As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1) rainfall intensity thresholds for postfire debris-flow initiation and (2) debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well these models performed over a 2-year period in the 2020 Grizzly Creek Fire burn perimeter in Glenwood Canyon, Colorado, USA, through the development of a debris-flow response inventory. The study area had the advantage of a network of 11 rain gauges for rainfall intensity measurements and repeat lidar data for volume estimates. Our observations showed that 89 % of observed debris flows in the first year postfire were triggered by rainfall rates higher than the fire-wide rainfall threshold produced by the current USGS operational model (M1). No debris flows were observed in the second year postfire, despite eight rainstorms with intensities higher than the modeled rainfall threshold. We found that the operational model for debris-flow initiation rainfall thresholds works well in this region during the first year but may be too conservative in year 2 due to vegetation recovery and sediment depletion. However, rainfall thresholds in the second year can be improved by using updated remote sensing imagery to recalculate the debris-flow initiation likelihood with the M1 model. The current volume model overestimates for this region by a median value of 4.4 times. However, the offset between the model estimates and observations is approximately linear, and the volumes from the Grizzly Creek debris flows had a similar magnitude to historic postfire debris flows in the region. Consequently, the current volume model could be adjusted with a regional correction factor.
The Plains Spotted Skunk (Spilogale interrupta) is a small carnivore native to central North America that has experienced significant population reductions, and there is a lack of information about the species that could inform conservation. Our study aimed to address knowledge gaps about the distribution and habitat associations of the species in South Dakota using species distribution modeling. We used species location data collected from state natural resource managers, trappers, and members of online social media groups dedicated to hunting and wildlife conservation; environmental predictors; and 6 predictive modeling algorithms (i.e., artificial neural networks, artificial classification tree analysis, generalized boosting models, maximum entropy, multivariate adaptive regression splines, and random forests) to develop climate and landcover ensemble distribution models. The most important climate and landcover predictors were mean temperature diurnal range (i.e., average monthly differences between daily high and low temperatures) and proportion of area classified as pasture. Ensemble model concordance identified approximately 31,300 km2 of potential Plains Spotted Skunk habitat primarily in eastern South Dakota and between the watersheds of the Missouri and James rivers. Our results offer insights that can guide conservation and inform effective management strategies for conserving Plains Spotted Skunk populations in the northern Great Plains. The promotion of low-intensity agricultural practices such as maintaining pastures, farm buildings, fences rows, and the management of woodland encroachment may improve habitat suitability and facilitate the recovery of plains spotted skunks in the region.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyae063","usgsCitation":"White, K.M., Cheeseman, A.E., Stafford, J.D., and Lonsinger, R.C., 2024, Pasture and diurnal temperature are key predictors of regional Plains Spotted Skunk (Spilogale interrupta) distribution: Journal of Mammalogy, https://doi.org/10.1093/jmammal/gyae063.","ipdsId":"IP-154327","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":462702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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Dakota\",\"nation\":\"USA \"}}]}","edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, Kara M.","contributorId":341115,"corporation":false,"usgs":false,"family":"White","given":"Kara","email":"","middleInitial":"M.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheeseman, Amanda E.","contributorId":341116,"corporation":false,"usgs":false,"family":"Cheeseman","given":"Amanda","email":"","middleInitial":"E.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":907963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stafford, Joshua D. 0000-0001-7590-8708 jstafford@usgs.gov","orcid":"https://orcid.org/0000-0001-7590-8708","contributorId":267260,"corporation":false,"usgs":true,"family":"Stafford","given":"Joshua","email":"jstafford@usgs.gov","middleInitial":"D.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907964,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lonsinger, Robert Charles 0000-0002-1040-7299","orcid":"https://orcid.org/0000-0002-1040-7299","contributorId":340524,"corporation":false,"usgs":true,"family":"Lonsinger","given":"Robert","email":"","middleInitial":"Charles","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907965,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257547,"text":"70257547 - 2024 - Climate, food and humans predict communities of mammals in the United States","interactions":[],"lastModifiedDate":"2024-09-10T15:02:17.761832","indexId":"70257547","displayToPublicDate":"2024-06-27T10:18:40","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Climate, food and humans predict communities of mammals in the United States","docAbstract":"The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities.
Conterminous United States.
2010–2021.
Twenty-five species of mammals.
We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities.
Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard-mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine-scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west.
Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13900","usgsCitation":"Kays, R., Snider, M.H., Hess, G., Cove, M.V., Jensen, A., Shamon, H., McShea, W.J., Rooney, B., Allen, M.L., Pekins, C.E., Wilmers, C., Pendergast, M.E., Green, A.M., Suraci, J., Leslie, M.S., Nasrallah, S., Farkas, D., Jordan, M., Grigione, M., LaScaleia, M., Davis, M.L., Hansen, C., Millspaugh, J., Lewis, J.S., Havrda, M., Long, R., Remine, K.R., Jaspers, K.J., Lafferty, D.J., Hubbard, T., Studds, C.E., Barthelmess, E.L., Andy, K., Romero, A., O’Neill, B.J., Hawkins, M.T., Lombardi, J.V., Sergeyev, M., Fisher-Reid, M.C., Rentz, M.S., Nagy, C., Davenport, J.D., Rega-Brodsky, C.C., Appel, C.L., Lesmeister, D.B., Giery, S.T., Whittier, C.A., Alston, J., Sutherland, C., Rota, C., Murphy, T., Lee, T.E., Mortelliti, A., Bergman, D.L., Compton, J.A., Gerber, B.D., Burr, J., Rezendes, K., DeGregorio, B.A., Wehr, N.H., Benson, J.F., O’Mara, M.T., Jachowski, D., Gray, M., Beyer, D.E., Belant, J., Horan, R.V., 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(USGS site ID 11502500) Rivers using high temporal resolution turbidity and streamflow data to develop surrogate regression models. Regression models were updated and validated for TP at the Williamson River site, and additional data improved a prior published TP model, increasing the coefficient of determination (R2) from 0.73 to 0.88. A new TP regression model was developed for the Sprague River site using 2 years of data and showed promising results with an R2 of 0.93. Suspended-sediment concentration (SSC) surrogate models were also updated at these sites using a longer period of record than the TP models and improved characterization of sediment transport conditions at these monitoring sites.
Computations of TP loads were compared to the annual loading capacity dictated by the total maximum daily load (TMDL) for Upper Klamath Lake and showed that the combined TP load of the Williamson and Sprague Rivers approaches the annual loading capacity in water years with high annual streamflow. TP loads were also compared to loads computed by the Klamath Tribes using a long-term dataset and a regression and interpolation algorithm (RIA). The comparison showed that the two methods report similar annual loads, with the surrogate regression method generally reporting lower loads than the RIA, and the RIA annual loads falling within the range of uncertainty of the surrogate regression model results. Determining the effect of habitat and stream restoration on basin-scale TP and suspended-sediment loading is challenging using the surrogate regression method at these sites given the short period of record that TP and suspended-sediment load (SSL) data are available. However, long-term analysis by the Klamath Tribes in their larger monitoring network could provide insight into the impact of restoration at smaller spatial scales compared to the basin-wide assessment produced in this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241034","collaboration":"Prepared in cooperation with the Klamath Tribes and the Oregon Watershed Enhancement Board","usgsCitation":"Schenk, L.N., and Simeone, C., 2024, Total phosphorus and suspended-sediment concentrations and loads from two main tributaries to Upper Klamath Lake, Oregon, 2014–20: U.S. Geological Survey Open-File Report 2024–1034, 18 p., https://doi.org/10.3133/ofr20241034.","productDescription":"viii, 18 p.","onlineOnly":"Y","ipdsId":"IP-155613","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":430574,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1034/ofr20241034.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1034"},{"id":430573,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1034/ofr20241034.jpg"},{"id":430577,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1034/ofr20241034.XML"},{"id":430576,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1034/images"},{"id":430575,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241034/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1034"},{"id":499255,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117101.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.39459049262162,\n 42.010424780518264\n ],\n [\n -119.81143415588565,\n 42.010424780518264\n ],\n [\n -119.81143415588565,\n 43.21983555337732\n ],\n [\n -122.39459049262162,\n 43.21983555337732\n ],\n [\n -122.39459049262162,\n 42.010424780518264\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
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601 SW Second Avenue, Suite 1950
Portland, Oregon 97204
Survival rate of the larval stage is an important driver of fish recruitment. To understand mechanisms regulating larval survival it is important to understand the relative importance of abiotic and biotic factors that shape larval spatial distributions. We studied larval Coregonus distributions in surface waters (surface to 1 m) by repeatedly sampling study sites in two lakes that varied greatly in trophic state and regional climate. We evaluated the importance of bathymetric depth, Julian Day, edible zooplankton densities (EZ, ind. L−1) and wind vectors on larval spatial distributions using generalized additive modeling. In both systems, larval counts declined in a negative exponential fashion with bathymetric depth, indicating shallow depths are critical nursery habitat. The north-south wind vectors and Julian Day (which was positively correlated with surface temperature) influenced larval distributions in Lake Geneva with larval counts related to both variables linearly, whereas the east-west wind vector and EZ were unimportant. Highest larval counts were during an offshore south wind and declined slightly with Julian Day. In Lake Superior, bathymetric depth and the east-west wind vector influenced larval distributions and were unrelated to EZ, Julian Day, and the north-south wind vector. Larval counts were highest when onshore southwest winds preceded sampling. Differences in how wind affected larval distribution (offshore vs. onshore) might be related to larval size with Lake Superior larvae considerably smaller (average length 12.9 mm vs. 15.9 mm); thus, more apt to be subjected to advection. Within coastal waters, Julian Day and wind vectors influence distributions, but their importance seemingly varies lake-to-lake.
","language":"English","publisher":"EcoSciences","doi":"10.1051/limn/2024013","usgsCitation":"Dobosenski, J.A., Yule, D.L., Guillard, J., Anneville, O., Isaac, E., Stockwell, J.D., Myers, J., Ackiss, A.S., Chapina, R.J., and Moore, S., 2024, Factors influencing larval coregonine spatial distribution in Lake Geneva (Europe) and Lake Superior (North America) during a single season near known spawning sites: International Journal of Limnology, v. 60, no. 12, 12, 21 p., https://doi.org/10.1051/limn/2024013.","productDescription":"12, 21 p.","ipdsId":"IP-159935","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":432483,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439333,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1051/limn/2024013","text":"Publisher Index Page"}],"country":"France, United States","state":"Minnesota","otherGeospatial":"Lake Geneva, Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.78267355691995,\n 47.99897216779462\n ],\n [\n -89.78267355691995,\n 47.90122910288531\n ],\n [\n -89.55798468980855,\n 47.90122910288531\n ],\n [\n -89.55798468980855,\n 47.99897216779462\n ],\n [\n -89.78267355691995,\n 47.99897216779462\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 6.238951771684498,\n 46.427296321727056\n ],\n [\n 6.238951771684498,\n 46.291519022939156\n ],\n [\n 6.534382971374612,\n 46.291519022939156\n ],\n [\n 6.534382971374612,\n 46.427296321727056\n ],\n [\n 6.238951771684498,\n 46.427296321727056\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Dobosenski, Jamie A.","contributorId":239602,"corporation":false,"usgs":false,"family":"Dobosenski","given":"Jamie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":909586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":909587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guillard, Jean","contributorId":342064,"corporation":false,"usgs":false,"family":"Guillard","given":"Jean","affiliations":[{"id":81834,"text":"Univ. Savoie Mont Blanc, INRAE, CARRTEL","active":true,"usgs":false}],"preferred":false,"id":909588,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anneville, Orlane","contributorId":147752,"corporation":false,"usgs":false,"family":"Anneville","given":"Orlane","affiliations":[{"id":16922,"text":"INRA UMR CARRTEL, Thonon-les-Bains, France","active":true,"usgs":false}],"preferred":false,"id":909589,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Isaac, Edmund J.","contributorId":342065,"corporation":false,"usgs":false,"family":"Isaac","given":"Edmund J.","affiliations":[{"id":81835,"text":"Grand Portage Band of Lake Superior Chippewa","active":true,"usgs":false}],"preferred":false,"id":909590,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stockwell, Jason D. 0000-0003-3393-6799","orcid":"https://orcid.org/0000-0003-3393-6799","contributorId":61004,"corporation":false,"usgs":false,"family":"Stockwell","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":909591,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Myers, Jared T. 0009-0004-9362-8792","orcid":"https://orcid.org/0009-0004-9362-8792","contributorId":44055,"corporation":false,"usgs":false,"family":"Myers","given":"Jared T.","affiliations":[{"id":6596,"text":"Quantitative Fisheries Center, Department of Fisheries and Wildlife Michigan State University","active":true,"usgs":false}],"preferred":false,"id":909592,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ackiss, Amanda Susanne 0000-0002-8726-7423","orcid":"https://orcid.org/0000-0002-8726-7423","contributorId":272165,"corporation":false,"usgs":true,"family":"Ackiss","given":"Amanda","email":"","middleInitial":"Susanne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":909593,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chapina, Rosaura J.","contributorId":342066,"corporation":false,"usgs":false,"family":"Chapina","given":"Rosaura","email":"","middleInitial":"J.","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":909594,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Moore, Seth A.","contributorId":342067,"corporation":false,"usgs":false,"family":"Moore","given":"Seth A.","affiliations":[{"id":81835,"text":"Grand Portage Band of Lake Superior Chippewa","active":true,"usgs":false}],"preferred":false,"id":909595,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70255885,"text":"70255885 - 2024 - Detection of periodic peaks in Karenia brevis concentration consistent with the time-delay logistic equation","interactions":[],"lastModifiedDate":"2024-07-10T12:08:55.656416","indexId":"70255885","displayToPublicDate":"2024-06-27T07:07:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Detection of periodic peaks in Karenia brevis concentration consistent with the time-delay logistic equation","docAbstract":"The logistic equation models single-species population growth with a sigmoid curve that begins as exponential and ends with an asymptotic approach to a final population determined by natural system carrying capacity. But the population of a natural system often does not stabilize as it approaches carrying capacity. Instead, it exhibits periodic change, sometimes with very large amplitudes. The time-delay modification of the logistic equation accounts for this behavior by connecting the present rate of population growth to conditions at an earlier time. The periodic change in population with time can progress from a monotonic approach to the carrying capacity; to oscillation around the carrying capacity; to limit-cycle periodic change; and, finally, to chaotic change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.174061","usgsCitation":"Kurtz, B.E., Landmeyer, J.E., and Culter, J.K., 2024, Detection of periodic peaks in Karenia brevis concentration consistent with the time-delay logistic equation: Science of the Total Environment, v. 946, 174061, 13 p., https://doi.org/10.1016/j.scitotenv.2024.174061.","productDescription":"174061, 13 p.","ipdsId":"IP-156971","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":439336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.174061","text":"Publisher Index Page"},{"id":430887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.22453932882675,\n 28.202140467401122\n ],\n [\n -83.22453932882675,\n 25.11977470858126\n ],\n [\n -80.62077956320161,\n 25.11977470858126\n ],\n [\n -80.62077956320161,\n 28.202140467401122\n ],\n [\n -83.22453932882675,\n 28.202140467401122\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"946","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kurtz, Bruce E.","contributorId":304961,"corporation":false,"usgs":false,"family":"Kurtz","given":"Bruce","email":"","middleInitial":"E.","affiliations":[{"id":35150,"text":"New College of Florida","active":true,"usgs":false}],"preferred":false,"id":905888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landmeyer, James E. 0000-0002-5640-3816","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":216137,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James","email":"","middleInitial":"E.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":905889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Culter, James K.","contributorId":304962,"corporation":false,"usgs":false,"family":"Culter","given":"James","email":"","middleInitial":"K.","affiliations":[{"id":66192,"text":"Mote Marine Laboratory and Aquarium","active":true,"usgs":false}],"preferred":false,"id":905890,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255923,"text":"70255923 - 2024 - Boulders modulate hillslope-channel coupling in the northern Alaska Range","interactions":[],"lastModifiedDate":"2024-09-11T16:17:12.569241","indexId":"70255923","displayToPublicDate":"2024-06-27T07:02:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Boulders modulate hillslope-channel coupling in the northern Alaska Range","docAbstract":"Active orogens balance tectonic rock uplift with erosion, commonly via river incision coupled to landslide denudation of “threshold” hillslopes, but sediment’s role in this feedback is unclear. We report fluvial geometry, and sediment size, prevalence, and mobility across two ≤600-m-tall gneissic northern Alaska Range anticlines that sustain steep landslide-clad hillslopes but differ 10× in late Pleistocene−recent rock uplift rate. Enigmatically, the river steepens and narrows prominently across the fold experiencing slow surface uplift (∼0.5 mm/yr) but remains low-gradient and wide downstream across the anticline undergoing rapid differential rock uplift (∼5 mm/yr). Frequent bedload mobilization across both folds implies fluvial equilibration to sediment transport despite discrepant channel forms and similarly prevalent hillslope-derived boulders. Boulder prevalence correlates significantly with channel slope and width on the slowly uplifting anticline, but weakly on the rapidly uplifting anticline. Strong correlations across the tectonically quiescent anticline may reflect local incision-suppressing boulder aggradation that forces the channel to steepen and narrow, consistent with field observations. Conversely, weak correlations across the rapidly uplifting anticline imply that boulders may modulate expected tectonic channel adjustment by preferentially aggrading to subdue slope, and deflecting frequently mobile bedload to drive lateral erosion that maintains channel width, steepens adjacent hillslopes, and perpetuates hillslope-channel coupling. Hence, hillslope-derived boulders may occupy important roles in regulating feedbacks between river incision and landslide erosion that differ fundamentally at high and low tectonic rates.
Developing conservation strategies for highly migratory marine species relies on understanding their spatial distributions. Nesting populations of female loggerhead (Caretta caretta) turtles typically travel from widely dispersed foraging areas and make use of common internesting areas between nesting events. Protection of these areas is essential to the conservation of this species. In this study, we used satellite tracking and behavioral switching state-space movement modeling to examine the internesting use-areas, migration patterns, and foraging area distribution of a previously uninvestigated nesting loggerhead population in southeast Florida. While these turtles spent much of their internesting period close to their nesting site, only 17.4% of the identified internesting area is within the boundaries currently designated under the US Endangered Species Act as critical loggerhead ‘nearshore reproductive habitat’. Additionally, 72% of turtles in this study (17 of 21) that were tracked to foraging grounds have foraging home ranges outside of the USA, with 62% of turtles (n = 13) in The Bahamas. Considering the proximity of their internesting areas to a large human population center and their largely international foraging distribution, this population could benefit from expanding federally designated critical habitat, along with developing collaborative conservation strategies between the USA and The Bahamas.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01339","usgsCitation":"Goodwin, G.D., Hart, K., Evans, A.C., and Burkholder, D.A., 2024, Satellite telemetry reveals high-use internesting areas and international foraging extent for loggerhead turtles tagged in southeast Florida, USA: Endangered Species Research, v. 54, p. 245-259, https://doi.org/10.3354/esr01339.","productDescription":"15 p.","startPage":"245","endPage":"259","ipdsId":"IP-160712","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01339","text":"Publisher Index Page"},{"id":431234,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.97747397991283,\n 26.62851048726462\n ],\n [\n -81.97747397991283,\n 24.887138979175802\n ],\n [\n -79.64837241741277,\n 24.887138979175802\n ],\n [\n -79.64837241741277,\n 26.62851048726462\n ],\n [\n -81.97747397991283,\n 26.62851048726462\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"54","noUsgsAuthors":false,"publicationDate":"2024-06-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Goodwin, Glenn D. 0000-0001-6802-9924","orcid":"https://orcid.org/0000-0001-6802-9924","contributorId":316773,"corporation":false,"usgs":false,"family":"Goodwin","given":"Glenn","email":"","middleInitial":"D.","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":906650,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":906651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evans, Abby C. 0009-0001-6097-6078","orcid":"https://orcid.org/0009-0001-6097-6078","contributorId":340233,"corporation":false,"usgs":false,"family":"Evans","given":"Abby","email":"","middleInitial":"C.","affiliations":[{"id":81512,"text":"Halmos College of Arts and Sciences, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":906652,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burkholder, Derek A. 0000-0001-6315-6932","orcid":"https://orcid.org/0000-0001-6315-6932","contributorId":289783,"corporation":false,"usgs":false,"family":"Burkholder","given":"Derek","email":"","middleInitial":"A.","affiliations":[{"id":62249,"text":"Halmos College of Natural Sciences and Oceanography, Department of Marine and Environmental Science, Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":906653,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256562,"text":"70256562 - 2024 - Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2024-08-22T16:05:19.777014","indexId":"70256562","displayToPublicDate":"2024-06-26T10:59:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3784,"text":"Wilson Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico","title":"Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico","docAbstract":"North American Black Terns (Chlidonias niger) breed primarily in the Prairie Pothole region of southern Canada and the northern United States, winter in Central and South American waters, and often migrate through the northern Gulf of Mexico (nGoM). This species has exhibited long-term population declines and is exposed to a myriad of anthropogenic threats in the nGoM, including oil spills, with an estimated 800–1,000 injured during the Deepwater Horizon oil spill, yet historical studies of Black Terns' use of the nGoM are sparse, with inconsistent spatial and temporal coverage. Using vessel-based observations collected from 2017 to 2019, we characterize Black Tern spatial and temporal occurrence in marine waters of the nGoM. We develop 2 separate habitat models: one describing spatial and temporal aspects of Black Terns occurrence and the other describing the relative density when present. In 10 months of survey effort, January–October, we observed Black Terns in 7 (Mar–May and Jul–Oct), predominantly on the continental shelf at <200 m depth. Relative densities were greatest in the fall, coinciding with Black Terns' southward migration. Spatial distribution and habitat models suggest an association with river mouths or ports, as well as cool, productive waters, frequently associated near the outflow of the Mississippi River and just off the coast from Corpus Christi, Texas. The enhanced understanding of Black Terns in the nGoM could inform the preparation for, and response to, future oiling events or provide insight into potential interactions with the installation of offshore wind farms and aquaculture.
","language":"English","publisher":"Wilson Ornithological Society","doi":"10.1676/23-00069","usgsCitation":"Michael, P.E., Gleason, J., Haney, J., Hixson, K.M., Satgé, Y., and Jodice, P.G., 2024, Black Terns (Chlidonias niger) beyond the breeding grounds: Occurrence, relative density, and habitat associations in the northern Gulf of Mexico: Wilson Journal of Ornithology, v. 136, no. 2, p. 220-236, https://doi.org/10.1676/23-00069.","productDescription":"17 p.","startPage":"220","endPage":"236","ipdsId":"IP-155261","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433067,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.06640768994139,\n 25.968336212595545\n ],\n [\n -81.56410467268525,\n 24.655591782200545\n ],\n [\n -81.0728587401813,\n 25.246086829050483\n ],\n [\n -82.48259218249659,\n 27.272254831037472\n ],\n [\n -82.73152989277146,\n 27.960240807935207\n ],\n [\n -82.69786543399637,\n 28.715489724979037\n ],\n [\n -84.01930283028096,\n 30.13642592586335\n ],\n [\n -85.21352357055073,\n 29.683019111608573\n ],\n [\n -86.43868847529218,\n 30.499185485782192\n ],\n [\n -87.48025217176053,\n 30.277107124160494\n ],\n [\n -88.01869425708601,\n 30.521051975468964\n ],\n [\n -89.17037649125658,\n 30.240796867164235\n ],\n [\n -89.90702908193344,\n 29.518977092248832\n ],\n [\n -90.49189050390542,\n 29.36185843606789\n ],\n [\n -91.74270448217844,\n 29.90068888910362\n ],\n [\n -93.01117136756345,\n 29.866650987283208\n ],\n [\n -94.66379808733016,\n 29.61102885408677\n ],\n [\n -94.72573793650419,\n 29.93320591649301\n ],\n [\n -94.98767798427114,\n 29.573223193987033\n ],\n [\n -95.28662091934446,\n 28.98698052519731\n ],\n [\n -96.75825455051769,\n 28.63898925070241\n ],\n [\n -97.57160445145992,\n 27.963607001385867\n ],\n [\n -97.86987981335206,\n 27.03862279886482\n ],\n [\n -97.06640768994139,\n 25.968336212595545\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"136","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Michael, Pamela E.","contributorId":341152,"corporation":false,"usgs":false,"family":"Michael","given":"Pamela","email":"","middleInitial":"E.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gleason, Jeffrey S.","contributorId":341153,"corporation":false,"usgs":false,"family":"Gleason","given":"Jeffrey S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haney, J. Christopher","contributorId":341154,"corporation":false,"usgs":false,"family":"Haney","given":"J. Christopher","affiliations":[{"id":81710,"text":"Terra Mar Applied Science","active":true,"usgs":false}],"preferred":false,"id":908008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hixson, Kathy M.","contributorId":341155,"corporation":false,"usgs":false,"family":"Hixson","given":"Kathy","email":"","middleInitial":"M.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908009,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Satgé, Yvan G.","contributorId":341156,"corporation":false,"usgs":false,"family":"Satgé","given":"Yvan G.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908010,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908011,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255599,"text":"sir20245056 - 2024 - Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois","interactions":[],"lastModifiedDate":"2026-02-03T19:43:57.736197","indexId":"sir20245056","displayToPublicDate":"2024-06-25T15:43:18","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5056","displayTitle":"Two-Dimensional Hydraulic Model for the Chain of Lakes on the Fox River near McHenry, Illinois","title":"Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois","docAbstract":"Forecasts of flows entering and leaving the Chain of Lakes on the Fox River in northeastern Illinois are critical information to water-resource managers operating the Stratton Dam at McHenry, Illinois. These managers determine the optimal operation of the Stratton Dam at McHenry, Ill., to manage Chain of Lakes pool levels and to help mitigate flooding in the Chain of Lakes system. In 2020, the U.S. Geological Survey (USGS) and the Illinois Department of Natural Resources–Office of Water Resources (IDNR–OWR) began a cooperative study to develop a system to enable engineers and planners to simulate and communicate water-surface elevations and flows and to proactively prepare for runoff events forecasted for the Chain of Lakes. The hydraulic model described in this report may be helpful to the IDNR–OWR for optimizing the operation of the Stratton Dam and includes the implementation of three newly installed torque-tube crest gates that became operational in 2020.
The hydraulic model for the Chain of Lakes was developed using the Hydrologic Engineering Center–River Analysis System program (version 6.5). The hydraulic model was used to simulate water-surface elevations and flows through the 18.5-mile Chain of Lakes system to 1.7 miles downstream from the Stratton Dam. Five USGS streamgages within the study area were used as reference points for model calibration and initial water-surface elevations for beginning a simulation. The hydraulic model was calibrated to three runoff events that incorporated the design specifications and observed gate operations of the Stratton Dam; furthermore, the hydraulic model simulated a validation event and a substantial flooding event during July 2017. The July 2017 event predated the torque-tube crest gate installation but nevertheless tested the performance of the model for such a substantial event. The model simulation results were a good fit to observed records at USGS streamgages with simulated peak water-surface elevations within −0.36–0.15 foot of observed events. The hydraulic model was then implemented into a forecast workflow that streamlines implementation of model inputs and documents the model outputs tailored to IDNR–OWS Stratton Dam operations and interpretations of simulated water-surface elevations and flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245056","collaboration":"Prepared in cooperation with the Illinois Department of Natural Resources–Office of Water Resources","usgsCitation":"Cigrand, C.V., and Ament, M.R., 2024, Two-dimensional hydraulic model for the Chain of Lakes on the Fox River near McHenry, Illinois: U.S. Geological Survey Scientific Investigations Report 2024–5056, 20 p., https://doi.org/10.3133/sir20245056.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137180","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499478,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117099.htm","linkFileType":{"id":5,"text":"html"}},{"id":430505,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":430504,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P16H3TDH","text":"USGS data release","linkHelpText":"Archive of the hydraulic model used in the two-dimensional simulation of the Chain of Lakes on the Fox River near McHenry, Illinois:"},{"id":430503,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245056/full"},{"id":430502,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5056/images/"},{"id":430501,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5056/sir20245056.XML"},{"id":430500,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5056/sir20245056.pdf","text":"Report","size":"3.5 MB","description":"SIR 2024–5056"},{"id":430499,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5056/coverthb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Fox River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.3061687403806,\n 42.29838954847517\n ],\n [\n -88.08497136642455,\n 42.29838954847517\n ],\n [\n -88.08497136642455,\n 42.4987780744203\n ],\n [\n -88.3061687403806,\n 42.4987780744203\n ],\n [\n -88.3061687403806,\n 42.29838954847517\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Advances in tagging technologies are expanding opportunities to estimate survival of fish and wildlife populations. Yet, capture and handling effects could impact survival outcomes and bias inference about natural mortality processes. We developed a multistage time-to-event model that can partition the survival process into sequential phases that reflect the tagged animal experience, including handling and release mortality, post-release recovery mortality, and subsequently, natural mortality. We demonstrate performance of multistage survival models through simulation testing and through fish and bird telemetry case studies. Models are implemented in a Bayesian framework and can accommodate left, right, and interval censorship events. Our results indicate that accurate survival estimates can be achieved with reasonable sample sizes (
Introduction: Terrestrial species in riverine ecosystems face unique constraints leading to diverging patterns of population structure, connectivity, and disease dynamics. Desert bighorn sheep (Ovis canadensis nelsoni) in Grand Canyon National Park, a large native population in the southwestern USA, offer a unique opportunity to evaluate population patterns and processes in a remote riverine system with ongoing anthropogenic impacts. We integrated non-invasive, invasive, and citizen-science methods to address questions on abundance, distribution, disease status, genetic structure, and habitat fragmentation.
Methods: We compiled bighorn sightings collected during river trips by park staff, commercial guides, and private citizens from 2000–2018 and captured bighorn in 2010–2016 to deploy GPS collars and test for disease. From 2011–2015, we non-invasively collected fecal samples and genotyped them at 9–16 microsatellite loci for individual identification and genetic structure. We used assignment tests to evaluate genetic structure and identify subpopulations, then estimated gene flow and recent migration to evaluate fragmentation. We used spatial capture-recapture to estimate annual population size, distribution, and trends after accounting for spatial variation in detection with a resource selection function model.
Results and discussion: From 2010–2018, 3,176 sightings of bighorn were reported, with sightings of 56–145 bighorn annually on formal surveys. From 2012–2016, bighorn exhibiting signs of respiratory disease were observed along the river throughout the park. Of 25 captured individuals, 56% were infected by Mycoplasma ovipneumoniae, a key respiratory pathogen, and 81% were recently exposed. Pellet sampling for population estimation from 2011–2015 yielded 1,250 genotypes and 453 individuals. We detected 6 genetic clusters that exhibited mild to moderate genetic structure (FST 0.022–0.126). The river, distance, and likely topography restricted recent gene flow, but we detected cross-river movements in one section via genetic recaptures, no subpopulation appeared completely isolated, and genetic diversity was among the highest reported. Recolonization of one large stretch of currently empty habitat appears limited by the constrained topology of this system. Annual population estimates ranged 536–552 (95% CrI range 451–647), lamb:ewe ratios varied, and no significant population decline was detected. We provide a multi-method sampling framework useful for sampling other wildlife in remote riverine systems.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2024.1377214","usgsCitation":"Epps, C.W., Holton, P.B., Monello, R.J., Crowhurst, R.S., Gaulke, S.M., Janousek, W.M., Creech, T.G., and Graves, T., 2024, Population and spatial dynamics of desert bighorn sheep in Grand Canyon during an outbreak of respiratory pneumonia: Frontiers in Ecology and Evolution, v. 12, 1377214, 22 p., https://doi.org/10.3389/fevo.2024.1377214.","productDescription":"1377214, 22 p.","ipdsId":"IP-137271","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":439348,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.3389/fevo.2024.1377214","text":"Publisher Index Page"},{"id":434937,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K89AA3","text":"USGS data release","linkHelpText":"Desert bighorn sheep (Ovis canadensis nelsoni) datasets from Grand Canyon National Park, 2010-2016"},{"id":430966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.5504498659519,\n 36.84244671894457\n ],\n [\n -114.04234802909026,\n 36.84244671894457\n ],\n [\n -114.04234802909026,\n 35.72909582502355\n ],\n [\n -111.5504498659519,\n 35.72909582502355\n ],\n [\n -111.5504498659519,\n 36.84244671894457\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Epps, Clinton W.","contributorId":198148,"corporation":false,"usgs":false,"family":"Epps","given":"Clinton","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":906239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holton, P. Brandon","contributorId":340119,"corporation":false,"usgs":false,"family":"Holton","given":"P.","email":"","middleInitial":"Brandon","affiliations":[],"preferred":false,"id":906240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monello, Ryan J.","contributorId":184143,"corporation":false,"usgs":false,"family":"Monello","given":"Ryan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":906241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crowhurst, Rachel S.","contributorId":198153,"corporation":false,"usgs":false,"family":"Crowhurst","given":"Rachel","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":906242,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gaulke, Sarah Mccrimmon 0000-0002-2657-5844","orcid":"https://orcid.org/0000-0002-2657-5844","contributorId":225564,"corporation":false,"usgs":true,"family":"Gaulke","given":"Sarah","email":"","middleInitial":"Mccrimmon","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":906243,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Janousek, William Michael 0000-0003-3978-1775","orcid":"https://orcid.org/0000-0003-3978-1775","contributorId":237980,"corporation":false,"usgs":true,"family":"Janousek","given":"William","email":"","middleInitial":"Michael","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":906244,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Creech, Tyler G.","contributorId":198152,"corporation":false,"usgs":false,"family":"Creech","given":"Tyler","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":906245,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":906246,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70255575,"text":"sir20245041 - 2024 - Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network","interactions":[],"lastModifiedDate":"2026-02-03T19:22:10.1439","indexId":"sir20245041","displayToPublicDate":"2024-06-25T09:45:01","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5041","displayTitle":"Representation of Surface-Water Flows Using Gradient-Related Discharge in an Everglades Network","title":"Representation of surface-water flows using Gradient-Related Discharge in an Everglades Network","docAbstract":"The Everglades Depth Estimation Network interpolates water-level gage data to produce daily water-level elevations for the Everglades in south Florida. These elevations were used to estimate flow vectors (gradients and directions) and volumetric flow rates using the Gradient-Related Discharge in an Everglades Network (GARDEN) application developed by the U.S. Geological Survey in cooperation with the U.S. Army Corps of Engineers. Flow rates in both the east-west and north-south directions were computed on a 400-meter square grid using modified parameters in the Manning’s equation. The frictional resistance parameter in the Manning’s equation was calibrated to measured flow rates at coastal creeks fed by Everglades Depth Estimation Network boundary flows. Levees and other features that act as barriers to flow were defined as “no-flow” grid cells where vectors were set to zero.
The flow volume magnitudes were calibrated with 2020 daily values of coastal river flows, and verification was performed using 2021 data. Within a given day, the measured coastal river flows fluctuate more than the GARDEN boundary flows because of tidal and wind forcings. Because the GARDEN boundary flows were the upstream water source for the coastal rivers, calibration focused on matching average daily flow volumes rather than daily fluctuations. The Pearson’s correlation coefficient is 0.766 for the 2020 calibration period and 0.566 for the 2021 verification period.
Applying GARDEN to periods with hydraulic-control-structure releases allows the propagation of structure flows to be seen in the daily flow-vector maps along with the multiday response of flows farther downgradient. Flow vectors may be overestimated near control structures because of difficulties in resolving the water gradient downstream from the structure. Flow vectors farther from the structure are more accurate than those near the structure.
Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
We present a hydroclimate synthesis of the southern Great Basin over the last two glacial-interglacial cycles focused on paleolakes in Death Valley (core DV93-1), Searles Valley (core SLAPP-SRLS17), Owens Valley (core OL92), and the Devils Hole cave. There is close agreement between the occurrence of lakes in Death Valley and the height of the water table in the Devils Hole (50 km east of Death Valley) during the last 200 kyr. Death Valley and Devils Hole have adjacent, partly overlapping, drainage areas and most likely did over the last 200 kyr. When the water table in the Devils Hole was above the threshold level of ∼5 m higher than the modern, permanent lakes existed in Death Valley. At water table elevations less than 5 m above the modern, ephemeral lakes, saline pans, and mudflats occurred in Death Valley. The close temporal agreement between inferred paleoenvironments from the sediments in the Death Valley core and the paleowater table elevation in Devils Hole suggests a common forcing and provides insight into climate variability in the southwestern United States over the last 200 kyr. Owens Valley and Searles Valley, which derived inflow waters from the Sierra Nevada via the Owens River, contain paleohydrologic records which match those from Death Valley and the Devils Hole in terms of timing and direction of water availability over the last 200 kyr, indicating a similar paleohydrologic history for the entire southern Great Basin region. Near the end of Marine Oxygen Isotope Stage 6 (MIS 6), 140 ka - 130 ka, Lake Manly in Death Valley became shallow and hypersaline, and ultimately dried up at 127.1 ka ±4.3 ka. The transition from glacial to interglacial vegetation, which involved the loss of Juniperus pollen and an increase in Quercus (oak) pollen, occurred in Death Valley core DV93-1 at 131.3 ka ±4.0 ka. Following the glacial to interglacial pollen shift, a large alkaline lake formed in Death Valley. Similar conditions (freshwater, high productivity, and a mixed, deeply oxygenated water column indicated by biomarkers) existed in Searles Lake between 135.3 +2.7/-2.9 ka and 130.1+2.7/-2.6 ka, also following the juniper-oak pollen transition. Sr isotopes in calcite and sulfate minerals (gypsum, glauberite, thenardite), and the rare occurrence of the sodium carbonate mineral northupite with a low 87Sr/86Sr ratio in core DV93-1, together with organic geochemical proxies from Searles core SLAPP-SRLS17, all suggest that at this time, late MIS 6 Lake Manly in Death Valley received alkaline water via spillover from Searles Valley into Death Valley through Panamint Valley. The hydrologic connection between Searles Valley, Panamint Valley, and Death Valley at Termination II (130 ka) is documented here for this system of pluvial lakes for the first time. The Devils Hole water table decreased to +6.5 m at 140.8 ka ±3.2 ka, rose briefly to +8 m at 137.6 ka ±0.5 ka, and then dropped 8 m by 120.36 ka ±0.45 ka, when it reached an elevation similar to the modern. The pluvial lakes in Death Valley and Searles Valley may have coincided with the rise of the Devils Hole water table at ∼137.6 ka ±0.5 ka years ago, although the age models for core DV93-1 and core SLAPP-SLRS17 during the end of MIS 6 carry large uncertainties.
Nutrient concentrations in many estuaries have increased over the past century due to increases in wastewater discharge and increased agricultural intensity, contributing to multiple environmental problems. Numerous biogeochemical and physical processes in estuaries influence nutrient concentrations during transport, resulting in complex spatial and temporal variability and challenges identifying predominant processes and their rates. Mechanistic models which require these rates to quantify biogeochemical processes become complex and difficult to calibrate as the number of processes and parameters grows, owing to the high dimensionality of the parameter space and the computational cost of simultaneously modeling the transport and transformations of constituents. We developed a modeling approach that decouples transport from transformations, enabling fast, data-driven exploration of the parameter space. The approach extracted information including water age, cumulative exposure to specific habitats, and mean water depth exposure from a hydrodynamic model. Using this information, a biogeochemical model was implemented to predict ammonium and nitrate concentrations in a Lagrangian frame. The model performed each simulation in milliseconds on a laptop computer, allowing the fitting of rate parameters for key transformations by optimization. The optimization used fixed station nitrate observations and the model was then validated against high-resolution mapping observations of ammonium and nitrate. The results suggest that the observed spatial and temporal variation can be largely represented with five transformation processes and their associated rates. Dissolved inorganic nitrogen (DIN) losses occurred only in shallow vegetated areas in the model, highlighting that biogeochemical processes in these areas should be included in DIN models.
The vertical accuracy of elevation data in coastal environments is critical because small variations in elevation can affect an area’s exposure to waves, tides, and storm-related flooding. Elevation data contractors typically quantify the vertical accuracy of lidar-derived digital elevation models (DEMs) on a per-project basis to gauge whether the datasets meet quality and accuracy standards. Here, we collated over 5200 contractor elevation checkpoints along the Atlantic and Gulf of Mexico coasts of the United States that were collected for project-level analyses produced for assessing DEMs acquired for the U.S. Geological Survey’s Three-Dimensional Elevation Program. We used land cover data to quantify non-vegetated vertical accuracy and vegetated vertical accuracy statistics (overall and by point spacing bins) and assessed elevation error by land cover class. We found the non-vegetated vertical accuracy had an overall root mean square error of 6.9 cm and vegetated areas had a 95th percentile vertical error of 22.3 cm. Point spacing was generally positively correlated to elevation accuracy, but sample size limited the ability to interpret results from accuracy by land cover, particularly in wetlands. Based on the specific questions a researcher may be asking, use of literature or fieldwork could assist with enhancing error statistics in underrepresented classes.
Spatial machine learning models can be developed from observations with substantial unexplainable variability, sometimes called ‘noise’. Traditional point-scale metrics (e.g., R2) alone can be misleading when evaluating these models. We present a multi-scale performance evaluation (MPE) using two additional scales (distributional and geostatistical). We apply the MPE framework to predictions of depth to bedrock (DTB) in the Delaware River Basin. Geostatistical analysis shows that approximately one third of the DTB variance is at spatial scale smaller than 2 km. Hence, we interpret our point-scale R2 of 0.3 (testing data) to be sufficient for regional-scale modelling. Bias-correction methods improve performance at two of the three MPE scales: point-scale change is negligible, while distributional and geostatistical performance improves. In contrast, bias correction applied to a global DTB model does not improve MPE performance. This work encourages scale-appropriate performance evaluations to enable effective model intercomparison.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2024.106124","usgsCitation":"Goodling, P.J., Belitz, K., Stackelberg, P.E., and Fleming, B.J., 2024, A spatial machine learning model developed from noisy data requires multiscale performance evaluation: Predicting depth to bedrock in the Delaware River Basin, USA: Environmental Modelling & Software, v. 179, 106124, 12 p., https://doi.org/10.1016/j.envsoft.2024.106124.","productDescription":"106124, 12 p.","ipdsId":"IP-160581","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":439361,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2024.106124","text":"Publisher Index Page"},{"id":430446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.88099946089724,\n 38.58741180591247\n ],\n [\n -74.71321333503417,\n 39.379784628066616\n ],\n [\n -74.91854009408658,\n 39.623906471535875\n ],\n [\n -74.56682974019151,\n 39.83490997578861\n ],\n [\n -74.83087158557463,\n 40.43445755432647\n ],\n [\n -74.69462804413362,\n 42.31099383658801\n ],\n [\n -75.89851464683143,\n 42.243461978517985\n ],\n [\n -76.67474108243883,\n 40.45538711590305\n ],\n [\n -76.4341425039691,\n 39.732367924983954\n ],\n [\n -75.81256791410406,\n 39.70992285882126\n ],\n [\n -75.68892404289328,\n 38.72600697054469\n ],\n [\n -74.88099946089724,\n 38.58741180591247\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"179","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904792,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":904793,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":904794,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fleming, Brandon J. 0000-0001-9649-7485 bjflemin@usgs.gov","orcid":"https://orcid.org/0000-0001-9649-7485","contributorId":4115,"corporation":false,"usgs":true,"family":"Fleming","given":"Brandon","email":"bjflemin@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":904795,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255961,"text":"70255961 - 2024 - Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data","interactions":[],"lastModifiedDate":"2024-07-11T14:25:45.706092","indexId":"70255961","displayToPublicDate":"2024-06-22T09:17:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data","docAbstract":"Evaluating the status and trends of natural-origin anadromous fish populations over time requires robust estimates of out-migrating juvenile abundance. Information on abundance is typically acquired by capturing actively migrating fish as they pass stationary monitoring platforms. Challenges to estimation include protracted migration timing, temporally varying capture probabilities and the contemporaneous presence of unmarked hatchery-origin fish. The confounding effects of unmarked hatchery fish are especially pernicious in systems hosting multiple hatchery programs with variable mark-rates among releases. Here, we address this problem for a regionally and culturally important population of Chinook salmon (Oncorhynchus tshawytscha) supported by a hatchery-supplementation program implemented in response to the listing of this population under the U.S. Endangered Species Act. We developed a model to estimate daily and annual abundance of naturally produced age-0 fall Chinook salmon passing Lower Granite Dam (Snake River, USA) for each of the last 30 years. We accounted for variable hatchery marking rates by integrating two related data sources: 1) release-recapture data of fish with individually identifiable tags and 2) counts of marked and unmarked sample of fish captured each day. We fit joint parameters for daily fish arrival and capture probabilities to these data to estimate the daily abundance of hatchery- and natural-origin fish. Our results show that from 1992 to 2021, the annual abundance of juvenile natural-origin Snake River fall Chinook salmon increased by two orders of magnitude. These results are the first comprehensive evaluation of multi-decadal trends in abundance and run-timing for this population. Our approach can be adapted to other runs and locations within the Columbia River basin or similar systems where out-migrating fish are monitored at fixed locations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fishres.2024.107098","usgsCitation":"Hance, D., Plumb, J., Perry, R., and Tiffan, K., 2024, Back from the brink: Estimating daily and annual abundance of natural-origin salmon smolts from 30-years of mixed-origin capture-recapture data: Fisheries Research, v. 278, 107098, 18 p., https://doi.org/10.1016/j.fishres.2024.107098.","productDescription":"107098, 18 p.","ipdsId":"IP-160680","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":434942,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EKDXW3","text":"USGS data release","linkHelpText":"Daily and annual abundances of natural- and hatchery-origin age-0 fall Chinook salmon (Oncorhynchus tshawytscha) passing Lower Granite Dam, Washington 1992 - 2021"},{"id":430961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.97264940326166,\n 47.24310996536303\n ],\n [\n -118.075231715107,\n 47.27051186495126\n ],\n [\n -118.03216123599826,\n 44.88742975557119\n ],\n [\n -115.98127625140233,\n 44.90409917890665\n ],\n [\n -115.97264940326166,\n 47.24310996536303\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"278","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Plumb, John 0000-0003-4255-1612","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":220178,"corporation":false,"usgs":true,"family":"Plumb","given":"John","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perry, Russell 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220189,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tiffan, Kenneth 0000-0002-5831-2846","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":217812,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":906154,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256164,"text":"70256164 - 2024 - Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS)","interactions":[],"lastModifiedDate":"2025-01-17T15:55:36.342599","indexId":"70256164","displayToPublicDate":"2024-06-22T06:52:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1248,"text":"Climate Dynamics","active":true,"publicationSubtype":{"id":10}},"title":"Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS)","docAbstract":"The mid-Piacenzian Warm Period (MPWP, ~ 3.264–3.025 Ma) is the most recent example of a persistently warmer climate in equilibrium with atmospheric CO2 concentrations similar to today. Towards studying patterns and dynamics of a warming climate the MPWP is often compared to today. Following the Pliocene Model Intercomparison Project, Phase 2 (PlioMIP2) protocol we prepare a water isotope-enabled Community Earth System Model (iCESM1.2) simulation that is warmer and wetter than the PlioMIP2 multi-model ensemble (MME). While our simulation resembles PlioMIP2 MME in many aspects we find added insights. (1) Considerable warmth at high latitudes exceeds previous simulations. Polar amplification (PA) is comparable to proxies, enabled by iCESM1.2’s high climate sensitivity and a distinct method of ocean initialization. (2) Major driver of warmth is the downward component of clear-sky surface long-wave radiation (ΔTrlds_clearsky). (3) In iCESM1.2 modulated dominance of dynamic (δDY) processes causes different low-latitude (~ 30 S°–10°N) precipitation response than the PlioMIP2 MME, where thermodynamic processes (δTH) dominate. (4) Modulated local condensation leads to lower δ18Op across tropical Indian Ocean and surrounding Asian-African-Australian monsoon regions. (5) We find contrasting changes in tropical atmospheric circulations (Hadley and Walker cells). Anomalous regional meridional (zonal) circulation, forced by changes in tropical-subtropical (tropical) diabatic processes, presents a more comprehensive perspective than explaining weakened and expanded Hadley circulation (strengthened and westward-shifted Walker circulation) via static stability. (6) Enhanced Atlantic meridional overturning circulation owes to a closed Bering Strait.
","language":"English","publisher":"Springer","doi":"10.1007/s00382-024-07304-0","usgsCitation":"Sun, Y., Su, B., Dowsett, H.J., Wu, H., Hu, J., Stepanek, C., Xiong, Z., Yuan, X., and Ramstein, G., 2024, Modeling the mid-Piacenzian warm climate using the water isotope-enabled Community Earth System Model (iCESM1.2-ITPCAS): Climate Dynamics, v. 62, p. 7741-7761, https://doi.org/10.1007/s00382-024-07304-0.","productDescription":"21 p.","startPage":"7741","endPage":"7761","ipdsId":"IP-145565","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":439362,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00382-024-07304-0","text":"Publisher Index Page"},{"id":431436,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","noUsgsAuthors":false,"publicationDate":"2024-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Sun, Yong","contributorId":336900,"corporation":false,"usgs":false,"family":"Sun","given":"Yong","email":"","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":906957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Su, Baohuang","contributorId":338746,"corporation":false,"usgs":false,"family":"Su","given":"Baohuang","email":"","affiliations":[],"preferred":false,"id":907051,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dowsett, Harry J. 0000-0003-1983-7524 hdowsett@usgs.gov","orcid":"https://orcid.org/0000-0003-1983-7524","contributorId":949,"corporation":false,"usgs":true,"family":"Dowsett","given":"Harry","email":"hdowsett@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":907052,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Haibin","contributorId":338744,"corporation":false,"usgs":false,"family":"Wu","given":"Haibin","email":"","affiliations":[],"preferred":false,"id":907053,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hu, Jun","contributorId":340390,"corporation":false,"usgs":false,"family":"Hu","given":"Jun","email":"","affiliations":[],"preferred":false,"id":907054,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stepanek, Christian","contributorId":220691,"corporation":false,"usgs":false,"family":"Stepanek","given":"Christian","email":"","affiliations":[{"id":40240,"text":"Alfred Wegener Institute-Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany","active":true,"usgs":false}],"preferred":false,"id":907055,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Xiong, Zhongyu","contributorId":340391,"corporation":false,"usgs":false,"family":"Xiong","given":"Zhongyu","email":"","affiliations":[],"preferred":false,"id":907056,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yuan, Xiayu","contributorId":338747,"corporation":false,"usgs":false,"family":"Yuan","given":"Xiayu","email":"","affiliations":[],"preferred":false,"id":907057,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ramstein, Gilles","contributorId":269585,"corporation":false,"usgs":false,"family":"Ramstein","given":"Gilles","email":"","affiliations":[{"id":55994,"text":"Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France","active":true,"usgs":false}],"preferred":false,"id":907058,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256471,"text":"70256471 - 2024 - Viability modeling for decision support with limited data: A lizard case study","interactions":[],"lastModifiedDate":"2024-12-10T15:01:27.166066","indexId":"70256471","displayToPublicDate":"2024-06-21T15:27:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17797,"text":"Journal of Fish and Wildlife Managment","active":true,"publicationSubtype":{"id":10}},"title":"Viability modeling for decision support with limited data: A lizard case study","docAbstract":"Plateau spot-tailed earless lizards, Holbrookia lacerata, are a species of ground lizard in central Texas that are under review for listing as endangered under the US Endangered Species Act, but heretofore no predictive models of population dynamics or viability have been developed. We used limited available data and published demographic rates in a PVA model to predict future status of these lizards under parametric and ecological uncertainty and temporal variability. Even in cases where data are sparse and life history information are limited, viability models can help clarify the consequences of management choices given the uncertainty. Our model predicted that on average populations will decline in in the future. Quasi-extinction probability was low 20 years into the future but up to 0.60. Extinction risk was highly dependent on the road mortality effect and the proportion of the population exposed to roadways, both of which are currently uncertain quantities. Despite these unknowns, our model enables managers to consider the future abundance and extinction risk for the species and make decisions about management to project the populations and also identifies key uncertainties for future research and monitoring.
","language":"English","publisher":"US Fish and Wildlife Service","doi":"10.3996/JFWM-23-024","usgsCitation":"Goode, A.B., Allan, N., and McGowan, C., 2024, Viability modeling for decision support with limited data: A lizard case study: Journal of Fish and Wildlife Managment, v. 15, no. 1, p. 70-86, https://doi.org/10.3996/JFWM-23-024.","productDescription":"17 p.","startPage":"70","endPage":"86","ipdsId":"IP-152348","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487513,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-23-024","text":"Publisher Index Page"},{"id":432057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Goode, Ashley B.C.","contributorId":340756,"corporation":false,"usgs":false,"family":"Goode","given":"Ashley","email":"","middleInitial":"B.C.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":907519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allan, Nathan","contributorId":340757,"corporation":false,"usgs":false,"family":"Allan","given":"Nathan","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":3381,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":907521,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70273359,"text":"70273359 - 2024 - Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska","interactions":[],"lastModifiedDate":"2026-01-09T17:22:33.919542","indexId":"70273359","displayToPublicDate":"2024-06-21T11:17:31","publicationYear":"2024","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":"Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska","docAbstract":"This work evaluates glacial dust as a source of sediment, and associated iron (Fe), to the Fe-limited Gulf of Alaska (GoA). A reanalysis of GoA sediment data, using rare earth elements and thorium as provenance tracers, suggests a flux to the ocean surface of Copper River (AK) glacial dust, and associated Fe, that is comparable to the flux of dust from Asia, at least 1,000 km from the narrow mountain valley glacial dust source area. This work suggests dust from Asia may not be the largest source of Fe to the GoA. Dust models fail to accurately simulate this glacial dust transport because their coarse resolution underestimates wind speeds, and the dust flux. This work suggests that glacial dust fluxes may have been important in the geologic past (e.g., the last glacial maximum) from locations where there was more extensive coverage by glaciers than at present.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GL106778","usgsCitation":"Crusius, J., Lao, C., Holmes, T.M., and Murray, J.W., 2024, Alaskan glacial dust is an important iron source to surface waters of the Gulf of Alaska: Geophysical Research Letters, v. 51, no. 12, e2023GL106778, 10 p., https://doi.org/10.1029/2023GL106778.","productDescription":"e2023GL106778, 10 p.","ipdsId":"IP-144402","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":498678,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gl106778","text":"Publisher Index Page"},{"id":498516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166,\n 61\n ],\n [\n -166,\n 48\n ],\n [\n -136,\n 48\n ],\n [\n -136,\n 61\n ],\n [\n -166,\n 61\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Crusius, John 0000-0003-2554-0831 jcrusius@usgs.gov","orcid":"https://orcid.org/0000-0003-2554-0831","contributorId":2155,"corporation":false,"usgs":true,"family":"Crusius","given":"John","email":"jcrusius@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":953433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lao, Carsten","contributorId":364912,"corporation":false,"usgs":false,"family":"Lao","given":"Carsten","affiliations":[{"id":87012,"text":"UW Dept of Chemistry","active":true,"usgs":false}],"preferred":false,"id":953434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holmes, Thomas M. 0000-0001-8061-4325","orcid":"https://orcid.org/0000-0001-8061-4325","contributorId":364913,"corporation":false,"usgs":false,"family":"Holmes","given":"Thomas","middleInitial":"M.","affiliations":[{"id":87014,"text":"U. Tasmania","active":true,"usgs":false}],"preferred":false,"id":953435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murray, J. W. 0000-0002-8577-7964","orcid":"https://orcid.org/0000-0002-8577-7964","contributorId":364914,"corporation":false,"usgs":false,"family":"Murray","given":"J.","middleInitial":"W.","affiliations":[{"id":87015,"text":"UW School of Oceanography","active":true,"usgs":false}],"preferred":false,"id":953436,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}