{"pageNumber":"122","pageRowStart":"3025","pageSize":"25","recordCount":165720,"records":[{"id":70262837,"text":"70262837 - 2024 - An intercomparison of DOC estimated from fDOM sensors in wildfire affected streams of the western United States","interactions":[],"lastModifiedDate":"2025-01-24T16:00:41.024114","indexId":"70262837","displayToPublicDate":"2024-12-25T08:54:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"An intercomparison of DOC estimated from fDOM sensors in wildfire affected streams of the western United States","docAbstract":"

Wildfires in the western United States (US) have been demonstrated to affect water quality, including dissolved organic carbon (DOC), in streams. Elevated post-wildfire DOC concentration poses a potential risk to drinking water treatment systems. In-stream measurements of fluorescent dissolved organic matter (fDOM), a proxy for DOC, have shown potential to detect dynamic changes in DOC. High frequency monitoring of water temperature, turbidity, and fDOM was used in conjunction with discrete sampling during targeted storm events and at fixed intervals to estimate DOC in five western US streams following wildfires in 2020 and 2021 with the objective to characterise and compare responses to wildfire among sites. The elevated turbidity conditions typical after wildfire presented a challenge to fDOM measurements and there was a need to identify appropriate turbidity corrections at burned sites. A combination of established and novel methods corrected fDOM concentrations for turbidity effects up to 800 Formazin nephelometric units (FNU). Pre-wildfire high frequency water quality data in adjacent burned and unburned watersheds allowed for separation of climate effects on DOC at one of the sites. Hydrology, climate and landcover were more important drivers of post-wildfire DOC yield than wildfire characteristics. Seasonal patterns of DOC were unchanged by wildfire in snowmelt-driven watersheds. Large, transient spikes in DOC concentration following frontal and convective storms were observed post-wildfire at all burned sites, but not at the unburned site. These spikes often exceeded operational thresholds for drinking water treatment. This study highlights the ability to develop high frequency DOC estimates in surface waters up to 800 FNU using fDOM sensors and targeted storm sampling and emphasises the value of high frequency pre-wildfire data in adjacent burned and unburned watersheds for separating climate and wildfire effects.

","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70023","usgsCitation":"Akie, G.A., Clow, D.W., Murphy, S.F., Clark, G.D., Meador, M.R., and Ebel, B., 2024, An intercomparison of DOC estimated from fDOM sensors in wildfire affected streams of the western United States: Hydrological Processes, v. 38, no. 12, e70023, 20 p., https://doi.org/10.1002/hyp.70023.","productDescription":"e70023, 20 p.","ipdsId":"IP-164617","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":489141,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70023","text":"Publisher Index Page"},{"id":481139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Colorado, Montana, 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\"}}]}","volume":"38","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Akie, Garrett Alexander 0000-0002-6356-7106","orcid":"https://orcid.org/0000-0002-6356-7106","contributorId":290236,"corporation":false,"usgs":true,"family":"Akie","given":"Garrett","email":"","middleInitial":"Alexander","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":924966,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clark, Gregory D. 0000-0003-0066-8193 gmclark@usgs.gov","orcid":"https://orcid.org/0000-0003-0066-8193","contributorId":224364,"corporation":false,"usgs":true,"family":"Clark","given":"Gregory","email":"gmclark@usgs.gov","middleInitial":"D.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":924967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meador, Michael R. 0000-0001-5956-3340 mrmeador@usgs.gov","orcid":"https://orcid.org/0000-0001-5956-3340","contributorId":219878,"corporation":false,"usgs":true,"family":"Meador","given":"Michael","email":"mrmeador@usgs.gov","middleInitial":"R.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":924968,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":924969,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267704,"text":"70267704 - 2024 - Gene flow prevents genetic diversity loss despite small effective population size in fragmented grizzly bear (Ursus arctos) populations","interactions":[],"lastModifiedDate":"2025-05-29T16:45:22.117743","indexId":"70267704","displayToPublicDate":"2024-12-25T08:37:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1324,"text":"Conservation Genetics","active":true,"publicationSubtype":{"id":10}},"title":"Gene flow prevents genetic diversity loss despite small effective population size in fragmented grizzly bear (Ursus arctos) populations","docAbstract":"

Genetic monitoring is important in small, fragmented populations that rely on gene flow to maintain genetic diversity. The Selkirk, Yaak, and Cabinet grizzly bear (Ursus arctos) populations are among the smallest in North America and are near the southernmost extent of the species’ range. These populations received little to no effective migration for generations but have recently experienced increased gene flow through natural migration and a population augmentation program. A long-term dataset of grizzly bear microsatellite genotypes from 1973 to 2021 presented a unique opportunity to examine genetic trends in these populations over time. We used this dataset of 464 bears to evaluate if gene flow affected observed heterozygosity (HO), expected heterozygosity (HE), allelic richness (AR), and average pairwise relatedness (r) in each of these populations. We also estimated effective population size (Ne) using the temporal and linkage disequilibrium (LD) methods. Post gene flow, AR increased in the Selkirk and Cabinet populations and r decreased in all three populations. We did not observe any significant changes in HE or HO, but HE values in our populations were significantly higher than those estimated using a model without gene flow. Our Ne estimates were consistent between the temporal and LD methods and ranged from 15.2 to 15.8, 15.4–17.5, and 5.6–8.9 for the Selkirk, Yaak, and Cabinet populations, respectively. Overall, our findings indicate that gene flow is increasing or maintaining genetic diversity in these populations. However, Ne remains low and additional connectivity or augmentation may be needed, particularly in the Cabinet population.

","language":"English","publisher":"Springer Nature","doi":"10.1007/s10592-024-01666-y","usgsCitation":"Turnock, M., Teisberg, J., Kasworm, W., Falcy, M.R., Proctor, M., and Waits, L., 2024, Gene flow prevents genetic diversity loss despite small effective population size in fragmented grizzly bear (Ursus arctos) populations: Conservation Genetics, v. 26, p. 279-291, https://doi.org/10.1007/s10592-024-01666-y.","productDescription":"13 p.","startPage":"279","endPage":"291","ipdsId":"IP-166778","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488451,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10592-024-01666-y","text":"Publisher Index Page"},{"id":486760,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Idaho, Montana, Washington","otherGeospatial":"British Columbia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.3821304045668,\n 50.132013169608314\n ],\n [\n -118.3821304045668,\n 47.95130403023194\n ],\n [\n -114.75140702492735,\n 47.95130403023194\n ],\n [\n -114.75140702492735,\n 50.132013169608314\n ],\n [\n -118.3821304045668,\n 50.132013169608314\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"26","noUsgsAuthors":false,"publicationDate":"2024-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Turnock, Megan F.","contributorId":356036,"corporation":false,"usgs":false,"family":"Turnock","given":"Megan F.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":938583,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Teisberg, Justin E.","contributorId":356039,"corporation":false,"usgs":false,"family":"Teisberg","given":"Justin E.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kasworm, Wayne F.","contributorId":356042,"corporation":false,"usgs":false,"family":"Kasworm","given":"Wayne F.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Falcy, Matthew Richard 0000-0002-3332-2239","orcid":"https://orcid.org/0000-0002-3332-2239","contributorId":288500,"corporation":false,"usgs":true,"family":"Falcy","given":"Matthew","email":"","middleInitial":"Richard","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938586,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Proctor, Michael F.","contributorId":356045,"corporation":false,"usgs":false,"family":"Proctor","given":"Michael F.","affiliations":[{"id":84901,"text":"Birchdale Ecological, Ltd.","active":true,"usgs":false}],"preferred":false,"id":938587,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waits, Lisette P.","contributorId":356046,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette P.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":938588,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263381,"text":"70263381 - 2024 - Sensitivity analysis of a dynamic vegetation-sediment transport model using equadratures: Exploring inorganic accretion on a marsh platform","interactions":[],"lastModifiedDate":"2025-02-10T16:17:14.052147","indexId":"70263381","displayToPublicDate":"2024-12-24T13:39:34","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7357,"text":"JGR Earth Surface","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity analysis of a dynamic vegetation-sediment transport model using equadratures: Exploring inorganic accretion on a marsh platform","docAbstract":"

Salt marsh systems require a net import of inorganic sediment to maintain their structure in response to sea‐level rise. Marshes are affected by physical processes including tides, waves, sediment transport, and the influence of vegetation, and these processes interact in complex ways leading to sediment accretion or erosion. We implement a 3‐D hydrodynamic sediment transport model in an idealized marsh‐bay complex with a gently sloping edge, and use it as a laboratory to explore the processes leading to bed elevation change through the bay‐marsh continuum. We use the novel equadratures method for efficient sensitivity analysis to test the roles of wave, vegetation, and sediment parameters on wave dissipation, bed shear stress, sediment fluxes, and deposition and erosion across a transect spanning bay shallows to the marsh. Within the explored bounds of parameter uncertainty, significant wave height (Hsig), settling velocity (ws), and critical shear stress (τcrit) most strongly affect accretion on the marsh platform. Deposition is affected more by parameter‐parameter interactions, that is, both τcrit and ws or both Hsig and ws, than by a single parameter varying alone. The sediment that accretes on the marsh platform originates beyond the marsh edge, indicating that the dynamics of the adjacent mudflat are important for predicting the fate of the marsh. Applying efficient sensitivity analysis techniques can empower process‐based models to test more parameters, larger ranges, and longer timeframes, enabling future predictions of marsh response to sea‐level rise based on physical processes

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JF007945","usgsCitation":"Allen, R., Ganju, N., Kalra, T., Aretxabaleta, A., and Lacy, J.R., 2024, Sensitivity analysis of a dynamic vegetation-sediment transport model using equadratures: Exploring inorganic accretion on a marsh platform: JGR Earth Surface, v. 129, no. 10, e2024JF007945, 21 p., https://doi.org/10.1029/2024JF007945.","productDescription":"e2024JF007945, 21 p.","ipdsId":"IP-159248","costCenters":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":487466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jf007945","text":"Publisher Index Page"},{"id":481871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"China Camp march, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5,\n 38.016667\n ],\n [\n -122.5,\n 38\n ],\n [\n -122.466667,\n 38\n ],\n [\n -122.466667,\n 38.016667\n ],\n [\n -122.5,\n 38.016667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"129","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Allen, Rachel 0000-0002-0287-6466","orcid":"https://orcid.org/0000-0002-0287-6466","contributorId":216002,"corporation":false,"usgs":true,"family":"Allen","given":"Rachel","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":926716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":926717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kalra, Tarandeep 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":304428,"corporation":false,"usgs":false,"family":"Kalra","given":"Tarandeep","email":"tkalra@usgs.gov","affiliations":[{"id":66067,"text":"Jupiter Intelligence, San Mateo, California","active":true,"usgs":false}],"preferred":false,"id":926718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":926719,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lacy, Jessica R. 0000-0002-2797-6172","orcid":"https://orcid.org/0000-0002-2797-6172","contributorId":201703,"corporation":false,"usgs":true,"family":"Lacy","given":"Jessica","email":"","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":926720,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261835,"text":"70261835 - 2024 - Self-guided decision support groundwater modelling with Python","interactions":[],"lastModifiedDate":"2024-12-30T15:49:23.123549","indexId":"70261835","displayToPublicDate":"2024-12-24T09:16:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":19861,"text":"Journal of Open Source Education","active":true,"publicationSubtype":{"id":10}},"title":"Self-guided decision support groundwater modelling with Python","docAbstract":"The GMDSI tutorial notebooks repository provides learners with a comprehensive set of tutorials for self-guided training on decision-support groundwater modelling using Python-based tools. Although targeted at groundwater modelling, they are based around model-agnostic tools and readily transferable to other environmental modelling workflows. The tutorials are divided into three parts. The first covers fundamental theoretical concepts. These are intended as background reading for reference on an as-needed basis. Tutorials in the second part introduce learners to some of the core concepts parameter estimation in a groundwater modelling context, as well as providing a gentle introduction to the PEST, PEST++ and pyEMU software. Lastly, the third part demonstrates how to implement highly-parameterized applied decision-support modelling workflows. The tutorials aim to provide examples of both “how to use” the software as well as “how to think” about using the software. A key advantage to using notebooks in this context is that the workflows described run the same code as practitioners would run on a large-scale real- world application. Using a small synthetic model facilitates rapid progression through the workflow.","language":"English","publisher":"Open Journals","doi":"10.21105/jose.00240","usgsCitation":"Hugman, R., White, J., Fienen, M., Hemmings, B., and Markovich, K., 2024, Self-guided decision support groundwater modelling with Python: Journal of Open Source Education, v. 7, no. 82, 240, 6 p., https://doi.org/10.21105/jose.00240.","productDescription":"240, 6 p.","ipdsId":"IP-166010","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":466700,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21105/jose.00240","text":"Publisher Index Page"},{"id":465530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"82","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hugman, Rui 0000-0003-0891-3886","orcid":"https://orcid.org/0000-0003-0891-3886","contributorId":299138,"corporation":false,"usgs":false,"family":"Hugman","given":"Rui","affiliations":[{"id":64778,"text":"Univeristy of Flinders","active":true,"usgs":false}],"preferred":false,"id":921991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":214251,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":921992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hemmings, Brioch","contributorId":260167,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":921994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Markovich, Katie","contributorId":347560,"corporation":false,"usgs":false,"family":"Markovich","given":"Katie","affiliations":[{"id":83190,"text":"INTERA Geosciences","active":true,"usgs":false}],"preferred":false,"id":921995,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262817,"text":"70262817 - 2024 - A comparison of survival and behavior of lake whitefish following transmitter implantation using electro- or chemical immobilization","interactions":[],"lastModifiedDate":"2025-01-23T15:25:55.35842","indexId":"70262817","displayToPublicDate":"2024-12-24T08:18:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of survival and behavior of lake whitefish following transmitter implantation using electro- or chemical immobilization","docAbstract":"

Background

The number of telemetry studies focused on lake whitefish (Coregonus clupeaformis) in the Laurentian Great Lakes has steadily increased over the last decade, but field tests of immobilization methods used for tag implantation, which have the potential to affect survival and behavior of fish after release, are lacking. We compared post-tagging survival and behavior of lake whitefish that were immobilized for tag implantation using electroimmobilization via a transcutaneous electrical nerve stimulation (TENS) unit or by chemical immobilization via exposure to 10% eugenol.

Results

Acoustic tags were implanted into 126 adult lake whitefish (N = 126; N = 67 TENS treatment group, N = 59 eugenol treatment group) collected from the Fox River, Wisconsin, during the spawning period in November 2021. We found no significant differences between treatments in the number of days that lake whitefish spent in the Fox River following tagging (TENS mean = 13.4 days, eugenol mean = 14.7), and also found that the proportions of fish within each treatment group that returned to the Fox River during fall 2022 (51% from TENS treatment group, 49% from eugenol treatment group) did not differ from the proportions for all fish that were confirmed to be alive at that time. The best Cormack–Jolly–Seber model indicated no differences in survival between the two treatment groups (monthly survival = 0.980, 95% CI 0.970–0.987). Fish immobilized using TENS underwent almost immediate induction and recovery from surgeries, while fish immobilized using eugenol had induction times that ranged 167–487 s (mean = 347 s) and recovery times that ranged 51–2358 s (mean = 1242 s).

Conclusions

Short- and long-term behavior (time to exit of Fox River, return to Fox River in the next spawning season) and monthly survival estimates of lake whitefish did not differ between the immobilization treatments. Either method may be suitable for immobilization during tag implantation, but the additional time needed for induction and recovery of fish when using eugenol may be a limiting factor in some field-based tagging situations.

","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-024-00393-y","usgsCitation":"Izzo, L., Dembkowski, D., Binder, T., Hansen, S., Vandergoot, C., and Isermann, D.A., 2024, A comparison of survival and behavior of lake whitefish following transmitter implantation using electro- or chemical immobilization: Animal Biotelemetry, v. 12, 39, 10 p., https://doi.org/10.1186/s40317-024-00393-y.","productDescription":"39, 10 p.","ipdsId":"IP-169427","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-024-00393-y","text":"Publisher Index Page"},{"id":480989,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Fox River, Green Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.05808785412178,\n 44.69153434457186\n ],\n [\n -88.05808785412178,\n 44.4951220149938\n ],\n [\n -87.86450882150862,\n 44.4951220149938\n ],\n [\n -87.86450882150862,\n 44.69153434457186\n ],\n [\n -88.05808785412178,\n 44.69153434457186\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-12-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Izzo, Lisa K.","contributorId":349826,"corporation":false,"usgs":false,"family":"Izzo","given":"Lisa K.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":924889,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dembkowski, Daniel J.","contributorId":349827,"corporation":false,"usgs":false,"family":"Dembkowski","given":"Daniel J.","affiliations":[{"id":65894,"text":"Wisconsin Cooperative Fishery Research Unit","active":true,"usgs":false}],"preferred":false,"id":924890,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Binder, Thomas R.","contributorId":349828,"corporation":false,"usgs":false,"family":"Binder","given":"Thomas R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":924891,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Scott P.","contributorId":349829,"corporation":false,"usgs":false,"family":"Hansen","given":"Scott P.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924892,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vandergoot, Christopher S.","contributorId":349830,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":924893,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isermann, Daniel A. 0000-0003-1151-9097 disermann@usgs.gov","orcid":"https://orcid.org/0000-0003-1151-9097","contributorId":5167,"corporation":false,"usgs":true,"family":"Isermann","given":"Daniel","email":"disermann@usgs.gov","middleInitial":"A.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924894,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261520,"text":"sim3514 - 2024 - Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California","interactions":[],"lastModifiedDate":"2025-08-15T16:11:32.54982","indexId":"sim3514","displayToPublicDate":"2024-12-23T10:32:03","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3514","displayTitle":"Geologic Map and Structure Sections Along the Southern Part of the Bartlett Springs Fault Zone and Adjacent Areas from Cache Creek to Lake Berryessa, Northern Coast Ranges, California","title":"Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California","docAbstract":"

Introduction

Located in the Coast Ranges of northern California, the Bartlett Springs Fault Zone is the easternmost fault in the San Andreas Fault system in northern California. The fault is a right-lateral, strike-slip structure considered capable of producing an earthquake of moment magnitude 7. The purpose of this mapping is to better characterize the geology and earthquake hazards associated with the southern part of the Bartlett Springs Fault Zone and to help identify any evidence of active uplift on the faults bounding the Coast Ranges. Although the area immediately surrounding the Bartlett Springs Fault Zone is sparsely populated, its southern segment presents a potential seismic hazard to northern California communities as far away as the San Francisco Bay region and Sacramento. There are also nearby water resources, mineral resources, and public lands used for public recreation.

The Coast Ranges of northern California are a series of northwest-southeast-oriented mountain ranges and valleys located north of the San Francisco Bay region, between the Pacific Ocean to the west and the Sacramento Valley to the east. The region has rugged terrain, high mountain peaks that reach more than 2,400 meters above sea level, isolated and narrow valley bottoms on which most human settlements are located, and large drainage systems that tend to follow the northwest-southeast-oriented topographic grain. The physiographic character of the region is shaped by its bedrock geology, deformational history, and active faulting.

The basement rocks of the northern Coast Ranges consist of the Franciscan Complex and the Great Valley complex, the latter of which consists of two informal units, the Coast Range ophiolite and the Great Valley sequence. The Franciscan Complex and the Great Valley complex are in structural contact along the Coast Range Fault, a regional-scale structure and fundamental crustal boundary.

The Franciscan Complex and the Great Valley complex are superposed by active, northwest-southeast-striking strike-slip faults that are associated with seismicity swarms. These active strike-slip faults can produce moderate to large earthquakes that have moment magnitudes of 7–8. In places, these active structures bound large ranges and valleys, suggesting that much of the modern topographic expression is the result of active deformation processes.

This report contains new 1:24,000-scale geologic mapping along the southern part of the Bartlett Springs Fault Zone between Clear Lake and Lake Berryessa. The map area spans 738 square kilometers in northern Napa County, southern Lake County, and parts of Yolo and Colusa Counties. The south and east borders of the map are 90 kilometers north of San Francisco and 70 kilometers west of Sacramento, respectively. The map area is within the Knoxville mining district, which has a history of mercury and gold mining dating back to the mid-19th century. The two main towns in the region, Lower Lake and Clearlake, California, are west-northwest of the map area. Approximately 71,000 people live in the cities and rural communities located within a 40-kilometer radius of the center of the map area.

The bedrock geology, cross sections, and structural data presented herein are critical for evaluating the long-term evolution of the Bartlett Springs Fault Zone. This work will supplement studies on local seismic hazards, liquefaction potential, landslide hazards, earthquake geology, natural resources, groundwater resources, engineering geology, and tectonic history by providing the background information for site-specific investigations on these subjects.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3514","usgsCitation":"Melosh, B.L., Bodtker, J.W., and Valin, Z.C., 2024, Geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California: U.S. Geological Survey Scientific Investigations Map 3514, 2 sheets, scale 1:24,000, 20 p. pamphlet, https://doi.org/10.3133/sim3514.","productDescription":"Pamphlet: vi, 20 p.; 2 Sheets: 46.15 x 78.86 inches and 58.26 x 41.78 inches; Data Release","numberOfPages":"20","additionalOnlineFiles":"Y","ipdsId":"IP-128914","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":494218,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118060.htm","linkFileType":{"id":5,"text":"html"}},{"id":465095,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1YJRCZD","text":"USGS Data Release","description":"Melosh, B.L., Bodtker, J.W., Valin, Z.C., and Sullivan, K., 2024, Geospatial database of the geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California: U.S. Geological Survey data release, https://doi.org/10.5066/P1YJRCZD.","linkHelpText":"Geospatial database of the geologic map and structure sections along the southern part of the Bartlett Springs Fault Zone and adjacent areas from Cache Creek to Lake Berryessa, northern Coast Ranges, California"},{"id":465094,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3514/covrthb.jpg"},{"id":465093,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_sheet2.pdf","text":"Sheet 2","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465092,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_sheet1.pdf","text":"Sheet 1","size":"30 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465091,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3514/sim3514_pamphlet.pdf","text":"Pamphlet","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Northern Coast Ranges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5457,\n 39.0012\n ],\n [\n -122.5457,\n 38.6099\n ],\n [\n -122.2368,\n 38.6099\n ],\n [\n -122.2368,\n 39.0012\n ],\n [\n -122.5457,\n 39.0012\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2024-12-23","noUsgsAuthors":false,"publicationDate":"2024-12-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Melosh, Benjamin L. 0000-0002-8017-7193","orcid":"https://orcid.org/0000-0002-8017-7193","contributorId":217215,"corporation":false,"usgs":true,"family":"Melosh","given":"Benjamin","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":920879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bodtker, Jackson W. 0000-0002-1576-0550","orcid":"https://orcid.org/0000-0002-1576-0550","contributorId":330697,"corporation":false,"usgs":true,"family":"Bodtker","given":"Jackson","email":"","middleInitial":"W.","affiliations":[],"preferred":true,"id":920880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valin, Zenon C. 0000-0001-6199-6700 zenon@usgs.gov","orcid":"https://orcid.org/0000-0001-6199-6700","contributorId":3742,"corporation":false,"usgs":true,"family":"Valin","given":"Zenon","email":"zenon@usgs.gov","middleInitial":"C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":920882,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261869,"text":"70261869 - 2024 - The anatomy of a drought in the upper San Francisco Estuary: Water quality and lower-trophic responses to multi-year droughts","interactions":[],"lastModifiedDate":"2024-12-31T16:51:27.536275","indexId":"70261869","displayToPublicDate":"2024-12-23T09:15:29","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":"The anatomy of a drought in the upper San Francisco Estuary: Water quality and lower-trophic responses to multi-year droughts","docAbstract":"Multi-year droughts are important and impactful features of California’s Mediterranean climate and can fundamentally affect the water quality and the ecosystem response of the San Francisco Estuary (Estuary) and the Sacramento-San Joaquin Delta (Delta). This study assesses data collected by long-term monitoring programs over the past 46 years (1975-2021) to evaluate how water quality in the Estuary changes during multi-year droughts. We found that multi-year droughts alter multiple physical and chemical parameters in the Estuary, increasing water temperature, salinity, water clarity, and nutrient levels. This trend was consistent across regions and seasons, with few exceptions. Increases in these parameters during droughts are likely caused by reduced Delta inflows that intensified in each successive dry year because of reduced precipitation and reservoir releases. Droughts did not substantially effect on tidal velocities within the Estuary, which remained mostly consistent across wet and dry periods. Trends in chlorophyll concentrations during droughts were more nuanced with higher concentrations occurring in the South-Central Delta region and during the winter and spring. Together, these results paint a picture of drought in the Estuary as warm, clear, high in nutrients, with patchy phytoplankton blooms (as indexed by chlorophyll), all of which have implications for higher trophic levels. Considering that droughts are expected to increase in frequency and intensity in California with climate change, it is imperative to understand the effects of multi-year droughts on the water quality conditions of the Estuary when making water management decisions.","language":"English","doi":"10.15447/sfews.2024v22iss1art1","usgsCitation":"Bosworth, D., Bashevkin, S., Bouma-Gregson, K., Rosemary Hartman, and Stumpner, E., 2024, The anatomy of a drought in the upper San Francisco Estuary: Water quality and lower-trophic responses to multi-year droughts: San Francisco Estuary and Watershed Science, v. 22, no. 1, Article 1, 33 p., https://doi.org/10.15447/sfews.2024v22iss1art1.","productDescription":"Article 1, 33 p.","ipdsId":"IP-148296","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":466701,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2024v22iss1art1","text":"Publisher Index Page"},{"id":465564,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"North Delta Confluence, Suisun Bay, Suisun Marsh, upper San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.13258485344167,\n 38.017503757798266\n ],\n [\n -121.61847863379901,\n 38.017503757798266\n ],\n [\n -121.61847863379901,\n 38.454570512931326\n ],\n [\n -122.13258485344167,\n 38.454570512931326\n ],\n [\n -122.13258485344167,\n 38.017503757798266\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bosworth, David 0000-0003-0740-3390","orcid":"https://orcid.org/0000-0003-0740-3390","contributorId":347649,"corporation":false,"usgs":false,"family":"Bosworth","given":"David","affiliations":[{"id":40593,"text":"CA Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":922094,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bashevkin, Samuel M.","contributorId":288941,"corporation":false,"usgs":false,"family":"Bashevkin","given":"Samuel M.","affiliations":[{"id":61910,"text":"Delta Science Program, Delta Stewardship Council","active":true,"usgs":false}],"preferred":false,"id":922095,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bouma-Gregson, Keith 0000-0002-0304-6034","orcid":"https://orcid.org/0000-0002-0304-6034","contributorId":311235,"corporation":false,"usgs":true,"family":"Bouma-Gregson","given":"Keith","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922096,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosemary Hartman 0000-0002-0642-183X","orcid":"https://orcid.org/0000-0002-0642-183X","contributorId":347657,"corporation":false,"usgs":false,"family":"Rosemary Hartman","affiliations":[{"id":40593,"text":"CA Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":922097,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stumpner, Elizabeth B. 0000-0003-2356-2244","orcid":"https://orcid.org/0000-0003-2356-2244","contributorId":330616,"corporation":false,"usgs":false,"family":"Stumpner","given":"Elizabeth B.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":922098,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263215,"text":"70263215 - 2024 - The emerging invasive species and climate change lexicon","interactions":[],"lastModifiedDate":"2025-02-03T15:22:15.310427","indexId":"70263215","displayToPublicDate":"2024-12-23T08:17:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"The emerging invasive species and climate change lexicon","docAbstract":"

The rapid diversification of terminology associated with invasion ecology is a known barrier to effective communication and management. These challenges are magnified by the addition of terms and concepts related to climate-induced range-shifting taxa and/or changes to impacts. Further, institutional policies and terminologies for invasive species introduce new ambiguities when considering climate change. To alleviate communication and application challenges, we introduce a conceptual framework that organizes climate-related invasion terms, revealing ambiguities and gaps. Additionally, we illustrate how these ambiguities can affect management with four case studies and consider situations where resolution can improve policy and management outcomes. The framework can help users avoid inconsistent use of terminology, and prioritize when to address management and policy consequences related to associated terminological ambiguity.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.tree.2024.08.005","usgsCitation":"Fusco, E.J., Falk, B., Heimowitz, P.J., Lieurance, D., Parsons, E., Rottler, C.M., Thurman, L., and Evans, A., 2024, The emerging invasive species and climate change lexicon: Trends in Ecology and Evolution, v. 39, no. 12, p. 1119-1129, https://doi.org/10.1016/j.tree.2024.08.005.","productDescription":"11 p.","startPage":"1119","endPage":"1129","ipdsId":"IP-159995","costCenters":[{"id":49226,"text":"Northwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":491008,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tree.2024.08.005","text":"Publisher Index Page"},{"id":481604,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fusco, Emily J.","contributorId":236821,"corporation":false,"usgs":false,"family":"Fusco","given":"Emily","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":925944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Falk, Bryan G. 0000-0002-9690-5626","orcid":"https://orcid.org/0000-0002-9690-5626","contributorId":265395,"corporation":false,"usgs":false,"family":"Falk","given":"Bryan G.","affiliations":[{"id":54672,"text":"National Park Service, Everglades National Park, 40001 SR 9336, Homestead, Florida 33034, USA","active":true,"usgs":false}],"preferred":false,"id":925945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heimowitz, Paul J. 0000-0001-7291-0175","orcid":"https://orcid.org/0000-0001-7291-0175","contributorId":334250,"corporation":false,"usgs":true,"family":"Heimowitz","given":"Paul","email":"","middleInitial":"J.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":925946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lieurance, Deah 0000-0001-8176-3146","orcid":"https://orcid.org/0000-0001-8176-3146","contributorId":293605,"corporation":false,"usgs":false,"family":"Lieurance","given":"Deah","email":"","affiliations":[{"id":63333,"text":"Agronomy Department, University of Florida","active":true,"usgs":false}],"preferred":false,"id":925947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Parsons, Elliott","contributorId":221123,"corporation":false,"usgs":false,"family":"Parsons","given":"Elliott","affiliations":[{"id":40328,"text":"State of Hawai‘i Division of Forestry and Wildlife, Pu‘u Wa‘awa‘a Forest Reserve","active":true,"usgs":false}],"preferred":false,"id":925948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rottler, Caitlin M.","contributorId":138853,"corporation":false,"usgs":false,"family":"Rottler","given":"Caitlin","email":"","middleInitial":"M.","affiliations":[{"id":12546,"text":"Univ of Wyoming, Department of Botany, 1000 E. University Ave., Laramie, WY 82071; Univ of WY, Program in Ecology, 1000 E. University Ave., Laramie, WY 82071 USA","active":true,"usgs":false}],"preferred":false,"id":925949,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thurman, Lindsey 0000-0003-3142-4909","orcid":"https://orcid.org/0000-0003-3142-4909","contributorId":269425,"corporation":false,"usgs":true,"family":"Thurman","given":"Lindsey","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":925950,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Evans, Annette","contributorId":300029,"corporation":false,"usgs":false,"family":"Evans","given":"Annette","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":925951,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70267722,"text":"70267722 - 2024 - A comparative framework to develop transferable species distribution models for animal telemetry data","interactions":[],"lastModifiedDate":"2025-05-29T14:19:47.317741","indexId":"70267722","displayToPublicDate":"2024-12-22T09:12:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"A comparative framework to develop transferable species distribution models for animal telemetry data","docAbstract":"

Species distribution models (SDMs) have become increasingly popular for making ecological inferences, as well as predictions to inform conservation and management. In predictive modeling, practitioners often use correlative SDMs that only evaluate a single spatial scale and do not account for differences in life stages. These modeling decisions may limit the performance of SDMs beyond the study region or sampling period. Given the increasing desire to develop transferable SDMs, a robust framework is necessary that can account for known challenges of model transferability. Here, we propose a comparative framework to develop transferable SDMs, which was tested using satellite telemetry data from green turtles (Chelonia mydas). This framework is characterized by a set of steps comparing among different models based on (1) model algorithm (e.g., generalized linear model vs. Gaussian process regression) and formulation (e.g., correlative model vs. hybrid model), (2) spatial scale, and (3) accounting for life stage. SDMs were fitted as resource selection functions and trained on data from the Gulf of Mexico with bathymetric depth, net primary productivity, and sea surface temperature as covariates. Independent validation datasets from Brazil and Qatar were used to assess model transferability. A correlative SDM using a hierarchical Gaussian process regression (HGPR) algorithm exhibited greater transferability than a hybrid SDM using HGPR, as well as correlative and hybrid forms of hierarchical generalized linear models. Additionally, models that evaluated habitat selection at the finest spatial scale and that did not account for life stage proved to be the most transferable in this study. The comparative framework presented here may be applied to a variety of species, ecological datasets (e.g., presence-only, presence-absence, mark-recapture), and modeling frameworks (e.g., resource selection functions, step selection functions, occupancy models) to generate transferable predictions of species–habitat associations. We expect that SDM predictions resulting from this comparative framework will be more informative management tools and may be used to more accurately assess climate change impacts on a wide array of taxa.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70136","usgsCitation":"Cullen, J., Domit, C.A., Lamont, M., Marshall, C., Santos, A.J., Sasso, C.R., Al Ansi, M., Hart, K., and Fuentes, M.M., 2024, A comparative framework to develop transferable species distribution models for animal telemetry data: Ecosphere, v. 15, no. 12, e70136, 20 p., https://doi.org/10.1002/ecs2.70136.","productDescription":"e70136, 20 p.","ipdsId":"IP-155304","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70136","text":"Publisher Index Page"},{"id":486722,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil, Qatar, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": 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83255000","active":true,"usgs":false}],"preferred":false,"id":938643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lamont, Margaret 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":222403,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938644,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, Christopher D.","contributorId":356063,"corporation":false,"usgs":false,"family":"Marshall","given":"Christopher D.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":938645,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Santos, Armando J.B.","contributorId":174284,"corporation":false,"usgs":false,"family":"Santos","given":"Armando","email":"","middleInitial":"J.B.","affiliations":[],"preferred":false,"id":938646,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sasso, Christopher R.","contributorId":296894,"corporation":false,"usgs":false,"family":"Sasso","given":"Christopher","email":"","middleInitial":"R.","affiliations":[{"id":64230,"text":"NOAA-NMFS Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":938647,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Al Ansi, Mehsin","contributorId":356065,"corporation":false,"usgs":false,"family":"Al Ansi","given":"Mehsin","affiliations":[{"id":54794,"text":"Qatar University","active":true,"usgs":false}],"preferred":false,"id":938648,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938649,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fuentes, Mariana M.P.B.","contributorId":331394,"corporation":false,"usgs":false,"family":"Fuentes","given":"Mariana","email":"","middleInitial":"M.P.B.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":938650,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70261948,"text":"70261948 - 2024 - Sequoia groves of Yosemite: Visitor use and impacts monitoring","interactions":[],"lastModifiedDate":"2025-01-06T14:57:47.15225","indexId":"70261948","displayToPublicDate":"2024-12-22T07:51:11","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Sequoia groves of Yosemite: Visitor use and impacts monitoring","docAbstract":"

Despite being long-lived and massive, giant sequoias (Sequoiadendron giganteum (Lindl.) J. Bucholz) are susceptible to erosion given their relatively shallow root structure. Human-caused soil compaction and vegetation loss through social trails are primary drivers of erosion in giant sequoia groves, particularly for trees that are near formal trails and access roads. We develop a method to observe and quantify the near-tree impacts from park visitors and to relate the overall amount of use with ground cover impact parameters to assess whether the desired conditions of each grove are being met for the park to maintain a spectrum of recreational opportunities. We collected data on visitation, ground cover, soil compaction, and social trailing using a combination of targeted surveys and observations at the three giant sequoia groves in Yosemite National Park. The Mariposa Grove receives the most visitation, and use levels among groves were consistent with relative size and facilities available. Selected parameters for ground cover data were analyzed by comparing values within undisturbed versus trampling-disturbed subplots at both 0–2 m and 2–8 m. Exposed soil cover and compaction were generally higher in anthropogenically disturbed subplots versus undisturbed subplots, and vegetation cover was reduced in some disturbed subplots. Each grove had one surveyed tree where average soil compaction was ≥2.2 kg/cm2, which may limit root growth and impact seedling regeneration. Each of the three groves had some trees with social trail presence, yet less than 7% of mature trees within any grove were impacted by social trails, and most social trails were rated as having low impairment. Coupling soil compaction measurements and estimates of trampling-disturbed areas with mapping of social trail conditions within groves provides a general assessment of visitor-associated impacts to sequoia groves and can facilitate a relatively rapid way to track hotspot (i.e., increasingly impacted) trees over time.

","language":"English","publisher":"MDPI","doi":"10.3390/f15122256","usgsCitation":"Shiflett, S., Jenkins, J., Mattos, R., Ibsen, P.C., and Athearn, N., 2024, Sequoia groves of Yosemite: Visitor use and impacts monitoring: Forests, v. 15, no. 12, 2256, 19 p., https://doi.org/10.3390/f15122256.","productDescription":"2256, 19 p.","ipdsId":"IP-170087","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":466702,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f15122256","text":"Publisher Index Page"},{"id":465665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.6799910466818,\n 37.9097240904418\n ],\n [\n -119.6799910466818,\n 37.729468979298204\n ],\n [\n -119.43926891028742,\n 37.729468979298204\n ],\n [\n -119.43926891028742,\n 37.9097240904418\n ],\n [\n -119.6799910466818,\n 37.9097240904418\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Shiflett, Sheri A.","contributorId":347753,"corporation":false,"usgs":false,"family":"Shiflett","given":"Sheri A.","affiliations":[{"id":39509,"text":"National Park Service, Yosemite National Park","active":true,"usgs":false}],"preferred":false,"id":922376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jenkins, Jeffery S.","contributorId":347754,"corporation":false,"usgs":false,"family":"Jenkins","given":"Jeffery S.","affiliations":[{"id":38695,"text":"University of California Merced","active":true,"usgs":false}],"preferred":false,"id":922377,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mattos, Rachel F.","contributorId":347755,"corporation":false,"usgs":false,"family":"Mattos","given":"Rachel F.","affiliations":[{"id":39509,"text":"National Park Service, Yosemite National Park","active":true,"usgs":false}],"preferred":false,"id":922378,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ibsen, Peter Christian 0000-0002-3436-9100","orcid":"https://orcid.org/0000-0002-3436-9100","contributorId":260735,"corporation":false,"usgs":true,"family":"Ibsen","given":"Peter","email":"","middleInitial":"Christian","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":922379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Athearn, Nicole D.","contributorId":347757,"corporation":false,"usgs":false,"family":"Athearn","given":"Nicole D.","affiliations":[{"id":39509,"text":"National Park Service, Yosemite National Park","active":true,"usgs":false}],"preferred":false,"id":922380,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267895,"text":"70267895 - 2024 - New tools for a legacy problem: How isotope tracers inform area of concern actions in the St. Louis River in Lake Superior","interactions":[],"lastModifiedDate":"2025-06-06T14:54:41.50936","indexId":"70267895","displayToPublicDate":"2024-12-22T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21812,"text":"Journal of Great Lakes Research (JGLR)","active":true,"publicationSubtype":{"id":10}},"title":"New tools for a legacy problem: How isotope tracers inform area of concern actions in the St. Louis River in Lake Superior","docAbstract":"

Numerous mercury (Hg) sources can contribute to biological burdens within the Great Lakes, including atmospheric deposition (e.g., precipitation), non-point source land runoff (e.g., watershed), and legacy contamination. Due to these different environmental entry points, it is often difficult to ascertain if legacy Hg contamination contributes to contemporary fish consumption advisories within Areas of Concern (AOCs), as designated by the United States-Canada Great Lakes Water Quality Agreement. In this study, we aimed to assess the contributions of legacy Hg to sediments in nearshore wetland habitats and co-located prey items (dragonfly larvae and yellow perch) within the St. Louis River AOC using Hg stable isotopes. We observed that nearshore sediments had the same Hg source portfolio as previously examined main channel sites. Furthermore, this data confirmed that two major Hg sources were contributing to sediments within nearshore regions of the St. Louis River AOC: legacy and watershed Hg. The contribution of legacy Hg was estimated in biota and demonstrated that up to 64% of the Hg in fish tissue in the lower estuary (St. Louis Bay) was from legacy sources, but that this percentage declined substantially when examining upstream regions of the AOC. These data indicate the influence of legacy Hg to the food web varies spatially within the St. Louis River. We further found that watershed Hg sources are an important Hg contributor to the St. Louis River, which likely applies to other impacted and unimpacted tributaries across the Great Lakes region.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102494","usgsCitation":"Janssen, S., Hoffman, J.C., and Krabbenhoft, D.P., 2024, New tools for a legacy problem: How isotope tracers inform area of concern actions in the St. Louis River in Lake Superior: Journal of Great Lakes Research (JGLR), v. 51, no. 1, 102494, 9 p., https://doi.org/10.1016/j.jglr.2024.102494.","productDescription":"102494, 9 p.","ipdsId":"IP-170550","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":497996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102494","text":"Publisher Index Page"},{"id":490198,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","otherGeospatial":"St. Louis River in Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.25467395030219,\n 47.005307600372475\n ],\n [\n -92.25467395030219,\n 46.60173825387926\n ],\n [\n -91.04335848560507,\n 46.60173825387926\n ],\n [\n -91.04335848560507,\n 47.005307600372475\n ],\n [\n -92.25467395030219,\n 47.005307600372475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":939289,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoffman, Joel C.","contributorId":84244,"corporation":false,"usgs":false,"family":"Hoffman","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":939290,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":939291,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261770,"text":"sir20245124 - 2024 - Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","interactions":[],"lastModifiedDate":"2025-08-15T16:13:12.075619","indexId":"sir20245124","displayToPublicDate":"2024-12-20T13:41:26","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5124","displayTitle":"Iodine-129 in the Eastern Snake River Plain Aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","title":"Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22","docAbstract":"

Between the 1950s and 1980s, wastewater generated at the Idaho National Laboratory contained Iodine-129 (129I); this wastewater was discharged directly into the eastern Snake River Plain (ESRP) aquifer through a deep disposal well, unlined infiltration ponds, or leaked from distribution systems below industrial facilities. During 2021–22, the U.S. Geological Survey, in cooperation with the U.S. Department of Energy and the Idaho Department of Environmental Quality Idaho National Laboratory Oversight Program, collected groundwater samples from 64 monitoring wells in the ESRP aquifer, 6 of which are part of a multilevel monitoring system, to determine the concentration of 129I in the groundwater. These samples were analyzed by accelerator mass spectrometry as part of a long-term ongoing study to track trends and occurrences of this carcinogenic, long-lived radionuclide in the environment. Concentrations ranged from slightly above the locally determined background concentration of 5.4×10−6 picocuries per liter, to just below the U.S. Environmental Protection Agency’s maximum contaminant level of 1 picocurie per liter. Discharge of wastewater containing 129I has been discontinued to the aquifer, and long-term trends from a subset (n=15) of sampled wells show decreasing 129I concentrations over the last three decades. Concentrations of 129I in groundwater from monitoring wells near facilities at the Idaho National Laboratory are affected by episodic recharge from an ephemeral surface-water source and by the fracture-flow dominated hydrologic regime in the ESRP aquifer. The spatially focused sampling effort has also identified a low-level 129I plume that affects long-term water quality near and downgradient from the Advanced Test Reactor Complex in the southwestern part of the facility that had not been clearly defined in previous sampling efforts, although the definition of the plume is somewhat limited by available data.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245124","collaboration":"Prepared in cooperation with the U.S. Department of Energy","programNote":"DOE/ID-22262","usgsCitation":"Treinen, K.C., Trcka, A.R., Krohe, N., and Lehotsky, G., 2024, Iodine-129 in the eastern Snake River Plain aquifer at and near the Idaho National Laboratory, Idaho, 2021–22: U.S. Geological Survey Scientific Investigations Report 2024–5124 (DOE/ID 22262), 27 p., https://doi.org/10.3133/sir20245124.","productDescription":"Report: vii, 27 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-150514","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":494219,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118236.htm","linkFileType":{"id":5,"text":"html"}},{"id":465410,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245124/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5124"},{"id":465409,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5124/sir20245124.pdf","text":"Report","size":"2.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5124"},{"id":465413,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5124/sir20245124.XML"},{"id":465412,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5124/images"},{"id":465411,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UWRYR4","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets for the U.S. Geological Survey—Idaho National Laboratory groundwater and surface-water monitoring networks, v1.1"},{"id":465408,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5124/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Eastern Snake River Plain aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.973611,\n 43.591667\n ],\n [\n -112.916667,\n 43.591667\n ],\n [\n -112.916667,\n 43.540278\n ],\n [\n -112.973611,\n 43.540278\n ],\n [\n -112.973611,\n 43.591667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4250

","tableOfContents":"","publishedDate":"2024-12-20","noUsgsAuthors":false,"publicationDate":"2024-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Treinen, Kerri C. 0000-0003-0645-6810 ktreinen@usgs.gov","orcid":"https://orcid.org/0000-0003-0645-6810","contributorId":296540,"corporation":false,"usgs":true,"family":"Treinen","given":"Kerri","email":"ktreinen@usgs.gov","middleInitial":"C.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Trcka, Allison R. 0000-0001-8498-4737 atrcka@usgs.gov","orcid":"https://orcid.org/0000-0001-8498-4737","contributorId":303227,"corporation":false,"usgs":true,"family":"Trcka","given":"Allison","email":"atrcka@usgs.gov","middleInitial":"R.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":false,"id":921730,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krohe, Nick","contributorId":347442,"corporation":false,"usgs":false,"family":"Krohe","given":"Nick","email":"","affiliations":[{"id":6912,"text":"Idaho Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":921731,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lehotsky, Genene","contributorId":347443,"corporation":false,"usgs":false,"family":"Lehotsky","given":"Genene","email":"","affiliations":[{"id":6912,"text":"Idaho Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":921732,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261777,"text":"70261777 - 2024 - Ecological trade-offs associated with fuel breaks in the sagebrush ecosystem","interactions":[],"lastModifiedDate":"2024-12-23T17:00:48.679613","indexId":"70261777","displayToPublicDate":"2024-12-20T09:44:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Ecological trade-offs associated with fuel breaks in the sagebrush ecosystem","docAbstract":"

Background

Unprecedented wildfire frequency, fueled by invasive annual grasses, threatens sagebrush ecosystems. To suppress wildfire and conserve sagebrush, land management agencies have installed fuel breaks across the sagebrush biome. However, despite the potential reduction in wildfire, fuel breaks may have ecological costs. Determining an acceptable balance between risks and benefits of fuel breaks is needed to avoid accelerating sagebrush loss, annual grass invasion, and habitat degradation. To evaluate the potential for ecological trade-offs to occur, we characterized the contexts in which known fuel breaks currently exist. We synthesized spatial data on all known fuel breaks and a suite of variables that may contribute to fuel break risks and benefits, including burn probabilities, predicted fuel break effectiveness, linear infrastructure, invasive annual grass cover, soil moisture conditions that confer resistance to invasion and resilience to disturbance, and priority wildlife habitats across the sagebrush biome.

Results

We found that within the sagebrush biome, fuel breaks are generally located in areas with high burn probability and are thus positioned well to intercept potential wildfires. However, fuel breaks are also frequently positioned in areas with lower predicted fuel break effectiveness relative to the sagebrush biome overall. Fuel breaks also are spatially associated with high invasive grass cover, indicating the need to better understand the causal relationship between fuel breaks and annual invasive grasses. We also show that the fuel break network is dense within priority wildlife habitats. Dense fuel breaks within wildlife habitats may trade off wildfire protection for decreased integrity of such habitats.

Conclusions

Our analyses describe the potential for fuel breaks to invoke ecological trade-offs and show that the balance of risks and benefits differs across sagebrush ecosystems. Strategic research and actions are needed to evaluate which factors tip the balance towards maximizing wildfire suppression while minimizing risk to sensitive ecological resources.

","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-024-00334-3","usgsCitation":"Roche, M.D., Saher, D., Buchholtz, E.K., Crist, M., Shinneman, D.J., Aldridge, C.L., Brussee, B.E., Coates, P.S., Weise, C.L., and Heinrichs, J., 2024, Ecological trade-offs associated with fuel breaks in the sagebrush ecosystem: Fire Ecology, v. 20, 107, 18 p., https://doi.org/10.1186/s42408-024-00334-3.","productDescription":"107, 18 p.","ipdsId":"IP-145103","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":466703,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-024-00334-3","text":"Publisher Index Page"},{"id":465427,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://doi.org/10.1186/s42408-024-00334-3"},{"id":465441,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.40464933105906,\n 47.68825926160255\n ],\n [\n -120.82712524399327,\n 38.98548876525665\n ],\n [\n -116.566007458342,\n 34.861574632752706\n ],\n [\n -113.0521168639216,\n 34.87879900031098\n ],\n [\n -111.11535062559898,\n 36.83110418392951\n ],\n [\n -110.30926485046137,\n 34.69158883186793\n ],\n [\n -107.22899556426468,\n 33.55864330375027\n ],\n [\n -105.7874928285311,\n 33.88069041964181\n ],\n [\n -104.12987144374344,\n 41.124690075951804\n ],\n [\n -102.99792925457677,\n 47.70535579401806\n ],\n [\n -115.73449001953601,\n 45.314501829559646\n ],\n [\n -117.83550292526795,\n 47.80646424271862\n ],\n [\n -121.40464933105906,\n 47.68825926160255\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","noUsgsAuthors":false,"publicationDate":"2024-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Roche, Morgan Dake 0000-0002-2276-3944","orcid":"https://orcid.org/0000-0002-2276-3944","contributorId":345794,"corporation":false,"usgs":true,"family":"Roche","given":"Morgan","email":"","middleInitial":"Dake","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":921784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saher, D. Joanne 0000-0002-2452-2570","orcid":"https://orcid.org/0000-0002-2452-2570","contributorId":288928,"corporation":false,"usgs":false,"family":"Saher","given":"D. 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The spatial heterogeneity of soil nutrients is crucial for the water bird and whole floodplain wetland ecosystem in large lakes, and it is influenced by the dramatic water level changes and sedimentation progress in West Dongting Lake (WDL). Soil samples were collected at various soil depths along the Yuan River and Li River that feed into WDL. The concentrations of soil total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), and soil grain size were tested. The stoichiometric ratios of C, N, P, and the mean value of soil grain size (Mz) were calculated. The differences of soil TOC, TN, TP and the stoichiometric ratio at different sites and soil depths were compared. Linear regression was used to explore the relationships of Mz and nutrient concentrations, and relationships between TOC, TN, and TP. Redundancy analysis was used to explore the relationship between soil nutrients, heavy metal concentrations, and plant community diversity. The results showed that the distributions of soil TOC, TN, and TP concentrations differed across regions in west Dongting Lake along the Yuan and Li Rivers. Total organic carbon concentration differed at different sedimentation depths. Soil grain size showed negative effect with soil TOC, TN, and TP concentrations in this region. Plant community diversity correlated positively with soil TOC and negatively with Hg. West Dongting Lake was N limited despite the high wet deposition of N. It could potentially be attributed to the insufficient presence of aerobic environments for microbes during intermittent flooding of the floodplain, coupled with feeble mineralization. This study can provide valuable insights for the conservation of water bird habitats and wetland ecosystems.

","language":"English","publisher":"MDPI","doi":"10.3390/w16243674","usgsCitation":"Lin, J., Wu, Y., Peng, D., Chen, M., Peng, L., Middleton, B., and Lei, T., 2024, Spatial differences in soil nutrients along a hydrographic gradient on floodplains in Dongting Lake: Water, v. 16, no. 24, 3674, 15 p., https://doi.org/10.3390/w16243674.","productDescription":"3674, 15 p.","ipdsId":"IP-132521","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":481042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w16243674","text":"Publisher Index Page"},{"id":480985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"West Dongting Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 111.958333,\n 29.0833\n ],\n [\n 111.958333,\n 28.8\n ],\n [\n 112.333,\n 28.8\n ],\n [\n 112.333,\n 29.0833\n ],\n [\n 111.958333,\n 29.0833\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"24","noUsgsAuthors":false,"publicationDate":"2024-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Jiayi","contributorId":348836,"corporation":false,"usgs":false,"family":"Lin","given":"Jiayi","affiliations":[{"id":80251,"text":"Southern University of Science and Technology, China","active":true,"usgs":false}],"preferred":false,"id":924846,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wu, Yuanmi","contributorId":349810,"corporation":false,"usgs":false,"family":"Wu","given":"Yuanmi","affiliations":[{"id":83517,"text":"Beijing Forestry University","active":true,"usgs":false}],"preferred":false,"id":924847,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peng, Dong","contributorId":224694,"corporation":false,"usgs":false,"family":"Peng","given":"Dong","email":"","affiliations":[{"id":40912,"text":"Beijing Forestry","active":true,"usgs":false}],"preferred":false,"id":924848,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Mingzhu","contributorId":303338,"corporation":false,"usgs":false,"family":"Chen","given":"Mingzhu","email":"","affiliations":[{"id":65768,"text":"Shenzhen Landscape Institute, Shenzhen","active":true,"usgs":false}],"preferred":false,"id":924849,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Peng, Lingli","contributorId":349811,"corporation":false,"usgs":false,"family":"Peng","given":"Lingli","affiliations":[{"id":83517,"text":"Beijing Forestry University","active":true,"usgs":false}],"preferred":false,"id":924850,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Middleton, Beth 0000-0002-1220-2326","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":222689,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":924851,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lei, Ting","contributorId":245022,"corporation":false,"usgs":false,"family":"Lei","given":"Ting","affiliations":[{"id":40912,"text":"Beijing Forestry","active":true,"usgs":false}],"preferred":false,"id":924852,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255373,"text":"tm6A63 - 2024 - SUTRA— A code for simulation of saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of the version 4.0 enhancements—Freeze-thaw capability, saturation and relative-permeability relations, spatially varying properties, and enhanced budget and velocity outputs","interactions":[],"lastModifiedDate":"2024-12-20T15:08:35.83011","indexId":"tm6A63","displayToPublicDate":"2024-12-20T09:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-A63","displayTitle":"SUTRA: A Code for Simulation of Saturated-Unsaturated, Variable-Density Groundwater Flow With Solute or Energy Transport—Documentation of the Version 4.0 Enhancements—Freeze-Thaw Capability, Saturation and Relative-Permeability Relations, Spatially Varying Properties, and Enhanced Budget and Velocity Outputs","title":"SUTRA— A code for simulation of saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of the version 4.0 enhancements—Freeze-thaw capability, saturation and relative-permeability relations, spatially varying properties, and enhanced budget and velocity outputs","docAbstract":"

Version 4.0 of the Saturated-Unsaturated Transport (SUTRA) software code provides the capability to simulate the freezing and thawing of groundwater during energy transport simulations under saturated and unsaturated conditions. In addition to the types of hydrogeologic processes that SUTRA has been able to simulate in the past, this version can be used to study the effects of the freeze-thaw process on the flow and energy dynamics of hydrogeologic systems. The freeze-thaw simulation capability accounts for the latent heat of fusion and allows thermal property values to vary with changing total-water saturation, liquid-water saturation, and ice saturation. It allows the effective permeability of the porous medium to change as a result of freezing and thawing. This version also provides several user-selectable relations for the dependence of total-water saturation on fluid pressure, the dependence of liquid-water saturation on temperature during freezing and thawing, and the dependence of relative permeability on liquid saturation, as well as three user-selectable formulae for defining the bulk thermal conductivity of a mixture of solid grains, liquid water, ice, and air. For unsaturated simulations without freezing, the selectable total-water saturation relations eliminate the need for the user to program these and their associated relative-permeability functions, as had been required in previous SUTRA versions. Optional nonlinear dependence of fluid density on temperature, which covers the range from supercooled (about −50 degrees Celsius) to superheated (about 400 degrees Celsius), is also provided.

Additionally, this version makes it possible to spatially vary parameters that, in previous versions of SUTRA, were required to be spatially uniform: solid-matrix properties, adsorption parameters, and parameters for production of solute mass or energy. Spatial variation is also allowed for the newly included freeze-thaw process parameters. Additional enhancements provide (1) output of water-mass and energy budgets that include values of all component terms in the governing balance equations, and (2) output of Darcy velocities (fluid fluxes), in addition to the velocity output provided by previous SUTRA versions. These enhanced outputs allow fuller interpretation of simulation results, especially for freeze-thaw phenomena.

The set of processes simulated by this version of SUTRA are useful for studying a wide range of hydrogeologic system types, conditions, and questions. For cryohydrogeologic simulations, however, this version of the code is limited in that (1) it does not simulate thermomechanical effects of freeze-thaw, (2) pressure changes due to water density change during freezing are neglected, (3) ice saturation cannot exceed the initial porosity of the simulated medium, and (4) cryosuction, the migration of liquid water toward freezing fronts, is neglected. Furthermore, this version does not account for air flow or for water vaporization and sublimation under unsaturated conditions.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6A63","usgsCitation":"Voss, C.I., Provost, A.M., McKenzie, J.M., and Kurylyk, B.L., 2024, SUTRA—A code for simulation of saturated-unsaturated, variable-density groundwater flow with solute or energy transport—Documentation of the version 4.0 enhancements—Freeze-thaw capability, saturation and relative-permeability relations, spatially varying properties, and enhanced budget and velocity outputs: U.S. Geological Survey Techniques and Methods, book 6, chap. A63, 91 p., https://doi.org/10.3133/tm6A63.","productDescription":"Report: vii, 91 p.; Software Release","numberOfPages":"91","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097937","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":430363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/a63/tm6a63.pdf","text":"Report","size":"3.80 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-A63 PDF"},{"id":430364,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm6A63/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"TM 6-A63 HTML"},{"id":430365,"rank":4,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9OL5IYX","text":"USGS software release","linkHelpText":"- SUTRA—A model for 2D or 3D saturated-unsaturated, variable-density groundwater flow with solute or energy transport"},{"id":430366,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/06/a63/tm6a63.XML","description":"TM 6-A63 XML"},{"id":430367,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/06/a63/images/"},{"id":430362,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/a63/coverthb.jpg"}],"contact":"

Director, Earth System Processes Division
Water Resources Mission Area
U.S. Geological Survey

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2024-12-20","noUsgsAuthors":false,"publicationDate":"2024-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Voss, Clifford I. 0000-0001-5923-2752","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":211844,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":904397,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Provost, Alden M. 0000-0002-4443-1107 aprovost@usgs.gov","orcid":"https://orcid.org/0000-0002-4443-1107","contributorId":2830,"corporation":false,"usgs":true,"family":"Provost","given":"Alden","email":"aprovost@usgs.gov","middleInitial":"M.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904398,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKenzie, Jeffrey M.","contributorId":176299,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jeffrey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":904399,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kurylyk, Barret L.","contributorId":176296,"corporation":false,"usgs":false,"family":"Kurylyk","given":"Barret","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":904400,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261117,"text":"70261117 - 2024 - Using mercury and lead stable isotopes to assess mercury, lead, and trace metal source contributions to Great Salt Lake, Utah, USA","interactions":[],"lastModifiedDate":"2024-11-25T15:48:55.638642","indexId":"70261117","displayToPublicDate":"2024-12-20T08:44:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Using mercury and lead stable isotopes to assess mercury, lead, and trace metal source contributions to Great Salt Lake, Utah, USA","docAbstract":"

Great Salt Lake is a critical habitat for migratory birds that is threatened by elevated metal concentrations, including mercury (Hg) and lead (Pb), and is subject to severe hydrologic changes, such as declining lake level. When assessing metal profiles recorded in Great Salt Lake sediment, a large data gap exists regarding the sources of metals within the system, which is complicated by various source inputs to the lake and complex biogeochemistry. Here, we leverage Hg and Pb stable isotopes to track relative changes in metal source contributions to Great Salt Lake over time. Mercury and Pb concentrations increase in sediments deposited after 1920 and peak between 1965 and 1995, following closure of several local smelters and the onset of increased emission controls. The nominal associations above are confirmed via Hg stable isotopes in pre-1920 background sediments, which reflect atmospheric inputs from regional and global origin, whereas Hg and Pb stable isotopes together indicate that elevated metal concentrations in mid-late 20th century sediments reflect increased mining/smelting inputs. The observed minimal rebound towards pre-1920 Pb isotope signatures in 21st century sediments indicates that mining/smelting inputs, though reduced, remain a primary source of Pb to Great Salt Lake. In contrast, the more pronounced rebound of Hg stable isotope signatures to pre-1920 values indicate a greater contribution of atmospheric inputs of regional/global origin to current Hg inputs, though Hg concentrations are ∼10 times greater than pre-1920 background values due to global increases in atmospheric Hg concentrations or possibly slow recovery from local contamination. The importance of regional/global Hg sources to the system suggests that reductions in Hg bioaccumulation in the open water food webs of Great Salt Lake are more dependent on national and global reductions in Hg emissions and management strategies to limit methylmercury production within system. This work highlights the utility of using coupled Hg and Pb stable isotope values to assess trace metal pollution sources and pathways in aquatic systems.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2024.177374","usgsCitation":"Lopez, S.F., Janssen, S., Tate, M., Fernandez, D.P., Anderson, C.R., Armstrong, G.J., Wang, T.C., and Johnson, W.P., 2024, Using mercury and lead stable isotopes to assess mercury, lead, and trace metal source contributions to Great Salt Lake, Utah, USA: Science of the Total Environment, v. 957, 177374, 14 p., https://doi.org/10.1016/j.scitotenv.2024.177374.","productDescription":"177374, 14 p.","ipdsId":"IP-170245","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":488066,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2024.177374","text":"Publisher Index Page"},{"id":464466,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Great Salt Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.27532189699335,\n 41.764305005205784\n ],\n [\n -113.27532189699335,\n 40.550910675427446\n ],\n [\n -111.82239945429947,\n 40.550910675427446\n ],\n [\n -111.82239945429947,\n 41.764305005205784\n ],\n [\n -113.27532189699335,\n 41.764305005205784\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"957","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lopez, Samuel Francisco 0000-0002-3544-7465","orcid":"https://orcid.org/0000-0002-3544-7465","contributorId":344607,"corporation":false,"usgs":true,"family":"Lopez","given":"Samuel","email":"","middleInitial":"Francisco","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tate, Michael T. 0000-0003-1525-1219 mttate@usgs.gov","orcid":"https://orcid.org/0000-0003-1525-1219","contributorId":3144,"corporation":false,"usgs":true,"family":"Tate","given":"Michael T.","email":"mttate@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fernandez, Diego P.","contributorId":138701,"corporation":false,"usgs":false,"family":"Fernandez","given":"Diego","email":"","middleInitial":"P.","affiliations":[{"id":12499,"text":"Univ. of Utah","active":true,"usgs":false}],"preferred":false,"id":919348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Christopher R.","contributorId":346496,"corporation":false,"usgs":false,"family":"Anderson","given":"Christopher","email":"","middleInitial":"R.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":919349,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Armstrong, Grace Jane 0009-0009-8132-9011","orcid":"https://orcid.org/0009-0009-8132-9011","contributorId":332127,"corporation":false,"usgs":true,"family":"Armstrong","given":"Grace","email":"","middleInitial":"Jane","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919350,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Thomas Charng-Shuen 0009-0001-2214-4721","orcid":"https://orcid.org/0009-0001-2214-4721","contributorId":331024,"corporation":false,"usgs":true,"family":"Wang","given":"Thomas","email":"","middleInitial":"Charng-Shuen","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":919351,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, William P.","contributorId":107288,"corporation":false,"usgs":false,"family":"Johnson","given":"William","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":919352,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70262849,"text":"70262849 - 2024 - Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA","interactions":[],"lastModifiedDate":"2025-01-24T15:33:05.221746","indexId":"70262849","displayToPublicDate":"2024-12-20T08:23:15","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA","docAbstract":"

The Magnet Cove alkaline‑carbonatite complex (MCC), located in the Ouachita Mountains of south-central Arkansas in the United States, hosts an extensive variety of rare rock types and critical mineral resources with physical properties (density and magnetization) that contrast significantly with the sedimentary rocks into which they have intruded. Newly acquired ground-based gravity and magnetic data were used to develop two-dimensional and three-dimensional geophysical models of the Cretaceous-aged Magnet Cove intrusive complex. The models reveal that the MCC: (1) widens out at middle crustal depths to as much 22 km across, and may reach a depth of 20 km; (2) has a total volume (exposed and subsurface) that may be over 800 km3; (3) is likely connected at depth to other intrusions in the Arkansas alkaline province; and (4) has a geometry that is aligned with pre-existing structures such as the Reelfoot rift and the Ouachita orogenic belt, some of which were likely structurally controlled by the Precambrian crystalline basement and the continent-ocean transition zone buried beneath the Ouachita orogen. For the first time, the magnetic models of the MCC account for the presence of strong remanent magnetization. This results in a geophysical workflow necessary to accurately interpret magnetic anomalies over the much larger Arkansas alkaline province, its geologic and structural framework, and critical mineral potential.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.tecto.2024.230545","usgsCitation":"Amaral, C.M., Lamb, A., and Dumond, G., 2024, Geophysical characterization of an alkaline‑carbonatite complex using gravity and magnetic methods at Magnet Cove, Arkansas, USA: Tectonophysics, v. 893, 230545, 17 p., https://doi.org/10.1016/j.tecto.2024.230545.","productDescription":"230545, 17 p.","ipdsId":"IP-155830","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":489907,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.tecto.2024.230545","text":"Publisher Index Page"},{"id":481137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Magnet Cove","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.87450349583777,\n 34.46740461224745\n ],\n [\n -92.87450349583777,\n 34.43682956355562\n ],\n [\n -92.80310027946261,\n 34.43682956355562\n ],\n [\n -92.80310027946261,\n 34.46740461224745\n ],\n [\n -92.87450349583777,\n 34.46740461224745\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"893","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Amaral, Chelsea Morgan 0000-0003-4632-4097","orcid":"https://orcid.org/0000-0003-4632-4097","contributorId":313539,"corporation":false,"usgs":true,"family":"Amaral","given":"Chelsea","email":"","middleInitial":"Morgan","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":925001,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, Andrew P. 0000-0001-7214-516X","orcid":"https://orcid.org/0000-0001-7214-516X","contributorId":349870,"corporation":false,"usgs":false,"family":"Lamb","given":"Andrew P.","affiliations":[{"id":83523,"text":"University of Arkansas Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":925002,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dumond, Gregory 0000-0002-3296-0976","orcid":"https://orcid.org/0000-0002-3296-0976","contributorId":349871,"corporation":false,"usgs":false,"family":"Dumond","given":"Gregory","affiliations":[{"id":83523,"text":"University of Arkansas Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":925003,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261721,"text":"sir20245117 - 2024 - Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24","interactions":[],"lastModifiedDate":"2025-08-15T16:14:31.530381","indexId":"sir20245117","displayToPublicDate":"2024-12-20T08:15:47","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5117","displayTitle":"Hydrologic and Hydraulic Analyses of Silver Creek and Selected Tributaries Associated with Scott Air Force Base, Illinois, 2022–24","title":"Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24","docAbstract":"

A hydrologic model of the Silver Creek Basin in southwest Illinois, and a hydraulic model of a selected reach of Silver Creek and local tributaries on and near Scott Air Force Base, Illinois, were developed to assess the effects of temporal land-use development in the Silver Creek Basin, the potential effects of projected changes based on future precipitation, and the effects of added detention storage in selected tributaries near Scott Air Force Base. The hydrologic model consists of a total of 52 scenarios—24 scenarios for an assessment of basin-wide changes in hydrology, and 28 scenarios for the hydraulic analysis of a focus area of Silver Creek and tributaries on and near Scott Air Force Base. Scenarios were run for precipitation events of 2-year through 500-year recurrence intervals (50-percent through 0.2-percent annual exceedance probability) and 24-hour durations.

The effects of detention structures added to Silver Creek tributaries throughout Scott Air Force Base were greater on water-level profiles (about 1 to 3 feet) than the effects of projected (2050) changes in precipitation (about 1 foot or less) in these basins. The results indicated that despite the increases in water-surface elevations resulting from projected increases in precipitation, the detention structures could provide a net reduction in water-surface elevations in the flood-prone western tributaries on the base. The effects of detention structures and projected precipitation also were assessed using the mapped extent of inundation for the simulated probabilistic precipitation scenarios. As an example, limited inundation of a residential area along Ash Creek was evident in the 5-year recurrence interval event for the scenarios without detention storage, whereas the first indications of flooding in the residential area from the scenario with detention storage were in the 50-year recurrence interval event.

Changes in hydrologic conditions followed a spatial pattern similar to that of the changes in land-cover development, with the greatest changes in the downstream one-half of the Silver Creek Basin and most pronounced in subbasins on and surrounding Scott Air Force Base. There was up to an estimated 54.6-percent increase in peak streamflows in subbasins on or near Scott Air Force Base from historical (1992) to current (2019) conditions, but changes in peak streamflows of as much as 144 percent are anticipated under the planned (to about 2050) land cover plus projected (2050) precipitation. The changes in the timing of peak streamflows were towards earlier peaks, with cumulative changes between historical and projected conditions approaching 0.75 hour (45 minutes) for a 2-year recurrence interval event. Results of the percentage change in cumulative event volume were similar to those of percentage change in peak streamflows in terms of magnitude of change and temporal and spatial distribution of changes. The greatest magnitude of percentage change in the assessed hydrologic properties was associated with the 2-year recurrence interval event, and the magnitude of the percentage change decreased with increasing probabilistic event recurrence interval. Subbasins with a substantial change in runoff yield between historical and current conditions were primarily in the downstream one-half of the Silver Creek Basin and most were within or adjacent to Scott Air Force Base. The magnitude of runoff yield changes increased with recurrence interval, and maximum changes were associated with subbasins on base and with the changes between the historical and current conditions.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245117","collaboration":"Prepared in cooperation with Scott Air Force Base","usgsCitation":"Cigrand, C.V., Heimann, D.C., and Rydlund, P.H., Jr., 2024, Hydrologic and hydraulic analyses of Silver Creek and selected tributaries associated with Scott Air Force Base, Illinois, 2022–24: U.S. Geological Survey Scientific Investigations Report 2024–5117, 87 p., https://doi.org/10.3133/sir20245117.","productDescription":"Report: x, 87 p.; Data Release; 2 Datasets","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135331","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":494220,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118090.htm","linkFileType":{"id":5,"text":"html"}},{"id":465320,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GBYP2K","text":"USGS data release","linkHelpText":"Archive of hydrologic and hydraulic models used in the analyses of Silver Creek Basin and selected tributaries associated with Scott Air Force Base, Illinois, 1992–2050"},{"id":465321,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://datagateway.nrcs.usda.gov/GDGOrder.aspx","text":"U.S. Department of Agriculture, Natural Resources Conservation Service database","linkHelpText":"- GeoSpatial data gateway"},{"id":465322,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":465316,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5117/sir20245117.pdf","text":"Report","size":"97.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5117"},{"id":465317,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5117/sir20245117.XML"},{"id":465318,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5117/images/"},{"id":465315,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5117/coverthb.jpg"},{"id":465319,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245117/full"}],"country":"United States","state":"Illinois","otherGeospatial":"Scott Air Force Base, Silver Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.8831289278596,\n 38.57664573726461\n ],\n [\n -89.8831289278596,\n 38.50342611477362\n ],\n [\n -89.77601466850366,\n 38.50342611477362\n ],\n [\n -89.77601466850366,\n 38.57664573726461\n ],\n [\n -89.8831289278596,\n 38.57664573726461\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2024-12-20","noUsgsAuthors":false,"publicationDate":"2024-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Cigrand, Charles V. 0000-0002-4177-7583","orcid":"https://orcid.org/0000-0002-4177-7583","contributorId":201575,"corporation":false,"usgs":true,"family":"Cigrand","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921587,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rydlund, Paul H. Jr. 0000-0001-9461-9944 prydlund@usgs.gov","orcid":"https://orcid.org/0000-0001-9461-9944","contributorId":3840,"corporation":false,"usgs":true,"family":"Rydlund","given":"Paul","suffix":"Jr.","email":"prydlund@usgs.gov","middleInitial":"H.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921588,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70261722,"text":"fs20243053 - 2024 - The 3D Elevation Program—Supporting the Kansas Economy","interactions":[],"lastModifiedDate":"2025-08-15T16:19:16.627318","indexId":"fs20243053","displayToPublicDate":"2024-12-19T14:20:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3053","displayTitle":"The 3D Elevation Program—Supporting the Kansas Economy","title":"The 3D Elevation Program—Supporting the Kansas Economy","docAbstract":"

Introduction 

High-resolution elevation data for Kansas inform decision making to improve the State’s economy. Existing elevation data coverage is used to support State water planning initiatives, facilitate infrastructure management, and improve resilience to natural disasters. The expanding availability of current and more accurate elevation data helps better support natural resources conservation, agriculture and precision farming, flood risk management, water supply planning, infrastructure and construction management, and geologic resource assessment and hazard mitigation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Kansas. The status of available and in-progress 3DEP baseline lidar data in Kansas is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $14.41 million in new benefits annually to the State. The top nine Kansas business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.

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Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192

Email: 3DEP@usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2024-12-19","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"DeVaughan, Claire 0000-0003-2449-3658 cdevaugh@usgs.gov","orcid":"https://orcid.org/0000-0003-2449-3658","contributorId":5861,"corporation":false,"usgs":true,"family":"DeVaughan","given":"Claire","email":"cdevaugh@usgs.gov","affiliations":[],"preferred":true,"id":921589,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70263847,"text":"70263847 - 2024 - Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories","interactions":[],"lastModifiedDate":"2025-02-26T20:56:20.109414","indexId":"70263847","displayToPublicDate":"2024-12-19T13:50:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories","docAbstract":"

Reed canarygrass (Phalaris arundinacea L.) is one of the most common invaders of floodplains and wetlands in North America. In the Upper Mississippi River floodplain, invasion by reed canarygrass in forest understories can inhibit forest regeneration when gaps form in the overstory. Understanding the distribution of reed canarygrass in forest understories is essential for effective management and control. We used an ensemble of species distribution models including Bayesian additive regression trees, boosted trees, and random forest algorithms to predict habitat suitability for reed canarygrass in forest understories across the Upper Mississippi River floodplain (~41,000 ha). Data from forest inventory study plots with reed canarygrass presence and absence were combined with 10 hypothesized environmental predictors of reed canarygrass invasion. We applied three approaches to better understand and incorporate the influence of spatial autocorrelation among our predictor variables, including random cross-validation, spatial cross-validation, and spatial cross-validation with Euclidean distance fields. Flood frequency, distance to contiguous floodplain, distance to forest edge, and distance to invaded wet meadow were among the most important environmental predictors across the three algorithms. Generally, the mean probability of reed canarygrass presence decreased with increasing flood depth, distance to contiguous floodplain, distance to invaded wet meadow, forest cover, and forest height, while relationships with other predictors were more variable. The ensemble of the three models (i.e., the average prediction) was used to map and summarize potential reed canary grass habitat suitability across the landscape. The maps generated quantified the habitat suitability for reed canarygrass and areas of agreement among the models in forest understories across the floodplain. This information can be used to better understand the extent of invasion, prioritize restoration efforts, and develop further research.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.70138","usgsCitation":"Delaney, J., Van Appledorn, M., De Jager, N.R., Bouska, K.L., and Rohweder, J.J., 2024, Spatial differences in predicted Phalaris arundinacea (reed canarygrass) occurrence in floodplain forest understories: Ecosphere, v. 15, no. 12, e70138, 19 p., https://doi.org/10.1002/ecs2.70138.","productDescription":"e70138, 19 p.","ipdsId":"IP-151054","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":487690,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.70138","text":"Publisher Index Page"},{"id":482505,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River floodplain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.10323647292768,\n 46.99782038374508\n ],\n [\n -95.10323647292768,\n 39.14905192203409\n ],\n [\n -86.81143448094211,\n 39.14905192203409\n ],\n [\n -86.81143448094211,\n 46.99782038374508\n ],\n [\n -95.10323647292768,\n 46.99782038374508\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"12","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Delaney, John T. 0000-0003-1038-0265","orcid":"https://orcid.org/0000-0003-1038-0265","contributorId":255630,"corporation":false,"usgs":true,"family":"Delaney","given":"John","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":928660,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":928661,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":928662,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":928663,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261666,"text":"sir20245051 - 2024 - Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia","interactions":[],"lastModifiedDate":"2025-08-15T16:20:41.023018","indexId":"sir20245051","displayToPublicDate":"2024-12-19T13:25:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5051","displayTitle":"Improving Time of Concentration Estimates for Small Rural Watersheds in the Appalachian Plateaus Physiographic Province, West Virginia","title":"Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia","docAbstract":"

The U.S. Geological Survey (USGS), in cooperation with the West Virginia Department of Transportation, Division of Highways, compared time of concentration (Tc) and related runoff characteristics measured at four field sites in West Virginia to estimates of these values made using accepted methods. These four sites were selected to represent a range of basin size, length, and slope, and a range of estimated Tc. Instrumentation included a rain gage and a streamgage at all sites. Two streamgages, USGS station number (no.) 03159718 Grasslick Creek tributary above Interstate 77 near Fairplain, West Virginia, (referred to as Fairplain in this report) and USGS station no. 03159823 Grass Run tributary above Interstate 77 near Ripley, W. Va., (referred to as Ripley in this report) were near each other in northwestern West Virginia at the outlets of small basins with moderate slope. The largest, longest, and flattest basin in the study was upstream from USGS station no. 03190307 Hedricks Creek Tributary above US–19 near Hico, W. Va. (Hico). The final gaged basin in the study, that of USGS station no. 03197062 Cookman Fork at Interstate 79 near Wallback, W. Va., (Wallback) in central West Virginia, had a drainage area nearly as large as Hico, but the basin was more compact.

Precipitation and streamflow data were collected at the streamgages between October 2017 and July 2020. Storms were identified and classified through an iterative process relying on inspecting graphs created from the precipitation and streamflow data. Three hydrograph time metrics that represent Tc were computed for this study: time to rise, time to recede from a high point on the hydrograph to an inflection on the recession, and the time between an inflection on the hyetograph and an inflection on the recession of the hydrograph (precipitation inflection to recession inflection or PI-to-RI).

Hico had the slowest time metrics: the streamgage had an average Tc of 34 and 32 minutes for time to rise and time to recede, respectively. The time between the PI-to-RI at Hico, 38 minutes, was the longest for any of the characteristics at any of the streamgages. Wallback had the second slowest time metrics. At Wallback, average Tc for time to rise and time to recede was similar, 23 and 25 minutes, respectively. The average time between the PI-to-RI for Wallback was greater than its time to rise or time to recede, 32 minutes. At Fairplain and Ripley, time to rise was 18 and 19 minutes, time to recede was 14 and 16 minutes, and time between the PI-to-RI was 22 and 27 minutes, respectively. At Ripley, PI-to-RI and time to rise were significantly different from each other. Differences in metrics were not statistically significant (p ≤0.05) among streamgages.

At all streamgages, predictions made with the “Rational Method” were within one average standard deviation of the overall mean Tc. The Rational Method was applied following two different procedures— (1) channel geometry was estimated using professional judgment and (2) channel geometry estimates were adjusted using regional equations. The three different time metrics had an inconsistent relation with the estimates. Some of the predictions differed from individual hydrograph time metrics by more than one standard deviation. Predicted values for the 10-year storm were within the interquartile range (IQR) for 4 of 12 combinations of streamgages and time metrics. Adjusted Tc predictions were within the IQR of PI-to-RI for Fairplain and Wallback, longer than the IQR of observed PI-to-RI at Hico, and shorter than the IQR of observed PI-to-RI at Ripley. The adjusted predictions of Tc were within the IQR of time-to-rise for Hico and Fairplain and were longer than the IQR for Ripley and Wallback. At Ripley, the predictions were not within the IQR for either PI-to-RI or time to rise, but instead, were between them. These lines of evidence do not indicate large, systematic errors in Tc estimates.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245051","collaboration":"Prepared in cooperation with West Virginia Department of Transportation, Division of Highways","usgsCitation":"Messinger, T., Holmes, D.A., Scott, J.D., and Kirk, D.W., 2024, Improving time of concentration estimates for small rural watersheds in the Appalachian Plateaus physiographic province, West Virginia: U.S. Geological Survey Scientific Investigations Report 2024–5051, 34 p., https://doi.org/10.3133/sir20245051.","productDescription":"Report: vii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135179","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":494223,"rank":7,"type":{"id":36,"text":"NGMDB Index 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Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2024-12-19","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Messinger, Terence 0000-0003-4084-9298 tmessing@usgs.gov","orcid":"https://orcid.org/0000-0003-4084-9298","contributorId":2717,"corporation":false,"usgs":true,"family":"Messinger","given":"Terence","email":"tmessing@usgs.gov","affiliations":[{"id":642,"text":"West Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921352,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holmes, Darrin A.","contributorId":347318,"corporation":false,"usgs":false,"family":"Holmes","given":"Darrin","email":"","middleInitial":"A.","affiliations":[{"id":83132,"text":"West Virginia Department of Transportation, Division of Highways","active":true,"usgs":false}],"preferred":false,"id":921353,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scott, James D. 0009-0005-7221-6139","orcid":"https://orcid.org/0009-0005-7221-6139","contributorId":347319,"corporation":false,"usgs":true,"family":"Scott","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921354,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirk, Douglas W.","contributorId":347320,"corporation":false,"usgs":false,"family":"Kirk","given":"Douglas","email":"","middleInitial":"W.","affiliations":[{"id":83132,"text":"West Virginia Department of Transportation, Division of Highways","active":true,"usgs":false}],"preferred":false,"id":921355,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261724,"text":"sir20245083 - 2024 - Three-dimensional hydrogeologic framework model of the Russian River watershed, California","interactions":[],"lastModifiedDate":"2025-08-15T16:18:01.611812","indexId":"sir20245083","displayToPublicDate":"2024-12-19T11:22:58","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5083","displayTitle":"Three-Dimensional Hydrogeologic Framework Model of the Russian River Watershed, California","title":"Three-dimensional hydrogeologic framework model of the Russian River watershed, California","docAbstract":"

The Russian River watershed is in northern Sonoma County and southern Mendocino County, California, in the northern part of the California Coast Ranges. The Russian River serves as a supply for agricultural irrigation and for municipal, domestic, and commercial uses. Through a cooperative agreement with the California State Water Resources Control Board and Sonoma County Water Agency, the U.S. Geological Survey has completed studies to better understand the hydrogeologic system and develop numerical hydrologic modeling tools to evaluate and aid in managing groundwater resources. This report focuses on the development of a digital three-dimensional hydrogeologic framework model of the Russian River watershed for use in groundwater resource assessment and numerical models.

The digital three-dimensional hydrogeologic framework model of the Russian River watershed portrays the altitude, thickness, and extent of five hydrogeologic units. These five hydrogeologic units include (1) a basement unit, (2) the Sonoma Volcanics, (3) a consolidated sedimentary rock unit, (4) an unconsolidated sediment unit, and (5) channel alluvium. Model input data were compiled from published geologic maps, interpreted well data, and a model of the top of basement derived from gravity data. These data were used to construct surfaces that represent the upper and lower subsurface boundaries of each hydrogeologic unit. Top surfaces were created for the five hydrogeologic units and then stacked in three dimensions to create a solid-volume digital model.

The digital three-dimensional hydrogeologic framework model described in this report and the corresponding data represent the generalized geometry of the subsurface geologic units; the model reproduces the input geologic data with reasonable accuracy and is consistent with previously published subsurface conceptualizations of the region. The model indicates the overall geometry of the basement within the watershed and the spatial extent, altitude, and thickness of the basin-filling units. The hydrogeologic framework model is at a scale and resolution appropriate for use as the foundation for a numerical hydrologic model of the study area.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245083","collaboration":"Prepared in cooperation with the California State Water Resources Control Board and Sonoma County Water Agency","programNote":"Water Availability and Use Science Program—Water Resources Mission Area","usgsCitation":"Cromwell, G., Sweetkind, D.S., Langenheim, V.E., and Ely, C.P., 2024, Three-dimensional hydrogeologic framework model of the Russian River watershed, California: U.S. Geological Survey Scientific Investigations Report 2024–5083, 25 p., https://doi.org/10.3133/sir20245083.","productDescription":"Report: viii, 25 p.; Data Release","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-122963","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":465343,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GPH5FW","text":"USGS Data Release","description":"Fenton, N.C., Cromwell, G., Sweetkind, D.S., Langenheim, V.E., Ely, C.P., Kohel, C.A., Martin, A.J., Morita, A.Y., Peterson, M.F., and Roberts, M.A., 2022, Hydrogeologic data of the Russian River watershed, Sonoma and Mendocino Counties, California (ver. 1.1, July 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9GPH5FW.","linkHelpText":"Hydrogeologic data of the Russian River watershed, Sonoma and Mendocino Counties, California (ver. 1.1, July 2023)"},{"id":465348,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5083/images"},{"id":465346,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5083/sir20245083.xml","linkFileType":{"id":8,"text":"xml"}},{"id":465347,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245083/full"},{"id":494221,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118084.htm"},{"id":465345,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5083/sir20245083.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":465344,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5083/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Russian River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.25,\n 39.25\n ],\n [\n -123.25,\n 38.333\n ],\n [\n -122.667,\n 38.333\n ],\n [\n -122.667,\n 39.25\n ],\n [\n -123.25,\n 39.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2024-12-19","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Cromwell, Geoffrey 0000-0001-8481-405X gcromwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8481-405X","contributorId":5920,"corporation":false,"usgs":true,"family":"Cromwell","given":"Geoffrey","email":"gcromwell@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921591,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweetkind, Donald S. 0000-0003-0892-4796","orcid":"https://orcid.org/0000-0003-0892-4796","contributorId":210808,"corporation":false,"usgs":true,"family":"Sweetkind","given":"Donald S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":921592,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213 zulanger@usgs.gov","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":151042,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","email":"zulanger@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921593,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ely, Christopher P. 0000-0001-5276-5046","orcid":"https://orcid.org/0000-0001-5276-5046","contributorId":219282,"corporation":false,"usgs":true,"family":"Ely","given":"Christopher P.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":921594,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261746,"text":"70261746 - 2024 - Mapping river flow from thermal images in approximately real time: Proof of concept on the Sacramento River, California, USA","interactions":[],"lastModifiedDate":"2024-12-20T16:58:23.798064","indexId":"70261746","displayToPublicDate":"2024-12-19T10:41:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Mapping river flow from thermal images in approximately real time: Proof of concept on the Sacramento River, California, USA","docAbstract":"

Image velocimetry has become an effective method of mapping flow conditions in rivers, but this analysis is typically performed in a post-processing mode after data collection is complete. In this study, we evaluated the potential to infer flow velocities in approximately real time as thermal images are being acquired from an uncrewed aircraft system (UAS). The sensitivity of thermal image velocimetry to environmental conditions was quantified by conducting 20 flights over four days and assessing the accuracy of image-derived velocity estimates via comparison to direct field measurements made with an acoustic Doppler current profiler (ADCP). This analysis indicated that velocity mapping was most reliable when the air was cooler than the water. We also introduced a workflow for River Velocity Measurement in Approximately Real Time (RiVMART) that involved transferring brief image sequences from the UAS to a ground station as distinct data packets. The resulting velocity fields were as accurate as those generated via post-processing. A new particle image velocimetry (PIV) algorithm based on staggered image sequences increased the number of image pairs available for a given image sequence duration and slightly improved accuracy relative to a standard PIV implementation. Direct, automated geo-referencing of image-derived velocity vectors based on information on the position and orientation of the UAS acquired during flight led to poor alignment with vectors that were geo-referenced manually by selecting ground control points from an orthophoto. This initial proof-of-concept investigation suggests that our workflow could enable highly efficient characterization of flow fields in rivers and might help support applications that require rapid response to changing conditions.

","language":"English","publisher":"MDPI","doi":"10.3390/rs16244746","usgsCitation":"Legleiter, C.J., Kinzel, P.J., Dille, M., Vespignani, M., Wong, U., Anderson, I.E., Hyde, E., Gazoorian, C.L., and Cramer, J.M., 2024, Mapping river flow from thermal images in approximately real time: Proof of concept on the Sacramento River, California, USA: Remote Sensing, v. 16, no. 24, 4746, 32 p., https://doi.org/10.3390/rs16244746.","productDescription":"4746, 32 p.","ipdsId":"IP-170700","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":466704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16244746","text":"Publisher Index Page"},{"id":465404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.98941342940537,\n 39.5356924740195\n ],\n [\n -122.01174409601143,\n 39.5356924740195\n ],\n [\n -122.01174409601143,\n 39.51821864106586\n ],\n [\n -121.98941342940537,\n 39.51821864106586\n ],\n [\n -121.98941342940537,\n 39.5356924740195\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"24","noUsgsAuthors":false,"publicationDate":"2024-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - 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2024 - The 3D Elevation Program—Supporting the Texas economy","interactions":[],"lastModifiedDate":"2025-08-15T16:25:59.587871","indexId":"fs20243054","displayToPublicDate":"2024-12-19T10:15:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3054","displayTitle":"The 3D Elevation Program—Supporting the Texas Economy","title":"The 3D Elevation Program—Supporting the Texas economy","docAbstract":"

Introduction 

High-resolution elevation data for Texas inform decision making to improve the State’s economy. Existing elevation data coverage is used to improve resiliency to natural disasters, manage energy infrastructure, and assess natural resources. The expanding availability of current and more accurate elevation data helps better support natural resources conservation, agriculture and precision farming, flood risk management, infrastructure and construction management, geologic resource assessment and hazard mitigation, coastal zone management, and identification of features of interest or concern, such as archaeological and historic sites. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey (USGS) in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Texas. The status of available and in-progress 3DEP baseline lidar data in Texas is shown in figure 1. 3DEP baseline lidar data include quality level 2 or better, 1-meter or better digital elevation models, and lidar point clouds, and must meet the Lidar Base Specification version 1.2 (https://www.usgs.gov/3dep/lidarspec) or newer requirements. The National Enhanced Elevation Assessment identified user requirements and conservatively estimated that availability of lidar data would result in at least $53.1 million in new benefits annually to the State. The top 10 Texas business uses for 3D elevation data, which are based on the estimated annual conservative benefits of 3DEP, are shown in table 2.

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\"}}]}","contact":"

Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192

Email: 3DEP@usgs.gov

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Monitoring animal body condition can provide insight on population responses to environmental change. Pacific walruses (Odobenus rosmarus divergens) are experiencing loss of their sea ice habitat which has decreased the time that females spend foraging during a critical period of pregnancy and lactation. Here we investigate the potential for body condition to track demographic change and be monitored via two-dimensional aerial imagery by (1) examining whether walrus somatic growth and body mass data tracked estimated historic demographic changes, (2) collecting morphometric and body mass data and aerial imagery of walruses in human care to determine if sex, age group, and body size and condition can be determined from imagery, and (3) examining aerial imagery from a large coastal haulout used primarily by females and young to estimate potential sample sizes of measurable walruses. Body mass and growth in body length decreased between the last 1970s and early 1980s concurrent with a period when the population apparently approached carrying capacity and subsequently declined. Measures from aerial imagery (1) accurately distinguished reproductive age females from subadults and adult males and (2) enabled body mass estimates with 6-7% error using either areal footprint or a combination of length and width. We found a mean of 216 ± 77 walruses appropriately positioned for measurement from aerial surveys of the haulout enabling measurements of ≥7000 individuals annually via repeated daily imagery. Our results suggest that body mass of reproductive age females and growth of dependent young may be useful indicators to augment monitoring of the Pacific walrus population and can be achieved via non-invasive aerial imagery collections.

","language":"English","publisher":"Inter-Research","doi":"10.3354/meps14738","usgsCitation":"Rode, K.D., Fischbach, A.S., Synnott, M., Stewart, J., Northcraft, N., Allen, E., Trotto, K., Vancsok, C., Issenjou, N., Ploof, S., Rager, S., DiRocco, S., Owens, S., and Prahl, A., 2024, Photogrammetry-based body condition for monitoring an Arctic marine mammal experiencing habitat loss: Marine Ecology Progress Series, v. 751, p. 211-227, https://doi.org/10.3354/meps14738.","productDescription":"17 p.","startPage":"211","endPage":"227","ipdsId":"IP-167535","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":466705,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps14738","text":"Publisher Index Page"},{"id":465444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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