The ability to quantify structural uncertainty in geological models that incorporate geophysical data is affected by two primary sources of uncertainty: geophysical parameter uncertainty and uncertainty in the relationship between geophysical parameters and geological properties of interest. Here, we introduce an open-source, trans-dimensional Bayesian Markov chain Monte Carlo (McMC) algorithm GeoBIPy—Geophysical Bayesian Inference in Python—for robust uncertainty analysis of time-domain or frequency-domain airborne electromagnetic (AEM) data. The McMC algorithm provides a robust assessment of geophysical parameter uncertainty using a trans-dimensional approach that lets the AEM data inform the level of model complexity necessary by allowing the number of model layers itself to be an unknown parameter. Additional components of the Bayesian algorithm allow the user to solve for parameters such as data errors or corrections to the measured instrument height above ground. Probability distributions for a user-specified number of lithologic classes are developed through posterior clustering of McMC-derived resistivity models. Estimates of geological model structural uncertainty are thus obtained through the joint probability of geophysical parameter uncertainty and the uncertainty in the definition of each class. Examples of the implementation of this algorithm are presented for both time-domain and frequency-domain AEM data acquired in Nebraska, USA.
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\"}}]}","volume":"224","issue":"17","noUsgsAuthors":false,"publicationDate":"2020-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":798227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foks, N. Leon 0000-0002-4907-3679","orcid":"https://orcid.org/0000-0002-4907-3679","contributorId":239959,"corporation":false,"usgs":false,"family":"Foks","given":"N. Leon","affiliations":[{"id":48073,"text":"Apogee Engineering, LLC; Contracted to USGS","active":true,"usgs":false}],"preferred":false,"id":798228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":798229,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70214063,"text":"70214063 - 2021 - High‐resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the U.S. Virgin Islands","interactions":[],"lastModifiedDate":"2021-02-03T23:28:47.997194","indexId":"70214063","displayToPublicDate":"2020-08-23T09:53:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2032,"text":"International Journal of Climatology","active":true,"publicationSubtype":{"id":10}},"title":"High‐resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the U.S. Virgin Islands","docAbstract":"The weather research and forecasting (WRF) model and a combination of the regional spectral model (RSM) and the Japanese Meteorological Agency Non‐Hydrostatic Model (NHM) were used to dynamically downscale selected CMIP5 global climate models to provide 2‐km projections with hourly model output for Puerto Rico and the U.S. Virgin Islands. Two 20‐year time slices were downscaled for historical (1986–2005) and future (2041–2060) periods following RCP8.5. Projected changes to mean and extreme temperature and precipitation were quantified for Holdridge life zones within Puerto Rico and for the U.S. Virgin Islands. The evaluation reveals a persistent cold bias for all islands in the U.S. Caribbean, a dry bias across Puerto Rico, and a wet bias on the windward side of mountains within the U.S. Virgin Islands. Despite these biases, model simulations show a robust drying pattern for all islands that is generally larger for Puerto Rico (25% annual rainfall reduction for some life zones) than the U.S. Virgin Islands (12% island average). The largest precipitation reductions are found during the more convectively active afternoon and evening hours. Within Puerto Rico, the model uncertainty increases for the wetter life zones, especially for precipitation. Across the life zones, both models project unprecedented maximum and minimum temperatures that may exceed 200 days annually above the historical baseline with only small changes to the frequency of extreme rainfall. By contrast, in the U.S. Virgin Islands, there is no consensus on the location of the largest drying relative to the windward and leeward side of the islands. However, the models project the largest increases in maximum temperature on the southern side of St. Croix and in higher elevations of St. Thomas and St. John.
","language":"English","publisher":"Wiley","doi":"10.1002/joc.6810","usgsCitation":"Bowden, J.H., Terando, A., Misra, V., Wootten, A., Bhardwaj, A., Boyles, R., Gould, W.A., Collazo, J.A., and Spero, T., 2021, High‐resolution dynamically downscaled rainfall and temperature projections for ecological life zones within Puerto Rico and for the U.S. Virgin Islands: International Journal of Climatology, v. 41, no. 2, p. 1305-1327, https://doi.org/10.1002/joc.6810.","productDescription":"23 p.","startPage":"1305","endPage":"1327","ipdsId":"IP-114087","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":454467,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8128702","text":"External Repository"},{"id":378663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"US Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3297119140625,\n 17.764381077782076\n ],\n [\n -65.1983642578125,\n 17.764381077782076\n ],\n [\n -65.1983642578125,\n 18.547324589827422\n ],\n [\n -67.3297119140625,\n 18.547324589827422\n ],\n [\n -67.3297119140625,\n 17.764381077782076\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -64.92988586425781,\n 17.662997014982857\n ],\n [\n -64.50828552246094,\n 17.662997014982857\n ],\n [\n -64.50828552246094,\n 17.790535393588975\n ],\n [\n -64.92988586425781,\n 17.790535393588975\n ],\n [\n -64.92988586425781,\n 17.662997014982857\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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0000-0002-1677-4292","orcid":"https://orcid.org/0000-0002-1677-4292","contributorId":212201,"corporation":false,"usgs":false,"family":"Bowden","given":"Jared","email":"","middleInitial":"H.","affiliations":[{"id":37102,"text":"Department of Applied Ecology, North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":799363,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam J. 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":216875,"corporation":false,"usgs":true,"family":"Terando","given":"Adam J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":799364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Misra, Vasu","contributorId":241025,"corporation":false,"usgs":false,"family":"Misra","given":"Vasu","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":799365,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wootten, Adrienne","contributorId":197529,"corporation":false,"usgs":false,"family":"Wootten","given":"Adrienne","affiliations":[],"preferred":false,"id":799366,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bhardwaj, Amit","contributorId":221025,"corporation":false,"usgs":false,"family":"Bhardwaj","given":"Amit","email":"","affiliations":[],"preferred":false,"id":799367,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boyles, Ryan 0000-0001-9272-867X","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":221983,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":799368,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gould, William A. 0000-0002-3720-9735","orcid":"https://orcid.org/0000-0002-3720-9735","contributorId":212196,"corporation":false,"usgs":false,"family":"Gould","given":"William","email":"","middleInitial":"A.","affiliations":[{"id":38452,"text":"USDA Forest Service International Institute of Tropical Forestry","active":true,"usgs":false}],"preferred":false,"id":799369,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":799370,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Spero, Tanya 0000-0002-1600-0422","orcid":"https://orcid.org/0000-0002-1600-0422","contributorId":241028,"corporation":false,"usgs":false,"family":"Spero","given":"Tanya","email":"","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":799371,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70217915,"text":"70217915 - 2021 - Step increase in eastern U.S. precipitation linked to Indian Ocean warming","interactions":[],"lastModifiedDate":"2021-02-10T18:36:20.998783","indexId":"70217915","displayToPublicDate":"2020-08-21T12:28:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Step increase in eastern U.S. precipitation linked to Indian Ocean warming","docAbstract":"A step increase in annual precipitation over the eastern U.S. in the early 1970’s commenced five decades of invigorated hydroclimate, with ongoing impacts on streamflow and water resources. Despite its far-reaching impacts, the dynamical origin of this change is unknown. Here, analyses of a century of atmospheric and oceanic data trace the dynamics to changes in the Indian Ocean. Spring and fall precipitation explain more than half the annual eastern-U.S. precipitation variance over the century, and changes in fall are predominantly responsible for the step increase. The driving mechanism is emergence of a pan-Pacific atmospheric wave emanating from deep convection over the warming Indian Ocean. Documentation of this fall teleconnection draws attention to projected anthropogenic increases in tropical oceanic heat content, and their potential impacts on hydroclimate of the midlatitudes.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088911","usgsCitation":"Strong, C., McCabe, G.J., and Weech, A., 2021, Step increase in eastern U.S. precipitation linked to Indian Ocean warming: Geophysical Research Letters, v. 47, no. 17, e2020GL088911; 10 p., https://doi.org/10.1029/2020GL088911.","productDescription":"e2020GL088911; 10 p.","ipdsId":"IP-118969","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":454468,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl088911","text":"Publisher Index Page"},{"id":383207,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Indian Ocean","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 66.796875,\n 23.885837699862005\n ],\n [\n 51.67968749999999,\n 11.178401873711785\n ],\n [\n 46.05468749999999,\n -1.0546279422758742\n ],\n [\n 42.1875,\n -12.211180191503997\n ],\n [\n 37.96875,\n -26.431228064506424\n ],\n [\n 42.5390625,\n -38.8225909761771\n ],\n [\n 66.4453125,\n -51.17934297928927\n ],\n [\n 106.171875,\n -49.15296965617039\n ],\n [\n 112.1484375,\n -35.7465122599185\n ],\n [\n 110.390625,\n -16.63619187839765\n ],\n [\n 99.140625,\n 9.102096738726456\n ],\n [\n 90.3515625,\n 22.59372606392931\n ],\n [\n 73.828125,\n 27.059125784374068\n ],\n [\n 66.796875,\n 23.885837699862005\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"17","noUsgsAuthors":false,"publicationDate":"2020-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Strong, Courtenay","contributorId":195262,"corporation":false,"usgs":false,"family":"Strong","given":"Courtenay","email":"","affiliations":[],"preferred":false,"id":810164,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":810165,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weech, Alexander","contributorId":248928,"corporation":false,"usgs":false,"family":"Weech","given":"Alexander","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":810166,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70235836,"text":"70235836 - 2021 - The processes of preferential flow in the unsaturated zone","interactions":[],"lastModifiedDate":"2022-08-23T14:31:02.397407","indexId":"70235836","displayToPublicDate":"2020-08-21T09:19:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3420,"text":"Soil Science Society of America Journal","active":true,"publicationSubtype":{"id":10}},"title":"The processes of preferential flow in the unsaturated zone","docAbstract":"Preferential flow, a major influence in unsaturated soil and rock almost everywhere, occurs by multiple phenomenologically distinct hydraulic processes. For the mode known as funneled flow, concentrated in particularly conductive portions of the medium, the surface-tension/viscous-flow processes of traditional unsaturated flow theory predominate. Fingered flow, through conductive paths of higher water content than surrounding material, requires amendments to traditional theory concerning instabilities and dynamic flow-regime boundaries. Macropore flow, the most recognized preferential flow mode, poses unanswered questions and major difficulties in practice. Accumulated evidence shows that water flows preferentially mostly through macropores that are (a) only partially filled with water, and (b) surrounded by matrix material that is drier, sometimes much drier, than saturation. With partial filling, geometric characteristics such as aperture have much less influence than was previously thought, and the intra-macropore configuration of the flowing water phase, about which little is conclusively known, is then a dominant controlling influence. With unsaturated surroundings, macropore/matrix exchange interactions control, for given input and medium, the initiating circumstances, conveyed flux, and duration of macropore flow. The multiple processes in play during such interactions have different sensitivities to the matrix water state and different directions of influence. The net influence of matrix water content on macropore flow is thus highly complex and a major research need. Additional high-priority topics are: flowpath connectivity, for watersheds as well as small scales; intra-macropore processes, to discern their importance and possible means of quantification; and the identification of measurable soil and rock properties that can be utilized predictively.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/saj2.20143","usgsCitation":"Nimmo, J.R., 2021, The processes of preferential flow in the unsaturated zone: Soil Science Society of America Journal, v. 85, no. 1, p. 1-27, https://doi.org/10.1002/saj2.20143.","productDescription":"27 p.","startPage":"1","endPage":"27","ipdsId":"IP-121207","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":405459,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"85","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-01-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":849505,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70212553,"text":"70212553 - 2021 - Using simulation to understand annual sea lamprey marking rates on lake trout","interactions":[],"lastModifiedDate":"2022-01-06T15:34:24.412426","indexId":"70212553","displayToPublicDate":"2020-08-19T08:58:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Using simulation to understand annual sea lamprey marking rates on lake trout","docAbstract":"Sea lampreys attack fish, killing some and leaving marks on others. Great Lakes fishery managers rely on observed marking rates to assess the success of the sea lamprey control program and estimate sea lamprey-induced mortality of lake trout. Because marking rates are only observed on survivors of sea lamprey attacks, they may not provide a reliable index of actual attack or mortality rates. To investigate the effect of survivor bias, we developed a simulation model representing a single season (June–December) of sea lamprey attacks. Simulated attack rates varied with month and lake trout size; simulated pierce and lethality rates varied with month alone. Surveyed marking rates were represented by simulated survivors in October; true rates were calculated from all simulated lake trout (dead and alive) in December. Simulation results were subsetted to include only those within the range of marking rates actually observed in the Great Lakes. Type A (piercing) marking rates were a good index of the sea lamprey attack rate and the sea lamprey-induced mortality rate if annual lethality rates were relatively constant. Type B (non-piercing) marking rates were a good index of the sea lamprey attack rate and the sea lamprey-induced mortality rate if annual pierce rates were relatively constant. Due to the uncertainty surrounding the pierce and lethality rates, we recommend that sea lamprey abundance information be incorporated in existing lake trout statistical catch-at-age models via a functional response component relating sea lamprey feeding to lake trout abundance, if possible.
","language":"English","publisher":"International Association for Great Lakes Research","doi":"10.1016/j.jglr.2020.08.008","issn":"0380-1330","usgsCitation":"Adams, J.V., Jones, M., and Bence, J., 2021, Using simulation to understand annual sea lamprey marking rates on lake trout: Journal of Great Lakes Research, v. 47, no. Suppl 1, p. S628-S638, https://doi.org/10.1016/j.jglr.2020.08.008.","productDescription":"11 p.","startPage":"S628","endPage":"S638","onlineOnly":"Y","ipdsId":"IP-109603","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454477,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2020.08.008","text":"Publisher Index Page"},{"id":377684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"Suppl 1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Adams, Jean V. 0000-0002-9101-068X jvadams@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-068X","contributorId":3140,"corporation":false,"usgs":true,"family":"Adams","given":"Jean","email":"jvadams@usgs.gov","middleInitial":"V.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":796837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Michael L.","contributorId":126763,"corporation":false,"usgs":false,"family":"Jones","given":"Michael L.","affiliations":[{"id":6600,"text":"Qauntitative Fisheries Center, Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":796838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bence, James R.","contributorId":95026,"corporation":false,"usgs":false,"family":"Bence","given":"James R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":796839,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211821,"text":"70211821 - 2021 - Evidence of host switching: Sea lampreys disproportionately attack Chinook salmon when lake trout abundance is low in Lake Ontario","interactions":[],"lastModifiedDate":"2022-01-06T12:15:30.319681","indexId":"70211821","displayToPublicDate":"2020-08-19T08:11:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of host switching: Sea lampreys disproportionately attack Chinook salmon when lake trout abundance is low in Lake Ontario","docAbstract":"Lake trout (Salvelinus namaycush) is the presumed preferred host of the invasive sea lamprey (Petromyzon marinus) in the Great Lakes, but little is understood about this preference outside of laboratory experiments. By preference we mean sea lamprey attacks on hosts are disproportionate to host relative abundance. The purpose of this study was to quantify host preference of sea lampreys in the field for the first time. We focused our analysis on Lake Ontario, where the two dominant host species for sea lampreys are lake trout and Chinook salmon (Oncorhynchus tshawytscha). Sea lampreys exhibited a strong preference for lake trout when host abundance was
Abandoned tropical pastures offer opportunities for passive and active restoration of native forest communities. Tree architecture of remnant canopy trees may be one important factor that can facilitate native plant recruitment in abandoned pastures but has largely been overlooked. Here, we evaluated patterns of native woody plant recruitment under remnant trees in abandoned pastures on Hawai’i Island and how these might be related to both tree architectural features and landscape variables. We measured native woody stems (excluding sprouts of the tree itself) in a 5 m radius around the base of each tree and modeled total basal area of native stems as a function of tree architectural characteristics. Recruitment was positively correlated with tree height as well as horizontal woody area below 1 m (tree structure that occurred below 1 m and was < 45° angle from the ground) around the base of trees. Tall trees likely attract more avian seed dispersers due to their higher visibility on the landscape and increased crown volume. Horizontal woody area likely provides establishment microsites that are above the pasture grass layer, similar to how dead or decaying logs act as nurse substrates. Unlike previous studies, we found little evidence that landscape variables such as distance to the intact forest or nearest canopy neighbor influenced understory recruitment. Tree architectural characteristics can be important predictors of native plant recruitment in abandoned tropical pastures and should be considered in addition to local and landscape-level variables.
","language":"English","publisher":"Springer","doi":"10.1007/s11258-020-01072-7","usgsCitation":"Rehm, E.M., Yelenik, S.G., Smith, M.P., and D’Antonio, C.M., 2021, Architecture of remnant trees influences native woody plant recruitment in abandoned Hawaiian pastures: Plant Ecology, v. 222, p. 659-667, https://doi.org/10.1007/s11258-020-01072-7.","productDescription":"9 p.","startPage":"659","endPage":"667","ipdsId":"IP-099626","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":386982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"222","noUsgsAuthors":false,"publicationDate":"2020-08-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Rehm, Evan M","contributorId":216487,"corporation":false,"usgs":false,"family":"Rehm","given":"Evan","email":"","middleInitial":"M","affiliations":[{"id":39457,"text":"University of California at Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":818707,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769 syelenik@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":5251,"corporation":false,"usgs":true,"family":"Yelenik","given":"Stephanie","email":"syelenik@usgs.gov","middleInitial":"G.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":818708,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Marley Puanani","contributorId":260775,"corporation":false,"usgs":false,"family":"Smith","given":"Marley","email":"","middleInitial":"Puanani","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818709,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"D’Antonio, Carla M.","contributorId":140014,"corporation":false,"usgs":false,"family":"D’Antonio","given":"Carla","email":"","middleInitial":"M.","affiliations":[{"id":13358,"text":"Environmental Studies, University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":818710,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212479,"text":"70212479 - 2021 - Assessing the assumptions of classification agreement, accuracy, and predictable healing time of sea lamprey wounds on lake trout","interactions":[],"lastModifiedDate":"2022-01-06T15:31:53.762691","indexId":"70212479","displayToPublicDate":"2020-08-14T09:40:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the assumptions of classification agreement, accuracy, and predictable healing time of sea lamprey wounds on lake trout","docAbstract":"Sea lamprey control in the Laurentian Great Lakes relies on records of sea lamprey wounds on lake trout to assess whether control efforts are supporting fisheries management targets. Wounding records have been maintained for 70 years under the assumption that they are a reliable and accurate reflection of sea lamprey damage inflicted on fish populations. However, two key assumptions underpinning the use of these data need thorough evaluation: sea lamprey wounds follow a predictable healing progression, and individuals classify wounds accurately and reliably. To assess these assumptions, we conducted a workshop where experienced professionals examined lake trout with known sea lamprey wounds. For most lake trout, pictures were taken at regular intervals during the healing process. Our evaluation of wound pictures found high variability in healing times and wound progressions that did not conform to the currently used classification system. Participants’ wound classification agreement and accuracy were low and misclassification rates were high for most wound types. Training provided during the workshops did not markedly improve these metrics. We assessed wound classification accuracy for the first time and found assumptions of high accuracy and agreement are not met. We recommend misclassification rates be incorporated into models using wound data, sensitivity analyses be conducted to assess the potential impact of wound misclassification on estimates of key metrics (such as sea lamprey-induced mortality for lake trout), and alternative biomarkers be developed to quantify wound status with greater accuracy and precision.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.07.016","usgsCitation":"Firkus, T., Murphy, C., Adams, J.V., Treska, T., and Fischer, G.J., 2021, Assessing the assumptions of classification agreement, accuracy, and predictable healing time of sea lamprey wounds on lake trout: Journal of Great Lakes Research, v. 47, no. Supp 1, p. 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University","active":true,"usgs":false}],"preferred":false,"id":796468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, Jean V. 0000-0002-9101-068X jvadams@usgs.gov","orcid":"https://orcid.org/0000-0002-9101-068X","contributorId":3140,"corporation":false,"usgs":true,"family":"Adams","given":"Jean","email":"jvadams@usgs.gov","middleInitial":"V.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":796469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Treska, Ted","contributorId":141105,"corporation":false,"usgs":false,"family":"Treska","given":"Ted","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":796470,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fischer, Gregory J.","contributorId":178010,"corporation":false,"usgs":false,"family":"Fischer","given":"Gregory","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":796471,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70213293,"text":"70213293 - 2021 - Mortality predispositions of conifers across western USA","interactions":[],"lastModifiedDate":"2020-12-29T21:33:00.393927","indexId":"70213293","displayToPublicDate":"2020-08-09T12:07:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2863,"text":"New Phytologist","active":true,"publicationSubtype":{"id":10}},"title":"Mortality predispositions of conifers across western USA","docAbstract":"In high‐latitude lakes, air temperature is an important driver of ice cover thickness and duration, which in turn influence water temperature and primary production supporting lake consumers and predators. In lieu of multidecadal observational records necessary to assess the response of lakes to long‐term warming, we used otolith‐based growth records from a long‐lived resident lake fish, lake trout (Salvelinus namaycush), as a proxy for production. Lake trout were collected from seven deep, oligotrophic lakes in Lake Clark National Park and Preserve on in southwest Alaska that varied in the presence of marine‐derived nutrients (MDN) from anadromous sockeye salmon (Oncorhynchus nerka). Linear mixed‐effects models were used to partition variation in lake trout growth by age and calendar‐year and model comparisons tested for a mean increase in lake trout growth with sockeye salmon presence. Year effects from the best mixed‐effects model were subsequently compared to indices of temperature, lake ice, and regional indices of sockeye salmon escapement. A strong positive correlation between annual lake trout growth and temperature suggested that warmer springs, earlier lake ice break‐up, and a longer ice‐free growing season increase lake trout growth via previously identified bottom‐up increases in production with warming. Accounting for differences in the presence or annual escapement of sockeye salmon with available data did not improve model fit. Collectively with other studies, the results suggest that productivity of subarctic lakes has benefitted from warming spring temperatures and that temperature can synchronise otolith growth across lakes with and without sockeye salmon MDN.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12566","usgsCitation":"von Biela, V.R., Black, B.A., Young, D.B., van der Sleen, P., Bartz, K.K., and Zimmerman, C.E., 2021, Lake trout growth is sensitive to spring temperature in southwest Alaska lakes: Ecology of Freshwater Fish, v. 30, no. 1, p. 88-99, https://doi.org/10.1111/eff.12566.","productDescription":"12 p.","startPage":"88","endPage":"99","ipdsId":"IP-108517","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":436679,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92YV00Z","text":"USGS data release","linkHelpText":"Lake Trout Otolith Growth Increment Measurements, Lake Clark National Park and Preserve, Alaska, 1979-2012"},{"id":378510,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lake Clark National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.852294921875,\n 59.58441353704829\n ],\n [\n -152.127685546875,\n 59.58441353704829\n ],\n [\n -152.127685546875,\n 61.59071955121135\n ],\n [\n -154.852294921875,\n 61.59071955121135\n ],\n [\n -154.852294921875,\n 59.58441353704829\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":799026,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Black, Bryan A.","contributorId":68448,"corporation":false,"usgs":false,"family":"Black","given":"Bryan","email":"","middleInitial":"A.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":799027,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Daniel","contributorId":58468,"corporation":false,"usgs":false,"family":"Young","given":"Daniel","affiliations":[{"id":35763,"text":"National Park Service, Lake Clark National Park and Preserve, Port Alsworth, AK","active":true,"usgs":false}],"preferred":false,"id":799028,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"van der Sleen, Peter","contributorId":203860,"corporation":false,"usgs":false,"family":"van der Sleen","given":"Peter","email":"","affiliations":[{"id":36731,"text":"University of Texas Marine Science Institute","active":true,"usgs":false}],"preferred":false,"id":799029,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bartz, Krista K.","contributorId":200705,"corporation":false,"usgs":false,"family":"Bartz","given":"Krista","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":799030,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":799031,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216198,"text":"70216198 - 2021 - Validation of the model-predicted spawning area of grass carp Ctenopharyngodon idella in the Sandusky River","interactions":[],"lastModifiedDate":"2023-01-19T16:30:04.172544","indexId":"70216198","displayToPublicDate":"2020-07-23T07:05:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Validation of the model-predicted spawning area of grass carp Ctenopharyngodon idella in the Sandusky River","title":"Validation of the model-predicted spawning area of grass carp Ctenopharyngodon idella in the Sandusky River","docAbstract":"Spawning of grass carp, Ctenopharyngodon idella, in the Great Lakes basin was verified when eight fertilized eggs were collected in the Sandusky River, a tributary to Lake Erie, in 2015. Using a fluvial drift model (FluEgg) and simulation modeling, researchers predicted the fertilization location for those eggs was 3.8 ± 1 km (95% credible interval, CI) downstream of Ballville Dam. In June 2018, simultaneous collection of fertilized eggs and adults within the model-predicted spawning area provided the opportunity to verify the fertilization location. We used estimated developmental time (Dt) of eggs calculated from developmental stages, water temperature, and an equation that predicts Dt from cumulative thermal units experienced by developing eggs, in two analyses. First, we regressed Dt versus location of capture and solved that equation for developmental time of 0 hrs (Dt0) to estimate fertilization location. Second, we used Dt in the Fluvial Drift Simulator (FluEgg) to simulate 23 scenarios representative of drift conditions throughout the spawning event using the model-predicted spawning area and the site of Ballville Dam as potential spawning locations. Regression analysis placed the mean fertilization location 3.36 km (95% CI 2.27, 4.24) downstream of the site of Ballville Dam, within the model-predicted spawning area. Drift models demonstrated the model-predicted spawning area was best supported. Histograms of fertilization times overlapped with capture times by boat electrofishing of diploid adult grass carp in the model-predicted spawning area. This suite of analyses confirms the model-predicted spawning area and validates the methodology used to locate it.
Barriers and pesticides have been used in streams to control sea lamprey in the Laurentian Great Lakes for nearly 70 years. Considerable effort has been spent to develop additional control measures, but much less effort has gone toward identifying how or where additional control measures might be cost-effectively integrated into the sea lamprey control program. We use a management strategy evaluation model in Lake Michigan to identify the stream types that would be most suitable for deploying traps to remove adults prior to spawning and estimate the likely impact on adult sea lamprey abundance in subsequent years under several trapping scenarios relative to status quo abundance. The greatest reduction in lake-wide adult sea lamprey abundance predicted by the model resulted when removing adult sea lampreys from streams that are difficult for control program personnel to treat with lampricide because lampricide applications would be required less frequently. Additionally, targeting streams which experience regular sea lamprey recruitment and streams with low adult sea lamprey density should result in reduced lake-wide abundance if trapping costs are relatively low or removal is high. Our results provide direction on where to trap and why, and indicate that trapping may be a valuable part of an integrated sea lamprey control approach advancing the goals of the Great Lakes Fishery Commission.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.06.023","usgsCitation":"Miehls, S.M., Dawson, H., Maguffee, A., Johnson, N., Jones, M., and Dobiesz, N., 2021, Where you trap matters: Implications for integrated sea lamprey management: Journal of Great Lakes Research, v. 47, no. Suppl 1, p. S320-S327, https://doi.org/10.1016/j.jglr.2020.06.023.","productDescription":"8 p.","startPage":"S320","endPage":"S327","onlineOnly":"N","ipdsId":"IP-115542","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454510,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2020.06.023","text":"Publisher Index Page"},{"id":378245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"Suppl 1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":798248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dawson, Heather","contributorId":96577,"corporation":false,"usgs":true,"family":"Dawson","given":"Heather","affiliations":[{"id":27267,"text":"University of Michigan-Flint","active":true,"usgs":false}],"preferred":false,"id":798249,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maguffee, Alex","contributorId":239976,"corporation":false,"usgs":false,"family":"Maguffee","given":"Alex","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":798250,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":798251,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, M.W.","contributorId":239977,"corporation":false,"usgs":false,"family":"Jones","given":"M.W.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":798252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dobiesz, Norine","contributorId":239978,"corporation":false,"usgs":false,"family":"Dobiesz","given":"Norine","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":798253,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211173,"text":"70211173 - 2021 - Trapping of suspended sediment by submerged aquatic vegetation in a tidal freshwater region: Field observations and long-term trends","interactions":[],"lastModifiedDate":"2021-03-19T20:16:25.981098","indexId":"70211173","displayToPublicDate":"2020-07-14T12:23:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Trapping of suspended sediment by submerged aquatic vegetation in a tidal freshwater region: Field observations and long-term trends","docAbstract":"Widespread invasion by non-native, submerged aquatic vegetation (SAV) may modify the sediment budget of an estuary, reducing the availability of inorganic sediment required by marshes to maintain their position in the tidal frame. The instantaneous trapping rate of suspended sediment in SAV patches in an estuary has not previously been quantified via field observations. In this study, flows of water and suspended sediment through patches of invasive SAV were measured at three tidally forced, freshwater sites, all located within the Sacramento-San Joaquin Delta in California. An acoustic Doppler current profiler deployed from a roving vessel provided velocity and backscatter data used to quantify fluxes of both water and suspended sediment. Sediment trapping efficiency, defined as instantaneous net trapped flux divided by incident flux, was positive in 24 of 29 cases, averaging + 5%. Coupled with 3 years of measured sediment flux data at one site, this suggests that trapping averages 3.7 kg m−2 year−1. This estimate compares favorably with the mean mass accumulation rate of 3.8 kg m−2 year−1 estimated from dated sediment cores collected at the study sites. Long-term measurements made upstream reveal a strong negative trend (− 1.8% year−1) in suspended sediment concentration, and intra-annual changes in both suspended sediment concentration and percent fines. The large footprint and high spatial density of invasive SAV coupled with declining sediment supply are diminishing downstream suspended sediment concentrations, potentially reducing the resiliency of marshes in the Delta and lower estuary to future sea-level rise.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00799-w","usgsCitation":"Work, P.A., Downing-Kunz, M.A., and Drexler, J.Z., 2021, Trapping of suspended sediment by submerged aquatic vegetation in a tidal freshwater region: Field observations and long-term trends: Estuaries and Coasts, v. 44, p. 734-739, https://doi.org/10.1007/s12237-020-00799-w.","productDescription":"6 p.","startPage":"734","endPage":"739","ipdsId":"IP-114567","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.32177734375,\n 37.60117623656667\n ],\n [\n -121.17919921875001,\n 37.60117623656667\n ],\n [\n -121.17919921875001,\n 38.543869175876154\n ],\n [\n -122.32177734375,\n 38.543869175876154\n ],\n [\n -122.32177734375,\n 37.60117623656667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2020-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Downing-Kunz, Maureen A. 0000-0002-4879-0318 mdowning-kunz@usgs.gov","orcid":"https://orcid.org/0000-0002-4879-0318","contributorId":3690,"corporation":false,"usgs":true,"family":"Downing-Kunz","given":"Maureen","email":"mdowning-kunz@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":792943,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211592,"text":"70211592 - 2021 - Group density, disease, and season shape territory size and overlap of social carnivores","interactions":[],"lastModifiedDate":"2021-01-18T23:01:19.187747","indexId":"70211592","displayToPublicDate":"2020-07-12T07:48:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Group density, disease, and season shape territory size and overlap of social carnivores","docAbstract":"1. The spatial organization of a population can influence the spread of information, behaviour, and pathogens. Territory size and territory overlap, components of spatial organization, provide key information as these metrics may be indicators of habitat quality, resource dispersion, contact rates, and environmental risk (e.g., indirectly transmitted pathogens). Furthermore, sociality and behaviour can also shape space use, and subsequently, how space use and habitat quality together impact demography.\n\n2. Our study aims to identify factors shaping the spatial organization of wildlife populations and assess the impact of epizootics on space use. We also use network analysis to describe spatial organization and connectivity of social groups. \n\n3. Here, we assessed the seasonal spatial organization of Serengeti lions and Yellowstone wolves at the group level. We examine the factors predicting mean territory size and mean territory overlap for each population using generalized additive models. We further explore the mechanisms by which disease perturbations could cause changes in spatial organization.\n\n4. We demonstrate that lions and wolves were similar in that group-level factors, such as number of groups, shaped spatial organization more than population-level factors, such as population density. Factors shaping territory size were slightly different than factors shaping territory overlap; for example, wolf pack size was an important predictor of territory overlap, but not territory size. Lion spatial networks were more highly connected, while wolf spatial networks varied seasonally. We found that resource dispersion may be more important for driving territory size and overlap for wolves than for lions. Additionally, canine distemper epizootics may alter lion spatial organization, highlighting the importance of including behavioural and movement ecology in studies of pathogen transmission dynamics. \n\n5. We provide insight about when we might expect to observe the impacts of resource dispersion, disease perturbations, and other ecological factors on spatial organization. Our work highlights the importance of monitoring and managing social carnivore populations at the group level. Future research should elucidate the complex relationships between demographics, social and spatial structure, abiotic and biotic conditions, and pathogen infections.","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.13294","usgsCitation":"Brandell, E., Fountain-Jones, N.M., Gilbertson, M.L., Cross, P., Hudson, P.J., Smith, D., Stahler, D.R., Packer, C., and Craft, M.E., 2021, Group density, disease, and season shape territory size and overlap of social carnivores: Journal of Animal Ecology, v. 90, no. 16, p. 87-101, https://doi.org/10.1111/1365-2656.13294.","productDescription":"15 p.","startPage":"87","endPage":"101","ipdsId":"IP-114169","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":454515,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9844152","text":"Publisher Index Page"},{"id":377003,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"90","issue":"16","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brandell, E. E. 0000-0002-2698-7013","orcid":"https://orcid.org/0000-0002-2698-7013","contributorId":236935,"corporation":false,"usgs":false,"family":"Brandell","given":"E. E.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":794743,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fountain-Jones, Nicholas M. 0000-0001-9248-8493","orcid":"https://orcid.org/0000-0001-9248-8493","contributorId":197452,"corporation":false,"usgs":false,"family":"Fountain-Jones","given":"Nicholas","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":794744,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilbertson, Marie L. J.","contributorId":212116,"corporation":false,"usgs":false,"family":"Gilbertson","given":"Marie","email":"","middleInitial":"L. J.","affiliations":[{"id":38415,"text":"Department of Veterinary Population Medicine, University of Minnesota, St. Paul, MN, USA","active":true,"usgs":false}],"preferred":false,"id":794745,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":794746,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hudson, P. J.","contributorId":236937,"corporation":false,"usgs":false,"family":"Hudson","given":"P.","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":794747,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Douglas W.","contributorId":179181,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas W.","affiliations":[],"preferred":false,"id":794748,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stahler, Daniel R.","contributorId":179180,"corporation":false,"usgs":false,"family":"Stahler","given":"Daniel","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":794749,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Packer, Craig 0000-0002-3939-8162","orcid":"https://orcid.org/0000-0002-3939-8162","contributorId":236938,"corporation":false,"usgs":false,"family":"Packer","given":"Craig","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":794750,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Craft, Meggan E.","contributorId":168372,"corporation":false,"usgs":false,"family":"Craft","given":"Meggan","email":"","middleInitial":"E.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":794751,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70216800,"text":"70216800 - 2021 - Food web fuel differs across habitats and seasons of a tidal freshwater estuary","interactions":[],"lastModifiedDate":"2020-12-30T14:47:27.113856","indexId":"70216800","displayToPublicDate":"2020-07-11T09:28:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Food web fuel differs across habitats and seasons of a tidal freshwater estuary","docAbstract":"Estuarine food webs are fueled by multiple different primary producers. However, identifying the relative importance of each producer to consumers is difficult, particularly for fishes that utilize multiple food sources due to both their mobility and their generally high trophic levels. Previous studies have documented broad spatial differences in the importance of primary producers to fishes within the Upper San Francisco Estuary, California, including separation between pelagic and littoral food webs. In this study, we evaluated the importance of primary producers to adult fishes in three closely spaced subregions that represented disparate habitat types (a tidal wetland channel, a turbid backwater channel, and a deep open-water channel), each a potential outcome of local restoration projects. Using stable isotope analysis coupled with a Bayesian mixing model, we identified significant differences in primary-producer contribution to fishes and invertebrates across habitats and seasons, especially in the relative contribution of submersed aquatic vegetation and phytoplankton. Most fishes utilized multiple primary producers and showed little segregation between pelagic and littoral food webs among habitats. Availability of primary producers differs seasonally and across multiple spatial scales, helping to buffer environmental variability and thus enhancing food web resilience. Ecosystem restoration may improve with emphasis on restoring a wide variety of primary producers to support consumers.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00762-9","usgsCitation":"Young, M.J., Howe, E.R., O’Rear, T., Berridge, K., and Moyle, P.B., 2021, Food web fuel differs across habitats and seasons of a tidal freshwater estuary: Estuaries and Coasts, v. 44, p. 286-301, https://doi.org/10.1007/s12237-020-00762-9.","productDescription":"16 p.","startPage":"286","endPage":"301","ipdsId":"IP-107933","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":454518,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12237-020-00762-9","text":"Publisher Index Page"},{"id":381104,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, Upper San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.80782318115233,\n 38.226314067139185\n ],\n [\n -121.67770385742186,\n 38.226314067139185\n ],\n [\n -121.67770385742186,\n 38.32011084501538\n ],\n [\n -121.80782318115233,\n 38.32011084501538\n ],\n [\n -121.80782318115233,\n 38.226314067139185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2020-07-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806323,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howe, Emily R.","contributorId":177088,"corporation":false,"usgs":false,"family":"Howe","given":"Emily","email":"","middleInitial":"R.","affiliations":[{"id":17978,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA","active":true,"usgs":false}],"preferred":false,"id":806324,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Rear, Teejay","contributorId":245510,"corporation":false,"usgs":false,"family":"O’Rear","given":"Teejay","email":"","affiliations":[{"id":49210,"text":"Univ. of California, Davis, Center for Watershed Sciences","active":true,"usgs":false}],"preferred":false,"id":806325,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berridge, Kathleen","contributorId":245511,"corporation":false,"usgs":false,"family":"Berridge","given":"Kathleen","email":"","affiliations":[{"id":49211,"text":"Univ. of California, Davis, Center for Watershed Sci. AND Environmental Science Associates","active":true,"usgs":false}],"preferred":false,"id":806326,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moyle, Peter B.","contributorId":117099,"corporation":false,"usgs":false,"family":"Moyle","given":"Peter","email":"","middleInitial":"B.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":806327,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218225,"text":"70218225 - 2021 - Estimating inundation of small waterbodies with sub-pixel analysis of Landsat imagery: Long-term trends in surface water area and evaluation of common drought indices","interactions":[],"lastModifiedDate":"2021-03-19T20:54:21.790299","indexId":"70218225","displayToPublicDate":"2020-07-10T12:29:14","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating inundation of small waterbodies with sub-pixel analysis of Landsat imagery: Long-term trends in surface water area and evaluation of common drought indices","docAbstract":"Small waterbodies are numerically dominant in many landscapes and provide several important ecosystem services, but automated measurement of waterbodies smaller than a standard Landsat pixel (0.09 ha) remains challenging. To further evaluate sub‐Landsat pixel techniques for estimating inundation extent of small waterbodies (basin area: 0.06–1.79 ha), we used a partial spectral unmixing method with matched filtering applied to September 1985–2018 Landsat 5 and eight imagery from southern Arizona, USA. We estimated trends in modeled surface water area each September and evaluated the ability of several common drought indices to explain variation in mean water area. Our methods accurately classified waterbodies as dry or inundated (Landsat 5: 91.3%; Landsat 8: 98.9%) and modeled and digitized surface water areas were strongly correlated (R2 = 0.70–0.92; bias = −0.024 to −0.015 ha). Estimated surface water area was best explained by the 3‐month seasonal standardized precipitation index (SPI03; July‒September). We found a wide range of estimated relationships between drought indices (e.g. SPI vs. Palmer Drought Severity Index) and estimated water area, even for different durations of the same drought index (e.g. SPI01 vs. SPI12). Mean waterbody surface area decreased by ~14% from September 1985 to September 2018, which matches declines in local annual precipitation and regional trends of reduced inundation extent of larger waterbodies. These results emphasize the importance of understanding local systems when relying on drought indices to infer variation in past or future surface water dynamics. Several challenges remain before widespread application of sub‐pixel methods is feasible, but our results provide further evidence that partial spectral unmixing with matched filtering provides reliable measures of inundation extent of small waterbodies.
","language":"English","publisher":"Zoological Society of London (Wiley)","doi":"10.1002/rse2.172","usgsCitation":"Sall, I., Jarchow, C., Sigafus, B.H., Eby, L., Forzley, M.J., and Hossack, B., 2021, Estimating inundation of small waterbodies with sub-pixel analysis of Landsat imagery: Long-term trends in surface water area and evaluation of common drought indices: Remote Sensing in Ecology and Conservation, v. 7, no. 1, p. 109-124, https://doi.org/10.1002/rse2.172.","productDescription":"16 p.","startPage":"109","endPage":"124","ipdsId":"IP-114730","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454519,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.172","text":"Publisher Index Page"},{"id":383382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona","otherGeospatial":"San Rafael Valley and neighboring areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.89599609375,\n 31.283245492650792\n ],\n [\n -110.2313232421875,\n 31.283245492650792\n ],\n [\n -110.2313232421875,\n 31.991771310172094\n ],\n [\n -110.89599609375,\n 31.991771310172094\n ],\n [\n -110.89599609375,\n 31.283245492650792\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-07-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Sall, Ibrahima 0000-0002-7526-636X","orcid":"https://orcid.org/0000-0002-7526-636X","contributorId":251750,"corporation":false,"usgs":false,"family":"Sall","given":"Ibrahima","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":810490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarchow, Christopher J. 0000-0002-0424-4104","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":211737,"corporation":false,"usgs":false,"family":"Jarchow","given":"Christopher J.","affiliations":[{"id":38314,"text":"USGS Southwest Biological Science Center, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":810491,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":810492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eby, Lisa A","contributorId":251751,"corporation":false,"usgs":false,"family":"Eby","given":"Lisa A","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":810493,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Forzley, Michael James 0000-0001-5307-8459","orcid":"https://orcid.org/0000-0001-5307-8459","contributorId":251752,"corporation":false,"usgs":true,"family":"Forzley","given":"Michael","email":"","middleInitial":"James","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810494,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810495,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211292,"text":"70211292 - 2021 - Interpreting and reporting 40Ar/39Ar geochronologic data","interactions":[],"lastModifiedDate":"2021-03-05T22:02:00.254822","indexId":"70211292","displayToPublicDate":"2020-07-01T09:41:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Interpreting and reporting 40Ar/39Ar geochronologic data","title":"Interpreting and reporting 40Ar/39Ar geochronologic data","docAbstract":"The 40Ar/39Ar dating method is among the most versatile of geochronometers, having the potential to date a broad variety of K-bearing materials spanning from the time of Earth’s formation into the historical realm. Measurements using modern noble-gas mass spectrometers are now producing 40Ar/39Ar dates with analytical uncertainties of ∼0.1%, thereby providing precise time constraints for a wide range of geologic and extraterrestrial processes. Analyses of increasingly smaller subsamples have revealed age dispersion in many materials, including some minerals used as neutron fluence monitors. Accordingly, interpretive strategies are evolving to address observed dispersion in dates from a single sample. Moreover, inferring a geologically meaningful “age” from a measured “date” or set of dates is dependent on the geological problem being addressed and the salient assumptions associated with each set of data. We highlight requirements for collateral information that will better constrain the interpretation of 40Ar/39Ar data sets, including those associated with single-crystal fusion analyses, incremental heating experiments, and in situ analyses of microsampled domains. To ensure the utility and viability of published results, we emphasize previous recommendations for reporting 40Ar/39Ar data and the related essential metadata, with the amendment that data conform to evolving standards of being findable, accessible, interoperable, and reusable (FAIR) by both humans and computers. Our examples provide guidance for the presentation and interpretation of 40Ar/39Ar dates to maximize their interdisciplinary usage, reproducibility, and longevity.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35560.1","usgsCitation":"Schaen, A.J., Jicha, B.R., Hodges, K.V., Vermeesch, P., Stelten, M.E., Mercer, C.M., Phillips, D., Rivera, T., Jourdan, F., Matchan, E.L., Hemming, S.R., Morgan, L.E., Kelley, S.P., Cassata, W.S., Heizler, M.T., Vasconcelos, P.M., Benowitz, J.A., Koppers, A.A., Mark, D.F., Niespolo, E.M., Sprain, C.J., Hames, W.E., Kuiper, K.F., Turrin, B., Renne, P.R., Ross, J., Nomade, S., Guillou, H., Webb, L.E., Cohen, B.A., Calvert, A.T., Joyce, N., Ganderod, M., Wijbrans, J., Ishizuka, O., He, H., Ramirez, A., Pfander, J., Lopez-Martinez, M., Qiu, H., and Singer, B.S., 2021, Interpreting and reporting 40Ar/39Ar geochronologic data: GSA Bulletin, v. 133, no. 3-4, p. 461-487, https://doi.org/10.1130/B35560.1.","productDescription":"17 p.","startPage":"461","endPage":"487","ipdsId":"IP-113901","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science 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Constraining sediment supply and net budgets is difficult due to multiple timescales of variability in hydrodynamic forcing and suspended-sediment concentrations, as well as the fundamental limitations of measurement and modeling technologies. We used two independent observational campaigns and one hydrodynamic modeling effort to estimate the sediment supply to Jamaica Bay, New York, USA, an urbanized embayment with a history of extensive wetland loss. We found that all three estimates indicate a net import to the system, ranging from 36 x 106 – 74 x 106 kg/y, with a mean estimate of 55,000 t/y +/- 31,000 t/y, which compares well with a prior estimate derived from radionuclide tracers. Net sediment import is controlled by flood-ebb asymmetry in bed shear stress, which results in higher suspended sediment concentrations on flood tide relative to ebb. This indicates a seaward source of sediment, likely offshore marine deposits and potentially sediment from the adjacent Hudson River-Estuary that is resuspended by waves in the coastal ocean. Despite the net sediment import, a simple sediment budget suggests that the rate of supply is not sufficient to maintain the present geomorphic planform of the system relative to sea-level rise. The convergent estimates from independent methods provide reasonable guidance as context for sediment-based restoration efforts.","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00784-3","usgsCitation":"Chant, R., Ralston, D.K., Ganju, N., Pianca, C., Simonson, A., and Cartwright, R., 2021, Sediment budget estimates for a highly impacted embayment with extensive wetland loss: Estuaries and Coasts, v. 44, p. 608-626, https://doi.org/10.1007/s12237-020-00784-3.","productDescription":"19 p.","startPage":"608","endPage":"626","ipdsId":"IP-104431","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":377319,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Grassey Bay, Jamaica Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.14947509765625,\n 40.50126945841645\n ],\n [\n -73.7457275390625,\n 40.50126945841645\n ],\n [\n -73.7457275390625,\n 40.74101426921151\n ],\n [\n -74.14947509765625,\n 40.74101426921151\n ],\n [\n -74.14947509765625,\n 40.50126945841645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Chant, Robert","contributorId":237975,"corporation":false,"usgs":false,"family":"Chant","given":"Robert","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":795713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ralston, David K. 0000-0002-0774-3101","orcid":"https://orcid.org/0000-0002-0774-3101","contributorId":195909,"corporation":false,"usgs":false,"family":"Ralston","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":795714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":795715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pianca, Casia","contributorId":237976,"corporation":false,"usgs":false,"family":"Pianca","given":"Casia","email":"","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":795716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simonson, Amy","contributorId":237977,"corporation":false,"usgs":false,"family":"Simonson","given":"Amy","affiliations":[{"id":47668,"text":"NYWSC","active":true,"usgs":false}],"preferred":false,"id":795717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cartwright, Richard","contributorId":237978,"corporation":false,"usgs":false,"family":"Cartwright","given":"Richard","affiliations":[{"id":47668,"text":"NYWSC","active":true,"usgs":false}],"preferred":false,"id":795718,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211520,"text":"70211520 - 2021 - Estimating age and growth of invasive sea lamprey: A review of approaches and investigation of a new method","interactions":[],"lastModifiedDate":"2022-01-05T17:47:39.475706","indexId":"70211520","displayToPublicDate":"2020-06-27T10:51:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimating age and growth of invasive sea lamprey: A review of approaches and investigation of a new method","docAbstract":"We review recent advances in age and growth estimation of invasive sea lamprey (Petromyzon marinus) in the Great Lakes and present a more accurate method for growth estimation. To forecast growth and prioritize streams for control actions, sea lamprey managers currently use an average daily growth model. Here, a new linear model that included stream and lake as contributing variables was investigated and found to outperform the currently used growth model (roughly a 10 mm difference at age 1). Length-at-age of larvae between ages 1 and 4 were also best forecasted by a linear model with the predictor variables including growing degree days, stream, lake, and larval lamprey density. The model predicts that larval sea lamprey grow faster in warm streams with low densities of lamprey larvae. More accurate growth models could allow sea lamprey control managers to improve decisions concerning how sea lamprey control effort is allocated among streams, and could help inform broader modeling efforts evaluating the population demographics of a lake-wide populations exposed to varying control and environmental scenarios. Priority areas for research include investigating if temperatures have increased in sea lamprey-producing streams in response to climate change, using close-kin mark-recapture to mark family groups at age 1 to age large larvae and transformers years later, and determining if sex determination is environmentally mediated by larval growth and density.
During the 2018 eruption of Kīlauea Volcano, Hawai'i, scientists relied heavily on a conceptual model of explosive eruptions triggered when lava‐lake levels drop below the water table. Numerical modeling of multiphase groundwater flow and heat transport revealed that, contrary to expectations, liquid water inflow to the drained magma conduit would likely be delayed by months to years, owing to the inability of liquid water to transit a zone of very hot rock. The summit of Kīlauea subsequently experienced an ∼2‐month period of consistent repeated collapses, and the crater now extends below the equilibrium position of the water table. Liquid water first emerged into the deepened crater in late July 2019. The timing of first appearance of liquid water (about 14 months postcollapse) and the rate of crater lake filling (currently ∼27 kg/s) were well‐predicted by the numerical modeling done in late spring 2018, which forecast liquid inflow after 3 to 24 months at rates of 10 to 100 kg/s. A second‐generation groundwater model, reflecting the current crater geometry, forecasts lake filling over the next several years. The successful 2018 to present forecasts with both models are based on unadjusted in situ permeability estimates (1 to 6 × 10−14 m2) and water‐table elevations (600 to 800 m) from a nearby research drillhole and geophysical surveys. Important unknowns that affect the reliability of longer‐term forecasts include the equilibrium water‐table geometry, the rate of evaporation from the hot and growing crater lake (currently ∼29,000 m2 at 70‐80 °C), and heterogenous permeability changes caused by the 2018 collapse.
Glacial aquifers are an important source of groundwater in the United States and require accurate characterization to make informed management decisions. One parameter that is crucial for understanding the movement of groundwater is hydraulic conductivity, K. Nuclear magnetic resonance (NMR) logging measures the NMR response associated with the water in geological materials. By utilizing an external magnetic field to manipulate the nuclear spins associated with 1H, the time‐varying decay of the nuclear magnetization is measured. This logging method could provide an effective way to estimate K at submeter vertical resolution, but the models that relate NMR measurements to K require calibration. At two field sites in a glacial aquifer in central Wisconsin, we collected a total of four NMR logs and obtained measurements of K in their immediate vicinity with a direct‐push permeameter (DPP). Using a bootstrap algorithm to calibrate the Schlumberger‐Doll Research (SDR) NMR‐K model, we estimated K to within a factor of 5 of the DPP measurements. The lowest levels of accuracy occurred in the lower‐K (K < 10−4 m/s) intervals. We also evaluated the applicability of prior SDR model calibrations. We found the NMR calibration parameters varied with K, suggesting the SDR model does not incorporate all the properties of the pore space that control K. Thus, the expected range of K in an aquifer may need to be considered during calibration of NMR‐K models. This study is the first step toward establishing NMR logging as an effective method for estimating K in glacial aquifers.
The migration of larval fish from spawning to rearing habitat in rivers is not well understood. This paper describes a methodology to predict larval drift using a Lagrangian particle-tracking (LPT) model with passive and active behavioural components loosely coupled to a quasi-three-dimensional hydraulic model. In the absence of measured larval drift, a heuristic approach is presented for the larval drift of two species of interest, white sturgeon (Acipenser transmontanus) and burbot (Lota lota), in the Kootenai River, Idaho. Previous studies found that many fish species prefer certain vertical zones within the water column; sturgeon tend to be found near the bottom and burbot close to the water surface. Limiting the vertical movement of larvae is incorporated into the active component of the LPT model. The results illustrate a pattern of drift where secondary flow in meander bends and other zones of flow curvature redistributes particles toward the outside of the bend for surface drifters and toward the inside of the bend for bottom drifters. This pattern periodically reinforces the intersection of drifting larvae with channel margins in meander bends. In the absence of measured larval drift data, the model provides a tool for hypothesis testing and a guide to both field and laboratory experiments to further define the role of active behaviour in drifting larvae.
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Incubation behaviours vary based on individual body condition, energy requirements and environmental factors. We assessed nest attendance patterns in Cinnamon Teal Spatula cyanoptera breeding in the San Luis Valley of Colorado in 2016–2017 using trail and video cameras to observe behaviours throughout incubation. We evaluated the effect of temporal, life-history and environmental covariates on the frequency and duration of incubation recesses as well as the incubation constancy. There was considerable model uncertainty among the models used to evaluate recess frequency. Recess duration varied according to the interaction between nest age and a quadratic effect of time of day, with hens on older nests taking longer recesses in the afternoon and hens on nests earlier in incubation taking longer recesses in the morning and evening. Incubation constancy decreased with higher ambient temperatures in the study area. This study provides evidence that Cinnamon Teal modify their behaviour during incubation according to the age of the nest and the time of day. These results improve our knowledge of Cinnamon Teal breeding ecology and shed light on the behaviours that fast-lived species may use to cope with environmental factors during nesting.
Spatial information on the distribution of ecosystem patterns and processes can be a critical component of designing and implementing effective management programs in river‐floodplain ecosystems. For example, translating how flood pulses detected within a stream gauge record are spatially manifested across a river‐valley bottom can be used to evaluate whether the current distribution of physical conditions has the potential to support priority habitats or if intervention is needed to meet desired goals. The size and complexity of large river‐floodplain systems can make mapping inundation dynamics a challenging task. We used a geospatial model to simulate 40 years (1972–2011) of daily surface‐water inundation depths for 11,331 km2 of the Upper Mississippi River System floodplain. We identified discrete inundation events at each 4‐m × 4‐m pixel in the model as sequential days of submergence. We then quantified and mapped four aspects of inundation regime – event frequency, duration, magnitude, and timing – for each pixel. The spatial distribution of inundation regime attributes varied within and among multiple levels of river organization, including navigation pools and geomorphic reaches, but only event timing exhibited a strong down‐river trend. Non‐linear relations among inundation attributes and their geospatial distributions likely reflect complex interactions among topographic, hydrologic, and anthropogenic constraints on flooding dynamics. Together, our results reveal spatial gradients in inundation dynamics not captured by hydrologic data alone. Characterizing such diversity in inundation dynamics is important for testing hypotheses about ecological processes, developing models of ecosystem functions, and informing management actions.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3628","usgsCitation":"Van Appledorn, M., De Jager, N.R., and Rohweder, J.J., 2021, Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications: River Research and Applications, v. 37, no. 2, p. 241-255, https://doi.org/10.1002/rra.3628.","productDescription":"15 p.","startPage":"241","endPage":"255","ipdsId":"IP-113745","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":383139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.3955078125,\n 38.51378825951165\n ],\n [\n -88.154296875,\n 40.245991504199026\n ],\n [\n -86.572265625,\n 41.07935114946899\n ],\n [\n -86.7919921875,\n 41.50857729743935\n ],\n [\n -88.0224609375,\n 42.45588764197166\n ],\n [\n -89.3408203125,\n 44.213709909702054\n ],\n [\n -91.7578125,\n 45.85941212790755\n ],\n [\n -92.63671875,\n 46.195042108660154\n ],\n [\n -92.59277343749999,\n 47.724544549099676\n ],\n [\n -94.8779296875,\n 47.249406957888446\n ],\n [\n -95.9326171875,\n 47.30903424774781\n ],\n [\n -95.4052734375,\n 44.84029065139799\n ],\n [\n -93.6474609375,\n 42.13082130188811\n ],\n [\n -93.515625,\n 39.605688178320804\n ],\n [\n -90.3955078125,\n 38.51378825951165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810091,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}