Stream entry of many fishes is influenced by environmental factors including water temperature, stream discharge, and photoperiod (Leggett 1977; Jonsson 1991). Environmental factors influence stream entry differently depending on the species and life stage of fish, likely because of varying physiologies and life histories (Lucas and Baras 2008). Many spring-run migratory fishes occupy Laurentian Great Lakes Basin (e.g., lake sturgeon, Acipenser fulvescens; steelhead, Oncorhynchus mykiss; white sucker, Catostomus commersonii; coho salmon, Oncorhynchus kisutch; and sea lamprey, Petromyzon marinus), and the timing of when they enter a river system each year is likely influenced by different environmental variables. For example, water temperature and stream discharge seem to be two of the triggers to upstream movement for many migratory fishes in the Great Lakes region (Hamel et al. 1997; Workman et al. 2002; Binder et al. 2010). Although much is known about the environmental cues for upstream migration of many species in the Great Lakes, understanding fish migration at higher spatial and temporal resolutions is needed (Landsman et al. 2011), especially in and around fishways. Therefore, our goal was to determine what factors influence the timing of fish entry into a Lake Michigan tributary at a relatively high temporal resolution using a noninvasive and continuous monitoring technique.
Enhanced understanding of the environmental cues that trigger fish movement in Great Lakes tributaries is important because it can improve recreation, native fish restoration, and invasive species control. For example, fisheries managers need to know when and at which life stage fish are moving into the rivers to establish closed seasons and catch limits. Additionally, knowing when native or invasive species move upstream can inform the operation of selective fishways and hydropower facilities that protect native or economically valuable fishes (e.g., lake sturgeon, white sucker, steelhead, coho salmon) while blocking invasive species (e.g., sea lamprey).
Here, we evaluate the environmental triggers of stream entry for three size classes of migratory fishes in the Lower Boardman River. Located in northern Michigan, the Boardman River is an important system for improving knowledge of fish migration because of its high productivity and value to the local community. Many fishes would benefit from increased habitat connectivity in the Boardman River, which connects a productive and relatively pristine tributary with a large and productive bay. The Boardman River is valued by both recreational and tribal stakeholders and is the focus of a restoration project aimed at improving habitat connectivity by removing and modifying all existing dams. For these reasons, the Great Lakes Fishery Commission is leading a project to replace the Union Street Dam on the Boardman River with a facility that integrates various technologies to pass desirable fish up- and down-stream while blocking and removing undesirable fish (the FishPass project). Using Dual-frequency Identification Sonar (DIDSON) cameras, we characterized the sizes of fishes migrating into the Boardman River below the Union Street Dam as well as the timing of their movements. Our results provide fine-scale details on the timing of fish movements in the Boardman River and directly inform the FishPass project. We were specifically interested in the movement timing of sea lamprey, an undesirable invasive fish that must not be passed upstream at FishPass, and lake sturgeon, a highly desired native fish that should pass upstream.
","language":"English","publisher":"Conservation Resource Alliance","usgsCitation":"McCann, E.L., Johnson, N., and Zielinski, D.P., 2018, Environmental factors influencing entry of fishes into a Great Lakes tributary during spring and summer, 24 p.","productDescription":"24 p.","ipdsId":"IP-096646","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":359936,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":359893,"type":{"id":15,"text":"Index Page"},"url":"https://theboardman.org/archived-documents/reports-documents.html"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c07a064e4b0815414cee781","contributors":{"authors":[{"text":"McCann, Erin L.","contributorId":195636,"corporation":false,"usgs":false,"family":"McCann","given":"Erin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":753047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":753046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zielinski, Daniel P.","contributorId":211034,"corporation":false,"usgs":false,"family":"Zielinski","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":34820,"text":"Great Lakes Fisheries Commission, Ann Arbor, MI","active":true,"usgs":false}],"preferred":false,"id":753048,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70201039,"text":"70201039 - 2018 - Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems","interactions":[],"lastModifiedDate":"2018-11-26T11:55:00","indexId":"70201039","displayToPublicDate":"2018-11-01T11:54:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems","docAbstract":"Earth observation data are increasingly used to provide consistent eco-physiological information over large areas through time. Production efficiency models (PEMs) estimate Gross Primary Production (GPP) as a function of the fraction of photosynthetically active radiation absorbed by the canopy, which is derived from Earth observation. GPP can be summed over the growing season and adjusted by a crop-specific harvest index to estimate yield. Although PEMs have many advantages over other crop yield models, they are not widely used, because performance is relatively poor. Here, a new PEM is presented that addresses deficiencies for macro-scale application: Production Efficiency Model Optimized for Crops (PEMOC). It was developed by optimizing functions from the literature with GPP estimated by eddy covariance flux towers in the United States. The model was evaluated using newly developed Earth observation products and county-level yield statistics for major crops. PEMOC generally performed better at the field and county level than another commonly used PEM, the Moderate Resolution Imaging Spectroradiometer GPP (MOD17). PEMOC and MOD17 estimates of GPP had an R2 and root mean squared error (RMSE) over the growing season of 0.71–0.89 (9.87–17.47 g CO2 d−1) and 0.59–0.83 (6.86–22.20 g CO2 d−1) with flux tower GPP. PEMOC produced R2s and RMSE of 0.70 (0.52), 0.60 (0.61), and 0.62 (0.59), while MOD17 produced R2s and RMSE of 0.65 (0.57), 0.53 (0.66), and 0.65 (0.57) with corn, soybean, and winter wheat crop yield anomalies. The sample size of rice was small, so yields were compared directly. PEMOC and MOD17 produced R2s and RMSE of 0.53 (3.42 t ha−1) and 0.40 (4.89 t ha−1). The most sizeable model improvements were seen for C3 and C4 crops during emergence/senescence and peak season, respectively. These improvements were attributed to C3 and C4 partitioning, optimized temperature and moisture constraints, and an evapotranspiration-based soil moisture index.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2018.08.001","usgsCitation":"Marshall, M., Tu, K., and Brown, J.F., 2018, Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems: Remote Sensing of Environment, v. 217, p. 258-271, https://doi.org/10.1016/j.rse.2018.08.001.","productDescription":"14 p.","startPage":"258","endPage":"271","ipdsId":"IP-082657","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468274,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2018.08.001","text":"Publisher Index Page"},{"id":359657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"217","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bfd146ee4b0815414ca38f6","contributors":{"authors":[{"text":"Marshall, Michael","contributorId":65216,"corporation":false,"usgs":true,"family":"Marshall","given":"Michael","email":"","affiliations":[],"preferred":false,"id":751963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tu, Kevin","contributorId":210791,"corporation":false,"usgs":false,"family":"Tu","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":751964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":751962,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200792,"text":"70200792 - 2018 - A multidisciplinary-based conceptual model of a fractured sedimentary bedrock aquitard: improved prediction of aquitard integrity","interactions":[],"lastModifiedDate":"2018-11-01T11:20:58","indexId":"70200792","displayToPublicDate":"2018-11-01T11:20:54","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"A multidisciplinary-based conceptual model of a fractured sedimentary bedrock aquitard: improved prediction of aquitard integrity","docAbstract":"A hydrogeologic conceptual model that improves understanding of variability in aquitard integrity is presented for a fractured sedimentary bedrock unit in the Cambrian-Ordovician aquifer system of midcontinent North America. The model is derived from multiple studies on the siliciclastic St. Lawrence Formation and adjacent strata across a range of scales and geologic conditions. These studies employed multidisciplinary techniques including borehole flowmeter logging, high-resolution depth-discrete multilevel well monitoring, fracture stratigraphy, fluorescent dye tracing, and three-dimensional (3D) distribution of anthropogenic tracers regionally. The paper documents a bulk aquitard that is highly anisotropic because of poor connectivity of vertical fractures across matrix with low permeability, but with ubiquitous bed parallel partings. The partings provide high bulk horizontal hydraulic conductivity, analogous to aquifers in the system, while multiple preferential termination horizons of vertical fractures serve as discrete low vertical hydraulic conductivity intervals inhibiting vertical flow. The aquitard has substantial variability in its ability to protect underlying groundwater from contamination. Across widespread areas where the aquitard is deeply buried by younger bedrock, preferential termination horizons provide for high aquitard integrity (i.e. protection). Protection is diminished close to incised valleys where stress release and weathering has enhanced secondary pore development, including better connection of fractures across these horizons. These conditions, along with higher hydraulic head gradients in the same areas and more complex 3D flow where the aquitard is variably incised, allow for more substantial transport to deeper aquifers. The conceptual model likely applies to other fractured sedimentary bedrock aquitards within and outside of this region.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-018-1794-2","usgsCitation":"Runkel, A.C., Tipping, R.G., Meyer, J.R., Steenberg, J.R., Retzler, A.J., Parker, B.L., Green, J.A., Barry, J.D., and Jones, P.M., 2018, A multidisciplinary-based conceptual model of a fractured sedimentary bedrock aquitard: improved prediction of aquitard integrity: Hydrogeology Journal, v. 26, no. 7, p. 2133-2159, https://doi.org/10.1007/s10040-018-1794-2.","productDescription":"27 p.","startPage":"2133","endPage":"2159","ipdsId":"IP-031151","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":359064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.6639404296875,\n 43.49676775343911\n ],\n [\n -91.23596191406249,\n 43.49676775343911\n ],\n [\n -91.23596191406249,\n 45.30193900072719\n ],\n [\n -93.6639404296875,\n 45.30193900072719\n ],\n [\n -93.6639404296875,\n 43.49676775343911\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-06-01","publicationStatus":"PW","scienceBaseUri":"5c10a8ffe4b034bf6a7e4eda","contributors":{"authors":[{"text":"Runkel, Anthony C.","contributorId":210350,"corporation":false,"usgs":false,"family":"Runkel","given":"Anthony","email":"","middleInitial":"C.","affiliations":[{"id":38105,"text":"Minnesota Geological Survey","active":true,"usgs":false}],"preferred":false,"id":750533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tipping, Robert G.","contributorId":210351,"corporation":false,"usgs":false,"family":"Tipping","given":"Robert","email":"","middleInitial":"G.","affiliations":[{"id":38105,"text":"Minnesota Geological Survey","active":true,"usgs":false}],"preferred":false,"id":750534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meyer, Jessica R.","contributorId":210352,"corporation":false,"usgs":false,"family":"Meyer","given":"Jessica","email":"","middleInitial":"R.","affiliations":[{"id":12660,"text":"University of Guelph","active":true,"usgs":false}],"preferred":false,"id":750535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steenberg, Julia R.","contributorId":210353,"corporation":false,"usgs":false,"family":"Steenberg","given":"Julia","email":"","middleInitial":"R.","affiliations":[{"id":38105,"text":"Minnesota Geological Survey","active":true,"usgs":false}],"preferred":false,"id":750536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Retzler, Andrew J.","contributorId":210354,"corporation":false,"usgs":false,"family":"Retzler","given":"Andrew","email":"","middleInitial":"J.","affiliations":[{"id":38105,"text":"Minnesota Geological Survey","active":true,"usgs":false}],"preferred":false,"id":750537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parker, Beth L.","contributorId":209230,"corporation":false,"usgs":false,"family":"Parker","given":"Beth","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":750538,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Green, Jeff A.","contributorId":210355,"corporation":false,"usgs":false,"family":"Green","given":"Jeff","email":"","middleInitial":"A.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":750539,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barry, John D.","contributorId":210356,"corporation":false,"usgs":false,"family":"Barry","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":750540,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jones, Perry M. 0000-0002-6569-5144 pmjones@usgs.gov","orcid":"https://orcid.org/0000-0002-6569-5144","contributorId":2231,"corporation":false,"usgs":true,"family":"Jones","given":"Perry","email":"pmjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750532,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70201054,"text":"70201054 - 2018 - Terrestrial wetlands","interactions":[],"lastModifiedDate":"2020-08-19T19:55:02.445324","indexId":"70201054","displayToPublicDate":"2018-11-01T11:17:07","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"chapter":"13","title":"Terrestrial wetlands","docAbstract":"1. The assessment of terrestrial wetland carbon stocks has improved greatly since the First State of the Carbon Cycle Report (CCSP 2007) because of recent national inventories and the development of a U.S. soils database. Terrestrial wetlands in North America encompass an estimated 2.2 million km2, which constitutes about 37% of the global wetland area, with a soil and vegetation carbon pool of about 161 petagrams of carbon that represents approximately 36% of global wetland carbon stock. Forested wetlands compose 55% of the total terrestrial wetland area, with the vast majority occurring in Canada. Organic soil wetlands or peatlands contain 58% of the total terrestrial wetland area and 80% of the carbon (high confidence, likely).
2. North American terrestrial wetlands currently are a carbon dioxide sink of about 123 teragrams of carbon (Tg C) per year, with approximately 53% occurring in forested systems. However, North American terrestrial wetlands are a natural source of methane (CH4), with mineral soil wetlands emitting 56% of the estimated total of 45 Tg C as CH4 (CH4 –C) per year (medium confidence, likely).
3. The current rate of terrestrial wetland loss is much less than historical rates (about 0.06% of the wetland area from 2004 to 2009), with restoration and creation nearly offsetting losses of natural wetlands. Although area losses are nearly offset, there is considerable uncertainty about the functional equivalence of disturbed, created, and restored wetlands when comparing them to undisturbed natural wetlands. Correspondingly, there remains considerable uncertainty about the effects of disturbance regimes on carbon stocks and greenhouse gas (GHG) fluxes. For this reason, studies and monitoring systems are needed that compare carbon pools, rates of carbon accumulation, and GHG fluxes across disturbance gradients, including restored and created wetlands. Those studies will produce data that are needed for model applications (high confidence, likely).
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Global Change Research Program","doi":"10.7930/SOCCR2.2018.Ch13","usgsCitation":"Kolka, R., Trettin, C., Tang, W., Krauss, K.W., Bansal, S., Drexler, J.Z., Wickland, K.P., Chimner, R.A., Hogan, D.M., Pindilli, E., Benscoter, B., Tangen, B., Kane, E.S., Bridgham, S.D., and Richardson, C.J., 2018, Terrestrial wetlands, 61 p., https://doi.org/10.7930/SOCCR2.2018.Ch13.","productDescription":"61 p.","startPage":"507","endPage":"567","ipdsId":"IP-084542","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":359710,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bfe65e0e4b0815414ca60f4","contributors":{"authors":[{"text":"Kolka, Randall","contributorId":115924,"corporation":false,"usgs":false,"family":"Kolka","given":"Randall","affiliations":[],"preferred":false,"id":752081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trettin, Carl","contributorId":210815,"corporation":false,"usgs":false,"family":"Trettin","given":"Carl","affiliations":[{"id":38151,"text":"USDA-Forest Service","active":true,"usgs":false}],"preferred":false,"id":752093,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tang, Wenwu","contributorId":210848,"corporation":false,"usgs":false,"family":"Tang","given":"Wenwu","email":"","affiliations":[],"preferred":false,"id":752082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krauss, Ken W. 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":208512,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","email":"","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":752083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":752080,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":752084,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":752085,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chimner, Rodney A.","contributorId":53346,"corporation":false,"usgs":false,"family":"Chimner","given":"Rodney","email":"","middleInitial":"A.","affiliations":[{"id":17860,"text":"Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":752086,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hogan, Dianna M. 0000-0003-1492-4514 dhogan@usgs.gov","orcid":"https://orcid.org/0000-0003-1492-4514","contributorId":131137,"corporation":false,"usgs":true,"family":"Hogan","given":"Dianna","email":"dhogan@usgs.gov","middleInitial":"M.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":752087,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pindilli, Emily 0000-0002-5101-1266 epindilli@usgs.gov","orcid":"https://orcid.org/0000-0002-5101-1266","contributorId":140262,"corporation":false,"usgs":true,"family":"Pindilli","given":"Emily","email":"epindilli@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":752088,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Benscoter, Brian","contributorId":210812,"corporation":false,"usgs":false,"family":"Benscoter","given":"Brian","affiliations":[{"id":15312,"text":"Florida Atlantic University","active":true,"usgs":false}],"preferred":false,"id":752089,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":752090,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kane, Evan S.","contributorId":11903,"corporation":false,"usgs":true,"family":"Kane","given":"Evan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":752094,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Bridgham, Scott D.","contributorId":177413,"corporation":false,"usgs":false,"family":"Bridgham","given":"Scott","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":752091,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Richardson, Curtis J.","contributorId":210814,"corporation":false,"usgs":false,"family":"Richardson","given":"Curtis","email":"","middleInitial":"J.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":752092,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70201093,"text":"70201093 - 2018 - A 3,000‐year lag between the geological and ecological shutdown of Florida's coral reefs","interactions":[],"lastModifiedDate":"2018-11-28T10:58:13","indexId":"70201093","displayToPublicDate":"2018-11-01T10:58:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"A 3,000‐year lag between the geological and ecological shutdown of Florida's coral reefs","docAbstract":"The global‐scale degradation of coral reefs has reached a critical threshold wherein further declines threaten both ecological functionality and the persistence of reef structure. Geological records can provide valuable insights into the long‐term controls on reef development that may be key to solving the modern coral‐reef crisis. Our analyses of new and existing coral‐reef cores from throughout the Florida Keys reef tract (FKRT) revealed significant spatial and temporal variability in reef development during the Holocene. Whereas maximum Holocene reef thickness in the Dry Tortugas was comparable to elsewhere in the western Atlantic, most of Florida's reefs had relatively thin accumulations of Holocene reef framework. During periods of active reef development, average reef accretion rates were similar throughout the FKRT at ~3 m/ky. The spatial variability in reef thickness was instead driven by differences in the duration of reef development. Reef accretion declined significantly from ~6,000 years ago to present, and by ~3,000 years ago, the majority of the FKRT was geologically senescent. Although sea level influenced the development of Florida's reefs, it was not the ultimate driver of reef demise. Instead, we demonstrate that the timing of reef senescence was modulated by subregional hydrographic variability, and hypothesize that climatic cooling was the ultimate cause of reef shutdown. The senescence of the FKRT left the ecosystem balanced at a delicate tipping point at which a veneer of living coral was the only barrier to reef erosion. Modern climate change and other anthropogenic disturbances have now pushed many reefs past that critical threshold and into a novel ecosystem state, in which reef structures built over millennia could soon be lost. The dominant role of climate in the development of the FKRT over timescales of decades to millennia highlights the potential vulnerability of both geological and ecological reef processes to anthropogenic climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14389","usgsCitation":"Toth, L., Kuffner, I.B., Stathakopoulos, A., and Shinn, E.A., 2018, A 3,000‐year lag between the geological and ecological shutdown of Florida's coral reefs: Global Change Biology, v. 24, no. 11, p. 5471-5483, https://doi.org/10.1111/gcb.14389.","productDescription":"13 p.","startPage":"5471","endPage":"5483","ipdsId":"IP-095331","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":359758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"11","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-21","publicationStatus":"PW","scienceBaseUri":"5bffb75de4b0815414ca8e4a","contributors":{"authors":[{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":752435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":752436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":752437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shinn, Eugene A.","contributorId":210858,"corporation":false,"usgs":false,"family":"Shinn","given":"Eugene","email":"","middleInitial":"A.","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":752438,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227673,"text":"70227673 - 2018 - Age and growth of a native, lightly exploited population of Coregonus clupeaformis (Lake Whitefish) in a small natural lake in Maine","interactions":[],"lastModifiedDate":"2022-01-26T16:33:43.599359","indexId":"70227673","displayToPublicDate":"2018-11-01T10:22:12","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Age and growth of a native, lightly exploited population of Coregonus clupeaformis (Lake Whitefish) in a small natural lake in Maine","docAbstract":"We assessed annual growth of Coregonus clupeaformis (Lake Whitefish) from a natural, lightly exploited population in a small lake in northern Maine using observed and back-calculated length-at-age data. We sampled Lake Whitefish from Clear Lake, ME, with gill nets and extracted otoliths from 57 fish. We incorporated age-at-length data into a von Bertalanffy growth function, which we employed to model growth trajectories from individual fish. We used these estimates to evaluate length-at-age variability within this population. Ages for Lake Whitefish varied from 8 y to 30 y. Among all fish, we characterized incremental growth by an average-growth coefficient of K = 0.156 and an estimated L∞ of 484 mm. The oldest individuals demonstrated the slowest incremental growth (K = 0.106) when compared to younger cohorts (K = 0.218). We observed an inverse relationship between L∞ and K and the estimated age-at-capture (R2 = 0.178 and 0.723, respectively), which suggests relatively slow growth and a smaller maximum size for the longest living members of the population. Our estimated parameters serve as a reference to inform management of populations of Lake Whitefish.
","language":"English","publisher":"Humboldt Field Research Institute; Eagle Hill Institute","doi":"10.1656/045.025.0406","usgsCitation":"Weaver, D.M., Ratten, S.K., Coghlan, S., Sherwood, G.D., and Zydlewski, J.D., 2018, Age and growth of a native, lightly exploited population of Coregonus clupeaformis (Lake Whitefish) in a small natural lake in Maine: Northeastern Naturalist, v. 25, no. 4, p. 599-610, https://doi.org/10.1656/045.025.0406.","productDescription":"12 p.","startPage":"599","endPage":"610","ipdsId":"IP-058610","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":394875,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Clear Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.14983749389648,\n 46.50831741322259\n ],\n [\n -69.11155700683594,\n 46.50831741322259\n ],\n [\n -69.11155700683594,\n 46.53595650395599\n ],\n [\n -69.14983749389648,\n 46.53595650395599\n ],\n [\n -69.14983749389648,\n 46.50831741322259\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weaver, Daniel M.","contributorId":272183,"corporation":false,"usgs":false,"family":"Weaver","given":"Daniel","email":"","middleInitial":"M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":831683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ratten, Silas K.","contributorId":272184,"corporation":false,"usgs":false,"family":"Ratten","given":"Silas","email":"","middleInitial":"K.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":831684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coghlan, Stephen M.","contributorId":272185,"corporation":false,"usgs":false,"family":"Coghlan","given":"Stephen M.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":831685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sherwood, Graham D.","contributorId":272186,"corporation":false,"usgs":false,"family":"Sherwood","given":"Graham","email":"","middleInitial":"D.","affiliations":[{"id":38441,"text":"Gulf of Maine Research Institute","active":true,"usgs":false}],"preferred":false,"id":831686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":831682,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70273348,"text":"70273348 - 2018 - Magmatic origin for sediment-hosted Au deposits, Guizhou Province, China: In situ chemistry and sulfur isotope composition of pyrites, Shuiyindong and Jinfeng deposits","interactions":[],"lastModifiedDate":"2026-01-07T16:01:30.702015","indexId":"70273348","displayToPublicDate":"2018-11-01T09:04:59","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Magmatic origin for sediment-hosted Au deposits, Guizhou Province, China: In situ chemistry and sulfur isotope composition of pyrites, Shuiyindong and Jinfeng deposits","docAbstract":"The southwest Guizhou Province, China, contains numerous sediment-hosted Au deposits with Au reserves greater than 700 tonnes. To date, the source of ore fluids that formed the Guizhou sediment-hosted Au deposits is controversial, hampering the formulation of genetic models. In this study, we selected the Shuiyindong and Jinfeng Au deposits, the largest strata-bound and fault-controlled deposits in Guizhou, respectively, for detailed research on pyrite chemistry and S isotope composition using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and laser ablation-multicollector-inductively coupled plasma-mass spectrometry (LA-MC-ICP-MS), respectively.
Petrography and pyrite chemistry studies distinguished five generations of pyrite. Among these, pre-ore pyrite 2 and ore pyrite are the most abundant types in the deposits. Pre-ore pyrite 2 is anhedral to euhedral and with ~2,639 ppm As and wider ranges of Cu, Sb, and Pb (<~22–4,837 ppm, <~6 to 532 ppm, and <~4 to 1,344 ppm, respectively). Gold in pre-ore pyrite 2 is below the detection limit of LA-ICP-MS (~2 ppm). Pre-ore pyrite 2 is interpreted to have a sedimentary (syngenetic or diagenetic) origin. Ore pyrite commonly rims the four identified pre-ore pyrites or occurs as individual, anhedral to euhedral crystals. Ore pyrite is enriched in Au (~641 ppm), As (~9,147 ppm), Cu (~1,043 ppm), Sb (~188 ppm), Hg (~43 ppm), and Tl (~22 ppm) in both deposits. Ore pyrite formed mainly by sulfidation of Fe in Fe-bearing host rocks, mainly Fe dolomite, and As, Cu, Sb, Hg, and Tl, also in ore fluids, were incorporated into ore pyrite.
In situ δ34S isotope ratios for pre-ore pyrite 2 and ore pyrite were measured by LA-MC-ICP-MS. Pre-ore pyrite 2 from Shuiyindong and Jinfeng deposits resulted in δ34S values ranging from −0.8 to +3.4‰ and from 5.1 to 10.5‰, respectively. Analyses of ore pyrite from the Shuiyindong have δ34S values that vary from −3.3 to +2.5‰, with a median of 0.7‰; analyses of ore pyrite from the Jinfeng range from 8.9 to 11.2‰, with a median at 10.3‰. Available bulk and in situ δ34S data in the literature for pre-ore pyrites 1 and 2 and ore-related sulfide minerals including ore pyrite, arsenopyrite, and late ore-stage stibnite, realgar, orpiment, and cinnabar from several Guizhou sediment-hosted Au deposits were compiled for comparison. Pre-ore-stage pyrites from Guizhou sediment-hosted Au deposits have a broad range of δ34S values, from −33.8 to + 17.9‰ (including in situ and available bulk δ34S data). Ore-related sulfide minerals in all Guizhou sediment-hosted Au deposits, except Jinfeng, have very similar δ34S values, and most data plot between ~−5 and +5‰. In the Jinfeng deposit, the ore-related sulfide minerals exhibit δ34S values ranging from 1.9 to 18.1‰, with most data plotting between 6 and 12‰.
The broad range of S isotope compositions for the sedimentary pyrites (pre-ore pyrites 1 and 2) indicate that S in these pre-ore pyrites was most likely generated by bacterial reduction from marine sulfate. The narrow range of δ34S values (~−5–+5‰) for ore-related sulfide minerals in all Guizhou sediment-hosted Au deposits, excepting the Jinfeng deposit, suggests that the deposits may have formed in response to a single widespread metallogenic event. As the S isotope fractionation between hydrothermal fluids and sulfide minerals in a sulfide-dominated system is small (<2‰) at ~250°C, the initial ore fluids that formed the Guizhou sediment-hosted Au deposits would have had δ34S values similar to the ore-related sulfide minerals, between ~−5 and +5‰. At Jinfeng, initial ore fluids may have mixed with local fluids with heavier δ34S, possibly basin brine (δ34Sbasin brine >18‰), resulting in elevated δ34S values of ore-related sulfide minerals and especially late ore-stage sulfide minerals.
Although few igneous rocks are exposed in the mining area around these deposits, there is evidence of magmatic activity ~20 km away. Furthermore, gravity and magnetic geophysical investigations indicate the presence of a pluton ~5 km below the surface of the Shuiyindong district. Based on in situ S isotope results and recent data indicating proximal intrusions, we interpret a deep magmatic S source for the ore fluids that formed the Guizhou sediment-hosted Au deposits. However, as the age for Au mineralization of Guizhou sediment-hosted Au deposits is still debated, the mineralization-magma connection remains hypothetical. Identifying an ore fluid source and time frame for Guizhou Au mineralization continues to be a critically important research goal for this district.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.5382/econgeo.2018.4607","usgsCitation":"Xie, Z., Xia, Y., Cline, J., Pribil, M., Koenig, A., Tan, Q., Wei, D., Wang, Z., and Yan, J., 2018, Magmatic origin for sediment-hosted Au deposits, Guizhou Province, China: In situ chemistry and sulfur isotope composition of pyrites, Shuiyindong and Jinfeng deposits: Economic Geology, v. 7, no. 113, p. 1627-1652, https://doi.org/10.5382/econgeo.2018.4607.","productDescription":"26 p.","startPage":"1627","endPage":"1652","ipdsId":"IP-097173","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":498381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Yunnan-Guizhou-Guangxi region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 102,\n 27\n ],\n [\n 102,\n 22\n ],\n [\n 108.5,\n 22\n ],\n [\n 108.5,\n 27\n ],\n [\n 102,\n 27\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"7","issue":"113","noUsgsAuthors":false,"publicationDate":"2018-11-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Xie, Zhuojun","contributorId":364901,"corporation":false,"usgs":false,"family":"Xie","given":"Zhuojun","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":953401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xia, Yong","contributorId":364903,"corporation":false,"usgs":false,"family":"Xia","given":"Yong","affiliations":[{"id":87003,"text":"State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":953402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cline, Jean","contributorId":364905,"corporation":false,"usgs":false,"family":"Cline","given":"Jean","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":953403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pribil, Michael J. 0000-0003-4859-8673 mpribil@usgs.gov","orcid":"https://orcid.org/0000-0003-4859-8673","contributorId":141158,"corporation":false,"usgs":true,"family":"Pribil","given":"Michael","email":"mpribil@usgs.gov","middleInitial":"J.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":953404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koenig, Alan 0000-0002-5230-0924","orcid":"https://orcid.org/0000-0002-5230-0924","contributorId":206119,"corporation":false,"usgs":true,"family":"Koenig","given":"Alan","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":953405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tan, Qingping","contributorId":364906,"corporation":false,"usgs":false,"family":"Tan","given":"Qingping","affiliations":[],"preferred":false,"id":953406,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wei, Dongtian","contributorId":364907,"corporation":false,"usgs":false,"family":"Wei","given":"Dongtian","affiliations":[],"preferred":false,"id":953407,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wang, Zepeng","contributorId":364908,"corporation":false,"usgs":false,"family":"Wang","given":"Zepeng","affiliations":[],"preferred":false,"id":953408,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yan, Jun","contributorId":364909,"corporation":false,"usgs":false,"family":"Yan","given":"Jun","affiliations":[],"preferred":false,"id":953409,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70204526,"text":"70204526 - 2018 - Modeling morphodynamic development in the Alviso Slough system, South San Francisco Bay, California","interactions":[],"lastModifiedDate":"2019-10-31T15:45:35","indexId":"70204526","displayToPublicDate":"2018-10-31T15:44:59","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Modeling morphodynamic development in the Alviso Slough system, South San Francisco Bay, California","docAbstract":"Alviso Slough area, South San Francisco Bay, California, is the site of an ongoing effort to restore former salt production ponds to intertidal habitat. As restoration proceeds and the levees surrounding the former salt production ponds are breached, the increase in tidal prism and associated sediment scour in the sloughs will remobilize legacy mercury deposits. A numerical model that is able to assess patterns of sediment transport, erosion, and the fate of remobilized sediments can improve mercury remobilization estimates and inform management actions.
The goals of the current research are to (1) validate a 2D geomorphic model for Alviso Slough using bathymetric surveys and to (2) apply the validated model for Alviso Slough to investigate scenarios of sea level rise and levee breaching on the long-term scour in Alviso Slough. The 2D geomorphic numerical model applies the Delft3D Flexible Mesh (software by Deltares) that describes detailed interaction between hydrodynamics, sediment transport, and geomorphic change on a high resolution mesh.
The morphodynamic modeling exercise shows that observed erosion and sedimentation patterns can be reproduced with skill. The associated suspended sediment concentrations are more difficult to reproduce. The model reveals tide residual flow patterns that are difficult to measure. These residual flow and transport patterns are the result of subtle, tide residual transport trends so that their effect becomes visible in multi-year simulations. Scenario model simulations show possible, illustrative impacts of sea level rise and potential management interventions (additional levee breaches).
","language":"English","publisher":"South Bay Salt Pond Restoration Project","usgsCitation":"Van der Wegen, M., Reyes, J., Jaffe, B., and Foxgrover, A., 2018, Modeling morphodynamic development in the Alviso Slough system, South San Francisco Bay, California, 30 p.","productDescription":"30 p.","ipdsId":"IP-102532","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":368849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":368846,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.southbayrestoration.org/document/modeling-morphodynamic-development-alviso-slough-system-south-san-francisco-bay-california"}],"country":"United States","state":"California","otherGeospatial":"Alviso Slough, South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.03767776489258,\n 37.415300132745415\n ],\n [\n -121.9563102722168,\n 37.415300132745415\n ],\n [\n -121.9563102722168,\n 37.47063471259065\n ],\n [\n -122.03767776489258,\n 37.47063471259065\n ],\n [\n -122.03767776489258,\n 37.415300132745415\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Van der Wegen, Mick","contributorId":191095,"corporation":false,"usgs":false,"family":"Van der Wegen","given":"Mick","email":"","affiliations":[],"preferred":false,"id":767396,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reyes, Johan","contributorId":217734,"corporation":false,"usgs":false,"family":"Reyes","given":"Johan","email":"","affiliations":[{"id":36631,"text":"IHE-Delft Institute for Water Education","active":true,"usgs":false}],"preferred":false,"id":767397,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaffe, Bruce 0000-0002-8816-5920 bjaffe@usgs.gov","orcid":"https://orcid.org/0000-0002-8816-5920","contributorId":217733,"corporation":false,"usgs":true,"family":"Jaffe","given":"Bruce","email":"bjaffe@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":767395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foxgrover, Amy 0000-0003-0638-5776 afoxgrover@usgs.gov","orcid":"https://orcid.org/0000-0003-0638-5776","contributorId":217735,"corporation":false,"usgs":true,"family":"Foxgrover","given":"Amy","email":"afoxgrover@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":767398,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204939,"text":"70204939 - 2018 - It matters when you measure it: Using snow-cover Normalised Difference Vegetation Index (NDVI) to isolate post-fire conifer regeneration","interactions":[],"lastModifiedDate":"2019-08-23T15:06:29","indexId":"70204939","displayToPublicDate":"2018-10-31T14:57:44","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"It matters when you measure it: Using snow-cover Normalised Difference Vegetation Index (NDVI) to isolate post-fire conifer regeneration","docAbstract":"Landsat Normalised Difference Vegetation Index (NDVI) is commonly used to monitor post-fire green-up; however, most studies do not distinguish new growth of conifer from deciduous or herbaceous species, despite potential consequences for local climate, carbon and wildlife. We found that dual season (growing and snow cover) NDVI improved our ability to distinguish conifer tree presence and density. We then examined the post-fire pattern (1984–2017) in Landsat NDVI for fires that occurred a minimum of 20 years ago (1986–1997). Points were classified into four categories depending on whether NDVI, 20 years post-fire, had returned to pre-fire values in only the growing season, only under snow cover, in both seasons or neither. We found that each category of points showed distinct patterns of NDVI change that could be used to characterise the average pre-fire and post-fire vegetation condition Of the points analysed, 43% showed a between-season disagreement if NDVI had returned to pre-fire values, suggesting that using dual-season NDVI can modify our interpretations of post-fire conditions. We also found an improved correlation between 5- and 20-year NDVI change under snow cover, potentially attributable to snow masking fast-growing herbaceous vegetation. This study suggests that snow-cover Landsat imagery can enhance characterisations of forest recovery following fire.
Restoration and rehabilitation of native vegetation in dryland ecosystems, which encompass over 40% of terrestrial ecosystems, is a common challenge that continues to grow as wildfire and biological invasions transform dryland plant communities. The difficulty in part stems from low and variable precipitation, combined with limited understanding about how weather conditions influence restoration outcomes, and increasing recognition that one‐time seeding approaches can fail if they do not occur during appropriate plant establishment conditions. The sagebrush biome, which once covered over 620,000 km2 of western North America, is a prime example of a pressing dryland restoration challenge for which restoration success has been variable. We analyzed field data on Artemisia tridentata (big sagebrush) restoration collected at 771 plots in 177 wildfire sites across its western range, and used process‐based ecohydrological modeling to identify factors leading to its establishment. Our results indicate big sagebrush occurrence is most strongly associated with relatively cool temperatures and wet soils in the first spring after seeding. In particular, the amount of winter snowpack, but not total precipitation, helped explain the availability of spring soil moisture and restoration success. We also find considerable interannual variability in the probability of sagebrush establishment. Adaptive management strategies that target seeding during cool, wet years or mitigate effects of variability through repeated seeding may improve the likelihood of successful restoration in dryland ecosystems. Given consistent projections of increasing temperatures, declining snowpack, and increasing weather variability throughout midlatitude drylands, weather‐centric adaptive management approaches to restoration will be increasingly important for dryland restoration success.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14374","usgsCitation":"Shriver, R.K., Andrews, C.M., Pilliod, D.S., Arkle, R., Welty, J.L., Germino, M., Duniway, M.C., Pyke, D.A., and Bradford, J.B., 2018, Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands: Global Change Biology, v. 24, no. 10, p. 4972-4982, https://doi.org/10.1111/gcb.14374.","productDescription":"11 p.","startPage":"4972","endPage":"4982","ipdsId":"IP-095817","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437704,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U67LQX","text":"USGS data release","linkHelpText":"Environmental conditions, covariate data used in model fitting, and long-term establishment predictions from 1979 to 2016 in the Great Basin, USA"},{"id":359048,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 37.5\n ],\n [\n -110,\n 37.5\n ],\n [\n -110,\n 45\n ],\n [\n -122,\n 45\n ],\n [\n -122,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"24","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-20","publicationStatus":"PW","scienceBaseUri":"5c10a900e4b034bf6a7e4ee0","contributors":{"authors":[{"text":"Shriver, Robert K. 0000-0002-4590-4834","orcid":"https://orcid.org/0000-0002-4590-4834","contributorId":210332,"corporation":false,"usgs":true,"family":"Shriver","given":"Robert","email":"","middleInitial":"K.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":750483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":750484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@usgs.gov","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":750485,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arkle, Robert 0000-0003-3021-1389 rarkle@usgs.gov","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":149893,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","email":"rarkle@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":750486,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Welty, Justin L. 0000-0001-7829-7324 jwelty@usgs.gov","orcid":"https://orcid.org/0000-0001-7829-7324","contributorId":4206,"corporation":false,"usgs":true,"family":"Welty","given":"Justin","email":"jwelty@usgs.gov","middleInitial":"L.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":750487,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":750488,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":750489,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":750490,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":750491,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70200758,"text":"70200758 - 2018 - Tropical storm-induced landslide potential using combined field monitoring and numerical modeling","interactions":[],"lastModifiedDate":"2018-10-31T14:10:54","indexId":"70200758","displayToPublicDate":"2018-10-31T14:10:50","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2327,"text":"Journal of Geotechnical and Geoenvironmental Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Tropical storm-induced landslide potential using combined field monitoring and numerical modeling","docAbstract":"When heavy rainfall, such as that associated with tropical storms, falls on steep hillsides, shallow landslides are often one of the damaging consequences. To assess landslide potential from heavy rainfall, a strategy of combined numerical simulation and field monitoring of variably saturated hillslope conditions is developed. To test the combined method, hillslope hydrologic data from paired field monitoring sites in western North Carolina are examined. The hydrologic data collected from the field monitoring site where no shallow landslide has occurred is used to identify and calibrate the hydromechanical parameters used in a numerical ground water flow model. The identified parameters are then used to simulate landslide potential at the two hillslopes during heavy rainfall associated with hurricanes Frances and Ivan (HFI) that impacted western North Carolina in 2004. Results identify the timing of instability at the shallow landslide site and show that the stable site remains stable during rainfall associated with the HFI tropical storms. Thus, the results demonstrate the effectiveness of combined numerical modeling and field monitoring to evaluate landslide potential under variably saturated conditions.
In the Upper Missouri River, Fort Peck and Garrison Dams limit the length of free‐flowing river available to the endangered pallid sturgeon. These barriers restrict the upstream migration of adults and downstream larval dispersal. A one‐dimensional (1D) modelling framework is currently in use to evaluate reservoir operation alternatives and to simulate drift of dispersing free embryos for different flow regimes and reservoir stages. This paper presents the results of a large‐scale tracer experiment conducted in 2016 and associated modelling performed to evaluate flow management scenarios that might aid species recovery. Breakthrough curves from the tracer experiment were used to infer the 1D longitudinal dispersion coefficient from a parameter optimization procedure. Simulations generated using the calibrated 1D advection–dispersion model were compared with field observations of the passive tracer and with larval fish collected during a previous experiment in 2007. When used with the appropriate range of dispersion coefficients, the 1D modelling framework agrees well with the available direct measurements of larval drift distances. Although we cannot unequivocally state whether insufficient length of free‐flowing river alone is causing recruitment failure, given the current thermal regime and our understanding of pallid sturgeon development, the time required for pallid sturgeon to transition to the benthos and initiate feeding might exceed the duration of drift available given constraints of reservoir operations.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3371","usgsCitation":"Erwin, S.O., Bulliner, E.A., Fischenich, C.J., Jacobson, R.B., Braaten, P., and Delonay, A.J., 2018, Evaluating flow management as a strategy to recover an endangered sturgeon species in the Upper Missouri River, USA: River Research and Applications, v. 34, no. 10, p. 1254-1266, https://doi.org/10.1002/rra.3371.","productDescription":"13 p.","startPage":"1254","endPage":"1266","ipdsId":"IP-098090","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":362980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Upper Missouri River ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.161376953125,\n 47.864773955792245\n ],\n [\n -102.733154296875,\n 47.864773955792245\n ],\n [\n -102.733154296875,\n 48.246625590713826\n ],\n [\n -104.161376953125,\n 48.246625590713826\n ],\n [\n -104.161376953125,\n 47.864773955792245\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"10","noUsgsAuthors":false,"publicationDate":"2018-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Erwin, Susannah O. 0000-0002-2799-0118 serwin@usgs.gov","orcid":"https://orcid.org/0000-0002-2799-0118","contributorId":5183,"corporation":false,"usgs":true,"family":"Erwin","given":"Susannah","email":"serwin@usgs.gov","middleInitial":"O.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":761016,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bulliner, Edward A. 0000-0002-2774-9295 ebulliner@usgs.gov","orcid":"https://orcid.org/0000-0002-2774-9295","contributorId":4983,"corporation":false,"usgs":true,"family":"Bulliner","given":"Edward","email":"ebulliner@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":761017,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fischenich, Craig J","contributorId":176226,"corporation":false,"usgs":false,"family":"Fischenich","given":"Craig","email":"","middleInitial":"J","affiliations":[],"preferred":false,"id":761018,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacobson, Robert B. 0000-0002-8368-2064 rjacobson@usgs.gov","orcid":"https://orcid.org/0000-0002-8368-2064","contributorId":1289,"corporation":false,"usgs":true,"family":"Jacobson","given":"Robert","email":"rjacobson@usgs.gov","middleInitial":"B.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":761019,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Braaten, Patrick 0000-0003-3362-420X pbraaten@usgs.gov","orcid":"https://orcid.org/0000-0003-3362-420X","contributorId":152682,"corporation":false,"usgs":true,"family":"Braaten","given":"Patrick","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":761020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeLonay, Aaron J. 0000-0002-3752-2799 adelonay@usgs.gov","orcid":"https://orcid.org/0000-0002-3752-2799","contributorId":2725,"corporation":false,"usgs":true,"family":"DeLonay","given":"Aaron","email":"adelonay@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":761021,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199426,"text":"ofr20181151 - 2018 - Using heat as a tracer to determine groundwater seepage in the Indian River Lagoon, Florida, April–November, 2017","interactions":[],"lastModifiedDate":"2018-11-14T09:49:32","indexId":"ofr20181151","displayToPublicDate":"2018-10-31T09:05:01","publicationYear":"2018","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":"2018-1151","title":"Using heat as a tracer to determine groundwater seepage in the Indian River Lagoon, Florida, April–November, 2017","docAbstract":"The U.S. Geological Survey, in cooperation with the St. Johns River Water Management District, conducted a study to examine water fluxes in two small study areas in the Indian River Lagoon. Vertical arrays of temperature sensors were placed at multiple locations in the lagoon bed to measure temperature time series in the vertical profile. These data at one of the study areas, Eau Gallie, were used in two numerical models, 1DTempPro and VFLUX, to estimate seepage flux rates into the lagoon. 1DTempPro uses an inverse-modeling approach to calibrate groundwater flux to the measured temperature time series. VFLUX isolates the fundamental frequency signal in the temperature data and utilizes the resulting amplitude and phase differences between sensor locations to determine vertical water flux.
Field measurements were made during two time periods, March 23 to April 28, 2017, and June 1 to November 3, 2017. Simulating the first, drier period at one location with 1DTempPro helped determine reasonable seepage fluctuations and provided guidelines for choosing which temperature sensor pairs used in the VFLUX simulations would produce the best results. VFLUX simulations at eight locations indicated daily average seepage flux rates of less than 20 centimeters per day (cm/d) and substantial seepage flux out to a distance of at least 110 meters from shore. The spatial variation in average seepage flux rates within 40 meters of shore seemed large, ranging from about 3 to 20 cm/d.
In the VFLUX application using the June 1–November 3, 2017 data, the seepage flux has a higher magnitude and fluctuation than the first simulation period, making the isolation of the fundamental temperature frequency signal in the temperature data difficult. However, useful partial or full simulations were achieved at 6 of the 10 locations. The storm surge of Hurricane Irma on September 10, 2017, changed the depths of the sensors relative to the lagoon bed and disrupted the ability of VFLUX to compute seepage flux for the posthurricane period. The June 1 to November 3, 2017, computed seepage flux rates were higher than those for the March 24 to April 28, 2017, period and were sometimes as great as 40 cm/d, and more than 60 cm/d at one location. The seepage time-series data collected during Hurricane Irma indicates a downward seepage flux as a result of the storm surge, followed by upwelling from precipitation recharge inland. The average seepage flux rates are higher than those during the March–April period and are over 25 cm/d near the coast and about 20 cm/d 130 meters offshore.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181151","collaboration":"Prepared in cooperation with the St. Johns River Water Management District","usgsCitation":"Swain, E.D., and Prinos, S.T., 2018, Using heat as a tracer to determine groundwater seepage in the Indian River Lagoon, Florida, April–November, 2017: U.S. Geological Survey Open-File Report 2018–1151, 18 p., https://doi.org/10.3133/ofr20181151.","productDescription":"Report: vi, 18 p.; Data Releases","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-096716","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":358771,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q8JGAO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model data sets for 1DTempPro and VFLUX simulation experiments to determine groundwater seepage in the Indian River Lagoon, Florida"},{"id":358770,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1151/ofr20181151.pdf","text":"Report","size":"7.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1151"},{"id":358769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1151/coverthb.jpg"},{"id":358772,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VM4B41","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Temperature data collected in the Indian River Lagoon to evaluate groundwater seepage, Brevard County, Florida, 2017–2018"}],"country":"United States","state":"Florida","otherGeospatial":"Indian River Lagoon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.91156005859375,\n 28.10832614221258\n ],\n [\n -80.452880859375,\n 28.10832614221258\n ],\n [\n -80.452880859375,\n 28.84707946871795\n ],\n [\n -80.91156005859375,\n 28.84707946871795\n ],\n [\n -80.91156005859375,\n 28.10832614221258\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Demonstrating disease impacts on the vital rates of free‐ranging mammalian hosts typically requires intensive, long‐term study. Evidence for chronic pathogens affecting reproduction but not survival is rare, but has the potential for wide‐ranging effects. Accurately quantifying disease‐associated reductions in fecundity is important for advancing theory, generating accurate predictive models, and achieving effective management. We investigated the impacts of brucellosis (Brucella abortus) on elk (Cervus canadensis) productivity using serological data from over 6,000 captures since 1990 in the Greater Yellowstone Ecosystem, USA. Over 1,000 of these records included known age and pregnancy status. Using Bayesian multilevel models, we estimated the age‐specific pregnancy probabilities of exposed and naïve elk. We then used repeat‐capture data to investigate the full effects of the disease on life history. Brucellosis exposure reduced pregnancy rates of elk captured in mid‐ and late‐winter. In an average year, we found 60% of exposed 2‐year‐old elk were pregnant compared to 91% of their naïve counterparts (a 31 percentage point reduction, 89% HPDI = 20%–42%), whereas exposed 3‐ to 9‐year‐olds were 7 percentage points less likely to be pregnant than naïve elk of their same age (89% HPDI = 2%–11%). We found these reduced rates of pregnancy to be independent from disease‐induced abortions, which afflict a portion of exposed elk. We estimate that the combination of reduced pregnancy by mid‐winter and the abortions following mid‐winter reduces the reproductive output of exposed female elk by 24%, which affects population dynamics to a similar extent as severe winters or droughts. Exposing hidden reproductive costs of disease is essential to avoid conflating them with the effects of climate and predation. Such reproductive costs cause complex population dynamics, and the magnitude of the effect we found should drive a strong selection gradient if there is heritable resistance.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.4521","usgsCitation":"Cotterill, G., Cross, P.C., Middleton, A.D., Rogerson, J.D., Scurlock, B., and Du Toit, J.T., 2018, Hidden cost of disease in a free‐ranging ungulate: brucellosis reduces mid‐winter pregnancy in elk: Ecology and Evolution, v. 8, no. 22, p. 10733-10742, https://doi.org/10.1002/ece3.4521.","productDescription":"10 p.","startPage":"10733","endPage":"10742","ipdsId":"IP-096975","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468280,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.4521","text":"Publisher Index Page"},{"id":358977,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"22","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-28","publicationStatus":"PW","scienceBaseUri":"5c08f1c6e4b0815414d0bbfd","contributors":{"authors":[{"text":"Cotterill, Gavin G.","contributorId":203301,"corporation":false,"usgs":false,"family":"Cotterill","given":"Gavin G.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":750321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":750320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Middleton, Arthur D.","contributorId":210264,"corporation":false,"usgs":false,"family":"Middleton","given":"Arthur","email":"","middleInitial":"D.","affiliations":[{"id":33770,"text":"University of California at Berkeley","active":true,"usgs":false}],"preferred":false,"id":750322,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogerson, Jared D.","contributorId":210265,"corporation":false,"usgs":false,"family":"Rogerson","given":"Jared","email":"","middleInitial":"D.","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":750323,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Scurlock, Brandon","contributorId":145744,"corporation":false,"usgs":false,"family":"Scurlock","given":"Brandon","email":"","affiliations":[{"id":16219,"text":"Wyoming Game and Fish Department, PO Box 850, Pinedale, Wyoming","active":true,"usgs":false}],"preferred":false,"id":750324,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Du Toit, Johan T. 0000-0003-0705-7117","orcid":"https://orcid.org/0000-0003-0705-7117","contributorId":210266,"corporation":false,"usgs":false,"family":"Du Toit","given":"Johan","email":"","middleInitial":"T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":750325,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70200728,"text":"70200728 - 2018 - Flooding tolerance of Sagittaria latifolia and Sagittaria rigida under controlled laboratory conditions","interactions":[],"lastModifiedDate":"2018-10-30T14:53:01","indexId":"70200728","displayToPublicDate":"2018-10-30T14:52:57","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Flooding tolerance of Sagittaria latifolia and Sagittaria rigida under controlled laboratory conditions","title":"Flooding tolerance of Sagittaria latifolia and Sagittaria rigida under controlled laboratory conditions","docAbstract":"Pool‐scale growing‐season water‐level reductions (drawdowns) have been implemented on the Upper Mississippi River in an effort to improve fish and wildlife habitat. Aquatic vegetation is a key habitat component, with perennial emergent species, such as Sagittaria latifolia and Sagittaria rigida, especially important. River managers have assumed the need for continuous drawdown during the growing season with limited reflooding and used this guidance in assessing the potential for an ecologically successful drawdown. However, information on the effects of growing‐season flooding episodes on survival and growth of Sagittaria is limited. To assess the flooding tolerance of S. latifoliaand S. rigida, we evaluated multiple levels of timing, duration, and depth on survival and productivity of plants. Plants were produced from S. latifolia and S. rigida seeds and S. latifolia tubers; all were reared under moist‐soil or shallow‐flooded rearing conditions. Mortality of plants was low (2%) among plants from large tubers, low (7%) among seedlings (and largely associated with early flooding treatments), and modest (11%) among plants from small tubers (with no clear effects of inundation). Flooding treatments generally had a positive effect on biomass production from seedlings, particularly when treatments occurred early, were relatively shallow, and were short in duration. There were no clear effects of depth, duration, or timing components of flooding treatments on plant biomass arising from tubers. This experiment indicates that S. latifolia and S. rigida are relatively tolerant of flooding events during the growing season and may actually benefit from some level of inundation.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3337","usgsCitation":"Kenow, K.P., Gray, B.R., and Lyons, J., 2018, Flooding tolerance of Sagittaria latifolia and Sagittaria rigida under controlled laboratory conditions: River Research and Applications, v. 34, no. 8, p. 1024-1031, https://doi.org/10.1002/rra.3337.","productDescription":"8 p.","startPage":"1024","endPage":"1031","ipdsId":"IP-096529","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437705,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q52NW4","text":"USGS data release","linkHelpText":"Sagittaria flooding tolerance experiment data"},{"id":358976,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"8","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-03","publicationStatus":"PW","scienceBaseUri":"5c10a902e4b034bf6a7e4eec","contributors":{"authors":[{"text":"Kenow, Kevin P. 0000-0002-3062-5197 kkenow@usgs.gov","orcid":"https://orcid.org/0000-0002-3062-5197","contributorId":3339,"corporation":false,"usgs":true,"family":"Kenow","given":"Kevin","email":"kkenow@usgs.gov","middleInitial":"P.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":750264,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Brian R. 0000-0001-7682-9550 brgray@usgs.gov","orcid":"https://orcid.org/0000-0001-7682-9550","contributorId":2615,"corporation":false,"usgs":true,"family":"Gray","given":"Brian","email":"brgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":750265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyons, James E.","contributorId":198859,"corporation":false,"usgs":false,"family":"Lyons","given":"James E.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":750266,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202164,"text":"70202164 - 2018 - Probabilistic substrate classification with multispectral acoustic backscatter: A comparison of discriminative and generative models","interactions":[],"lastModifiedDate":"2019-02-12T11:09:23","indexId":"70202164","displayToPublicDate":"2018-10-30T11:09:15","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1816,"text":"Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Probabilistic substrate classification with multispectral acoustic backscatter: A comparison of discriminative and generative models","docAbstract":"We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully-connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of allowable proximity and backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model (GMM) that doesn’t include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF therefore may prove to be a powerful ‘spatially aware’ alternative to other discriminative classifiers.
","language":"English","publisher":"MDPI","doi":"10.3390/geosciences8110395","usgsCitation":"Buscombe, D.D., and Grams, P.E., 2018, Probabilistic substrate classification with multispectral acoustic backscatter: A comparison of discriminative and generative models: Geosciences, v. 8, no. 11, p. 1-21, https://doi.org/10.3390/geosciences8110395.","productDescription":"Article 395; 21 p.","startPage":"1","endPage":"21","ipdsId":"IP-095788","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468281,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geosciences8110395","text":"Publisher Index Page"},{"id":361166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":757055,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":757054,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70220345,"text":"70220345 - 2018 - Late Cretaceous-Cenozoic exhumation of the western Brooks Range, Alaska, revealed from apatite and zircon fission track data","interactions":[],"lastModifiedDate":"2021-05-06T12:17:07.340898","indexId":"70220345","displayToPublicDate":"2018-10-30T07:08:39","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Late Cretaceous-Cenozoic exhumation of the western Brooks Range, Alaska, revealed from apatite and zircon fission track data","docAbstract":"We report data for 112 apatite and 31 zircon fission track (AFT and ZFT) outcrop sandstone samples along a transect that spans the western Brooks Range. Sampling targeted structures that modify the Middle Jurassic‐Early Cretaceous early Brookian orogen. The AFT samples record latest Cretaceous to Eocene in situ exhumational cooling and resolve two kinematic phases. The first phase was focused at 65–60 Ma. To the north, cooling age patterns at this time are attributable to wide‐spaced fault‐related folding. Farther south, within the allochthon belt, exhumation was related to uplift of a broad region, likely in the hanging wall of deep‐seated faults that extend into basement rocks. The second kinematic phase occurred around ~45 Ma. It was characterized by north and east directed thrusting to the north, and coeval extension in the allochthon belt to the south. The ZFT cooling ages are all Early Cretaceous or older and put an upper limit on the magnitude of Cenozoic exhumation across the western Brooks Range. Synthesis of exhumation patterns and structural styles show that Paleocene rejuvenation of contraction was roughly contemporaneous along the entire ~1,000‐km orogen. Later, around ~45 Ma in the Eocene, contraction in the frontal parts of the orogen was contemporaneous with extension interior to the orogen. Following previous authors, we suggest that the Paleocene rejuvenation was a far‐field response to subduction of a mid‐ocean ridge in southern Alaska. However, by the Eocene, strain patterns in the western Brooks Range changed, possibly to accommodate rotations of fault blocks in southwestern Alaska.
Mechanical oscillations or vibrations on spacecraft, also called pointing jitter, cause geometric distortions and/or smear in high-resolution digital images acquired from orbit. Geometric distortion is especially a problem with pushbroom sensors, such as the High Resolution Imaging Science Experiment (HiRISE) instrument on-board the Mars Reconnaissance Orbiter (MRO). Geometric distortions occur at a range of frequencies that may not be obvious in the image products, but can cause problems with stereo image correlation in the production of digital elevation models, and in measuring surface changes in time series with orthorectified images. The HiRISE focal plane comprises a staggered array of fourteen charge-coupled devices (CCDs) with pixel instantaneous field of view (IFOV) of 1 microradian. The high spatial resolution of HiRISE makes it both sensitive to, and an excellent recorder of jitter. We present an algorithm using Fourier analysis to resolve the jitter function for a HiRISE image that is then used to update instrument pointing information to remove geometric distortions from the image. Implementation of the jitter analysis and image correction is performed on selected HiRISE images made available to the public. Results show marked reduction of geometric distortions. This work has applications to similar cameras operating now (such as the Lunar Reconnaissance Orbiter Camera Narrow Angle Camera (LROC NAC) on-board the Lunar Reconnaissance Orbiter) and to the design of future instruments (such as the Europa Imaging System, planned for the Europa Clipper mission).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Planetary remote sensing and mapping","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Taylor & Francis","doi":"10.1201/9780429505997-8","usgsCitation":"Sutton, S., Boyd, A., Kirk, R.L., Cook, D., Backer, J., Fennema, A., Heyd, R., McEwen, A., and Mirchandani, S., 2018, Correcting spacecraft jitter in HiRISE images, chap. 8 of Planetary remote sensing and mapping, p. 91-106, https://doi.org/10.1201/9780429505997-8.","productDescription":"16 p.","startPage":"91","endPage":"106","ipdsId":"IP-095557","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":460827,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1201/9780429505997-8","text":"External Repository"},{"id":377831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sutton, S.S.","contributorId":239566,"corporation":false,"usgs":false,"family":"Sutton","given":"S.S.","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":797246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boyd, A.K.","contributorId":202342,"corporation":false,"usgs":false,"family":"Boyd","given":"A.K.","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":797247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kirk, Randolph L. 0000-0003-0842-9226 rkirk@usgs.gov","orcid":"https://orcid.org/0000-0003-0842-9226","contributorId":2765,"corporation":false,"usgs":true,"family":"Kirk","given":"Randolph","email":"rkirk@usgs.gov","middleInitial":"L.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, Debbie 0000-0001-9973-9929","orcid":"https://orcid.org/0000-0001-9973-9929","contributorId":202343,"corporation":false,"usgs":true,"family":"Cook","given":"Debbie","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Backer, Jean 0000-0002-6010-3867","orcid":"https://orcid.org/0000-0002-6010-3867","contributorId":202344,"corporation":false,"usgs":true,"family":"Backer","given":"Jean","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":797250,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fennema, A.","contributorId":202345,"corporation":false,"usgs":false,"family":"Fennema","given":"A.","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":797251,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heyd, R.","contributorId":202346,"corporation":false,"usgs":false,"family":"Heyd","given":"R.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":797252,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McEwen, A.S.","contributorId":202347,"corporation":false,"usgs":false,"family":"McEwen","given":"A.S.","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":797253,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mirchandani, S.D.","contributorId":202348,"corporation":false,"usgs":false,"family":"Mirchandani","given":"S.D.","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":797254,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70206274,"text":"70206274 - 2018 - Effects of an extreme flood event on federally endangered Diamond Darter abundances","interactions":[],"lastModifiedDate":"2019-10-29T08:13:31","indexId":"70206274","displayToPublicDate":"2018-10-29T08:12:27","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5153,"text":"The American Midland Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Effects of an extreme flood event on federally endangered Diamond Darter abundances","docAbstract":"Extreme flood events can substantially affect riverine systems, modifying instream habitat and influencing fish assemblages and densities. Rare species are especially vulnerable to these disturbance events because of their small population size and often reduced phenotypic heterogeneity. In June 2016 the lower Elk River in West Virginia experienced severe flooding, resulting in a peak discharge that exceeded the 0.005 annual exceedance probability (>200 y flood) in the main stem. We obtained pre-flood and postflood population count data and estimated abundances for one cohort of the federally endangered Diamond Darter (Crystallaria cincotta) at 15 sites. While both the total count data and total estimated abundance decreased following the flood, our analyses did not indicate the extreme flood event strongly impacted Diamond Darter abundance. This indicates individuals are able to withstand high velocities and resist displacement or mortality. In addition site-level abundances were estimated at three sentinel sites during 2015 and 2016 using a multinomial N-mixture model that accounted for variation in detectability resulting from water temperature. Mean estimated abundance varied among the three sites and between the 2 y. Our results suggest there is substantial variation in year-class strength between the two cohorts we sampled. It is suggested that survey efforts at established sentinel sites be continued on an annual basis in order to help determine factors influencing year-class strength.
","language":"English","publisher":"United States Department of Agriculture","doi":"10.1674/0003-0031-180.1.108","usgsCitation":"Welsh, S., 2018, Effects of an extreme flood event on federally endangered Diamond Darter abundances: The American Midland Naturalist, v. 180, p. 108-118, https://doi.org/10.1674/0003-0031-180.1.108.","productDescription":"11 p.","startPage":"108","endPage":"118","ipdsId":"IP-088254","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":368688,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"180","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":152088,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart A.","email":"swelsh@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":774050,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70200658,"text":"70200658 - 2018 - River network saturation concept: factors influencing the balance of biogeochemical supply and demand of river networks","interactions":[],"lastModifiedDate":"2018-12-05T14:08:35","indexId":"70200658","displayToPublicDate":"2018-10-26T16:39:28","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"River network saturation concept: factors influencing the balance of biogeochemical supply and demand of river networks","docAbstract":"River networks modify material transfer from land to ocean. Understanding the factors regulating this function for different gaseous, dissolved, and particulate constituents is critical to quantify the local and global effects of climate and land use change. We propose the River Network Saturation (RNS) concept as a generalization of how river network regulation of material fluxes declines with increasing flows due to imbalances between supply and demand at network scales. River networks have a tendency to become saturated (supply ≫ demand) under higher flow conditions because supplies increase faster than sink processes. However, the flow thresholds under which saturation occurs depends on a variety of factors, including the inherent process rate for a given constituent and the abundance of lentic waters such as lakes, ponds, reservoirs, and fluvial wetlands within the river network. As supply increases, saturation at network scales is initially limited by previously unmet demand in downstream aquatic ecosystems. The RNS concept describes a general tendency of river network function that can be used to compare the fate of different constituents among river networks. New approaches using nested in situ high-frequency sensors and spatially extensive synoptic techniques offer the potential to test the RNS concept in different settings. Better understanding of when and where river networks saturate for different constituents will allow for the extrapolation of aquatic function to broader spatial scales and therefore provide information on the influence of river function on continental element cycles and help identify policy priorities.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-018-0488-0","usgsCitation":"Wollheim, W., Bernal, S., Burns, D., Czuba, J., Driscoll, C., Hansen, A., Hensley, R., Hosen, J., Inamdar, S., Kaushall, S., Koenig, L., Lu, Y.H., Marzadri, A., Raymond, P.A., Scott, D., Stewart, R., Vidon, P., and Wohl, E., 2018, River network saturation concept: factors influencing the balance of biogeochemical supply and demand of river networks: Biogeochemistry, v. 141, no. 3, p. 503-521, https://doi.org/10.1007/s10533-018-0488-0.","productDescription":"19 p.","startPage":"503","endPage":"521","ipdsId":"IP-092249","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":468283,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/99225","text":"External Repository"},{"id":358854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","issue":"3","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-29","publicationStatus":"PW","scienceBaseUri":"5c08f1c6e4b0815414d0bbff","contributors":{"authors":[{"text":"Wollheim, W.M.","contributorId":210143,"corporation":false,"usgs":false,"family":"Wollheim","given":"W.M.","email":"","affiliations":[{"id":38082,"text":"Univ. of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":750014,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernal, S.","contributorId":210144,"corporation":false,"usgs":false,"family":"Bernal","given":"S.","email":"","affiliations":[{"id":38083,"text":"Center for Advanced studies of Blanes (CEAB-CSIC)","active":true,"usgs":false}],"preferred":false,"id":750015,"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":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Czuba, J.A.","contributorId":210145,"corporation":false,"usgs":false,"family":"Czuba","given":"J.A.","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":750016,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Driscoll, C.T.","contributorId":210146,"corporation":false,"usgs":false,"family":"Driscoll","given":"C.T.","email":"","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false}],"preferred":false,"id":750017,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hansen, A.T.","contributorId":210147,"corporation":false,"usgs":false,"family":"Hansen","given":"A.T.","email":"","affiliations":[{"id":27811,"text":"Univ. of Minnesota","active":true,"usgs":false}],"preferred":false,"id":750018,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hensley, R.T.","contributorId":210148,"corporation":false,"usgs":false,"family":"Hensley","given":"R.T.","email":"","affiliations":[{"id":38084,"text":"Univ. of Florida","active":true,"usgs":false}],"preferred":false,"id":750019,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hosen, J.D. 0000-0003-2559-0687","orcid":"https://orcid.org/0000-0003-2559-0687","contributorId":210149,"corporation":false,"usgs":false,"family":"Hosen","given":"J.D.","affiliations":[{"id":38085,"text":"Yale Univ.","active":true,"usgs":false}],"preferred":false,"id":750020,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Inamdar, Shreeram","contributorId":177337,"corporation":false,"usgs":false,"family":"Inamdar","given":"Shreeram","affiliations":[],"preferred":false,"id":750053,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kaushall, S.S.","contributorId":210150,"corporation":false,"usgs":false,"family":"Kaushall","given":"S.S.","email":"","affiliations":[{"id":38074,"text":"Univ. of Maryland","active":true,"usgs":false}],"preferred":false,"id":750021,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Koenig, L. E.","contributorId":210151,"corporation":false,"usgs":false,"family":"Koenig","given":"L. E.","affiliations":[{"id":38082,"text":"Univ. of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":750022,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lu, Y. H.","contributorId":210159,"corporation":false,"usgs":false,"family":"Lu","given":"Y.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":750023,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Marzadri, A.","contributorId":210152,"corporation":false,"usgs":false,"family":"Marzadri","given":"A.","affiliations":[{"id":13466,"text":"Univ. of Idaho","active":true,"usgs":false}],"preferred":false,"id":750024,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Raymond, P. A.","contributorId":210153,"corporation":false,"usgs":false,"family":"Raymond","given":"P.","email":"","middleInitial":"A.","affiliations":[{"id":38085,"text":"Yale Univ.","active":true,"usgs":false}],"preferred":false,"id":750025,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Scott, D.","contributorId":210154,"corporation":false,"usgs":false,"family":"Scott","given":"D.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":750026,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Stewart, R.J.","contributorId":210155,"corporation":false,"usgs":false,"family":"Stewart","given":"R.J.","email":"","affiliations":[{"id":38082,"text":"Univ. of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":750027,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Vidon, P.G.","contributorId":210156,"corporation":false,"usgs":false,"family":"Vidon","given":"P.G.","email":"","affiliations":[{"id":38086,"text":"State University of New York College of Environmental Science and Forestry (SUNY-ESF)","active":true,"usgs":false}],"preferred":false,"id":750028,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Wohl, E. 0000-0001-7435-5013","orcid":"https://orcid.org/0000-0001-7435-5013","contributorId":210157,"corporation":false,"usgs":false,"family":"Wohl","given":"E.","email":"","affiliations":[{"id":13407,"text":"Colorado State Univ.","active":true,"usgs":false}],"preferred":false,"id":750029,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70200657,"text":"70200657 - 2018 - Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters","interactions":[],"lastModifiedDate":"2018-12-05T14:09:21","indexId":"70200657","displayToPublicDate":"2018-10-26T16:35:43","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters","docAbstract":"In the Anthropocene, watershed chemical transport is increasingly dominated by novel combinations of elements, which are hydrologically linked together as ‘chemical cocktails.’ Chemical cocktails are novel because human activities greatly enhance elemental concentrations and their probability for biogeochemical interactions and shared transport along hydrologic flowpaths. A new chemical cocktail approach advances our ability to: trace contaminant mixtures in watersheds, develop chemical proxies with high-resolution sensor data, and manage multiple water quality problems. We explore the following questions: (1) Can we classify elemental transport in watersheds as chemical cocktails using a new approach? (2) What is the role of climate and land use in enhancing the formation and transport of chemical cocktails in watersheds? To address these questions, we first analyze trends in concentrations of carbon, nutrients, metals, and salts in fresh waters over 100 years. Next, we explore how climate and land use enhance the probability of formation of chemical cocktails of carbon, nutrients, metals, and salts. Ultimately, we classify transport of chemical cocktails based on solubility, mobility, reactivity, and dominant phases: (1) sieved chemical cocktails (e.g., particulate forms of nutrients, metals and organic matter); (2) filtered chemical cocktails (e.g., dissolved organic matter and associated metal complexes); (3) chromatographic chemical cocktails (e.g., ions eluted from soil exchange sites); and (4) reactive chemical cocktails (e.g., limiting nutrients and redox sensitive elements). Typically, contaminants are regulated and managed one element at a time, even though combinations of elements interact to influence many water quality problems such as toxicity to life, eutrophication, infrastructure corrosion, and water treatment. A chemical cocktail approach significantly expands evaluations of water quality signatures and impacts beyond single elements to mixtures. High-frequency sensor data (pH, specific conductance, turbidity, etc.) can serve as proxies for chemical cocktails and improve real-time analyses of water quality violations, identify regulatory needs, and track water quality recovery following storms and extreme climate events. Ultimately, a watershed chemical cocktail approach is necessary for effectively co-managing groups of contaminants and provides a more holistic approach for studying, monitoring, and managing water quality in the Anthropocene.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-018-0502-6","usgsCitation":"Kaushal, S., Gold, A.J., Bernal, S., Newcomer Johnson, T., Addy, K., Burgin, A., Burns, D., Coble, A.A., Hood, E.W., Lu, Y., Mayer, P., Minor, E.C., Schroth, A.W., Vidon, P., Wilson, H.F., Xenopolous, M.A., Doody, T., Galella, J.G., Goodling, P., Haviland, K., Haq, S., Wessel, B., Wood, K.L., Jaworski, N., and Belt, K., 2018, Watershed ‘chemical cocktails’: forming novel elemental combinations in Anthropocene fresh waters: Biogeochemistry, v. 141, no. 3, p. 281-305, https://doi.org/10.1007/s10533-018-0502-6.","productDescription":"25 p.","startPage":"281","endPage":"305","ipdsId":"IP-093496","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":468284,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/nrs_facpubs/407","text":"External 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Norbert","contributorId":210141,"corporation":false,"usgs":false,"family":"Jaworski","given":"Norbert","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":750011,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Belt, Kenneth T.","contributorId":210142,"corporation":false,"usgs":false,"family":"Belt","given":"Kenneth T.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":750012,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70201193,"text":"70201193 - 2018 - Comment on “The earliest modern humans outside Africa”","interactions":[],"lastModifiedDate":"2018-12-05T10:46:15","indexId":"70201193","displayToPublicDate":"2018-10-26T10:46:09","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal 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(Reports, 26 January 2018, p. 456) interpreted the Misliya-1 fossil maxilla as evidence of the earliest known anatomically modern human outside Africa. However, the fossil’s reported age of 177,000 to 194,000 years relies on flawed interpretations of uranium-series data. We contend that those data support a minimum age of no more than ~60,000 to 70,000 years.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.aat6598","usgsCitation":"Sharp, W.D., and Paces, J.B., 2018, Comment on “The earliest modern humans outside Africa”: Science, v. 362, no. 6413, p. 1-2, https://doi.org/10.1126/science.aat6598.","productDescription":"eaat6598; 2 p.","startPage":"1","endPage":"2","ipdsId":"IP-096569","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":359957,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"362","issue":"6413","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c08f1c7e4b0815414d0bc03","contributors":{"authors":[{"text":"Sharp, Warren D.","contributorId":72272,"corporation":false,"usgs":true,"family":"Sharp","given":"Warren","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":753138,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paces, James B. 0000-0002-9809-8493 jbpaces@usgs.gov","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":2514,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"jbpaces@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":753137,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223856,"text":"70223856 - 2018 - A dirty dozen ways to die: Metrics and modifiers of mortality driven by drought and warming for a tree species","interactions":[],"lastModifiedDate":"2021-09-10T14:34:55.369322","indexId":"70223856","displayToPublicDate":"2018-10-26T09:06:02","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"title":"A dirty dozen ways to die: Metrics and modifiers of mortality driven by drought and warming for a tree species","docAbstract":"Tree mortality events driven by drought and warmer temperature, often amplified by pests and pathogens, are emerging as one of the predominant climate change impacts on plants. Understanding and predicting widespread tree mortality events in the future is vital as they affect ecosystem goods and services provided by forests and woodlands, including carbon storage needed to help offset warming. Additionally, if extensive enough, tree die-off events can influence not only local climate but also climate and vegetation elsewhere via ecoclimate teleconnections. Consequently, recent efforts have focused on improving predictions of tree mortality. One of the most commercially important genera of trees is Pinus, and the most studied species globally for drought-induced tree mortality is piñon pine, Pinus edulis. Numerous metrics have been developed in association with predicting mortality thresholds or variations in mortality for this species. In this article, we compiled metrics associated with drought and warming related mortality that were developed for P. edulis or for which P. edulis was a key example species used in a calculation or prediction. We grouped these metrics into three categories: (i) those related to simple climate variables, (ii) those related to physiological responses, and (iii) those that require multi-step calculations or modeling using climate, ecohydrological, and/or ecophysiological data; and we identified the spatial-temporal scale of each of these metrics. We also compiled factors shown to modify rates or sensitivities of mortality. The metrics to predict mortality include empirical ones which often have implicit linkages to expected mechanisms, and more mechanistic ones related to physiological drivers. The metrics for P. edulis have similarities with those available for other species of Pinus. Expected future mortality events will provide an opportunity to observationally and experimentally test and compare these metrics related to tree mortality for P. edulis via near-term ecological forecasting. The metrics for P. edulis may also be useful as potential analogs for other genera. Improving predictions of tree mortality for this species and others will be increasingly important as an aid to move toward anticipatory management.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffgc.2018.00004","usgsCitation":"Breshears, D.D., Carroll, C.J., Redmond, M.D., Wion, A.P., Allen, C.D., Cobb, N.S., Meneses, N., Field, J.P., Wilson, L.A., Law, D., McCabe, L.M., and Newell-Bauer, O., 2018, A dirty dozen ways to die: Metrics and modifiers of mortality driven by drought and warming for a tree species: Frontiers in Forests and Global Change, v. 1, 4, 10 p., https://doi.org/10.3389/ffgc.2018.00004.","productDescription":"4, 10 p.","ipdsId":"IP-099758","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468285,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2018.00004","text":"Publisher Index Page"},{"id":389056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","noUsgsAuthors":false,"publicationDate":"2018-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Breshears, David D.","contributorId":51620,"corporation":false,"usgs":false,"family":"Breshears","given":"David","email":"","middleInitial":"D.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":822993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carroll, Charles J. 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To study the factors that determine seasonality, a multiyear study at seven sites across the geographic range of I. scapularis systematically collected questing ticks by flagging/dragging, and feeding ticks by capture of their hosts. The observed phenology patterns were consistent with previous studies reporting geographic variation in seasonal tick activity. Predictions of seasonal activity for each site were obtained from an I. scapularis simulation model calibrated using contemporaneous weather data. A range of scenarios for life-cycle processes—including different regimes of temperature-independent behavioral and developmental diapause, variations in temperature–development rate relationships, and temperature-dependent tick activity—were used in model formulations. These formulations produced a range of simulations of seasonal activity for each site and were compared against the field observed tick data using negative binomial regression models. Best fit scenarios were chosen for each site on the basis of Akaike’s information criterion and regression model parameters. This analysis suggests that temperature-independent diapause mechanisms explain some key observed variations in I. scapularis seasonality, and are responsible in part for geographic variations in I. scapularis seasonality in the United States. However, diapause appears to operate in idiosyncratic ways in different regions of the United States, so further studies on populations in different regions will be needed to enable predictive modeling of climatic and climate change effects on I. scapularis seasonal activity and pathogen transmission.
","language":"English","publisher":"Entomological Society of America","doi":"10.1093/jme/tjy104","usgsCitation":"Ogden, N.H., Pang, G., Ginsberg, H., Hickling, G., Burke, R.L., Beati, L., and Tsao, J.I., 2018, Evidence for geographic variation in life-cycle processes affecting phenology of the Lyme disease vector Ixodes scapularis (Acari: Ixodidae) in the United States: Journal of Medical Entomology, v. 55, no. 6, p. 1386-1401, https://doi.org/10.1093/jme/tjy104.","productDescription":"16 p.","startPage":"1386","endPage":"1401","ipdsId":"IP-095991","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":468286,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jme/tjy104","text":"Publisher Index Page"},{"id":358827,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"6","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-07","publicationStatus":"PW","scienceBaseUri":"5c10a915e4b034bf6a7e4f67","contributors":{"authors":[{"text":"Ogden, Nicholas H.","contributorId":147667,"corporation":false,"usgs":false,"family":"Ogden","given":"Nicholas","email":"","middleInitial":"H.","affiliations":[{"id":16890,"text":"Public Health Agency of Canada","active":true,"usgs":false}],"preferred":false,"id":749791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pang, Genevieve","contributorId":71087,"corporation":false,"usgs":true,"family":"Pang","given":"Genevieve","affiliations":[],"preferred":false,"id":749795,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":749790,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hickling, Graham J.","contributorId":88639,"corporation":false,"usgs":true,"family":"Hickling","given":"Graham J.","affiliations":[],"preferred":false,"id":749792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burke, Russell L.","contributorId":127374,"corporation":false,"usgs":false,"family":"Burke","given":"Russell","email":"","middleInitial":"L.","affiliations":[{"id":6921,"text":"Hofstra University","active":true,"usgs":false}],"preferred":false,"id":749793,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beati, Lorenza","contributorId":148019,"corporation":false,"usgs":false,"family":"Beati","given":"Lorenza","email":"","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":749794,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tsao, Jean I.","contributorId":140905,"corporation":false,"usgs":false,"family":"Tsao","given":"Jean","email":"","middleInitial":"I.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":749796,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}