In 2019 the U.S. Geological Survey (USGS) launched a pilot regional Integrated Water Availability Assessment (IWAA) in the Delaware River Basin (fig. 1). IWAA is intended to explore, test, and refine systems and processes for assessing water availability for human and ecological uses and understanding their underlying controls. Water quality plays an important role in supporting ecological health and determining the suitability of water for human consumption, recreation, agriculture, and industry. Understanding how water quality has changed over time in response to natural and human-induced changes in landscape and climate identifies potential challenges in safeguarding water for all uses. The USGS has evaluated water-quality trends across the Nation, and 22 of the evaluated sites are in the Delaware River Basin. These 22 sites are in the Appalachian Plateau, Valley and Ridge, Piedmont, and Coastal Plain Physiographic Provinces. Data from these sites indicate decadal to multidecadal changes in water quality and provide an initial look at how nutrient concentrations, such as total phosphorous, total nitrogen, and nitrate, and salinity indicators, such as specific conductance, sulfate, and chloride, have varied over time in the basin. The time period of the evaluation ranged from 1972 to 2012.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203007","collaboration":"Integrated Water Availability Assessments Program","usgsCitation":"Murphy, J.C., and Shoda, M.E., 2020, A historical look at changing water quality in the Delaware River basin: U.S. Geological Survey Fact Sheet 2020–3007, 2 p., https://doi.org/10.3133/fs20203007.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-113624","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":373184,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TQ5ZS3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality trends and trend component estimates for the Nation's rivers and streams using Weighted Regressions on Time, Discharge, and Season (WRTDS) models and generalized flow normalization, 1972–2012"},{"id":373182,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3007/coverthb2.jpg"},{"id":373183,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3007/fs20203007.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3007"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.5,\n 38.625\n ],\n [\n -74.5,\n 38.625\n ],\n [\n -74.5,\n 43\n ],\n [\n -76.5,\n 43\n ],\n [\n -76.5,\n 38.625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Program Coordinator, Water Availability and Use Science Program
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","tableOfContents":"The evolution of male signals and female preferences remains a central question in the study of animal communication. The sensory trap model suggests males evolve signals that mimic cues used in nonsexual contexts and thus manipulate female behavior to generate mating opportunities. Much evidence supports the sensory trap model, but how females glean reliable information from both mimetic signals and their model cues remains unknown. We discovered a mechanism whereby a manipulative male signal guides reliable communication in sea lamprey (Petromyzon marinus). Migratory sea lamprey follow a larval cue into spawning streams; once sexually mature, males release a pheromone that mimics the larval cue and attracts females. Females conceivably benefit from the mimetic pheromone during mate search but must discriminate against the model cue to avoid orienting toward larvae in nearby nursery habitats. We tested the hypothesis that spawning females respond to petromyzonol sulfate (PZS) as a behavioral antagonist to avoid attraction to the larval cue while tracking the male pheromone despite each containing attractive 3-keto petromyzonol sulfate (3kPZS). We found 1) PZS inhibited electrophysiological responses to 3kPZS and abated preferences for 3kPZS when mixed at the same or greater concentrations, 2) larvae released more PZS than 3kPZS whereas males released more 3kPZS than PZS, and 3) mixtures of 3kPZS and PZS applied at ratios measured in larval and male odorants resulted in the discrimination observed between the natural odors. Our study elucidates how communication systems that arise via deception can facilitate reliable communication.
Landslides in Puerto Rico range from nuisances to deadly events. Centuries of agricultural and urban modification of the landscape have perturbed many already unstable hillsides on the tropical island. One of the main triggers of mass wasting on the island is the high-intensity rainfall that is associated with tropical atmospheric systems. Puerto Rico’s geographic position and rugged topography render millions of residents vulnerable to widespread landslide events. In this study, a high-resolution (5 meters), high-intensity rainfall-induced landslide susceptibility model was produced using the frequency-ratio method. Datasets utilized in the model included a complete-island landslide inventory created from imagery obtained after Hurricanes Irma and María impacted the island during September 2017, slope inclination, land-surface curvature, soil type, geologic terrane, mean annual precipitation, land use, soil moisture, and distance to roadways and streams. The final data product (plate 1) is a statistically viable representation of where landslides are likely to initiate during or soon after intense rainfall, with a robust receiver operating characteristic area-under-curve value of 0.87. The model output raster pixel values were binned into 100 equal-area quantiles and then classified into Low, Moderate, High, Very High, and Extremely High classes of susceptibility. The Extremely High susceptibility classification represents the most vulnerable 1 percent of the island, whereas Very High, High, Moderate, and Low classifications cover 9, 20, 30, and 40 percent of the island, respectively. The susceptibility map is intended to assist in planning future development, mitigation measures, and post-event emergency response; however, it is not a substitute for site-specific, slope-stability assessments performed by licensed geologists and engineers. Additionally, the map does not portray locations where landslide material may travel after mobilization, and which may be at extreme risk; nor does it necessarily portray where landslides may occur during earthquakes or mass wasting triggered by prolonged, relatively low-intensity rainfall.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201022","collaboration":"Prepared in cooperation with the University of Puerto Rico at Mayagüez","usgsCitation":"Hughes, K.S., and Schulz, W.H., 2020, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-File Report 2020–1022, 91 p., 1 plate, scale 1:150,000, https://doi.org/10.3133/ofr20201022.","productDescription":"Report: viii, 91 pages; 2 Sheets: 49.11 x 33.86 inches; Application Sites; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-116377","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":373245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_pamphlet.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1022 pamphlet","linkHelpText":"English 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Rico"},{"id":373241,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://hazards.colorado.edu/uploads/documents/PuertoRico_LandslideGuide_2020.pdf","text":"Landslide Guide for Residents of Puerto Rico"},{"id":373242,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://hazards.colorado.edu/uploads/documents/PuertoRico_GuiaDerrumbe_2020.pdf","text":"Guía sobre deslizamientos de tierra para residentes de Puerto Rico"},{"id":373256,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_kmz.zip","text":"Landslide susceptibility map as a Google Earth file","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2020-1022 kmz"},{"id":373246,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/SI_raster_for_SMAP.zip","text":"SI raster for SMAP","linkFileType":{"id":6,"text":"zip"},"description":"SI raster for SMAP"},{"id":373247,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet.pdf","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico","linkFileType":{"id":1,"text":"pdf"},"description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico"},{"id":376481,"rank":16,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp.pdf","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico","linkFileType":{"id":1,"text":"pdf"},"description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico"},{"id":376485,"rank":18,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp_georeferenced.tif","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por 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Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado","linkFileType":{"id":1,"text":"pdf"},"description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3736572265625,\n 17.832374329567518\n ],\n [\n -65.577392578125,\n 17.832374329567518\n ],\n [\n -65.577392578125,\n 18.58377568837094\n ],\n [\n -67.3736572265625,\n 18.58377568837094\n ],\n [\n -67.3736572265625,\n 17.832374329567518\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geologic Hazards Science Center
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Box 25046, MS-966
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Life history theory examines how characteristics of organisms, such as age and size at maturity, may vary through natural selection as evolutionary responses that optimize fitness. Here we ask how predictions of age and size at maturity differ for the three classical fitness functions–intrinsic rate of natural increase r, net reproductive rate R0, and reproductive value Vx−for semelparous species. We show that different choices of fitness functions can lead to very different predictions of species behavior. In one’s efforts to understand an organism’s behavior and to develop effective conservation and management policies, the choice of fitness function matters. The central ingredient of our approach is the maturation reaction norm (MRN), which describes how optimal age and size at maturation vary with growth rate or mortality rate. We develop a practical geometric construction of MRNs that allows us to include different growth functions (linear growth and nonlinear von Bertalanffy growth in length) and develop two-dimensional MRNs useful for quantifying growth-mortality trade-offs. We relate our approach to Beverton-Holt life history invariants and to the Stearns-Koella categorization of MRNs. We conclude with a detailed discussion of life history parameters for Great Lakes Chinook Salmon and demonstrate that age and size at maturity are consistent with predictions using R0 (but not r or Vx) as the underlying fitness function.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0228990","usgsCitation":"Breck, J.E., Simon, C.P., Rutherford, E.S., Low, B.S., Lamberson, P.J., and Rogers, M.W., 2020, The geometry of reaction norms yields insights on classical fitness functions for Great Lakes salmon: PLoS ONE, v. 15, no. 3, p. 1-35, https://doi.org/10.1371/journal.pone.0228990.","productDescription":"e0228990, 35 p.","startPage":"1","endPage":"35","ipdsId":"IP-111222","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457359,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0228990","text":"Publisher Index Page"},{"id":395682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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],\n [\n -88.52783203125,\n 46.89023157359399\n ],\n [\n -88.4619140625,\n 46.70973594407157\n ],\n [\n -88.13232421875,\n 46.81509864599243\n ],\n [\n -87.69287109375,\n 46.649436163350245\n ],\n [\n -87.34130859375,\n 46.42271253466717\n ],\n [\n -87.0556640625,\n 46.49839225859763\n ],\n [\n -86.81396484375,\n 46.37725420510028\n ],\n [\n -86.59423828125,\n 46.37725420510028\n ],\n [\n -86.17675781249999,\n 46.58906908309182\n ],\n [\n -85.6494140625,\n 46.604167162931844\n ],\n [\n -85.166015625,\n 46.7549166192819\n ],\n [\n -85.10009765625,\n 46.46813299215554\n ],\n [\n -84.79248046875,\n 46.37725420510028\n ],\n [\n -84.22119140625,\n 46.5739667965278\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-16","publicationStatus":"PW","contributors":{"editors":[{"text":"Hovel, Rachel A.","contributorId":171740,"corporation":false,"usgs":false,"family":"Hovel","given":"Rachel","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":834064,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Breck, James E.","contributorId":171518,"corporation":false,"usgs":false,"family":"Breck","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":834011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simon, Carl P.","contributorId":275339,"corporation":false,"usgs":false,"family":"Simon","given":"Carl","email":"","middleInitial":"P.","affiliations":[{"id":56762,"text":"The University of Michigan","active":true,"usgs":false}],"preferred":false,"id":834012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rutherford, Edward S.","contributorId":54161,"corporation":false,"usgs":true,"family":"Rutherford","given":"Edward","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":834013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Low, Bobbi S.","contributorId":189289,"corporation":false,"usgs":false,"family":"Low","given":"Bobbi","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":834014,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lamberson, P. J.","contributorId":275342,"corporation":false,"usgs":false,"family":"Lamberson","given":"P.","email":"","middleInitial":"J.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":834015,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rogers, Mark W. 0000-0001-7205-5623","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":245525,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834016,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212618,"text":"70212618 - 2020 - Pavement alters delivery of sediment and fallout radionuclides to urbanstreams","interactions":[],"lastModifiedDate":"2020-08-24T15:59:04.913792","indexId":"70212618","displayToPublicDate":"2020-03-16T09:48:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Pavement alters delivery of sediment and fallout radionuclides to urbanstreams","docAbstract":"Sediment from urban impervious surfaces has the potential to be an important vector for contaminants, particularly where stormwater culverts and other buried channels draining large impervious areas exit from underground pipes into open channels. To better understand urban sediment sources and their relation to fallout radionuclides, we collected samples of rainfall, urban sediment (pavement sediment, topsoil), streambank sediment, and fluvial sediment (suspended sediment and bed sediment) for 7Be, 210Pbex, and 137Cs analysis. The results indicate that each rainfall event tags pavement sediment with elevated activities of 7Be and 210Pbex such that runoff from impervious surfaces in the buried channel part of the stream network contains the highest activities. Pavement sediment, because it is characteristically a thin veneer, has a small mass to rainwater ratio resulting in a greater tagging of 7Be and 210Pbex activity than does topsoil on a per gram basis. An unmixing model indicated that suspended-sediment samples collected at the culvert outlet from the buried-channel network are from pavement sediment sources (45 ± 25%) with a smaller component of topsoil (22 ± 19%), and a component from streambanks (32 ± 35%) that we infer to be older channel material and subsoil eroded from within the culvert system. Downstream from the culvert, suspended sediment collected from the open-channel parts of the stream had 7Be and 210Pbex activities that were substantially reduced by the contribution of sediment from streambanks (57 ± 15%), with pavement contributions decreasing to 15 (±9%) and topsoil contributing 28 (±7%). The results highlight the utility of 7Be, 210Pbex, and 137Cs as tracers of urban sediment sources, resulting in a unique radionuclide signature for urban watersheds compared to other sediment-source settings.
Mercury (Hg) is a global pollutant that affects biota in remote settings due to atmospheric deposition of inorganic Hg, and its conversion to methylmercury (MeHg), the bioaccumulating and toxic form. Characterizing biotic MeHg is important for evaluating aquatic ecosystem responses to changes in Hg inputs. Aquatic insects possess many qualities desired for MeHg biomonitoring, but are not widely used, largely because of limited information regarding percentages of total mercury (THg) composed of MeHg (i.e., MeHg%) in various taxa. Here, we examine taxonomic, spatial, and seasonal variation in MeHg% of stream-dwelling predator and primary-consumer insects from nine streams in the Adirondack region (NY, USA). Predator MeHg% was high (median 94%) and did not differ significantly among five taxa. MeHg% in selected dragonflies (the most abundant predators, Odonata: Aeshnidae and Libellulidae) exhibited little seasonal and spatial variation, and THg concentration was strongly correlated with aqueous (filtered) MeHg (FMeHg; rs = 0.76). In contrast, MeHg% in primary consumers—shredders (northern caddisflies [Trichoptera: Limnephilidae]) and scrapers (flathead mayflies [Ephemeroptera: Heptageniidae]), were lower (medians 52% and 35%, respectively), and differed significantly between taxa, among sites, and seasonally. Correlations of THg with FMeHg were weak (shredders, rs = 0.45, p = 0.09) or not significant (scrapers, p = 0.89). The higher MeHg% of predators corresponded with their higher trophic positions (indicated by nitrogen stable isotopes). Results suggest obligate predators hold the most promise for the use of THg as a surrogate for MeHg biomonitoring with aquatic insects within the Adirondack region.
","language":"English","publisher":"Springer","doi":"10.1007/s10646-020-02191-7","usgsCitation":"Riva-Murray, K., Bradley, P., and Brigham, M.E., 2020, Methylmercury-Total mercury ratios in predator and primary consumer insects from Adirondack streams (New York, USA): Ecotoxicology, v. 29, https://doi.org/10.1007/s10646-020-02191-7.","productDescription":"15 p.","startPage":"1658","ipdsId":"IP-086907","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":373331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.35498046875,\n 42.827638636242284\n ],\n [\n -73.2568359375,\n 42.827638636242284\n ],\n [\n -73.2568359375,\n 45.24395342262324\n ],\n [\n -76.35498046875,\n 45.24395342262324\n ],\n [\n -76.35498046875,\n 42.827638636242284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","edition":"1644","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-16","publicationStatus":"PW","contributors":{"authors":[{"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":785016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradley, Paul M. 0000-0001-7522-8606","orcid":"https://orcid.org/0000-0001-7522-8606","contributorId":221226,"corporation":false,"usgs":true,"family":"Bradley","given":"Paul M.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785018,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brigham, Mark E. 0000-0001-7412-6800 mbrigham@usgs.gov","orcid":"https://orcid.org/0000-0001-7412-6800","contributorId":1840,"corporation":false,"usgs":true,"family":"Brigham","given":"Mark","email":"mbrigham@usgs.gov","middleInitial":"E.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785017,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228662,"text":"70228662 - 2020 - The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss","interactions":[],"lastModifiedDate":"2022-03-11T16:34:08.740184","indexId":"70228662","displayToPublicDate":"2020-03-15T11:24:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":857,"text":"Aquaculture Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss","title":"The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss","docAbstract":"Swimming exercise and dissolved oxygen (DO) are important parameters to consider when operating intensive salmonid aquaculture facilities. While previous research has focused on each of these two variables in rainbow trout Oncorhynchus mykiss, studies examining both variables in combination, and their potential interaction, are absent from the scientific literature. Both swimming exercise (usually measured in body lengths per second, or BL/s) and DO can be readily controlled in modern aquaculture systems; therefore, we sought to evaluate the effects of these variables, separately and combined, on several outcomes in rainbow trout including growth performance, fin health and survival. Rainbow trout fry (18 g) were stocked into 12 circular 0.5 m3 tanks, provided with either high (1.5–2 BL/s) or low (approximately 0.5 BL/s) swimming exercise and high (100% saturation) or low (70% saturation) DO, and grown to approximately 1 kg. By the conclusion of the study, higher DO was independently associated with significantly (p < .05) increased growth performance. Significant differences were not noted in other outcomes, namely feed conversion, condition factor and mortality, although caudal and right pectoral fin damage was associated with low oxygen and low swimming exercise treatments respectively. Cardiosomatic index was significantly higher among exercised fish. These results suggest that swimming exercise and DO at saturation during the culture of rainbow trout can be beneficial to producers through improved growth performance and cardiac health.
","language":"English","publisher":"Wiley","doi":"10.1111/are.14600","usgsCitation":"Waldrop, T., Summerfelt, S., Mazik, P.M., Kenney, P.B., and Good, C., 2020, The effects of swimming exercise and dissolved oxygen on growth performance, fin condition and survival of rainbow trout Oncorhynchus mykiss: Aquaculture Research, v. 51, no. 6, p. 2582-2589, https://doi.org/10.1111/are.14600.","productDescription":"8 p.","startPage":"2582","endPage":"2589","ipdsId":"IP-113465","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457365,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/are.14600","text":"Publisher Index Page"},{"id":397025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Waldrop, Thomas","contributorId":279449,"corporation":false,"usgs":false,"family":"Waldrop","given":"Thomas","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Summerfelt, Steven","contributorId":279450,"corporation":false,"usgs":false,"family":"Summerfelt","given":"Steven","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kenney, P. Brett","contributorId":279452,"corporation":false,"usgs":false,"family":"Kenney","given":"P.","email":"","middleInitial":"Brett","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":834955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Good, Christopher","contributorId":279454,"corporation":false,"usgs":false,"family":"Good","given":"Christopher","affiliations":[{"id":33606,"text":"The Conservation Fund Freshwater Institute","active":true,"usgs":false}],"preferred":false,"id":834956,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210136,"text":"70210136 - 2020 - North Carolina State climate report","interactions":[],"lastModifiedDate":"2020-05-15T14:30:20.518142","indexId":"70210136","displayToPublicDate":"2020-03-15T09:27:17","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"North Carolina State climate report","docAbstract":"Our scientific understanding of the climate system strongly supports the conclusion that North Carolina’s climate has changed in recent decades and the expectation that large changes—much larger than at any time in the state’s history—will occur if current trends in greenhouse gas concentrations continue. Even under a scenario where emissions peak around 2050 and decline thereafter, North Carolina will experience substantial changes in climate. The projected changes with the highest level of scientific confidence include increases in temperature, increases in summer absolute humidity, increases in sea level, and increases in extreme precipitation. It is also likely that there will be increases in the intensity of the strongest hurricanes. \nA full appreciation for past and future changes in North Carolina’s climate requires a global perspective. Earth’s climate has warmed substantially since the late 19th century, with most of that warming occurring in the last 50 years. This warming trend is clear from global temperature records and many other indicators, including rising global sea levels and rapid decreases in arctic sea ice cover. Scientists have very high confidence that this warming is largely due to human activities that have significantly increased atmospheric concentrations of carbon dioxide (CO2) and other greenhouse gases. Exhaustive research has examined other potential causes of this warming, and the increase in greenhouse gas concentrations is the only plausible cause that is consistent with the observed data and the physics that governs the climate system.","language":"English","publisher":"NCICS","collaboration":"North Carolina State University, NC Department of Environmental Quality","usgsCitation":"Kunkel, K.E., Easterling, D.R., Ballinger, A., Bililign, S., Champion, S., Corbett, D.R., Dello, K., Dissen, J., Kossin, J.P., Lackmann, G., Luettich, R., Perry, B., Robinson, W., Stevens, L.E., Stewart, B.C., and Terando, A., 2020, North Carolina State climate report, 236 p,.","productDescription":"236 p,","ipdsId":"IP-115496","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":374872,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":374858,"type":{"id":15,"text":"Index 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wildfires","interactions":[],"lastModifiedDate":"2022-02-14T15:33:01.949286","indexId":"70228573","displayToPublicDate":"2020-03-15T09:19:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","title":"Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","docAbstract":"The combined effects of long-term fire suppression, logging, and overgrazing have negatively impacted many southwestern U.S. forests, resulting in decreased habitat quality for wildlife, and more frequent and severe wildfires. In response, land management agencies are implementing large-scale forest restoration treatments, but data on how wildlife respond to restoration treatments and wildfires are often limited. We investigated bed and den site selection of American black bears (Ursus americanus) using GPS location data and a use/available study design to assess the influence of habitat characteristics, including wildfires, prescribed burns, and thinning treatments on bed and den site selection in the Jemez Mountains, New Mexico. The most supported models suggested that black bears were more likely to select bed sites with a combination of low horizontal visibility (β = −0.007, SE = 0.002; P = 0.002) and high stand basal area (β = 0.013, SE = 0.005; P = 0.004). The highest-ranking model for den site selection indicated that black bears were more likely to select den sites with low horizontal visibility (β = −0.0102, SE = 0.004; P = 0.006). Black bears used all disturbed sites to varying degrees (45% of study area), although 48% of bed sites were located in undisturbed habitat (55% of study area) while only 11% and 2% of bed sites were located in thinned and prescribed burn sites, respectively. Thirty-nine percent of bed sites were located in previous wildfire locations; however, 67% of these sites were in areas with low burn severity. Thirty-eight percent of den sites were located in previously disturbed habitat, 8 of these sites were burned by wildfires. In order to develop effective management plans for black bears, it is essential to understand responses to landscape-scale habitat disturbances due to wildfires and restoration activities, all of which are becoming more prevalent and widespread across southwestern forests. Accounting for the timing, size, and proximity of future restoration efforts would aid in mitigating potential short-term negative effects on black bears.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2020.117904","usgsCitation":"Bard, S.M., and Cain, J.W., 2020, Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires: Forest Ecology and Management, v. 460, p. 1-11, https://doi.org/10.1016/j.foreco.2020.117904.","productDescription":"117904, 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-112372","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":457367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1595500","text":"Publisher Index Page"},{"id":395886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Collaborative Forest Landscape Restoration Program area, Jemez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.67587280273438,\n 35.65004306288284\n ],\n [\n -106.67587280273438,\n 35.622698214535184\n ],\n [\n -106.62506103515625,\n 35.623256366178964\n ],\n [\n -106.42936706542969,\n 35.85455268869835\n ],\n [\n -106.39503479003906,\n 35.85343961959182\n ],\n [\n -106.39022827148438,\n 36.00800626603582\n ],\n [\n -106.62368774414062,\n 36.00911716117325\n ],\n [\n -106.68960571289062,\n 35.884043325566886\n ],\n [\n -106.86882019042969,\n 35.879592612012026\n ],\n [\n -106.86744689941405,\n 35.821153818963175\n ],\n [\n -106.85714721679688,\n 35.8217105820067\n ],\n [\n -106.85302734374999,\n 35.649485098277204\n ],\n [\n -106.67587280273438,\n 35.65004306288284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"460","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bard, Susan M.","contributorId":264967,"corporation":false,"usgs":false,"family":"Bard","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":834645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834644,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209101,"text":"70209101 - 2020 - Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","interactions":[],"lastModifiedDate":"2020-03-16T16:52:49","indexId":"70209101","displayToPublicDate":"2020-03-14T16:47:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2049,"text":"International Journal of Greenhouse Gas Control","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","title":"Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale","docAbstract":"Residual oil zones (ROZs) are increasingly gaining interest as potential reservoirs for carbon dioxide (CO2) storage. Here, we present a national- and regional-scale methodology for estimating prospective CO2 storage resources in residual oil zones. This methodology uses a volumetric equation that accounts for CO2 storage as a free phase in pore space and as a dissolved phase in oil and does not assume any oil production associated with CO2 storage. Reservoir modeling and the CO2-SCREEN tool are used to demonstrate that CO2 storage in residual oil zones will predominantly take place in the free phase (approximately 92–97%) with some storage as dissolution in oil (approximately 3–8 %). Based on this preliminary demonstration, the CO2 storage efficiency for ROZs using this national- and regional-scale method ranges from 0.61 to 7.1 %. This range indicates ROZs have a similar efficiency potential for storing CO2 as deep saline formations (0.51–5.4 %).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ijggc.2020.103006","usgsCitation":"Sanguinito, S., Singh, H., Myshakin, E.M., Goodman, A.L., Dilmore, R.M., Grant, T.C., Morgan, D., Bromhal, G., Warwick, P., Brennan, S.T., Freeman, P., Karacan, C.O., Gorecki, C., Peck, W., Burton-Kelly, M., Dotzenrod, N., Frailey, S., and Pawar, R., 2020, Methodology for estimating the prospective CO2 storage resource of residual oil zones at the national and regional scale: International Journal of Greenhouse Gas Control, v. 96, 103006, 8 p., https://doi.org/10.1016/j.ijggc.2020.103006.","productDescription":"103006, 8 p.","ipdsId":"IP-108213","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":457370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1780239","text":"Publisher Index 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sbrennan@usgs.gov","orcid":"https://orcid.org/0000-0002-7102-9359","contributorId":559,"corporation":false,"usgs":true,"family":"Brennan","given":"Sean","email":"sbrennan@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":784936,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":784937,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":208012,"corporation":false,"usgs":false,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[],"preferred":false,"id":784938,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Gorecki, Charles","contributorId":223395,"corporation":false,"usgs":false,"family":"Gorecki","given":"Charles","email":"","affiliations":[{"id":40709,"text":"Energy & Environmental Research Center, University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":784939,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Peck, Wesley","contributorId":223396,"corporation":false,"usgs":false,"family":"Peck","given":"Wesley","email":"","affiliations":[{"id":40709,"text":"Energy & Environmental Research Center, University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":784940,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Burton-Kelly, Matthew","contributorId":223397,"corporation":false,"usgs":false,"family":"Burton-Kelly","given":"Matthew","email":"","affiliations":[{"id":40709,"text":"Energy & Environmental Research Center, University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":784941,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Dotzenrod, Neil","contributorId":223398,"corporation":false,"usgs":false,"family":"Dotzenrod","given":"Neil","email":"","affiliations":[{"id":40709,"text":"Energy & Environmental Research Center, University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":784942,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Frailey, Scott","contributorId":177268,"corporation":false,"usgs":false,"family":"Frailey","given":"Scott","email":"","affiliations":[],"preferred":false,"id":784943,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Pawar, Rajesh 0000-0003-1422-7532","orcid":"https://orcid.org/0000-0003-1422-7532","contributorId":223399,"corporation":false,"usgs":false,"family":"Pawar","given":"Rajesh","email":"","affiliations":[{"id":37625,"text":"Earth and Environmental Sciences Division, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":784944,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70209447,"text":"70209447 - 2020 - Validation of a screening method for the detection of colistin-resistant E. coli containing mcr-1 in feral swine feces","interactions":[],"lastModifiedDate":"2020-05-05T17:21:58.161449","indexId":"70209447","displayToPublicDate":"2020-03-14T07:28:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2390,"text":"Journal of Microbiological Methods","active":true,"publicationSubtype":{"id":10}},"title":"Validation of a screening method for the detection of colistin-resistant E. coli containing mcr-1 in feral swine feces","docAbstract":"A method was developed and validated for the detection of colistin-resistant Escherichia coli containing mcr-1 in the feces of feral swine. Following optimization of an enrichment method using EC broth supplemented with colistin (1 µg/mL) and vancomycin (8 µg/mL), aliquots derived from 100 feral swine fecal samples were spiked with of one of five different mcr-1 positive E. coli strains (between 100 and 104 CFU/g), for a total of 1,110 samples tested. Enrichments were then screened using a simple boil-prep and a previously developed real-time PCR assay for mcr-1 detection. The sensitivity of the method was determined in swine feces, with mcr-1 E. coli inoculums of 0.1-9.99 CFU/g (n = 340), 10-49.99 CFU/g (n = 170), 50-99 CFU/g (n = 255), 100-149 CFU/g (n = 60), and 200-2,200 CFU/g (n = 175), which were detected with 32%, 72%, 88%, 95%, and 98% accuracy, respectively. Uninoculated controls (n = 100) were negative for mcr-1 following enrichment.","language":"English","publisher":"Elsevier ","doi":"10.1016/j.mimet.2020.105892","collaboration":"","usgsCitation":"Chandler, J.C., Franklin, A.B., Bevins, S.N., Bentler, K.T., Bonnedahl, J., Ahlstrom, C., Bisha, B., and Shriner, S.A., 2020, Validation of a screening method for the detection of colistin-resistant E. coli containing mcr-1 in feral swine feces: Journal of Microbiological Methods, v. 172, 105892, 5 p., https://doi.org/10.1016/j.mimet.2020.105892.","productDescription":"105892, 5 p.","ipdsId":"IP-114741","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":457373,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.mimet.2020.105892","text":"Publisher Index Page"},{"id":373833,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"172","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chandler, Jeffrey C","contributorId":223870,"corporation":false,"usgs":false,"family":"Chandler","given":"Jeffrey","email":"","middleInitial":"C","affiliations":[{"id":40781,"text":"USDA/APHIS/WS, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":786508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Franklin, Alan B.","contributorId":101999,"corporation":false,"usgs":false,"family":"Franklin","given":"Alan","email":"","middleInitial":"B.","affiliations":[{"id":12434,"text":"USDA, Wildlife Services, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":786509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bevins, Sarah N.","contributorId":212845,"corporation":false,"usgs":false,"family":"Bevins","given":"Sarah","email":"","middleInitial":"N.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":786510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bentler, Kevin T","contributorId":223871,"corporation":false,"usgs":false,"family":"Bentler","given":"Kevin","email":"","middleInitial":"T","affiliations":[{"id":40781,"text":"USDA/APHIS/WS, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":786511,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonnedahl, Jonas","contributorId":181800,"corporation":false,"usgs":false,"family":"Bonnedahl","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":786512,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":786513,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bisha, Bledar","contributorId":223872,"corporation":false,"usgs":false,"family":"Bisha","given":"Bledar","email":"","affiliations":[{"id":40782,"text":"Department of Animal Science, University of Wyoming,","active":true,"usgs":false}],"preferred":false,"id":786514,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shriner, Susan A.","contributorId":168690,"corporation":false,"usgs":false,"family":"Shriner","given":"Susan","email":"","middleInitial":"A.","affiliations":[{"id":13407,"text":"Colorado State Univ.","active":true,"usgs":false}],"preferred":false,"id":786515,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70209158,"text":"70209158 - 2020 - A 'weight of evidence' approach to evaluating structural equation models","interactions":[],"lastModifiedDate":"2020-03-19T19:09:47","indexId":"70209158","displayToPublicDate":"2020-03-13T19:08:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5943,"text":"One Ecosystem","active":true,"publicationSubtype":{"id":10}},"title":"A 'weight of evidence' approach to evaluating structural equation models","docAbstract":"It is possible that model selection has been the most researched and most discussed topic in the history of both statistics and structural equation modeling (SEM). The reason for this is because selecting one model for interpretive use from amongst many possible models is both essential and difficult. The published protocols and advice for model evaluation and selection in SEM studies are complex and difficult to integrate with current approaches used in biology. Opposition to the use of p-values and decision thresholds has been voiced by the statistics community, yet certain phases of model evaluation have been historically tied to reliance on p-values. In this paper, I outline an approach to model evaluation, comparison and selection based on a weight-of-evidence paradigm. The details and proposed sequence of steps are illustrated using a real-world example. At the end of the paper, I briefly discuss the current state of knowledge and a possible direction for future studies.","language":"English","publisher":"Pensoft Publisher","doi":"10.3897/oneeco.5.e50452","usgsCitation":"Grace, J., 2020, A 'weight of evidence' approach to evaluating structural equation models: One Ecosystem, v. 5, e50452, https://doi.org/10.3897/oneeco.5.e50452.","productDescription":"e50452","ipdsId":"IP-115758","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":457375,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/oneeco.5.e50452","text":"Publisher Index Page"},{"id":373395,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Grace, James 0000-0001-6374-4726","orcid":"https://orcid.org/0000-0001-6374-4726","contributorId":219648,"corporation":false,"usgs":true,"family":"Grace","given":"James","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":785160,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70228651,"text":"70228651 - 2020 - Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty","interactions":[],"lastModifiedDate":"2022-02-16T18:02:33.912037","indexId":"70228651","displayToPublicDate":"2020-03-13T11:56:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty","docAbstract":"Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13–20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2126","usgsCitation":"Pepin, K.M., Smyser, T.J., Davis, A., Miller, R., McKee, S., VerCauteren, K.C., Kendall, W.L., and Slootmaker, C., 2020, Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty: Ecological Applications, v. 30, no. 6, e02126, 15 p., https://doi.org/10.1002/eap.2126.","productDescription":"e02126, 15 p.","ipdsId":"IP-113401","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":457377,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/812305","text":"External Repository"},{"id":396025,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Pepin, Kim M.","contributorId":279406,"corporation":false,"usgs":false,"family":"Pepin","given":"Kim","email":"","middleInitial":"M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smyser, Timothy J.","contributorId":279407,"corporation":false,"usgs":false,"family":"Smyser","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Amy J.","contributorId":279408,"corporation":false,"usgs":false,"family":"Davis","given":"Amy J.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Ryan S.","contributorId":279409,"corporation":false,"usgs":false,"family":"Miller","given":"Ryan S.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834936,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKee, Sophie","contributorId":279410,"corporation":false,"usgs":false,"family":"McKee","given":"Sophie","email":"","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834937,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"VerCauteren, Kurt C.","contributorId":279413,"corporation":false,"usgs":false,"family":"VerCauteren","given":"Kurt","email":"","middleInitial":"C.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834938,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834932,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Slootmaker, Chris","contributorId":279414,"corporation":false,"usgs":false,"family":"Slootmaker","given":"Chris","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":834939,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210755,"text":"70210755 - 2020 - Eradication of peste des petits ruminants and the wildlife-livestock interface","interactions":[],"lastModifiedDate":"2020-06-23T15:30:31.423094","indexId":"70210755","displayToPublicDate":"2020-03-13T10:24:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5720,"text":"Frontiers in Veterinary Science","onlineIssn":"2297-1769","active":true,"publicationSubtype":{"id":10}},"title":"Eradication of peste des petits ruminants and the wildlife-livestock interface","docAbstract":"Growing evidence suggests that multiple wildlife species can be infected with peste des petits ruminants virus (PPRV), with important consequences for the potential maintenance of PPRV in communities of susceptible hosts, and the threat that PPRV may pose to the conservation of wildlife populations and resilience of ecosystems. Significant knowledge gaps in the epidemiology of PPRV across the ruminant community (wildlife and domestic), and the understanding of infection in wildlife and other atypical host species groups (e.g., camelidae, suidae, and bovinae) hinder our ability to apply necessary integrated disease control and management interventions at the wildlife-livestock interface. Similarly, knowledge gaps limit the inclusion of wildlife in the FAO/OIE Global Strategy for the Control and Eradication of PPR, and the framework of activities in the PPR Global Eradication Programme that lays the foundation for eradicating PPR through national and regional efforts. This article reports on the first international meeting on, “Controlling PPR at the livestock-wildlife interface,” held in Rome, Italy, March 27–29, 2019. A large group representing national and international institutions discussed recent advances in our understanding of PPRV in wildlife, identified knowledge gaps and research priorities, and formulated recommendations. The need for a better understanding of PPRV epidemiology at the wildlife-livestock interface to support the integration of wildlife into PPR eradication efforts was highlighted by meeting participants along with the reminder that PPR eradication and wildlife conservation need not be viewed as competing priorities, but instead constitute two requisites of healthy socio-ecological systems.
Morris Lake, also known as Newton Reservoir, has been the source of drinking water for the Town of Newton, New Jersey, since the early 1900s. Although Morris Lake has been used as a source of drinking water for many years, its capacity was previously uncertain. In April 2018, the U.S. Geological Survey and the New Jersey Department of Environmental Protection conducted a bathymetric survey of Morris Lake using a multibeam echosounder to map the reservoir. The points measured with the multibeam echosounder were combined with light detection and ranging data above the water surface and processed to create a 3.3-foot (1 meter) raster grid of the bathymetric surface, bathymetric contours at 2-foot intervals of depth and elevation, and an elevation-area-capacity table.
The results of the bathymetric survey show that Morris Lake has a maximum depth of just over 119 feet with an average depth of 42 feet. Like the surrounding topography, parts of the reservoir are extremely steep. The capacity of the reservoir at full spillway level is 1,980 million gallons, with a corresponding surface area of 145 acres. The accuracy of the mapped multibeam echosounder bathymetric data was evaluated using a quality assurance dataset collected with a single-beam echosounder; 9,386 quality assurance points were spatially joined with the mapped raster surface to compute measurement errors. The calculated median point error for Morris Lake was 0.23 foot, the median absolute error was 0.35 foot, and the 95-percent accuracy was 2.68 feet. The largest errors occurred in the steepest areas of the reservoir and in unmeasured areas. Geospatial files of the bathymetry data, including the mapped bathymetric surface, contours, and capacity tables, quality assurance points, and associated metadata are available for download as part of an accompanying U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205010","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Nystrom, E.A., and Collenburg, J.V., 2020, Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5010, 14 p., https://doi.org/10.3133/sir20205010.","productDescription":"Report: vii, 14 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103879","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":399631,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109786.htm"},{"id":373089,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P977GO3J","text":"USGS data release","linkHelpText":"Geospatial Bathymetry Dataset and Elevation-Area-Capacity Table for Morris Lake (Newton Reservoir), New Jersey"},{"id":373091,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5010/sir20205010.pdf","text":"Report","size":"4.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5010"},{"id":373090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5010/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Morris Lake (Newton Reservoir)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.62128639221191,\n 41.0387074972886\n ],\n [\n -74.59296226501463,\n 41.0387074972886\n ],\n [\n -74.59296226501463,\n 41.05366055046841\n ],\n [\n -74.62128639221191,\n 41.05366055046841\n ],\n [\n -74.62128639221191,\n 41.0387074972886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Streamflow generation in mountain watersheds is strongly influenced by snow accumulation and melt, and multiple studies have found that snow loss leads to earlier snowmelt timing and declines in annual streamflow. However, hydrologic responses to snow loss are heterogeneous, and not all areas experience streamflow declines. This research examines whether streamflow generation is different for rainfall versus snowmelt inputs. We compiled a sample of 57 small U.S. Geological Survey watersheds in the western United States containing a Natural Resource Conservation Service Snow Telemetry site and having ratios of mean annual peak snow water equivalent to precipitation ratios >0.25. Daily streamflow was separated into quickflow and baseflow using a digital filter, and quickflow was then divided into quickflow response intervals using thresholds in quickflow slope. Each quickflow response interval was categorized by its fraction of input from snowmelt. Most sites exhibited two streamflow generation peaks each year, with one peak in the winter when runoff efficiency is greatest, and the second in the spring during peak snowmelt input. On average, study watersheds were dominated by snowmelt inputs (70%), and snowmelt and mixed inputs usually generated greater streamflow than rainfall because of higher inputs and longer durations. However, rainfall produced high streamflow generation in winter, when watersheds have their highest runoff efficiency (81%) across all input types. We demonstrate that while snowmelt is important for streamflow generation due to high input over long periods, increases in rain and mixed input during wet winter periods can countervail tendencies for reduced streamflow with declining snowpacks.
The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104692","usgsCitation":"Stanley, T.A., Kirschbaum, D.B., Sobieszczyk, S., Jasinski, M.F., Borak, J.S., and Slaughter, S.L., 2020, Building a landslide hazard indicator with machine learning and land surface models: Environmental Modelling & Software, v. 129, 104692, 15 p., https://doi.org/10.1016/j.envsoft.2020.104692.","productDescription":"104692, 15 p.","ipdsId":"IP-114297","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":457392,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2020.104692","text":"Publisher Index Page"},{"id":410787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, 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Landfill leachate, WWTP influent, and WWTP effluent samples were collected from three landfill-WWTP systems and compared with analogous influent and effluent samples from two WWTPs that did not receive leachate. Samples were analyzed for 73 per-/poly-fluoroalkyl substances (PFAS), 109 pharmaceuticals, and 21 hormones and related compounds. PFAS were detected more frequently in leachate (92%) than in influent (55%). Total PFAS concentrations in leachate (93,100 ng/L) were more than ten times higher than in influent (6,950 ng/L), and effluent samples (3,730 ng/L). Concentrations of bisphenol A; the nonprescription pharmaceuticals cotinine, lidocaine, nicotine; and the prescription pharmaceuticals amphetamine, carisoprodol, pentoxifylline, and thiabendazole were an order of magnitude higher in landfill leachate than WWTP influent. Leachate load contributions for PFAS (0.78 to 31 g/d), bisphenol A (0.97 to 8.3 g/d), and nonprescription (2.0 to 3.1 g/d) and prescription (0.48 to 2.5 g/d) pharmaceuticals to WWTP influent were generally low (<10 g/d) for most compounds because of high influent-to-leachate volumetric ratios (0.983). No clear differences in concentrations were apparent between effluents from WWTPs receiving landfill leachate and those that did not receive landfill leachate.","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D0EW00045K","usgsCitation":"Masoner, J.R., Kolpin, D.W., Cozzarelli, I.M., Smalling, K.L., Bolyard, S., Field, J., Furlong, E.T., Gray, J.L., Lozinski, D., Reinhart, D., Rodowa, A., and Bradley, P.M., 2020, Landfill leachate contributes per-/poly-fluoroalkyl substances (PFAS) and pharmaceuticals to municipal wastewater: Environmental Science: Water Research & Technology, v. 6, p. 1300-1311, https://doi.org/10.1039/D0EW00045K.","productDescription":"12 p.","startPage":"1300","endPage":"1311","ipdsId":"IP-116926","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":457394,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1039/d0ew00045k","text":"Publisher Index Page"},{"id":437056,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97LMTKZ","text":"USGS data release","linkHelpText":"Target-Chemical Concentrations in Landfill Leachate and Wastewater Treatment Influent and Effluent"},{"id":373360,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Masoner, Jason R. 0000-0002-4829-6379 jmasoner@usgs.gov","orcid":"https://orcid.org/0000-0002-4829-6379","contributorId":3193,"corporation":false,"usgs":true,"family":"Masoner","given":"Jason","email":"jmasoner@usgs.gov","middleInitial":"R.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - 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