The Taishanmiao granitic batholith, located in the Eastern Qinling Orogen in Henan Province, China, contains numerous small (mostly tens of centimeters in maximum dimension) bodies exhibiting textures and mineralogy characteristics of simple quartz and alkali feldspar pegmatites. Analysis of melt inclusions (MI) and fluid inclusions (FI) in pegmatitic quartz, combined with Rhyolite-MELTS modeling of the crystallization of the granite, have been applied to develop a conceptual model of the physical and geochemical processes associated with the formation of the pegmatites. These results allow us to consider the formation of the Taishanmiao pegmatites within the context of varios models that have been proposed for pegmatite formation.
Field observations and geochemical data indicate that the pegmatites represent the latest stage in the crystallization of the Taishanmiao granite and occupy ≤4 vol% of the syenogranite phase of the batholith. Results of Rhyolite-MELTS modeling suggest that the pegmatite-forming melts can be produced through continuous fractional crystallization of the Taishanmiao granitic magma, consistent with the designation of the pegmatites as a miarolitic class, segregation-type pegmatites rather than the more common intrusive-type of pegmatite. The mineral assemblage predicted by Rhyolite-MELTS after ~96% of the original granite-forming melt had crystallized consists of ~51 vol% alkali feldspar, 34 vol% quartz, 14 vol% plagioclase, 0.1 vol% biotite, and 1 vol% magnetite, similar to the alkali feldspar + quartz dominated mineralogy of the pegmatites. Moreover, the modeled residual melt composition following crystallization of ~96% of the original melt is similar to the composition of homogenized MI in quartz within the pegmatite. Rhyolite-MELTS predicts that the granite-forming melt remained volatile-undersaturated during crystallization of the batholith and contained ~6.3 wt% H2O and ~500 ppm CO2 after ~96% crystallization when the pegmatites began to develop. The Rhyolite-MELTS prediction that the melt was volatile-undersaturated at the time the pegmatites began to form, but became volatile-saturated during the early stages of pegmatite formation, is consistent with the presence of some inclusion assemblages consisting of only MI, while others contain co-existing MI and FI. The relationship between halogen (F and Cl) and Na abundances in MI is also consistent with the interpretation that the very earliest stages of pegmatite formation occurred in the presence of a volatile-undersaturated melt and that the melt became volatile saturated as crystallization progressed.
We propose a closed system, isochoric model for the formation of the pegmatites. Accordingly, the Taishanmiao granite crystallized isobarically at ~3.3 kbar, and the pegmatites began to form at ~734 °C and ~ 3.3 kbar, after ~96% of the original granitic melt had crystallized. During the final stages of crystallization of the granite, small pockets of the remaining residual melt became isolated within the enclosing granite and evolved as constant mass (closed), constant volume (isochoric) systems, similar to the manner in which volatile-rich melt inclusions in igneous phenocrysts evolve during post-entrapment crystallization under isochoric conditions. As a result of the negative volume change associated with crystallization, pressure in the pegmatite initially decreases as crystals form, and this leads to volatile exsolution from the melt phase. The changing PTX conditions produce a pressure-induced “liquidus deficit” that is analogous to liquidus undercooling and results in crystal growth as required to return the system to equilibrium PTX conditions. Owing to the complex closed system, isochoric PVTX evolution of the melt-crystal-volatile system, the pressure does not decrease rapidly or monotonically during pegmatite formation but, rather, gradually fluctuates such that at some stages in the evolution of the pegmatite the pressure is decreasing while at other times the pressure increases as the system cools to maintain mass and volume balance. This behavior, in turn, leads to alternating episodes of precipitation and dissolution that serve to coarsen (ripen) the crystals to produce the pegmatitic texture. The evolution of the pegmatitic melt described here is analogous to that which has been well-documented to occur in volatile-rich MI that undergo closed system, isochoric, post-entrapment crystallization.
","language":"English","publisher":"Mineralogical Society of America","doi":"10.2138/am-2021-7637","usgsCitation":"Yuan, Y., Moore, L., McAleer, R.J., Yuan, S., Ouyang, H., Belkin, H.E., Mao, J., Sublett, M.D., and Bodnar, R., 2021, Formation of miarolitic-class, segregation-type pegmatites in the Taishanmiao batholith, China: The role of pressure fluctuations and volatile exsolution during pegmatite formation in a closed, isochoric system: American Mineralogist, v. 106, no. 10, p. 1559-1573, https://doi.org/10.2138/am-2021-7637.","productDescription":"15 p.","startPage":"1559","endPage":"1573","ipdsId":"IP-119402","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":467224,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/111949","text":"External 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source increase hybridization with invasive Alabama Bass","docAbstract":"“Bartram’s Bass” Micropterus sp. cf. coosae is endemic to the upper Savannah River basin of the southeastern United States and is threatened by hybridization with invasive Alabama Bass Micropterus henshalli. Bartram’s Bass have been functionally extirpated from reservoirs, and hybrid individuals have been detected in several tributaries. However, the extent of introgression in tributaries is currently unknown. Our objectives were to (1) assess the distribution of Bartram’s Bass, native Largemouth Bass M. salmoides, invasive Alabama Bass, and their hybrids in streams of the upper Savannah River basin and (2) quantify effects of abiotic variables on the distribution of each species. We sampled 154 locations in 2017 and 2018 and assigned genetic identity using hydrolysis probes and microsatellites. We used conditional inference trees to quantify variables affecting the occurrence of each species and hybrids. We observed widespread hybridization across the basin. Pure Bartram’s Bass were collected at 27% (42) of sites, among which only 12 sites contained pure Bartram’s Bass and no other congeners. Thirty sites where pure Bartram’s Bass were collected contained hybrids. In the montane Blue Ridge ecoregion, occurrence of pure Bartram’s Bass was negatively affected by low levels of local-scale developed land cover. In the lower-relief Piedmont ecoregion, pure Bartram’s Bass were positively associated with watershed-scale forest land cover and stream gradient. Distance from a reservoir was positively associated with occurrence of pure Bartram’s Bass in both ecoregions. Pure Bartram’s Bass are likely to occur with high probability in only 16% of nonimpounded stream segments; this represents a conservative estimate, and the true number is likely lower. However, future work accounting for incomplete detection of Bartram’s Bass will help to improve confidence in true extirpations. Conservation efforts may be more successful if implemented on stream segments farther from reservoirs or upstream of dispersal barriers preventing colonization of Alabama Bass.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10637","usgsCitation":"Peoples, B., Judson, E., Darden, T.L., Farrae, D.J., Kubach, K., Leitner, J., and Scott, M.C., 2021, Modeling distribution of endemic Bartram’s Bass Micropterus sp. cf. coosae: Disturbance and proximity to invasion source increase hybridization with invasive Alabama Bass: North American Journal of Fisheries Management, v. 41, no. 5, p. 1309-1321, https://doi.org/10.1002/nafm.10637.","productDescription":"13 p.","startPage":"1309","endPage":"1321","ipdsId":"IP-152697","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":432883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, North Carolina, South Carolina","otherGeospatial":"Savannah River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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Kevin","contributorId":341212,"corporation":false,"usgs":false,"family":"Kubach","given":"Kevin","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907848,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Leitner, Jean","contributorId":177201,"corporation":false,"usgs":false,"family":"Leitner","given":"Jean","email":"","affiliations":[],"preferred":false,"id":910836,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott, Mark C.","contributorId":341033,"corporation":false,"usgs":false,"family":"Scott","given":"Mark","email":"","middleInitial":"C.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":907849,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224620,"text":"sir20215065 - 2021 - Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013","interactions":[],"lastModifiedDate":"2021-10-01T12:09:28.489755","indexId":"sir20215065","displayToPublicDate":"2021-09-30T21:14:22","publicationYear":"2021","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":"2021-5065","displayTitle":"Conceptual and Numerical Groundwater Flow Model of the Cedar River Alluvial Aquifer System with Simulation of Drought Stress on Groundwater Availability near Cedar Rapids, Iowa, for 2011 through 2013","title":"Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013","docAbstract":"Between July 2011 and February 2013, the City of Cedar Rapids observed water level declines in their horizontal collector wells approaching 11 meters. As a result, pumping from these production wells had to be halted, and questions were raised about the reliability of the alluvial aquifer under future drought conditions. The U.S. Geological Survey, in cooperation with the City of Cedar Rapids, completed a study to better understand the effects of drought stress on the Cedar River alluvial aquifer using a numerical groundwater flow model. Previously published groundwater flow models were combined with newly collected airborne, waterborne, down-hole, and land-based geophysical survey data and provided a detailed three-dimensional lithologic model of the Cedar River alluvial aquifer and surrounding area. An improved conceptual model for the groundwater flow system and a lithologic model were used to build and inform a numerical groundwater flow model capable of simulating water levels observed in the City of Cedar Rapids horizontal collector wells during the 2012 drought. Model performance was assessed primarily on the ability of the model to simulate water table elevation at six monitoring wells. Statistical tests were used to assess the numerical model during the calibration period, and results varied from satisfactory to unsatisfactory, likely because of stage changes in the Cedar River and production well withdrawal rates near monitoring wells. Simulated water levels during the 2012 drought indicated a depression near the horizontal collector wells, although simulated elevations at these locations and at monitoring wells were generally overestimated compared to measured values. The numerical groundwater flow model was modified to account for a decrease in seepage rate caused by low flow in the Cedar River and increased production. With seepage rate modification, model results improved; the simulated water table elevations were like those observed in horizontal collector and monitoring wells. Results demonstrated the ability of the model to simulate water levels observed in the horizontal collector wells during the 2012 drought when accounting for a decrease in infiltration from the Cedar River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215065","collaboration":"Prepared in cooperation with the City of Cedar Rapids","usgsCitation":"Haj, A.E., Ha, W.S., Gruhn, L.R., Bristow, E.L., Gahala, A.M., Valder, J.F., Johnson, C.D., White, E.A., and Sterner, S.P., 2021, Conceptual and numerical groundwater flow model of the Cedar River alluvial aquifer system with simulation of drought stress on groundwater availability near Cedar Rapids, Iowa, for 2011 through 2013: U.S. Geological Survey Scientific Investigations Report 2021–5065, 59 p., https://doi.org/10.3133/sir20215065.","productDescription":"Report: ix, 59 p.; Appendix; 3 Data Releases; Dataset","numberOfPages":"74","onlineOnly":"Y","ipdsId":"IP-118762","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":390066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5065/coverthb.jpg"},{"id":390067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065.pdf","text":"Report","size":"8.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5065"},{"id":390069,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BS882S","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Airborne electromagnetic and magnetic survey data and inverted resistivity models, Cedar Rapids, Iowa, May 2017"},{"id":390070,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96CF4L5","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Cedar River alluvial aquifer near Cedar Rapids, Iowa"},{"id":390071,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YXJDHX","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Geophysical data collected in the Cedar River floodplain, Cedar Rapids, Iowa, 2015–2017"},{"id":390072,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":390068,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065_appendix.pdf","text":"Poster","size":"3.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5065 Appendix","linkHelpText":"— Geophysical methods used to better characterize surface water, alluvial aquifer, and bedrock aquifer interaction in the Cedar River Valley, Iowa"},{"id":390073,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5065/sir20215065.xml","size":"367 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5065 xml"},{"id":390074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5065/images"}],"country":"United States","state":"Iowa","city":"Cedar Rapids","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.76759719848633,\n 41.99139471889533\n ],\n [\n -91.69189453125,\n 41.99139471889533\n ],\n [\n -91.69189453125,\n 42.03565184193029\n ],\n [\n -91.76759719848633,\n 42.03565184193029\n ],\n [\n -91.76759719848633,\n 41.99139471889533\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
A U.S. Geological Survey-led assessment of data gaps and science needs across the Great Lakes ecosystem indicated the following:
• Expanded data collection or monitoring would provide basic ecosystem, social, and public health data to manage the Great Lakes system and to develop and test models and decision support tools.
• New science and advanced technologies (for example, sensors and high-performance computing capability) would improve the understanding of critical threats, such as harmful algae blooms and high-water levels.
Although there is significant scientific knowledge in specific areas or for specific topics, managers could use improved models and decision support tools, strengthened by extensive data collection and developed at multiple scales, to better inform decision making in the future. Enhanced coordination of agency efforts and associated data collection across data types (for example, prey fish populations and water levels) is needed to effectively manage the Great Lakes.
This report highlights the data gaps; benefits of better, more structured coordination; and areas of concern specifically related to data collection/measurement and science efforts. It summarizes and analyzes stakeholder feedback and information from review of scientific literature. Finally, the report outlines steps necessary to create an integrated Great Lakes science plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211096","usgsCitation":"Carl, L.M., Hortness, J.E., and Strach, R.M., 2021, U.S. Geological Survey Great Lakes Science Forum—Summary of remaining data and science needs and next steps: U.S. Geological Survey Open-File Report 2021–1096, 4 p., https://doi.org/10.3133/ofr20211096.","productDescription":"iii, 4 p.","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-133589","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"links":[{"id":390007,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1096/ofr20211096.xml","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2021–1096 xml"},{"id":390006,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1096/ofr20211096.pdf","text":"Report","size":"655 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1096"},{"id":390005,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1096/coverthb.jpg"}],"contact":"Director, Midwest Regional Director’s Office
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
Stocking of Rainbow Trout Oncorhynchus mykiss commonly provides seasonal or mitigation fisheries; however, these fish are usually small and ecosystem effects are spatially or temporally limited. Yet agencies receive requests to stock Rainbow Trout in relatively natural settings (i.e., not tailwater or mitigation fisheries), where introductions may have greater ecosystem consequences. The size of introduced fish is an important factor in determining biotic interactions with native species; therefore, our objectives were to assess the seasonal feeding ecology and microhabitat use of large (265–530 mm TL) nonnative Emmerson strain Rainbow Trout in a relatively unaltered, groundwater-influenced, warmwater stream of the Ozark Highlands. Rainbow Trout consumed a variety of prey; however, diets differed between cool (winter and spring) and warm (summer) seasons. Cool-season Rainbow Trout exhibited a mixed feeding strategy, with individual specialization on crayfishes and fishes and generalist feeding on Ephemeroptera and Diptera, but Gastropoda were the dominant prey. Feeding strategy in the warm season switched to individual specialization on numerous prey types. Overall, larger prey resources were important components of Rainbow Trout diets. Piscivory was relatively high in both seasons, and crayfishes were one of the most important prey types across seasons. Selection of coarse substrates and deeper-water microhabitats (>0.95 m) was similar between seasons. Rainbow Trout selected the lowest-velocity microhabitats available during the warm season and moderate velocities in the cool season. Rainbow Trout were five times more likely to be associated with cover in the warm season. Due to their higher temperature tolerance, Emmerson strain Rainbow Trout may persist in Ozark Highland streams, where they disrupt local food webs and occupy habitat otherwise selected by native fish, such as Neosho Smallmouth Bass Micropterus dolomieu velox. If native species conservation is a priority for agencies, then caution regarding Rainbow Trout stockings may be warranted.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10694","usgsCitation":"Rodger, A.W., Wolf, S.L., Starks, T.A., Burroughs, J.P., and Brewer, S.K., 2021, Seasonal diet and habitat use of large, introduced Rainbow Trout in an Ozark Highland stream: North American Journal of Fisheries Management, v. 41, no. 6, p. 1764-1780, https://doi.org/10.1002/nafm.10694.","productDescription":"17 p.","startPage":"1764","endPage":"1780","ipdsId":"IP-129494","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":397173,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Spavinaw Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13336181640625,\n 36.22987352301491\n ],\n [\n -94.37393188476562,\n 36.22987352301491\n ],\n [\n -94.37393188476562,\n 36.46657630040234\n ],\n [\n -95.13336181640625,\n 36.46657630040234\n ],\n [\n -95.13336181640625,\n 36.22987352301491\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Rodger, A. W.","contributorId":288610,"corporation":false,"usgs":false,"family":"Rodger","given":"A.","email":"","middleInitial":"W.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838135,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolf, S. L.","contributorId":288613,"corporation":false,"usgs":false,"family":"Wolf","given":"S.","email":"","middleInitial":"L.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":838136,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Starks, T. A.","contributorId":288616,"corporation":false,"usgs":false,"family":"Starks","given":"T.","email":"","middleInitial":"A.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838137,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burroughs, J. P.","contributorId":288619,"corporation":false,"usgs":false,"family":"Burroughs","given":"J.","email":"","middleInitial":"P.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":838138,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":838139,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229778,"text":"70229778 - 2021 - Livestock grazing, climatic variation, and breeding phenology jointly shape disease dynamics and survival in a wild amphibian","interactions":[],"lastModifiedDate":"2022-03-17T16:05:37.252377","indexId":"70229778","displayToPublicDate":"2021-09-30T10:49:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Livestock grazing, climatic variation, and breeding phenology jointly shape disease dynamics and survival in a wild amphibian","docAbstract":"Wildlife responses to infectious disease can be influenced by environmental stressors that alter host-pathogen dynamics. We investigated how livestock grazing, climatic variation, and breeding phenology influence disease prevalence and annual survival in boreal toad (Anaxyrus boreas boreas) populations challenged with Batrachochytrium dendrobatidis (Bd), a fungal pathogen implicated in global amphibian declines. We conducted a five-year (2015–2019) capture-recapture study of boreal toads (n = 1301) inhabiting pastures grazed by cattle in western Wyoming, USA. We employed structural equation models to determine whether the effects of climatic variation on Bd prevalence were direct or mediated through effects on breeding phenology and multi-state models to explore the interplay of grazing, weather, and Bd infection on adult survival. Higher winter snowpack was linked with shorter spring breeding seasons, which were associated with lower Bd prevalence. Boreal toads infected with Bd suffered increased mortality, but only at relatively cool temperatures. Although cattle grazing created warmer microclimates, likely by reducing vegetation cover, grazing-induced habitat changes did not scale up to influence adult survival. Our results suggest that boreal toads in cooler environments face increased risk of disease-induced mortality, possibly because infected individuals are not able to elevate body temperature to reduce or clear infection. More generally, we demonstrate that host-pathogen dynamics can be shaped jointly by independent and interactive effects of livestock grazing, breeding season length, and climatic variation. Future investigations of wildlife responses to disease therefore may benefit from considering anthropogenic land use and climatic regimes, including the effect of weather on host phenology.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2021.109247","usgsCitation":"Barrile, G., Walters, A.W., and Chalfoun, A.D., 2021, Livestock grazing, climatic variation, and breeding phenology jointly shape disease dynamics and survival in a wild amphibian: Biological Conservation, v. 261, 109247, 10 p., https://doi.org/10.1016/j.biocon.2021.109247.","productDescription":"109247, 10 p.","ipdsId":"IP-127277","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":450597,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2021.109247","text":"Publisher Index Page"},{"id":397254,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Wyoming Range, Buck Creek,Chall Creek, Wind River Range, Lower Gypsum Creek , Upper Gypsum Creek;","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.050048828125,\n 42.73289174571287\n ],\n [\n -109.16015624999999,\n 42.73289174571287\n ],\n [\n -109.16015624999999,\n 43.375108633273086\n ],\n [\n -110.050048828125,\n 43.375108633273086\n ],\n [\n -110.050048828125,\n 42.73289174571287\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"261","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":288734,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel M.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":838252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chalfoun, Anna D. 0000-0002-0219-6006 achalfoun@usgs.gov","orcid":"https://orcid.org/0000-0002-0219-6006","contributorId":197589,"corporation":false,"usgs":true,"family":"Chalfoun","given":"Anna","email":"achalfoun@usgs.gov","middleInitial":"D.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838253,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232197,"text":"70232197 - 2021 - Evaluating the role of active management in mature Douglas-fir (Pseudotsuga menziesii) stands for songbird conservation","interactions":[],"lastModifiedDate":"2022-06-13T15:41:33.151946","indexId":"70232197","displayToPublicDate":"2021-09-30T10:32:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluating the role of active management in mature Douglas-fir (Pseudotsuga menziesii) stands for songbird conservation","title":"Evaluating the role of active management in mature Douglas-fir (Pseudotsuga menziesii) stands for songbird conservation","docAbstract":"Forest birds, particularly those associated with late-successional forests, are of widespread conservation interest. Although birds are among the more widely studied taxa of forest wildlife, relatively few studies have examined the long-term effects of active management (i.e., intentional stand density reduction) on the forest bird assemblage. This is an important omission, as changes in stand structure and composition over the decades following harvesting may influence wildlife utilization of forest stands. We developed an observational study to evaluate forest bird response to stand structural development multiple decades following harvesting with differing levels of overstory density reduction, and in unmanaged stands. We focused, in particular, on songbirds and cavity excavating species associated with old-growth Douglas-fir forests, and the active management strategies that we examined – thinning and structural retention harvesting – were selected based on their potential to accelerate stand structural development. Our 18 mature stands (age range: 107–187 years) were located in the western hemlock zone of western Oregon, and bird surveys were conducted an average of 41 years and 22 years post-harvest in thinned and retention harvest stands, respectively. Poisson generalized linear models were formulated to evaluate the effects of management condition on forest birds. Although five species were associated with specific management conditions, relative abundance was not statistically different between unmanaged, thinned and retention harvest stands for a majority of the species in our analysis. Species richness was also relatively invariant across management conditions. Our results do suggest that retention harvesting adversely affects some songbird species associated with old-growth forests – Pacific-slope flycatcher (Empidonax difficilis) and Pacific wren (Troglodytes pacificus) for example - but our findings also indicate that retention harvesting has long-term benefits for birds associated with early-successional forests. This includes migrant species that have experienced significant population declines in Western North America over recent decades, such as Wilson’s and MacGillivray’s warblers (Cardellina pusilla and Geothlypis tolmiei, respectively). Overall, our results imply that for many species of forest birds, including those associated with old-growth forests, managers have some flexibility in overstory density management where long-term species persistence is an objective. Equally significant, our results provide strong support for the application of variable retention harvesting as a tool for the conservation of bird species associated with early-successional forests, while also reinforcing the value of mature structural legacies for bird species associated with late-successional forests. While also reinforcing the value of mature structural legacies for birds associated with late-successional forests.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119609","usgsCitation":"Williams, N., Hagar, J., and Powers, M., 2021, Evaluating the role of active management in mature Douglas-fir (Pseudotsuga menziesii) stands for songbird conservation: Forest Ecology and Management, v. 502, 119609, 15 p., https://doi.org/10.1016/j.foreco.2021.119609.","productDescription":"119609, 15 p.","ipdsId":"IP-126150","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":450602,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119609","text":"Publisher Index Page"},{"id":402089,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.5849609375,\n 43.004647127794435\n ],\n [\n -121.11328124999999,\n 43.004647127794435\n ],\n [\n -121.11328124999999,\n 44.96479793033101\n ],\n [\n -124.5849609375,\n 44.96479793033101\n ],\n [\n -124.5849609375,\n 43.004647127794435\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"502","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Neil","contributorId":292425,"corporation":false,"usgs":false,"family":"Williams","given":"Neil","email":"","affiliations":[{"id":62230,"text":"Oregon State University, Corvallis","active":true,"usgs":false}],"preferred":false,"id":844544,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagar, Joan 0000-0002-3044-6607 joan_hagar@usgs.gov","orcid":"https://orcid.org/0000-0002-3044-6607","contributorId":3369,"corporation":false,"usgs":true,"family":"Hagar","given":"Joan","email":"joan_hagar@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":844545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powers, Matthew","contributorId":202554,"corporation":false,"usgs":false,"family":"Powers","given":"Matthew","affiliations":[{"id":36477,"text":"Department of Forest Engineering Resources and Management, Oregon State University, Corvallis, OR 97331 USA. matthew.powers@oregonstate.edu","active":true,"usgs":false}],"preferred":false,"id":844546,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256767,"text":"70256767 - 2021 - Floodplain forest tree seedling response to variation in flood timing and duration","interactions":[],"lastModifiedDate":"2024-09-06T15:25:58.126396","indexId":"70256767","displayToPublicDate":"2021-09-30T10:22:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Floodplain forest tree seedling response to variation in flood timing and duration","docAbstract":"The regeneration process is a sensitive period within life cycles of floodplain tree species and can strongly influence forest community composition. Yet, fundamental information remains limited on the relationship between regeneration processes and the flood disturbances that, together, construct floodplain forest landscapes. In a controlled greenhouse experiment we tested the effects of complete submergence on six temperate floodplain forest species to understand how flood timing and duration influence seedling survival. Groups of overcup oak (Quercus lyrata), Nuttall oak (Quercus texana), willow oak (Quercus phellos), sugarberry (Celtis laevigata), green ash (Fraxinus pennsylvanica), and American elm (Ulmus americana) seedlings were submerged for either 5, 15, 25, or 0 days (control) at the ages of 3-weeks, 6-weeks, and 9-weeks post-emergence. All species demonstrated a higher sensitivity to flooding at age 3-weeks compared to 6- and 9- weeks, indicating substantial changes in seedling resilience within the first months following emergence. Additionally, the heavier-seeded Q. lyrata, Q. texana, and Q. phellos were less or equally vulnerable to flooding compared to the lighter-seeded C. laevigata, F. pennsylvanica, and U. americana across all age groups, especially at 3-weeks post-emergence. The results of this study have implications for understanding woody species regeneration ecology and changes in floodplain forest composition, particularly in the context of hydrologic modifications.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119660","usgsCitation":"Kroschel, W., and King, S.L., 2021, Floodplain forest tree seedling response to variation in flood timing and duration: Forest Ecology and Management, v. 502, 119660, 10 p., https://doi.org/10.1016/j.foreco.2021.119660.","productDescription":"119660, 10 p.","ipdsId":"IP-127728","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433558,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"502","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kroschel, W.A.","contributorId":341796,"corporation":false,"usgs":false,"family":"Kroschel","given":"W.A.","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":908901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sammy L. 0000-0002-5364-6361 sking@usgs.gov","orcid":"https://orcid.org/0000-0002-5364-6361","contributorId":557,"corporation":false,"usgs":true,"family":"King","given":"Sammy","email":"sking@usgs.gov","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908902,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70267787,"text":"70267787 - 2021 - Fire refugia in old-growth forests: Predicting habitat persistence to support land management in an era of rapid global change","interactions":[],"lastModifiedDate":"2025-06-03T14:12:39.411691","indexId":"70267787","displayToPublicDate":"2021-09-30T10:11:42","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":9141,"text":"Final Report","active":true,"publicationSubtype":{"id":2}},"title":"Fire refugia in old-growth forests: Predicting habitat persistence to support land management in an era of rapid global change","docAbstract":"Recent stand-replacing wildfires in late-successional and old-growth (LSOG) forests have increased land manager interest in fire refugia, which could provide vital habitat for threatened and endangered species during a time of rapid change. The overall goal of this project was to model, map, and share information essential for the conservation of LSOG forest ecosystems in the U.S. Pacific Northwest, within a diverse co-production team of state and federal land managers. We developed statistical models of contemporary (2002-2017) fire refugia, non-stand-replacing fire (NSR), and high-severity fire based on topography, fuels, fire weather, fire behavior and climate. Independent models were built for two ecoregions (Figure 1), one encompassing the Douglas-fir/western hemlock forests of the northwestern portion of our study area and the other encompassing dry-mixed conifer forests of the eastern Cascades and Klamath-Siskiyou region. We used these models to produce probability surface maps for fire refugia, NSR, and high-severity fire under low, moderate, and extreme fire weather and fire growth scenarios. These maps and associated products provide timely information about the likely persistence, change, and loss of LSOG forests under current and future climate conditions.","language":"English","publisher":"Oregon State University","usgsCitation":"Naficy, C.E., Meigs, G., Gregory, M., Davis, R., Bell, D.M., Dugger, K., Wiens, J.D., and Krawchuk, M.A., 2021, Fire refugia in old-growth forests: Predicting habitat persistence to support land management in an era of rapid global change: Final Report, 39 p.","productDescription":"39 p.","ipdsId":"IP-135786","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489323,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://firerefugia.forestry.oregonstate.edu/findings"},{"id":489389,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.03230454175225,\n 48.98129296044047\n ],\n [\n -123.23629505743158,\n 49.021189699759475\n ],\n [\n -122.36411639022937,\n 47.53290523042941\n ],\n [\n -122.735187412137,\n 48.16641453033125\n ],\n [\n -124.8905678391362,\n 48.456759846434124\n ],\n [\n -124.28837165472417,\n 44.57003722150395\n ],\n [\n -124.68283057104817,\n 42.67310902261141\n ],\n [\n -124.20999230992997,\n 41.18665658280105\n ],\n [\n -124.55296007164978,\n 40.23706044199622\n ],\n [\n -123.88224360324604,\n 39.64402305418474\n ],\n [\n -123.79771977835343,\n 38.75178009790977\n ],\n [\n -122.3664015891323,\n 37.62043867256661\n ],\n [\n -120.98881658816403,\n 38.95590221404629\n ],\n [\n -119.90347242862897,\n 40.51176766538154\n ],\n [\n -119.03230454175225,\n 48.98129296044047\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Naficy, Cameron E.","contributorId":298154,"corporation":false,"usgs":false,"family":"Naficy","given":"Cameron","email":"","middleInitial":"E.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":938881,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meigs, Garrett W.","contributorId":356212,"corporation":false,"usgs":false,"family":"Meigs","given":"Garrett W.","affiliations":[{"id":37093,"text":"Washington State Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":938882,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gregory, Matt J.","contributorId":356213,"corporation":false,"usgs":false,"family":"Gregory","given":"Matt J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":938883,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Ray","contributorId":356214,"corporation":false,"usgs":false,"family":"Davis","given":"Ray","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":938884,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bell, David M.","contributorId":34423,"corporation":false,"usgs":true,"family":"Bell","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":938885,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938886,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wiens, J. David 0000-0002-2020-038X jwiens@usgs.gov","orcid":"https://orcid.org/0000-0002-2020-038X","contributorId":468,"corporation":false,"usgs":true,"family":"Wiens","given":"J.","email":"jwiens@usgs.gov","middleInitial":"David","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":false,"id":938887,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Krawchuk, Meg A.","contributorId":187425,"corporation":false,"usgs":false,"family":"Krawchuk","given":"Meg","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":938888,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70225594,"text":"70225594 - 2021 - Optical properties of water for prediction of wastewater contamination, human-associated bacteria, and fecal indicator bacteria in surface water at three watershed scales","interactions":[],"lastModifiedDate":"2021-10-26T14:45:49.936829","indexId":"70225594","displayToPublicDate":"2021-09-30T09:37:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Optical properties of water for prediction of wastewater contamination, human-associated bacteria, and fecal indicator bacteria in surface water at three watershed scales","docAbstract":"Relations between spectral absorbance and fluorescence properties of water and human-associated and fecal indicator bacteria were developed for facilitating field sensor applications to estimate wastewater contamination in waterways. Leaking wastewater conveyance infrastructure commonly contaminates receiving waters. Methods to quantify such contamination can be time consuming, expensive, and often nonspecific. Human-associated bacteria are wastewater specific but require discrete sampling and laboratory analyses, introducing latency. Human sewage has fluorescence and absorbance properties different than those of natural waters. To assist real-time field sensor development, this study investigated optical properties for use as surrogates for human-associated bacteria to estimate wastewater prevalence in environmental waters. Three spatial scales were studied: Eight watershed-scale sites, five subwatershed-scale sites, and 213 storm sewers and open channels within three small watersheds (small-scale sites) were sampled (996 total samples) for optical properties, human-associated bacteria, fecal indicator bacteria, and, for selected samples, human viruses. Regression analysis indicated that bacteria concentrations could be estimated by optical properties used in existing field sensors for watershed and subwatershed scales. Human virus occurrence increased with modeled human-associated bacteria concentration, providing confidence in these regressions as surrogates for wastewater contamination. Adequate regressions were not found for small-scale sites to reliably estimate bacteria concentrations likely due to inconsistent local sanitary sewer inputs.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.1c02644","usgsCitation":"Corsi, S., DeCicco, L.A., Hansen, A., Lenaker, P.L., Bergamaschi, B.A., Pellerin, B., Dila, D., Bootsma, M., Spencer, S., Borchardt, M.A., and McLellan, S.L., 2021, Optical properties of water for prediction of wastewater contamination, human-associated bacteria, and fecal indicator bacteria in surface water at three watershed scales: Environmental Science and Technology, v. 55, no. 20, p. 13770-13782, https://doi.org/10.1021/acs.est.1c02644.","productDescription":"13 p.","startPage":"13770","endPage":"13782","ipdsId":"IP-132758","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":450607,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index 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- Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California","interactions":[],"lastModifiedDate":"2022-02-07T15:35:39.438342","indexId":"70228198","displayToPublicDate":"2021-09-30T09:30:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California","docAbstract":"Migratory waterfowl are the primary reservoir of avian influenza viruses (AIV) which can be spread to commercial poultry. Surveillance efforts that track the location and abundance of wild waterfowl and link those data to inform assessments of risk and sampling for AIV currently do not exist. To assist surveillance and minimize poultry exposure to AIV, here we explored the utility of remotely sensed MODerate Resolution Imaging Spectroradiometer (MODIS) satellite imagery in combination with land-based climate measurements (e.g., temperature and precipitation) to predict waterfowl location and abundance in near real-time in the California Central Valley (CCV), where both wild waterfowl and domestic poultry are densely located. Specifically, remotely collected MODIS and climate data were integrated into a previously developed Boosted Regression Tree (BRT) model to predict and visualize waterfowl distributions across the CCV. Daily model-based predictions are publicly available during the winter as part of the dynamic California Waterfowl Tracker (CWT) web-app hosted on the University of California’s Cooperative Extension webpage. In this study, we analyzed 52 days of model predictions and produced daily spatio-temporal maps of waterfowl concentrations near the 605 commercial poultry farms in the CCV during January and February of 2019. Exposure of each poultry farm to waterfowl during each day was classified as “high”, “medium”, “low”, or “none” depending on the density of waterfowl within 4 km of a farm. Results indicated that farms were at substantially greater risk of “exposure” in January, when CCV waterfowl populations peak, than in February. For example, during January, 33% (199/605) of the farms were exposed ≥ 1 day to “high” waterfowl density versus 19% (115/605) of the farms in February. In addition to demonstrating the overall variability of waterfowl location and density, these data demonstrate how remote sensing can be used to better triage AIV surveillance and biosecurity efforts via the utilization of a functional web-app based tool. The ability to leverage remote sensing is an integral advancement toward improving AIV surveillance in waterfowl in close proximity to commercial poultry. Expansion of these types of remote sensing methods linked to a user-friendly web-tool could be further developed across the continental U.S. The BRT model incorporated into the CWT reflects a first attempt to give an accurate representation of waterfowl distribution and density relative to commercial poultry.","language":"English","publisher":"American Association of Avian Pathologists","doi":"10.1637/aviandiseases-D-20-00137","usgsCitation":"Acosta, S., Kelman, T., Feirer, S., Matchett, E., Smolinsky, J.A., Pitesky, M.E., and Buler, J.J., 2021, Using the California Waterfowl Tracker to assess proximity of waterfowl to commercial poultry in the Central Valley of California: Avian Diseases, v. 65, no. 3, p. 483-492, https://doi.org/10.1637/aviandiseases-D-20-00137.","productDescription":"10 p.","startPage":"483","endPage":"492","ipdsId":"IP-125341","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":395530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley of California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.958984375,\n 40.44694705960048\n ],\n [\n -122.51953124999999,\n 38.95940879245423\n ],\n [\n -121.70654296874999,\n 37.54457732085582\n ],\n [\n -120.08056640625,\n 35.92464453144099\n ],\n [\n -119.20166015625,\n 35.15584570226544\n ],\n [\n -118.43261718749999,\n 35.38904996691167\n ],\n [\n -119.0478515625,\n 36.73888412439431\n ],\n [\n -120.89355468749999,\n 38.238180119798635\n ],\n [\n -122.3876953125,\n 40.29628651711716\n ],\n [\n -122.958984375,\n 40.44694705960048\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"65","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Acosta, Sarai","contributorId":274842,"corporation":false,"usgs":false,"family":"Acosta","given":"Sarai","email":"","affiliations":[{"id":56669,"text":"Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA USA","active":true,"usgs":false}],"preferred":false,"id":833381,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelman, Todd","contributorId":274843,"corporation":false,"usgs":false,"family":"Kelman","given":"Todd","email":"","affiliations":[{"id":56669,"text":"Department of Population Health and Reproduction, School of Veterinary Medicine, University of California, Davis, CA USA","active":true,"usgs":false}],"preferred":false,"id":833382,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feirer, Shane","contributorId":274844,"corporation":false,"usgs":false,"family":"Feirer","given":"Shane","email":"","affiliations":[{"id":56670,"text":"Hopland Research & Extension Center, UC-Agriculture and Natural Resources. 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Although our three BCIs are suitable for Antillean manatees, BCI1 is more practical as it does not require information about weight, which can be a metric logistically difficult to collect under particular circumstances. BCI1 was significantly different among environments, revealing that the phenotypic plasticity of the subspecies have originated at least two ecotypes—coastal marine and riverine—of Antillean manatees.
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B.","contributorId":289868,"corporation":false,"usgs":false,"family":"Morales-Vela","given":"J.","email":"","middleInitial":"B.","affiliations":[{"id":62273,"text":"El Colegio de la Frontera Sur, Mexico","active":true,"usgs":false}],"preferred":false,"id":839950,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Olivera-Gomez, L. D.","contributorId":289869,"corporation":false,"usgs":false,"family":"Olivera-Gomez","given":"L. D.","affiliations":[{"id":62272,"text":"Universidad Juarez Autónoma de Tabasco, Mexico","active":true,"usgs":false}],"preferred":false,"id":839951,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Padilla-Saldivar, Janneth Adriana","contributorId":140524,"corporation":false,"usgs":false,"family":"Padilla-Saldivar","given":"Janneth","email":"","middleInitial":"Adriana","affiliations":[{"id":13524,"text":"El Colegio de la Frontera Sur, Quintana Roo, Mexico","active":true,"usgs":false}],"preferred":false,"id":839952,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Powell, James A.","contributorId":288150,"corporation":false,"usgs":false,"family":"Powell","given":"James A.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":839953,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Reid, James P. 0000-0002-8497-1132","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":206849,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839954,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Rieucau, G.","contributorId":289870,"corporation":false,"usgs":false,"family":"Rieucau","given":"G.","email":"","affiliations":[{"id":62267,"text":"Fundación Internacional para la Naturaleza y la Sustentabilidad, Mexico","active":true,"usgs":false}],"preferred":false,"id":839955,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Mignucci-Gianonni, Antonio A.","contributorId":289871,"corporation":false,"usgs":false,"family":"Mignucci-Gianonni","given":"Antonio","email":"","middleInitial":"A.","affiliations":[{"id":62269,"text":"Caribbean Manatee Conservation Center, Inter American University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":839956,"contributorType":{"id":1,"text":"Authors"},"rank":24}]}} ,{"id":70224634,"text":"70224634 - 2021 - Machine learning can assign geologic basin to produced water samples using major ion geochemistry","interactions":[],"lastModifiedDate":"2021-11-16T15:48:00.581979","indexId":"70224634","displayToPublicDate":"2021-09-30T08:16:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2832,"text":"Natural Resources Research","onlineIssn":"1573-8981","printIssn":"1520-7439","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning can assign geologic basin to produced water samples using major ion geochemistry","docAbstract":"Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO3), Ca, Na, Cl, Mg, and sulfate (SO4). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO3, Ca, Na, Cl, Mg, SO4, pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.
","language":"English","publisher":"Springer Link","doi":"10.1007/s11053-021-09949-8","usgsCitation":"Shelton, J., Jubb, A., Saxe, S., Attanasi, E., Milkov, A., Engle, M.A., Freeman, P., Shaffer, C., and Blondes, M., 2021, Machine learning can assign geologic basin to produced water samples using major ion geochemistry: Natural Resources Research, v. 30, p. 4147-4163, https://doi.org/10.1007/s11053-021-09949-8.","productDescription":"17 p.","startPage":"4147","endPage":"4163","ipdsId":"IP-126045","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":450614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11053-021-09949-8","text":"Publisher Index Page"},{"id":390110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saxe, Samuel 0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":824456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milkov, Alexei","contributorId":266160,"corporation":false,"usgs":false,"family":"Milkov","given":"Alexei","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":824458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engle, Mark A 0000-0001-5258-7374","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":228981,"corporation":false,"usgs":false,"family":"Engle","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":41535,"text":"The University of Texas at El Paso, Department of Geological Sciences, El Paso, TX 79968","active":true,"usgs":false}],"preferred":false,"id":824459,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824460,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaffer, Christopher","contributorId":266161,"corporation":false,"usgs":false,"family":"Shaffer","given":"Christopher","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":824461,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824462,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225551,"text":"70225551 - 2021 - Clays are not created equal: How clay mineral type affects soil parameterization","interactions":[],"lastModifiedDate":"2021-10-22T12:42:02.867477","indexId":"70225551","displayToPublicDate":"2021-09-30T07:38:20","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":"Clays are not created equal: How clay mineral type affects soil parameterization","docAbstract":"Clay minerals dominate the soil colloidal fraction and its specific surface area. Differences among clay mineral types significantly influence their effects on soil hydrological and mechanical behavior. Presently, the soil clay content is used to parameterize soil hydraulic and mechanical properties (SHMP) for land surface models while disregarding the type of clay mineral. This undifferentiated use of clay leads to inconsistent parameterization, particularly between tropical and temperate soils, as shown herein. We capitalize on recent global maps of clay minerals that exhibit strong climatic and spatial segregation of active and inactive clays to consider spatially resolved clay mineral types in SHMP estimation. Clay mineral-informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.
Water resources in the Big Lost River Basin, Idaho are vital to irrigated agriculture, domestic, municipal and other uses but declining groundwater levels, diminished streamflows, and concern about drought motivated an evaluation of water resources in the basin. This multichapter volume documents the findings of a hydrogeologic investigation of the Big Lost River Basin that was jointly conducted by the U.S. Geological Survey, Idaho Department of Water Resources, and Idaho Geological Survey from 2018 through 2021. Chapter A (Zinsser, 2021) describes the hydrogeologic framework of the Big Lost River Basin. The framework presents a conceptual definition of four hydrogeologic units, a three-dimensional hydrogeologic framework model representing the spatial occurrence of the hydrogeologic units, and a description of groundwater occurrence and movement. Chapter B (Dudunake and Zinsser, 2021) describes streamflow gains from and losses to groundwater in the Big Lost River between Mackay Reservoir and south of Arco. Streamflow losses and gains were estimated from a series of four measurement events completed during spring and fall conditions from 2019 to 2021. Chapter C (Clark, 2022) describes budgets for the Big Lost River Basin from 2000 to 2019. The groundwater budgets provide annual estimates for aquifer inflows and outflows and include representations of average, wet, and dry conditions. Collectively, these reports present a characterization of water resources in the Big Lost River Basin that will help address current challenges in water-resources management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215078","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Zinsser, L.M., ed., Characterization of water resources in the Big Lost River Basin, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5078, https://doi.org/10.3133/sir20215078.","onlineOnly":"Y","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":389977,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5078/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.16992187499999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 43.22919511396498\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The State of Hawai'i passed legislation to be carbon neutral by 2045, a goal that will partly depend on carbon sequestration by terrestrial ecosystems. However, there is considerable uncertainty surrounding the future direction and magnitude of the land carbon sink in the Hawaiian Islands. We used the Land Use and Carbon Scenario Simulator (LUCAS), a spatially explicit stochastic simulation model that integrates landscape change and carbon gain-loss, to assess how projected future changes in climate and land use will influence ecosystem carbon balance in the Hawaiian Islands under all combinations of two radiative forcing scenarios (RCPs 4.5 and 8.5) and two land use scenarios (low and high) over a 90 year timespan from 2010 to 2100. Collectively, terrestrial ecosystems of the Hawaiian Islands acted as a net carbon sink under low radiative forcing (RCP 4.5) for the entire 90 year simulation period, with low land use change further enhancing carbon sink strength. In contrast, Hawaiian terrestrial ecosystems transitioned from a net sink to a net source of CO2 to the atmosphere under high radiative forcing (RCP 8.5), with high land use accelerating this transition and exacerbating net carbon loss. A sensitivity test of the CO2 fertilization effect on plant productivity revealed it to be a major source of uncertainty in projections of ecosystem carbon balance, highlighting the need for greater mechanistic understanding of plant productivity responses to rising atmospheric CO2. Long-term model projections such as ours that incorporate the interactive effects of land use and climate change on regional ecosystem carbon balance will be critical to evaluating the potential of ecosystem-based climate mitigation strategies.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac2347","usgsCitation":"Selmants, P., Sleeter, B.M., Liu, J., Wilson, T., Trauernicht, C., Frazier, A.G., and Asner, G.P., 2021, Ecosystem carbon balance in the Hawaiian Islands under different scenarios of future climate and land use change: Environmental Research Letters, v. 16, 104020, 14 p., https://doi.org/10.1088/1748-9326/ac2347.","productDescription":"104020, 14 p.","ipdsId":"IP-119813","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450627,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac2347","text":"Publisher Index Page"},{"id":436181,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AWLFKZ","text":"USGS data release","linkHelpText":"Land change and carbon balance projections for the Hawaiian 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Clay","contributorId":221125,"corporation":false,"usgs":false,"family":"Trauernicht","given":"Clay","email":"","affiliations":[{"id":40329,"text":"University of Hawai‘i at Mānoa, Department of Natural Resources and Environmental Management","active":true,"usgs":false}],"preferred":false,"id":824903,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Frazier, Abby G.","contributorId":221112,"corporation":false,"usgs":false,"family":"Frazier","given":"Abby","email":"","middleInitial":"G.","affiliations":[{"id":40321,"text":"USDA Forest Service, Pacific Southwest Research Station","active":true,"usgs":false}],"preferred":false,"id":824904,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":824905,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254942,"text":"70254942 - 2021 - Demographic risk assessment for a harvested species threatened by climate change: Polar bears in the Chukchi Sea","interactions":[],"lastModifiedDate":"2024-06-11T14:59:07.050153","indexId":"70254942","displayToPublicDate":"2021-09-28T09:48:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Demographic risk assessment for a harvested species threatened by climate change: Polar bears in the Chukchi Sea","docAbstract":"Climate change threatens global biodiversity. Many species vulnerable to climate change are important to humans for nutritional, cultural, and economic reasons. Polar bears Ursus maritimus are threatened by sea-ice loss and represent a subsistence resource for Indigenous people. We applied a novel population modeling-management framework that is based on species life history and accounts for habitat loss to evaluate subsistence harvest for the Chukchi Sea (CS) polar bear subpopulation. Harvest strategies followed a state-dependent approach under which new data were used to update the harvest on a predetermined management interval. We found that a harvest strategy with a starting total harvest rate of 2.7% (˜85 bears/yr at current abundance), a 2:1 male-to-female ratio, and a 10-yr management interval would likely maintain subpopulation abundance above maximum net productivity level for the next 35 yr (approximately three polar bear generations), our primary criterion for sustainability. Plausible bounds on starting total harvest rate were 1.7–3.9%, where the range reflects uncertainty due to sampling variation, environmental variation, model selection, and differing levels of risk tolerance. The risk of undesired demographic outcomes (e.g., overharvest) was positively related to harvest rate, management interval, and projected declines in environmental carrying capacity; and negatively related to precision in population data. Results reflect several lines of evidence that the CS subpopulation has been productive in recent years, although it is uncertain how long this will last as sea-ice loss continues. Our methods provide a template for balancing trade-offs among protection, use, research investment, and other factors. Demographic risk assessment and state-dependent management will become increasingly important for harvested species, like polar bears, that exhibit spatiotemporal variation in their response to climate change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2461","usgsCitation":"Regehr, E.V., Runge, M.C., Von Duyke, A.L., Wilson, R., Polasek, L., Rode, K.D., Hostetter, N.J., and Converse, S.J., 2021, Demographic risk assessment for a harvested species threatened by climate change: Polar bears in the Chukchi Sea: Ecological Applications, v. 31, no. 8, e02461, 13 p., https://doi.org/10.1002/eap.2461.","productDescription":"e02461, 13 p.","ipdsId":"IP-119837","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450636,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2461","text":"External Repository"},{"id":429876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.44241992486556,\n 73.07923094153199\n ],\n [\n -179.9,\n 73.07923094153199\n ],\n [\n -179.9,\n 66.33440002284189\n ],\n [\n -157.44241992486556,\n 66.33440002284189\n ],\n [\n -157.44241992486556,\n 73.07923094153199\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":902940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":902941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Von Duyke, Andrew L.","contributorId":214208,"corporation":false,"usgs":false,"family":"Von Duyke","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":38995,"text":"North Slope Borough Department of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":903129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Ryan R. ","contributorId":222456,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan R. ","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":903130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Polasek, Lori","contributorId":338318,"corporation":false,"usgs":false,"family":"Polasek","given":"Lori","email":"","affiliations":[],"preferred":false,"id":903131,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":902942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":903132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902943,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224534,"text":"ofr20211080 - 2021 - Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making","interactions":[],"lastModifiedDate":"2021-09-29T11:36:22.700641","indexId":"ofr20211080","displayToPublicDate":"2021-09-28T09:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1080","displayTitle":"Optimization of Salt Marsh Management at the Rachel Carson National Wildlife Refuge, Maine, Through Use of Structured Decision Making","title":"Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making","docAbstract":"Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop an example of a prototype tool for optimizing tidal marsh management decisions for selected marsh management units at the Rachel Carson National Wildlife Refuge in Maine. The goal was to create a prototype that could be available for future implementation. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of seven marsh management units within the refuge and estimated the outcomes of each action in terms of regional performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale.
Management costs were estimated using limited available information, and estimated costs of individual management actions reflected relative differences among actions rather than actual expected expenditures. Results from this prototype showed how, for the objectives, actions, and estimated outcomes used for this example, total management benefits may increase consistently up to a certain estimated cost, and may continue to increase, at a lower rate, with further expenditures. Potential management actions in optimal portfolios at moderate total estimated costs included breaching or removing dikes, roads, or embankments; planting Spartina alterniflora (smooth cordgrass); and digging runnels, or shallow creeks, on the marsh platform to improve surface-water drainage. Potential management actions in optimal portfolios at high estimated costs (for example, up to $550,000) included breaching embankments to restore tidal exchange followed by planting salt marsh vegetation. The potential management benefits were derived from predicted increases in the numbers of tidal marsh obligate birds and spiders (as an indicator of trophic health), and expected improvement in the capacity of marsh elevation to keep pace with sea-level rise and reduced duration of marsh-surface inundation. The prototype presented here does not resolve current management decisions; rather, it provides a framework for decision making at the Rachel Carson National Wildlife Refuge that can be updated for implementation as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., O’Brien, K.M., Benvenuti, B., and Kleinert, R., 2021, Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1080, 35 p., https://doi.org/10.3133/ofr20211080.","productDescription":"vi, 35 p.","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126540","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":389743,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1080/coverthb.jpg"},{"id":389744,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.pdf","text":"Report","size":"4.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1080"},{"id":389737,"rank":1,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":389746,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1080/images/"},{"id":389747,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.XML"}],"country":"United States","state":"Maine","otherGeospatial":"Rachel Carson National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.63796997070312,\n 43.20417480788432\n ],\n [\n -70.61325073242188,\n 43.153101551466385\n ],\n [\n -70.477294921875,\n 43.257205668363206\n ],\n [\n -70.43472290039062,\n 43.38508989465156\n ],\n [\n -70.53634643554688,\n 43.393073720674415\n ],\n [\n -70.63796997070312,\n 43.31418735795809\n ],\n [\n -70.63796997070312,\n 43.20417480788432\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The Los Angeles Coastal Plain (LACP) covers about 580 square miles and is the largest coastal plain of semiarid southern California. The LACP is heavily developed with mostly residential, commercial, and industrial land uses that rely heavily on groundwater for water supply. In 2010, the LACP was home to about 14 percent of California’s population, or about 5.4 million residents. The LACP is also a major commercial and industrial hub with industries including manufacturing, aerospace, entertainment, and tourism.
There has been a heavy reliance on groundwater from the LACP for many years. An average of 305,000 acre-feet per year (acre-ft/yr) of groundwater was used annually from the LACP from 1971 to 2015. The need to replenish the groundwater basins within the LACP was recognized as far back as the 1930s, when spreading grounds were first used to replenish groundwater basins and store water underground during times of water surplus to meet demands in times of shortage. Seawater intrusion resulting from freshwater pumping was first observed in the 1940s. As a result, injection of imported water through wells at what is now the West Coast Basin Barrier Project began on an experimental basis in 1951. Managed aquifer recharge from the spreading grounds and barrier wells is now a substantial component of the LACP’s groundwater supply. The average annual recharge from water spreading from 1971 to 2015 was about 120,000 acre-ft/yr, and the average annual injection into the barrier wells was about 33,000 acre-ft/yr. Other inflows include areal recharge, underflow from San Gabriel and San Fernando Valleys, and onshore flow from the ocean. The average annual recharge from these sources was 100,000 acre-feet (acre-ft) from 1971 to 2015. Additionally, cross-boundary flow from Orange County into the western Orange County subareas of the LACP was simulated as 48,000 acre-ft from 1971 to 2015.
This study, conducted in cooperation with the Water Replenishment District of Southern California (WRD), involved an assessment of the historical and present status of groundwater resources in the LACP and the development of tools to better understand the groundwater system. These efforts were built upon results from previous studies and incorporate new information and developments in modeling capabilities to provide a more detailed analysis of the aquifer systems.
This study includes a comprehensive compilation of geologic and hydrologic data (Chapter A), development of a chronostratigraphic model that provides a detailed description of the LACP aquifer systems (Chapter B), characterization of the groundwater hydrology of the LACP, including a down-hole analysis of grain size using lithologic and geophysical logs (Chapter C), and development and application of the Los Angeles Coastal Plain Groundwater-flow Model (LACPGM) to simulate past groundwater conditions, estimate groundwater-budget components and flow paths, and approximate future groundwater conditions under different scenarios (Chapter D).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215088","collaboration":"Prepared in cooperation with the Water Replenishment District of Southern California","usgsCitation":"Paulinski, S., ed., 2021, Development of a groundwater-simulation model in the Los Angeles Coastal Plain, Los Angeles County, California (ver. 1.1, May 2023): U.S. Geological Survey Scientific Investigations Report 2021-5088, 489 p., https://doi.org/10.3133/sir20215088.","productDescription":"Report: xiii, 489 p.; Data Release","numberOfPages":"489","onlineOnly":"Y","ipdsId":"IP-023155","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":389755,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H15ZAX","linkHelpText":"MODFLOW-USG model used to evaluate water management issues in the Los Angeles Coastal Plain, California"},{"id":389754,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5088/sir20215088_v1.1.pdf","text":"Report","size":"66 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389753,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5088/covrthb_.jpg"},{"id":416877,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5088/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":436182,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TJD4IE","text":"USGS data release","linkHelpText":"MODFLOW-6 model to update and extend the Los Angeles Coastal Plain Groundwater Model"},{"id":500446,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111785.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"Los Angeles County","otherGeospatial":"Los Angeles Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.90802001953125,\n 33.59860671494885\n ],\n [\n -117.59490966796875,\n 33.876116579321206\n ],\n [\n -117.82012939453125,\n 34.14249823152873\n ],\n [\n -118.20327758789062,\n 34.23337699755914\n ],\n [\n -118.53973388671874,\n 34.03672867489511\n ],\n [\n -118.41476440429686,\n 33.80083235326659\n ],\n [\n -118.24722290039061,\n 33.72776616734189\n ],\n [\n -117.90802001953125,\n 33.59860671494885\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: September 2021; Version 1.1: May 2023","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
A numerical groundwater flow model of the Hualapai Valley Basin in northwestern Arizona was developed to assist water-resource managers in understanding the potential effects of projected groundwater withdrawals on groundwater levels in the basin. The Hualapai Valley Hydrologic Model (HVHM) simulates the hydrologic system for the years 1935 through 2219, including future withdrawal scenarios that simulate large-scale agricultural expansion with and without enhanced groundwater recharge from potential new infiltration basin projects. HVHM is a highly parameterized model (75,586 adjustable parameters) capable of simulating grid-scale variability in aquifer properties (for example, conductivity, specific yield, and specific storage) and system stresses (for instance, natural recharge and groundwater withdrawals). Parameter estimation and uncertainty quantification were performed using an iterative ensemble smoother software (PESTPP-IES) to produce an ensemble of models fit to historical data. Results via the future withdrawal scenario from this ensemble indicate that mean groundwater level will decline at wells in the Kingman subbasin 87 to 128 feet by the year 2050 and 204 to 241 feet by the year 2080. Mean groundwater level is expected to decline at wells in the Hualapai subbasin between 44 and 210 feet by 2050 and between 107 and 350 feet by 2080. The enhanced recharge scenario results show potential for these declines to be partially mitigated in the Kingman subbasin by between 8 and 23 feet in 2050 and between 23 and 43 feet in 2080. The enhanced recharge scenario has no simulated effect on groundwater levels in the Hualapai subbasin. All planned enhanced infiltration projects are located in the Kingman subbasin, which is simulated to become hydraulically disconnected from the Hualapai subbasin owing to groundwater-level declines before 2050. Mean depth to water in the Kingman subbasin as simulated in the future withdrawal scenario will exceed 1,200 feet between the years 2155 and 2214 (median year 2171). In the future withdrawal plus enhanced recharge scenario, mean depth to water in the Kingman subbasin exceeds 1,200 feet between the years 2163 and 2207 (median year 2180), except for one model realization in which the subbasin does not reach an mean depth to water of 1,200 feet by the end of forecast simulation (year 2220). Simulated dewatering of the basin margins reduces scenario pumping rates by as much as 7 percent in 2029 and 12 percent in 2079 below specified rates. Forecasts of groundwater-level declines are based on the reduced simulated pumping rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215077","collaboration":"Prepared in cooperation with Mohave County and the City of Kingman","usgsCitation":"Knight, J.E., Gungle, B., and Kennedy, J.R., 2021, Assessing potential groundwater-level declines from future withdrawals in the Hualapai Valley, northwestern Arizona: U.S. Geological Survey Scientific Investigations Report, 63 p., https://doi.org/10.3133/sir20215077.","productDescription":"Report: vii, 63 p.; Data Release","numberOfPages":"63","onlineOnly":"Y","ipdsId":"IP-118946","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":436183,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MJRMSQ","text":"USGS data release","linkHelpText":"Repeat microgravity data from the Hualapai Valley, Mohave County, Arizona, 2008-2019"},{"id":389758,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20125275","text":"Scientific Investigations Report 2012-5275","linkHelpText":"— Hydrogeologic framework and estimates of groundwater storage for the Hualapai Valley, Detrital Valley, and Sacramento Valley basins, Mohave County, Arizona"},{"id":389739,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5077/sir20215077.pdf","text":"Report","size":"26 MB"},{"id":389759,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20135122","text":"Scientific Investigations Report 2013-5122","linkHelpText":"— Preliminary groundwater flow model of the basin-fill aquifers in Detrital, Hualapai, and Sacramento Valleys, Mohave County, northwestern Arizona"},{"id":389740,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9017DI9","linkHelpText":"Data release for transient groundwater model of the Hualapai Valley Groundwater Basin, Mohave County, Arizona"},{"id":389738,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5077/covrthb.jpg"},{"id":389756,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20075182","text":"Scientific Investigations Report 2007-5182","linkHelpText":"— Ground-Water Occurrence and Movement, 2006, and Water-Level Changes in the Detrital, Hualapai, and Sacramento Valley Basins, Mohave County, Arizona"},{"id":389757,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20115159","text":"Scientific Investigations Report 2011-5159","linkHelpText":"— Groundwater budgets for Detrital, Hualapai, and Sacramento Valleys, Mohave County, Arizona, 2007-08"}],"country":"United States","state":"Arizona","otherGeospatial":"Hualapai Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.5,\n 36\n ],\n [\n -113.5,\n 36\n ],\n [\n -113.5,\n 35\n ],\n [\n -114.5,\n 35\n ],\n [\n -114.5,\n 36\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Basin-centric long short-term memory (LSTM) network models have recently been shown to be an exceptionally powerful tool for stream temperature (Ts) temporal prediction (training in one period and predicting in another period at the same sites). However, spatial extrapolation is a well-known challenge to modelling Ts and it is uncertain how an LSTM-based daily Ts model will perform in unmonitored or dammed basins. Here we compiled a new benchmark dataset consisting of >400 basins across the contiguous United States in different data availability groups (DAG, meaning the daily sampling frequency) with and without major dams, and studied how to assemble suitable training datasets for predictions in basins with or without temperature monitoring. For prediction in unmonitored basins (PUB), LSTM produced a root-mean-square error (RMSE) of 1.129°C and an R2 of 0.983. While these metrics declined from LSTM's temporal prediction performance, they far surpassed traditional models' PUB values, and were competitive with traditional models' temporal prediction on calibrated sites. Even for unmonitored basins with major reservoirs, we obtained a median RMSE of 1.202°C and an R2 of 0.984. For temporal prediction, the most suitable training set was the matching DAG that the basin could be grouped into (for example, the 60% DAG was most suitable for a basin with 61% data availability). However, for PUB, a training dataset including all basins with data was consistently preferred. An input-selection ensemble moderately mitigated attribute overfitting. Our results indicate there are influential latent processes not sufficiently described by the inputs (e.g., geology, wetland covers), but temporal fluctuations can still be predicted well, and LSTM appears to be a highly accurate Ts modelling tool even for spatial extrapolation.
In regions where water resources are scarce and in high demand, it is important to safeguard against contamination of groundwater aquifers by oil-field fluids (water, gas, oil). In this context, the geochemical characterisation of these fluids is critical so that anthropogenic contaminants can be readily identified. The first step is characterising pre-development geochemical fluid signatures (i.e., those unmodified by hydrocarbon resource development) and understanding how these signatures may have been perturbed by resource production, particularly in the context of enhanced oil recovery (EOR) techniques. Here, we present noble gas isotope data in fluids produced from oil wells in several water-stressed regions in California, USA, where EOR is prevalent. In oil-field systems, only casing gases are typically collected and measured for their noble gas compositions, even when oil and/or water phases are present, due to the relative ease of gas analyses. However, this approach relies on a number of assumptions (e.g., equilibrium between phases, water-to-oil ratio (WOR) and gas-to-oil ratio (GOR) in order to reconstruct the multiphase subsurface compositions. Here, we adopt a novel, more rigorous approach, and measure noble gases in both casing gas and produced fluid (oil-water-gas mixtures) samples from the Lost Hills, Fruitvale, North and South Belridge (San Joaquin Basin, SJB) and Orcutt (Santa Maria Basin) Oil Fields. Using this method, we are able to fully characterise the distribution of noble gases within a multiphase hydrocarbon system. We find that measured concentrations in the casing gases agree with those in the gas phase in the produced fluids and thus the two sample types can be used essentially interchangeably.
EOR signatures can readily be identified by their distinct air-derived noble gas elemental ratios (e.g., 20Ne/36Ar), which are elevated compared to pre-development oil-field fluids, and conspicuously trend towards air values with respect to elemental ratios and overall concentrations. We reconstruct reservoir 20Ne/36Ar values using both casing gas and produced fluids and show that noble gas ratios in the reservoir are strongly correlated (r2 = 0.88–0.98) to the amount of water injected within ~500 m of a well. We suggest that the 20Ne/36Ar increase resulting from injection is sensitive to the volume of fluid interacting with the injectate, the effective water-to-oil ratio, and the composition of the injectate. Defining both the pre-development and injection-modified hydrocarbon reservoir compositions are crucial for distinguishing the sources of hydrocarbons observed in proximal groundwaters, and for quantifying the transport mechanisms controlling this occurrence.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120540","usgsCitation":"Tyne, R.L., Barry, P.H., Karolytė, R., Bryne, D.J., Kulongoski, J.T., Hillegonds, D., and Ballentine, C.J., 2021, Investigating the effect of enhanced oil recovery on the noble gas signature of casing gases and produced waters from selected California oil fields: Chemical Geology, v. 584, 120540, 10 p., https://doi.org/10.1016/j.chemgeo.2021.120540.","productDescription":"120540, 10 p.","ipdsId":"IP-126638","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":450655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120540","text":"Publisher Index Page"},{"id":393752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"Fruitvale, Lost Hills and North and South Belridge Oil Fields","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.73150634765625,\n 34.4069096565206\n ],\n [\n -119.24011230468749,\n 34.4069096565206\n ],\n [\n -119.24011230468749,\n 35.85566574217861\n ],\n [\n -120.73150634765625,\n 35.85566574217861\n ],\n [\n -120.73150634765625,\n 34.4069096565206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"584","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tyne, R. L.","contributorId":205891,"corporation":false,"usgs":false,"family":"Tyne","given":"R.","email":"","middleInitial":"L.","affiliations":[{"id":37187,"text":"Department of Earth Sciences, University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barry, P. 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J.","contributorId":270730,"corporation":false,"usgs":false,"family":"Bryne","given":"D.","email":"","middleInitial":"J.","affiliations":[{"id":56201,"text":"Dept. of Earth Sci., University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829845,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829846,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hillegonds, D.J.","contributorId":205892,"corporation":false,"usgs":false,"family":"Hillegonds","given":"D.J.","email":"","affiliations":[{"id":37187,"text":"Department of Earth Sciences, University of Oxford, Oxford, UK","active":true,"usgs":false}],"preferred":false,"id":829847,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ballentine, C. J.","contributorId":224737,"corporation":false,"usgs":false,"family":"Ballentine","given":"C.","email":"","middleInitial":"J.","affiliations":[{"id":40928,"text":"Oxford University","active":true,"usgs":false}],"preferred":false,"id":829848,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224530,"text":"70224530 - 2021 - A simplified method for rapid estimation of emergency water supply needs after earthquakes","interactions":[],"lastModifiedDate":"2022-12-23T17:20:52.03908","indexId":"70224530","displayToPublicDate":"2021-09-25T09:53:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"A simplified method for rapid estimation of emergency water supply needs after earthquakes","docAbstract":"Researchers are investigating the problem of estimating households with potable water service outages soon after an earthquake. Most of these modeling approaches are computationally intensive, have large proprietary data collection requirements or lack precision, making them unfeasible for rapid assessment, prioritization, and allocation of emergency water resources in large, complex disasters. This study proposes a new simplified analytical method—performed without proprietary water pipeline data—to estimate water supply needs after earthquakes, and a case study of its application in the HayWired earthquake scenario. In the HayWired scenario—a moment magnitude (Mw) 7.0 Hayward Fault earthquake in the San Francisco Bay Area, California (USA)—an analysis of potable water supply in two water utility districts was performed using the University of Colorado Water Network (CUWNet) model. In the case study, application of the simplified method extends these estimates of household water service outage to the nine counties adjacent to the San Francisco Bay, aggregated by a ~250 m2 (nine-arcsecond) grid. The study estimates about 1.38 million households (3.7 million residents) out of 7.6 million residents (2017, ambient, nighttime population) with potable water service outage soon after the earthquake—about an 8% increase from the HayWired scenario estimates.
","language":"English","publisher":"MDPI","doi":"10.3390/w13192635","usgsCitation":"Toland, J.C., and Wein, A., 2021, A simplified method for rapid estimation of emergency water supply needs after earthquakes: Water, v. 13, 2635, 27 p., https://doi.org/10.3390/w13192635.","productDescription":"2635, 27 p.","ipdsId":"IP-132813","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450658,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13192635","text":"Publisher Index Page"},{"id":389813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.79968261718749,\n 37.24782120155428\n ],\n [\n -121.55273437499999,\n 37.24782120155428\n ],\n [\n -121.55273437499999,\n 38.324420427006544\n ],\n [\n -122.79968261718749,\n 38.324420427006544\n ],\n [\n -122.79968261718749,\n 37.24782120155428\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2021-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Toland, Joseph Charles 0000-0002-0092-0320","orcid":"https://orcid.org/0000-0002-0092-0320","contributorId":265976,"corporation":false,"usgs":true,"family":"Toland","given":"Joseph","email":"","middleInitial":"Charles","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":823911,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":823912,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224317,"text":"fs20213044 - 2021 - Managing water resources on Long Island, New York, with integrated, multidisciplinary science","interactions":[],"lastModifiedDate":"2021-09-27T12:11:24.513816","indexId":"fs20213044","displayToPublicDate":"2021-09-24T14:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3044","displayTitle":"Managing Water Resources on Long Island, New York, with Integrated, Multidisciplinary Science","title":"Managing water resources on Long Island, New York, with integrated, multidisciplinary science","docAbstract":"Nutrients, harmful algal blooms, and synthetic chemicals like per- and polyfluoroalkyl substances (PFAS) and 1,4-dioxane threaten Long Island’s water resources by affecting the quality of drinking water and ecologically sensitive habitats that support the diverse wildlife throughout the island. Understanding the occurrence, fate, and transport of these potentially harmful chemicals is critical to protect these vital resources. The U.S. Geological Survey (USGS) is collecting and analyzing data to support informed water-resource management decisions. This fact sheet introduces ongoing efforts and future areas of study aimed to help water professionals develop a comprehensive science strategy to address contamination of the Long Island aquifer system, the sole source of drinking water for nearly 3 million people. These studies include surface and groundwater collection and groundwater flow modeling. Funding for the data collection has been provided by the USGS, New York State Department of Environmental Conservation, New York City Department of Environmental Protection, Suffolk County Water Authority, Nassau County Department of Public Works, State and local agencies, and Tribal and Federal partners. Without the foresight and long-term commitment of these funding partners, evaluating sustainability and planning for future water needs would not be possible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213044","usgsCitation":"Breault, R.F., Masterson, J.P., Schubert, C.E., and Herdman, L.M., 2021, Managing water resources on Long Island, New York, with integrated, multidisciplinary science: U.S. Geological Survey Fact Sheet 2021–3044, 4 p., https://doi.org/10.3133/fs20213044.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-131602","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":389579,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3044/fs20213044.pdf","text":"Report","size":"14.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3044"},{"id":389578,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3044/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.0478515625,\n 40.538851525354666\n ],\n [\n -73.7677001953125,\n 40.538851525354666\n ],\n [\n -73.1304931640625,\n 40.60561205826018\n ],\n [\n -72.5537109375,\n 40.76806170936614\n ],\n [\n -71.9549560546875,\n 40.97575093157534\n ],\n [\n -71.83959960937499,\n 41.10832999732831\n ],\n [\n -72.1417236328125,\n 41.24064190269475\n ],\n [\n -72.8338623046875,\n 41.10005163093046\n ],\n [\n -73.5205078125,\n 40.96330795307353\n ],\n [\n -73.883056640625,\n 40.83043687764923\n ],\n [\n -74.06982421875,\n 40.713955826286046\n ],\n [\n -74.0863037109375,\n 40.61812224225511\n ],\n [\n -74.0478515625,\n 40.538851525354666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349