Functional connectivity (i.e., the movement of individuals across a landscape) is essential for the maintenance of genetic variation and persistence of rare species. However, illuminating the processes influencing functional connectivity and ultimately translating this knowledge into management practice remains a fundamental challenge. Here, we combine various population structure analyses with pairwise, population-specific demographic modeling to investigate historical functional connectivity in Graham’s beardtongue (Penstemon grahamii), a rare plant narrowly distributed across a dryland region of the western US. While principal component and population structure analyses indicated an isolation-by-distance pattern of differentiation across the species’ range, spatial inferences of effective migration exposed an abrupt shift in population ancestry near the range center. To understand these seemingly conflicting patterns, we tested various models of historical gene flow and found evidence for recent admixture (~ 3400 generations ago) between populations near the range center. This historical perspective reconciles population structure patterns and suggests management efforts should focus on maintaining connectivity between these previously isolated lineages to promote the ongoing transfer of genetic variation. Beyond providing species-specific knowledge to inform management options, our study highlights how understanding demographic history may be critical to guide conservation efforts when interpreting population genetic patterns and inferring functional connectivity.
The travel time or “age” of groundwater affects catchment responses to hydrologic changes, geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.
The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model for Assateague Island in eastern Maryland and Virginia to assess the effects of sea-level rise on the groundwater system. Sea-level rise is expected to increase the altitude of the water table in barrier island aquifer systems, possibly leading to adverse effects to ecosystems on the barrier islands. The potential effects of sea-level rise were evaluated by simulating groundwater conditions under sea-level-rise scenarios of 20 centimeters (cm), 40 cm, and 60 cm. Results show that as sea level rises, low-lying areas of the island originally represented as receiving freshwater recharge in the baseline scenario are inundated by saltwater. This change from freshwater recharge to saltwater decreases the overall amount of freshwater recharging the system. As the water table rises in response to the higher sea levels, freshwater flow out of the system changes, with more freshwater leaving as submarine groundwater discharge and less freshwater leaving as seeps and evapotranspiration. At the current land-surface altitude, as much as 50 percent of the island may be inundated with a 60-cm rise in sea level, and the low-lying marshes may change from freshwater to saltwater.
Groundwater levels at 32 wells were monitored for as long as 12 months between October 2014 and September 2015 on Assateague Island. Results from objective classification analysis of 14 shallow monitoring wells show two dominant processes affecting groundwater levels in two different settings on the island. On the western side of the island, between the primary dune and the inland bays, water levels clearly respond to precipitation events. This side of the island is more protected from ocean tides and typically is more vegetated than the eastern side. On the eastern side of the island, between the Atlantic Ocean and the primary dune, water levels clearly respond to tidal events. Specific conductance was measured at four wells, two on the western part of the island and two on the eastern part of the island. Specific conductance values in the two wells west of the primary dune show episodic decreases, coinciding with precipitation events. Specific conductance values in the two wells on the eastern side of the primary dune show episodic increases, coinciding with high-tide events. These high frequency monitoring data are intended to aid in designing a monitoring network that can document both short-term and long-term hydrologic processes on Assateague Island National Seashore.
This study uses a modeling approach consistent with models developed for Gateway National Recreation Area, Sandy Hook Unit (New Jersey) and Fire Island National Seashore (New York). Combined, these models are meant to improve the regional capabilities for predicting climate-change effects on barrier islands and provide resource managers with a common set of tools for adaptation and mitigation of potentially adverse effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205104","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fleming, B.J., Raffensperger, J.P., Goodling, P.J., and Masterson, J., 2021, Simulated effects of sea-level rise on the shallow, fresh groundwater system of Assateague Island, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2020–5104, 62 p., https://doi.org/10.3133/sir20205104.","productDescription":"Report: viii, 62 p.; Data Release","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094959","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":387028,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AJOLRK","text":"USGS data release","linkHelpText":"MODFLOW-NWT model with SWI2 used to evaluate the water-table response to sea-level rise and change in recharge, Assateague Island, Maryland and Virginia"},{"id":387027,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5104/sir20205104.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5104"},{"id":387026,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5104/coverthb.jpg"},{"id":387041,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205117","text":"Scientific Investigations Report 2020–5117","linkHelpText":"- Simulation of Water-Table and Freshwater/Saltwater Interface Response to Climate-Change-Driven Sea-Level Rise and Changes in Recharge at Fire Island National Seashore, New York"},{"id":387040,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.87376937332855\n ],\n [\n -75.3826904296875,\n 37.83473402375478\n ],\n [\n -75.30441284179688,\n 37.88027325525864\n ],\n [\n -75.15335083007812,\n 38.11727165830543\n ],\n [\n -75.12039184570312,\n 38.29101446582335\n ],\n [\n -75.17120361328125,\n 38.22847167526397\n ],\n [\n -75.28793334960938,\n 38.0513353697269\n ],\n [\n -75.3826904296875,\n 37.93769926732864\n ],\n [\n -75.42388916015625,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Landscape features influence wildlife movements across spatial scales and have the potential to influence the spread of disease. Chronic wasting disease (CWD) is a fatal prion disease affecting members of the family Cervidae, particularly white-tailed deer (Odocoileus virginianus), and the first positive CWD case in a wild deer in Ohio, USA, was recorded in 2020. Landscape genetics approaches are increasingly used to better understand potential pathways for CWD spread in white-tailed deer, but little is known about genetic structure of white-tailed deer in Ohio. The objectives of our study were to evaluate spatial genetic structure in white-tailed deer across Ohio and compare the support for isolation by distance (IBD) and isolation by landscape resistance (IBR) models in explaining this structure. We collected genetic data from 619 individual deer from 24 counties across Ohio during 2007–2009. We used microsatellite genotypes from 619 individuals genotyped at 11 loci and haplotypes from a 547-base pair fragment of the mitochondrial DNA control region. We used spatial and non-spatial genetic clustering tests to evaluate genetic structure in both types of genetic data and empirically optimized landscape resistance surfaces to compare IBD and IBR using microsatellite data. Non-spatial genetic clustering tests failed to detect spatial genetic structure, whereas spatial genetic clustering tests indicated subtle spatial genetic structure. The IBD model consistently outperformed IBR models that included land cover, traffic volume, and streams. Our results indicated widespread genetic connectivity of white-tailed deer across Ohio and negligible effects of landscape features. These patterns likely reflect some combination of minimal resistive effects of landscape features on white-tail deer movement in Ohio and the effects of regional recolonization or translocation. We encourage continued CWD surveillance in Ohio, particularly in the proximity of confirmed cases.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22120","usgsCitation":"Bauder, J., Anderson, C.S., Gibbs, H., Tonkovich, M., and Walter, W., 2021, Landscape features fail to explain spatial genetic structure in white-tailed deer across Ohio, USA: Journal of Wildlife Management, v. 85, no. 8, p. 1669-1684, https://doi.org/10.1002/jwmg.22120.","productDescription":"16 p.","startPage":"1669","endPage":"1684","ipdsId":"IP-128673","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"85","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Bauder, Javan M.","contributorId":288641,"corporation":false,"usgs":false,"family":"Bauder","given":"Javan M.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":838159,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Christine S.","contributorId":288642,"corporation":false,"usgs":false,"family":"Anderson","given":"Christine","email":"","middleInitial":"S.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838160,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibbs, H. Lisle","contributorId":288643,"corporation":false,"usgs":false,"family":"Gibbs","given":"H. Lisle","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":838161,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tonkovich, Michael J.","contributorId":288644,"corporation":false,"usgs":false,"family":"Tonkovich","given":"Michael J.","affiliations":[{"id":13589,"text":"Ohio DNR","active":true,"usgs":false}],"preferred":false,"id":838162,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838158,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223767,"text":"70223767 - 2021 - Growth of greater white-fronted goose goslings relates to population dynamics at multiple scales","interactions":[],"lastModifiedDate":"2021-10-18T14:28:57.67922","indexId":"70223767","displayToPublicDate":"2021-09-03T10:15:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Growth of greater white-fronted goose goslings relates to population dynamics at multiple scales","docAbstract":"The abundance of greater white-fronted geese (Anser albifrons frontalis) on the Arctic Coastal Plain (ACP) of northern Alaska, USA, has more than tripled since the late 1990s; however, recent rate of annual population growth has declined as population size increased, which may indicate white-fronted geese on the ACP are approaching carrying capacity. We examined rates of gosling growth in greater white-fronted geese at 3 sites on the ACP during 2012–2014 to assist with predictions of future population trends and assess evidence for density-dependent constraints on recruitment. We marked goslings at hatch with individually coded webtags and conducted brood drives during early August to capture, measure, and weigh goslings. Annual estimates of gosling mass at 32 days old (range = 1,190–1,685) indicate that goslings had obtained >60% of asymptotic size. This rate of growth corresponds with that of other goose species and populations with access to high-quality forage and no limitations on forage availability, and is consistent with the overall increase in abundance of white-fronted geese at the ACP scale. Contrary to most previous investigations, age-adjusted mass of goslings did not decline with hatch date. Goslings grew faster in coastal areas than at inland freshwater sites. Taken together, these findings suggest forage was not limiting gosling growth rates in either ecosystem, but forage was of greater quality in coastal areas where goose foraging habitat is expanding because of permafrost subsidence. Spatial patterns of gosling growth corresponded with local-scale patterns of population density and population change; the areas with greatest rates of gosling growth were those with the greatest population density and rates of population increase. We found little evidence to suggest forage during brood rearing was limiting population increase of white-fronted geese on the ACP. Factors responsible for the apparent slowing of ACP-wide population growth are likely those that occur in stages of the annual cycle outside of the breeding grounds.
","language":"English","publisher":"Wildlife Society","doi":"10.1002/jwmg.22115","usgsCitation":"Fondell, T.F., Meixell, B.W., and Flint, P.L., 2021, Growth of greater white-fronted goose goslings relates to population dynamics at multiple scales: Journal of Wildlife Management, v. 85, no. 8, p. 1591-1600, https://doi.org/10.1002/jwmg.22115.","productDescription":"10 p.","startPage":"1591","endPage":"1600","ipdsId":"IP-113368","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":436212,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92OQ9A4","text":"USGS data release","linkHelpText":"Capture and Measurement Data of Greater White-Fronted Geese (Anser albifrons) from the Arctic Coastal Plain of Alaska, 2012-2014"},{"id":436211,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92OQ9A4","text":"USGS data release","linkHelpText":"Capture and Measurement Data of Greater White-Fronted Geese (Anser albifrons) from the Arctic Coastal Plain of Alaska, 2012-2014"},{"id":388878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"National Petroleum Reserve–Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.5107421875,\n 69.10777677331498\n ],\n [\n -148.16162109375,\n 69.10777677331498\n ],\n [\n -148.16162109375,\n 71.42017915498717\n ],\n [\n -158.5107421875,\n 71.42017915498717\n ],\n [\n -158.5107421875,\n 69.10777677331498\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"85","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Fondell, Thomas F tfondell@usgs.gov","contributorId":219605,"corporation":false,"usgs":false,"family":"Fondell","given":"Thomas","email":"tfondell@usgs.gov","middleInitial":"F","affiliations":[{"id":40039,"text":"USGS Alaska Science Center (Deceased)","active":true,"usgs":false}],"preferred":false,"id":822587,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meixell, Brandt W. 0000-0002-6738-0349 bmeixell@usgs.gov","orcid":"https://orcid.org/0000-0002-6738-0349","contributorId":138716,"corporation":false,"usgs":true,"family":"Meixell","given":"Brandt","email":"bmeixell@usgs.gov","middleInitial":"W.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":822588,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Paul L. 0000-0002-8758-6993 pflint@usgs.gov","orcid":"https://orcid.org/0000-0002-8758-6993","contributorId":3284,"corporation":false,"usgs":true,"family":"Flint","given":"Paul","email":"pflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":822589,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223746,"text":"70223746 - 2021 - Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages","interactions":[],"lastModifiedDate":"2021-09-07T14:51:20.460964","indexId":"70223746","displayToPublicDate":"2021-09-03T09:46:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1702,"text":"Frontiers in Microbiology","onlineIssn":"1664-302X","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages","title":"Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages","docAbstract":"Sea lamprey (SL; Petromyzon marinus), one of the oldest living vertebrates, have a complex metamorphic life cycle. Following hatching, SL transition into a microphagous, sediment burrowing larval stage, and after 2–10+ years, the larvae undergo a dramatic metamorphosis, transforming into parasitic juveniles that feed on blood and bodily fluids of fishes; adult lamprey cease feeding, spawn, and die. Since gut microbiota are critical for the overall health of all animals, we examined the microbiota associated with SLs in each life history stage. We show that there were significant differences in the gut bacterial communities associated with the larval, parasitic juvenile, and adult life stages. The transition from larval to the parasitic juvenile stage was marked with a significant shift in bacterial community structure and reduction in alpha diversity. The most abundant SL-associated phyla were Proteobacteria, Fusobacteria, Bacteroidetes, Verrucomicrobia, Actinobacteria, and Firmicutes, with their relative abundances varying among the stages. Moreover, while larval SL were enriched with unclassified Fusobacteriaceae, unclassified Verrucomicrobiales and Cetobacterium, members of the genera with fastidious nutritional requirements, such as Streptococcus, Haemophilus, Cutibacterium, Veillonella, and Massilia, were three to four orders of magnitude greater in juveniles than in larvae. In contrast, adult SLs were enriched with Aeromonas, Iodobacter, Shewanella, and Flavobacterium. Collectively, our findings show that bacterial communities in the SL gut are dramatically different among its life stages. Understanding how these communities change over time within and among SL life stages may shed more light on the role that these gut microbes play in host growth and fitness.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmicb.2021.706683","usgsCitation":"Mathai, P., Byappanahalli, M., Johnson, N.S., and Sadowsky, M.J., 2021, Gut microbiota associated with different sea lamprey (Petromyzon marinus) life stages: Frontiers in Microbiology, v. 12, 706683, 11 p., https://doi.org/10.3389/fmicb.2021.706683.","productDescription":"706683, 11 p.","ipdsId":"IP-129553","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450943,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmicb.2021.706683","text":"Publisher Index Page"},{"id":388873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Lake Huron watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.24316406249999,\n 42.85180609584705\n ],\n [\n -79.56298828125,\n 42.85180609584705\n ],\n [\n -79.56298828125,\n 47.502358951968574\n ],\n [\n -84.24316406249999,\n 47.502358951968574\n ],\n [\n -84.24316406249999,\n 42.85180609584705\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Mathai, P 0000-0001-5261-9911","orcid":"https://orcid.org/0000-0001-5261-9911","contributorId":265312,"corporation":false,"usgs":false,"family":"Mathai","given":"P","email":"","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":822538,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":241924,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822539,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sadowsky, Michael J.","contributorId":34003,"corporation":false,"usgs":false,"family":"Sadowsky","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":822541,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224925,"text":"70224925 - 2021 - Watershed sediment yield following the 2018 Carr Fire, Whiskeytown National Recreation Area, northern California","interactions":[],"lastModifiedDate":"2021-10-05T12:21:37.890807","indexId":"70224925","displayToPublicDate":"2021-09-03T07:18:54","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Watershed sediment yield following the 2018 Carr Fire, Whiskeytown National Recreation Area, northern California","docAbstract":"Wildfire risk has increased in recent decades over many regions, due to warming climate and other factors. Increased sediment export from recently burned landscapes can jeopardize downstream infrastructure and water resources, but physical landscape response to fire has not been quantified for some at-risk areas, including much of northern California, USA. We measured sediment yield from three watersheds (13–29 km2) that drain to Whiskeytown Lake, California, within the area burned by the 2018 Carr Fire. Structure-from-Motion photogrammetry on aerial images combined with sonar bathymetric mapping of submerged areas indicated first-year post-fire sediment yields of 4,080 ± 598 t/km2 (Brandy Creek), 2,700 ± 527 t/km2 (Boulder Creek), and 305 ± 58.0 t/km2 (Whiskey Creek)—some of the first post-fire yields measured in northern California and 64, 42, and 4.8 times greater than pre-fire yields, respectively. These were measured during a wet year and resulted largely from rilling erosion and fluvial sediment transport, without post-fire debris flows. Rilling preferentially developed in contact with dirt roads, aided by thin soils and exposed bedrock, and on slopes vegetated by chaparral pre-fire. The second post-fire year (a dry year) was characterized by fluvial reworking and delta progradation of the first-year deposits and relatively little new sediment export. First-year sedimentation of 111,000 m3 represented minor loss of storage capacity in Whiskeytown Lake but would be detrimental to smaller reservoirs; in general, increased sediment yields from western US watersheds as fire and extreme rainfall increase will likely pose risks to water quality and storage.
Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.
Livebearers in the family Poeciliidae are some of the most widely introduced fishes. Native poeciliid translocations within the U.S. are mostly due to deliberate stocking for mosquito control. Introductions of exotic poeciliids, those not native to the U.S., are more likely to be due to release from aquaria or escape from farms. Many of these non-natives originate from warm climate regions, contrasting with the relatively cold climates in the U.S. Thus, thermal springs may increase the possible range of these species. Our primary objective was to examine the importance of climate and thermal springs in affecting the distribution of translocated and non-native poeciliids in the U.S. This objective was addressed using a national database of poeciliid introductions. Records were dominated by a handful of states and most introductions led to established populations. While translocated mosquitofish were found across many states and climates, non-natives were found almost exclusively in warm climate states and territories (e.g., Florida, Hawaii, Puerto Rico), especially where air temperatures remained above freezing. Outside warm climate states, 46% of established non-native populations were located at thermal spring sources. These results indicate that thermal springs extend the distribution of non-natives, but were relatively unimportant for translocated poeciliids.
Costs to producing sexual signals can create selective pressures on males to invest signaling effort in particular contexts. When the benefits of signaling vary consistently across time, males can optimize signal investment to specific temporal contexts using biological rhythms. Sea lamprey, Petromyzon marinus, have a semelparous life history, are primarily nocturnal, and rely on pheromone communication for reproduction; however, whether male investment in pheromone transport and release matches increases in spawning activity remains unknown. By measuring (1) 3keto-petromyzonol sulfate (3kPZS, a main pheromone component) and its biosynthetic precursor PZS in holding water and tissue samples at six points over the course of 24 hours and (2) 3kPZS release over the course of several days, we demonstrate that 3kPZS release exhibits a consistent diel pattern across several days with elevated pheromone release just prior to sunset and at night. Trends in hepatic concentrations and circulatory transport of PZS and 3kPZS were relatively consistent with patterns of 3kPZS release and suggest the possibility of direct upregulation in pheromone transport and release rather than observed release patterns being solely a byproduct of increased behavioral activity. Our results suggest males evolved a signaling strategy that synchronizes elevated pheromone release with nocturnal increases in sea lamprey behavior. This may be imperative to ensure that male signaling effort is not wasted in a species having a single, reproductive event.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icb/icab190","usgsCitation":"Fissette, S.D., Bussy, U., Huerta, B., Brant, C.O., Li, K., Johnson, N.S., and Li, W., 2021, Diel patterns of pheromone release by male sea lamprey: Integrative and Comparative Biology, v. 61, no. 5, p. 1795-1810, https://doi.org/10.1093/icb/icab190.","productDescription":"16 p.","startPage":"1795","endPage":"1810","ipdsId":"IP-131044","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":450955,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icb/icab190","text":"Publisher Index Page"},{"id":412801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"61","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-09-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Fissette, Skye D.","contributorId":150994,"corporation":false,"usgs":false,"family":"Fissette","given":"Skye","email":"","middleInitial":"D.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bussy, Ugo","contributorId":150993,"corporation":false,"usgs":false,"family":"Bussy","given":"Ugo","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huerta, Belinda","contributorId":222210,"corporation":false,"usgs":false,"family":"Huerta","given":"Belinda","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brant, Cory O.","contributorId":126746,"corporation":false,"usgs":false,"family":"Brant","given":"Cory","email":"","middleInitial":"O.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Li, Ke","contributorId":172267,"corporation":false,"usgs":false,"family":"Li","given":"Ke","email":"","affiliations":[],"preferred":false,"id":863804,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":863628,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Weiming","contributorId":126748,"corporation":false,"usgs":false,"family":"Li","given":"Weiming","email":"","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":863629,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225501,"text":"70225501 - 2021 - Individual variation in temporal dynamics of post-release habitat selection","interactions":[],"lastModifiedDate":"2021-10-18T11:28:27.245581","indexId":"70225501","displayToPublicDate":"2021-09-03T06:26:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9319,"text":"Frontiers in Conservation Science","active":true,"publicationSubtype":{"id":10}},"title":"Individual variation in temporal dynamics of post-release habitat selection","docAbstract":"Translocated animals undergo a phase of behavioral adjustment after being released in a novel environment, initially prioritizing exploration and gradually shifting toward resource exploitation. This transition has been termed post-release behavioral modification. Post-release behavioral modification may also manifest as changes in habitat selection through time, and these temporal dynamics may differ between individuals. We aimed to evaluate how post-release behavioral modification is reflected in temporal dynamics of habitat selection and its variability across individuals using a population of translocated female greater sage-grouse as a case study. Sage-grouse were translocated from Wyoming to North Dakota (USA) during the summers of 2018–2020. We analyzed individual habitat selection as a function of sagebrush cover, herbaceous cover, slope, and distance to roads. Herbaceous cover is a key foraging resource for sage-grouse during summer; thus, we expected a shift from exploration to exploitation to manifest as temporally-varying selection for herbaceous cover. For each individual sage-grouse (N = 26), we tested two competing models: a null model with no time-dependence and a model with time-dependent selection for herbaceous cover. We performed model selection at the individual level using an information-theoretic approach. Time-dependence was supported for five individuals, unsupported for seven, and the two models were indistinguishable based on AICc for the remaining fourteen. We found no association between the top-ranked model and individual reproductive status (brood-rearing or not). We showed that temporal dynamics of post-release habitat selection may emerge in some individuals but not in others, and that failing to account for time-dependence may hinder the detection of steady-state habitat selection patterns. These findings demonstrate the need to consider both temporal dynamics and individual variability in habitat selection when conducting post-release monitoring to inform translocation protocols.
Lead (Pb) exposure is a widespread wildlife conservation threat. Although commonly associated with Pb-based ammunition from big-game hunting, small mammals (e.g., ground squirrels) shot for recreational or pest-management purposes represent a potentially important Pb vector in agricultural regions. We measured the responses of avian scavengers to pest-shooting events and examined their Pb exposure through consumption of shot mammals. There were 3.4-fold more avian scavengers at shooting fields relative to those at fields with no recent shooting, and avian scavengers spent 1.8-fold more time feeding after recent shooting events. We isotopically labeled shot ground squirrels in the field with an enriched 15N isotope tracer; 6% of avian scavengers sampled within a 39 km radius reflected this tracer in their blood. However, 33% of the avian scavengers within the average foraging dispersal distance of nests (0.6−3.7 km) were labeled, demonstrating the importance of these shooting fields as a source of food for birds nesting in close proximity. Additionally, Pb concentrations in 48% of avian scavengers exceeded subclinical poisoning benchmarks for sensitive species (0.03−0.20 μg/g w/w), and those birds exhibited reduced δ-aminolevulinic acid dehydratase activity, indicating a biochemical effect of Pb. The use of shooting to manage small mammal pests is a common practice globally. Efforts that can reduce the use of Pb-based ammunition may lessen the negative physiological effects of Pb exposure on avian scavengers.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.1c01041","usgsCitation":"Herring, G., Eagles-Smith, C., Goodell, J., Buck, J.A., and Willacker, J., 2021, Small mammal shooting as a conduit for lead exposure in avian scavengers: Environmental Science and Technology, v. 55, no. 18, p. 12272-12280, https://doi.org/10.1021/acs.est.1c01041.","productDescription":"9 p.","startPage":"12272","endPage":"12280","ipdsId":"IP-126933","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":397858,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","county":"Lake County, Malheur 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Garth 0000-0003-1106-4731 gherring@usgs.gov","orcid":"https://orcid.org/0000-0003-1106-4731","contributorId":4403,"corporation":false,"usgs":true,"family":"Herring","given":"Garth","email":"gherring@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goodell, John","contributorId":289382,"corporation":false,"usgs":false,"family":"Goodell","given":"John","email":"","affiliations":[{"id":62121,"text":"High Desert Museum","active":true,"usgs":false}],"preferred":false,"id":839077,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buck, Jeremy A.","contributorId":195480,"corporation":false,"usgs":false,"family":"Buck","given":"Jeremy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":839078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Willacker, James 0000-0002-6286-5224","orcid":"https://orcid.org/0000-0002-6286-5224","contributorId":207883,"corporation":false,"usgs":true,"family":"Willacker","given":"James","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":839079,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223740,"text":"70223740 - 2021 - Unexpected diversity of Endozoicomonas in deep-sea corals","interactions":[],"lastModifiedDate":"2021-09-03T12:12:53.673111","indexId":"70223740","displayToPublicDate":"2021-09-02T07:09:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2663,"text":"Marine Ecology Progress Series","active":true,"publicationSubtype":{"id":10}},"title":"Unexpected diversity of Endozoicomonas in deep-sea corals","docAbstract":"ABSTRACT: The deep ocean hosts a large diversity of azooxanthellate cold-water corals whose associated microbiomes remain to be described. While the bacterial genus Endozoicomonas has been widely identified as a dominant associate of tropical and temperate corals, it has rarely been detected in deep-sea corals. Determining microbial baselines for these cold-water corals is a critical first step to understanding the ecosystem services their microbiomes contribute, while providing a benchmark against which to measure responses to environmental change or anthropogenic effects. Samples of Acanthogorgia aspera, A. spissa, Desmophyllum dianthus, and D. pertusum (Lophelia pertusa) were collected from western Atlantic sites off the US east coast and from the northeastern Gulf of Mexico. Microbiomes were characterized by 16S rRNA gene amplicon surveys. Although D. dianthus and D. pertusum have recently been combined into a single genus due to their genetic similarity, their microbiomes were significantly different. The Acanthogorgia spp. were collected from submarine canyons in different regions, but their microbiomes were extremely similar and dominated by Endozoicomonas. This is the first report of coral microbiomes dominated by Endozoicomonas occurring below 1000 m, at temperatures near 4°C. D. pertusum from 2 Atlantic sites were also dominated by distinct Endozoicomonas, unlike D. pertusum from other sites described in previous studies, including the Gulf of Mexico, the Mediterranean Sea and a Norwegian fjord.
","language":"English","publisher":"Inter-Research","doi":"10.3354/meps13844","usgsCitation":"Kellogg, C.A., and Pratte, Z.A., 2021, Unexpected diversity of Endozoicomonas in deep-sea corals: Marine Ecology Progress Series, v. 673, p. 1-15, https://doi.org/10.3354/meps13844.","productDescription":"15 p.","startPage":"1","endPage":"15","ipdsId":"IP-126734","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":450959,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps13844","text":"Publisher Index Page"},{"id":436213,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z1HPKR","text":"USGS data release","linkHelpText":"Cold-water Coral Microbiomes (Acanthogorgia spp. Desmophyllum dianthus, and Lophelia pertusa) from the Gulf of Mexico and Atlantic Ocean off the Southeast Coast of the United States-Raw Data"},{"id":388829,"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 -74.8828125,\n 37.405073750176925\n ],\n [\n -74.6630859375,\n 35.42486791930558\n ],\n [\n -75.673828125,\n 34.161818161230386\n ],\n [\n -78.046875,\n 33.247875947924385\n ],\n [\n -79.453125,\n 32.32427558887655\n ],\n [\n -79.4970703125,\n 31.541089879585808\n ],\n [\n -77.87109375,\n 31.316101383495624\n ],\n [\n -74.4873046875,\n 32.509761735919426\n ],\n [\n -71.71875,\n 34.77771580360469\n ],\n [\n -71.455078125,\n 36.4566360115962\n ],\n [\n -71.89453125,\n 37.405073750176925\n ],\n [\n -72.5537109375,\n 37.50972584293751\n ],\n [\n -73.47656249999999,\n 37.82280243352756\n ],\n [\n -74.1796875,\n 37.96152331396614\n ],\n [\n -74.8828125,\n 37.405073750176925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"673","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":822526,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratte, Zoe A.","contributorId":214260,"corporation":false,"usgs":false,"family":"Pratte","given":"Zoe","email":"","middleInitial":"A.","affiliations":[{"id":27526,"text":"Georgia Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":822527,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70230525,"text":"70230525 - 2021 - Historical changes in plant water use and need in the continental United States","interactions":[],"lastModifiedDate":"2022-04-15T12:11:42.091767","indexId":"70230525","displayToPublicDate":"2021-09-02T07:07:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Historical changes in plant water use and need in the continental United States","docAbstract":"A robust method for characterizing the biophysical environment of terrestrial vegetation uses the relationship between Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD). These variables are usually estimated from a water balance model rather than measured directly and are often more representative of ecologically-significant changes than temperature or precipitation. We evaluate trends and spatial patterns in AET and CWD in the Continental United States (CONUS) during 1980–2019 using a gridded water balance model. The western US had linear regression slopes indicating increasing CWD and decreasing AET (drying), while the eastern US had generally opposite trends. When limits to plant performance characterized by AET and CWD are exceeded, vegetation assemblages change. Widespread increases in aridity throughout the west portends shifts in the distribution of plants limited by available moisture. A detailed look at Sequoia National Park illustrates the high degree of fine-scale spatial variability that exists across elevation and topographical gradients. Where such topographical and climatic diversity exists, appropriate use of our gridded data will require sub-setting to an appropriate area and analyzing according to categories of interest such as vegetation communities or across obvious physical gradients. Recent studies have successfully applied similar water balance models to fire risk and forest structure in both western and eastern U.S. forests, arid-land spring discharge, amphibian colonization and persistence in wetlands, whitebark pine mortality and establishment, and the distribution of arid-land grass species and landscape scale vegetation condition. Our gridded dataset is available free for public use. Our findings illustrate how a simple water balance model can identify important trends and patterns at site to regional scales. However, at finer scales, environmental heterogeneity is driving a range of responses that may not be simply characterized by a single trend.
Chemical contamination of freshwaters is a global problem. In the United States alone, millions of kilometers of rivers and hectares of lakes and wetlands are impaired from contamination by chemicals including mercury, pesticides, polychlorinated biphenyls (PCBs), and trace metals (US Environmental Protection Agency, 2017). Efforts to mitigate the risks of contamination have largely focused on aquatic endpoints. However, these contaminants pose a risk not only to life in freshwater ecosystems but also to the terrestrial organisms that depend on freshwater ecosystems for food.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.5203","usgsCitation":"Kraus, J.M., Wesner, J., and Walters, D., 2021, Insect-mediated contaminant flux at the land–water interface: Are ecological subsidies driving exposure or is exposure driving subsidies?: Environmental Toxicology and Chemistry, v. 40, no. 11, p. 2953-2958, https://doi.org/10.1002/etc.5203.","productDescription":"6 p.","startPage":"2953","endPage":"2958","ipdsId":"IP-127477","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":450962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5203","text":"Publisher Index Page"},{"id":391424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Johanna M. 0000-0002-9513-4129 jkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-9513-4129","contributorId":4834,"corporation":false,"usgs":true,"family":"Kraus","given":"Johanna","email":"jkraus@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826402,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wesner, Jeff S.","contributorId":268319,"corporation":false,"usgs":false,"family":"Wesner","given":"Jeff S.","affiliations":[{"id":55622,"text":"University of South Dakota, Department of Biology, 414 E. Clark St., Vermillion, SD","active":true,"usgs":false}],"preferred":false,"id":826403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205915,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826404,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225724,"text":"70225724 - 2021 - Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise","interactions":[],"lastModifiedDate":"2021-11-05T11:58:08.77405","indexId":"70225724","displayToPublicDate":"2021-09-02T06:57:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise","docAbstract":"Tilting of the ground due to loading by the variable atmosphere is known to corrupt very long period horizontal seismic records (below 10 mHz) even at the quietest stations. At BFO (Black Forest Observatory, SW-Germany), the opportunity arose to study these disturbances on a variety of simultaneously operated state-of-the-art broad-band sensors. A series of time windows with clear atmospherically caused effects was selected and attempts were made to model these ‘signals’ in a deterministic way. This was done by simultaneously least-squares fitting the locally recorded barometric pressure and its Hilbert transform to the ground accelerations in a bandpass between 100 and 3600 s periods. Variance reductions of up to 97 per cent were obtained. We show our results by combining the ‘specific pressure induced accelerations’ for the two horizontal components of the same sensor as vectors on a horizontal plane, one for direct pressure and one for its Hilbert transform. It turned out that at BFO the direct pressure effects are large, strongly position dependent and largely independent of atmospheric events for instruments installed on piers, while three post-hole sensors are only slightly affected. The infamous ‘cavity effects’ are invoked to be responsible for these large effects on the pier sensors. On the other hand, in the majority of cases all sensors showed very similar magnitudes and directions for the vectors obtained for the regression with the Hilbert transform, but highly variable from event to event especially in direction. Therefore, this direction most certainly has to do with the gradient of the pressure field moving over the station which causes a larger scale deformation of the crust. The observations are very consistent with these two fundamental mechanisms of how fluctuations of atmospheric surface pressure causes tilt noise. The results provide a sound basis for further improvements of the models for these mechanisms. The methods used here can already help to reduce atmospherically induced noise in long-period horizontal seismic records.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab336","usgsCitation":"Zurn, W., Forbriger, T., Widmer-Schnidrig, R., Duffner, P., and Ringler, A.T., 2021, Modelling tilt noise caused by atmospheric processes at long periods for several horizontal seismometers at BFO—A reprise: Geophysical Journal International, v. 228, no. 2, p. 927-943, https://doi.org/10.1093/gji/ggab336.","productDescription":"17 p.","startPage":"927","endPage":"943","ipdsId":"IP-131609","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":450964,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.5445/ir/1000140172","text":"External Repository"},{"id":391423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"228","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Zurn, W.","contributorId":268322,"corporation":false,"usgs":false,"family":"Zurn","given":"W.","affiliations":[{"id":55624,"text":"Black Forest Observatory (Schiltach)","active":true,"usgs":false}],"preferred":false,"id":826410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Forbriger, T.","contributorId":268323,"corporation":false,"usgs":false,"family":"Forbriger","given":"T.","email":"","affiliations":[{"id":55625,"text":"Black Forest Observatory (Schiltach); Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":826411,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Widmer-Schnidrig, R.","contributorId":221153,"corporation":false,"usgs":false,"family":"Widmer-Schnidrig","given":"R.","email":"","affiliations":[{"id":40338,"text":"Black Forest Observatory, Institute of Geodesy, Stuttgart University, Wolfach, Germany","active":true,"usgs":false}],"preferred":false,"id":826412,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duffner, P.","contributorId":268324,"corporation":false,"usgs":false,"family":"Duffner","given":"P.","email":"","affiliations":[{"id":55625,"text":"Black Forest Observatory (Schiltach); Karlsruhe Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":826413,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":826414,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223694,"text":"sir20205150 - 2021 - Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013","interactions":[],"lastModifiedDate":"2021-09-02T11:51:45.887677","indexId":"sir20205150","displayToPublicDate":"2021-09-01T16:37:37","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5150","displayTitle":"Precipitation-Runoff Processes in the Merced River Basin, Central California, with Prospects for Streamflow Predictability, Water Years 1952–2013","title":"Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013","docAbstract":"The U.S. Geological Survey, in cooperation with the California Department of Water Resources (DWR), has constructed a new spatially detailed Precipitation-Runoff Modeling System (PRMS) model for the Merced River Basin, California, which is a tributary of the San Joaquin River in California. Operated through an Object User Interface (OUI) with Ensemble Streamflow Prediction (ESP) and daily climate distribution preprocessing functionality, the model is calibrated primarily to simulate (and eventually, forecast) year-to-year variations of inflows to Lake McClure during the critical April–July snowmelt season. The model is intended to become part of a suite of methods used by DWR for estimating daily streamflow from the Merced River Basin, especially during the snowmelt season. This study describes the results of the application of an analysis tool that simulates responses to climate and land-use variations at a higher spatial resolution than previously available to DWR.
A geographic information system was used to delineate the model domain, that is, areas draining to a single outlet at U.S. Geological Survey streamflow-gaging station 11270900, Merced River below Merced Falls Dam, near Snell, CA (also known as California Data Exchange Center station MRC), and subdrainage areas, including four draining to internal gages used as calibration targets. Using this delineation, three contiguous subbasins were recognized and, along with the model domain and nested calibration targets, are the simulation units evaluated in this report.
An auto-calibration tool, LUCA (Let Us CAlibrate), was used for each calibration node, from headwaters to basin outlet, and then parameters were manually adjusted to complete the calibration. The main objective was to match April–July snowmelt seasonal discharge values of simulated streamflow to observed (measured or reconstructed) discharge values. Calibration or validation periods used site-specific streamflows—mostly from October 1, 1988, through September 30, 2013—but differed according to the period-of-record available for the measurements collected at internal gages or reconstructed flows for the single outlet.
The accuracy of the Merced PRMS streamflow simulations varied seasonally, as compared to observed values. Based on statistical results, the Merced PRMS model satisfactorily simulated snowmelt seasonal streamflows. April–July calibrations for all areas had small negative bias (not greater than 7 percent) and low relative error (less than 8 percent). Less satisfactory performance for other seasons was attributed to several factors: (1) high uncertainty in low or zero flows in summer and fall, (2) lack of accounting for basin withdrawals and anthropogenic water use, (3) unavailability and (or) inaccuracy of observed (measured) meteorological input data, and (4) uncertainty in reconstructed streamflow data.
With some additional refinement, the Merced PRMS model may be used for forecasting seasonal and longer-term streamflow variations; evaluating forecasted and past climate and land cover changes; providing water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites within the basin; and aiding environmental studies, hydraulic design, water management, and water-quality projects in the Merced River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205150","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Koczot, K.M., Risley, J.C., Gronberg, J.M., Donovan, J.M., and McPherson, K.R., 2021, Precipitation-runoff processes in the Merced River Basin, Central California, with prospects for streamflow predictability, water years 1952–2013: U.S. Geological Survey Scientific Investigations Report 2020–5150, 61 p., https://doi.org/10.3133/sir20205150.","productDescription":"Report: ix, 61 p.; 1 Figure: 16.0 x 10.0 inches; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-028665","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":388739,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KFR","linkHelpText":"Archive of Merced River Basin Precipitation-Runoff Modeling System, with forecasting, climate-file preparation, and data-visualization tools"},{"id":388738,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150_fig11_sheet.pdf","text":"Figure 11 (16\" x 10\" sheet)","size":"7 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Physical architecture of the Merced River Basin Precipitation-Runoff Modeling System."},{"id":388737,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5150/sir20205150.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388736,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5150/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Merced River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.3055419921875,\n 36.88401445049676\n ],\n [\n -119.27307128906249,\n 36.88401445049676\n ],\n [\n -119.27307128906249,\n 37.69251435532741\n ],\n [\n -121.3055419921875,\n 37.69251435532741\n ],\n [\n -121.3055419921875,\n 36.88401445049676\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The spread of the invasive and fire-adapted buffelgrass (Cenchrus ciliaris L.) threatens desert ecosystems by competing for resources, increasing fuel loads, and creating wildfire connectivity. The Rincon Mountain District of Saguaro National Park addressed this natural resource threat with the use of glyphosate-based herbicides (GBHs). In 2010, the Rincon Mountain District initiated an aerial restoration plan to control dense buffelgrass patches in remote areas and implemented a trial project to evaluate the effects of aerial restoration techniques that included the helicopter application of GBHs. In 2014, more than 250 acres of buffelgrass in the Rincon Mountain District were treated with the aerial application of GBHs. This widespread aerial application of GBHs continued through 2018, but the potential transport and effects to aquatic ecosystems were unknown.
In 2015–18, the U.S. Geological Survey, in cooperation with the National Park Service, studied the occurrence, distribution, fate, and transport of glyphosate in surface water and sediments derived from areas that were treated during past and current aerial herbicide applications. Three watersheds, treated with different regimens of GBHs, were sampled for glyphosate and the primary metabolite of glyphosate, aminomethylphosphonic acid (AMPA), during various hydrologic flow conditions. Water and aquatic sediment were collected from three watersheds, each in a different stage of application during the U.S. Geological Survey study. The unnamed watershed above the Loma Verde Trailhead referred to by the National Park Service as “Loma Verde canyon” had received no aerial treatment since 2014, whereas the Box Canyon watershed was aerially treated every year beginning in 2014. The Madrona Canyon watershed was first sprayed in 2016 and aerial application continued once a year though the entirety of the study. In addition, terrestrial soil samples were sampled from areas sprayed to understand dissipation rates and herbicide transport via sediments washing away during rainfall runoff. The concentrations present in water and sediment samples were compared to ecological benchmarks and characterized within the context of the environmental conditions of the park setting.
Of the 48 water samples collected and analyzed for glyphosate and AMPA, 10.4 percent and 14.6 percent were detected above the laboratory minimum detection limit, respectively. Mean water concentrations, calculated using specific statistical methods for non-detects, were equal to the laboratory minimum detection limit of 0.02 microgram per liter for samples collected in all the watersheds. In aquatic sediments, glyphosate and AMPA were detected in 10.7 and 25.0 percent of the samples, whereas 89.5 and 100 percent of the terrestrial soil samples had detections for glyphosate and AMPA, respectively. Mean aquatic sediment concentrations were 1.13 and 4.42 micrograms per kilogram (μg/kg) for glyphosate and AMPA, respectively. Mean terrestrial soil concentrations were orders of magnitude greater than water and aquatic sediment with concentrations of 678 μg/kg for AMPA and 1,240 μg/kg for glyphosate. Hours after glyphosate-based herbicide was applied, the concentrations of glyphosate and AMPA were present in terrestrial soil samples near or above the laboratory maximum detection limit of 5,000 μg/kg. The Box Canyon watershed was the most intensively treated watershed in terms of total land area treated, total amount of GBH applied, and number of years treated. The frequent and large volume of treatment resulted in the highest number of detections of glyphosate and AMPA in water (3 and 7 detections, respectively) and in aquatic sediment (2 and 6 detections, respectively) samples. In comparison, the other two watersheds had two or fewer detections for glyphosate and AMPA in water and aquatic sediment.
Glyphosate detected in pools was associated with increased rainfall closer in time to the last herbicide treatment. Glyphosate and AMPA concentration ratios above one, along with stable-isotope and tritium results, indicated that runoff processes were the primary transport mechanism for the two compounds when found in streams and pools rather than subsurface recharge or deeper flow paths. One pool in a small tributary of Box Canyon consistently had detections of glyphosate and AMPA in aquatic sediments, but these frequent concentrations were likely related to the intensive application upstream, near the steep terrain above the head of the channel that supplies the downstream pool. Intense flows during summer rainfall events move treated sediments into this channel where vegetation and the incised bedrock banks of the pool retained those sediments and ultimately led to frequent detections of both compounds. Isotope results in most of the pools and tinajas indicated that the water source had residence time representative of recently recharged waters, on the order of years.
No water concentrations exceeded published criteria for human health or aquatic life. Median and maximum glyphosate and AMPA water concentrations were lower than those reported in other national assessments, but maximum concentrations observed in individual runoff samples were higher than median concentrations measured in the national assessments. A similar finding was observed with aquatic sediment concentrations measured in the Rincon Mountain District. Results from the study were compared and assessed in the context of other studies examining GBHs and their effects on amphibians, fish, and macroinvertebrates. This comparison was used to generalize the potential risk to aquatic species similar to those species in the Rincon Mountain District. Concentrations of published effect levels were several orders of magnitude greater than the highest concentration detected in water at the Rincon Mountain District. Most published studies evaluate acute and chronic toxicity for glyphosate and GBHs, and these criteria may not be representative of environmental conditions in the Rincon Mountain District. The classic lethal dose studies conducted in a controlled laboratory setting may not be suitable for comparison to the longer, variable, low-dose exposure conditions in the pools and tinajas in the Rincon Mountain District. However, this study determined that the fate of GBHs transported from treated areas to potential aquatic habitat was highly variable in occurrence, timing, and concentrations. This variability in glyphosate concentrations was too high, and the potential exposure was determined to be far too complex to directly compare with the results from controlled studies.
This study provides the first information collected on GBHs used to control invasive buffelgrass in a remote, mountainous, and semiarid setting. The information about the transport and fate of herbicide application near aquatic habitat will help to inform managers about the broader ecosystem implications and provide useful information to other agencies implementing buffelgrass remediation strategies near aquatic habitat.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215039","collaboration":"Prepared in cooperation with the National Park Service and Saguaro National Park","usgsCitation":"Paretti, N.V., Beisner, K.R., Gungle, B., Meyer, M.T., Kunz, B.K., Hermosillo, E., Cederberg, J.R., and Mayo, J.P., 2021, Occurrence, fate, and transport of aerially applied herbicides to control invasive buffelgrass within Saguaro National Park Rincon Mountain District, Arizona, 2015–18: U.S. Geological Survey Scientific Investigations Report 2021–5039, 65 p., https://doi.org/10.3133/sir20215039.","productDescription":"Report: ix, 65 p.; Dataset","numberOfPages":"65","onlineOnly":"Y","ipdsId":"IP-099223","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":404268,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215039/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Scientific Investigations Report 2021–5039"},{"id":388749,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5039/images"},{"id":388746,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5039/covrthb.jpg"},{"id":388747,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5039/sir20215039.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388748,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5039/sir20215039.xml"},{"id":388760,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":404267,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223029","text":"Fact Sheet 2022-3029","description":"Paretti, N.V., and Gungle, B., 2022, Occurrence and transport of aerially applied herbicides to control invasive buffelgrass in Rincon Mountain District, Saguaro National Park, Arizona: U.S. Geological Survey Fact Sheet 2022-3029, 6 p., https://doi.org/10.3133/fs20223029.","linkHelpText":"- Occurrence and Transport of Aerially Applied Herbicides to Control Invasive Buffelgrass in Rincon Mountain District, Saguaro National Park, Arizona"}],"country":"United States","state":"Arizona","otherGeospatial":"Saguaro National Park Rincon Mountain District","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.75248718261719,\n 32.027870563435584\n ],\n [\n -110.37483215332031,\n 32.027870563435584\n ],\n [\n -110.37483215332031,\n 32.27320009948135\n ],\n [\n -110.75248718261719,\n 32.27320009948135\n ],\n [\n -110.75248718261719,\n 32.027870563435584\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Misunderstandings regarding the term “false positive” present a significant hurdle to broad adoption of eDNA monitoring methods. Here, we identify three challenges to clear communication of false-positive error between scientists, managers, and the public. The first arises from a failure to distinguish between false-positive eDNA detection at the sample level and false-positive inference of taxa presence at the site level. The second is based on the large proportion of false positives that may occur when true-positive detections are likely to be rare, even when rates of contamination or other error are low. And the third misunderstanding occurs when conventional species detection approaches, often based on direct capture, are used to confirm eDNA approaches without acknowledging or quantifying the conventional approach's detection probability. The solutions to these issues include careful and consistent communication of error definitions, managing expectations of error rates, and providing a balanced discussion not only of alternative sources of species DNA, but also of the detection limitations of conventional methods. We argue that the benefit of addressing these misunderstandings will be increased confidence in the utility of eDNA methods and, ultimately, improved resource management using eDNA approaches.
","language":"English","publisher":"Wiley","doi":"10.1002/edn3.194","usgsCitation":"Darling, J., Jerde, C.L., and Sepulveda, A., 2021, What do you mean by false positive?: Environmental DNA, v. 3, no. 5, p. 879-883, https://doi.org/10.1002/edn3.194.","productDescription":"5 p.","startPage":"879","endPage":"883","ipdsId":"IP-124696","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":450967,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/edn3.194","text":"Publisher Index Page"},{"id":411273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Darling, John A. 0000-0002-4776-9533","orcid":"https://orcid.org/0000-0002-4776-9533","contributorId":260860,"corporation":false,"usgs":false,"family":"Darling","given":"John A.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":860685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jerde, Christopher L. 0000-0002-8074-3466","orcid":"https://orcid.org/0000-0002-8074-3466","contributorId":210301,"corporation":false,"usgs":false,"family":"Jerde","given":"Christopher","email":"","middleInitial":"L.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":860686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":860687,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230783,"text":"70230783 - 2021 - Incorporating uncertainty into groundwater salinity mapping using AEM data","interactions":[],"lastModifiedDate":"2022-04-26T16:03:13.299546","indexId":"70230783","displayToPublicDate":"2021-09-01T10:58:08","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Incorporating uncertainty into groundwater salinity mapping using AEM data","docAbstract":"Airborne electromagnetic surveys provide spatially extensive resistivity information that can be useful for groundwater salinity mapping; however, the transformation from geophysical data to salinity interpretations carries uncertainty. We compare two quantitative approaches to salinity mapping recently applied to address water resource management objectives: the location of the depth to the freshwater-brine interface at Paradox Valley, Colorado, and 3D categorical mapping of fresh, brackish, and saline groundwater near oil and gas fields of the San Joaquin Valley, California. These different approaches were driven by a combination of the availability of water quality observations, the hydrogeologic setting, and study objectives.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"First international meeting for applied geoscience & energy expanded abstracts","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"First International Meeting for Applied Geoscience & Energy (IMAGE ’21)","conferenceDate":"Sep 26-Oct1, 2021","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/segam2021-3584073.1","usgsCitation":"Ball, L.B., and Minsley, B.J., 2021, Incorporating uncertainty into groundwater salinity mapping using AEM data, in First international meeting for applied geoscience & energy expanded abstracts, Sep 26-Oct1, 2021, p. 3105-3109, https://doi.org/10.1190/segam2021-3584073.1.","productDescription":"5 p.","startPage":"3105","endPage":"3109","ipdsId":"IP-128031","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":399677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ball, Lyndsay B. 0000-0002-6356-4693 lbball@usgs.gov","orcid":"https://orcid.org/0000-0002-6356-4693","contributorId":1138,"corporation":false,"usgs":true,"family":"Ball","given":"Lyndsay","email":"lbball@usgs.gov","middleInitial":"B.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":841357,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":841358,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70270492,"text":"70270492 - 2021 - G-LiHT user guide","interactions":[],"lastModifiedDate":"2025-08-21T16:02:56.444112","indexId":"70270492","displayToPublicDate":"2021-09-01T10:51:30","publicationYear":"2021","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"G-LiHT user guide","docAbstract":"No abstract available.
","language":"English","publisher":"NASA","usgsCitation":"Wirt, B., 2021, G-LiHT user guide (Version 2.0), 26 p.","productDescription":"26 p.","ipdsId":"IP-133130","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":494391,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":494395,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://lpdaac.usgs.gov/documents/1312/G-LiHT_User_Guide_V2.pdf","linkFileType":{"id":1,"text":"pdf"}}],"edition":"Version 2.0","noUsgsAuthors":false,"publicationDate":"2021-09-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Wirt, Bradford 0000-0001-6115-6963","orcid":"https://orcid.org/0000-0001-6115-6963","contributorId":359919,"corporation":false,"usgs":false,"family":"Wirt","given":"Bradford","affiliations":[{"id":85935,"text":"KBR, Inc, contracted to USGS","active":true,"usgs":false}],"preferred":false,"id":946459,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237357,"text":"70237357 - 2021 - LakeEnsemblR: An R package that facilitates ensemble modelling of lakes","interactions":[],"lastModifiedDate":"2022-10-11T15:49:16.703697","indexId":"70237357","displayToPublicDate":"2021-09-01T10:40:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"LakeEnsemblR: An R package that facilitates ensemble modelling of lakes","docAbstract":"Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105101","usgsCitation":"Moore, T.N., Mesman, J., Ladwig, R., Feldbauer, J., Olsson, F., Pilla, R.M., Shatwell, T., Venkiteswaran, J.J., Delany, A.D., Dugan, H., Rose, K.C., and Read, J., 2021, LakeEnsemblR: An R package that facilitates ensemble modelling of lakes: Environmental Modelling & Software, v. 143, 105101, 14 p., https://doi.org/10.1016/j.envsoft.2021.105101.","productDescription":"105101, 14 p.","ipdsId":"IP-122731","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":450973,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105101","text":"Publisher Index Page"},{"id":408161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Tadhg N.","contributorId":297476,"corporation":false,"usgs":false,"family":"Moore","given":"Tadhg","email":"","middleInitial":"N.","affiliations":[{"id":64406,"text":"Dundalk Institute of Technology, Centre for Freshwater and Environmental Studies, Dundalk, Co. Louth, Ireland","active":true,"usgs":false}],"preferred":false,"id":854248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mesman, Jorrit P.","contributorId":297477,"corporation":false,"usgs":false,"family":"Mesman","given":"Jorrit P.","affiliations":[{"id":64408,"text":"University of Geneva, Department F.A. Forel for Environmental and Aquatic Sciences, Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":854249,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladwig, Robert","contributorId":265278,"corporation":false,"usgs":false,"family":"Ladwig","given":"Robert","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":854250,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feldbauer, Johannes 0000-0002-8238-5375","orcid":"https://orcid.org/0000-0002-8238-5375","contributorId":268217,"corporation":false,"usgs":false,"family":"Feldbauer","given":"Johannes","email":"","affiliations":[{"id":55600,"text":"Technische Universität Dresden","active":true,"usgs":false}],"preferred":false,"id":854251,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olsson, Freya","contributorId":297478,"corporation":false,"usgs":false,"family":"Olsson","given":"Freya","email":"","affiliations":[{"id":64410,"text":"UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Bailrigg, Lancaster, UK","active":true,"usgs":false}],"preferred":false,"id":854252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilla, Rachel M. 0000-0001-9156-9486","orcid":"https://orcid.org/0000-0001-9156-9486","contributorId":261758,"corporation":false,"usgs":false,"family":"Pilla","given":"Rachel","email":"","middleInitial":"M.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":854253,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shatwell, Tom","contributorId":297279,"corporation":false,"usgs":false,"family":"Shatwell","given":"Tom","email":"","affiliations":[{"id":64343,"text":"Helmholtz Centre for Environmental Research - UFZ, Department Lake Research, Magdeburg, Germany","active":true,"usgs":false}],"preferred":false,"id":854254,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Venkiteswaran, Jason J.","contributorId":297479,"corporation":false,"usgs":false,"family":"Venkiteswaran","given":"Jason","email":"","middleInitial":"J.","affiliations":[{"id":64411,"text":"Wilfrid Laurier University, Department of Geography and Environmental Studies, Waterloo, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":854255,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delany, Austin D.","contributorId":297480,"corporation":false,"usgs":false,"family":"Delany","given":"Austin","email":"","middleInitial":"D.","affiliations":[{"id":64412,"text":"University of Wisconsin – Madison, Center for Limnology, Madison, Wisconsin, USA","active":true,"usgs":false}],"preferred":false,"id":854256,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dugan, Hilary","contributorId":150191,"corporation":false,"usgs":false,"family":"Dugan","given":"Hilary","affiliations":[{"id":17938,"text":"Center for Limnology University of Wisconsin, Madison, WI 53706, US","active":true,"usgs":false}],"preferred":false,"id":854257,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Kevin C.","contributorId":174809,"corporation":false,"usgs":false,"family":"Rose","given":"Kevin","email":"","middleInitial":"C.","affiliations":[{"id":12656,"text":"Rensselaer Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":854258,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854259,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70224964,"text":"70224964 - 2021 - Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt","interactions":[],"lastModifiedDate":"2021-10-11T15:41:58.169094","indexId":"70224964","displayToPublicDate":"2021-09-01T10:37:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7359,"text":"Journal of Geophysical Research Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt","docAbstract":"Freshwater streams can exchange nutrients and carbon with the surrounding terrestrial environment through various mechanisms including physical erosion, flooding, leaf drop, and snowmelt. These aquatic-terrestrial interactions are crucial in carbon mobilization, transformation, ecosystem productivity, and have important implications for the role of freshwater ecosystems in the global carbon budget. We utilized high-frequency oxygen, temperature, and carbon dioxide (CO2) data to infer watershed connectivity in Boulder Creek, a mid-sized (1160 km2) watershed located in Colorado, USA. Daily modeled gross primary production (GPP), ecosystem respiration (ER), net ecosystem production (NEP), and reaeration coefficients (K600) were paired with high-frequency, in-situ dissolved CO2 data to characterize changes in metabolic regime and carbon flux on a stream influenced by seasonal snowmelt. GPP and ER were correlated (ρ = −0.72, p ≪ 0.001) during the non-snowmelt period and NEP was frequently negative. Mean FCO2 during the non-snowmelt period was approximately 302 (±171) mmol C m−2 d−1 and was primarily supported by watershed CO2 inputs. During snowmelt, GPP and ER were not significantly correlated (ρ = −0.22, p = 0.05), and mean NEP was significantly more negative than during non-snowmelt. Watershed connectivity was higher during snowmelt, as evidenced by significantly higher FCO2 (843 ± 338 mmol C m−2 d−1) and greater allochthonous CO2 inputs than during non-snowmelt periods, emphasizing the effects of seasonal differences in aquatic-terrestrial linkages in this stream. We suggest that our understanding of watershed carbon budgets is subject to temporal dynamics which control the degree of connectivity between terrestrial and aquatic ecosystems.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JG006296","usgsCitation":"Reed, A.P., Stets, E.G., Murphy, S.F., and Mullins, E., 2021, Aquatic-terrestrial linkages control metabolism and carbon dynamics in a mid-sized, urban stream influenced by snowmelt: Journal of Geophysical Research Biogeosciences, v. 126, no. 9, e2021JG006296, 16 p., https://doi.org/10.1029/2021JG006296.","productDescription":"e2021JG006296, 16 p.","ipdsId":"IP-113327","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":450975,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jg006296","text":"Publisher Index Page"},{"id":436214,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P991TMNQ","text":"USGS data release","linkHelpText":"Modeled Stream Metabolism in Boulder Creek near Boulder, CO (2016 - 2018)"},{"id":390389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Boulder","otherGeospatial":"Boulder Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.43922424316406,\n 39.95343802330847\n ],\n [\n -105.15975952148438,\n 39.95343802330847\n ],\n [\n -105.15975952148438,\n 40.054949943999496\n ],\n [\n -105.43922424316406,\n 40.054949943999496\n ],\n [\n -105.43922424316406,\n 39.95343802330847\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Ariel P. 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":219992,"corporation":false,"usgs":true,"family":"Reed","given":"Ariel","email":"","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stets, Edward G. 0000-0001-5375-0196 estets@usgs.gov","orcid":"https://orcid.org/0000-0001-5375-0196","contributorId":194490,"corporation":false,"usgs":true,"family":"Stets","given":"Edward","email":"estets@usgs.gov","middleInitial":"G.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":824894,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":824895,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullins, Emily 0000-0002-6710-0327","orcid":"https://orcid.org/0000-0002-6710-0327","contributorId":219993,"corporation":false,"usgs":true,"family":"Mullins","given":"Emily","email":"","affiliations":[],"preferred":true,"id":824896,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}