{"pageNumber":"102","pageRowStart":"2525","pageSize":"25","recordCount":41032,"records":[{"id":70251052,"text":"70251052 - 2024 - Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","interactions":[],"lastModifiedDate":"2024-01-19T15:15:53.833192","indexId":"70251052","displayToPublicDate":"2024-01-04T09:15:25","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":17129,"text":"STAC Workshop Report","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"24-002","title":"Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","docAbstract":"The Chesapeake Bay Program’s Watershed Model (CBWM) has been used as an accounting tool for the Chesapeake Bay Total Maximum Daily Load (TMDL). However, some of the fundamental parameters that underpin the watershed model may not represent local watershed characteristics at all scales. Significant investments have been made by state and local governments, and other local stakeholders, who are interested in validating loads and progress in implementing measures to achieve the pollutant reductions called for in the TMDL through local monitoring data. For the purposes of this STAC workshop, local monitoring is considered any relevant data collected by a local, regional, state, or federal organization that has not been used previously in the development, calibration, or validation of the CBWM. Some of these local monitoring efforts have been collecting data over the past 5-10 years, with some datasets extending back over more than two decades. However, the data and the CBWM are often not directly comparable due to differences in temporal and spatial scales or because the water quality parameters being monitored are not those estimated by the model. Therefore, a Scientific and Technical Advisory Committee (STAC) workshop was convened to bring together Chesapeake Bay Program (CBP) modelers, local and state government stakeholders, and scientists who are monitoring and analyzing local water quality data to recommend ways in which local monitoring data can be used to inform the CBWM, identify gaps between modeled and monitored data, and validate model predictions at the local scale.\n\nThe workshop, “Using Local Monitoring Results to Inform the Chesapeake Bay Program’s Watershed Model”, was held in March 2023 to provide insight on the scope of local water quality monitoring efforts within and outside of the Bay watershed that could be used to inform the CBWM. Scientists and managers developed recommendations that could be used by modelers for either calibration or knowledge generation to inform the Phase 7 version of the CBWM currently under development for a 2027 decision by the CBP, recommendations for how local monitoring efforts could be designed or altered to better inform the CBWM, and recommendations for how monitored trends could be used in management. The preliminary presentations for the workshop provided essential background information on the CBWM and data used to parameterize it. This information was the foundation for discussions on existing data gaps, the importance of current local monitoring networks, and best practices for developing future monitoring networks. More information on this STAC-funded effort including workshop presentation slides and recordings can be accessed on the workshop webpage. \n\nConfidence in the loading estimates of the CBWM is critical because of its role as the accounting mechanism for measuring progress toward the Bay TMDL’s nutrient and sediment reduction goals. Those who are being asked or required to pay for these reductions, from state and local government managers to farmers, property owners and developers, must have confidence in the scientific validity of the CBWM’s loading estimates or trust in the restoration effort will dissipate. Toward that end, several local entities have invested in extensive urban, suburban, and agricultural monitoring programs to characterize nutrient and sediment loading (among other water quality parameters) at a relatively fine scale (from a few acres to 5 square miles). Monitoring networks outside of the Bay watershed were also included as their relevance and similarities to Bay watershed landscapes, hydrology, and climate conditions can help build the body of knowledge necessary for better parameterization of the CBWM.\nLocal monitoring results could be analyzed for loads and trends for calibration of Phase 7, comparison against trends, informing the structure and parameterization of the model, and potentially in policy evaluation. The effectiveness of management practices at the small watershed scale is a primary question of watershed managers that could be addressed by local monitoring, but to do so study design and statistical techniques may need to be altered if these datasets are intended to inform parameterization of the Bay modeling tools. The partnership would benefit from the redesign of some existing monitoring programs so that they are hypothesis-driven, with fully described inputs, outputs, and practices. New statistical tools could be applied to evaluate the relative importance of various drivers affecting water quality and influenced by hydrogeologic setting and watershed condition.","language":"English","publisher":"Chesapeake Bay Program STAC (Scientific and Technical Advisory Committee)","usgsCitation":"Berger, K., Filippino, K.C., Shenk, G.W., Goulet, N., Lookenbill, M., Moyer, D.L., Noe, G.E., Porter, A.J., Shallenberger, J., Thomas, B., and Yactayo, G., 2024, Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model: STAC Workshop Report 24-002, 35 p.","productDescription":"35 p.","ipdsId":"IP-160274","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":424622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":424607,"rank":1,"type":{"id":15,"text":"Index 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For species with complex life histories, climate change can have contrasting effects for different life stages within locally adapted populations and may result in responses counter to general climate change predictions. Using data from two, 14-year demographic studies for a North American montane amphibian, Cascades frog (Rana cascadae), we quantified how aspects of current climate influenced annual survival of larvae and adult stages and modeled the stochastic population growth rate (λs) of each population for current (1980–2006) and future periods (2080s). Climate drivers of survival for the populations were similar for larvae (i.e., decreases in precipitation lead to pond drying and mortality), but diverged for terrestrial stages where decreases in winter length and summer precipitation had opposite effects. By the 2080s, we predict one population will be in sharp decline (λs = 0.90), while the other population will remain nearly stable (λs = 0.99) in the absence of other stressors, such as mortality due to disease. Our case study demonstrates a result counter to many climate envelope predictions in that stage-specific responses to local climate and hydrology result in a higher extinction risk for the more northern population.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecochg.2023.100081","usgsCitation":"Kissel, A.M., Palen, W.J., Adams, M.J., and Garwood, J.M., 2024, Contrasting demographic responses under future climate for two populations of a montane amphibian: Climate Change Ecology, v. 7, 100081, 10 p., https://doi.org/10.1016/j.ecochg.2023.100081.","productDescription":"100081, 10 p.","ipdsId":"IP-115669","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":440801,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecochg.2023.100081","text":"Publisher Index Page"},{"id":430755,"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 -128.01194792245104,\n 50.743714435865\n ],\n [\n -128.01194792245104,\n 36.39514683322275\n ],\n [\n -115.61936979745127,\n 36.39514683322275\n ],\n [\n -115.61936979745127,\n 50.743714435865\n ],\n [\n -128.01194792245104,\n 50.743714435865\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kissel, Amanda M.","contributorId":211917,"corporation":false,"usgs":false,"family":"Kissel","given":"Amanda","email":"","middleInitial":"M.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":905576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palen, Wendy J.","contributorId":211918,"corporation":false,"usgs":false,"family":"Palen","given":"Wendy","email":"","middleInitial":"J.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":905577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":905578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garwood, Justin M","contributorId":217674,"corporation":false,"usgs":false,"family":"Garwood","given":"Justin","email":"","middleInitial":"M","affiliations":[{"id":39681,"text":"California Dept fish wildlife","active":true,"usgs":false}],"preferred":false,"id":905579,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252027,"text":"70252027 - 2024 - Major fluvial erosion and a 500-Mt sediment pulse triggered by lava-dam failure, Río Coca, Ecuador","interactions":[],"lastModifiedDate":"2024-03-11T12:02:54.212891","indexId":"70252027","displayToPublicDate":"2024-01-04T06:54:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Major fluvial erosion and a 500-Mt sediment pulse triggered by lava-dam failure, Río Coca, Ecuador","docAbstract":"

The failure of a 144-m-high lava-dam waterfall on the Río Coca, Ecuador, in February 2020 initiated a catastrophic watershed reset—regressive erosion upstream and a massive sediment pulse downstream—as the river evolves towards a new equilibrium grade. The evolution of this river corridor after a sudden base-level fall embodies the “complex response” concepts long understood through laboratory experiments, numerical modelling and smaller-scale field studies, but that have not been observed in the field before on this scale. This paper presents geomorphic and geotechnical data to characterize the evolution of the Río Coca since 2020. In the three years after the lava-dam failure, the erosion front migrated almost 13 km upstream along the mainstem river and triggered secondary headcuts that began migrating up tributaries. Erosion of the mainstem and tributary valleys generated a sediment pulse estimated to be 277 million m3 and ~500 million tonnes (Mt) over three years, depositing sediment tens of meters thick over tens of kilometres downstream from the former waterfall. This sediment pulse is one of the largest in modern times, comparable to the annual sediment load of a major continent-draining river but with orders-of-magnitude greater sediment yield. Geomorphic adjustment of the Río Coca represents a highly unusual natural disaster threatening life, property, water quality, the regional economy, major infrastructure and energy security. However, this event also provides a rare opportunity to learn how a large autogenic watershed disturbance and recovery evolve, with important lessons for interpreting the sedimentary record of volcanic landscapes.

","language":"English","publisher":"Wiley","doi":"10.1002/esp.5751","usgsCitation":"Barrera Crespo, P.D., Espinoza Giron, P., Bedoya, R., Gibson, S., East, A.E., Langendoen, E., and Boyd, P.M., 2024, Major fluvial erosion and a 500-Mt sediment pulse triggered by lava-dam failure, Río Coca, Ecuador: Earth Surface Processes and Landforms, v. 49, no. 3, p. 1058-1080, https://doi.org/10.1002/esp.5751.","productDescription":"23 p.","startPage":"1058","endPage":"1080","ipdsId":"IP-155338","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5751","text":"Publisher Index Page"},{"id":426486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Río Coca","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.02554831641997,\n -0.2842980721613628\n ],\n [\n -78.02554831641997,\n -1.4706593291238619\n ],\n [\n -76.79508146704426,\n -1.4706593291238619\n ],\n [\n -76.79508146704426,\n -0.2842980721613628\n ],\n [\n -78.02554831641997,\n -0.2842980721613628\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrera Crespo, Pedro D.","contributorId":334693,"corporation":false,"usgs":false,"family":"Barrera Crespo","given":"Pedro","email":"","middleInitial":"D.","affiliations":[{"id":80211,"text":"Corporacion Electrica del Ecuador","active":true,"usgs":false}],"preferred":false,"id":896276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Espinoza Giron, Pablo","contributorId":334694,"corporation":false,"usgs":false,"family":"Espinoza Giron","given":"Pablo","email":"","affiliations":[{"id":80211,"text":"Corporacion Electrica del Ecuador","active":true,"usgs":false}],"preferred":false,"id":896277,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bedoya, Renan","contributorId":334695,"corporation":false,"usgs":false,"family":"Bedoya","given":"Renan","email":"","affiliations":[{"id":80211,"text":"Corporacion Electrica del Ecuador","active":true,"usgs":false}],"preferred":false,"id":896278,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gibson, Stanford","contributorId":334541,"corporation":false,"usgs":false,"family":"Gibson","given":"Stanford","email":"","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":896279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":896280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Langendoen, Eddy J.","contributorId":256774,"corporation":false,"usgs":false,"family":"Langendoen","given":"Eddy J.","affiliations":[{"id":51861,"text":"USDA National Sedimentation Laboratory, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":896281,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boyd, Paul M","contributorId":215066,"corporation":false,"usgs":false,"family":"Boyd","given":"Paul","email":"","middleInitial":"M","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":896282,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250624,"text":"ofr20231097 - 2024 - Non-negligible near-term risk of extinction to the eastern migratory population of monarch butterflies—An updated assessment (2006–22)","interactions":[],"lastModifiedDate":"2024-01-25T20:08:49.8811","indexId":"ofr20231097","displayToPublicDate":"2024-01-03T10:09:17","publicationYear":"2024","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":"2023-1097","displayTitle":"Non-Negligible Near-Term Risk of Extinction to the Eastern Migratory Population of Monarch Butterflies—An Updated Assessment (2006–22)","title":"Non-negligible near-term risk of extinction to the eastern migratory population of monarch butterflies—An updated assessment (2006–22)","docAbstract":"

The eastern migratory population of monarch butterflies (Danaus plexippus) started declining as early as the mid-1970s and seemed to stop declining by the early 2000s; the population now (about 2022) persists at a much-reduced abundance. Stochastic variation in abundance, at levels typical of monarch butterflies and other insects, was assessed to determine whether this population is at heightened risk of quasi-extinction, a level of abundance below which recovery of the migratory behavior is uncertain. Using previously published Bayesian state-space modeling methods it was determined roughly equivalent risk of quasi-extinction as was reported in 2016 for the species (28.7 percent [1.9–81.0 credible interval] and 52.0 percent [3.2–97.7 credible interval] at the 10- and 20-year marks, respectively). Though highly uncertain, the risk is non-negligibly positive. Warning signal analysis indicates the current dynamic is dominated by stochastic variation, which seems to be heightening risk with the passage of time. Increasing breeding opportunities through restoration of milkweed in its northern breeding locations seems to be the most promising means of mitigating extinction risk for this species.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231097","usgsCitation":"Thogmartin, W.E., 2024, Non-negligible near-term risk of extinction to the eastern migratory population of monarch butterflies—An updated assessment (2006–22): U.S. Geological Survey Open-File Report 2023–1097, 10 p., https://doi.org/10.3133/ofr20231097.","productDescription":"Report: iii, 10 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152775","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":423797,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WRARO7","text":"USGS data release","linkHelpText":"Eastern migratory monarch butterfly population estimates and associated early warning signals (2006–22)"},{"id":423796,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1097/images/"},{"id":423798,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231097/full"},{"id":423795,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1097/ofr20231097.XML"},{"id":423794,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1097/ofr20231097.pdf","text":"Report","size":"949 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1097"},{"id":423793,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1097/coverthb.jpg"}],"contact":"

Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, Wisconsin 54603

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2024-01-03","noUsgsAuthors":false,"publicationDate":"2024-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":890608,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70250774,"text":"70250774 - 2024 - Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling","interactions":[],"lastModifiedDate":"2024-01-04T12:58:28.072553","indexId":"70250774","displayToPublicDate":"2024-01-03T06:55:05","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17114,"text":"Natural Hazards and Earth Systems Sciences (NHESS)","active":true,"publicationSubtype":{"id":10}},"title":"Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling","docAbstract":"

Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and polygon), and better accommodating the varying data accuracy and precision in landslide inventories. However, the use of slope units in regional (> 100 km2) landslide studies remains limited due, in part, to the large computational costs and/or poor reproducibility with current delineation methods. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units that leverages tools from within TauDEM and GRASS, using an R interface. The algorithm uses geomorphic laws to define the appropriate scaling of the slope units representative of hillslope processes, avoiding the often ambiguous determination of slope unit size. We then demonstrate how slope units enable more robust regional-scale landslide susceptibility and event-specific landslide occurrence maps.


","language":"English","publisher":"European Geophysical Union","doi":"10.5194/nhess-24-1-2024","collaboration":"Oregon State, Kentucky Geological Survey","usgsCitation":"Woodard, J.B., Mirus, B., Wood, N.J., Allstadt, K.E., Leshchinsky, B., and Crawford, M., 2024, Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling: Natural Hazards and Earth Systems Sciences (NHESS), v. 24, no. 1, p. 1-12, https://doi.org/10.5194/nhess-24-1-2024.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-146317","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":440808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-24-1-2024","text":"Publisher Index Page"},{"id":424109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Woodard, Jacob Bryson 0000-0002-3095-0774","orcid":"https://orcid.org/0000-0002-3095-0774","contributorId":305507,"corporation":false,"usgs":true,"family":"Woodard","given":"Jacob","email":"","middleInitial":"Bryson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":891372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":891373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":891374,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":891375,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leshchinsky, Ben","contributorId":332926,"corporation":false,"usgs":false,"family":"Leshchinsky","given":"Ben","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":891376,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crawford, Matthew","contributorId":332927,"corporation":false,"usgs":false,"family":"Crawford","given":"Matthew","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":891377,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70251172,"text":"70251172 - 2024 - Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients","interactions":[],"lastModifiedDate":"2024-01-25T12:44:18.618329","indexId":"70251172","displayToPublicDate":"2024-01-03T06:39:41","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients","docAbstract":"

Mangrove is one of the most productive and sensitive ecosystems in the world. Due to the complexity and specificity of mangrove habitat, the development of mangrove is regulated by several factors. Species distribution models (SDMs) are effective tools to identify the potential habitats for establishing and regenerating the ecosystem. Such models usually include exclusively environmental factors. Nevertheless, recent studies have challenged this notion and highlight the importance of including biotic interactions. Both factors are necessary for a mechanistic understanding of the mangrove distribution in order to promote the protection and restoration of mangroves. Thus, we present a novel approach of combining environmental factors and interactions with salt marsh for projecting mangrove distributions at the global level and within latitudinal zones. To test the salt marsh interaction, we fit the MaxEnt model with two predicting sets: (1) environments only and (2) environments + salt marsh interaction index (SII). We found that both sets of models had good predictive ability, although the SII improved model performance slightly. Potential distribution areas of mangrove decrease with latitudes, and are controlled by biotic and abiotic factors. Temperature, precipitation and wind speed are generally critical at both global scale and ecotones along latitudes. SII is important on global scale, with a contribution of 5.9%, ranking 6th, and is particularly critical in the 10–30°S and 20–30°N zone. Interactions with salt marsh, including facilitation and competition, are shown to affect the distribution of mangroves at the zone of coastal ecotone, especially in the latitudinal range from 10° - 30°. The contribution of SII to mangrove distribution increases with latitudes due to the difference in the adaptive capacity of salt marsh plants and mangroves to environments. Totally, this study identified and quantified the effects of salt marsh on mangrove distribution by establishing the SII. The results not only facilitate to establish a more accurate mangrove distribution map, but also improve the efficiency of mangrove restoration by considering the salt marsh interaction in the mangrove management projects. In addition, the method of incorporating biotic interaction into SDMs through establish the biotic interaction index has contributed to the development of SDMs.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.119892","usgsCitation":"Cui, L., DeAngelis, D., Berger, U., Cao, M., Zhang, Y., Zhang, X., and Jiang, J., 2024, Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients: Journal of Environmental Management, v. 351, 119892, 13 p., https://doi.org/10.1016/j.jenvman.2023.119892.","productDescription":"119892, 13 p.","ipdsId":"IP-142484","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":424944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"351","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cui, Lina","contributorId":333612,"corporation":false,"usgs":false,"family":"Cui","given":"Lina","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":893339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berger, Uta","contributorId":224016,"corporation":false,"usgs":false,"family":"Berger","given":"Uta","affiliations":[{"id":40811,"text":"TU Dresden, Institute of Forest Growth and Computer Science, Germany","active":true,"usgs":false}],"preferred":false,"id":893340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cao, Minmin","contributorId":333613,"corporation":false,"usgs":false,"family":"Cao","given":"Minmin","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Yaqi","contributorId":333614,"corporation":false,"usgs":false,"family":"Zhang","given":"Yaqi","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893342,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Xiaomin","contributorId":333615,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaomin","email":"","affiliations":[{"id":79948,"text":"Zhejiang Academy of Forestry","active":true,"usgs":false}],"preferred":false,"id":893343,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Jiang","contributorId":191968,"corporation":false,"usgs":false,"family":"Jiang","given":"Jiang","email":"","affiliations":[],"preferred":false,"id":893344,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70250880,"text":"70250880 - 2024 - Estimating lithium concentrations in groundwater used as drinking water for the conterminous United States","interactions":[],"lastModifiedDate":"2024-01-25T14:57:06.787905","indexId":"70250880","displayToPublicDate":"2024-01-02T10:47:01","publicationYear":"2024","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":"Estimating lithium concentrations in groundwater used as drinking water for the conterminous United States","docAbstract":"

Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, >4 to ≤10 μg/L, >10 to ≤30 μg/L, and >30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47–65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km2 and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose–response to low (<30 μg/L) concentrations of Li.

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0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":891896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Eric E.","contributorId":333096,"corporation":false,"usgs":false,"family":"Brown","given":"Eric","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":891897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saftner, Daniel","contributorId":333090,"corporation":false,"usgs":false,"family":"Saftner","given":"Daniel","email":"","affiliations":[{"id":16138,"text":"Desert Research 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Science Center","active":true,"usgs":true}],"preferred":true,"id":891901,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891902,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256598,"text":"70256598 - 2024 - Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale","interactions":[],"lastModifiedDate":"2024-08-01T14:48:54.943118","indexId":"70256598","displayToPublicDate":"2024-01-02T09:45:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale","docAbstract":"

The foundation of earthquake monitoring is the ability to rapidly detect, locate, and estimate the size of seismic sources. Earthquake magnitudes are particularly difficult to rapidly characterize because magnitude types are only applicable to specific magnitude ranges, and location errors propagate to substantial magnitude errors. We developed a method for rapid estimation of single‐station earthquake magnitudes using raw three‐component P waveforms observed at local to teleseismic distances, independent of prior size or location information. We used the MagNet regression model architecture (Mousavi and Beroza, 2020b), which combines convolutional and recurrent neural networks. We trained our model using ∼2.4 million P‐phase arrivals labeled by the authoritative magnitude assigned by the U.S. Geological Survey. We tested input data parameters (e.g., window length) that could affect the performance of our model in near‐real‐time monitoring applications. At the longest waveform window length of 114 s, our model (Artificial Intelligence Magnitude [AIMag]) is accurate (median estimated magnitude within ±0.5 magnitude units from catalog magnitude) between M 2.3 and 7.6. However, magnitudes above M ∼7 are more underestimated as true magnitude increases. As the windows are shortened down to 1 s, the point at which higher magnitudes begin to be underestimated moves toward lower magnitudes, and the degree of underestimation increases. The over and underestimation of magnitudes for the smallest and largest earthquakes, respectively, are potentially related to the limited number of events in these ranges within the training data, as well as magnitude saturation effects related to not capturing the full source time function of large earthquakes. Importantly, AIMag can determine earthquake magnitudes with individual stations’ waveforms without instrument response correction or knowledge of an earthquake’s source‐station distance. This work may enable monitoring agencies to more rapidly recognize large, potentially tsunamigenic global earthquakes from few stations, allowing for faster event processing and reporting. This is critical for timely warnings for seismic‐related hazards.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230171","usgsCitation":"Dybing, S., Yeck, W.L., Cole, H.M., and Melgar, D., 2024, Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale: Bulletin of the Seismological Society of America, v. 114, no. 3, p. 1523-1538, https://doi.org/10.1785/0120230171.","productDescription":"16 p.","startPage":"1523","endPage":"1538","ipdsId":"IP-158857","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":432030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"114","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Dybing, Sydney","contributorId":341314,"corporation":false,"usgs":false,"family":"Dybing","given":"Sydney","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":908222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":908223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cole, Hank M. 0000-0003-1684-9116","orcid":"https://orcid.org/0000-0003-1684-9116","contributorId":335228,"corporation":false,"usgs":true,"family":"Cole","given":"Hank","email":"","middleInitial":"M.","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":908224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melgar, Diego","contributorId":341315,"corporation":false,"usgs":false,"family":"Melgar","given":"Diego","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":908225,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256115,"text":"70256115 - 2024 - Mass-balance-consistent geological stock accounting: A new approach toward sustainable management of mineral resources","interactions":[],"lastModifiedDate":"2024-07-23T13:40:07.15414","indexId":"70256115","displayToPublicDate":"2024-01-02T08:33:44","publicationYear":"2024","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":"Mass-balance-consistent geological stock accounting: A new approach toward sustainable management of mineral resources","docAbstract":"

Global resource extraction raises concerns about environmental pressures and the security of mineral supply. Strategies to address these concerns depend on robust information on natural resource endowments, and on suitable methods to monitor and model their changes over time. However, current mineral resources and reserves reporting and accounting workflows are poorly suited for addressing mineral depletion or answering questions about the long-term sustainable supply. Our integrative review finds that the lack of a robust theoretical concept and framework for mass-balance (MB)-consistent geological stock accounting hinders systematic industry-government data integration, resource governance, and strategy development. We evaluate the existing literature on geological stock accounting, identify shortcomings of current monitoring of mine production, and outline a conceptual framework for MB-consistent system integration based on material flow analysis (MFA). Our synthesis shows that recent developments in Earth observation, geoinformation management, and sustainability reporting act as catalysts that make MB-consistent geological stock accounting increasingly feasible. We propose first steps for its implementation and anticipate that our perspective as “resource realists” will facilitate the integration of geological and anthropogenic material systems, help secure future mineral supply, and support the global sustainability transition.

","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.3c03088","usgsCitation":"Simoni, M.U., Drielsma, J.A., Ericsson, M., Gunn, A.G., Heiberg, S., Heldal, T.A., Nassar, N.T., Petavratz, E., and Muller, D.B., 2024, Mass-balance-consistent geological stock accounting: A new approach toward sustainable management of mineral resources: Environmental Science and Technology, v. 58, p. 971-990, https://doi.org/10.1021/acs.est.3c03088.","productDescription":"20 p.","startPage":"971","endPage":"990","ipdsId":"IP-145992","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":440814,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c03088","text":"Publisher Index Page"},{"id":431350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Simoni, Mark U.","contributorId":340251,"corporation":false,"usgs":false,"family":"Simoni","given":"Mark","email":"","middleInitial":"U.","affiliations":[{"id":81520,"text":"Norwegian University of Science and Technology, Norway","active":true,"usgs":false}],"preferred":false,"id":906749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drielsma, Johannes A.","contributorId":340252,"corporation":false,"usgs":false,"family":"Drielsma","given":"Johannes","email":"","middleInitial":"A.","affiliations":[{"id":81521,"text":"Drielsma Resources Europe, Germany","active":true,"usgs":false}],"preferred":false,"id":906750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ericsson, Magnus","contributorId":340253,"corporation":false,"usgs":false,"family":"Ericsson","given":"Magnus","email":"","affiliations":[{"id":81522,"text":"Luleå University of Technology, Sweden","active":true,"usgs":false}],"preferred":false,"id":906751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gunn, Andrew G.","contributorId":340254,"corporation":false,"usgs":false,"family":"Gunn","given":"Andrew","email":"","middleInitial":"G.","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heiberg, Sigurd","contributorId":340255,"corporation":false,"usgs":false,"family":"Heiberg","given":"Sigurd","email":"","affiliations":[{"id":81523,"text":"Petronavit AS, Norway","active":true,"usgs":false}],"preferred":false,"id":906753,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heldal, Tom A.","contributorId":340256,"corporation":false,"usgs":false,"family":"Heldal","given":"Tom","email":"","middleInitial":"A.","affiliations":[{"id":35509,"text":"Geological Survey of Norway","active":true,"usgs":false}],"preferred":false,"id":906754,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":906755,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Petavratz, Evi","contributorId":340257,"corporation":false,"usgs":false,"family":"Petavratz","given":"Evi","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906756,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Muller, Daniel B.","contributorId":340258,"corporation":false,"usgs":false,"family":"Muller","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":39348,"text":"Norwegian University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":906757,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70251342,"text":"70251342 - 2024 - Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios","interactions":[],"lastModifiedDate":"2024-02-06T13:19:35.568516","indexId":"70251342","displayToPublicDate":"2024-01-02T07:15:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios","docAbstract":"

Most of the world’s river-floodplain ecosystems are simultaneously undergoing modifications to their hydrological regimes and experiencing species invasions, making it unclear whether invasive species are the main drivers of ecosystem change or simply responding to changes in the hydrological regime.

We simulated patterns of forest recruitment and succession in a 2500-ha portion of the Upper Mississippi River floodplain with and without removal of invasive Phalaris arundinacea and under two different future 100-year hydrological scenarios: a future maintaining the average flooding conditions of the past 40 years (random) and a future that projects an observed upward 40-year trend in flooding conditions forward (trending). By comparing scenarios that included Phalaris removal and ones that did not, we were able to identify the conditions where Phalaris was the main driver of forest loss vs. the conditions where hydrology was the main driver of forest loss. Areas where Phalaris was the main driver of forest loss had mean annual flood inundation durations that were similar to areas that did not lose forest cover (60–90 growing season days), while areas where flooding was the main driver of forest loss had longer mean inundation durations (102–124 growing season days). In comparison to the random hydrology scenario, the trending scenario produced a decrease in the area over which Phalaris was identified as the main driver of forest loss and an increase in the area over which flood inundation was identified as the main driver of forest loss. Thus, if the observed trends in flooding continue, our model projects an increase in the area over which eradicating Phalaris is unlikely to result in the maintenance of forest cover. We used the Resist-Accept-Direct (RAD) framework to discuss potential management options to resist changes and maintain forest cover where Phalaris is likely to be the main driver of forest loss and to accept or direct changes in areas where forest loss is likely driven by hydrological change.

","language":"English","publisher":"Springer Nature","doi":"10.1007/s11273-023-09969-6","usgsCitation":"De Jager, N.R., Rohweder, J.J., Van Appledorn, M., Hlavacek, E., and Meier, A., 2024, Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios: Wetlands Ecology and Management, v. 32, p. 153-170, https://doi.org/10.1007/s11273-023-09969-6.","productDescription":"18 p.","startPage":"153","endPage":"170","ipdsId":"IP-149601","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":435067,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971TC5G","text":"USGS data release","linkHelpText":"Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios"},{"id":425437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.3876472088107,\n 43.699879451781044\n ],\n [\n -91.3876472088107,\n 43.281352841078245\n ],\n [\n -91.02775519900284,\n 43.281352841078245\n ],\n [\n -91.02775519900284,\n 43.699879451781044\n ],\n [\n -91.3876472088107,\n 43.699879451781044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":894163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":894164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":894165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hlavacek, Enrika 0000-0002-9872-2305","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":297184,"corporation":false,"usgs":false,"family":"Hlavacek","given":"Enrika","affiliations":[{"id":48800,"text":"Former USGS, UMESC employee","active":true,"usgs":false}],"preferred":false,"id":894166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meier, Andy","contributorId":333863,"corporation":false,"usgs":false,"family":"Meier","given":"Andy","email":"","affiliations":[{"id":79993,"text":"U.S. Army Corps of Engineers (USACE)","active":true,"usgs":false}],"preferred":false,"id":894167,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70259501,"text":"70259501 - 2024 - Snake River Fall Chinook Salmon research and monitoring","interactions":[],"lastModifiedDate":"2024-10-10T16:16:16.36293","indexId":"70259501","displayToPublicDate":"2024-01-01T10:59:34","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Snake River Fall Chinook Salmon research and monitoring","docAbstract":"In Chapter 1, we report on development and application of an integrated population model (IPM) for the natural-origin fall Chinook salmon population upstream of Lower Granite Dam. This year’s efforts represent the third update to the model. Initial efforts focused on generating juvenile and adult abundance estimates, with estimates of uncertainty, for informing the life-cycle model and estimating the effects of covariates on key demographic parameters. The goals of this year’s report are to 1) describe the modifications and advances made since the previous report, 2) to annually update and report the abundance estimates and other quantities used in the model, 3) to provide annual estimates of population parameters estimated by the IPM, and 4) to outline the next year’s tasks for advancing and/or applying the model.\n Since our last report on the life-cycle model, we have made a number of changes including: 1) incorporating jack abundance and age-structure data into the observation model, 2) changing smolt-to-adult survival (SAR) for subyearling and yearling to partial SARs that represent the joint probability surviving and entering the ocean at a given juvenile age, 3) combining age categories for rarely observed ages, 4) using scale data from unmarked fish to estimate age structure, and 5) generating composite life-cycle demographic parameters (cumulative capacity and productivity) from stage-specific parameters. We also generated juvenile abundance estimates, extended the model to include three additional brood years (1992– 2021), and ran the model to forecast returns to Lower Granite in 2022. \n For posterior medians of life stage-specific parameters, we estimated a mean productivity of 438 natural-origin juvenile recruits per female spawner, a capacity of 1.36 million juveniles, and a mean smolt-to-adult survival (SAR) of 1.2%. We detected strong density-dependent regulation, with juvenile recruits per spawner declining to about 50 juvenile recruits per female spawner at high spawner abundance. Across the entire life cycle, these stage-specific parameters resulted in a median cumulative intrinsic productivity of 1.93 adult female recruits per female spawner and a median equilibrium abundance of 2,851 female spawners (7,842 total spawners). Annual juvenile productivity varied from about 250–1,000 juveniles per spawner but displayed no temporal trends or patterns. For the three most recent brood years added to the model, recruits per spawner were higher than average but well within the range of uncertainty observed over the entire time series. In contrast to juvenile recruitment variability, SAR varied considerably among years and exhibited two periods of high survival (1996–2001 and 2007–2012) when SAR ranged from 2% to 6% and cumulative productivity ranged from 2 to 8 recruits per spawner. Partial SARs revealed that yearling outmigrants contributed substantially to the high SARs in the first high-survival period, but the second period was dominated by subyearlings. Yearlings contributed >30% to SAR in most years prior to 2007, and <30% since 2007.\n\nOur two-stage IPM provides a wealth of information about population dynamics affecting two key life-stage transitions (spawner to juvenile, and juvenile to spawner) centered on passage at Lower Granite Dam. By summarizing these stage-specific demographic parameters across the entire life cycle, this information will be useful for informing the recovery status of this threatened population. Whereas previous versions introduced hydrosystem and ocean covariates into the model, this phase of model development focused on solidifying the underlying model structure by introducing the concept of partial SARs and developing composite productivity and capacity as a function of underlying stage-specific parameters. Given this advancement, our next steps are to re-incorporate covariates into the model, specifically to understand how different factors affect partial SARs of subyearling and yearlings. Longer term model developments include:1) incorporating hatchery fish to explicitly estimate their survival as an alternative method for estimating natural-origin age composition, 2) expanding the model’s structure to include the three major spawning aggregates, 3) more explicitly modeling hydrosystem effects including transportation, and 4) using the model to assess retrospective and prospective management actions.\n\nIn 2022, the U.S. Geological Survey (USGS) focused adult salmon survey efforts in the Snake River on deepwater redd searches and fish collection for parentage-based tagging (PBT) analyses. We use used a boat-mounted underwater video camera to count 99 deepwater redds at 16 of the 29 sites surveyed. Redd depths averaged 4.4 m. In conjunction with the Idaho Power Company, we collected genetic samples from 318 live fall Chinook salmon (Oncorhynchus tshawytscha) and 19 carcasses at 40 unique geographic locations that spanned 91 river kilometers. Eighty fish were collected at three sites (High Range [rkm 332.3], Dug Bar [rkm 315.4], and Three Creek [rkm 384.0]), which accounted for 23% of all collected fish in 2022. Most (333 fish) post-spawned salmon were collected from early to mid-November just after the peak of spawning. A summary of 2021 PBT results produced by the Idaho Power Company can be found in Appendix A.2.\n\nBeach seining and PIT tagging of subyearling fall Chinook salmon was conducted in Snake and Salmon rivers to obtain information on population metrics and growth as well as to provide data for ongoing life-cycle modeling. In the Snake River, we collected 7,496 subyearlings, tagged 4,139, and recaptured 502 (12.1%). Using 8-mm tags in 45–49-mm fish allowed us to represent an additional 25% of the juvenile population through PIT tagging beyond just using standard 9- and 12-mm tags. In the Salmon River, we captured 206 natural subyearlings with the majority (52%) of fish being captured at two sites: rkm 20 and 26. We tagged 145 subyearlings and recaptured 9 fish. \n\nMany of the subyearlings we tagged in the Snake River were detected passing Lower Granite Dam, but only 4 fish tagged in the Salmon River were detected. In total we detected 484 (11.3%) tagged fish at Lower Granite Dam, and detection rates varied by tag size and passage route. More subyearlings were detected passing via the removable spill weir (RSW) earlier in the season while more fish were detected passing through the juvenile fish bypass system (JBS) earlier in the season while more fish were detected passing via the removable spill weir (RSW) later in the season. In general, fish tagged with 12-mm PIT tags had higher detection rates than fish tagged with smaller tags. Survival to Lower Granite Dam was low and ranged from 0.22 to 0.36. Season-wide, growth of subyearlings was higher in the lower reach than in the upper reach of the Snake River.","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Perry, R., Hance, D., Plumb, J., Tiffan, K.F., Bickford, B., Benson, S.L., Rhodes, T., Brink, S., and Alcorn, B., 2024, Snake River Fall Chinook Salmon research and monitoring, v, 110 p.","productDescription":"v, 110 p.","ipdsId":"IP-159991","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":462763,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org"},{"id":462792,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.73530070271516,\n 46.5793713644326\n ],\n [\n -117.73530070271516,\n 44.66424938201729\n ],\n [\n -115.87996433031464,\n 44.66424938201729\n ],\n [\n -115.87996433031464,\n 46.5793713644326\n ],\n [\n -117.73530070271516,\n 46.5793713644326\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915620,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Perry, Russell W. 0000-0003-4110-8619","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":220177,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915509,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915510,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plumb, John 0000-0003-4255-1612","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":223236,"corporation":false,"usgs":true,"family":"Plumb","given":"John","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tiffan, Kenneth F. 0000-0002-5831-2846 ktiffan@usgs.gov","orcid":"https://orcid.org/0000-0002-5831-2846","contributorId":3200,"corporation":false,"usgs":true,"family":"Tiffan","given":"Kenneth","email":"ktiffan@usgs.gov","middleInitial":"F.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915621,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bickford, Brad 0000-0003-3756-6588","orcid":"https://orcid.org/0000-0003-3756-6588","contributorId":220180,"corporation":false,"usgs":true,"family":"Bickford","given":"Brad","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915512,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Benson, Scott Louis 0000-0003-0397-1200","orcid":"https://orcid.org/0000-0003-0397-1200","contributorId":303796,"corporation":false,"usgs":true,"family":"Benson","given":"Scott","email":"","middleInitial":"Louis","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915514,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rhodes, Tobyn 0000-0002-4023-4827","orcid":"https://orcid.org/0000-0002-4023-4827","contributorId":220181,"corporation":false,"usgs":true,"family":"Rhodes","given":"Tobyn","email":"","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":915513,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brink, Steve","contributorId":222508,"corporation":false,"usgs":false,"family":"Brink","given":"Steve","email":"","affiliations":[],"preferred":false,"id":915516,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Alcorn, 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The U.S. Geological Survey is developing the National Crustal Model (NCM) for seismic hazard studies to facilitate modeling site, path, and source components of seismic hazard across the conterminous United States. The NCM is composed of a 1km grid of geophysical profiles, extending from the Earth’s surface into the upper mantle. It is constructed from a threedimensional (3D) geologic framework and geophysical rules that use (1) a petrologic and mineral physics database; (2) a 3D temperature model; and (3) a calibrated rock type- and age-dependent porosity model. Parameters needed to estimate site response for existing ground motion models (GMMs), including the time-averaged velocity in the upper 30 meters (VS30), the depths to 1.0 and 2.5 km/s shear-wave velocity (Z1.0 and Z2.5), and sediment thickness, can be computed from the NCM. As GMMs continue to improve in the future, other metrics could also be extracted or derived from the NCM, such as fundamental period, site attenuation (ko), a fully frequency-dependent site response function, or 3D geophysical volumes for wavefield simulations. Application of the NCM may also benefit other aspects of seismic hazard analysis, including better accounting for path-dependent attenuation and geometric spreading, more accurate estimation of earthquake source properties such as hypocentral location and stress drop, and calculation of crustal strength profiles that inform estimates of the base of seismicity.

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Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":910091,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moschetti, Morgan P. 0000-0001-7261-0295 mmoschetti@usgs.gov","orcid":"https://orcid.org/0000-0001-7261-0295","contributorId":1662,"corporation":false,"usgs":true,"family":"Moschetti","given":"Morgan","email":"mmoschetti@usgs.gov","middleInitial":"P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":910090,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science 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Shallow landslides are often triggered during rainfall events, which can increase subsurface soil water pressure and destabilize hillslopes. The likelihood of regional shallow landslide initiation is often assessed through a comparison of rainfall intensity and duration to pre-established thresholds. While informative for landslide warning, this exclusive focus on rainfall exceeding thresholds does not consider the meteorological conditions producing the rainfall. Here, we ask the question, are there common meteorological characteristics that lead to landslide-triggering precipitation? We develop a catalog of 18 post-1995 widespread, impactful shallow landslide events occurring within 13 storms across California, USA, where initiation time could be constrained to a ≤6-h window. We examine storm characteristics during the landslide initiation window using atmospheric reanalysis products, radar observations, and quantitative precipitation estimates. We find that, while there are some common atmospheric characteristics across landslide events, they can occur under a range of atmospheric conditions. For example, all Northern California landslide events assessed are associated with moderate to strong atmospheric rivers (ARs), while Southern California landslides feature non-AR to strong AR conditions. The storm events evaluated herein share many characteristics of hydrologically important storms in California that did not necessarily result in landslides; thus, atmospheric characteristics alone may not be sufficient to determine whether landslides will occur. However, documenting the characteristics of landslide-triggering storms defines the conditions under which landslides tend to occur, provides analog events that can be useful in forecast applications, helps define future research directions relating to atmospheric conditions and landslides, and supports interdisciplinary research efforts.

","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-24-0003.1","usgsCitation":"Oakley, N.S., Perkins, J.P., Bartlett, S.M., Collins, B.D., Comstock, K.H., Brien, D.L., Burgess, W., and Corbett, S.C., 2024, Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA): Earth Interactions, v. 28, no. 1, e240003, 19 p., https://doi.org/10.1175/EI-D-24-0003.1.","productDescription":"e240003, 19 p.","ipdsId":"IP-157035","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":363,"text":"Landslide Hazards Program","active":false,"usgs":true}],"links":[{"id":467041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-24-0003.1","text":"Publisher Index Page"},{"id":465146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"28","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Oakley, Nina S.","contributorId":197885,"corporation":false,"usgs":false,"family":"Oakley","given":"Nina","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":921116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perkins, Jonathan P. 0000-0002-6113-338X","orcid":"https://orcid.org/0000-0002-6113-338X","contributorId":237053,"corporation":false,"usgs":true,"family":"Perkins","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bartlett, Samuel M.","contributorId":347239,"corporation":false,"usgs":false,"family":"Bartlett","given":"Samuel","email":"","middleInitial":"M.","affiliations":[{"id":83104,"text":"Center for Western Weather and Water Extremes, Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":921118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":921119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Comstock, Karimah Halona 0009-0003-3662-5678","orcid":"https://orcid.org/0009-0003-3662-5678","contributorId":335639,"corporation":false,"usgs":true,"family":"Comstock","given":"Karimah","email":"","middleInitial":"Halona","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":921120,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brien, Dianne L. 0000-0003-3227-7963 dbrien@usgs.gov","orcid":"https://orcid.org/0000-0003-3227-7963","contributorId":229851,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":921121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burgess, W.P.","contributorId":347240,"corporation":false,"usgs":false,"family":"Burgess","given":"W.P.","email":"","affiliations":[{"id":83107,"text":"California Geological Survey, Sacramento","active":true,"usgs":false}],"preferred":false,"id":921122,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Corbett, Skye C. 0000-0003-3277-1021 scorbett@usgs.gov","orcid":"https://orcid.org/0000-0003-3277-1021","contributorId":200617,"corporation":false,"usgs":true,"family":"Corbett","given":"Skye","email":"scorbett@usgs.gov","middleInitial":"C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921123,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70250999,"text":"70250999 - 2024 - Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses","interactions":[],"lastModifiedDate":"2024-02-02T14:59:59.031674","indexId":"70250999","displayToPublicDate":"2024-01-01T08:50:09","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses","docAbstract":"

RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Colorado River Basin.

The RiverWare model described in this chapter presents development of the Colorado River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer gage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Colorado River Basin POR hydrology will be in a later section.

Calibration results will also be shown that illustrate the overall model performance for the POR. The time window simulation run is for January 01, 1930 – September 30, 2019. This time window captures all big events occurred over the Colorado River basin. Each simulated water year was inspected individually to better validate the results.

Historical pool elevations along with observed inflows and outflows were compared against the model simulated results.

","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"USACE Fort Worth District, FEMA Region 6, NWS WGRFC","usgsCitation":"Wallace, D., and Watson, K.M., 2024, Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses: Interagency Flood Risk Management Report, 166 p.","productDescription":"166 p.","ipdsId":"IP-127610","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":424561,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/"},{"id":425286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lower Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.8,\n 28.65\n ],\n [\n -95.8,\n 32\n ],\n [\n -101,\n 32\n ],\n [\n -101,\n 28.65\n ],\n [\n -95.8,\n 28.65\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":220786,"corporation":false,"usgs":true,"family":"Wallace","given":"David","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892730,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259275,"text":"70259275 - 2024 - Ecology of Lake Erie - Chemistry, plankton & planktivory: A synthesis","interactions":[],"lastModifiedDate":"2024-10-03T13:31:43.005985","indexId":"70259275","displayToPublicDate":"2024-01-01T08:28:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18728,"text":"Aquatic Ecosystem Health and Management","active":true,"publicationSubtype":{"id":10}},"title":"Ecology of Lake Erie - Chemistry, plankton & planktivory: A synthesis","docAbstract":"

As with other large lake ecosystems worldwide, Lake Erie can be considered a moving target for management, owing to physicochemical and biological changes brought on by anthropogenic change, both planned (e.g. nutrient and fisheries management) and unplanned (e.g. climate change, invasive species, modified land-use activities). These changes have challenged efforts to conserve biodiversity, sustain exploitable resources, and maintain the integrity of services valued by society both within the Lake Erie basin and (Fraker et al., 2022; Fussell et al., 2016; Sinclair et al., 2021; Sinclair et al., 2023) and outside of it (Allan et al., 2013; Jenny et al., 2020; Sterner et al., 2017). Some of these changes and their ramifications for management were documented in the first of four AEHM special issues devoted to the Lake Erie ecosystem (the fourth issue of 2023, volume 26, issue 4; see overview by Ludsin et al., 2023). That special issue focused explicitly on nutrient inputs and availability in Lake Erie and the lower food web, including planktonic and benthic microbial (including cyanobacteria), algal, and invasive dreissenid mussel communities. Similar to the previous Lake Erie special issue, this second one has focused on documenting the state of the lake, providing ecological understanding that could potentially benefit management. While some overlap in topics exists between issues, the studies conducted herein were completely independent of those previous investigations and offer unique insights. Specifically, the contributions to this current issue center on: 1) dynamics of water chemistry in Lake Erie’s central basin (i.e. bottom hypoxia; Ackerman et al., 2024) and western basin (i.e. mercury; Starr et al., 2024); 2) changes in primary producer biomass (Lesht et al., 2024), cyanotoxins (i.e. microcystin; Zastepa et al., 2024), and water quality (e.g. water clarity and dissolved nutrients; Howell et al., 2024); and 3) larval fish foraging (i.e. Lake Whitefish; Coregonus clupeaformis; Amidon et al., 2024) and community structure and phenology (DeBruyne et al., 2024). Below we summarize the major findings of these papers and offer a synthetic perspective on the value of this research for understanding the state of Lake Erie and enhancing management.

","language":"English","publisher":"Michigan State University Press","doi":"10.14321/aehm.027.01.116","usgsCitation":"Ludsin, S., Munawar, M., DeBruyne, R.L., Howell, E.T., Tyson, J., and Watkins, J.M., 2024, Ecology of Lake Erie - Chemistry, plankton & planktivory: A synthesis: Aquatic Ecosystem Health and Management, v. 27, no. 1, p. 116-124, https://doi.org/10.14321/aehm.027.01.116.","productDescription":"9 p.","startPage":"116","endPage":"124","ipdsId":"IP-163615","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":462529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -78.90437674926393,\n 42.88573072124271\n ],\n [\n -79.52141499356523,\n 42.87769210959476\n ],\n [\n -80.22678399206747,\n 42.79281581499302\n ],\n [\n -80.51880625151767,\n 42.57814946253396\n ],\n [\n -81.02026663661093,\n 42.67543230196293\n ],\n [\n -81.30128454543694,\n 42.67543535756306\n ],\n [\n -81.53822853578485,\n 42.56596464399709\n ],\n [\n -81.9019334399238,\n 42.33014170165541\n ],\n [\n -81.95153424579222,\n 42.26492779780335\n ],\n [\n -82.12236039987853,\n 42.236375180224144\n ],\n [\n -82.45298532777961,\n 42.08524033767702\n ],\n [\n -82.5191058069868,\n 41.92964634487325\n ],\n [\n -82.61828632462084,\n 42.04024072452731\n ],\n [\n -82.91034362126736,\n 41.987015817617134\n ],\n [\n -83.11972864041921,\n 42.077062926530004\n ],\n [\n -83.10317152070901,\n 42.24453450962994\n ],\n [\n -83.29610139408294,\n 41.9419412108324\n ],\n [\n -83.3622121286252,\n 41.92964573816295\n ],\n [\n -83.51644448225035,\n 41.69555451568095\n ],\n [\n -82.9488535420495,\n 41.41103910261083\n ],\n [\n -82.71193183972241,\n 41.41929329585025\n ],\n [\n -82.47491670465367,\n 41.35733064230888\n ],\n [\n -81.99008606791249,\n 41.477110666960954\n ],\n [\n -81.70899983775189,\n 41.46067864477564\n ],\n [\n -81.25720217469238,\n 41.736707291689584\n ],\n [\n -80.6842102922714,\n 41.88460348122109\n ],\n [\n -79.96258495960994,\n 42.16700280222196\n ],\n [\n -79.42295622097835,\n 42.38302164176207\n ],\n [\n -79.37861904307078,\n 42.4440510296389\n ],\n [\n -79.15795348814065,\n 42.52534601120243\n ],\n [\n -79.00930804606551,\n 42.69563463834368\n ],\n [\n -78.83853732874603,\n 42.76038448982763\n ],\n [\n -78.84931646792415,\n 42.86956831700121\n ],\n [\n -78.90437674926393,\n 42.88573072124271\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"27","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ludsin, Stuart A.","contributorId":270532,"corporation":false,"usgs":false,"family":"Ludsin","given":"Stuart A.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":914751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munawar, Mohiuddin","contributorId":344801,"corporation":false,"usgs":false,"family":"Munawar","given":"Mohiuddin","email":"","affiliations":[{"id":13015,"text":"Department of Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":914752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeBruyne, Robin L. 0000-0002-9232-7937 rdebruyne@usgs.gov","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":4936,"corporation":false,"usgs":true,"family":"DeBruyne","given":"Robin","email":"rdebruyne@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":914753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howell, E. Todd","contributorId":344802,"corporation":false,"usgs":false,"family":"Howell","given":"E.","email":"","middleInitial":"Todd","affiliations":[{"id":82411,"text":"Ontario Ministry of the Environment, Conservation, and Parks","active":true,"usgs":false}],"preferred":false,"id":914754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tyson, Jeffrey","contributorId":344803,"corporation":false,"usgs":false,"family":"Tyson","given":"Jeffrey","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":914755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watkins, James M.","contributorId":189286,"corporation":false,"usgs":false,"family":"Watkins","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":914756,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70250215,"text":"70250215 - 2024 - Need and vision for global medium-resolution Landsat and Sentinel-2 data products","interactions":[],"lastModifiedDate":"2024-05-20T13:56:53.021648","indexId":"70250215","displayToPublicDate":"2024-01-01T06:39:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Need and vision for global medium-resolution Landsat and Sentinel-2 data products","docAbstract":"

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly &#x2264;\">≤30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2023.113918","usgsCitation":"Radeloff, V., Roy, D., Wulder, M., Anderson, M., Cook, B., Crawford, C., Friedl, M., Gao, F., Gorelick, N., Hansen, M., Healey, S., Hostert, P., Hulley, G., Huntington, J., Johnson, D., Neigh, C., Lyapustin, A., Lymburner, L., Pahlevan, N., Pekel, J., Scambos, T.A., Schaaf, C., Strobl, P., Vermote, E., Woodcock, C., Zhang, H.K., and Zhu, Z., 2024, Need and vision for global medium-resolution Landsat and Sentinel-2 data products: Remote Sensing of Environment, v. 300, 113918, 26 p., https://doi.org/10.1016/j.rse.2023.113918.","productDescription":"113918, 26 p.","ipdsId":"IP-156892","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":440830,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2023.113918","text":"Publisher Index 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quail","interactions":[],"lastModifiedDate":"2025-05-02T16:48:52.098498","indexId":"70266260","displayToPublicDate":"2024-01-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Longer holding times decrease dispersal but increase mortality of translocated scaled quail","docAbstract":"

Scaled quail (Callipepla squamata) decline caused by habitat loss and fragmentation increased interest in translocation to reestablish populations. Yet factors determining translocation success are poorly understood. We tested hypotheses concerning the influence of source population and variation in delayed release strategy (1–9 weeks) on mortality and dispersal of wild-caught, translocated scaled quail. We trapped and translocated quail from 2016–2017 from source populations in the Edwards Plateau and Rolling Plains ecoregions to a large contiguous (>40,000 ha) release site in Knox County, Texas, USA. We evaluated mortality and dispersal of translocated females as a function of source population, holding time prior to release, age, release location, and year using a multi-state mark-recapture model with state uncertainty. Scaled quail translocated within the Rolling Plains were more likely to exhibit philopatry to the release site. Quail with longer holding times had higher mortality but lower dispersal rates. The Edwards Plateau is a suitable source site for translocation in the Rolling Plains. The reduced dispersal but higher mortality of translocated scaled quail associated with longer holding times creates a decision tradeoff for managers.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22498","usgsCitation":"Ruzicka, R., Rollins, D., Doherty, P.F., and Kendall, W.L., 2024, Longer holding times decrease dispersal but increase mortality of translocated scaled quail: Journal of Wildlife Management, v. 88, no. 1, e22498, 16 p., https://doi.org/10.1002/jwmg.22498.","productDescription":"e22498, 16 p.","ipdsId":"IP-151614","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487938,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22498","text":"Publisher Index Page"},{"id":485349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Knox County","otherGeospatial":"South Wichita River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.2487789756043,\n 34.08946446033667\n ],\n [\n -103.2487789756043,\n 31.880566287138876\n ],\n [\n -100.09201762067653,\n 31.880566287138876\n ],\n [\n -100.09201762067653,\n 34.08946446033667\n ],\n [\n -103.2487789756043,\n 34.08946446033667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"88","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruzicka, Rebekah E.","contributorId":354109,"corporation":false,"usgs":false,"family":"Ruzicka","given":"Rebekah E.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rollins, Dale","contributorId":140708,"corporation":false,"usgs":false,"family":"Rollins","given":"Dale","email":"","affiliations":[],"preferred":false,"id":935109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doherty, Paul F. Jr.","contributorId":37636,"corporation":false,"usgs":false,"family":"Doherty","given":"Paul","suffix":"Jr.","email":"","middleInitial":"F.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935111,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264095,"text":"70264095 - 2024 - Merging integrated population models and individual-based models to project population dynamics of recolonizing species","interactions":[],"lastModifiedDate":"2025-03-06T15:59:15.6884","indexId":"70264095","displayToPublicDate":"2024-01-01T00:00:00","publicationYear":"2024","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":"Merging integrated population models and individual-based models to project population dynamics of recolonizing species","docAbstract":"

Recolonizing species exhibit unique population dynamics, namely dispersal to and colonization of new areas, that have important implications for management. A resulting challenge is how to simultaneously model demographic and movement processes so that recolonizing species can be accurately projected over time and space. We introduce a framework for spatially explicit projection modeling that harnesses the rigorous parameter estimation made possible by an integrated population model (IPM) and the flexible movement modeling made possible by an individual-based model (IBM). Our framework has two components: [1] a Bayesian IPM-driven age- and state-structured population model that governs the population state process and estimation of demographic rates, and [2] an IBM-driven spatial model that allows for the projection of dispersal and habitat colonization. We applied this model framework to estimate current and project future dynamics of gray wolves (Canis lupus) in Washington State, USA. We used data from 74 telemetered wolves and yearly pup and pack counts to parameterize the model, and then projected statewide dynamics over 50 years. Mean population growth was 1.29 (95 % Bayesian Credible Interval = 1.26–1.33) during initial recolonization from 2009 to 2020 and decreased to 1.02 (95 % Prediction Interval = 0.98–1.04) in the projection period (2021–2070). Our results suggest that gray wolves have an ~100 % probability of colonizing the last of Washington State's three specified recovery regions by 2030, regardless of alternative assumptions about how dispersing wolves select new territories. Our spatially explicit projection model can be used to project the dynamics of any species for which spatial spread is an important driver of population dynamics.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.110340","usgsCitation":"Petracca, L., Gardner, B., Maletzke, B., and Converse, S.J., 2024, Merging integrated population models and individual-based models to project population dynamics of recolonizing species: Biological Conservation, v. 289, 110340, 14 p., https://doi.org/10.1016/j.biocon.2023.110340.","productDescription":"110340, 14 p.","ipdsId":"IP-151020","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimates of Southern White-tailed Ptarmigan daily nest survival from multiple sites in the Southern Rocky Mountains of Colorado","docAbstract":"

Estimating vital rates of avian species is important to understand population dynamics and develop potential conservation strategies that target rates for management. Avian species have reduced potential for high annual fecundity in alpine ecosystems due to a short breeding window and harsh weather conditions. We located nests from Southern White-tailed Ptarmigan (Lagopus leucura altipetens) across six study sites in the Southern Rocky Mountains of Colorado to estimate daily nest survival from 2013–2017. We used a known-fate hierarchical nest survival model and fit several covariates, including environmental conditions representing daily weather events and shrub cover, to describe variation in daily survival and derive estimates of nest success. We located and monitored 198 nests from 128 radio-marked ptarmigan hens. The mean nest success estimated as a derived parameter from daily nest survival was 45.6% (95% credible interval [CI]: 31.2–59.6%) and ranged from 40.3% to 50.3% across sites. Variation in daily nest survival was poorly described by the covariates we fit (95% CI of most slope coefficients overlapped 0), although there was some support for a negative effect of relative elevation (nests at lower elevations within a site survived at higher rates) and a positive effect of nest age (older nests survived at higher rates). We examined how variation in nest success was likely to influence the finite rate of population growth using a simple simulation with an age-transition matrix parameterized with previously reported fecundity and survival estimates. We found that the finite growth rate was predicted to increase 18.7% when evaluated from the lower to upper 95% CI estimated values of nest success, conditional on the other vital rates used in our simulation. We discuss the broader implications of these findings in the context of managing for nest survival of Southern White-tailed Ptarmigan.

","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ACE-02566-190104","usgsCitation":"Wann, G.T., Seglund, A.E., Street, P.A., Parker, N.J., Nelson, S.L., Runge, J.P., Braun, C.E., and Aldridge, C.L., 2024, Estimates of Southern White-tailed Ptarmigan daily nest survival from multiple sites in the Southern Rocky Mountains of Colorado: Avian Conservation and Ecology, v. 19, no. 1, 4, 14 p., https://doi.org/10.5751/ACE-02566-190104.","productDescription":"4, 14 p.","ipdsId":"IP-153968","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.5751/ace-02566-190104","text":"Publisher Index Page"},{"id":464593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Southern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.04311121117411,\n 41.012655687234144\n ],\n [\n -109.04311121117411,\n 36.91934059975698\n ],\n [\n -104.69133822114236,\n 36.91934059975698\n ],\n [\n -104.69133822114236,\n 41.012655687234144\n ],\n [\n -109.04311121117411,\n 41.012655687234144\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wann, Gregory T. 0000-0001-9076-7819 wanng@usgs.gov","orcid":"https://orcid.org/0000-0001-9076-7819","contributorId":3855,"corporation":false,"usgs":true,"family":"Wann","given":"Gregory","email":"wanng@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":919595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seglund, Amy E.","contributorId":218686,"corporation":false,"usgs":false,"family":"Seglund","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":919596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Street, Phillip A.","contributorId":346426,"corporation":false,"usgs":false,"family":"Street","given":"Phillip","email":"","middleInitial":"A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":919597,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parker, Nicholas J.","contributorId":341574,"corporation":false,"usgs":false,"family":"Parker","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":919598,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, Shelley L.","contributorId":346576,"corporation":false,"usgs":false,"family":"Nelson","given":"Shelley","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":919599,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Runge, Jonathan P.","contributorId":196756,"corporation":false,"usgs":false,"family":"Runge","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":919600,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Braun, Clait E.","contributorId":200013,"corporation":false,"usgs":false,"family":"Braun","given":"Clait","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":919601,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":919602,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70263244,"text":"70263244 - 2024 - Individual-based ecological particle tracking model (ECO-PTM) for simulating juvenile chinook salmon migration and survival through the Sacramento – San Joaquin Delta","interactions":[],"lastModifiedDate":"2025-02-03T15:49:36.99796","indexId":"70263244","displayToPublicDate":"2024-01-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Individual-based ecological particle tracking model (ECO-PTM) for simulating juvenile chinook salmon migration and survival through the Sacramento – San Joaquin Delta","docAbstract":"Recovery of endangered salmon species in the Central Valley of California amidst prolonged drought and climate change necessitates innovative water management actions that balance species recovery and California's water demands. We describe an individual-based ecological particle tracking model (ECO-PTM) that can be used to assess the efficacy of proposed actions. Based on a random walk theory, the model tracks individual particles’ travel time, routing and survival in a flow field simulated by the Delta Simulation Model 2 hydrodynamic module (DSM2 HYDRO). The random walk particles are parameterized to have fish-like swimming behaviors, including upstream/downstream swimming, probabilistic holding behaviors, and stochastic swimming velocities. Particle routing at key junctions is based on well-established statistical models, and route-specific survival is calculated using the XT mean free-path length model. Behavioral parameters were estimated by fitting several competing models to a multiyear dataset of travel times from acoustic tagged juvenile salmon. The model’s baseline simulations under historical flow conditions from 1991 to 2016 successfully replicated essential relationships between salmon outmigration survival and hydrodynamic conditions, consistent with previous studies and the STARS (Survival Travel Time and Routing Simulation) statistical simulation model. Simulation results for management scenarios revealed multifaceted influences on fish survival, including Delta flow, flow at key junctions, route alterations, seasons, and water year characteristics. Importantly, these results highlight ECO-PTM’s potential to predict fish survival outcomes of proposed actions, serving as a foundation for informed future research, decision-making, and effective management strategies to enhance the survival prospects of out-migrating salmonids within the Sacramento-San Joaquin Delta ecosystem.","language":"English","publisher":"eScholarship","doi":"10.15447/sfews.2024v22iss4art4","usgsCitation":"Wang, X., Perry, R.W., Pope, A., Jackson, D., and Hance, D., 2024, Individual-based ecological particle tracking model (ECO-PTM) for simulating juvenile chinook salmon migration and survival through the Sacramento – San Joaquin Delta: San Francisco Estuary and Watershed Science, v. 22, no. 4, 4, 23 p., https://doi.org/10.15447/sfews.2024v22iss4art4.","productDescription":"4, 23 p.","ipdsId":"IP-163182","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":486828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2024v22iss4art4","text":"Publisher Index Page"},{"id":481609,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento – San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.56056300774354,\n 38.494229534007104\n ],\n [\n -121.56056300774354,\n 37.898481687362576\n ],\n [\n -121.08815089836878,\n 37.898481687362576\n ],\n [\n -121.08815089836878,\n 38.494229534007104\n ],\n [\n -121.56056300774354,\n 38.494229534007104\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Xiaochun","contributorId":225264,"corporation":false,"usgs":false,"family":"Wang","given":"Xiaochun","email":"","affiliations":[{"id":41085,"text":"California Department of Water Resources, Sacramento, CA, 95819","active":true,"usgs":false}],"preferred":false,"id":925998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":925999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":926000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Doug","contributorId":315556,"corporation":false,"usgs":false,"family":"Jackson","given":"Doug","email":"","affiliations":[{"id":68352,"text":"QEDA Consulting, LLC., 4007 Densmore Avenue N., Seattle, WA, 98103","active":true,"usgs":false}],"preferred":false,"id":926001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":926002,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251369,"text":"70251369 - 2024 - Variable climate-growth relationships of quaking aspen (Populus tremuloides) among Sky Island mountain ranges in the Great Basin, Nevada, USA","interactions":[],"lastModifiedDate":"2024-02-07T13:23:28.375501","indexId":"70251369","displayToPublicDate":"2023-12-30T07:21:42","publicationYear":"2024","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":"Variable climate-growth relationships of quaking aspen (Populus tremuloides) among Sky Island mountain ranges in the Great Basin, Nevada, USA","docAbstract":"

The Great Basin is an arid province located in the interior western United States. The region encompasses millions of hectares and quaking aspen (Populus tremuloides Michx.) forests comprise a minor portion of the total area. However, montane aspen forests play a disproportionately large role in providing ecosystem services in the region, including water retention, biodiversity, wildlife habitat, livestock forage, and recreational uses. With warming temperatures, increasing evaporative demand, and heightened precipitation variability, the future of aspen has become a critical concern. Using dendroecological approaches, we assessed growth patterns of 20 aspen stands across three geographically isolated “sky island” mountain ranges spanning portions of the north-central Great Basin. We anticipated that the growth of Great Basin aspen would be strongly influenced by regional climatic patterns and largely in synchrony. Results revealed a more complex growth dynamic that varied among mountain ranges and across environmental gradients. In particular, aspen climate-growth relationships in the slightly dryer Ruby Mountains were strongly and positively correlated (r > 0.5) with previous fall to winter moisture availability. The Jarbidge Mountains had a positive but modest relationship with previous fall to winter moisture availability (r > 0.3). Climate-growth response in the Santa Rosa Mountains, the wettest range, showed no significant response to moisture availability during any time period examined but had greater tree-ring growth with warmer May temperatures. Although tree-ring centennial (1910 – 2010) growth trends were positive for all three mountain ranges, only the Santa Rosa Mountains maintained a positive recent growth trend (1970 – 2010). Moreover, distinct temporal shifts in tree growth-climate relationships in each mountain range suggest potentially unique aspen population adaptations to climate variability. For instance, in two of the mountain ranges, there was a shift from positive/neutral to negative growth relationships with temperature starting around the 1963 – 1987 time period, while tree growth also began simultaneously responding more positively to moisture availability. These growth shifts and observed enhanced sensitivities to monthly and seasonal climate variables over time may reflect dynamic tree growth responses caused by ongoing global climate change, but that may be tempered by local or regional factors, such as the relative availability and timing of soil moisture provided by spring snowmelt. A better understanding of biogeographic variation and causality in aspen growth could provide multiple management pathways governed by resilience characteristics in the face of future anthropogenic and climatic threats.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2023.121664","usgsCitation":"Senfeldr, M., Shinneman, D.J., McIlroy, S., Rogers, P., and DeRose, R.J., 2024, Variable climate-growth relationships of quaking aspen (Populus tremuloides) among Sky Island mountain ranges in the Great Basin, Nevada, USA: Forest Ecology and Management, v. 554, 121664, 13 p., https://doi.org/10.1016/j.foreco.2023.121664.","productDescription":"121664, 13 p.","ipdsId":"IP-158673","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":425469,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.29798298247125,\n 41.94769808682884\n ],\n [\n -117.29798298247125,\n 38.318330957085834\n ],\n [\n -113.76668592101488,\n 38.318330957085834\n ],\n [\n -113.76668592101488,\n 41.94769808682884\n ],\n [\n -117.29798298247125,\n 41.94769808682884\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"554","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Senfeldr, Martin 0000-0002-8314-6632","orcid":"https://orcid.org/0000-0002-8314-6632","contributorId":333915,"corporation":false,"usgs":false,"family":"Senfeldr","given":"Martin","email":"","affiliations":[{"id":80011,"text":"Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood Technology, Mendel University in Brno, Czech Republic","active":true,"usgs":false}],"preferred":false,"id":894297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","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":894298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McIlroy, Susan K. 0000-0001-5088-3700 smcilroy@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-3700","contributorId":169446,"corporation":false,"usgs":true,"family":"McIlroy","given":"Susan","email":"smcilroy@usgs.gov","middleInitial":"K.","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":894299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogers, Paul 0000-0001-5978-8910","orcid":"https://orcid.org/0000-0001-5978-8910","contributorId":333916,"corporation":false,"usgs":false,"family":"Rogers","given":"Paul","email":"","affiliations":[{"id":80012,"text":"Western Aspen Alliance, Department of Environment & Society, Ecology Center, Utah State University","active":true,"usgs":false}],"preferred":false,"id":894300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeRose, R. Justin 0000-0002-4849-7744","orcid":"https://orcid.org/0000-0002-4849-7744","contributorId":333917,"corporation":false,"usgs":false,"family":"DeRose","given":"R.","email":"","middleInitial":"Justin","affiliations":[{"id":80013,"text":"Department of Wildland Resources and Ecology Center, Utah State University","active":true,"usgs":false}],"preferred":false,"id":894301,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70251050,"text":"70251050 - 2024 - The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy","interactions":[],"lastModifiedDate":"2024-01-19T13:03:23.917405","indexId":"70251050","displayToPublicDate":"2023-12-30T06:57:57","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy","docAbstract":"

Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous ‘blue carbon’ studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon-storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R-shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision-making.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.17098","usgsCitation":"Holmquist, J., Klinges, D.H., Lonneman, M., Wolfe, J., Boyd, B.M., Eagle, M.J., Sanderman, J., Todd-Brown, K., Brown, L.N., Belshe, E.F., Chapman, S.K., Corstanje, R., Janousek, C.N., Morris, J.T., Noe, G.E., Rovai, A.S., Spivak, A.C., Vahsen, M., Windham-Myers, L., Kroeger, K.D., and Megonigal, P., 2024, The Coastal Carbon Library and Atlas: Open source soil data and tools supporting blue carbon research and policy: Global Change Biology, v. 30, no. 1, e17098, 35 p., https://doi.org/10.1111/gcb.17098.","productDescription":"e17098, 35 p.","ipdsId":"IP-155461","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440836,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.17098","text":"Publisher Index Page"},{"id":424617,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-12-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Holmquist, James R.","contributorId":272628,"corporation":false,"usgs":false,"family":"Holmquist","given":"James R.","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":892870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klinges, David H.","contributorId":333483,"corporation":false,"usgs":false,"family":"Klinges","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":79892,"text":"Smithsonian Research Center and University of Florida","active":true,"usgs":false}],"preferred":false,"id":892871,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lonneman, Michael","contributorId":333484,"corporation":false,"usgs":false,"family":"Lonneman","given":"Michael","email":"","affiliations":[{"id":79894,"text":"Smithsonian Research Center","active":true,"usgs":false}],"preferred":false,"id":892872,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wolfe, Jaxine","contributorId":332003,"corporation":false,"usgs":false,"family":"Wolfe","given":"Jaxine","email":"","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":892873,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boyd, Brandon M.","contributorId":261538,"corporation":false,"usgs":false,"family":"Boyd","given":"Brandon","email":"","middleInitial":"M.","affiliations":[{"id":52868,"text":"U.S. Army Corps of Engineers, Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":892874,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":892876,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sanderman, Jonathan","contributorId":187477,"corporation":false,"usgs":false,"family":"Sanderman","given":"Jonathan","email":"","affiliations":[],"preferred":false,"id":892877,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Todd-Brown, Katherine","contributorId":240705,"corporation":false,"usgs":false,"family":"Todd-Brown","given":"Katherine","affiliations":[{"id":34255,"text":"Wilfred Laurier University","active":true,"usgs":false}],"preferred":false,"id":892878,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Brown, Lauren N.","contributorId":173461,"corporation":false,"usgs":false,"family":"Brown","given":"Lauren","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":892875,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Belshe, E. 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Baruch Institute for Marine and Coastal Sciences, University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":892883,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":892884,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Rovai, Andre S.","contributorId":167671,"corporation":false,"usgs":false,"family":"Rovai","given":"Andre","email":"","middleInitial":"S.","affiliations":[{"id":24801,"text":"Federal University of Santa Catarina, Dept. Ecology and Zoology, Brazil","active":true,"usgs":false}],"preferred":false,"id":892885,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Spivak, Amanda C.","contributorId":191376,"corporation":false,"usgs":false,"family":"Spivak","given":"Amanda","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":892886,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Vahsen, Megan","contributorId":333487,"corporation":false,"usgs":false,"family":"Vahsen","given":"Megan","email":"","affiliations":[{"id":39511,"text":"Notre Dame University","active":true,"usgs":false}],"preferred":false,"id":892887,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892888,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":892889,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Megonigal, Patrick","contributorId":211641,"corporation":false,"usgs":false,"family":"Megonigal","given":"Patrick","affiliations":[{"id":38291,"text":"Smithsonian Environmental Research Center, Edgewater, MD 21037, USA","active":true,"usgs":false}],"preferred":false,"id":892890,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70240773,"text":"70240773 - 2024 - Conventional rare earth element mineral deposits: The global landscape","interactions":[],"lastModifiedDate":"2024-01-12T15:25:32.908721","indexId":"70240773","displayToPublicDate":"2023-12-29T09:22:13","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Conventional rare earth element mineral deposits: The global landscape","docAbstract":"

Four conventional mineral deposit types—carbonatite, alkaline igneous, heavy mineral sand, and regolith-hosted ion-adsorption clay deposits—currently supply global markets with the rare earth elements (REEs) and rare earth oxides (REOs) necessary to meet the technological needs of global communities. The unique properties of REEs make them useful in a wide variety of applications, such as alloys, batteries, catalysts, magnets, phosphors, and polishing compounds. Rare earth element minerals are complex in both composition and structure. Carbonate, oxide, silicate, and phosphate-type minerals contain highly variable amounts of rare earths. Most rare earth-bearing minerals contain mainly lighter rare earths, a mixture of all the rare earths, or only the heavier rare earths.

Diverse technological applications require the full range of light, middle, and heavy rare earths. The production of these elements, in particular the heavy rare earths, remains highly dependent on deposits from China. Diversification of rare earth supply chains is contingent on expanded knowledge of globally distributed resources and an understanding of the degree to which those resources have been explored and evaluated. The knowledge of tectonic setting, typical rock associations, deposit morphology, and deposit genesis has led to the discovery of many conventional-type rare earth deposit types. Recent developments are anticipated to result in further discoveries that have the potential to meet the ever-expanding applications of REEs and REOs to address modern societal needs.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Rare earth metals and minerals industries: Status and prospects","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","usgsCitation":"Foley, N.K., and Ayuso, R.A., 2024, Conventional rare earth element mineral deposits: The global landscape, chap. of Rare earth metals and minerals industries: Status and prospects, p. 17-56.","productDescription":"40 p.","startPage":"17","endPage":"56","ipdsId":"IP-138267","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":424380,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Foley, Nora K. 0000-0003-0124-3509 nfoley@usgs.gov","orcid":"https://orcid.org/0000-0003-0124-3509","contributorId":4010,"corporation":false,"usgs":true,"family":"Foley","given":"Nora","email":"nfoley@usgs.gov","middleInitial":"K.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":864786,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":864787,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231903,"text":"70231903 - 2024 - Energy-related rare earth element sources","interactions":[],"lastModifiedDate":"2024-01-12T15:12:45.934983","indexId":"70231903","displayToPublicDate":"2023-12-29T09:08:25","publicationYear":"2024","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"Energy-related rare earth element sources","docAbstract":"

Energy-related materials such as coal, coal-bearing wastes, and coal combustion products are traditionally thought of as sources or by-products of electric power generation. Increasingly, these materials are considered resources for their content of rare earth elements (REEs) and other useful constituents. In this chapter, we examine the distribution, modes of occurrence, and relative extractability of REEs from coal-derived materials. We also consider economic factors associated with recovery of REEs from these sources. While several coal-derived sources show promise for REE recovery at the pilot scale, in all cases, REE contents are much below those of primary ores, such that extraction and concentrating the REEs require new and innovative approaches that are largely developmental.

Among coal-related sources, fly ash is the most REE-enriched, as REEs from coal are strongly retained in these refractory solids remaining after coal combustion. Partitioning of coal-derived elements into fly ash has been known for decades but this has yet to be commercially exploited. A key drawback shown in this chapter is that a significant fraction of REEs in fly ash is contained in highly insoluble aluminosilicate glasses that make up the largest portion of this material. In addition to testing chemical or physical pretreatment approaches to help improve the extractability of REEs from fly ash, current research is applying modern analytical approaches to better understand the distribution of REEs on increasingly smaller scales, in the interest of targeting their recovery.

Next-most REE-enriched among coal-related materials are solid waste products of coal mining and wastes from coal preparation, both of which are REE-enriched relative to coal itself. These waste coals concentrate mineralogical constituents that are excluded during mining or removed during coal preparation because they do not contribute to the heating value of coal for power generation. Recovery of REEs from coal waste has shown promise at the pilot scale and has the added benefit of converting a waste into useful constituents.

Total REE contents of commercial coals are, on average, much below the 300 parts per million interest level for REE recovery set by the U.S. Department of Energy (DOE). However, as reviewed in this chapter, certain horizons within coal beds show preferential REE enrichment and could be targeted by selective mining. Beyond this, certain coals are REE-enriched overall due to their unique geologic histories involving derivation from REE-enriched sediment sources, deposition of volcanic ash during coal formation, or interaction of coal with REE-bearing fluids.

Acidic drainage from abandoned coal mines is produced by the breakdown of pyrite (FeS2), which is unstable in oxygenated conditions. While these acidic fluids have lower REE contents than any of the coal-based solids described above, they are proportionally enriched in certain heavy rare earths, especially yttrium (Y). Precipitates from coal-based acid-mine drainage concentrate REEs to levels that are of interest for recovery, and these are also promising sources for extraction at the pilot scale.

","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Rare earth metals and minerals industries: Status and prospects","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","usgsCitation":"Kolker, A., Lefticariu, L., and Anderson, S.T., 2024, Energy-related rare earth element sources, chap. 3 of Rare earth metals and minerals industries: Status and prospects, p. 57-102.","productDescription":"46 p.","startPage":"57","endPage":"102","ipdsId":"IP-137470","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":424379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":424378,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://link.springer.com/chapter/10.1007/978-3-031-31867-2_3"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kolker, Allan 0000-0002-5768-4533 akolker@usgs.gov","orcid":"https://orcid.org/0000-0002-5768-4533","contributorId":643,"corporation":false,"usgs":true,"family":"Kolker","given":"Allan","email":"akolker@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":844062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lefticariu, Liliana 0000-0003-3413-654X","orcid":"https://orcid.org/0000-0003-3413-654X","contributorId":251875,"corporation":false,"usgs":false,"family":"Lefticariu","given":"Liliana","email":"","affiliations":[{"id":13212,"text":"Southern Illinois University","active":true,"usgs":false}],"preferred":false,"id":844063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Steven T. 0000-0003-3481-3424 sanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-3481-3424","contributorId":2532,"corporation":false,"usgs":true,"family":"Anderson","given":"Steven","email":"sanderson@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":844064,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250775,"text":"70250775 - 2024 - Hydrothermal monazite and xenotime chemistry as genetic discriminators for intrusion-related and orogenic gold deposits: Implications for an orogenic origin of the Pogo gold deposit, Alaska","interactions":[],"lastModifiedDate":"2024-05-20T15:17:04.532583","indexId":"70250775","displayToPublicDate":"2023-12-29T07:01:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2746,"text":"Mineralium Deposita","active":true,"publicationSubtype":{"id":10}},"title":"Hydrothermal monazite and xenotime chemistry as genetic discriminators for intrusion-related and orogenic gold deposits: Implications for an orogenic origin of the Pogo gold deposit, Alaska","docAbstract":"

Attempts to geochemically distinguish between metamorphic-hydrothermal systems that form orogenic gold deposits and both reduced and oxidized magmatic-hydrothermal systems using isotopes or metal associations have proven ambiguous, particularly for orogenic gold and reduced intrusion-related gold systems. The absence of conclusive geochemical discriminators and the overlap in geologic characteristics have led to gold deposit models being potentially incorrectly applied, which in turn negatively affect regional mineral exploration and mine planning. In this study, in situ electron microprobe geochemical analyses of hydrothermal monazite and xenotime crystals associated with different types of gold-bearing deposits are shown to be effective geochemical discriminators. There are notable differences in mineral chemistry such as rare earth element (REE) profiles, total light REE, Dy, Er, Pr, Y, Nd/Sm, and La/Sm that distinguish monazite precipitated from metamorphic-hydrothermal fluids that form orogenic gold deposits and those precipitated from magmatic-hydrothermal fluids that form both porphyry Cu-Mo-Au and reduced intrusion-related gold deposits. Notable differences in overall xenotime abundances and concentrations of heavy REEs, Ca, and Sc are distinctive between the different deposit classes for xenotime. The origin of the controversially classified Pogo gold deposit, Tintina gold province, Alaska, which has been characterized as both a reduced intrusion-related and an orogenic gold deposit, is tested based upon the noted chemical differences associated with these hydrothermal phosphates. The findings of this study have implications for exploration and mine development in the Tintina gold province and other areas that contain deposits that are controversially classified as either orogenic or as magmatic-hydrothermal gold deposits.

","language":"English","publisher":"Springer","doi":"10.1007/s00126-023-01240-5","usgsCitation":"Taylor, R., Graham, G.E., and Lowers, H.A., 2024, Hydrothermal monazite and xenotime chemistry as genetic discriminators for intrusion-related and orogenic gold deposits: Implications for an orogenic origin of the Pogo gold deposit, Alaska: Mineralium Deposita, v. 59, p. 949-967, https://doi.org/10.1007/s00126-023-01240-5.","productDescription":"19 p.","startPage":"949","endPage":"967","ipdsId":"IP-153907","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":440841,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00126-023-01240-5","text":"Publisher Index Page"},{"id":424110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -158.5757283796243,\n 66.30882355010831\n ],\n [\n -158.5757283796243,\n 59.39793159658413\n ],\n [\n -143.28798884528499,\n 59.39793159658413\n ],\n [\n -143.28798884528499,\n 66.30882355010831\n ],\n [\n -158.5757283796243,\n 66.30882355010831\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"59","noUsgsAuthors":false,"publicationDate":"2023-12-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Ryan D. 0000-0002-8845-5290","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":201948,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":891378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":891379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowers, Heather A. 0000-0001-5360-9264 hlowers@usgs.gov","orcid":"https://orcid.org/0000-0001-5360-9264","contributorId":191307,"corporation":false,"usgs":true,"family":"Lowers","given":"Heather","email":"hlowers@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":891380,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}