Anthropogenic eutrophication contributes to harmful blooms of cyanobacteria in freshwater ecosystems worldwide. In Upper Klamath Lake, Oregon, massive blooms of Aphanizomenon flos-aquae and smaller blooms of other cyanobacteria are associated with cyanotoxins, hypoxia, high pH, high concentrations of ammonia, and potentially hypercapnia. Recovery of the endangered Lost River sucker Deltistes luxatus and shortnose sucker Chasmistes brevirostris in Upper Klamath Lake is obstructed by low survival in the juvenile life stage. Water quality associated with the harmful algal blooms and their decomposition (crashes) is often singled out as the primary cause of juvenile sucker mortality. We investigated this general hypothesis with a review of relevant literature and data from decades of monitoring in Upper Klamath Lake. Microcystins, hepatotoxins produced by some cyanobacteria, are unlikely to be directly lethal to suckers; potential effects of other cyanotoxins that are present in the lake warrant investigation. Dissolved-oxygen saturation declined following bloom crashes, but was infrequently low enough for long enough in Upper Klamath Lake to cause direct sucker mortality. Hypercapnia could potentially reach lethal concentrations in the fall and winter, but did not appear to be associated with the summer algal blooms. pH was highest during peaks in cyanobacteria growth, but infrequently reached directly lethal levels (> 10.3). However, pH frequently reached an observed sub-lethal effect level for juvenile suckers (10.0). Un-ionized ammonia rarely exceeded even the lowest effect level measured for suckers. Rather than act as a direct cause of large-scale mortality, the available evidence suggests that water quality associated with massive blooms of cyanobacteria in Upper Klamath Lake contributes to chronic stress for juvenile suckers and may increase mortality due to other factors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2020.101847","usgsCitation":"Burdick, S.M., Hewitt, D., Martin, B.A., Schenk, L.N., and Rounds, S.A., 2020, Effects of harmful algal blooms and associated water-quality on endangered Lost River and shortnose suckers: Harmful Algae, v. 97, 101847, 20 p., https://doi.org/10.1016/j.hal.2020.101847.","productDescription":"101847, 20 p.","ipdsId":"IP-109018","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":375815,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.81777954101561,\n 42.08905095219165\n ],\n [\n -121.73675537109374,\n 42.21733067375916\n ],\n [\n -121.78619384765624,\n 42.36970554816487\n ],\n [\n -121.89605712890624,\n 42.49235259142821\n ],\n [\n -121.90017700195312,\n 42.53992763032448\n ],\n [\n -121.92489624023436,\n 42.60566321006408\n ],\n [\n -122.00454711914061,\n 42.58544425738491\n ],\n [\n -122.04849243164061,\n 42.508552415528634\n ],\n [\n -122.09518432617186,\n 42.48323834594139\n ],\n [\n -122.08007812499999,\n 42.430552260619564\n ],\n [\n -122.04986572265624,\n 42.412304442420684\n ],\n [\n -122.01690673828124,\n 42.37274928510041\n ],\n [\n -121.98669433593749,\n 42.40115038362433\n ],\n [\n -121.97433471679689,\n 42.371734722510496\n ],\n [\n -121.95373535156249,\n 42.38086519582321\n ],\n [\n -121.92214965820311,\n 42.355499492256534\n ],\n [\n -121.95510864257811,\n 42.34433533786168\n ],\n [\n -121.93862915039061,\n 42.28340504748081\n ],\n [\n -121.90017700195312,\n 42.3047373589234\n ],\n [\n -121.85760498046875,\n 42.245801966774025\n ],\n [\n -121.81915283203126,\n 42.21936476344714\n ],\n [\n -121.80679321289062,\n 42.19596877629178\n ],\n [\n -121.86721801757812,\n 42.16645713841854\n ],\n [\n -121.84249877929688,\n 42.08599350447723\n ],\n [\n -121.82601928710938,\n 42.09007006868398\n ],\n [\n -121.81777954101561,\n 42.08905095219165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"97","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":791212,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hewitt, David A. 0000-0002-5387-0275","orcid":"https://orcid.org/0000-0002-5387-0275","contributorId":225441,"corporation":false,"usgs":true,"family":"Hewitt","given":"David A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":791213,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Barbara A. 0000-0002-9415-6377 barbara_ann_martin@usgs.gov","orcid":"https://orcid.org/0000-0002-9415-6377","contributorId":2855,"corporation":false,"usgs":true,"family":"Martin","given":"Barbara","email":"barbara_ann_martin@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":791214,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791215,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rounds, Stewart A. 0000-0002-8540-2206","orcid":"https://orcid.org/0000-0002-8540-2206","contributorId":205029,"corporation":false,"usgs":true,"family":"Rounds","given":"Stewart","email":"","middleInitial":"A.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791216,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228940,"text":"70228940 - 2020 - Habitat associations and distributions of two endemic crayfishes, Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988 and Faxonius (Billecambarus) harrisonii (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA","interactions":[],"lastModifiedDate":"2022-02-24T16:03:02.114215","indexId":"70228940","displayToPublicDate":"2020-06-20T09:59:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"displayTitle":"Habitat associations and distributions of two endemic crayfishes, Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988 and Faxonius (Billecambarus) harrisonii (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA","title":"Habitat associations and distributions of two endemic crayfishes, Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988 and Faxonius (Billecambarus) harrisonii (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA","docAbstract":"Understanding the habitat associations and distributions of rare species is important to inform management and policy decisions. Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988, the freckled crayfish, and Faxonius (Billecambarus) harrisonii (Faxon, 1884), the belted crayfish, are two of Missouri’s endemic crayfish species. Both species are listed as Vulnerable (S3) on Missouri’s Species and Communities of Conservation Concern Checklist due to their limited range within the Meramec River drainage (MRD) and the impact of anthropogenic activities therein. Their distributional overlap offers an opportunity for multi-species research to address gaps in information required for conservation. We sampled 140 sites throughout the MRD during the summers of 2017 and 2018 for crayfishes and associated habitat variables, which we related to crayfish presence in an occupancy modeling framework. We found that C. maculatus occupancy was associated with larger stream size, boulder substrate, dolomite lithology, aquatic vegetation beds, dissolved oxygen, and pool mesohabitat. Faxonius harrisonii occupancy increased with boulder substrate, aquatic vegetation beds, the presence of C. maculatus, and decreased in third-order streams. We also expanded the known range for both species within the MRD. Range estimates (watershed area) for C. maculatus and F. harrisonii were 4,347 km2 and 3,690 km2, respectively. This study demonstrates the importance of targeted rather than opportunistic sampling for species distribution.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/jcbiol/ruaa033","usgsCitation":"Chilton, J., Rosenberger, A.E., and DiStefano, R., 2020, Habitat associations and distributions of two endemic crayfishes, Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988 and Faxonius (Billecambarus) harrisonii (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA, v. 40, no. 4, p. 351-363, https://doi.org/10.1093/jcbiol/ruaa033.","productDescription":"13 p.","startPage":"351","endPage":"363","ipdsId":"IP-123414","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jcbiol/ruaa033","text":"Publisher Index Page"},{"id":396426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Meramec River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.90887451171875,\n 37.54239958054064\n ],\n [\n -90.428466796875,\n 37.54239958054064\n ],\n [\n -90.428466796875,\n 38.59970036588819\n ],\n [\n -91.90887451171875,\n 38.59970036588819\n ],\n [\n -91.90887451171875,\n 37.54239958054064\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Chilton, J.","contributorId":280068,"corporation":false,"usgs":false,"family":"Chilton","given":"J.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":835981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DiStefano, Robert J.","contributorId":213268,"corporation":false,"usgs":false,"family":"DiStefano","given":"Robert J.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":835982,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210930,"text":"70210930 - 2020 - Divergent biotic and abiotic filtering of root endosphere and rhizosphere soil fungal communities along ecological gradients","interactions":[],"lastModifiedDate":"2020-07-07T14:16:05.435292","indexId":"70210930","displayToPublicDate":"2020-06-20T09:12:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1619,"text":"FEMS Microbiology Ecology","onlineIssn":"1574-6941","printIssn":"0168-6496","active":true,"publicationSubtype":{"id":10}},"title":"Divergent biotic and abiotic filtering of root endosphere and rhizosphere soil fungal communities along ecological gradients","docAbstract":"Plant roots assemble two distinct microbial compartments: the rhizosphere (microbes in soil surrounding roots) and the endosphere (microbes within roots). Our knowledge of fungal community assembly in these compartments is limited, especially in wetlands. We tested the hypothesis that biotic factors would have direct effects on rhizosphere and endosphere assembly, while abiotic factors would have direct and indirect effects. Using a field study, we examined the influences of salinity, water level and biotic factors on baldcypress (Taxodium distichum) fungal communities. We found that endosphere fungi were correlated with host density and canopy cover as opposed to rhizosphere, suggesting that hosts can impose selective filters on fungi colonizing into their roots. Meanwhile, local abiotic conditions strongly influenced both rhizosphere and endosphere diversity in opposite patterns: e.g. highest endosphere diversity (hump-shaped) while lowest rhizosphere diversity (U-shaped) at intermediate salinity levels. These results indicate that the assembly and structure for the root endosphere and rhizosphere within a host can be shaped by different processes. Our results also highlight the importance of assessing how environmental changes affect plant and plant-associated fungal communities in wetland ecosystems where saltwater intrusion and sea level rise are major threats to both plant and fungal communities.","language":"English","publisher":"Oxford Academic","doi":"10.1093/femsec/fiaa124","usgsCitation":"Lumibao, C.Y., Kimbrough, E., Day, R., Conner, W.H., Krauss, K., and Van Bael, S.A., 2020, Divergent biotic and abiotic filtering of root endosphere and rhizosphere soil fungal communities along ecological gradients: FEMS Microbiology Ecology, v. 96, no. 7, fiaa124, https://doi.org/10.1093/femsec/fiaa124.","productDescription":"fiaa124","ipdsId":"IP-102460","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":376149,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Lumibao, Candice Y 0000-0002-1414-7949","orcid":"https://orcid.org/0000-0002-1414-7949","contributorId":228830,"corporation":false,"usgs":false,"family":"Lumibao","given":"Candice","email":"","middleInitial":"Y","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":792200,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kimbrough, Elizabeth 0000-0002-4007-6304","orcid":"https://orcid.org/0000-0002-4007-6304","contributorId":228831,"corporation":false,"usgs":false,"family":"Kimbrough","given":"Elizabeth","email":"","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":792201,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":222817,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792202,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conner, William H.","contributorId":79376,"corporation":false,"usgs":false,"family":"Conner","given":"William","email":"","middleInitial":"H.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":792203,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":792204,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Van Bael, Sunshine A 0000-0001-7317-3533","orcid":"https://orcid.org/0000-0001-7317-3533","contributorId":228832,"corporation":false,"usgs":false,"family":"Van Bael","given":"Sunshine","email":"","middleInitial":"A","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":792205,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228559,"text":"70228559 - 2020 - Using reproductive potential to assess oyster population sustainability","interactions":[],"lastModifiedDate":"2022-02-14T21:02:57.728758","indexId":"70228559","displayToPublicDate":"2020-06-19T16:02:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using reproductive potential to assess oyster population sustainability","docAbstract":"Ensuring that oysters remain sustainable in the face of significant coastal restoration activities, high local subsidence rates, and predicted sea-level rise requires a deeper understanding of basic population demographics, including reproductive potential. We quantified fecundity (eggs ind−1) of oysters at high- and low-salinity sites during a fall and spring spawn season. We assessed the relationships between oyster size, the relative proportion of females across size classes, and fecundity. Finally, we quantified reproductive potential (eggs m−2) of an engineered reef by connecting fecundity with annual oyster population demographic data as a means to assess population sustainability. The proportion of females generally increased with shell height, achieving a population with >50% females in Biloxi oysters >75 mm, and Grand Isle oysters >100 mm. Fecundity across both sites and seasons ranged from approximately 2,000 to >55 million eggs oyster−1. Mean fecundity generally increased with shell height, varying significantly by site, with Grand Isle (high salinity) oysters having greater fecundity than Biloxi (low salinity) oysters. Fecundity did not differ by season. Mean reproductive potential (eggs m−2) was driven by density and size distribution. Reefs with high densities and higher counts of market-sized oysters had reproductive potentials 5× greater than those with low densities and low counts of juvenile oysters. With increasing changes in water quality from coastal management and climate, impacts on oyster reproduction may critically impact population sustainability. Reproductive potential provides critical data to assess individual reef ecosystem services, and to assess the potential for maintenance of local metapopulations.
","language":"English","publisher":"Society for Ecological Restoration","doi":"10.1111/rec.13225","usgsCitation":"Marshall, D., Moore, S., Sutor, M., La Peyre, J.F., and La Peyre, M., 2020, Using reproductive potential to assess oyster population sustainability: Restoration Ecology, v. 28, no. 6, p. 1621-1632, https://doi.org/10.1111/rec.13225.","productDescription":"12 p.","startPage":"1621","endPage":"1632","ipdsId":"IP-118044","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":499854,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.lsu.edu/animalsciences_pubs/795","text":"External Repository"},{"id":395940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.2691650390625,\n 29.563901551414418\n ],\n [\n -89.3353271484375,\n 29.563901551414418\n ],\n [\n -89.3353271484375,\n 30.259067203213018\n ],\n [\n -90.2691650390625,\n 30.259067203213018\n ],\n [\n -90.2691650390625,\n 29.563901551414418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Danielle A.","contributorId":239867,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle A.","affiliations":[{"id":48014,"text":"School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":834590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Samuel C.","contributorId":276133,"corporation":false,"usgs":false,"family":"Moore","given":"Samuel C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":834591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sutor, Malinda","contributorId":276134,"corporation":false,"usgs":false,"family":"Sutor","given":"Malinda","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":834592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Peyre, Jerome F.","contributorId":177346,"corporation":false,"usgs":false,"family":"La Peyre","given":"Jerome","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":834593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":834594,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210730,"text":"ofr20201056 - 2020 - Envisioning a multi-agency and multi-academic institution geomorphology data exchange portal","interactions":[],"lastModifiedDate":"2020-06-22T11:33:00.458228","indexId":"ofr20201056","displayToPublicDate":"2020-06-19T11:14:24","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1056","displayTitle":"Envisioning a Multi-Agency and Multi-Academic Institution Geomorphology Data Exchange Portal","title":"Envisioning a multi-agency and multi-academic institution geomorphology data exchange portal","docAbstract":"Access to bathymetry and geomorphology data for rivers and reservoirs is a critical need in multiple agencies and academia. These data are needed to make water-resource-management decisions regarding river restoration, resource protection, infrastructure design and sustainability, and flood-risk reduction, and during natural disasters. Sharing of data increases decision-making capacity by incorporating information from entire watersheds, provides knowledge from similar settings being managed or studied by other entities, and helps meet the goals of the Federal Open Water Data Initiative. Addressing these needs across broad spatial and temporal scales would be made more efficient if these data were available in consistent formats with standardized metadata and were either stored in a centralized database or integrated with existing geospatial datasets. Because of renewed interest and technological advances, representatives from multiple Federal agencies and academic institutions have created a new working group to scope the development of a Geomorphology Data Exchange Portal to increase access to needed data. The working group has developed a vision for the Portal and outlined possible approaches to achieve the vision. Short-term approaches may include leveraging existing data-access portals and data-processing tools and integrating geomorphology data with existing national geospatial datasets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201056","collaboration":"USGS Water Observing Systems ProgramOffice of Associate Director, Water
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Climate change is projected to alter the elevation and latitude of treeline globally. Seed germination and seedling survival are critical controls on treeline expansion. Neighbouring alpine plants, either through competition for resources or through altered microclimate, also affect seedlings emerging in the alpine zone. With warming, alpine plant species may interact with each other more or less strongly.
To determine whether establishing tree seedlings and an alpine herb are similarly sensitive to alpine plant neighbours under ambient and altered climate.
We imposed active heating, watering, and removed all plants adjacent to emerging conifer seedlings and an alpine herb.
Picea engelmannii seedlings showed lower survival compared with Pinus flexilis 3 weeks following neighbour removal, and after 1 year only survived in watered plots. Pinus seedlings responded to neighbour removal by lowering the quantum yield of photosynthesis (ϕPSII). Contrary to expectations from the stress gradient hypothesis, survival was reduced without neighbours near the low-elevation range limit of Chionophila jamesii.
Pinus flexilis has higher expansion potential into the alpine, while Picea engelmannii requires moist conditions that could be facilitated by neighbours to expand its range. This implies likely range expansion by P. flexilis with consequences for alpine plant diversity and ecosystem function.
The city of Omaha, Nebraska, has a combined sewer system in some areas of the city. In Omaha, Nebr., a moderate amount of rainfall will lead to the combination of stormwater and untreated sewage or wastewater being discharged directly into the Missouri River and Papillion Creek and is called a combined sewer overflow (CSO) event. In 2009, the city of Omaha began the implementation of their Long Term Control Plan (LTCP) to mitigate the effects of CSOs on the Missouri River and Papillion Creek. As part of the LTCP, the city partnered with the U.S. Geological Survey (USGS) in 2012 to begin monitoring in the Missouri River. Since 2012, monthly discrete water-quality samples for many constituents have been collected from the Missouri River at four sites. At 3 of the 4 sites, water quality has been monitored continuously for selected constituents and physical properties. These discrete water-quality samples and continuous water-quality monitoring data (from July 2012 to 2020) have been collected to better understand the water quality of the Missouri River, how it is changing with time, how it changes upstream from the city of Omaha to downstream, and how it varies during base-flow conditions and during periods of runoff.
The purpose of this report is to document the development of Escherichia coli (E. coli) concentration models for these four Missouri River sites. Analysis was completed using the first 5 years of data (through 2016) to determine if the current approach is sufficient to meet future analysis goals and to understand if proposed models such as Load Estimator (LOADEST) models will be able to represent water-quality changes in the Missouri River.
Multiple linear regression models were developed to estimate E. coli concentration using LOADEST as implemented in the rloadest package in the R statistical software program. A set of explanatory variables, including streamflow and streamflow anomalies, precipitation, information about CSOs, and continuous water quality, were evaluated for potential inclusion in regression models. The best model at Missouri River at NP Dodge Park at Omaha, Nebr. (USGS station 412126095565201; hereafter “NP Dodge”) included basin explanatory variables of upstream antecedent precipitation index measured at Tekamah, Nebr.; decimal time; season; and turbidity. The best model at Missouri River at Freedom Park Omaha, Nebr. (USGS station 411636095535401; hereafter “Freedom Park”) included the same explanatory variables as the NP Dodge model with the addition of turbidity anomalies and flow anomalies. The best models at the two downstream sites (Missouri River near Council Bluffs, Iowa, USGS station 06610505 and Missouri River near La Platte, Nebr., USGS station 410333095530101) included the same explanatory variables as the Freedom Park model with the addition of local antecedent precipitation index as measured at Eppley Airport in Omaha, Nebr., and additional turbidity and flow anomalies. The final selected models were the best models given our modeling design constraint in which explanatory variables included in the model for the upstream site were included in the downstream models.
Explanatory variables currently (2020) being collected and included in the selected models through 2016 explained 64–75 percent of the variability of E. coli concentration in the Missouri River. Explaining 64–75 percent of the variability might be considered low when working with physical constituents (total nitrogen or sediment), but with the natural variability of biological constituents such as E. coli, the uncertainty of E. coli laboratory measurements, and the added complexity of modeling in a large drainage basin with multiple sources, these results are adequate and indicate that the explanatory variables being collected and models such as LOADEST can represent water-quality changes in the Missouri River for E. coli concentration from 2012 to 2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205045","collaboration":"Prepared in cooperation with the city of Omaha, Nebraska","usgsCitation":"Densmore, B.K., Hall, B.M., and Moser, M.T., 2020, Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: U.S. Geological Survey Scientific Investigations Report 2020–5045, 24 p., https://doi.org/10.3133/sir20205045.","productDescription":"Report: vi, 24 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-098296","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":375621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5045/coverthb.jpg"},{"id":375622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5045/sir20205045.pdf","text":"Report","size":"9.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5045"},{"id":375623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S6WSV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: Model Inputs and Outputs"}],"country":"United States","state":"Nebraska","city":"Omaha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.23611450195312,\n 41.166249339092\n ],\n [\n -95.78155517578124,\n 41.15901221836655\n ],\n [\n -95.7843017578125,\n 41.37783904584602\n ],\n [\n -95.95321655273436,\n 41.37886950966323\n ],\n [\n -96.23611450195312,\n 41.37165592008984\n ],\n [\n -96.23611450195312,\n 41.166249339092\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
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The North Dakota hydrology manual prepared by the U.S. Department of Agriculture, Soil Conservation Service, presents methodologies primarily used for developing hydrology for onfarm conservation practices, watershed projects, Resource Conservation and Development project measures, and river basin studies. The manual includes data necessary for determining hydrologic factors and developing a design discharge for a given site and intended purpose. The U.S. Geological Survey, in cooperation with the North Dakota Natural Resources Conservation Service, developed methods to reproduce and update the annual yield maps for chapter 7 of the North Dakota hydrology manual. Annual yields, in acre-feet per square mile, for the 50- and 80-percent exceedance probabilities and expected percentage of snowmelt runoff isolines were estimated using U.S. Geological Survey streamflow data from 1931 to 2016 for 71 selected streamgages with drainage areas of 505 square miles or less. An application of a modified Maintenance of Variance Extension Type III was used to estimate missing annual streamflow volumes. An alternate expected percentage of snowmelt runoff isolines was estimated using High Plains Climatic Center precipitation and snowmelt data from 1931 to 2016 for 85 selected sites. The final expected percentage of snowmelt runoff isolines was estimated using streamflow data instead of precipitation and snowfall depth data. A snowmelt runoff seasonal period of March–May produced better isoline slopes than a November–May runoff seasonal period. Slopes of the expected percentage of snowmelt runoff isolines were sensitive to amounts of missing record. Suitable isoline slopes appeared when the missing record was set to 50 percent (43 years) and 66 percent (57 years) for the 86-year period of 1931–2016.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195144","collaboration":"Prepared in cooperation with the Natural Resources Conservation Service—North Dakota","usgsCitation":"Williams-Sether, T., and Wheeling, S.L., 2020, Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016: U.S. Geological Survey Scientific Investigations Report 2019–5144, 37 p., https://doi.org/10.3133/sir20195144.","productDescription":"Report: vii, 38 p.; Dataset; 2 Appendixes","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-104356","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":375620,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5144"},{"id":375416,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5144/coverthb.jpg"},{"id":375418,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_1.xlsx","text":"Appendix 1","size":"136 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5144 Appendix 1","linkHelpText":"—Table 1.1. 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Dakota\",\"nation\":\"USA \"}}]}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503–1608
Mountain View Road
Rapid City, SD 57702
To address a major limitation of the functionality of the Missouri statewide StreamStats application in the urban areas of St. Louis County and the City of St. Louis, Missouri, the U.S. Geological Survey, in cooperation with the Metropolitan St. Louis Sewer District, defined watershed boundaries and hydrography for the study area using high-resolution 3-meter digital elevation data derived from light detection and ranging sources, high-resolution 6-inch imagery, and storm sewer network geospatial data. The combined sanitary sewers, a part of the storm sewer network, were integrated into the open channel hydrography and elevation data using a new Arc Hydro stormwater tool developed to facilitate the incorporation of the combined sanitary sewer network into the StreamStats application.
The combined sanitary sewer network was edited for connectivity and flow direction before integration into the Missouri-St. Louis StreamStats application. Inlet structures in the geospatial data were defined as HydroJunction features that allow for stormwater runoff to enter the combined sanitary sewer network. An Arc Hydro stormwater processing workflow and a sewershed delineation tool were developed to integrate the combined sanitary sewer network with the hydrographic dataset and digital elevation model in the study area.
The StreamStats application developed for the study area provides various data exploration tools that can be used to examine the spatial data and to obtain general descriptive information and flow statistics at streamgages in the study area. Watersheds and sewersheds can be delineated and basin characteristics can be determined at any point on the open channel network or the combined sanitary sewer network in the study area. Peak-flow statistics can be computed at any point on the open channel network. A report summarizing the results is generated by the StreamStats application and can be downloaded and used in other software.
The Missouri-St. Louis StreamStats application is limited to the area inside St. Louis County and the City of St. Louis and excludes locations on the main stem of the Mississippi, Missouri, and Meramec Rivers. The limitations of the Missouri-St. Louis StreamStats application include possible inaccuracies using regression equations for peak-flow statistics developed assuming natural flow conditions and topographically derived watersheds determined from a coarser resolution of data than is used in this application. Additionally, published regression equations for peak-flow statistics did not incorporate any pipe flow or sewershed delineations when they were developed, which limits the applicability of peak-flow statistics to basins based on primarily topographic delineation. Inaccuracies in resolution, completeness, location, or attribution of geospatial elevation data, hydrographic data, derived stream lines, derived watershed boundaries, and combined sanitary sewer data can limit the accuracy and functionality of the Missouri-St. Louis StreamStats application.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205040","collaboration":"Prepared in cooperation with the Metropolitan St. Louis Sewer District","usgsCitation":"Southard, R.E., Haluska, T., Richards, J.M., Ellis, J.T., Dartiguenave, C., and Djokic, D., 2020, Missouri StreamStats—St. Louis County and the City of St. Louis urban application: U.S. Geological Survey Scientific Investigations Report 2020–5040, 27 p., https://doi.org/10.3133/sir20205040.","productDescription":"Report: vii, 27 p.; Appendix; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-098907","costCenters":[{"id":36532,"text":"Central Midwest Water Science 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This report presents a novel conceptual framework and approach for conducting a geologically based environmental assessment, or geoenvironmental assessment, of undiscovered uranium resources within an area likely to contain uranium deposits. The framework is based on a source-to-receptor model that prioritizes the most likely contaminant sources, contaminant pathways, and affected environmental media for three common uranium extraction methods—open pit or underground mining with milling and in situ recovery (ISR). Data on regional geology, hydrology, and climate, as well as historical uranium mining and milling records are used to estimate the probable amounts of waste rock, tailings, wastewater, surface land disturbance, and subsurface aquifer disturbance for likely mining methods. Constituents of concern that might take the form of leachates, dust, radon, and sediments formed by chemical and physical weathering are also identified in the geoenvironmental assessment. Finally, areas where constituents of concern are likely to occur and persist in air, land, surface water, and groundwater are indicated by the potential for dispersion of dust by wind, accumulation of radon because of air stagnation, dispersion of sediments and wastewater by runoff, and infiltration of wastewater or leachates with consideration of the likely mobility of contaminants in surface water and groundwater. The geoenvironmental assessment output can be summarized in the following primary products: (1) a descriptive geoenvironmental model; (2) maps and statistics of variables that indicate the potential for constituents of concern to occur and persist in air, land, surface water, and groundwater within a tract that is geologically permissive for the occurrence of uranium; and (3) tables providing estimated or indicated quantities of waste rock, tailings, wastewater, dust, and radon emissions that could be associated with undiscovered uranium resources, if extracted, for each permissive tract. The uranium geoenvironmental assessment could help natural resource managers to prioritize and (or) identify (1) important potential contaminant pathways, (2) management practices required depending on the types of constituents that could be of concern, (3) areas for response in the event of accidental release, and (4) future directions for study. Furthermore, indicators of rock and water volumes potentially associated with an undiscovered uranium deposit may be evaluated to make quantitative comparisons of water required for uranium production or potential waste products generated during uranium extraction from areas permissive for uranium resource occurrence throughout the United States.
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Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
A method to estimate the probable high groundwater level in Massachusetts, excluding Cape Cod and the islands, was developed in 1981. The method uses a groundwater measurement from a test site, groundwater measurements from an index well, and a distribution of high groundwater levels from wells in similar geologic and topographic settings. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, conducted an update to the Frimpter method for estimating the probable high groundwater levels in Massachusetts. The study evaluated the potential changes to the method resulting from four decades of additional groundwater-level data and the expansion of the network of wells for monitoring groundwater levels. The differences and potential benefits of daily, as opposed to monthly, measurements in the application of the method were examined because of the increased availability of high-frequency (subdaily) groundwater-level data. The study also considered long-term trends in groundwater levels that may alter the accuracy of the method. Finally, the accuracy of the estimated high groundwater levels was evaluated, and improved implementation guidance was prepared.
For this study, groundwater levels in 153 wells in Massachusetts and surrounding States with records with lengths of 16 to 78 years were analyzed. The highest recorded groundwater levels ranged from 1.2 feet (ft) above land surface (flooded conditions) to 45.8 ft below land surface, with a median of 4.6 ft below land surface. The maximum annual groundwater-level range was 1.4 to 17.9 ft, with a median of 5.5 ft.
The within-month variation, maximum annual groundwater-level range, and highest recorded groundwater level were computed using daily mean groundwater-level values from 28 wells with continuous records. The use of daily data resulted in larger maximum annual groundwater-level ranges (0.02 to 2.94 ft larger, with a median of 0.58 ft larger) and shallower highest-recorded groundwater levels (0.0 to 1.60 ft shallower, with a median of 0.18 ft shallower) than computations based on monthly measurements in the same wells.
Statistical tests showed moderate to strong evidence of trends in measurements of both high and low groundwater levels within most of the periods during which water levels were analyzed. High groundwater levels rose beneath the land surface at most sites during four of the six periods used for analysis (1966–2015, 1986–2015, 1991–2010, and 1981–2010). Low groundwater levels also increased at many sites during most of the periods evaluated, but this trend was less widespread than the similar trends in high groundwater levels, and the trend was to deeper low groundwater levels at more sites than the trend to deeper high groundwater levels. There was no clear trend in annual groundwater-level ranges at most sites during the six periods analyzed.
In general, the Frimpter method predicted shallower (higher) high groundwater levels than were observed but correctly classified sites according to their suitabilities for unmounded septic systems. The mean error of the predictions (difference between the estimated and observed groundwater levels) ranged from −3.23 ft to −1.40 ft for various approaches to estimating the groundwater-level range and selecting an index well. The method correctly classified 83 to 86 percent of monitoring-well sites according to their suitability for an unmounded septic system for many approaches to estimating the annual groundwater-level range and selecting an index well. The approach selected for estimating the annual groundwater-level range and selecting an index well will depend upon the importance of an accurate estimate of the high groundwater level as compared to the importance of an estimated high groundwater level that is less likely to be exceeded.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205036","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2020, Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts: U.S. Geological Survey Scientific Investigations Report 2020–5036, 45 p., https://doi.org/10.3133/sir20205036.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"45","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103689","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":375551,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NM2PHP","text":"USGS data release","linkHelpText":"Data on well characteristics and well-pair characteristics for estimating high groundwater levels in selected areas of Massachusetts"},{"id":375553,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5036/sir20205036.pdf","text":"Report","size":"7.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5036"},{"id":375554,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5036/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Massachusetts, New Hampshire, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.8343505859375,\n 42.90011265525328\n ],\n [\n -73.2952880859375,\n 42.9524020856897\n ],\n [\n -73.27880859375,\n 42.65820178455667\n ],\n [\n -73.5150146484375,\n 42.12267315117256\n ],\n [\n -73.5479736328125,\n 41.393294288784865\n ],\n [\n -73.54248046875,\n 41.29431726315258\n ],\n [\n -73.487548828125,\n 41.20345619205131\n ],\n [\n -73.7347412109375,\n 41.10005163093046\n ],\n [\n -73.65234375,\n 41.000629848685385\n ],\n [\n -72.9547119140625,\n 41.14143302653628\n ],\n [\n -72.0538330078125,\n 41.17451935556443\n ],\n [\n -71.43310546875,\n 41.29431726315258\n ],\n [\n -70.6475830078125,\n 41.21585377825921\n ],\n [\n -69.7686767578125,\n 41.16211393939692\n ],\n [\n -69.8785400390625,\n 41.87774145109676\n ],\n [\n -70.1806640625,\n 42.17968819665961\n ],\n [\n -70.57617187499999,\n 42.718768102606326\n ],\n [\n -70.8343505859375,\n 42.90011265525328\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
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Acid deposition has declined across eastern North America and northern Europe due to reduced emissions of sulfur and nitrogen oxides. Ecosystem recovery has been slow with limited improvement in surface water chemistry. Delayed recovery has encouraged acid-neutralization strategies to accelerate recovery of impaired biological communities. Lime application has been shown to increase pH and dissolved organic carbon (DOC), which could also drive increased mobilization of mercury (Hg) to surface waters. A four-year study was conducted within Honnedaga Lake’s watershed in the Adirondack region of New York to compare the effects of watershed and direct channel lime additions on Hg in stream water and macroinvertebrates. All treatments sharply increased stream pH and DOC concentrations, but large differences in the duration of impacts were apparent. The watershed treatment resulted in multi-year increases in concentrations and loads of total Hg (150%; 390%), DOC (190%; 350%) and nutrients, whereas total Hg and DOC increased for short periods (72–96 h) after channel treatments. No response of Hg in macroinvertebrates was evident following the watershed treatment, but a potential short-term and spatially constrained increase occurred after the channel treatment. Our observations indicate that both treatment approaches mobilize Hg, but that direct channel liming mobilizes considerably less than watershed liming over any period longer than a few days. During the final study year, increased methyl Hg concentrations were observed across reference and treated streams, which may reflect an extended dry period, highlighting that climate variation may also affect Hg dynamics.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10646-020-02224-1","usgsCitation":"Millard, G., Riva-Murray, K., Burns, D., Montesdeoca, M.S., and Driscoll, C., 2020, The impact of lime additions on mercury dynamics in stream chemistry and macroinvertebrates: A comparison of watershed and direct stream addition management strategies: Ecotoxicology, v. 29, p. 1627-1643, https://doi.org/10.1007/s10646-020-02224-1.","productDescription":"17 p.","startPage":"1627","endPage":"1643","ipdsId":"IP-109979","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":436928,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C17PA0","text":"USGS data release","linkHelpText":"Methylmercury and associated data in macroinvertebrates from tributaries of Honnedaga Lake and from the Middle Branch Black River in New York."},{"id":378508,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Honnedaga Lake watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.89448547363281,\n 43.469864270218416\n ],\n [\n -74.74754333496092,\n 43.469864270218416\n ],\n [\n -74.74754333496092,\n 43.56496912804994\n ],\n [\n -74.89448547363281,\n 43.56496912804994\n ],\n [\n -74.89448547363281,\n 43.469864270218416\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Millard, Geoffrey D.","contributorId":240873,"corporation":false,"usgs":false,"family":"Millard","given":"Geoffrey D.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riva-Murray, Karen 0000-0001-6683-2238 krmurray@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-2238","contributorId":168876,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":799039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":799041,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montesdeoca, Mario S.","contributorId":240877,"corporation":false,"usgs":false,"family":"Montesdeoca","given":"Mario","email":"","middleInitial":"S.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799042,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Driscoll, Charles T.","contributorId":240874,"corporation":false,"usgs":false,"family":"Driscoll","given":"Charles T.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":799040,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210700,"text":"70210700 - 2020 - Snow processes in mountain forests: Interception modeling for coarse-scale applications","interactions":[],"lastModifiedDate":"2020-06-18T14:54:10.16543","indexId":"70210700","displayToPublicDate":"2020-06-15T09:50:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Snow processes in mountain forests: Interception modeling for coarse-scale applications","docAbstract":"Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.
","language":"English","doi":"10.5194/hess-24-2545-2020","usgsCitation":"Helbig, N., Moeser, C.D., Teich, M., Vincent, L., Lejeune, Y., Sicart, J., and Monnet, J., 2020, Snow processes in mountain forests: Interception modeling for coarse-scale applications: Hydrology and Earth System Sciences, v. 24, p. 2545-2560, https://doi.org/10.5194/hess-24-2545-2020.","productDescription":"16 p.","startPage":"2545","endPage":"2560","ipdsId":"IP-111174","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":456397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-2545-2020","text":"Publisher Index Page"},{"id":375684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France, United States","state":"Utah","otherGeospatial":"French Alps, Rocky Mountains","volume":"24","noUsgsAuthors":false,"publicationDate":"2020-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Helbig, N. 0000-0002-8663-7306","orcid":"https://orcid.org/0000-0002-8663-7306","contributorId":225392,"corporation":false,"usgs":false,"family":"Helbig","given":"N.","email":"","affiliations":[{"id":41093,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland","active":true,"usgs":false}],"preferred":false,"id":791020,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791021,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teich, M. 0000-0002-8850-9279","orcid":"https://orcid.org/0000-0002-8850-9279","contributorId":225393,"corporation":false,"usgs":false,"family":"Teich","given":"M.","email":"","affiliations":[{"id":41094,"text":"Austrian Research Centre for Forests (BFW), Innsbruck, Austria","active":true,"usgs":false}],"preferred":false,"id":791022,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vincent, L.","contributorId":225394,"corporation":false,"usgs":false,"family":"Vincent","given":"L.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791023,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lejeune, Y.","contributorId":225395,"corporation":false,"usgs":false,"family":"Lejeune","given":"Y.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791024,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sicart, J.-E.","contributorId":225396,"corporation":false,"usgs":false,"family":"Sicart","given":"J.-E.","email":"","affiliations":[{"id":41096,"text":"Université Grenoble Alpes, CNRS, IRD, Grenoble INP, Institut des Géosciences de l’Environnement (IGE) - UMR 5001,","active":true,"usgs":false}],"preferred":false,"id":791025,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monnet, J.-M.","contributorId":225397,"corporation":false,"usgs":false,"family":"Monnet","given":"J.-M.","email":"","affiliations":[{"id":41097,"text":"Univ. Grenoble Alpes, Irstea, LESSEM, 38000 Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791026,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210859,"text":"70210859 - 2020 - Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States","interactions":[],"lastModifiedDate":"2020-06-30T13:29:12.764279","indexId":"70210859","displayToPublicDate":"2020-06-15T08:24:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States","docAbstract":"The National Park Service (NPS) manages hundreds of parks in the United States, and many contain important aquatic ecosystems and/or threatened and endangered aquatic species vulnerable to hydro-climatic change. Effective management of park resources under future hydro-climatic uncertainty requires information on both baseline conditions and the range of projected future conditions. A monthly water balance model was used to assess baseline (1981-1999) conditions and a range of projected future hydro-climatic conditions in 374 NPS parks. General circulation model outputs representing 214 future climate simulations were used to drive the model. Projected future changes in temperature (T), precipitation (P), and runoff (R) are expressed as departures from historical baselines. Climate simulations indicate increasing T in 2030 for all parks with 50th percentile simulations projecting increases of 1.67 oC or more in 50% of parks. Departures in 2030 P indicate a mix of mostly increases and some decreases, with 50th percentile simulations projecting increases in P in more than 70% of parks. Departures in R for 2030 are mostly decreases , with the 50th percentile simulations projecting decreases in R in more than 50% of parks in all seasons except winter. Hence in many parks, R is projected to decrease even when P is projected to increase because of increasing T in all NPS parks. Projected changes in future hydro-climatic conditions can also be assessed for individual parks, and Rocky Mountain National Park and Congaree National Park are used as examples.","language":"English","publisher":"MDPI","doi":"10.3390/w12061704","usgsCitation":"Battaglin, W., Hay, L., Lawrence, D.J., McCabe, G.J., and Norton, P.A., 2020, Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States: Water, v. 6, no. 12, 1704, 24 p., https://doi.org/10.3390/w12061704.","productDescription":"1704, 24 p.","ipdsId":"IP-117255","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":456399,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12061704","text":"Publisher Index Page"},{"id":376013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n 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Center","active":true,"usgs":true}],"preferred":true,"id":791750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren","contributorId":209524,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","affiliations":[],"preferred":true,"id":791751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, David J.","contributorId":34374,"corporation":false,"usgs":true,"family":"Lawrence","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":791752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":791753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Norton, Parker A. 0000-0002-4638-2601 pnorton@usgs.gov","orcid":"https://orcid.org/0000-0002-4638-2601","contributorId":2257,"corporation":false,"usgs":true,"family":"Norton","given":"Parker","email":"pnorton@usgs.gov","middleInitial":"A.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791754,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211015,"text":"70211015 - 2020 - Conceptualizing alternate regimes in a large floodplain-river ecosystem","interactions":[],"lastModifiedDate":"2020-07-10T13:20:01.000249","indexId":"70211015","displayToPublicDate":"2020-06-15T08:16:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Conceptualizing alternate regimes in a large floodplain-river ecosystem","docAbstract":"Regime shifts –persistent changes in the structure and function of an ecosystem - are well-documented in many ecosystems but remain poorly understood in floodplain-river ecosystems. We apply a resilience perspective to large floodplain-river ecosystems by presenting three examples of plausible sets of alternate regimes that are relevant to natural resource management interests within the Upper Mississippi River and Illinois River. These alternate regimes include: 1) a clear water and abundant vegetation regime vs. a turbid water and sparse vegetation regime in lentic, off-channel areas, 2) a diverse native fish community regime vs. an invasive-dominated fish community regime, and 3) a regime characterized by a diverse and dynamic mosaic of floodplain vegetation types vs. one characterized as a persistent invasive wet meadow monoculture. For each set of potential alternate regimes, we synthesize known or hypothesized feedback mechanisms that reinforce regimes, controlling variables that drive regime transitions, and restoration pathways. The conceptual models presented here provide a framework for synthesizing our understanding of the dynamics of this ecosystem and are relevant to other large floodplain-river ecosystems that face similar human pressures across the world. The models are currently being used to prioritize future research, test hypotheses, and inform restoration and management on the Upper Mississippi River and Illinois River. Through sharing our approach, we provide a case study in which we document an important step in operationalizing resilience concepts for the management of natural resources.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2020.110516","usgsCitation":"Bouska, K.L., Houser, J.N., De Jager, N.R., Drake, D.C., Collins, S.F., Gibson-Reniemer, C.K., and Thomsen, M.A., 2020, Conceptualizing alternate regimes in a large floodplain-river ecosystem: Journal of Environmental Management, v. 264, 110516, 15 p., https://doi.org/10.1016/j.jenvman.2020.110516.","productDescription":"110516, 15 p.","ipdsId":"IP-108847","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":376247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin, Iowa, Illinois, Missouri","otherGeospatial":"Upper Mississippi River, Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.58154296875,\n 37.020098201368114\n ],\n [\n -88.22021484375,\n 37.020098201368114\n ],\n [\n -88.22021484375,\n 45.27488643704891\n ],\n [\n -93.58154296875,\n 45.27488643704891\n ],\n [\n -93.58154296875,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"264","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houser, Jeffrey N. 0000-0003-3295-3132 jhouser@usgs.gov","orcid":"https://orcid.org/0000-0003-3295-3132","contributorId":2769,"corporation":false,"usgs":true,"family":"Houser","given":"Jeffrey","email":"jhouser@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":792433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Scott F.","contributorId":172292,"corporation":false,"usgs":false,"family":"Collins","given":"Scott","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":792434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibson-Reniemer, Caniel K.","contributorId":228874,"corporation":false,"usgs":false,"family":"Gibson-Reniemer","given":"Caniel","email":"","middleInitial":"K.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":792435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thomsen, Meredith A.","contributorId":228875,"corporation":false,"usgs":false,"family":"Thomsen","given":"Meredith","email":"","middleInitial":"A.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":792436,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225148,"text":"70225148 - 2020 - Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska","interactions":[],"lastModifiedDate":"2021-10-14T12:44:11.60355","indexId":"70225148","displayToPublicDate":"2020-06-15T07:41:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska","docAbstract":"The U.S. Department of Interior National Park Service is charged with both monitoring avian communities and evaluating the influence of visitors to National Parks on sensitive species; however, this task is challenging considering that sampling programs often involve multiple species, each with differing behavior, habitat requirements, and detectability. Our objectives were to build a model to describe the status of waterbirds in Glacier Bay National Park, Alaska, USA, and assess effects of area closures on these species. We used a Bayesian multistate occupancy model to describe the status of multiple species and make the best possible use of existing survey data. We modeled up to 5 states per species and evaluated predictors of occupancy, nesting, and abundance, as well as survey-related predictors of state-dependent detection probability. We found that occupancy probability varied across species and habitats (islands vs. glacial outwashes). For most species, occupancy probability was substantially greater at sites occupied in the year previous (site persistence). We found weak evidence that area closures affected the occurrence of species in the study, but this was largely because most sites were closed for the entirety of the study period. The probability of detecting occurrence, nesting, and abundance varied across species and survey methods (ground vs. vessel). Detection parameters provided valuable information for enhancing the efficiency of future surveys, by identifying preferred survey methods and sampling periods for specific waterbird species. © 2020 The Wildlife Society.
Environmental change associated with anthropogenic disturbance can lower habitat quality, especially for sensitive species such as many amphibians. Variation in environmental quality may affect an organism’s physiological health and, ultimately, survival and fitness. Using multiple health measures can aid in identifying populations at increased risk of declines. Our objective was to measure environmental variables at multiple spatial scales and their effect on three indicators of health in ornate chorus frog (Pseudacris ornata) tadpoles to identify potential correlates of population declines. To accomplish this, we measured a glucocorticoid hormone (corticosterone; CORT) profile associated with the stress response, as well as the skin mucosal immune function (combined function of skin secretions and skin bacterial community) and bacterial communities of tadpoles from multiple ponds. We found that water quality characteristics associated with environmental variation, including higher water temperature, conductivity and total dissolved solids, as well as percent developed land nearby, were associated with elevated CORT release rates. However, mucosal immune function, although highly variable, was not significantly associated with water quality or environmental factors. Finally, we examined skin bacterial diversity as it aids in immunity and is affected by environmental variation. We found that skin bacterial diversity differed between ponds and was affected by land cover type, canopy cover and pond proximity. Our results indicate that both local water quality and land cover characteristics are important determinants of population health for ornate chorus frogs. Moreover, using these proactive measures of health over time may aid in early identification of at-risk populations that could prevent further declines and aid in management decisions.
","language":"English","publisher":"The Society for Experimental Biology","doi":"10.1093/conphys/coaa047","usgsCitation":"Goff, C.B., Walls, S., Rodriguez, D., and Gabor, C.S., 2020, Changes in physiology and microbial diversity in larval ornate chorus frogs are associated with habitat quality: Conservation Physiology, v. 8, no. 1, coaa047, 19 p., https://doi.org/10.1093/conphys/coaa047.","productDescription":"coaa047, 19 p.","ipdsId":"IP-109179","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":456407,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coaa047","text":"Publisher Index Page"},{"id":436929,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UBF9SM","text":"USGS data release","linkHelpText":"Corticosterone release rates, water quality, microbiome, and mucosome data for analysis of Pseudacris ornata sites"},{"id":411709,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Goff, Cory B.","contributorId":300720,"corporation":false,"usgs":false,"family":"Goff","given":"Cory","email":"","middleInitial":"B.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":861287,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walls, Susan 0000-0001-7391-9155","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":216235,"corporation":false,"usgs":true,"family":"Walls","given":"Susan","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":861288,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodriguez, David","contributorId":300721,"corporation":false,"usgs":false,"family":"Rodriguez","given":"David","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":861289,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gabor, Caitlin S.","contributorId":300722,"corporation":false,"usgs":false,"family":"Gabor","given":"Caitlin","email":"","middleInitial":"S.","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":861290,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70274789,"text":"70274789 - 2020 - Reassessing particulate organic carbon dynamics in the highly disturbed San Francisco Bay Estuary","interactions":[],"lastModifiedDate":"2026-04-09T15:01:46.432427","indexId":"70274789","displayToPublicDate":"2020-06-15T00:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23798,"text":"Frontiers Earth Science - Biogeoscience","active":true,"publicationSubtype":{"id":10}},"title":"Reassessing particulate organic carbon dynamics in the highly disturbed San Francisco Bay Estuary","docAbstract":"Environmental research has been shifting toward a new normal in which a primary focus is to capture change that may be accelerating. In this study, we collected particulate samples in the northern San Francisco Bay Estuary (SFBE) in the fall of 2011 through the spring of 2012 in order to assess vascular plant contributions across both time and space and to compare our findings with a similar set of samples from 1990 to 1992. Across the ∼20-year span, we detected (1) decreasing C:Na ratios (averages ± SD of 12.5 ± 2.5 vs. 8.8 ± 1.4, significant t-test with p < 0.0001); (2) distinct shifts in chlorophyll vs. salinity, with higher chlorophyll concentrations shifting toward freshwater; and (3) greater relative proportions of vascular plant carbon that also appears less degraded (as indicated by lignin measurements) shifting from freshwater toward higher salinities. Lignin compositional data (syringyl:vanillyl and cinnamyl:vanillyl) suggest that increased lignin content in the more saline samples could be derived from wetland materials, while a two-endmember mixing model indicates that a significant portion of the particulate organic carbon (POC) in the western sites (50–60% as an upper bound, 13–15% as a lower bound) could be wetland-derived. This has potential implications for the lower food web, given recent work that demonstrates selective feeding by copepods on wetland detrital material in the northern SFBE. The latter has ramifications for proposed wetland restoration within the SFBE and Sacramento River/San Joaquin River Delta system, namely, that restored wetlands could confer important benefits toward the food web. Equally important is to prioritize continued monitoring of particulate organic matter cycling in the SFBE system to make sure that changing conditions are accounted for in any management decision.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2020.00185","usgsCitation":"Hernes, P.J., Dyda, R.Y., and Bergamaschi, B.A., 2020, Reassessing particulate organic carbon dynamics in the highly disturbed San Francisco Bay Estuary: Frontiers Earth Science - Biogeoscience, v. 8, 185, 13 p., https://doi.org/10.3389/feart.2020.00185.","productDescription":"185, 13 p.","ipdsId":"IP-117634","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":502493,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00185","text":"Publisher Index Page"},{"id":502352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin River Delta, San Francisco Bay Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.2443083713161,\n 38.39872273873195\n ],\n [\n -122.2443083713161,\n 37.81288212710655\n ],\n [\n -121.45817059994386,\n 37.81288212710655\n ],\n [\n -121.45817059994386,\n 38.39872273873195\n ],\n [\n -122.2443083713161,\n 38.39872273873195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Hernes, Peter J.","contributorId":139730,"corporation":false,"usgs":false,"family":"Hernes","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":12894,"text":"Department of Land, Air, and Water Resources, University of California, One Shields Avenue, Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":959148,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dyda, Rachael Y. 0000-0002-4616-7231","orcid":"https://orcid.org/0000-0002-4616-7231","contributorId":369567,"corporation":false,"usgs":false,"family":"Dyda","given":"Rachael","middleInitial":"Y.","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":959149,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":959150,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227712,"text":"70227712 - 2020 - Model-based clustering reveals patterns in central place use of a marine top predator","interactions":[],"lastModifiedDate":"2022-01-27T16:07:29.994101","indexId":"70227712","displayToPublicDate":"2020-06-12T10:02:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Model-based clustering reveals patterns in central place use of a marine top predator","docAbstract":"Satellite telemetry data are commonly used to quantify habitat selection, examine animal movements, and delineate home ranges. These data also contain valuable information concerning dens, nests, roosts, and other central places that are often associated with important life history events and may exhibit unique characteristics; however, using satellite telemetry data to study central places is complicated by common nuances like locational error and animal movement. We coupled a novel modeling framework that accounts for these nuances with an Argos satellite telemetry dataset to examine the spatiotemporal behavior associated with harbor seal haul-out sites on Kodiak Island, Alaska, USA. The methodology incorporates an observation model that accommodates multiple sources of uncertainty in telemetry data and a flexible Bayesian nonparametric model to uncover latent clustering in the telemetry locations. We also contribute extensions to examine the effect of covariates on site selection and to obtain population-level inference concerning central place use. Harbor seal haul-out sites generally occurred in inlets and bays, areas that are isolated from the open water of the Gulf of Alaska. Most individuals selected haul-out sites that were protected from wave exposure. The effects of bathymetry and shoreline complexity on haul-out site selection were variable among individual seals, as were the effects of time of day, time since low tide, and day of year on temporal patterns of haul-out use. As repositories of satellite telemetry data on a wide variety of species accumulate, so do opportunities for using this information to learn about the locations of central places, as well as the temporal patterns in their use. The model-based approach we describe offers a practical and rigorous means for gaining insight concerning these sensitive locations, knowledge of which is important for the effective management and conservation of many species.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3123","usgsCitation":"Brost, B.M., Hooten, M., and Small, R., 2020, Model-based clustering reveals patterns in central place use of a marine top predator: Ecosphere, e03123, 15 p., https://doi.org/10.1002/ecs2.3123.","productDescription":"e03123, 15 p.","ipdsId":"IP-079248","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":456420,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3123","text":"Publisher Index Page"},{"id":394975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.171142578125,\n 56.32262930069559\n ],\n [\n -151.885986328125,\n 57.76865857271793\n ],\n [\n -152.479248046875,\n 58.019737000187305\n ],\n [\n -153.74267578125,\n 58.04300405858762\n ],\n [\n -155.115966796875,\n 57.320589769167135\n ],\n [\n -154.171142578125,\n 56.32262930069559\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-06-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Brost, Brian M.","contributorId":272252,"corporation":false,"usgs":false,"family":"Brost","given":"Brian","email":"","middleInitial":"M.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":831864,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":831863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Small, Robert J.","contributorId":272253,"corporation":false,"usgs":false,"family":"Small","given":"Robert J.","affiliations":[{"id":56329,"text":"akfg","active":true,"usgs":false}],"preferred":false,"id":831865,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210718,"text":"70210718 - 2020 - Regional patterns in hydrologic response, a new three-component metric for hydrograph analysis and implications for ecohydrology, Northwest Volcanic Aquifer Study Area, USA","interactions":[],"lastModifiedDate":"2020-08-06T19:30:06.543616","indexId":"70210718","displayToPublicDate":"2020-06-12T09:48:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Regional patterns in hydrologic response, a new three-component metric for hydrograph analysis and implications for ecohydrology, Northwest Volcanic Aquifer Study Area, USA","docAbstract":"Oregon, California, Idaho, Nevada and Utah
Spatial patterns of hydrologic response were examined for the Northwest Volcanic Aquifer Study Area (NVASA). The utility of established hydrograph-separation methods for assessing hydrologic response in permeable volcanic terranes was assessed and a new three-component metric for hydrograph analysis was developed. The new metric, which partitions streamflow into subcomponents defined by the timescales of hydrologic response (e.g., fast-runoff, intermediate-interflow and slow-baseflow), was used to gain a fundamental understanding of the regional hydrology, investigate sub-regional differences, influencing factors, and ecohydrological implications.
The combined effects of NVASA’s physiography, climate and geology create a strongly coupled surface-groundwater system that produces copious baseflow and limited quantities of runoff and interflow. Patterns of hydrologic response are influenced by the type and rate of precipitation and permeability of the underlying geology. Under variable precipitation conditions the hydrologic response of volcanic terranes with similar permeability and subsurface-storage capacity can be significantly different. From a water management and ecohydrology perspective, understanding regional patterns of hydrologic response and sub-regional differences is fundamental. Results indicate that minimum-flow methods provide the most conservative estimate of baseflow and may be the most robust for filtering out snowmelt bias in baseflow estimates. Baseflow contributes ∼75% of the perennial streamflow across the NVASA and represents a critical component of the regional water supply that provides critical cold-water habitat.
Karst is a landscape formed from the dissolution of soluble rock or rock containing minerals that are easily dissolved from within the rock. The landscape is characterized by sinkholes, caves, losing streams, springs, and underground drainage systems, which rapidly move water through the karst. The two forms of karst in New York State include carbonate karst, which forms in carbonate rock (limestone, marble, and dolostone), and evaporite karst, which forms in rock that contains the evaporite minerals gypsum and halite.
Past and recent studies of karst across the State have shown that areas of focused recharge in karstic carbonate rock allow contaminants to enter aquifer systems with little attenuation. Focused areas of recharge need to be identified to help prevent such contamination from sources on or adjacent to the karst. The New York State Departments of Environmental Conservation and Health are collaborating with the agricultural community to make farmers and farm-planning advisors more aware of karst and how to manage daily farming activities to reduce their impact on surface water and groundwater resources, especially in karst areas. There is also a need to make regulators, planners, and the general public aware of New York’s karst resources and to properly protect and manage these resources to protect the quality of groundwater and surface water that can flow into, through, and from karst bedrock.
Using publicly available geospatial data, karst bedrock and closed depressions over or near karst rock were identified across New York. Carbonate, evaporite, and marble geologic units were selected from a statewide 1:250,000-scale bedrock geology dataset. The selected geologic units were intersected with 7.5-minute quadrangle maps to define the study area.
The U.S. Geological Survey has compiled an inventory of closed depressions from statewide digital contour data, scanned 7.5-minute topographic maps known as a digital raster graphics, and light detection and ranging (lidar) digital elevation models. Analysis of the data resulted in the identification of 5,023 closed depressions statewide. The inventory was conducted to eliminate duplication of results from analysis of the three data sources. A series of overlay analyses was conducted using the closed depressions and thematic data known to be key factors in determining the probability of a closed depression contributing to focused groundwater recharge; the thematic data include bedrock geology, soil type, soil infiltration rate, and land cover.
Though the extent of karst development is important in understanding the interaction between surface water and groundwater in karst terrains, some of the worst cases of groundwater contamination in karst can occur where only minor karst features might be present. The presence of karst—be it a short section of a solutioned fracture or an extensive cave system—requires careful consideration, forward-looking environmental planning, and consistent water-quality protection to preserve New York State’s water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205030","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Kappel, W.M., Reddy, J.E., and Root, J.C., 2020, Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features: U.S. Geological Survey Scientific Investigations Report 2020–5030, 74 p., https://doi.org/10.3133/sir20205030.","productDescription":"Report: viii, 74 p.","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090019","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":375401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5030/coverthb.jpg"},{"id":375404,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGN5IJ","text":"USGS data release","linkHelpText":"Data for statewide assessment of New York’s karst aquifers with an inventory of closed-depression and focused-recharge features"},{"id":375534,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030.pdf","text":"Report","size":"19.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5030"},{"id":375482,"rank":2,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030_table1.pdf","text":"Table 1","size":"140 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Stratigraphic column of New York State bedrock indicating those units in which karst features might be present"}],"country":"United States","state":"New 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York\",\"nation\":\"USA \"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349