that spawning populations (and their corresponding DPS) from distant locations may potentially be impacted by offshore activities. In fact, activities in this region of the New York Bight could negatively impact Atlantic Sturgeon population from at least four different DPSs. Genetic or genomic assignment testing remains an essential tool to characterize potential impacts to Atlantic Sturgeon populations and should be applied more broadly to better characterize potential impacts of activities in other locations.
","language":"English","publisher":"Bureau of Ocean Energy Management","usgsCitation":"Kazyak, D.C., Aunins, A.W., Johnson, R.L., Lubinski, B.A., Eackles, M.S., and King, T.L., 2020, Using advanced population genomics to better understand the relationship between offshore and spawning habitat use for Atlantic Sturgeon: OCS Study 2020-062, vi, 70 p.","productDescription":"vi, 70 p.","ipdsId":"IP-106640","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":417577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417553,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://espis.boem.gov/final%20reports/BOEM_2020-062.pdf"}],"country":"United States","otherGeospatial":"Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.78806562180561,\n 30.580780235117913\n ],\n [\n -77.30336432135029,\n 29.69193789019691\n ],\n [\n -64.22711779996946,\n 41.900389189548946\n ],\n [\n -67.3512701866663,\n 45.015746962117504\n ],\n [\n -69.19972084180974,\n 45.07365899620572\n ],\n [\n -71.17441845739293,\n 43.73099181927631\n ],\n [\n -71.83104405363528,\n 41.84410310133748\n ],\n [\n -74.31268565149895,\n 41.0826339553866\n ],\n [\n -77.28914013501216,\n 39.074309300310176\n ],\n [\n -76.84797677722777,\n 36.18896372481389\n ],\n [\n -80.79266974786641,\n 32.79868499554179\n ],\n [\n -82.1343310450048,\n 31.38292965735748\n ],\n [\n -81.78806562180561,\n 30.580780235117913\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874141,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874142,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Robin L. 0000-0003-4314-3792 rjohnson1@usgs.gov","orcid":"https://orcid.org/0000-0003-4314-3792","contributorId":224717,"corporation":false,"usgs":true,"family":"Johnson","given":"Robin","email":"rjohnson1@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874143,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874144,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874145,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, Tim L. tlking@usgs.gov","contributorId":3520,"corporation":false,"usgs":true,"family":"King","given":"Tim","email":"tlking@usgs.gov","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":874258,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222101,"text":"70222101 - 2020 - Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States","interactions":[],"lastModifiedDate":"2021-07-21T11:56:46.001135","indexId":"70222101","displayToPublicDate":"2020-01-01T07:07:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2344,"text":"Journal of Hydrometeorology","active":true,"publicationSubtype":{"id":10}},"title":"Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States","docAbstract":"Atmospheric rivers (ARs) significantly influence precipitation and hydrologic variability in many areas of the world, including the western United States. As ARs are increasingly recognized by the research community and the public, there is a need to more precisely quantify and communicate their hydrologic impacts, which can vary from hazardous to beneficial depending on location and on the atmospheric and land surface conditions prior to and during the AR. This study leverages 33 years of atmospheric and hydrologic data for the western United States to 1) identify how water vapor amount, wind direction and speed, temperature, and antecedent soil moisture conditions influence precipitation and hydrologic responses (runoff, recharge, and snowpack) using quantile regression and 2) identify differences in hydrologic response types and magnitudes across the study region. Results indicate that water vapor amount serves as a primary control on precipitation amounts. Holding water vapor constant, precipitation amounts vary with wind direction, depending on location, and are consistently greater at colder temperatures. Runoff efficiencies further covary with temperature and antecedent soil moisture, with precipitation falling as snow and greater available water storage in the soil column mitigating flood impacts of large AR events. This study identifies the coastal and maritime mountain ranges as areas with the greatest potential for hazardous flooding and snowfall impacts. This spatially explicit information can lead to better understanding of the conditions under which ARs of different precipitation amounts are likely to be hazardous at a given location.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-19-0119.1","usgsCitation":"Albano, C.M., Dettinger, M.D., and Harpold, A., 2020, Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States: Journal of Hydrometeorology, v. 21, p. 143-159, https://doi.org/10.1175/JHM-D-19-0119.1.","productDescription":"17 p.","startPage":"143","endPage":"159","ipdsId":"IP-108504","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":458275,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jhm-d-19-0119.1","text":"Publisher Index Page"},{"id":387290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.3095703125,\n 31.541089879585808\n ],\n [\n -108.9404296875,\n 31.541089879585808\n ],\n [\n -108.9404296875,\n 49.26780455063753\n ],\n [\n -127.3095703125,\n 49.26780455063753\n ],\n [\n -127.3095703125,\n 31.541089879585808\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Albano, Christine M.","contributorId":169455,"corporation":false,"usgs":false,"family":"Albano","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":819519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dettinger, Michael D. 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":149896,"corporation":false,"usgs":true,"family":"Dettinger","given":"Michael","email":"mddettin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":819520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harpold, Adrian","contributorId":184147,"corporation":false,"usgs":false,"family":"Harpold","given":"Adrian","affiliations":[],"preferred":false,"id":819521,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208625,"text":"70208625 - 2020 - Temporospatial shifts in Sandhill Crane staging in the Central Platte River Valley in response to climatic variation and habitat change","interactions":[],"lastModifiedDate":"2020-12-15T20:16:16.059853","indexId":"70208625","displayToPublicDate":"2019-12-31T14:44:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2785,"text":"Monographs of the Western North American Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Temporospatial shifts in Sandhill Crane staging in the Central Platte River Valley in response to climatic variation and habitat change","docAbstract":"Over 80% of the Mid-Continent Sandhill Crane (Antigone canadensis) Population (MCP), estimated at over 660,000 individuals, stops in the Central Platte River Valley (CPRV) during spring migration from mid-February through mid-April. Research suggests that the MCP may be shifting its distribution spatially and temporally within the CPRV. From 2002 to 2017, we conducted weekly aerial surveys of Sandhill Cranes staging in the CPRV to examine temporal and spatial trends in their abundance and distribution. Then, we used winter temperature and drought severity measures from key wintering and early migratory stopover locations to assess the impacts of weather patterns on annual migration chronology in the CPRV. We also evaluated channel width and land cover characteristics using aerial imagery from 1938, 1998, and 2016 to assess the relationship between habitat change and the spatial distribution of the MCP in the CPRV. We used generalized linear models, cumulative link models, and Akaike’s information criterion corrected for small sample sizes (AICc) to compare temporal and spatial models. Temperatures and drought conditions at wintering and migration locations that are heavily used by Greater Sandhill Cranes (A. c. tabida) best predicted migration chronology of the MCP to the CPRV. The spatial distribution of roosting Sandhill Cranes from 2015 to 2017 was best predicted by the proportion of width reduction in the main channel since 1938 (rather than its width in 2016) and the proportion of land cover as prairie-meadow habitat within 800 m of the Platte River. Our data suggest that Sandhill Cranes advanced their migration by an average of just over 1 day per year from 2002 to 2017, and that they continued to shift eastward, concentrating at eastern reaches of the CPRV. Climate change, land use change, and habitat loss have all likely contributed to Sandhill Cranes coming earlier and staying longer in fewer reaches of the CPRV, increasing their site use intensity. These historically unprecedented densities may present a disease risk to Sandhill Cranes and other waterbirds, including Whooping Cranes (Grus americana). Our models suggest that conservation actions may be maintaining Sandhill Crane densities in areas that would otherwise be declining in use. We suggest that management actions intended to mitigate trends in the distribution of Sandhill Cranes, including wet meadow restoration, may similarly benefit prairie- and braided river–endemic species of concern.
","language":"English","publisher":"BioOne","doi":"10.3398/042.011.0104","usgsCitation":"Caven, A.J., Brinley Buckley, E.M., King, K.C., Wiese, J.D., Baasch, D.M., Wright, G.D., Harner, M.J., Pearse, A.T., Rabbe, M., Varner, D., Krohn, B., Arcilla, N., Schroeder, K.D., and Dinan, K.F., 2020, Temporospatial shifts in Sandhill Crane staging in the Central Platte River Valley in response to climatic variation and habitat change: Monographs of the Western North American Naturalist, v. 11, p. 33-76, https://doi.org/10.3398/042.011.0104.","productDescription":"44 p.","startPage":"33","endPage":"76","ipdsId":"IP-102357","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458279,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3398/042.011.0104","text":"Publisher Index 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The model framework was iteratively developed through evaluation of model results at multiple checkpoints. A methodology was developed for characterizing regional wave and water levels, and the numerical model XBeach was used to simulate the potential impacts from a wide range of storm events. Simulations quantified the potential for erosion, overwash, and inundation of the pre- and post-restoration beach and dune system and were used as a preliminary screening of restoration designs. The model framework also incorporated a computationally efficient method to evaluate the impacts of storms, long-term shoreline changes, and relative sea level rise over a 15-year time period in order to evaluate the effect of the preferred restoration alternative on habitat distribution. 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","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography","doi":"10.1002/lol2.10134","usgsCitation":"Wagner, T., Noah R., O.L., Bartley, M.L., Hanks, E., Schliep, E.M., Wikle, N.B., King, K.B., McCullough, I., Stachelek, J., Cheruvelil, K.S., Filstrup, C.T., Lapierre, J., Liu, B., Sorrano, P., Tan, P., Wang, Q., Webster, K., and Zhou, J., 2020, Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data: Limnology and Oceanography Letters, v. 5, no. 2, p. 228-235, https://doi.org/10.1002/lol2.10134.","productDescription":"8 p.","startPage":"228","endPage":"235","ipdsId":"IP-109351","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":488957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lol2.10134","text":"Publisher Index Page"},{"id":395550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":833307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Noah R., oa Lottig Lottig","contributorId":274769,"corporation":false,"usgs":false,"family":"Noah R.","given":"oa","suffix":"Lottig","email":"","middleInitial":"Lottig","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":833308,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartley, Meridith L.","contributorId":274772,"corporation":false,"usgs":false,"family":"Bartley","given":"Meridith","email":"","middleInitial":"L.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":833309,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanks, Ephraim M.","contributorId":274775,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":833310,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schliep, Erin M.","contributorId":274778,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":833311,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wikle, Nathan B.","contributorId":274780,"corporation":false,"usgs":false,"family":"Wikle","given":"Nathan","email":"","middleInitial":"B.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":833312,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"King, Katelyn B. 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S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833313,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCullough, Ian","contributorId":274784,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833314,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stachelek, Jemma","contributorId":274864,"corporation":false,"usgs":false,"family":"Stachelek","given":"Jemma","email":"","affiliations":[],"preferred":false,"id":833315,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cheruvelil, Kendra S.","contributorId":172029,"corporation":false,"usgs":false,"family":"Cheruvelil","given":"Kendra","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":833316,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Filstrup, Christopher T.","contributorId":169032,"corporation":false,"usgs":false,"family":"Filstrup","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":833440,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lapierre, Jean-Francois","contributorId":264522,"corporation":false,"usgs":false,"family":"Lapierre","given":"Jean-Francois","affiliations":[{"id":54487,"text":"University of Montreal","active":true,"usgs":false}],"preferred":false,"id":833441,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Liu, Boyang","contributorId":274865,"corporation":false,"usgs":false,"family":"Liu","given":"Boyang","email":"","affiliations":[],"preferred":false,"id":833442,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sorrano, Patricia","contributorId":204929,"corporation":false,"usgs":false,"family":"Sorrano","given":"Patricia","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833443,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tan, Pang-Ning","contributorId":172193,"corporation":false,"usgs":false,"family":"Tan","given":"Pang-Ning","affiliations":[],"preferred":false,"id":833444,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Wang, Q.","contributorId":83761,"corporation":false,"usgs":true,"family":"Wang","given":"Q.","affiliations":[],"preferred":false,"id":833445,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Webster, Katherine","contributorId":274866,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","affiliations":[],"preferred":false,"id":833446,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Zhou, Jiayu","contributorId":204926,"corporation":false,"usgs":false,"family":"Zhou","given":"Jiayu","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":833447,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70222540,"text":"70222540 - 2020 - Metal bioavailability models: Current status, lessons learned, considerations for regulatory use, and the path forward","interactions":[],"lastModifiedDate":"2021-08-03T13:47:20.331188","indexId":"70222540","displayToPublicDate":"2019-12-27T08:45:36","publicationYear":"2020","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}},"title":"Metal bioavailability models: Current status, lessons learned, considerations for regulatory use, and the path forward","docAbstract":"Since the early 2000s, biotic ligand models and related constructs have been a dominant paradigm for risk assessment of aqueous metals in the environment. We critically review 1) the evidence for the mechanistic approach underlying metal bioavailability models; 2) considerations for the use and refinement of bioavailability-based toxicity models; 3) considerations for the incorporation of metal bioavailability models into environmental quality standards; and 4) some consensus recommendations for developing or applying metal bioavailability models. We note that models developed to date have been particularly challenged to accurately incorporate pH effects because they are unique with multiple possible mechanisms. As such, we doubt it is ever appropriate to lump algae/plant and animal bioavailability models; however, it is often reasonable to lump bioavailability models for animals, although aquatic insects may be an exception. Other recommendations include that data generated for model development should consider equilibrium conditions in exposure designs, including food items in combined waterborne–dietary matched chronic exposures. Some potentially important toxicity-modifying factors are currently not represented in bioavailability models and have received insufficient attention in toxicity testing. Temperature is probably of foremost importance; phosphate is likely important in plant and algae models. Acclimation may result in predictions that err on the side of protection. Striking a balance between comprehensive, mechanistically sound models and simplified approaches is a challenge. If empirical bioavailability tools such as multiple-linear regression models and look-up tables are employed in criteria, they should always be informed qualitatively and quantitatively by mechanistic models. If bioavailability models are to be used in environmental regulation, ongoing support and availability for use of the models in the public domain are essential.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.4560","usgsCitation":"Mebane, C.A., Chowdhury, M., De Schamphelaere, K.A., Lofts, S., Paquin, P.R., Santore, R.C., and Wood, C.M., 2020, Metal bioavailability models: Current status, lessons learned, considerations for regulatory use, and the path forward: Environmental Toxicology and Chemistry, v. 39, no. 1, p. 60-84, https://doi.org/10.1002/etc.4560.","productDescription":"25 p.","startPage":"60","endPage":"84","ipdsId":"IP-110208","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":458289,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.4560","text":"Publisher Index Page"},{"id":387661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chowdhury, M. Jasim","contributorId":261730,"corporation":false,"usgs":false,"family":"Chowdhury","given":"M. Jasim","affiliations":[{"id":52970,"text":"International Lead Association, Durham, North Carolina, USA","active":true,"usgs":false}],"preferred":false,"id":820504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Schamphelaere, Karel A.C.","contributorId":261731,"corporation":false,"usgs":false,"family":"De Schamphelaere","given":"Karel","email":"","middleInitial":"A.C.","affiliations":[{"id":52971,"text":"Ghent University, Gent, Belgium","active":true,"usgs":false}],"preferred":false,"id":820505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lofts, Stephen","contributorId":261732,"corporation":false,"usgs":false,"family":"Lofts","given":"Stephen","email":"","affiliations":[{"id":52972,"text":"Centre for Ecology and Hydrology, Bailrigg, Lancaster, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":820506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paquin, Paul R.","contributorId":261733,"corporation":false,"usgs":false,"family":"Paquin","given":"Paul","email":"","middleInitial":"R.","affiliations":[{"id":52973,"text":"HDR, New York, New York, USA","active":true,"usgs":false}],"preferred":false,"id":820507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santore, Robert C.","contributorId":202449,"corporation":false,"usgs":false,"family":"Santore","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":36447,"text":"Windward Environmental LLC, Syracuse, NY","active":true,"usgs":false}],"preferred":false,"id":820508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wood, Chris M.","contributorId":261734,"corporation":false,"usgs":false,"family":"Wood","given":"Chris","email":"","middleInitial":"M.","affiliations":[{"id":52974,"text":"University of British Columbia, Vancouver, British Columbia, Canada.","active":true,"usgs":false}],"preferred":false,"id":820509,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70208469,"text":"70208469 - 2020 - Microbial source tracking (MST) in Chattahoochee River National Recreation Area: Seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use","interactions":[],"lastModifiedDate":"2020-02-11T10:05:32","indexId":"70208469","displayToPublicDate":"2019-12-26T10:04:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Microbial source tracking (MST) in Chattahoochee River National Recreation Area: Seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use","title":"Microbial source tracking (MST) in Chattahoochee River National Recreation Area: Seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use","docAbstract":"Escherichia coli levels in recreational waters are often used to predict when fecal-associated pathogen levels are a human health risk. The reach of the Chattahoochee River that flows through the Chattahoochee River National Recreation Area (CRNRA), located in the Atlanta-metropolitan area, is a popular recreation area that frequently exceeds the U.S. Environmental Protection Agency beach action value (BAV) for E. coli. A BacteriALERT program has been implemented to provide real-time E. coli estimates in the reach and notify the public of potentially harmful levels of fecal-associated pathogens as indicated by surrogate models based on real-time turbidity measurements from continuous water quality monitoring stations. However, E. coli does not provide information about the sources of fecal contamination and its accuracy as a human health indicator is questionable when sources of contamination are non-human. The objectives of our study were to investigate, within the Park and surrounding watersheds, seasonal and precipitation-related patterns in microbial source tracking marker concentrations of possible sources (human, dog, and ruminant), assess correlations between source contamination levels and culturable E. coli levels, determine which sources best explained model-based E. coli estimates above the BAV and detection of esp2 (a marker for the esp gene associated with pathogenic strains of Enterococcus faecium and Enterococcus faecalis), and investigate associations between source contamination levels and land use features. Three BacteriALERT sites on the Chattahoochee River were sampled six times per season in the winter and summer from December 2015 through September 2017, and 11 additional stream sites (synoptic sites) from the CRNRA watershed were sampled once per season. Samples were screened with microbial source tracking (MST) quantitative PCR (qPCR) markers for humans (HF183 Taqman), dogs (DogBact), and ruminants (Rum2Bac), the esp2 qPCR marker, and culturable E. coli. At the BacteriALERT sites, HF183 Taqman concentrations were higher under wet conditions DogBact concentrations were greater in the winter and under wet conditions, and Rum2Bac concentrations were comparatively low throughout the study with no difference across seasons or precipitation conditions. Concentrations of HF183 Taqman, DogBact, and Rum2Bac were positively correlated with culturable E. coli concentrations; however, DogBact had the largest R2 value among the three markers, and the forward stepwise regression indicated it was the best predictor of culturable E. coli concentrations at the BacteriALERT sites. Recursive partitioning indicated that BAV exceedances of model-based E. coli estimates were best explained by DogBact concentrations ≥3 gene copies per mL (CN/mL). Detections of esp2 at BacteriALERT sites were best explained by DogBact concentrations ≥11 CN/mL, while detections of esp2 at synoptic sites were best explained by HF183 Taqman ≥29 CN/mL. At the synoptic sites, HF183 Taqman levels were associated with wastewater treatment plant density. However, this relationship was driven primarily by a single site, suggesting possible conveyance issues in that catchment. esp2 detections at synoptic sites were positively associated with development within a 2-km radius and negatively associated with development within the catchment, suggesting multiple sources of esp2 in the watershed. DogBact and Rum2Bac were not associated with the land use features included in our analyses. Implications for Park management include: 1) fecal contamination levels were highest during wet conditions and in the off season when fewer visitors are expected to be participating in water-based recreation, 2) dogs are likely contributors to fecal contamination in the CRNRA and may be sources of pathogenic bacteria indicating further investigation of the origins of this contamination may be warranted as would be research to understand the human health risks from exposure to dog fecal contamination, and 3) high levels of the human marker at one site in the CRNRA watershed suggests more extensive monitoring in that catchment may locate the origin of human fecal contamination detected during this study.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2019.115435","usgsCitation":"McKee, A.M., Molina, M., Cyterski, M., and Couch, A., 2020, Microbial source tracking (MST) in Chattahoochee River National Recreation Area: Seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use: Water Research, v. 171, 115435, 12 p., https://doi.org/10.1016/j.watres.2019.115435.","productDescription":"115435, 12 p.","ipdsId":"IP-105660","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":458294,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2019.115435","text":"Publisher Index Page"},{"id":437182,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957P46S","text":"USGS data release","linkHelpText":"Microbial Source Tracking Marker Concentrations in the Chattahoochee River National Recreation Area Watershed in 2015-2017, Georgia, USA"},{"id":372227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Chattahoochee River National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.51370239257812,\n 33.90347621404078\n ],\n [\n -83.91769409179688,\n 33.90347621404078\n ],\n [\n -83.91769409179688,\n 34.250405862125\n ],\n [\n -84.51370239257812,\n 34.250405862125\n ],\n [\n -84.51370239257812,\n 33.90347621404078\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"171","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Anna M. 0000-0003-2790-5320 amckee@usgs.gov","orcid":"https://orcid.org/0000-0003-2790-5320","contributorId":166725,"corporation":false,"usgs":true,"family":"McKee","given":"Anna","email":"amckee@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Molina, Marirosa","contributorId":220538,"corporation":false,"usgs":false,"family":"Molina","given":"Marirosa","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":782033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cyterski, Mike","contributorId":222389,"corporation":false,"usgs":false,"family":"Cyterski","given":"Mike","email":"","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":782034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Couch, Ann","contributorId":222390,"corporation":false,"usgs":false,"family":"Couch","given":"Ann","email":"","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":782035,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227717,"text":"70227717 - 2020 - Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA","interactions":[],"lastModifiedDate":"2022-01-27T16:55:07.591983","indexId":"70227717","displayToPublicDate":"2019-12-25T10:48:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7470,"text":"Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA","title":"Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA","docAbstract":"Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream-segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large-river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AICC-selected parameterization. Although all models had similarly high performance and evaluation metrics, the AICC-selected models were more inclusive of westward-situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5913","usgsCitation":"Taylor, A., Hafen, T., Holley, C.T., Gonzalez, A., and Long, J.M., 2020, Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA: Ecology & Evolution, v. 10, no. 2, p. 705-717, https://doi.org/10.1002/ece3.5913.","productDescription":"13 p.","startPage":"705","endPage":"717","ipdsId":"IP-108639","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":458296,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5913","text":"Publisher Index Page"},{"id":394979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Colorado, Kansas, Missouri, Nebraska, New Mexico, Texas","otherGeospatial":"Arkansas River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.314453125,\n 34.08906131584994\n ],\n [\n -91.845703125,\n 34.08906131584994\n ],\n [\n -91.845703125,\n 39.30029918615029\n ],\n [\n -107.314453125,\n 39.30029918615029\n ],\n [\n -107.314453125,\n 34.08906131584994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, A. T.","contributorId":264887,"corporation":false,"usgs":false,"family":"Taylor","given":"A. T.","affiliations":[{"id":54572,"text":"University of Central Oklahoma","active":true,"usgs":false}],"preferred":false,"id":831896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hafen, T.","contributorId":272271,"corporation":false,"usgs":false,"family":"Hafen","given":"T.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":831897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holley, Colt Taylor 0000-0003-4172-4331","orcid":"https://orcid.org/0000-0003-4172-4331","contributorId":272272,"corporation":false,"usgs":true,"family":"Holley","given":"Colt","email":"","middleInitial":"Taylor","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":831898,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gonzalez, A.","contributorId":272273,"corporation":false,"usgs":false,"family":"Gonzalez","given":"A.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":831899,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":831900,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208003,"text":"70208003 - 2020 - Assessing the water quality impacts of two Category-5 hurricanes on St. Thomas, Virgin Islands","interactions":[],"lastModifiedDate":"2020-01-23T09:34:37","indexId":"70208003","displayToPublicDate":"2019-12-24T09:28:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the water quality impacts of two Category-5 hurricanes on St. Thomas, Virgin Islands","docAbstract":"Managing waterborne and water-related diseases is one of the most critical factors in the aftermath of hurricane-induced natural disasters. The goal of the study was to identify water-quality impairments in order to set the priorities for post-hurricane relief and to guide future decisions on disaster preparation and relief administration. Field investigations were carried out on St. Thomas, U.S. Virgin Islands as soon as the disaster area became accessible after the back-to-back hurricane strikes by Irma and Maria in 2017. Water samples were collected from individual household rain cisterns, the coastal ocean, and street-surface runoffs for microbial concentration. The microbial community structure and the occurrence of potential human pathogens were investigated in samples using next generation sequencing. Loop mediated isothermal amplification was employed to detect fecal indicator bacteria, Enterococcus faecalis. The results showed both fecal indicator bacteria and Legionella genetic markers were prevalent but were low in concentration in the water samples. Among the 22 cistern samples, 86% were positive for Legionella and 82% for Escherichia-Shigella. Enterococcus faecalis was detected in over 68% of the rain cisterns and in 60% of the coastal waters (n = 20). Microbial community composition in coastal water samples was significantly different from cistern water and runoff water. Although identification at bacterial genus level is not direct evidence of human pathogens, our results suggest cistern water quality needs more organized attention for protection of human health, and that preparation and prevention measures should be taken before natural disasters strike.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2019.115440","usgsCitation":"Jiang, S., Han, M., Chandrasekaran, S., Fang, Y., and Kellogg, C.A., 2020, Assessing the water quality impacts of two Category-5 hurricanes on St. Thomas, Virgin Islands: Water Research, v. 171, 115440, 9 p., https://doi.org/10.1016/j.watres.2019.115440.","productDescription":"115440, 9 p.","ipdsId":"IP-109410","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458299,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2019.115440","text":"Publisher Index Page"},{"id":371493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"St. Thomas, U.S, Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.09811401367188,\n 18.24761153423444\n ],\n [\n -64.72457885742188,\n 18.24761153423444\n ],\n [\n -64.72457885742188,\n 18.419684546193967\n ],\n [\n -65.09811401367188,\n 18.419684546193967\n ],\n [\n -65.09811401367188,\n 18.24761153423444\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"171","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jiang, Sunny","contributorId":221746,"corporation":false,"usgs":false,"family":"Jiang","given":"Sunny","email":"","affiliations":[{"id":40412,"text":"University of California, Irvine, CA","active":true,"usgs":false}],"preferred":false,"id":780109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Han, Muyue","contributorId":221747,"corporation":false,"usgs":false,"family":"Han","given":"Muyue","email":"","affiliations":[{"id":40412,"text":"University of California, Irvine, CA","active":true,"usgs":false}],"preferred":false,"id":780110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chandrasekaran, Srikiran","contributorId":221748,"corporation":false,"usgs":false,"family":"Chandrasekaran","given":"Srikiran","email":"","affiliations":[{"id":40412,"text":"University of California, Irvine, CA","active":true,"usgs":false}],"preferred":false,"id":780111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fang, Yingcong","contributorId":221749,"corporation":false,"usgs":false,"family":"Fang","given":"Yingcong","email":"","affiliations":[{"id":40412,"text":"University of California, Irvine, CA","active":true,"usgs":false}],"preferred":false,"id":780112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":780108,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209758,"text":"70209758 - 2020 - Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics","interactions":[],"lastModifiedDate":"2020-04-28T14:24:02.273893","indexId":"70209758","displayToPublicDate":"2019-12-24T08:13:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics","docAbstract":"Understanding the effectiveness of environmental flow deliveries along rivers requires monitoring vegetation. Monitoring data are often collected at multiple spatial scales. For riparian vegetation, optical remote sensing methods can estimate growth responses at the riparian corridor scale, and field‐based measures can quantify species composition; however, the extent to which these different measures are duplicative or complementary is important to understand when planning monitoring programmes with limited resources. In this study, we analysed riparian vegetation growth in the delta of the Colorado River in response to an experimental pulse flow. Our goal was to compare ground‐based measurements of vegetation structure and composition with satellite‐based Landsat radiometric variables, such as the normalized difference vegetation index (NDVI). We made this comparison in 21 transects following the delivery of 131.8 million cubic meters (mcm) of water in the stream channel during the spring of 2014 as a pulse flow and 38.4 mcm as base flows. Vegetation cover increased 14% and NDVI increased 0.02 (15%) by October 2015, and both variables returned to pre‐pulse flow values in October 2016. Observed changes in vegetation structure and composition did not persist after the second year. The highest increase in vegetation cover in October 2014 and October 2015 resulted from species that could respond rapidly to additional water such as reeds (Arundo donax and Phragmites australis), cattail (Typha domingensis), and herbaceous plants. Dominant shrubs, saltcedar (Tamarix spp.) and arrowweed (Pluchea sericea), both indicative of nonrestored habitats showed variable increases in cover, and native trees (Salicaceae family) presented low increases (1%). The strong NDVI–vegetation cover relationship indicates that NDVI is appropriate to detect changes at the riparian corridor scale but needs to be complemented with ground data to determine the contributions by different species to the observed trends.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13689","collaboration":"","usgsCitation":"Gomez-Sapiens, M.M., Jarchow, C., Flessa, K.W., Shafroth, P.B., Glenn, E., and Nagler, P.L., 2020, Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics: Hydrological Processes, v. 34, no. 8, p. 1682-1696, https://doi.org/10.1002/hyp.13689.","productDescription":"15 p.","startPage":"1682","endPage":"1696","ipdsId":"IP-109952","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":488909,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/659868","text":"External Repository"},{"id":374314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.3070068359375,\n 31.5691754490709\n ],\n [\n -114.70275878906249,\n 31.5691754490709\n ],\n [\n -114.70275878906249,\n 32.708733368521585\n ],\n [\n -115.3070068359375,\n 32.708733368521585\n ],\n [\n -115.3070068359375,\n 31.5691754490709\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomez-Sapiens, Martha M.","contributorId":58172,"corporation":false,"usgs":true,"family":"Gomez-Sapiens","given":"Martha","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":787897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":787898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flessa, Karl W.","contributorId":175308,"corporation":false,"usgs":false,"family":"Flessa","given":"Karl","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":787899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":787900,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":787901,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":787902,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70207572,"text":"70207572 - 2020 - Algal toxins in Alaskan seabirds: Evaluating the role of saxitoxin and domoic acid in a large-scale die-off of Common Murres","interactions":[],"lastModifiedDate":"2019-12-26T13:30:58","indexId":"70207572","displayToPublicDate":"2019-12-23T13:28:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"Algal toxins in Alaskan seabirds: Evaluating the role of saxitoxin and domoic acid in a large-scale die-off of Common Murres","docAbstract":"Elevated seawater temperatures are linked to the development of harmful algal blooms (HABs), which pose a growing threat to marine birds and other wildlife. During late 2015 and early 2016, a massive die-off of Common Murres (Uria algae; hereafter, murres) was observed in the Gulf of Alaska coincident with a strong marine heat wave. Previous studies have documented illness and death among seabirds resulting from exposure to the HAB neurotoxins saxitoxin (STX) and domoic acid (DA). Given the unusual mortality event, corresponding warm water anomalies, and recent detection of STX and DA throughout coastal Alaskan waters, HABs were identified as a possible factor of concern. To evaluate whether algal toxins may have contributed to murre deaths, we tested for STX and DA in a suite of tissues obtained from beach-cast murre carcasses associated with the die-off as well as from apparently healthy murres and Black-legged Kittiwakes (Rissa tridactyla; hereafter, kittiwakes) in the preceding and following summers. We also tested forage fish and marine invertebrates collected in the Gulf of Alaska in 2015–2017 to evaluate potential sources of HAB toxin exposure for seabirds. Saxitoxin was present in multiple tissue types of both die-off (36.4%) and healthy (41.7%) murres and healthy kittiwakes (54.2%). Among birds, we detected the highest concentrations of STX in liver tissues (range 1.4 –10.8 µg 100 g-1) of die-off murres. Saxitoxin was relatively common in forage fish (20.3%) and marine invertebrates (53.8%). No established toxicity limits currently exist for seabirds, but concentrations of STX in birds and forage fish in our study were lower than values reported from most other bird die-offs in which STX intoxication was causally linked. We detected low concentrations of DA in a single bird sample and in 33.3% of marine invertebrates and 4.0% of forage fish samples. Although these results do not support the hypothesis that acute exposure to STX or DA was a primary factor in the 2015–2016 die-off event, additional information about the sensitivity of murres to these toxins is needed before we can discount their potential role in the die-off. The widespread occurrence of STX in seabirds, forage fish, and marine invertebrates in the Gulf of Alaska indicates that algal toxins should be considered in future assessments of seabird health, especially given the potential for greater occurrence of HABs in the future.","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2019.101730","usgsCitation":"Van Hemert, C.R., Schoen, S.K., Litaker, R.W., Smith, M.M., Arimitsu, M.L., Piatt, J.F., Holland, W., Hardison, R., and Pearce, J.M., 2020, Algal toxins in Alaskan seabirds: Evaluating the role of saxitoxin and domoic acid in a large-scale die-off of Common Murres: Harmful Algae, v. 92, 101730, https://doi.org/10.1016/j.hal.2019.101730.","productDescription":"101730","ipdsId":"IP-111669","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":458309,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2019.101730","text":"Publisher Index Page"},{"id":437184,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UNY0FR","text":"USGS data release","linkHelpText":"SUPERSEDED: Data Associated with Algal Toxin Testing of Common Murres (Uria aalge) and Forage Fish in Alaska, 2015–2017"},{"id":370688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.01708984375,\n 58.73400476743346\n ],\n [\n -143.08593749999997,\n 58.73400476743346\n ],\n [\n -143.08593749999997,\n 62.36999628130772\n ],\n [\n -155.01708984375,\n 62.36999628130772\n ],\n [\n -155.01708984375,\n 58.73400476743346\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"92","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Van Hemert, Caroline R. 0000-0002-6858-7165 cvanhemert@usgs.gov","orcid":"https://orcid.org/0000-0002-6858-7165","contributorId":3592,"corporation":false,"usgs":true,"family":"Van Hemert","given":"Caroline","email":"cvanhemert@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":778565,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoen, Sarah K. 0000-0002-5685-5185 sschoen@usgs.gov","orcid":"https://orcid.org/0000-0002-5685-5185","contributorId":5136,"corporation":false,"usgs":true,"family":"Schoen","given":"Sarah","email":"sschoen@usgs.gov","middleInitial":"K.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":778566,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Litaker, R. 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Mean groundwater age, groundwater age distribution, and susceptibility to land surface contamination, using a new metric (Susceptibility Index; SI) based on the full age distribution and less prone to bias than estimated mean age, is reported for 168 public and domestic wells across the aquifer. Comparison of groundwater age metrics between well networks of varying spatial scale suggest an extensive sample network of equally spaced, long screened interval wells can be used to characterize aquifer wide groundwater age. Estimated mean age ranges from 1 to 50,000 years and, according to the composite age distribution, approximately 63 percent of all sampled water recharged after 1950 (i.e., modern) and 18 percent of the sampled water was recharged greater than 10,000 years ago. The later finding strongly suggests a connection between the glacial aquifer and underlying bedrock aquifers. Statistical analysis of glacial aquifer hydrogeology and age metrics show groundwater ages are young (less than few 100 years) and more susceptible to land surface contamination (larger SI) in unconfined and shallow portions of the aquifer. Old groundwater (greater than 1000 years) is more often associated with thicker sequences of fine grain sediments and/or shallow bedrock. Calculated SI is shown to be more strongly related to the number of land surface contaminants detected than mean age or fraction modern. Statistical analysis of SI and hydrogeology indicates SI is largely dictated by well depth and confinement. This study demonstrates how sample network design can be used to characterize groundwater age of large aquifers with a limited number of samples and how interpretation of environmental tracers can be used to improve conceptual models of groundwater aquifers and identify groundwater susceptible to contamination.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124505","collaboration":"","usgsCitation":"Solder, J.E., Jurgens, B., Stackelberg, P.E., and Shope, C., 2020, Environmental tracer evidence for connection between shallow and bedrock aquifers and high intrinsic susceptibility to contamination of the conterminous U.S. glacial aquifer: Journal of Hydrology, v. 583, 124505, 12 p., https://doi.org/10.1016/j.jhydrol.2019.124505.","productDescription":"124505, 12 p.","ipdsId":"IP-090099","costCenters":[{"id":610,"text":"Utah Water Science 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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":786518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shope, Christopher L. 0000-0003-4209-049X","orcid":"https://orcid.org/0000-0003-4209-049X","contributorId":223873,"corporation":false,"usgs":false,"family":"Shope","given":"Christopher L.","affiliations":[{"id":40783,"text":"State of Utah Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":786519,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70207547,"text":"70207547 - 2020 - An experimental evaluation of the feasibility of inferring concentrations of a visible tracer dye from remotely sensed data in turbid rivers","interactions":[],"lastModifiedDate":"2019-12-24T12:08:16","indexId":"70207547","displayToPublicDate":"2019-12-22T11:55:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"An experimental evaluation of the feasibility of inferring concentrations of a visible tracer dye from remotely sensed data in turbid rivers","docAbstract":"The movement of contaminants and biota within river channels is influenced by the flow field via various processes of dispersion. Understanding and modeling of these processes thus can facilitate applications ranging from the prediction of travel times for spills of toxic materials to the simulation of larval drift for endangered species of fish. A common means of examining dispersion in rivers involves conducting tracer experiments with a visible tracer dye. Whereas conventional in situ instruments can only measure variations in dye concentration over time at specific, fixed locations, remote sensing could provide more detailed, spatially distributed information for characterizing dispersion patterns and validating two-dimensional numerical models. Although previous studies have demonstrated the potential to infer dye concentrations from remotely sensed data in clear-flowing streams, whether this approach can be applied to more turbid rivers remains an open question. To evaluate the feasibility of mapping spatial patterns of dispersion in streams with greater turbidity, we conducted an experiment that involved manipulating dye concentration and turbidity while acquiring field spectra and hyperspectral and RGB (red, green, blue) images from a small Unoccupied Aircraft System (sUAS). Applying an Optimal Band Ratio Analysis (OBRA) algorithm to these data sets indicated strong relationships between reflectance (i.e., water color) and Rhodamine WT dye concentration across four different turbidity levels from 40-60 NTU. Moreover, we obtained high correlations between spectrally based quantities (i.e., band ratios) and dye concentration for the original, essentially continuous field spectra; field spectra resampled to the bands of a five-band imaging system and an RGB camera; and both hyperspectral and RGB images acquired from a sUAS during the experiment. The results of this study thus confirmed the potential to map dispersion patterns of tracer dye via remote sensing and suggested that this approach can be extended to more turbid rivers than those examined previously.","language":"English","publisher":"MDPI","doi":"10.3390/rs12010057","usgsCitation":"Legleiter, C.J., Manley, P., Erwin, S.O., and Bulliner, E.A., 2020, An experimental evaluation of the feasibility of inferring concentrations of a visible tracer dye from remotely sensed data in turbid rivers: Remote Sensing, v. 12, no. 1, 57, 21 p., https://doi.org/10.3390/rs12010057.","productDescription":"57, 21 p.","ipdsId":"IP-112896","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":458311,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12010057","text":"Publisher Index Page"},{"id":437185,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ZRGKQ","text":"USGS data release","linkHelpText":"Field spectra, UAS-based hyperspectral and RGB images, and in situ measurements of turbidity and Rhodamine WT dye concentration from an experiment conducted at the USGS Columbia Environmental Research Center, Columbia, MO, on April 2, 2019"},{"id":370672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Columbia","otherGeospatial":"Columbia Environmental Research Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.28494167327881,\n 38.905995699991145\n ],\n [\n -92.27007150650024,\n 38.905995699991145\n ],\n [\n -92.27007150650024,\n 38.91711561447239\n ],\n [\n -92.28494167327881,\n 38.91711561447239\n ],\n [\n -92.28494167327881,\n 38.905995699991145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":778425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manley, Paul 0000-0001-6062-1149","orcid":"https://orcid.org/0000-0001-6062-1149","contributorId":221490,"corporation":false,"usgs":false,"family":"Manley","given":"Paul","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":778426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erwin, Susannah O. 0000-0002-2799-0118 serwin@usgs.gov","orcid":"https://orcid.org/0000-0002-2799-0118","contributorId":5183,"corporation":false,"usgs":true,"family":"Erwin","given":"Susannah","email":"serwin@usgs.gov","middleInitial":"O.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":778427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bulliner, Edward A. 0000-0002-2774-9295 ebulliner@usgs.gov","orcid":"https://orcid.org/0000-0002-2774-9295","contributorId":4983,"corporation":false,"usgs":true,"family":"Bulliner","given":"Edward","email":"ebulliner@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":778428,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215153,"text":"70215153 - 2020 - A hydrologic landscapes perspective on groundwater connectivity of depressional wetlands","interactions":[],"lastModifiedDate":"2020-10-08T14:52:59.912851","indexId":"70215153","displayToPublicDate":"2019-12-21T09:46:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"A hydrologic landscapes perspective on groundwater connectivity of depressional wetlands","docAbstract":"Simplification of communities is a common consequence of anthropogenic modification. However, the prevalence and mechanisms of biotic homogenization among wetland systems require further examination. Biota of wetlands in the North American Prairie Pothole Region are adapted to high spatial and temporal variability in ponded-water duration and salinity. Recent climate change, however, has resulted in decreased hydrologic variability. Land-use changes have exacerbated this loss of variability. We used aquatic-macroinvertebrate data from 16 prairie-pothole wetlands sampled between 1992 and 2015 to explore homogenization of wetland communities. Macroinvertebrate communities of small wetlands that continued to cycle between wet and dry phases experienced greater turnover and supported unique taxa compared to larger wetlands that shifted towards less dynamic permanently ponded, lake-like regimes. Temporal turnover in beta-diversity was lowest in these permanently ponded wetlands. Additionally, wetlands that shifted to permanently ponded regimes also experienced a shift from palustrine to lacustrine communities. While increased pond permanence can increase species and overall beta-diversity in local areas previously lacking lake communities, homogenization of wetland communities at a larger, landscape scale can result in an overall loss of biodiversity as the diverse communities of many wetland systems become increasingly similar to those of lakes.
","language":"English","publisher":"Springer International Publishing","doi":"10.1007/s10750-019-04154-4","usgsCitation":"McLean, K., Mushet, D.M., Sweetman, J.N., Anteau, M.J., and Wiltermuth, M.T., 2020, Invertebrate communities of Prairie-Pothole wetlands in the age of the aquatic Homogenocene: Hydrobiologia, v. 847, p. 3773-3793, https://doi.org/10.1007/s10750-019-04154-4.","productDescription":"21 p.","startPage":"3773","endPage":"3793","ipdsId":"IP-111199","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":370671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Stutsman County","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.77999114990234,\n 47.820762392755846\n ],\n [\n -100.63407897949219,\n 47.820762392755846\n ],\n [\n -100.63407897949219,\n 47.939116930322\n ],\n [\n -100.77999114990234,\n 47.939116930322\n ],\n [\n -100.77999114990234,\n 47.820762392755846\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"847","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778407,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778408,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetman, Jon N. 0000-0002-9849-7355","orcid":"https://orcid.org/0000-0002-9849-7355","contributorId":221489,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","email":"","middleInitial":"N.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":778409,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778410,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778411,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207540,"text":"70207540 - 2020 - Alternative stable states in inherently unstable systems","interactions":[],"lastModifiedDate":"2020-02-06T11:23:06","indexId":"70207540","displayToPublicDate":"2019-12-20T11:43:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Alternative stable states in inherently unstable systems","docAbstract":"Alternative stable states are nontransitory states within which communities can exist. However, even highly dynamic communities can be viewed within the framework of stable‐state theory if an appropriate “ecologically relevant” time scale is identified. The ecologically relevant time scale for dynamic systems needs to conform to the amount of time needed for a system's community to complete an entire cycle through its normal range of variation. For some systems, the ecologically relevant period can be relatively short (eg, tidal systems), for others it can be decadal (eg, prairie wetlands). We explore the concept of alternative stable states in unstable systems using the highly dynamic wetland ecosystems of North America's Prairie Pothole Region. The communities in these wetland ecosystems transition through multiple states in response to decadal‐long climate oscillations that cyclically influence ponded‐water depth, permanence, and chemistry. The perspective gained by considering dynamic systems in the context of stable‐state theory allows for an increased understanding of how these systems respond to changing drivers that can push them past tipping points into alternative states. Incorporation of concepts inherent to stable‐state theory has been suggested as a key scientific element upon which to base sustainable environmental management.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ece3.5944","usgsCitation":"Mushet, D.M., McKenna, O.P., and McLean, K., 2020, Alternative stable states in inherently unstable systems: Ecology and Evolution, v. 10, no. 2, p. 843-850, https://doi.org/10.1002/ece3.5944.","productDescription":"8 p.","startPage":"843","endPage":"850","ipdsId":"IP-102838","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5944","text":"Publisher Index Page"},{"id":370670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"Prairie Potholes Wetlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.27929687499999,\n 56.12106042504407\n ],\n [\n -113.64257812499999,\n 49.03786794532644\n ],\n [\n -113.37890625,\n 47.81315451752768\n ],\n [\n -102.12890625,\n 47.87214396888731\n ],\n [\n -99.66796875,\n 44.08758502824516\n ],\n [\n -93.955078125,\n 42.032974332441405\n ],\n [\n -92.63671875,\n 42.22851735620852\n ],\n [\n -95.00976562499999,\n 47.69497434186282\n ],\n [\n -99.84374999999999,\n 51.23440735163459\n ],\n [\n -106.61132812499999,\n 54.059387886623576\n ],\n [\n -109.86328125,\n 55.677584411089526\n ],\n [\n -116.27929687499999,\n 56.12106042504407\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":778404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778405,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237931,"text":"70237931 - 2020 - The method controls the story - Sampling method impacts on the detection of pore-water nitrogen concentrations in streambeds","interactions":[],"lastModifiedDate":"2022-11-01T16:00:00.148611","indexId":"70237931","displayToPublicDate":"2019-12-19T09:50:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"The method controls the story - Sampling method impacts on the detection of pore-water nitrogen concentrations in streambeds","docAbstract":"Biogeochemical gradients in streambeds are steep and can vary over short distances often making adequate characterisation of sediment biogeochemical processes challenging. This paper provides an overview and comparison of streambed pore-water sampling methods, highlighting their capacity to address gaps in our understanding of streambed biogeochemical processes. This work reviews and critiques available pore-water sampling techniques to characterise streambed biogeochemical conditions, including their characteristic spatial and temporal resolutions, and associated advantages and limitations. A field study comparing three commonly-used pore-water sampling techniques (multilevel mini-piezometers, miniature drivepoint samplers and diffusive equilibrium in thin-film gels) was conducted to assess differences in observed nitrate and ammonium concentration profiles. Pore-water nitrate concentrations did not differ significantly between sampling methods (p-value = 0.54) with mean concentrations of 2.53, 4.08 and 4.02 mg l−1 observed with the multilevel mini-piezometers, miniature drivepoint samplers and diffusive equilibrium in thin-film gel samplers, respectively. Pore-water ammonium concentrations, however, were significantly higher in pore-water extracted by multilevel mini-piezometers (3.83 mg l−1) and significantly lower where sampled with miniature drivepoint samplers (1.05 mg l−1, p-values <0.01). Differences in observed pore-water ammonium concentration profiles between active (suction: multilevel mini-piezometers) and passive (equilibrium; diffusive equilibrium in thin-film gels) samplers were further explored under laboratory conditions. Measured pore-water ammonium concentrations were significantly greater when sampled by diffusive equilibrium in thin-film gels than with multilevel mini-piezometers (all p-values ≤0.02).
The findings of this study have critical implications for the interpretation of field-based research on hyporheic zone biogeochemical cycling and highlight the need for more systematic testing of sampling protocols. For the first time, the impact of different active and passive pore-water sampling methods is addressed systematically here, highlighting to what degree the choice of pore-water sampling methods affects research outcomes, with relevance for the interpretation of previously published work as well as future studies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.136075","usgsCitation":"Comer-Warner, S., Knapp, J.L., Blaen, P.J., Klaar, M., Shelley, F., Zarnetske, J.P., Lee-Cullen, J., Folegot, S., Kurz, M., Lewandowski, J., Harvey, J., Ward, A., Mendoza-Lera, C., Ullah, S., Datry, T., Kettridge, N., Gooddy, D., Drummond, J., Marti, E., Milner, A., Hannah, D., and Krause, S., 2020, The method controls the story - Sampling method impacts on the detection of pore-water nitrogen concentrations in streambeds: Science of the Total Environment, v. 709, 136075, 19 p., https://doi.org/10.1016/j.scitotenv.2019.136075.","productDescription":"136075, 19 p.","ipdsId":"IP-114183","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":458336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2019.136075","text":"Publisher Index Page"},{"id":408992,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"709","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Comer-Warner, Sophie 0000-0003-1260-3151","orcid":"https://orcid.org/0000-0003-1260-3151","contributorId":298689,"corporation":false,"usgs":false,"family":"Comer-Warner","given":"Sophie","email":"","affiliations":[{"id":64658,"text":"Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.","active":true,"usgs":false}],"preferred":false,"id":856251,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knapp, Julia LA","contributorId":243624,"corporation":false,"usgs":false,"family":"Knapp","given":"Julia","email":"","middleInitial":"LA","affiliations":[{"id":48754,"text":"Department of Environmental Systems Science, ETH Zurich","active":true,"usgs":false}],"preferred":false,"id":856252,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blaen, Phillip J","contributorId":242774,"corporation":false,"usgs":false,"family":"Blaen","given":"Phillip","email":"","middleInitial":"J","affiliations":[{"id":48522,"text":"School of Geography, Earth & Environmental Sciences, University of Birmingham","active":true,"usgs":false}],"preferred":false,"id":856253,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaar, Megan","contributorId":298690,"corporation":false,"usgs":false,"family":"Klaar","given":"Megan","email":"","affiliations":[{"id":64659,"text":"School of Geography and Water, University of Leeds, Leeds, U.K.","active":true,"usgs":false}],"preferred":false,"id":856254,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shelley, Felicity","contributorId":298691,"corporation":false,"usgs":false,"family":"Shelley","given":"Felicity","email":"","affiliations":[{"id":64660,"text":"Queen Mary University of London, Mile End Road, London E1 4NS, UK","active":true,"usgs":false}],"preferred":false,"id":856255,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zarnetske, Jay P.","contributorId":210073,"corporation":false,"usgs":false,"family":"Zarnetske","given":"Jay","email":"","middleInitial":"P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":856256,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lee-Cullen, Joseph","contributorId":298692,"corporation":false,"usgs":false,"family":"Lee-Cullen","given":"Joseph","email":"","affiliations":[{"id":64662,"text":"Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI, USA","active":true,"usgs":false}],"preferred":false,"id":856257,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Folegot, Silvia","contributorId":298693,"corporation":false,"usgs":false,"family":"Folegot","given":"Silvia","email":"","affiliations":[{"id":64663,"text":"Faculty of Science and Technology, Free University of Bozen-Bolzano, Universitätsplatz 5 - piazza Università, 5 39100, Bozen-Bolzano","active":true,"usgs":false}],"preferred":false,"id":856258,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kurz, Marie","contributorId":242783,"corporation":false,"usgs":false,"family":"Kurz","given":"Marie","affiliations":[{"id":38143,"text":"The Academy of Natural Sciences of Drexel University","active":true,"usgs":false}],"preferred":false,"id":856259,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lewandowski, Jorg","contributorId":298694,"corporation":false,"usgs":false,"family":"Lewandowski","given":"Jorg","affiliations":[{"id":64664,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Department of Ecohydrology, Müggelseedamm 310, D-12587 Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":856260,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856261,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ward, Adam 0000-0002-6376-0061","orcid":"https://orcid.org/0000-0002-6376-0061","contributorId":296003,"corporation":false,"usgs":false,"family":"Ward","given":"Adam","email":"","affiliations":[{"id":40154,"text":"Indiana University Bloomington","active":true,"usgs":false}],"preferred":false,"id":856384,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Mendoza-Lera, Clara","contributorId":298695,"corporation":false,"usgs":false,"family":"Mendoza-Lera","given":"Clara","email":"","affiliations":[{"id":64665,"text":"Department of Freshwater Conservation Brandenburg University of Technology BTU Cottbus–Senftenberg Bad Saarow Germany","active":true,"usgs":false}],"preferred":false,"id":856262,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ullah, Sami","contributorId":298696,"corporation":false,"usgs":false,"family":"Ullah","given":"Sami","email":"","affiliations":[{"id":64658,"text":"Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K.","active":true,"usgs":false}],"preferred":false,"id":856263,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Datry, Thibault 0000-0003-1390-6736","orcid":"https://orcid.org/0000-0003-1390-6736","contributorId":225166,"corporation":false,"usgs":false,"family":"Datry","given":"Thibault","email":"","affiliations":[{"id":41062,"text":"Centre de Lyon-Villeurbanne, 69626 Villeurbanne CEDEX, France","active":true,"usgs":false}],"preferred":false,"id":856385,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kettridge, Nicholas","contributorId":298784,"corporation":false,"usgs":false,"family":"Kettridge","given":"Nicholas","email":"","affiliations":[],"preferred":false,"id":856386,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Gooddy, Daren","contributorId":298785,"corporation":false,"usgs":false,"family":"Gooddy","given":"Daren","email":"","affiliations":[],"preferred":false,"id":856387,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Drummond, Jennifer","contributorId":298786,"corporation":false,"usgs":false,"family":"Drummond","given":"Jennifer","affiliations":[],"preferred":false,"id":856388,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Marti, Eugenia","contributorId":243628,"corporation":false,"usgs":false,"family":"Marti","given":"Eugenia","affiliations":[{"id":48756,"text":"Integrative Freshwater Ecology Group, Center for Advanced Studies of Blanes","active":true,"usgs":false}],"preferred":false,"id":856389,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Milner, Alexander","contributorId":242787,"corporation":false,"usgs":false,"family":"Milner","given":"Alexander","affiliations":[{"id":48522,"text":"School of Geography, Earth & Environmental Sciences, University of Birmingham","active":true,"usgs":false}],"preferred":false,"id":856390,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Hannah, David","contributorId":242779,"corporation":false,"usgs":false,"family":"Hannah","given":"David","affiliations":[{"id":48522,"text":"School of Geography, Earth & Environmental Sciences, University of Birmingham","active":true,"usgs":false}],"preferred":false,"id":856391,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Krause, Stefan","contributorId":242782,"corporation":false,"usgs":false,"family":"Krause","given":"Stefan","email":"","affiliations":[{"id":48522,"text":"School of Geography, Earth & Environmental Sciences, University of Birmingham","active":true,"usgs":false}],"preferred":false,"id":856392,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70211528,"text":"70211528 - 2020 - Arsenic-related oxidative stress in experimentally dosed wild great tit nestlings","interactions":[],"lastModifiedDate":"2020-07-30T16:56:55.361041","indexId":"70211528","displayToPublicDate":"2019-12-16T11:52:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic-related oxidative stress in experimentally dosed wild great tit nestlings","docAbstract":"Arsenic (As) is broadly distributed due to natural and anthropogenic sources, and it may cause adverse effects in birds. However, research on other elements (Pb, Hg and Cd) has been prioritized, resulting in scarce data on As exposure and related effects in wild birds. One of the mechanisms responsible for As toxicity is oxidative stress. Therefore, the aim of this study was to investigate if environmentally relevant As levels affected oxidative stress biomarkers in great tits (Parus major). This is the first field experiment studying the effects of As on oxidative stress in wild passerines. Wild great tit nestlings were orally dosed with sodium arsenite (Control: water, Low dose: 0.2 μg g−1 d−1 and High dose: 1 μg g−1 d−1; from day 3 to day 13 post-hatching). We intended to reach As concentrations similar to those at which passerines are exposed to at actual polluted areas. We compared the responses to the experimental manipulations (High, Low and Control groups) with those in an As/metal-exposed population breeding close to a Cu–Ni smelter in Finland (Smelter group). A set of antioxidants (tGSH, GSH:GSSG ratio, CAT, SOD, GST and GPx), and oxidative damage biomarkers (lipid peroxidation, protein carbonylation, 8-hydroxy-2′-deoxyguanosine formation in DNA, and telomere length) were explored in blood. Arsenic administration had no significant effect on most of the biomarkers measured: only the CAT activity was lower in the High As group and the GPx activity was enhanced in the Smelter group compared to the Control. Our results suggest that the dose and duration of the As exposure was not enough to induce oxidative damage in red cells of great tit nestlings. In spite of this, nestlings dosed with 1 μg g−1 d−1 of sodium arsenite showed non-significantly higher oxidative stress biomarkers than controls, suggesting that we were close to an effect level for the redox-defense system. Oxidative effects at equivalent As levels combined with other stressors cannot be dismissed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2019.113813","usgsCitation":"Sanchez-Virosta, P., Espin, S., Ruiz, S., Panda, B., Ilmonen, P., Schultz, S.L., Karouna-Renier, N., Garcia-Fernandez, A.J., and Eeva, T., 2020, Arsenic-related oxidative stress in experimentally dosed wild great tit nestlings: Environmental Pollution, v. 259, 113813, 7 p., https://doi.org/10.1016/j.envpol.2019.113813.","productDescription":"113813, 7 p.","ipdsId":"IP-106899","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458355,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2019.113813","text":"Publisher Index Page"},{"id":376914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Finland","city":"Harjavalta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 22.071533203125,\n 61.266271866180446\n ],\n [\n 22.269287109374996,\n 61.266271866180446\n ],\n [\n 22.269287109374996,\n 61.32431537628559\n ],\n [\n 22.071533203125,\n 61.32431537628559\n ],\n [\n 22.071533203125,\n 61.266271866180446\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sanchez-Virosta, Pablo","contributorId":236867,"corporation":false,"usgs":false,"family":"Sanchez-Virosta","given":"Pablo","email":"","affiliations":[{"id":25452,"text":"University of Turku","active":true,"usgs":false}],"preferred":false,"id":794518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Espin, Silvia","contributorId":236868,"corporation":false,"usgs":false,"family":"Espin","given":"Silvia","email":"","affiliations":[{"id":25452,"text":"University of Turku","active":true,"usgs":false}],"preferred":false,"id":794519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ruiz, Sandra","contributorId":236869,"corporation":false,"usgs":false,"family":"Ruiz","given":"Sandra","email":"","affiliations":[{"id":25452,"text":"University of Turku","active":true,"usgs":false}],"preferred":false,"id":794520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Panda, Bineet","contributorId":236870,"corporation":false,"usgs":false,"family":"Panda","given":"Bineet","email":"","affiliations":[{"id":25452,"text":"University of Turku","active":true,"usgs":false}],"preferred":false,"id":794521,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ilmonen, Petteri","contributorId":236871,"corporation":false,"usgs":false,"family":"Ilmonen","given":"Petteri","email":"","affiliations":[{"id":25452,"text":"University of Turku","active":true,"usgs":false}],"preferred":false,"id":794522,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schultz, Sandra L. 0000-0003-3394-2857 sschultz@usgs.gov","orcid":"https://orcid.org/0000-0003-3394-2857","contributorId":5966,"corporation":false,"usgs":true,"family":"Schultz","given":"Sandra","email":"sschultz@usgs.gov","middleInitial":"L.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":794523,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karouna-Renier, Natalie 0000-0001-7127-033X nkarouna@usgs.gov","orcid":"https://orcid.org/0000-0001-7127-033X","contributorId":200983,"corporation":false,"usgs":true,"family":"Karouna-Renier","given":"Natalie","email":"nkarouna@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":794524,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Garcia-Fernandez, Antonio J.","contributorId":236872,"corporation":false,"usgs":false,"family":"Garcia-Fernandez","given":"Antonio","email":"","middleInitial":"J.","affiliations":[{"id":47555,"text":"University of Murcia","active":true,"usgs":false}],"preferred":false,"id":794525,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eeva, Tapio","contributorId":236873,"corporation":false,"usgs":false,"family":"Eeva","given":"Tapio","email":"","affiliations":[{"id":25452,"text":"University of Turku","active":true,"usgs":false}],"preferred":false,"id":794526,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209440,"text":"70209440 - 2020 - Time scales of arsenic variability and the role of high-frequency monitoring at three water-supply wells in New Hampshire, USA","interactions":[],"lastModifiedDate":"2020-05-05T12:11:42.664539","indexId":"70209440","displayToPublicDate":"2019-12-14T19:51:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Time scales of arsenic variability and the role of high-frequency monitoring at three water-supply wells in New Hampshire, USA","docAbstract":"Groundwater geochemistry, redox process classification, high-frequency physicochemical and hydrologic measurements, and climate data were analyzed to identify controls on arsenic (As) concentration changes. Groundwater was monitored in two public-supply wells (one glacial aquifer and one bedrock aquifer), and one bedrock-aquifer domestic well in New Hampshire, USA, from 2014 to 2018 to identify time scales of and controls on As concentration changes. Concentrations of As and other geochemical constituents were measured bimonthly. Specific conductance (SC), pH, dissolved oxygen, and pumping rate/water level were measured at high frequency (every 5 to 15 min). Median (and 95% confidence interval) As concentrations at the three wells were 4.1 (3.7–4.6), 18.9 (17.2–23.6), and 37.5 (30.4–42.9) μg/L. Arsenic variability in each of the three wells, in relative standard deviation, ranged from 9 to 12%. Median quarterly As concentrations were highest in all wells in the spring. The bedrock-aquifer public-supply well As concentration increased over the period of study while pumping rate decreased. In the public-supply wells, As variability was correlated with SC and pH, and As species were related to SC, pH, pumping, precipitation, and changes in redox process. Specific conductance also had a seasonal pattern in the two public-supply wells and was correlated with Na and Cl. Excess Na in water samples suggests possible ion exchange with dissolved Ca, creating more capacity to dissolve CaCO3 from calcareous rocks, which can increase pH and in turn, As concentrations in wells. High-frequency monitoring data are cost effective to collect, which could be advantageous in other parts of the United States and in the many parts of the world where glacial aquifers are in direct contact with other water supply aquifers or where water from different aquifers have potential to mix.
Remediation of mercury (Hg) contaminated sites has long relied on traditional approaches, such as removal and containment/capping. Here we review contemporary practices in the assessment and remediation of industrial-scale Hg contaminated sites and discuss recent advances. Significant improvements have been made in site assessment, including the use of XRF to rapidly identify the spatial extent of contamination, Hg stable isotope fractionation to identify sources and transformation processes, and solid-phase characterization (XAFS) to evaluate Hg forms. The understanding of Hg bioavailability for methylation has been improved by methods such as sequential chemical extractions and porewater measurements, including the use of diffuse gradient in thin-film (DGT) samplers. These approaches have shown varying success in identifying bioavailable Hg fractions and further study and field applications are needed. The downstream accumulation of methylmercury (MeHg) in biota is a concern at many contaminated sites. Identifying the variables limiting/controlling MeHg production—such as bioavailable inorganic Hg, organic carbon, and/or terminal electron acceptors (e.g. sulfate, iron) is critical. Mercury can be released from contaminated sites to the air and water, both of which are influenced by meteorological and hydrological conditions. Mercury mobilized from contaminated sites is predominantly bound to particles, highly correlated with total sediment solids (TSS), and elevated during stormflow. Remediation techniques to address Hg contamination can include the removal or containment of Hg contaminated materials, the application of amendments to reduce mobility and bioavailability, landscape/waterbody manipulations to reduce MeHg production, and food web manipulations through stocking or extirpation to reduce MeHg accumulated in desired species. These approaches often rely on knowledge of the Hg forms/speciation at the site, and utilize physical, chemical, thermal and biological methods to achieve remediation goals. Overall, the complexity of Hg cycling allows many different opportunities to reduce/mitigate impacts, which creates flexibility in determining suitable and logistically feasible remedies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.136031","usgsCitation":"Eckley, C.S., Gilmour, C.C., Janssen, S., Luxton, T., Randall, P.M., Whalin, L., and Austin, C., 2020, The assessment and remediation of mercury contaminated sites: A review of current approaches: Science of the Total Environment, v. 707, 136031, 19 p., https://doi.org/10.1016/j.scitotenv.2019.136031.","productDescription":"136031, 19 p.","ipdsId":"IP-111241","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":458364,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6980986","text":"External Repository"},{"id":370681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"707","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eckley, Chris S.","contributorId":167256,"corporation":false,"usgs":false,"family":"Eckley","given":"Chris","email":"","middleInitial":"S.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":778497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilmour, Cynthia C","contributorId":221508,"corporation":false,"usgs":false,"family":"Gilmour","given":"Cynthia","email":"","middleInitial":"C","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":778498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luxton, Todd P","contributorId":221509,"corporation":false,"usgs":false,"family":"Luxton","given":"Todd P","affiliations":[{"id":40396,"text":"US Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":778499,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Randall, Paul M","contributorId":221510,"corporation":false,"usgs":false,"family":"Randall","given":"Paul","email":"","middleInitial":"M","affiliations":[{"id":40396,"text":"US Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":778500,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whalin, Lindsay","contributorId":221511,"corporation":false,"usgs":false,"family":"Whalin","given":"Lindsay","email":"","affiliations":[{"id":40397,"text":"San Francisco Bay Water Board","active":true,"usgs":false}],"preferred":false,"id":778501,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Austin, Carrie","contributorId":221512,"corporation":false,"usgs":false,"family":"Austin","given":"Carrie","email":"","affiliations":[{"id":40397,"text":"San Francisco Bay Water Board","active":true,"usgs":false}],"preferred":false,"id":778502,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70218231,"text":"70218231 - 2020 - Seismo-acoustic evidence for vent drying during shallow submarine eruptions at Bogoslof volcano, Alaska","interactions":[],"lastModifiedDate":"2021-02-19T17:59:44.99221","indexId":"70218231","displayToPublicDate":"2019-12-13T11:53:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7594,"text":"Bulletin of Volcanology Special Issue on the Bogoslof Eruption","active":true,"publicationSubtype":{"id":10}},"title":"Seismo-acoustic evidence for vent drying during shallow submarine eruptions at Bogoslof volcano, Alaska","docAbstract":"Characterizing the state of the volcanic vent is key for interpreting observational datasets and accurately assessing volcanic hazards. This is particularly true for remote, complex eruptions such as the 2016–2017 Bogoslof volcano, Alaska eruption sequence. Bogoslof’s eruptions in this period were either shallow submarine or subaerial, or some combination of both. Our results demonstrate how low-frequency sound waves (infrasound), integrated with seismic and satellite data, can provide unique insight into shallow vent processes, otherwise not available. We use simple metrics, such as the infrasound frequency index (FI), event duration, and acoustic-seismic amplitude ratio, to look at changes in the elastic energy radiation and infer changes in seawater access to the vent. Satellite imagery before and after selected eruptions is used to ground-truth inferences on vent conditions. High FI and gradual increases in infrasound frequency content at Bogoslof correspond with transitions from submarine to subaerial vent conditions and a diminished or absent role of water, likely resulting in a drying out of the vent region. Event durations generally correlate with high FI and the range of FI values for each event, suggesting long duration events were more effective at drying out the vent region. A trend from low to high acoustic-seismic amplitude ratios for some long duration events also suggests an increase in acoustic efficiency as the vent dried out. We demonstrate that infrasound can serve as a robust indicator of seawater involvement for Bogoslof and other shallow submarine eruptions that may not be inferable from other datasets, particularly in near-real-time.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-019-1326-5","usgsCitation":"Fee, D., Lyons, J.J., Haney, M.M., Wech, A., Waythomas, C.F., Diefenbach, A., Lopez, T., Van Eaton, A.R., and Schneider, D.J., 2020, Seismo-acoustic evidence for vent drying during shallow submarine eruptions at Bogoslof volcano, Alaska: Bulletin of Volcanology Special Issue on the Bogoslof Eruption, v. 82, 2, 14 p., https://doi.org/10.1007/s00445-019-1326-5.","productDescription":"2, 14 p.","ipdsId":"IP-107901","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":458366,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-019-1326-5","text":"Publisher Index Page"},{"id":383378,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof volcano","geographicExtents":"{\n \"type\": 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djschneider@usgs.gov","orcid":"https://orcid.org/0000-0001-9092-1054","contributorId":198601,"corporation":false,"usgs":true,"family":"Schneider","given":"David","email":"djschneider@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":810544,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70208852,"text":"70208852 - 2020 - Traveling to thermal refuges during stressful temperatures leads to foraging constraints in a central-place forager","interactions":[],"lastModifiedDate":"2020-03-03T11:28:40","indexId":"70208852","displayToPublicDate":"2019-12-13T11:24:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2373,"text":"Journal of Mammalogy","onlineIssn":"1545-1542","printIssn":"0022-2372","active":true,"publicationSubtype":{"id":10}},"title":"Traveling to thermal refuges during stressful temperatures leads to foraging constraints in a central-place forager","docAbstract":"Central-place foragers can be constrained by the distance between habitats. When an organism relies on a central place for thermal refuge, the distance to food resources can potentially constrain foraging behavior. We investigated the effect of distance between thermal refuges and forage patches of the cold-intolerant marine mammal, the Florida manatee (Trichechus manatus latirostris), on foraging duration. We tested the alternative hypotheses of time minimization and energy maximization as a response to distance between habitats. We also determined if manatees mitigate foraging constraints with increased visits to closer thermal refuges. We used hidden Markov models to assign discrete behaviors from movement parameters as a function of water temperature and assessed the influence of distance on foraging duration in water temperatures above (> 20°C) and below (≤ 20°C) the lower critical limit of the thermoneutral zone of manatees. We found that with increased distance, manatees decreased foraging duration in cold water temperature and increased foraging duration in warmer temperatures. We also found that manatees returned to closer thermal refuges more often. Our results suggest that the spatial relationship of thermal and forage habitats can impact behavioral decisions regarding foraging. Addressing foraging behavior questions while considering thermoregulatory behavior implicates the importance of understanding changing environments on animal behavior, particularly in the face of current global change.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jmammal/gyz197","usgsCitation":"Haase, C.G., Fletcher, R.J., Slone, D.H., Reid, J.P., and Butler, S.M., 2020, Traveling to thermal refuges during stressful temperatures leads to foraging constraints in a central-place forager: Journal of Mammalogy, v. 101, no. 1, p. 271-280, https://doi.org/10.1093/jmammal/gyz197.","productDescription":"10 p.","startPage":"271","endPage":"280","ipdsId":"IP-093855","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyz197","text":"Publisher Index Page"},{"id":372851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"101","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Haase, Catherine G. 0000-0002-7682-0625 chaase@usgs.gov","orcid":"https://orcid.org/0000-0002-7682-0625","contributorId":195794,"corporation":false,"usgs":true,"family":"Haase","given":"Catherine","email":"chaase@usgs.gov","middleInitial":"G.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fletcher, Robert J. Jr.","contributorId":41294,"corporation":false,"usgs":true,"family":"Fletcher","given":"Robert","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":783668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slone, Daniel H. 0000-0002-9903-9727 dslone@usgs.gov","orcid":"https://orcid.org/0000-0002-9903-9727","contributorId":205617,"corporation":false,"usgs":true,"family":"Slone","given":"Daniel","email":"dslone@usgs.gov","middleInitial":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reid, James P. 0000-0002-8497-1132 jreid@usgs.gov","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":3460,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"jreid@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":783670,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Susan M. 0000-0003-3676-9332 sbutler@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-9332","contributorId":195796,"corporation":false,"usgs":true,"family":"Butler","given":"Susan","email":"sbutler@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783671,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209441,"text":"70209441 - 2020 - Chronic and episodic acidification of streams along the Appalachian Trail corridor, eastern United States","interactions":[],"lastModifiedDate":"2020-05-04T18:25:19.107285","indexId":"70209441","displayToPublicDate":"2019-12-12T07:59:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Chronic and episodic acidification of streams along the Appalachian Trail corridor, eastern United States","docAbstract":"Acidic atmospheric deposition has adversely affected aquatic ecosystems globally. As emissions and deposition of sulfur (S) and nitrogen (N) have declined in recent decades across North America and Europe, ecosystem recovery is evident in many surface waters. However, persistent chronic and episodic acidification remain important concerns in vulnerable regions. We evaluated acidification in 269 headwater streams during 2010–2012 along the Appalachian Trail (AT) that transits several ecoregions and is located downwind of high levels of S and N emission sources. Discharge was estimated by matching sampled streams to those of a nearby gaged stream and assuming equivalent daily mean flow percentiles. Charge balance acid‐neutralizing capacity (ANC) values were adjusted to the 15th (Q15) and 85th flow percentiles (Q85) by applying the ANC/discharge slope among sample pairs collected at each stream. A site‐based approach was applied to streams sampled twice or more and a second regression‐based approach to streams sampled once to estimate episodic acidification magnitudes as the ANC difference from Q15 to Q85. Streams with ANC <0 μeq/L doubled from 16% to 32% as discharge increased from Q15 to Q85 according to the site‐based approach. The proportion of streams with ANC <0 μeq/L at low flow and high flow decreased from north to south. Base cation dilution explained the greatest amount of episodic acidification among streams and variation in sulfate (SO42−) concentrations was a secondary explanatory variable. Episodic SO42− patterns varied geographically with dilution dominant in northern streams underlain by soils developed in glacial sediment and increased concentrations dominant in southern streams with older, highly weathered soils. Episodic acidification increased as low‐flow ANC increased, exceeding 90 μeq/L in 25% of streams. Episodic increases in ANC were the dominant pattern in streams with low‐flow ANC values <30 μeq/L. Chronic and episodic acidification remain an ecological concern among AT streams. The approach developed here could be applied to estimate the magnitude and extent of chronic and episodic acidification in other regions recovering from decreasing levels of atmospheric S and N deposition.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13668","collaboration":"","usgsCitation":"Burns, D., McDonnell, T., Rice, K.C., Lawrence, G.B., and Sullivan, T., 2020, Chronic and episodic acidification of streams along the Appalachian Trail corridor, eastern United States: Hydrological Processes, v. 34, p. 1498-1513, https://doi.org/10.1002/hyp.13668.","productDescription":"16 p.","startPage":"1498","endPage":"1513","ipdsId":"IP-109972","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":458377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13668","text":"Publisher Index Page"},{"id":373837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Georgia, Maine, Massachusetts, Maryland, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Tennessee, Vermont, Virginia","otherGeospatial":"Appalachian Trail corridor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.671875,\n 32.509761735919426\n ],\n [\n -82.08984375,\n 32.02670629333614\n ],\n [\n -79.62890625,\n 33.02708758002874\n ],\n [\n -76.9921875,\n 35.67514743608467\n ],\n [\n -76.5966796875,\n 37.61423141542417\n ],\n [\n -76.552734375,\n 38.89103282648846\n ],\n [\n -75.2783203125,\n 40.413496049701955\n ],\n [\n -71.7626953125,\n 42.52069952914966\n ],\n [\n -70.3564453125,\n 43.644025847699496\n ],\n [\n -69.521484375,\n 44.465151013519616\n ],\n [\n -68.15917968749999,\n 45.058001435398275\n ],\n [\n -68.02734375,\n 46.164614496897094\n ],\n [\n -68.291015625,\n 46.6795944656402\n ],\n [\n -69.345703125,\n 46.46813299215554\n ],\n [\n -70.5322265625,\n 45.213003555993964\n ],\n [\n -72.158203125,\n 44.653024159812\n ],\n [\n -74.8388671875,\n 43.389081939117496\n ],\n [\n -75.76171875,\n 42.00032514831621\n ],\n [\n -78.22265625,\n 40.68063802521456\n ],\n [\n -79.013671875,\n 39.87601941962116\n ],\n [\n -80.244140625,\n 38.37611542403604\n ],\n [\n -81.650390625,\n 35.28150065789119\n ],\n [\n -83.8037109375,\n 34.08906131584994\n ],\n [\n -84.111328125,\n 33.50475906922609\n ],\n [\n -83.671875,\n 32.509761735919426\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","noUsgsAuthors":false,"publicationDate":"2020-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":786490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDonnell, Todd","contributorId":223867,"corporation":false,"usgs":false,"family":"McDonnell","given":"Todd","affiliations":[{"id":40780,"text":"E&S Environmental Chemistry","active":true,"usgs":false}],"preferred":false,"id":786491,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rice, Karen C. 0000-0002-9356-5443 kcrice@usgs.gov","orcid":"https://orcid.org/0000-0002-9356-5443","contributorId":178269,"corporation":false,"usgs":true,"family":"Rice","given":"Karen","email":"kcrice@usgs.gov","middleInitial":"C.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":786492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786493,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sullivan, Timothy","contributorId":223868,"corporation":false,"usgs":false,"family":"Sullivan","given":"Timothy","affiliations":[{"id":40780,"text":"E&S Environmental Chemistry","active":true,"usgs":false}],"preferred":false,"id":786494,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}