Managing species in a rapidly changing climate requires knowledge of how species will respond to climate change and other threats while simultaneously developing management actions to reduce threats. Amphibians are one of the most threatened taxa on earth and often serve as the ‘canary in the coalmine’ for the health of ecosystems that countless other species and humans rely on. To understand the status of and guide management for the boreal toad (Anaxyrus boreas boreas), an imperiled amphibian species in the North Central region, we coproduced several products with the Boreal Toad Conservation Team. These products included 1) reconstructed seasonal hydrology patterns for historical boreal toad breeding wetlands and high elevation watersheds in the Southern Rocky Mountain Region (SRMR) from remotely sensed data, 2) current and future predictions of drying rates for historical breeding wetlands, 3) current and future predictions on the status of the boreal toad in the SRMR, and 4) a web tool to guide management actions. While the boreal toad is considered a ‘data rich’ species given data collection efforts that span multiple decades, many amphibian species are considered ‘data poor’, meaning managers lack data on the biology, ecology, or status of the species needed to make sound decisions. To address this knowledge gap, we also quantified drying patterns across watersheds for two ‘data poor’ species in the North Central region at risk from climate change: the Great Basin spadefoot toad (Spea intermontana) and the wood frog (Lithobates sylvaticus). These new data can guide management decisions for these species by allowing managers to understand habitat changes with respect to water availability, a crucial element for amphibian survival and persistence. Together, these products demonstrate how cutting-edge technology and analytical methods can produce a range of useful information to support amphibian conservation.
","language":"English","publisher":"Nrrth Central Climate Adaptation Center","usgsCitation":"Kissel, A.M., Muths, E., Lacey, M., Popescu, V.D., Dyck, M., and Littlefield, C., 2025, A framework for guiding management decisions for amphibians in an uncertain future, 56 p.","productDescription":"56 p.","ipdsId":"IP-174467","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":486563,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/6009c26fd34e162231fb2333","linkFileType":{"id":5,"text":"html"}},{"id":501210,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Wyoming","otherGeospatial":"southern Rocky Mountain region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108,\n 41.25\n ],\n [\n -108,\n 36.5\n ],\n [\n -105,\n 36.5\n ],\n [\n -105,\n 41.25\n ],\n [\n -108,\n 41.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kissel, Amanda Marie 0000-0002-6346-7455","orcid":"https://orcid.org/0000-0002-6346-7455","contributorId":334356,"corporation":false,"usgs":true,"family":"Kissel","given":"Amanda","email":"","middleInitial":"Marie","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":938369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":245922,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":938370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lacey, Mae","contributorId":355913,"corporation":false,"usgs":false,"family":"Lacey","given":"Mae","affiliations":[{"id":13470,"text":"Conservation Science Partners","active":true,"usgs":false}],"preferred":false,"id":938371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Popescu, Viorel D.","contributorId":169697,"corporation":false,"usgs":false,"family":"Popescu","given":"Viorel","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":938372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dyck, Marissa","contributorId":355915,"corporation":false,"usgs":false,"family":"Dyck","given":"Marissa","affiliations":[{"id":16829,"text":"University of Victoria","active":true,"usgs":false}],"preferred":false,"id":938373,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Littlefield, Caitlin","contributorId":352216,"corporation":false,"usgs":false,"family":"Littlefield","given":"Caitlin","affiliations":[],"preferred":false,"id":938374,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266500,"text":"ofr20251019 - 2025 - The feasibility of using lidar-derived digital elevation models for gravity data reduction","interactions":[],"lastModifiedDate":"2025-07-07T14:15:33.584578","indexId":"ofr20251019","displayToPublicDate":"2025-05-12T08:40:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-1019","displayTitle":"The Feasibility of Using Lidar-Derived Digital Elevation Models for Gravity Data Reduction","title":"The feasibility of using lidar-derived digital elevation models for gravity data reduction","docAbstract":"Gravity data require submeter elevation accuracy for data processing, and differential global navigation satellite system (dGNSS) equipment is commonly used to acquire three-dimensional positional data to achieve such accuracy. However, lidar (light detection and ranging) data are commonly used to develop digital elevation models (DEMs) of Earth’s surface. Therefore, using elevations from lidar-derived DEMs for gravity-data acquisition and reduction may improve field efficiency and reduce cost. This study examines the feasibility of using DEMs for gravity-data reduction by comparing dGNSS elevation data from 435 gravity stations in Michigan, Wyoming, and Colorado with their respective DEM elevations. The results show that the average difference between DEM and dGNSS elevations is 13 centimeters (cm) and that 93 percent of those differences are less than 50 cm, even in areas with steep terrain. Because an elevation discrepancy of 50 cm corresponds to an error of roughly 0.1 milligals (mGal) in the simple Bouguer gravity anomaly, the results suggest that lidar-derived DEMs are a viable source for acquiring the elevation data needed to process gravity data, thus improving both the cost and efficiency of data collection for regional surveys where an accuracy of less than 1.0 mGal is desired.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251019","programNote":"Mineral Resources Program","usgsCitation":"Murchek, J.T., Drenth, B.J., Reitman, J.J., Anderson, E.D., Magnin, B.P., and DeGraff, J.M., 2025, The feasibility of using lidar-derived digital elevation models for gravity data reduction (ver. 1.1, July 2025): U.S. Geological Survey Open-File Report 2025–1019, 33 p., https://doi.org/10.3133/ofr20251019.","productDescription":"vii, 33 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-163043","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":491565,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1019/coverthb2.jpg"},{"id":491633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118572.htm"},{"id":491634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1019 PDF"},{"id":491636,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1019 XML"},{"id":491635,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251019/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1019 HTML"},{"id":491638,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2025/1019/versionHist.txt","size":"654 B","linkFileType":{"id":2,"text":"txt"}},{"id":491637,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1019/images/"}],"edition":"Version 1.0: May 12, 2025; Version 1.1: July 1, 2025","contact":"Director, Energy and Minerals Mission Area
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12201 Sunrise Valley Drive
Reston, VA 20192-0002
Throughout the history of oil and gas production in the United States, millions of wells have been drilled for exploration and energy production. Hundreds of thousands of unplugged wells are no longer actively producing and are currently under orphan status, with no responsible party obligated for plugging. Orphan wells can pose threats to water resources by providing pathways for contaminants such as hydrocarbons and brines to migrate into water-supply aquifers. In this study, we investigate the potential threats to groundwater resources posed by orphan wells at the national scale. Water-quality data is extremely sparse in relation to orphan wells nationally and may not be suitable for identifying contamination from oil and gas development. We used geospatial and statistical methods to evaluate which principal and secondary aquifer systems may be most susceptible to contamination from orphan wells. Analysis involved three sets of susceptibility factors including: 1) factors related to the number and density of orphan wells; 2) factors that can threaten well integrity and contribute to transport of contaminants; and 3) factors related to groundwater withdrawal rates and the affected populations/communities in the event of water quality disturbances. From a dataset of 117,672 documented orphan wells, 64,203 fall within a principal aquifer system, while the remainder fall within a secondary aquifer system.
By assessing the combination of well integrity and hydrogeologic factors within these aquifer systems, five groupings of principal aquifers were identified, where groups ranged from aquifer systems with high numbers of orphan wells, multiple well integrity threats and high withdrawals, to aquifers with a relatively low number of orphan wells, limited well integrity threats and minimal water use. Three regions of the country emerge containing aquifers with higher susceptibility to contamination from orphan oil and gas wells. These regions include 1) The Appalachian Basin (including the Pennsylvanian Aquifer System), 2) The Gulf Coast Aquifers (including the Coastal Lowlands Aquifer system) and 3) The California Aquifers (including the California Coastal Basin Aquifer system). This work is the first multivariate geospatial investigation of orphan wells and groundwater resources on a national scale, and sheds light on which aquifers are most susceptible to groundwater contamination from orphan wells.
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ngianoutsos@usgs.gov","orcid":"https://orcid.org/0000-0002-6510-6549","contributorId":3607,"corporation":false,"usgs":true,"family":"Gianoutsos","given":"Nicholas","email":"ngianoutsos@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":933139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jahn, Kalle 0000-0002-4976-0137","orcid":"https://orcid.org/0000-0002-4976-0137","contributorId":333053,"corporation":false,"usgs":true,"family":"Jahn","given":"Kalle","email":"","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gutchess, Kristina 0000-0002-9745-5049","orcid":"https://orcid.org/0000-0002-9745-5049","contributorId":353190,"corporation":false,"usgs":true,"family":"Gutchess","given":"Kristina","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933141,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70271374,"text":"70271374 - 2025 - Global methane budget 2000-2020","interactions":[],"lastModifiedDate":"2025-09-10T14:32:19.040545","indexId":"70271374","displayToPublicDate":"2025-05-09T09:24:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Global methane budget 2000-2020","docAbstract":"Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020.
The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches.
For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters.
The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified.
For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget.
We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.
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State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":948309,"contributorType":{"id":1,"text":"Authors"},"rank":66},{"text":"Zhu, Qing","contributorId":260547,"corporation":false,"usgs":false,"family":"Zhu","given":"Qing","affiliations":[],"preferred":false,"id":948310,"contributorType":{"id":1,"text":"Authors"},"rank":67},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false},{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false}],"preferred":false,"id":948311,"contributorType":{"id":1,"text":"Authors"},"rank":68},{"text":"Zhuang, Qianlai","contributorId":207137,"corporation":false,"usgs":false,"family":"Zhuang","given":"Qianlai","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":948312,"contributorType":{"id":1,"text":"Authors"},"rank":69}]}} ,{"id":70266225,"text":"tm7C29 - 2025 - Bayesian mapping of regionally grouped, sparse, univariate earth science data","interactions":[],"lastModifiedDate":"2025-05-12T15:26:22.543681","indexId":"tm7C29","displayToPublicDate":"2025-05-08T12:05:00","publicationYear":"2025","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":"7-C29","displayTitle":"Bayesian Mapping of Regionally Grouped, Sparse, Univariate Earth Science Data","title":"Bayesian mapping of regionally grouped, sparse, univariate earth science data","docAbstract":"Some earth science data are naturally grouped by region, and it is often desirable to map these data by region. However, if there are only a few samples within each region, then the map should be smoothed in an appropriate way to mitigate the problems that arise from having only a few samples. A smoothing algorithm based on a Bayesian hierarchical model is developed and presented in this report. This algorithm has several features that make it especially suitable for mapping earth science data: it can account for measurements that are censored, it can process multiple datasets with different measurement errors and different censoring thresholds, and it can calculate the uncertainty in any statistic that is mapped. The algorithm is demonstrated by mapping gold concentrations that are measured in streambed sediments in the Taylor Mountains quadrangle in southwestern Alaska.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7: Bayesian Mapping of Regionally Grouped, Sparse, Univariate Earth Science Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm7C29","programNote":"Mineral Resources Program","usgsCitation":"Ellefsen, K.J., Wang, B., and Goldman, M.A., 2025, Bayesian mapping of regionally grouped, sparse, univariate earth science data: U.S. Geological Survey Techniques and Methods, book 7, chap. C29, 20 p., https://doi.org/10.3133/tm7C29.","productDescription":"iv, 20 p.","onlineOnly":"Y","ipdsId":"IP-148248","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":485233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c29/coverthb2.jpg"},{"id":485714,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm7C29/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 7C29"},{"id":485567,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/07/c29/tm7c29.xml"},{"id":485566,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/07/c29/images"},{"id":485235,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P14X4CKG","text":"USGS software release","linkHelpText":"Software for Bayesian mapping of regionally grouped, sparse, univariate earth science data (program BMRGSU)"},{"id":485234,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c29/tm7c29.pdf","text":"Report","size":"9.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7C29"}],"country":"United States","state":"Alaska","otherGeospatial":"Taylor Mountains quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159,\n 61\n ],\n [\n -159,\n 60\n ],\n [\n -156,\n 60\n ],\n [\n -156,\n 61\n ],\n [\n -159,\n 61\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
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Emerging wildlife pathogens often display geographic variability due to landscape heterogeneity. Modeling approaches capable of learning complex, non-linear spatial dynamics of diseases are needed to rigorously assess and mitigate the effects of pathogens on wildlife health and biodiversity. We propose a novel machine learning (ML)-guided approach that leverages prior physical knowledge of ecological systems, using partial differential equations. We present our approach, taking advantage of the universal function approximation property of neural networks for flexible representation of the underlying dynamics of the geographic spread and growth of wildlife diseases. We demonstrate the benefits of our approach by comparing its forecasting power with commonly used methods and highlighting the obtained insights on disease dynamics. Additionally, we show the theoretical guarantees for the approximation error of our model. We illustrate the implementation of our ML-guided approach using data from white-nose syndrome (WNS) outbreaks in bat populations across the US. WNS is an infectious fungal disease responsible for significant declines in bat populations. Our results on WNS are useful for disease surveillance and bat conservation efforts. Our methods can be broadly used to assess the effects of environmental and anthropogenic drivers impacting wildlife health and biodiversity.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/eds.2025.3","usgsCitation":"Reyes, J., Oh, G., McGahan, I., Ma, T., Russell, R., Walsh, D.P., and Zhu, J., 2025, Learning complex spatial dynamics of wildlife diseases with machine learning-guided partial differential equations: Environmental Data Science, v. 4, e28, 23 p., https://doi.org/10.1017/eds.2025.3.","productDescription":"e28, 23 p.","ipdsId":"IP-160182","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490167,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/eds.2025.3","text":"Publisher Index Page"},{"id":489410,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":356170,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oh, Gina","contributorId":333634,"corporation":false,"usgs":false,"family":"Oh","given":"Gina","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian","contributorId":333637,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ma, Ting Fung","contributorId":356257,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":938923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russell, Robin 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":333621,"corporation":false,"usgs":false,"family":"Russell","given":"Robin","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938925,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zhu, Jun","contributorId":356177,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938926,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266525,"text":"70266525 - 2025 - Marginalizing time in habitat selection and species distribution models improves inference","interactions":[],"lastModifiedDate":"2025-05-09T15:11:57.95249","indexId":"70266525","displayToPublicDate":"2025-05-08T08:01:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Marginalizing time in habitat selection and species distribution models improves inference","docAbstract":"Aim
Recent methodological advances for studying how animals move and use space with telemetry data have focused on fine-scale, more mechanistic inference. However, in many cases, researchers and managers remain interested in larger scale questions regarding species distribution and habitat use across study areas, landscapes, or seasonal ranges. Point processes offer a unified framework for many methods applied in studies of species distribution and resource selection; however, challenges remain in terms of dealing with temporal autocorrelation common in many types of telemetry data collected from animal locations.
Innovation
Space–time point processes (STPPs) have a unique property, in that marginalising time offers a connection between individual animal movement and broader point processes, yet this property has seen little attention in both statistical and applied research. In this paper, we first present some of the details of this marginalisation property and methods for applying marginalised STPPs (mSTTPs) to autocorrelated telemetry data and then apply a mSTTP in a case study on the summer space use and habitat selection of female caribou (Rangifer tarandus) in Denali National Park and Preserve, Alaska.
Main Conclusions
The case study demonstrated that an mSTPP approach can improve inference over other commonly used methods in terms of its ability to account for temporal autocorrelation and offers greater precision in parameter estimates and improved predictions of space use. As this method fits conveniently into the existing point process frameworks, it offers a practical solution to dealing with temporal autocorrelation inherent to many types of telemetry data when research questions center around broader scale patterns of animal habitat selection and space use.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.70028","usgsCitation":"Eisaguirre, J.M., Adams, L., Borg, B., and Johnson, H.E., 2025, Marginalizing time in habitat selection and species distribution models improves inference: Diversity and Distributions, v. 31, no. 5, e70028, 9 p., https://doi.org/10.1111/ddi.70028.","productDescription":"e70028, 9 p.","ipdsId":"IP-170546","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":488294,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.70028","text":"Publisher Index Page"},{"id":485648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Denali National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151.66979451782657,\n 64.15039809953939\n ],\n [\n -151.66979451782657,\n 63.132768852129516\n ],\n [\n -147.1767819339525,\n 63.132768852129516\n ],\n [\n -147.1767819339525,\n 64.15039809953939\n ],\n [\n -151.66979451782657,\n 64.15039809953939\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Eisaguirre, Joseph Michael 0000-0002-0450-8472","orcid":"https://orcid.org/0000-0002-0450-8472","contributorId":301980,"corporation":false,"usgs":true,"family":"Eisaguirre","given":"Joseph","email":"","middleInitial":"Michael","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":936464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Layne G. 0000-0001-6212-2896 ladams@usgs.gov","orcid":"https://orcid.org/0000-0001-6212-2896","contributorId":2776,"corporation":false,"usgs":true,"family":"Adams","given":"Layne G.","email":"ladams@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":936465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borg, Bridget","contributorId":173862,"corporation":false,"usgs":false,"family":"Borg","given":"Bridget","affiliations":[{"id":27306,"text":"Denali Natil Park and Preserve","active":true,"usgs":false}],"preferred":false,"id":936466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":936467,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267683,"text":"70267683 - 2025 - Interpreting a sudden population decline in a long-lived species (Malaclemys terrapin rhizophorarum)","interactions":[],"lastModifiedDate":"2025-05-29T15:03:15.907361","indexId":"70267683","displayToPublicDate":"2025-05-07T07:57:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Interpreting a sudden population decline in a long-lived species (Malaclemys terrapin rhizophorarum)","docAbstract":"Long-term ecological studies are critical for providing insight into population dynamics and detecting population declines, particularly for species of conservation concern. However, spatiotemporal variation and logistical challenges make the identification of sudden population declines difficult. We conducted an in-water capture-mark-recapture study of mangrove diamond-backed terrapins (Malaclemys terrapin rhizophorarum) within Big Sable Creek, in Everglades National Park, Florida. We used an 18-year dataset (2001 to 2019) incorporating year, sex, hurricane occurrence, and sampling effort to estimate survival using Cormack–Jolly–Seber (CJS) models in Program Mark. Annual survivorship estimates were high from 2001 to 2003 for both sexes (91%–96%) and variable from 2006 to 2014 (77%–92%). Beginning in 2015, survival estimates exhibited a steeper decline (females: 65%, males 75%), and dropped to below 36% by 2018. Because the driver of this apparent population decline is unknown, we created a population projection matrix and used model-estimated annual survival to simulate annual terrapin population size. We then generated competing scenarios of low survival at various age classes to attempt to reproduce a simulated decline mirroring what we observed from our capture data. A scenario of low adult survival (75%–85%) from 2012 to 2018, possibly in conjunction with no reproduction after 2010, provides estimates of abundance that appear to match simulated annual population size and may indicate that adult emigration/human removal or a drastic drop in recruitment could be responsible for the apparent decline in survival. We explore reasons for this apparent decline and highlight difficulties common to long-term studies that may influence how declines are interpreted.
","language":"English","publisher":"British Ecological Society","doi":"10.1002/ece3.71347","usgsCitation":"Guzy, J.C., Smith, B., Denton, M., Cherkiss, M., Roche, D., Crowder, A., and Hart, K., 2025, Interpreting a sudden population decline in a long-lived species (Malaclemys terrapin rhizophorarum): Ecology and Evolution, v. 15, no. 5, e71347, 16 p., https://doi.org/10.1002/ece3.71347.","productDescription":"e71347, 16 p.","ipdsId":"IP-168394","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488447,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71347","text":"Publisher Index Page"},{"id":486731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Cape Sable, Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.29674951955056,\n 25.648529217591914\n ],\n [\n -81.29674951955056,\n 25.09922085696259\n ],\n [\n -80.79518175403075,\n 25.09922085696259\n ],\n [\n -80.79518175403075,\n 25.648529217591914\n ],\n [\n -81.29674951955056,\n 25.648529217591914\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Guzy, Jacquelyn C. 0000-0003-2648-398X","orcid":"https://orcid.org/0000-0003-2648-398X","contributorId":288520,"corporation":false,"usgs":true,"family":"Guzy","given":"Jacquelyn","email":"","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Brian J. 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":139672,"corporation":false,"usgs":false,"family":"Smith","given":"Brian J.","affiliations":[{"id":12876,"text":"Cherokee Nation Technology Solutions","active":true,"usgs":false}],"preferred":false,"id":938537,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Denton, Mathew 0000-0002-1024-3722","orcid":"https://orcid.org/0000-0002-1024-3722","contributorId":210504,"corporation":false,"usgs":true,"family":"Denton","given":"Mathew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938538,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cherkiss, Michael 0000-0002-7802-6791","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":218466,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938539,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roche, David 0000-0002-3329-2746 droche@usgs.gov","orcid":"https://orcid.org/0000-0002-3329-2746","contributorId":204332,"corporation":false,"usgs":true,"family":"Roche","given":"David","email":"droche@usgs.gov","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938540,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crowder, Andrew G.","contributorId":355985,"corporation":false,"usgs":false,"family":"Crowder","given":"Andrew G.","affiliations":[{"id":84891,"text":"Xylem Analytics","active":true,"usgs":false}],"preferred":false,"id":938541,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":218324,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938542,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266492,"text":"70266492 - 2025 - Using long-term ecological datasets to unravel the impacts of short-term meteorological disturbances on phytoplankton communities","interactions":[],"lastModifiedDate":"2025-05-08T14:12:51.567682","indexId":"70266492","displayToPublicDate":"2025-05-06T09:07:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Using long-term ecological datasets to unravel the impacts of short-term meteorological disturbances on phytoplankton communities","docAbstract":"Extreme meteorological events such as storms are increasing in frequency and intensity, but our knowledge of their impacts on aquatic ecosystems and emergent system properties is limited. Understanding the ecological impacts of storms on the dynamics of primary producers remains a challenge that needs to be addressed to assess the vulnerability of freshwater ecosystems to extreme weather conditions and climate change.
One promising approach to gain insights into storm impacts on phytoplankton community dynamics is to analyse long-term monitoring datasets. However, such an approach requires disentangling the impacts of short-term meteorological disturbances from the effects of the seasonal trajectories of meteorological conditions. To this end, we applied boosted regression tree models to phytoplankton time series from eight relatively large lakes on four continents, coupled with a procedure adapted to detect and quantify rare events.
Overall, the patterns and potential drivers we identified provide important insights into the responses of lakes to short-term meteorological events and highlight differences in the response of phytoplankton communities according to lake morphological characteristics. Our results indicated that deepened thermoclines and lake-specific combinations of drivers describing altered thermal structures caused deviations from the typical trajectories of seasonal phytoplankton succession. For shallow polymictic lakes, shifts in phytoplankton succession also depended on changes in light availability.
Overall, our study highlights the value of long-term monitoring to improve our understanding of phytoplankton sensitivity to short-term meteorological disturbances.
Warming rivers and interactions with non-native species impact salmonid species globally. Understanding how hydroclimatic conditions synergistically and independently interact with non-native species is critical for effectively managing salmonids into the future. We used a 10-year mark–recapture dataset to assess how native Yellowstone cutthroat trout (YCT) Oncorhynchus virginalis bouvieri and non-native brown trout Salmo trutta growth rates and apparent survival were affected by hydroclimatic conditions and (for YCT) the presence of brown trout in a tributary. Growth (YCT) and survival (both species across size classes) were negatively related to warming stream temperatures. Brown trout growth was positively related to increasing daily streamflow variability (a proxy for streamflow), but this variable was not included in the top YCT growth model. Density-dependent effects appeared to be non-existent (growth) or weakly positive (survival). When sympatric with brown trout, YCT displayed worse survival than allopatric YCT across environmental conditions. Broadly, we found native and non-native trout respond to different hydroclimatic conditions that shift with changing climatic conditions, and brown trout represent an additional threat to YCT survival.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2024-0211","usgsCitation":"Heinle, K., Al-Chokhachy, R., Sepulveda, A., and Verhille, C.E., 2025, Native Yellowstone cutthroat trout Oncorhynchus virginalis bouvieri growth and survival in a headwater stream primarily driven by warming stream temperatures, with non-native brown trout Salmo trutta posing an additional threat to survival: Canadian Journal of Fisheries and Aquatic Sciences, v. 82, p. 1-17, https://doi.org/10.1139/cjfas-2024-0211.","productDescription":"17 p.","startPage":"1","endPage":"17","ipdsId":"IP-168237","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":490403,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1WUVM5S","text":"USGS data release","linkHelpText":"Trout mark-recapture and stream temperature and streamflow data from Duck Creek, Montana"},{"id":486636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"East Fork Duck Creek, Henry Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.38754194568855,\n 45.95593917254632\n ],\n [\n -110.38754194568855,\n 45.80046830120395\n ],\n [\n -110.17467211725662,\n 45.80046830120395\n ],\n [\n -110.17467211725662,\n 45.95593917254632\n ],\n [\n -110.38754194568855,\n 45.95593917254632\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2025-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Heinle, Kadie B.","contributorId":355955,"corporation":false,"usgs":false,"family":"Heinle","given":"Kadie B.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":938444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Al-Chokhachy, Robert 0000-0002-2136-5098","orcid":"https://orcid.org/0000-0002-2136-5098","contributorId":222450,"corporation":false,"usgs":true,"family":"Al-Chokhachy","given":"Robert","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":938445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":938446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verhille, Christine E.","contributorId":174642,"corporation":false,"usgs":false,"family":"Verhille","given":"Christine","email":"","middleInitial":"E.","affiliations":[{"id":13461,"text":"U.C. Davis","active":true,"usgs":false}],"preferred":false,"id":938447,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267313,"text":"70267313 - 2025 - Modeling lamprey distribution using flow, geomorphology, and elevation in a terminal lake system","interactions":[],"lastModifiedDate":"2025-06-16T14:01:42.236379","indexId":"70267313","displayToPublicDate":"2025-05-06T08:31:51","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Modeling lamprey distribution using flow, geomorphology, and elevation in a terminal lake system","docAbstract":"Objective
Lampreys are an ecologically important group of fishes. Several species are imperiled and lack key distribution and habitat data. The terminal Goose Lake Basin, U.S.A. is home to two such species, the Goose Lake Lamprey, Entosphenus sp. (formally undescribed), and the Pit-Klamath Brook Lamprey, E. lethophagus. Species distribution models (SDMs) are useful for identifying key habitats; however, SDMs are subject to accuracy impairments caused by scale mismatches and spatial autocorrelation—both exacerbated by the hierarchical structure of dendritic stream networks. Our goal was to relate lamprey presence–absence to ecological drivers and predict the distribution of lampreys across the Goose Lake Basin.
Methods
Using a dataset pooling approach, we integrated count and presence–absence data from five surveys and relevant habitat variables from publicly available, geospatial datasets to build logistic regression models. To account for potential mismatches of scale, we compared three sample grains for slope and sinuosity (i.e., stream segment lengths: 250, 500, and 1,000 m), and two scales of elevation (site and watershed). We accounted for spatial autocorrelation by incorporating network-based and Euclidean spatial dependencies using a spatial stream network (SSN) modeling approach. Using the best-fit spatial and non-spatial models, we predicted basin-wide lamprey distribution.
Result
Flow, sinuosity at our largest sample grain (1,000 m), and watershed-scale elevation were positively associated with lamprey presence, whereas slope was negatively associated. The non-spatial model predicted lamprey presence among sinuous, low-gradient streams, whereas the spatial model, which identified Euclidean and flow-connected spatial relationships, predicted contiguous patches with a high probability of occurrence near areas with previously observed presences.
Conclusions
Our study revealed ecological relationships and produced an accurate basinwide SDM. Prediction and inference improved after accounting for spatial relationships across multiple scales. Developing accurate and efficient modeling strategies that incorporate the hierarchical structure inherent to stream ecosystems aids in the management and conservation of native fishes such as lampreys.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/tafafs/vnaf017","usgsCitation":"Dickey, J., Clemens, B.J., Dumelle, M., and Davis, M.J., 2025, Modeling lamprey distribution using flow, geomorphology, and elevation in a terminal lake system: Transactions of the American Fisheries Society, v. 154, no. 3, p. 322-338, https://doi.org/10.1093/tafafs/vnaf017.","productDescription":"17 p.","startPage":"322","endPage":"338","ipdsId":"IP-170580","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486248,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"154","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Dickey, Jacob C.","contributorId":350824,"corporation":false,"usgs":false,"family":"Dickey","given":"Jacob C.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":937699,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clemens, Benjamin J.","contributorId":195098,"corporation":false,"usgs":false,"family":"Clemens","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":937700,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dumelle, Michael 0000-0002-3393-5529","orcid":"https://orcid.org/0000-0002-3393-5529","contributorId":355601,"corporation":false,"usgs":false,"family":"Dumelle","given":"Michael","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":937701,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":937702,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266474,"text":"70266474 - 2025 - Failure to meet the exchangeability assumption in Bayesian multispecies occupancy models: Implications for study design","interactions":[{"subject":{"id":70266474,"text":"70266474 - 2025 - Failure to meet the exchangeability assumption in Bayesian multispecies occupancy models: Implications for study design","indexId":"70266474","publicationYear":"2025","noYear":false,"title":"Failure to meet the exchangeability assumption in Bayesian multispecies occupancy models: Implications for study design"},"predicate":"SUPERSEDED_BY","object":{"id":70272628,"text":"70272628 - 2025 - When do single-species occupancy models outperform multispecies models?","indexId":"70272628","publicationYear":"2025","noYear":false,"title":"When do single-species occupancy models outperform multispecies models?"},"id":1}],"supersededBy":{"id":70272628,"text":"70272628 - 2025 - When do single-species occupancy models outperform multispecies models?","indexId":"70272628","publicationYear":"2025","noYear":false,"title":"When do single-species occupancy models outperform multispecies models?"},"lastModifiedDate":"2025-11-26T14:27:45.5494","indexId":"70266474","displayToPublicDate":"2025-05-06T08:30:40","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19846,"text":"BioRxiv","active":true,"publicationSubtype":{"id":32}},"title":"Failure to meet the exchangeability assumption in Bayesian multispecies occupancy models: Implications for study design","docAbstract":"Bayesian hierarchical models are ubiquitous in ecology. Random effect model structures are often employed that treat individual effects as deviations from larger population-level effects. In this way individuals are assumed to be \"exchangeable\" samples. Ecologists may address this exchangeability assumption intuitively, but might in certain modeling contexts ignore it altogether, including in situations where it may have large implications for study design. Multispecies occupancy models based on detection/non-detection data are an approach that can be utilized by those tasked with monitoring rare and endangered species because most literature suggests that, compared to single species occupancy models, improved parameter estimates are assured. Yet, we illustrate through a power analysis how sampling requirements to detect experimental treatment effects vary tremendously depending on whether the species exchangeability assumption is met. The degree to which species in a community respond similarly to covariates governs the ability to accurately estimate parameters using multispecies occupancy models. Detecting small or moderate changes in occupancy resulting from habitat restoration treatments may be impossible for small datasets (e.g., < 36 sampling locations, each surveyed < 8 times) even with a paired treatment-control design if the exchangeability assumption is violated. By contrast, when the assumption is met, small effects may be confidently estimated with as few as 12 sampling locations (6 pairs) and 6-8 survey events. Often, it may be impossible to know whether the exchangeability assumption is met. The statistical power needed to accurately estimate species-specific effects using detection/non-detection multispecies occupancy models depends on the unknown values of treatment effects and whether responses by species in the community diverge. When the species exchangeability assumption is violated, and at lower levels of sampling effort, multispecies occupancy models may provide worse inference than single species occupancy models.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2025.04.30.651473","usgsCitation":"Cotterill, G.G., Keinath, D.A., and Graves, T., 2025, Failure to meet the exchangeability assumption in Bayesian multispecies occupancy models: Implications for study design: BioRxiv, preprint posted May 06, 2025, https://doi.org/10.1101/2025.04.30.651473.","productDescription":"31 p.","ipdsId":"IP-176524","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":488154,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1101/2025.04.30.651473","text":"Publisher Index Page"},{"id":485549,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-05-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Cotterill, Gavin G. 0000-0002-1408-778X","orcid":"https://orcid.org/0000-0002-1408-778X","contributorId":346534,"corporation":false,"usgs":true,"family":"Cotterill","given":"Gavin","middleInitial":"G.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":936157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keinath, Douglas A.","contributorId":274356,"corporation":false,"usgs":false,"family":"Keinath","given":"Douglas","email":"","middleInitial":"A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":936158,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":936159,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266328,"text":"sir20255030 - 2025 - Methodology for defining and compiling abandoned and active hydrocarbon well inventories","interactions":[],"lastModifiedDate":"2025-05-27T16:04:41.799878","indexId":"sir20255030","displayToPublicDate":"2025-05-05T13:15:00","publicationYear":"2025","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":"2025-5030","displayTitle":"Methodology for Defining and Compiling Abandoned and Active Hydrocarbon Well Inventories","title":"Methodology for defining and compiling abandoned and active hydrocarbon well inventories","docAbstract":"Hydrocarbon wells are not active forever; when they become permanently disused (abandoned), well infrastructure must be remediated or repurposed. Knowing which wells are abandoned is the initial and often complicated step in taking responsibility for well infrastructure. Each State creates laws and regulates hydrocarbon operations, which includes well abandonment. The existence of multiple regulating authorities means definitions of abandonment are mostly found in legal documents are broadly defined or other terms are used. This report presents a technical approach to defining hydrocarbon well abandonment using well production data and identifies abandoned hydrocarbon wells using the new definition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255030","programNote":"Energy Resources Program","usgsCitation":"Varela, B.A., and Buursink, M.L., 2025, Methodology for defining and compiling abandoned and active hydrocarbon well inventories: U.S. Geological Survey Scientific Investigations Report 2025–5030, 7 p., https://doi.org/10.3133/sir20255030.","productDescription":"iii, 7 p.","onlineOnly":"Y","ipdsId":"IP-163767","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":485406,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255030/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-5030"},{"id":485403,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5030/sir20255030.xml"},{"id":485852,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118566.htm","linkFileType":{"id":5,"text":"html"}},{"id":485402,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5030/images"},{"id":485353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5030/sir20255030.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-5030"},{"id":485352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5030/coverthb.jpg"}],"contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
Prey species adjust their behavior along human-use gradients by balancing risks from predators and humans. During hunting seasons, prey often exhibit strong antipredator responses to humans but may develop tolerance in suburban areas to exploit human-mediated resources. Additionally, areas with high human activity may offer reduced predation risk if apex predators avoid such locations. This study examined mule deer Odocoileus hemionus behavioral responses to risks from humans and their primary predators, cougars Puma concolor, contextualized by differences in risk levels between study sites, individual risk exposure, and human habituation. We framed our investigation using three non-mutually exclusive hypotheses: (H1) neutral impact, (H2) human shielding (human tolerance driven by cougar avoidance), and (H3) super-additive risk (human avoidance dominating behavior). We controlled for deer phenology and diel period, recognizing that deer behavior varies with these temporal dynamics. Spatiotemporal cougar encounter risk was quantified using GPS collar data, while spatiotemporal human encounter risk and use intensity were quantified using GPS smartphone data. Our results supported H2 and H3, emphasizing the significance of site- and individual-level variation in risk exposure and human use intensity. Deer managed cougar risk adaptively, but humans emerged as the dominant perceived risk, varying by study site. At the site with higher cougar density and lower human hunting pressure, deer exhibited antipredator responses to humans based on individual exposure to human activity, except during hunting season, when tolerance for cougars increased. Conversely, humans were the dominant risk at the site with lower cougar density and greater human hunting pressure. Deer behavior varied significantly across a gradient of human use, influenced by nuanced human presence and predation risks, which were discernible using human smartphone data.
","language":"English","publisher":"Nordic Society Oikos","doi":"10.1002/ecog.07626","usgsCitation":"Abernathy, H., Dittmer, M., Stoner, D., Kent Hersey, Schoenecker, K., Jackson, P., Engebretsen, K., Young, J., and Wittemyer, G., 2025, Dynamic riskscapes for prey: Disentangling the impact of human and cougar presence on deer behavior using GPS smartphone locations: Ecography, v. 2025, no. 8, e07626, 16 p., https://doi.org/10.1002/ecog.07626.","productDescription":"e07626, 16 p.","ipdsId":"IP-167773","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":492206,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":492489,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecog.07626","text":"Publisher Index Page"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Book Cliffs Mountain Range, Pine Valley Mountain Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.9861944832185,\n 40.12839200868561\n ],\n [\n -109.9861944832185,\n 39.165811964831306\n ],\n [\n -108.78276027284889,\n 39.165811964831306\n ],\n [\n -108.78276027284889,\n 40.12839200868561\n ],\n [\n -109.9861944832185,\n 40.12839200868561\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.03221329371398,\n 38.23924320343863\n ],\n [\n -114.03221329371398,\n 37.05942746113389\n ],\n [\n -112.56984003409535,\n 37.05942746113389\n ],\n [\n -112.56984003409535,\n 38.23924320343863\n ],\n [\n -114.03221329371398,\n 38.23924320343863\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2025","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Abernathy, Heather","contributorId":357961,"corporation":false,"usgs":false,"family":"Abernathy","given":"Heather","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":942909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dittmer, Mark","contributorId":357963,"corporation":false,"usgs":false,"family":"Dittmer","given":"Mark","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":942910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoner, David","contributorId":357965,"corporation":false,"usgs":false,"family":"Stoner","given":"David","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":942911,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kent Hersey","contributorId":357967,"corporation":false,"usgs":false,"family":"Kent Hersey","affiliations":[{"id":85569,"text":"Utah DWR","active":true,"usgs":false}],"preferred":false,"id":942912,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":942913,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jackson, Pat","contributorId":357968,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","affiliations":[{"id":85566,"text":"NDOW","active":true,"usgs":false}],"preferred":false,"id":942914,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Engebretsen, Kristin","contributorId":357969,"corporation":false,"usgs":false,"family":"Engebretsen","given":"Kristin","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":942915,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Young, Julie","contributorId":357970,"corporation":false,"usgs":false,"family":"Young","given":"Julie","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":942916,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wittemyer, George","contributorId":357971,"corporation":false,"usgs":false,"family":"Wittemyer","given":"George","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":942917,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70267285,"text":"70267285 - 2025 - Practical genetic diversity protection: an accessible framework for IUCN subpopulation and Evolutionarily Significant Unit identification","interactions":[],"lastModifiedDate":"2025-05-20T14:09:13.655275","indexId":"70267285","displayToPublicDate":"2025-05-05T09:05:07","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18754,"text":"EcoEvoRxiv","active":true,"publicationSubtype":{"id":32}},"title":"Practical genetic diversity protection: an accessible framework for IUCN subpopulation and Evolutionarily Significant Unit identification","docAbstract":"The International Union for Conservation of Nature (IUCN) sets global conservation standards, including the Red List of Threatened Species and the Green Status of Species. Recent analyses showed that genetic diversity has not been effectively considered by IUCN species assessments, despite being fundamental to species’ fitness and adaptive potential. Incorporation of genetic diversity into IUCN assessments can support its successful long-term conservation. To enhance the preservation of genetic diversity, assessments should include genetically meaningful within-species units. Subpopulations are recognized units by the IUCN for protecting natural connectivity, however infrequently evaluated. Evolutionarily Significant Units (ESUs) are currently not recognized as a formal unit by the IUCN. However, incorporating ESUs into conservation frameworks could significantly enhance our capacity to identify and protect adaptive genetic diversity. To facilitate inclusion of these units in IUCN assessments, we outline a widely applicable framework for their identification that uses non-molecular and molecular data for global accessibility.
","language":"English","publisher":"EcoEvoRxiv","doi":"10.32942/X2RK9Q","usgsCitation":"Geue, J.C., Bertola, L.D., Bloomer, P., Bruniche-Olsen, A., da Silva, J.M., DeWoody, J., Fedorca, A., Godoy, J.A., Grueber, C.E., Hunter, M., Hvilsom, C., Russo, I.M., Jensen, E.L., Kopatz, A., MacDonald, A.J., Pérez-Espona, S., Piaggio, A.J., Pierson, J., Senn, H., Segelbacher, G., Sunnucks, P., van Oosterhout, C., and Leigh, D.M., 2025, Practical genetic diversity protection: an accessible framework for IUCN subpopulation and Evolutionarily Significant Unit identification: EcoEvoRxiv, preprint posted May 05, 2025, https://doi.org/10.32942/X2RK9Q.","productDescription":"64 p.","ipdsId":"IP-175610","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":489215,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.32942/x2rk9q","text":"Publisher Index Page"},{"id":486208,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Geue, Julia C. 0000-0002-1038-8614","orcid":"https://orcid.org/0000-0002-1038-8614","contributorId":343901,"corporation":false,"usgs":false,"family":"Geue","given":"Julia","email":"","middleInitial":"C.","affiliations":[{"id":82252,"text":"Biology Department, Trent University \nPeterborough, Canada","active":true,"usgs":false}],"preferred":false,"id":937594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bertola, Laura D.","contributorId":239924,"corporation":false,"usgs":false,"family":"Bertola","given":"Laura","email":"","middleInitial":"D.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":937595,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bloomer, Paulette","contributorId":239925,"corporation":false,"usgs":false,"family":"Bloomer","given":"Paulette","email":"","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":937596,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bruniche-Olsen, Anna 0000-0002-3364-2064","orcid":"https://orcid.org/0000-0002-3364-2064","contributorId":333554,"corporation":false,"usgs":false,"family":"Bruniche-Olsen","given":"Anna","email":"","affiliations":[{"id":79924,"text":"Section for Computational and RNA Biology, Department of Biology, University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":937597,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"da Silva, Jessica M.","contributorId":290139,"corporation":false,"usgs":false,"family":"da Silva","given":"Jessica","email":"","middleInitial":"M.","affiliations":[{"id":62352,"text":"South African National Biodiversity Institute, Kirstenbosch Research Centre, Rhodes Drive, Private Bag X7, 7735 Cape Town, South Africa","active":true,"usgs":false}],"preferred":false,"id":937598,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeWoody, J. 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USA","interactions":[],"lastModifiedDate":"2025-06-03T15:34:51.475791","indexId":"70267823","displayToPublicDate":"2025-05-04T08:28:53","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Metal fingerprints of Eocene rhyolite magmas coincident with Carlin-type gold deposition in Nevada USA","docAbstract":"Eocene magmatic systems contemporaneous with world-class Carlin-type Au deposits in Nevada (USA) have been proposed by some researchers as a key ingredient for Au mineralization, though evidence conclusively demonstrating their genetic relationship remains tenuous. This study provides the first direct evidence of the pre-eruptive metal budget of volatile- and metal-charged silicic magmas coincident in time (~41 to 34 Ma) and space (within 5 km) with Carlin-type Au deposits. We characterize the pre-eruptive metal fingerprints of these diverse magmatic systems to assess their potential as sources of metals for Carlin-type Au mineralization. Metal abundances from quartz-hosted melt inclusions (Au, Te, Ag, Sb, Tl, Mo, W, Sn, As, Pb, Co, Cu, Ni, and Zn) characterized in situ by SHRIMP-RG and LA-ICP-MS represent our best (and only) estimates for the pre-eruptive metal budget in these systems. Median metal concentrations are generally within one order of magnitude of average upper crust and average continental rhyolite values. But there are two notable exceptions, with median Au contents extending >1 order of magnitude higher than average upper crust and median Cu contents ranging >1 order of magnitude lower than upper crust. Despite this, melts contain lower Au/Cu (<0.1), Au/Ag (<5), and Au/Tl (<0.3) than most ore-grade Carlin-type rock samples and quartz-hosted fluid inclusions, regardless of their age and timing relative to nearby Carlin-type Au mineralization. The metal fingerprints of these magmatic systems, de-fined both by traditional and multivariate compositional data analysis techniques, are distinct from one another. Yet none are particularly specialized, e.g., high Au/Cu, in terms of being ideal ingredients as postulated by magmatic models for Carlin-type Au mineralization. Magmatic Au contents do not appear to be correlated with rhyolite “flavors” in the way that Cu, Sn, and Nb contents are. Fluid/melt partitioning modeling and magma volume estimates support the idea that a diverse array of non-specialized silicic magmas could feasibly contribute some or potentially all of the Au, Ag, and Cu in Carlin-type systems. The compositional diversity among contemporaneous magmatic systems could possibly contribute to some of the diversity observed across Carlin-type Au districts in Nevada.","language":"English","publisher":"MDPI","doi":"10.3390/min15050479","usgsCitation":"Mercer, C.N., Babel, H., Mercer, C.M., and Hofstra, A.H., 2025, Metal fingerprints of Eocene rhyolite magmas coincident with Carlin-type gold deposition in Nevada USA: Minerals, v. 15, no. 5, 479, 29 p., https://doi.org/10.3390/min15050479.","productDescription":"479, 29 p.","ipdsId":"IP-170125","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":490665,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min15050479","text":"Publisher Index Page"},{"id":490404,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UWAW28","text":"USGS data release","linkHelpText":"Melt inclusion and mineral geochemical analyses supporting the evaluation of petrogenesis, degassing, and metallogenic potential of mid-Cenozoic rhyolite magmas in northern Nevada, USA (ver. 2.0, March 2025)"},{"id":489471,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Mercer, Celestine N. 0000-0001-8359-4147 cmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-8359-4147","contributorId":4006,"corporation":false,"usgs":true,"family":"Mercer","given":"Celestine","email":"cmercer@usgs.gov","middleInitial":"N.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":939032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Babel, Hannah R.","contributorId":356285,"corporation":false,"usgs":false,"family":"Babel","given":"Hannah R.","affiliations":[{"id":40814,"text":"University of Bergen, Norway","active":true,"usgs":false}],"preferred":false,"id":939033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mercer, Cameron Mark 0000-0003-0534-848X","orcid":"https://orcid.org/0000-0003-0534-848X","contributorId":301880,"corporation":false,"usgs":true,"family":"Mercer","given":"Cameron","email":"","middleInitial":"Mark","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":939034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hofstra, Albert H. 0000-0002-2450-1593 ahofstra@usgs.gov","orcid":"https://orcid.org/0000-0002-2450-1593","contributorId":1302,"corporation":false,"usgs":true,"family":"Hofstra","given":"Albert","email":"ahofstra@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":939035,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266346,"text":"70266346 - 2025 - One-hundred fundamental, open questions to integrate methodological approaches in lake ice research","interactions":[],"lastModifiedDate":"2025-05-05T14:19:08.992831","indexId":"70266346","displayToPublicDate":"2025-05-03T09:11:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"One-hundred fundamental, open questions to integrate methodological approaches in lake ice research","docAbstract":"The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real-time in situ monitoring, remote sensing, and modeling. The recent progress in low-power in situ sensing and data telemetry allows continuous tracing of under-ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process-based or statistical data-driven), with the recent technological advancements and integration of machine learning and hybrid process-based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less-studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross-pollination of intensive and extensive modes of study to broaden knowledge of the winter period.
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This species is listed as a threatened species under the California Endangered Species Act and has been petitioned for listing under the United States Endangered Species Act. In 2017, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, initiated a Clear Lake Hitch monitoring program to generate information annually on relative abundance and size structure. The monitoring program was organized around a conceptual life cycle diagram, focused on life stages approximately ≥1 year of age, and incorporated a probabilistic study design involving approximately 10 days of short-duration (approximately 40 minutes) gillnet sampling undertaken during daytime. This report documents monitoring program activities from 2017 to 2023 and presents the results of an evaluation of the monitoring program. The evaluation was done after the 2023 sampling event, following 6 years of implementation, which is the approximate generation cycle of Clear Lake Hitch. The results of the evaluation indicated the following: (1) gillnets used in the monitoring program were effective at capturing Clear Lake Hitch aged 1 year or more; (2) the study design was effective at generating the information needed to characterize Clear Lake Hitch relative abundance and size structure, and meaningful operational efficiencies can be obtained by implementing simple changes; and (3) future sampling can be scaled to approximately 4–7 days of effort and maintain at least 80-percent confidence in detecting at least a 25-percent change in abundance, assuming past work productivity is maintained and future data are typical of previous data.
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U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Multiple generations of spring-fed streams traversed ∼800 km2 of the Las Vegas Valley in southern Nevada between ca. 10.9 ka and 8.5 ka, depositing an extensive tufa network. The scale of this network and diversity of tufa morphologies is novel in North America and offers an opportunity to obtain quantitative paleoclimate data for the region during the early Holocene. We determined isotopic compositions and estimated past temperatures using clumped isotope data from early Holocene tufa on the valley floor (698 m) as well as tufa forming today at higher elevation in the nearby Spring Mountains at Cold Creek Spring (1856 m). Modern and fossil tufa yielded comparably low δ18O values, implying that source waters for both were derived from high-elevation winter precipitation. Clumped isotope temperatures of modern tufa average 15.8 ± 2.5 °C, aligning with mean summer temperatures of the emergent spring water, and indicate equilibrium conditions of tufa formation. The early Holocene tufa yielded similar clumped isotope temperatures, averaging 15.2 ± 3.9 °C, meaning it precipitated at temperatures that occur at much higher elevations today. The Las Vegas tufa record, combined with nearby and temporally correlative paleospring and lacustrine records, suggest that cool/wet conditions prevailed throughout the Mojave Desert during the early Holocene. These records also demonstrate that spring ecosystems responded to millennial-scale hydroclimate variations that supersede climate change driven solely by insolation. The previously unrecognized pattern of ecosystem response to hydroclimate documented here may assist in understanding climate drivers for the early Holocene and provide critical information for the fate of groundwater-dependent ecosystems in the southwestern United States.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G53174.1","usgsCitation":"Springer, K.B., Hudson, A.M., Pigati, J.S., Huntington, K.W., and Schauer, A.J., 2025, An early Holocene wet period in the southwestern United States: Geology, v. 53, no. 8, p. 631-635, https://doi.org/10.1130/G53174.1.","productDescription":"5 p.","startPage":"631","endPage":"635","ipdsId":"IP-147460","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":485440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Las Vegas Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.75,\n 36.75\n ],\n [\n -115.75,\n 36\n ],\n [\n -115,\n 36\n ],\n [\n -115,\n 36.75\n ],\n [\n -115.75,\n 36.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"8","noUsgsAuthors":false,"publicationDate":"2025-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":935810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hudson, Adam M. 0000-0002-3387-9838 ahudson@usgs.gov","orcid":"https://orcid.org/0000-0002-3387-9838","contributorId":195419,"corporation":false,"usgs":true,"family":"Hudson","given":"Adam","email":"ahudson@usgs.gov","middleInitial":"M.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":935811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":935812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huntington, Katharine W.","contributorId":195423,"corporation":false,"usgs":false,"family":"Huntington","given":"Katharine","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":935813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schauer, Andrew J.","contributorId":140713,"corporation":false,"usgs":false,"family":"Schauer","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":935814,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70272582,"text":"70272582 - 2025 - Spatially explicit capture-mark-recapture to evaluate demographic status of the Louisiana black bear","interactions":[],"lastModifiedDate":"2025-11-24T15:39:23.408333","indexId":"70272582","displayToPublicDate":"2025-05-02T08:25:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit capture-mark-recapture to evaluate demographic status of the Louisiana black bear","docAbstract":"Louisiana black bears (Ursus americanus luteolus) occur in semi-isolated fragments of bottomland hardwood forest in the lower Mississippi Alluvial Valley and were listed as threatened under the United States Endangered Species Act in 1992. A population viability analysis based on radio-telemetry and capture-mark-recapture (CMR) data collected from 2002 to 2012 revealed that the probability of bears persisting in at least 1 subpopulation was >0.999, which prompted the United States Fish and Wildlife Service to remove the Louisiana black bear from the threatened species list in 2016. A post-delisting monitoring plan was developed, which included continued noninvasive CMR surveys to monitor subpopulation trends. We used genetic CMR data based on non-invasively collected hair samples for a post-delisting assessment of the demographic status of the black bear subpopulations in Louisiana, USA. The assessment included primary range in the Tensas River Basin (TRB; 2006–2020), the Upper Atchafalaya River Basin (UARB; 2007–2020), and the Three Rivers Complex (TRC; 2014 and 2021), where bears had been reintroduced beginning in 2001, and adjacent areas of possible range expansion (i.e., secondary range). We used spatially explicit closed-population capture-recapture models to estimate abundance (N), density (D), and the realized population growth rate (λ) for female bears at TRB, UARB, and TRC. Model-averaged estimates of N for primary range at TRB ranged from 135 (95% CI = 121–149) female bears in 2006 to 140 (95% CI = 123–157) in 2020 with mean λ of 1.003 (95% CI = 0.991–1.015), which suggested a stable primary population. An estimated 56 (95% CI = 19–92) additional female bears occurred in secondary range at TRB in 2020. Model-averaged estimates of N for primary range at UARB ranged from 35 (95% CI = 28–43) females in 2007 to 33 (95% CI = 28–43) in 2020, with mean λ = 0.996 (95% CI = 0.972–1.020), again suggesting a stable population. We estimated 3 (95% CI = 0–6) additional female bears occurred in secondary range at UARB during 2020. The estimate of N at TRC was 28 (95% CI = 18–44) females in 2014 and 42 (95% CI = 19–94) in 2021. The number of bears in the secondary ranges suggests some bears may have emigrated from the primary areas and colonized secondary areas, likely facilitated by increases in bottomland hardwood forests adjacent to the core populations. The stable primary populations, the reintroduced population at TRC, and the number of bears in secondary range adjacent to TRB indicate the overall number of Louisiana black bears has increased since monitoring began.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70023","usgsCitation":"Clark, J.D., Adams, H.L., Augustine, B., Berry, J.R., Champagne, D., Davidson, M., Hanks, J., Laufenberg, J.S., and Murphy, S.M., 2025, Spatially explicit capture-mark-recapture to evaluate demographic status of the Louisiana black bear: Journal of Wildlife Management, v. 89, no. 5, e70023, 19 p., https://doi.org/10.1002/jwmg.70023.","productDescription":"e70023, 19 p.","ipdsId":"IP-167830","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":496825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Mississippi Alluvial Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.838224038886,\n 32.76704554295411\n ],\n [\n -92.00742445013995,\n 31.79517824097138\n ],\n [\n -91.838224038886,\n 30.560509464604237\n ],\n [\n -91.33801935478277,\n 30.560509464604237\n ],\n [\n -91.4335879079766,\n 31.604282104237825\n ],\n [\n -90.97400724274002,\n 32.3050125648729\n ],\n [\n -91.838224038886,\n 32.76704554295411\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Clark, Joseph D. 0000-0002-8547-8112 jclark1@usgs.gov","orcid":"https://orcid.org/0000-0002-8547-8112","contributorId":2265,"corporation":false,"usgs":true,"family":"Clark","given":"Joseph","email":"jclark1@usgs.gov","middleInitial":"D.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":950851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Heidi L.","contributorId":362940,"corporation":false,"usgs":false,"family":"Adams","given":"Heidi","middleInitial":"L.","affiliations":[{"id":54517,"text":"Louisiana Tech University","active":true,"usgs":false}],"preferred":false,"id":950852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Augustine, Ben 0000-0001-6935-6361","orcid":"https://orcid.org/0000-0001-6935-6361","contributorId":245736,"corporation":false,"usgs":true,"family":"Augustine","given":"Ben","email":"","affiliations":[{"id":49304,"text":"Department of Natural Resources, Cornell University","active":true,"usgs":false}],"preferred":false,"id":950853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Berry, John R. III","contributorId":362941,"corporation":false,"usgs":false,"family":"Berry","given":"John","suffix":"III","middleInitial":"R.","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":950854,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Champagne, Dustin","contributorId":362942,"corporation":false,"usgs":false,"family":"Champagne","given":"Dustin","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":950855,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Davidson, Maria","contributorId":139273,"corporation":false,"usgs":false,"family":"Davidson","given":"Maria","email":"","affiliations":[{"id":12717,"text":"Louisiana Department of Wildlife and Fisheries","active":true,"usgs":false}],"preferred":false,"id":950856,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanks, John","contributorId":360685,"corporation":false,"usgs":false,"family":"Hanks","given":"John","affiliations":[{"id":62256,"text":"LDWF","active":true,"usgs":false}],"preferred":false,"id":950857,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Laufenberg, Jared S.","contributorId":28899,"corporation":false,"usgs":false,"family":"Laufenberg","given":"Jared","email":"","middleInitial":"S.","affiliations":[{"id":7006,"text":"Department of Forestry, Wildlife and Fisheries, University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":950858,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murphy, Sean M. 0000-0002-9404-8878","orcid":"https://orcid.org/0000-0002-9404-8878","contributorId":346967,"corporation":false,"usgs":true,"family":"Murphy","given":"Sean","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":950859,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70263372,"text":"70263372 - 2025 - Airborne geophysical analysis to decipher salinization for coastal Louisiana","interactions":[],"lastModifiedDate":"2025-02-07T20:08:24.240308","indexId":"70263372","displayToPublicDate":"2025-05-01T13:04:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Airborne geophysical analysis to decipher salinization for coastal Louisiana","docAbstract":"Coastal Louisiana is known for saltwater intrusion that threatens wetlands, aquifers, and rivers. However, the extent of saltwater intrusion is not well understood. This study develops an innovative framework with airborne electromagnetic (AEM) data to map chloride concentration distributions for wetlands in the Mississippi River deltaic plain and Chenier plain as well as for the Mississippi River Valley alluvial aquifer (MRVA) and Chicot aquifer. Moreover, the framework maps chloride concentrations along the Mississippi River and Atchafalaya River. Key components in the framework include the establishment of resistivity-to-chloride concentration transformation, 3D resistivity architecture building through geostatistics, and the employment of a lithologic model. The transformation functions correlate AEM resistivity data with porewater salinity measurements and groundwater and river chloride samples. The results show that AEM data reliably infers soil water chloride concentrations and correlates well with the distribution of various marsh types. AEM data reveals extensive saltwater presence at depth and near the coast, originating from salt domes and the Gulf of Mexico, respectively. The saltwater upconing pattern in the Chicot aquifer is likely due to excessive groundwater withdrawals. The AEM data also confirms a distinct tongue of saltwater intruding into the Atchafalaya Basin from the Gulf. The AEM data helps to identify faults that are obscured or eroded at the surface, which appear as leaky barriers in the subsurface where dramatic changes in chloride concentration are apparent. Finally, this study uses the AEM data to infer the presence of an extensive seawater wedge in the Mississippi River and Atchafalaya River.","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2025.123215","usgsCitation":"Attia, M., Tsai, F.T., Yang, S., Minsley, B.J., and Kress, W., 2025, Airborne geophysical analysis to decipher salinization for coastal Louisiana: Water Research, v. 271, 123215, 15 p., https://doi.org/10.1016/j.watres.2025.123215.","productDescription":"123215, 15 p.","ipdsId":"IP-172321","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":489931,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2025.123215","text":"Publisher Index Page"},{"id":481808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"coastal Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.73623237534059,\n 30.56098510625779\n ],\n [\n -93.73623237534059,\n 28.997878726132328\n ],\n [\n -89.2134138791802,\n 28.997878726132328\n ],\n [\n -89.2134138791802,\n 30.56098510625779\n ],\n [\n -93.73623237534059,\n 30.56098510625779\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"271","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Attia, Michael","contributorId":350698,"corporation":false,"usgs":false,"family":"Attia","given":"Michael","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":926677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tsai, Frank T.-C.","contributorId":305938,"corporation":false,"usgs":false,"family":"Tsai","given":"Frank","email":"","middleInitial":"T.-C.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":926678,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Shuo","contributorId":350699,"corporation":false,"usgs":false,"family":"Yang","given":"Shuo","affiliations":[{"id":49206,"text":"INTERA Incorporated","active":true,"usgs":false}],"preferred":false,"id":926679,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":926680,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":926681,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70265915,"text":"cir1551 - 2025 - U.S. Geological Survey Colorado River Basin science and technology collaboration meetings on drought (2021)—Synthesis of findings","interactions":[],"lastModifiedDate":"2025-09-11T19:01:41.957549","indexId":"cir1551","displayToPublicDate":"2025-05-01T11:15:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1551","displayTitle":"U.S. Geological Survey Colorado River Basin Science and Technology Collaboration Meetings on Drought (2021)—Synthesis of Findings","title":"U.S. Geological Survey Colorado River Basin science and technology collaboration meetings on drought (2021)—Synthesis of findings","docAbstract":"Ongoing, prolonged, and severe drought and water overuse during the first two decades of the 21st century have reduced water supplies of the Colorado River Basin, with effects cascading to ecosystems and human communities throughout the basin. In June and July 2021, the U.S. Geological Survey (USGS) Colorado River Basin Actionable and Strategic Integrated Science and Technology initiative team held a series of 12 collaboration meetings with USGS scientists and managers to discuss complicated, integrated science challenges and solutions related to drought in the Colorado River Basin. These Science and Technology collaboration meetings were structured to identify challenges experienced by meeting participants when working on complex problems, explore opportunities for coproducing scientific information, and envision future collaborative programs that leverage new technology. The 12 meetings were attended by 79 USGS staff representing 43 unique affiliations (for example, USGS science centers, mission areas, and regional offices). Meeting participants submitted 865 individual responses to six general discussion prompt topics (“Challenges,” “Knowledge Gaps,” “Existing Capabilities,” “Strategies and Actions,” “Example Applications,” and “Next Steps”) using a structured online collaboration tool. However, specific questions or tasks from each general discussion prompt varied by meeting topic. Terms from the USGS Thesaurus (https://apps.usgs.gov/thesaurus/) and USGS Data Lifecycle Model (https://www.usgs.gov/data-management/data-lifecycle) were used to identify and summarize participant responses relevant to science integration, stakeholder engagement, and information management technology. From these responses, opportunities for the Colorado River Basin Actionable and Strategic Integrated Science and Technology initiative to facilitate science integration in the Colorado River Basin are highlighted in this report, including (a) pursuing specific interdisciplinary research topics that require integrating knowledge across spatial and temporal scales, (b) connecting scientists across disciplines, (c) reducing barriers to stakeholder engagement, (d) identifying new technologies, and (e) facilitating data access. Multiple strategies for designing future Science and Technology collaboration meetings are also outlined in this circular to better collect and analyze participant responses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/cir1551","usgsCitation":"Monroe, A.P., Alexander, J.S., Anderson, E.D., Anderson, P.J., Andrews, W.J., Driscoll, J.M., Frus, R.J., Hevesi, J.A., Jones, D.K., Thomas, K.A., Tillery, A.C., Torregrosa, A., and Dahm, K.G., 2025, U.S. Geological Survey Colorado River Basin science and technology collaboration meetings on drought (2021)—Synthesis of findings: U.S. Geological Survey Circular 1551, 17 p., https://doi.org/10.3133/cir1551.","productDescription":"Report: iv, 17 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-159062","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":485359,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1551/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Circular 1551"},{"id":484771,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QIBOJY","text":"USGS data release","linkHelpText":"Summary of Responses at the 2021 Colorado River Basin Science and Technology Meetings"},{"id":484770,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1551/cir1551.pdf","text":"Report","size":"3.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1551"},{"id":484769,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1551/coverthb.jpg"},{"id":485258,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1551/cir1551.xml"},{"id":485257,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1551/images"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Sonora, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.84288087645007,\n 39.71384827828035\n ],\n [\n -105.7152848621287,\n 40.31627361955492\n ],\n [\n -107.15413242417426,\n 42.58617572195294\n ],\n [\n -110.24707077090045,\n 42.71786640310043\n ],\n [\n -110.89757634299599,\n 41.77462246338027\n ],\n [\n -110.95331264260932,\n 40.79783839963443\n ],\n [\n -112.038576374738,\n 37.469561144392586\n ],\n [\n -115.98036476910397,\n 38.79317671202148\n ],\n [\n -115.44123475079675,\n 32.992451748753595\n ],\n [\n -114.82264190472011,\n 31.47874298522271\n ],\n [\n -112.27051510504592,\n 30.18537034627093\n ],\n [\n -108.23443902124117,\n 30.2194618603899\n ],\n [\n -106.42157190796223,\n 36.17948752808586\n ],\n [\n -105.84288087645007,\n 39.71384827828035\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
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Federally managed public lands and waters attract millions of visitors each year, generating significant economic benefits for surrounding communities. Accurate visitation data are crucial for guiding policy decisions and managing resources effectively. This report explores the methods employed by agencies to collect and use data on recreational visitation to Federal lands and waters. Visitation estimation practices across seven agencies are reviewed, revealing similarities such as the use of automated counters for on-site data collection, alongside differences in reporting frequencies, visit definitions, and public access to data. Emerging technologies, including social media, mobile device activity, and community science, are also evaluated for their potential to improve visitation estimation. Although these technologies offer promising opportunities, they come with challenges such as data biases, the need for calibration, costs, and privacy concerns. The report concludes with opportunities to enhance data collection, coordination, and accessibility, ensuring more efficient resource management and informed decision making.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20255022","collaboration":"Prepared in cooperation with the U.S. Department of the Interior Office of Policy Analysis and University of Washington","programNote":"Land Management Research Program","usgsCitation":"Hanson, D., Wilkins, E.J., Wood, S.A., Crowley, C., Boone, W., and Schuster, R., 2025, Monitoring recreation on federally managed lands and waters—Visitation estimation: U.S. Geological Survey Scientific Investigations Report 2025–5022, 46 p., https://doi.org/10.3133/sir20255022.","productDescription":"vii, 46 p.","onlineOnly":"Y","ipdsId":"IP-172048","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":485325,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255022/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2025-5022"},{"id":485230,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5022/sir20255022.xml"},{"id":485229,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5022/images"},{"id":485175,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5022/coverthb.jpg"},{"id":485176,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5022/sir20255022.pdf","text":"Report","size":"1.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5022"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118