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Informing and assessing conservation and control efforts frequently requires information from captures. When little is understood about spatial and temporal fish distributions, captures can be infrequent and costly. If successful management depends on effective management response, optimizing for efficiency may be the difference between success and failure. We compared per vessel hour capture efficiencies for invasive grass carp (Ctenopharyngodon idella) between two methods: electrofishing-only (electrofishing) and in combination with a trammel net (combination). Capture and effort information including 174 captures from 1853 capture attempts from 1706 total hours of effort in the Sandusky River, OH, USA from 2020–2023 was used to fit a generalized linear model. Captures were allowed to vary by river kilometer, month, and year to account for unequal capture rates and effort. Captures were offset by total vessel hours or the count of independent efforts to compare methods that prioritize detection at a single location (e.g., combination) to methods that prioritize exploiting more locations (e.g., electrofishing). Including trammel nets was intended to increase single site detection, but we found that electrofishing-only was at least 2.4x more efficient (catch per vessel hour) than when combined with a trammel net with no significant difference in catch per removal effort. Complex methods intended to increase single site detection may reduce the number of efforts completed. Therefore, overall capture efficiency and total capture numbers for rare fish may be increased through methods that prioritize per-hour efficiency.
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unresolved. For four regions of North America that include parts of the United States and Canada (Sacramento-San Joaquin River Basins, Lower Colorado River Basin, Great Lakes Region, Mid-Atlantic Region), we construct and compare classification tree models to reveal robust predictors for the establishment and ecological impact stages of freshwater fish invasion. We subsequently apply the models to identify invasive fish species in trade and conduct pathway analyses to determine which trades (aquarium, biological supply, live bait, live food, water garden) and source continents pose the greatest risk to each region. Model results differed by invasion stage and region. Across regions, establishment models shared climate-related predictors including climate match and temperature tolerance. Three of the four impact models contained prior establishment success. The greatest number of species (548) were predicted to establish in the Sacramento-San Joaquin while the fewest (5) were predicted to establish in the Mid-Atlantic. Forty species were predicted to establish in multiple regions, five of which were also predicted to have high impact. The aquarium trade and Asia supplied the most species predicted to establish. Taken together, the results highlight region-specific models, indicating no universal model predicts invasion. Climate-related and prior establishment variables were most useful to risk assessments. The regional models, and identified high-risk pathways and potential invaders, could be applied to prevent future fish invasions in North America.
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To evaluate the predictive capabilities of data-driven ensemble modeling approaches, we conducted surveys for bats and signs of bat use, such as urine and guano staining, at bridges across the southwestern United States. We developed a bat roost discovery tool using Bayesian Additive Regression Trees (BART) and evaluated the predictive ability of this model against other commonly used approaches. We found that the lack of nearby water resources was associated with a lower predicted probability of bat presence or signs of bat use at bridges. While the presence of nearby water resources was associated with higher average predicted probability of bat presence or signs of bat use, high uncertainty surrounding these estimates indicates that other factors also play a role in determining which bridge roosts bats are more likely to use. As such, our model could be particularly useful for predicting which bridges can be excluded from survey efforts due to low probability of bat presence or signs of bat use. We extrapolated our model to unsurveyed bridges across the study region and provide an interactive dashboard application interface for the exploration of these results. 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\"}}]}","volume":"60","noUsgsAuthors":false,"publicationDate":"2025-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Oram, Jacob 0009-0001-8405-529X","orcid":"https://orcid.org/0009-0001-8405-529X","contributorId":353522,"corporation":false,"usgs":false,"family":"Oram","given":"Jacob","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":934780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wray, Amy Kristine 0000-0001-9685-8308","orcid":"https://orcid.org/0000-0001-9685-8308","contributorId":334941,"corporation":false,"usgs":true,"family":"Wray","given":"Amy","email":"","middleInitial":"Kristine","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Helen Trice 0000-0001-5449-4331","orcid":"https://orcid.org/0000-0001-5449-4331","contributorId":336752,"corporation":false,"usgs":true,"family":"Davis","given":"Helen","middleInitial":"Trice","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"de Wit, Luz A.","contributorId":353942,"corporation":false,"usgs":false,"family":"de Wit","given":"Luz A.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":934783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Frick, Winifred F. 0000-0002-9469-1839","orcid":"https://orcid.org/0000-0002-9469-1839","contributorId":337076,"corporation":false,"usgs":false,"family":"Frick","given":"Winifred","email":"","middleInitial":"F.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":934784,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hoegh, Andrew B.","contributorId":166684,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":934785,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X kirvine@usgs.gov","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":2218,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn","email":"kirvine@usgs.gov","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":934786,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pollock, Patrick","contributorId":353945,"corporation":false,"usgs":false,"family":"Pollock","given":"Patrick","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":934787,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schuhmann, Andrea Nichole 0009-0005-8244-4303","orcid":"https://orcid.org/0009-0005-8244-4303","contributorId":329059,"corporation":false,"usgs":true,"family":"Schuhmann","given":"Andrea","email":"","middleInitial":"Nichole","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934788,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tousley, Frank Charles 0000-0002-6859-7558","orcid":"https://orcid.org/0000-0002-6859-7558","contributorId":304216,"corporation":false,"usgs":true,"family":"Tousley","given":"Frank","middleInitial":"Charles","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934789,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Reichert, Brian E. 0000-0002-9640-0695","orcid":"https://orcid.org/0000-0002-9640-0695","contributorId":204260,"corporation":false,"usgs":true,"family":"Reichert","given":"Brian","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934790,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70265231,"text":"70265231 - 2025 - Linking environmental variability to long-term demographic change of an endangered species using integrated population models","interactions":[],"lastModifiedDate":"2025-05-12T15:43:41.572164","indexId":"70265231","displayToPublicDate":"2025-03-21T09:21:56","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Linking environmental variability to long-term demographic change of an endangered species using integrated population models","docAbstract":"The Southeastern United States has many lakes, streams, and reservoirs that serve as important drinking water sources with recreational, agricultural, and ecological uses. However, harmful algal blooms (HABs) are becoming more common in these waters, causing health issues for humans and animals. HABs have been listed as a contaminant of emerging concern, and the magnitude, frequency, and duration of HABs appear to be increasing at the global scale. While it is well known that nutrients stimulate algae growth, it is not clear how climate change and other parameters stimulate the development of toxin production by HABs. The scientific literature describes parameters, such as storm occurrence, temperature, dissolved metals, erosion of soils, increasing length of growing season, discharge, and hydroperiod, that may affect algae growth and toxin production. Climate and hydrologic models address many of the physical and environmental parameters that influence HABs, but no climate models directly address HABs. This report compiles information from the existing literature pertaining to HABs and the modeling and forecasting of HABS. This compilation is done through the incorporation of climate change models. HAB research that involves climate change will require multiple disciplines that bring together ecologists, hydrologists, climatologists, engineers, economists, and new technology. Resource managers could use geographic data about the occurrence and distribution of HABs to develop models that identify waterbodies more vulnerable to HAB events. Development of such models will require teams capable of integrating biological, chemical, and physical factors. Model development will require additional research that can resolve anthropogenic and climate-related environmental factors to identify trends in freshwater HABs. The complexity and interconnectedness of the parameters that influence HAB occurrences will make model development challenging and require rigorous regional calibration.
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Contact Us- USGS Publications Warehouse
","tableOfContents":"The 1973 Oklahoma Groundwater Law (Oklahoma Statute §82–1020.5) requires that the Oklahoma Water Resources Board conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin. Every 20 years, the Oklahoma Water Resources Board is required to update the hydrologic investigation on which the maximum annual yield determinations were based. The maximum annual yield allocated per acre of land is used to set the equal-proportionate share pumping rate. The maximum annual yield of 5,913,600 acre-feet per year and equal-proportionate-share of 2.1 acre-feet per acre per year currently (2025) in place for the Antlers aquifer were issued by the Oklahoma Water Resources Board on February 14, 1995. Because more than 20 years have elapsed since the 1995 final order for the Antlers aquifer was issued, the U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, completed an in-depth hydrologic study that included a hydrogeologic framework and conceptual groundwater-flow model for the 1980–2022 study period.
The results of an analysis of land use, long-term climate patterns, streamflow and base-flow patterns, historical groundwater use, as well as groundwater-level fluctuations across the Antlers aquifer are described. In addition, groundwater quality was analyzed for total dissolved solids concentrations and major ions for the Antlers aquifer. An updated hydrogeologic framework was developed that included refining the aquifer boundary in Oklahoma, the creation of new potentiometric surface and saturated thickness of fresh groundwater maps, one multiple-well aquifer test, slug tests, and an analysis of lithologic logs across the aquifer. A conceptual groundwater flow model and water budget were developed by incorporating estimates of recharge from precipitation, saturated-zone evapotranspiration, streambed seepage, lateral groundwater flows, vertical leakage, and withdrawals from groundwater wells.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"In this commentary, we aim to (1) describe ways that hydrological intensification and hydrological whiplash (sub-seasonal transitions between hydrological extremes) may impact water management decision-making, (2) introduce the complexities of identifying and quantifying hydrological extreme transitions, (3) discuss the processes controlling hydrological transitions and trends in hydrological extremes through time, (4) discuss considerations involved in modeling hydrological extreme transitions, and (5) motivate additional research by suggesting priority research questions that diverge from an assumption of independence between extreme events.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70113","usgsCitation":"Hammond, J., Anderson, B., Simeone, C., Brunner, M., Munoz-Castro, E., Archfield, S.A., Magee, E., and Armitage, R., 2025, Hydrological whiplash: Highlighting the need for better understanding and quantification of sub-seasonal hydrological extreme transitions: Hydrological Processes, v. 39, no. 3, e70113, 9 p., https://doi.org/10.1002/hyp.70113.","productDescription":"e70113, 9 p.","ipdsId":"IP-174142","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":504817,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":484020,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930712,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Bailey","contributorId":352305,"corporation":false,"usgs":false,"family":"Anderson","given":"Bailey","affiliations":[{"id":40606,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":930715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Simeone, Caelan 0000-0003-3263-6452","orcid":"https://orcid.org/0000-0003-3263-6452","contributorId":221008,"corporation":false,"usgs":true,"family":"Simeone","given":"Caelan","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brunner, Manuela","contributorId":352306,"corporation":false,"usgs":false,"family":"Brunner","given":"Manuela","affiliations":[{"id":40606,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":930716,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Munoz-Castro, Eduardo","contributorId":352307,"corporation":false,"usgs":false,"family":"Munoz-Castro","given":"Eduardo","affiliations":[{"id":40606,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":930717,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":930714,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Magee, Eugene","contributorId":352308,"corporation":false,"usgs":false,"family":"Magee","given":"Eugene","affiliations":[{"id":84169,"text":"UK Centre for Ecology & Hydrology (UKCEH)","active":true,"usgs":false}],"preferred":false,"id":930718,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Armitage, Rachael","contributorId":352309,"corporation":false,"usgs":false,"family":"Armitage","given":"Rachael","affiliations":[{"id":84169,"text":"UK Centre for Ecology & Hydrology (UKCEH)","active":true,"usgs":false}],"preferred":false,"id":930719,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266530,"text":"70266530 - 2025 - A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales","interactions":[],"lastModifiedDate":"2025-05-09T14:47:14.01538","indexId":"70266530","displayToPublicDate":"2025-03-19T09:42:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales","docAbstract":"The use of quantitative real-time PCR (qPCR) to monitor pathogens is common; however, quantitative frameworks that consider the observation process, dynamics in pathogen presence, and pathogen load are lacking. This can be problematic in the early stages of disease progression, where low level detections may be treated as ‘inconclusive’ and excluded from analyses. Alternatively, a framework that accounts for imperfect detection would provide more robust inferences. To better estimate pathogen dynamics, we developed a hierarchical multi-scale dynamic occupancy hurdle model (MS-DOHM). The model used data gathered during sampling for Pseudogymnoascus destructans (Pd), the causative agent of white-nose syndrome, a fungal disease that has cause severe declines in several species of hibernating bats in North America. The model allowed us to estimate initial occupancy, colonization, persistence and prevalence of Pd at bat hibernacula. Additionally, utilizing the relationship between cycle threshold and pathogen load, we estimated pathogen detectability and modeled expected colony and bat pathogen loads. To assess the ability of MS-DOHM to estimate pathogen dynamics, we compared MS-DOHM’s results to those of a dynamic occupancy model and naïve detection/non-detection. MS-DOHM’s estimates of site-level pathogen presence were up to 11.9% higher than estimates from the dynamic occupancy model and 35.7% higher than naïve occupancy. Including prevalence and load in our modeling framework resulted in estimates of pathogen arrival that were two to three years earlier compared to the dynamic occupancy and naïve detection/non-detection, respectively. Compared to naïve values, MS-DOHM predicted greater pathogen loads on colonies; however, we found no difference between model estimates and naïve values of prevalence. While the model predicted no declines in site-level prevalence, there were instances where pathogen load decreased in colonies that had been Pd positive for longer periods of time. Our findings demonstrate that accounting for pathogen load and prevalence at multiple scales changes our understanding of Pd dynamics, potentially allowing earlier conservation intervention. Additionally, we found that accounting for pathogen load and prevalence within hibernacula and among individuals resulted in a better fitting model with greater predictive ability.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-93865-x","usgsCitation":"Gridder, J., Udell, B.J., Reichert, B., Foster, J., Kendall, W.L., Cheng, T., and Frick, W.F., 2025, A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales: Scientific Reports, v. 15, 9423, 10 p., https://doi.org/10.1038/s41598-025-93865-x.","productDescription":"9423, 10 p.","ipdsId":"IP-166127","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":490111,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-93865-x","text":"Publisher Index Page"},{"id":485644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.26363777562113,\n 49.48504400263704\n ],\n [\n -97.26363777562113,\n 32.49418643417637\n ],\n [\n -70.38881830989425,\n 32.49418643417637\n ],\n [\n -70.38881830989425,\n 49.48504400263704\n ],\n [\n -97.26363777562113,\n 49.48504400263704\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Gridder, John F.","contributorId":354814,"corporation":false,"usgs":false,"family":"Gridder","given":"John F.","affiliations":[{"id":84669,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":936476,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Udell, Bradley James 0000-0001-5225-4959","orcid":"https://orcid.org/0000-0001-5225-4959","contributorId":271174,"corporation":false,"usgs":true,"family":"Udell","given":"Bradley","email":"","middleInitial":"James","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":936477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reichert, Brian E. 0000-0002-9640-0695","orcid":"https://orcid.org/0000-0002-9640-0695","contributorId":204260,"corporation":false,"usgs":true,"family":"Reichert","given":"Brian","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":936478,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foster, Jeffery T.","contributorId":351633,"corporation":false,"usgs":false,"family":"Foster","given":"Jeffery T.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":936479,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kendall, William Louis 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":257230,"corporation":false,"usgs":false,"family":"Kendall","given":"William","email":"","middleInitial":"Louis","affiliations":[{"id":51981,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, 201 J.V.K. Wagar Building 1484 Campus Delivery, Fort Collins, CO 80523, USA","active":true,"usgs":false}],"preferred":false,"id":936480,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheng, Tina L.","contributorId":127716,"corporation":false,"usgs":false,"family":"Cheng","given":"Tina L.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":936481,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Frick, Winifred F. 0000-0002-9469-1839","orcid":"https://orcid.org/0000-0002-9469-1839","contributorId":337076,"corporation":false,"usgs":false,"family":"Frick","given":"Winifred","email":"","middleInitial":"F.","affiliations":[{"id":12591,"text":"Bat Conservation International","active":true,"usgs":false}],"preferred":false,"id":936482,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270681,"text":"70270681 - 2025 - A comprehensive freshwater mussel database for the Duck River Drainage, Tennessee","interactions":[],"lastModifiedDate":"2025-08-25T13:35:28.429518","indexId":"70270681","displayToPublicDate":"2025-03-19T09:36:42","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5373,"text":"Cooperator Science Series","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"CSS-166-2025","title":"A comprehensive freshwater mussel database for the Duck River Drainage, Tennessee","docAbstract":"We have developed a comprehensive database for freshwater mussels for the Duck River drainage in Tennessee, including its largest tributary, the Buffalo River. This database is intended to serve as an expandable template that could be applied statewide. The Duck River is one of the most biologically diverse rivers in the world, with historically over 70 mussel species, and it has been selected as a priority watershed by multiple management and conservation entities. The database for this system compiles over 7,000 mussel records, spanning 200 years, from multiple Federal, State, academic, and private entities, representing 77 native species. The database is spatially explicit and includes temporal and methodological data for each record, and notes of negative survey data were made when possible. The database can facilitate the creation of distribution maps for each species and temporal maps of species richness to show watershed-wide trends. This project addresses the present lack of a centralized mussel database in Tennessee for a critical system. It will be available to facilitate species status assessments, inform conservation planning, and serve as a model for similar databases for other Tennessee watersheds.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/css36499787","usgsCitation":"Womble, K.I., and Rosenberger, A.E., 2025, A comprehensive freshwater mussel database for the Duck River Drainage, Tennessee: Cooperator Science Series CSS-166-2025, ii, 100 p., https://doi.org/10.3996/css36499787.","productDescription":"ii, 100 p.","ipdsId":"IP-174003","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":496393,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/css36499787","text":"Publisher Index Page"},{"id":494515,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Duck River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88,\n 36.15\n ],\n [\n -88,\n 35\n ],\n [\n -86,\n 35\n ],\n [\n -86,\n 36.15\n ],\n [\n -88,\n 36.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2025-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Womble, Kristen Irwin","contributorId":360120,"corporation":false,"usgs":false,"family":"Womble","given":"Kristen","middleInitial":"Irwin","affiliations":[{"id":56209,"text":"Tennessee Tech University","active":true,"usgs":false}],"preferred":false,"id":946812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":946813,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264714,"text":"70264714 - 2025 - Specific conductance and water type as a proxy model for salinity and total dissolved solids measurements in the Upper Colorado River Basin","interactions":[],"lastModifiedDate":"2025-03-20T14:36:34.442104","indexId":"70264714","displayToPublicDate":"2025-03-19T09:32:46","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Specific conductance and water type as a proxy model for salinity and total dissolved solids measurements in the Upper Colorado River Basin","docAbstract":"Salinity levels in streams and tributaries of the Colorado River Basin have been a major concern for the United States and Mexico for over 50 years as the water is used by millions of people for domestic and industrial purposes. Recently, the United States Geological Survey expanded stream monitoring networks including the number of sites where continuous (15-min) specific conductance is measured in the Colorado River Headwaters and Gunnison River Basin located east of the Colorado-Utah state line (hereafter, UCOL). The purpose of this study is to apply a proxy method to determine salinity and total dissolved solids concentrations from specific conductance and major-ion water type that is applicable to monitoring sites in the UCOL. Within the UCOL, carbonate rich waters originate from high-elevation mountain regions in the eastern UCOL, calcium sulfate rich waters are mainly found in the western half of the UCOL including the Gunnison River Basin, and waters of variable composition are found along the lower reaches of the Colorado River and Eagle River. It was found that the chemistry of sites with variable composition changes seasonally and is impacted by both geogenic and anthropogenic processes, potentially including seasonal application of deicing road salt. The specific conductance – water type proxy can be used to reliably (±10 %) predict salinity and total dissolved solids at 66 monitoring sites in the UCOL. The method is rapid, can generate high-resolution measurements, is cost-effective, and greatly expands the utility of specific conductance measurements. Furthermore, the high-resolution estimates provide an accurate approach to determining long-term salinity loads as short-term events are accurately accounted for.
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We show that the source motions combined with a wave propagation code create surface ground motions that fall within the range of epistemic uncertainties for the Next Generation Attenuation‐West2 set of empirical models. We show the model is well calibrated where there are good data constraints, and has good correspondence in regions with fewer data constraints. We show magnitude, distance, and mechanism dependence all arising naturally from the same underlying friction. The deterministic physics‐based approach provides an opportunity for better understanding the physical origins of ground motions. For example, we find that reduced stress drops in shallow layers relative to constant stress drop with depth lead to peak ground velocities in the near field that better match empirical models. The simulators may also provide better extrapolations into regimes that are poorly empirically constrained by data because physics, rather than surface shaking data parameterizations, is underlying the extrapolations. Having shown the model is credible, we apply it to a problem where observations are lacking. We examine the case of crustal faults above a shallow subduction interface seen to break coseismically in simulations of the New Zealand fault system. These types of events were left out of consideration in the most recent New Zealand national seismic hazard model due to the modeling complexity and lack of observational data to constrain ground‐motion models (GMMs). Here, we show that in the model, by breaking up the coseismic crustal and interface rupturing fault motions into two separate subevents, and then recombining the resulting ground‐motion measures in a square‐root‐of‐sum‐of‐squares incoherent manner, we reproduce well the ground‐motion measures from the full event rupture. 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An invasive species is an organism that is not native to a locality and causes (or is likely to cause) harm. An introduced species is one that is nonnative to a locality and occurs there because of human activities or their consequences, including the species’ intentional or unintentional escape, release, dissemination, or placement. The United States Register of Introduced and Invasive Species (US-RIIS, ver. 2.0) contains 14,700 records for three localities: Alaska (545 records), Hawaii (5,628 records), and the conterminous United States (L48; 8,527 records). For these localities, the US-RIIS catalogs introduced species that have become established, thus providing a basis for their prioritization and management. To be included on the US-RIIS, a species must be nonnative to the entire locality and reproducing anywhere in the locality. Each US-RIIS record has information on taxonomy, dates of introduction (where available; version 2.0 for 47 percent of the records), invasion status (invasive or introduced), use for biocontrol (if applicable), and a citation for the information source(s). The US-RIIS was designed to be compatible with country contributions to the Global Register of Introduced and Invasive Species Initiative, which compiles annotated and verified country-wide inventories of introduced and invasive species. Within the US-RIIS, the density of introduced species per 10,000 square kilometers among the localities ranges markedly, from 3 in Alaska to 1,988 in Hawaii (11 in the L48). The comparative taxonomic composition of the largest groups in the sublists also varies: the Alaska sublist has a majority of flowering plants; Hawaii has a majority of insects; and the L48 is about equally divided between insects and flowering plants. 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U.S. Geological Survey
Box 25046, MS 302
Denver, CO 80225-0046
Objective
Although conserving fish biodiversity in lotic systems is challenging, protected areas can provide refuge from certain environmental stressors. In the Appalachian region, USA, the National Park Service manages Delaware Water Gap National Recreation Area (DEWA) and New River Gorge National Park & Preserve (NERI), which contain abundant and diverse freshwater resources. To assess the effectiveness of these protected areas in conserving stream fishes, we evaluated decadal changes and ecological drivers of species occupancy and detection.
Methods
Using fish assemblage data from backpack electrofishing surveys conducted in both parks during 2013–2014 and 2022–2023, we quantified temporal differences in species occupancy and detection probabilities using a Bayesian hierarchical multispecies occupancy modeling approach. For the 2022–2023 survey, we included habitat variables as predictors of occupancy and detection.
Results
Community composition and occupancy probabilities for species in both parks remained similar through time, with the most recent occupancy estimates ranging from 0.07 (90% CI = 0.02, 0.14) for Variegate Darter Etheostoma variatum and Rainbow Darter E. caeruleum to 0.73 (90% credible interval = 0.59, 0.85) for Blacknose Dace Rhinichthys atratulus. Changes in occupancy were more prominent at Delaware Water Gap National Recreation Area than New River Gorge National Park & Preserve, with Yellow Perch Perca flavescens having a posterior mean difference of −0.17 [90% credible interval = −0.35, −0.01] and American Eel Anguilla rostrata having a high posterior probability (>80%) of occupancy increasing by at least 1%. Habitat variables were related to community structure, but effects varied in significance, magnitude, and direction among species and parks. Conversely, species-specific detection probabilities were comparatively less affected by environmental and sampling effort predictors.
Conclusions
Between 2013 and 2023, occupancy estimates for 44 fish species across two protected, ecologically diverse landscapes remained relatively stable. Furthermore, we highlight the efficacy of national parks in maintaining freshwater fish biodiversity amidst rapid global change.
The history-matching approach to parameter estimation with models enables a powerful offshoot analysis of data worth—using the uncertainty of a model forecast as a metric for the worth of data. Adding observation data will either have no impact on forecast uncertainty or will reduce it. Removing existing data will either have no impact on forecast uncertainty or will increase it. The history-matching framework makes it possible to perform this quantitative analysis leveraging the connections among observations, model parameters, and model forecasts. We show this behavior on a specific groundwater flow model of the Mississippi Alluvial Plain and show where the analysis can be informative for considering the potential design of an observation network based on existing or potential observations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20255007","usgsCitation":"Fienen, M.N., Schachter, L.A., and Hunt, R.J., 2025, A model uncertainty quantification protocol for evaluating the value of observation data: U.S. Geological Survey Scientific Investigations Report 2025–5007, 12 p., https://doi.org/10.3133/sir20255007.","productDescription":"vi; 12 p.","numberOfPages":"22","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-171702","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":483399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2025/5007/sir20255007.pdf","text":"Report","size":"7.93 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025–5007"},{"id":483398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2025/5007/coverthb.jpg"},{"id":483404,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20255007/full","text":"Report"},{"id":483400,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2025/5007/sir20255007.XML","text":"Report","description":"SIR 2025–5007"},{"id":483403,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2025/5007/images"},{"id":492790,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118495.htm","linkFileType":{"id":5,"text":"html"}}],"contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, Wisconsin 53726
Population monitoring is essential to document recovery efforts for threatened and endangered species. Mexican wolves (Canis lupus baileyi) are an endangered subspecies of gray wolves that historically occupied large portions of the American Southwest and Mexico. Recently, the Mexican wolf population in the United States has been growing rapidly and traditional approaches for population monitoring (e.g., capture and radio collaring) are becoming difficult and expensive as wolves expand into new areas. We developed predictive models of pup-rearing habitat (i.e., den and rendezvous sites) that could help guide future population monitoring efforts. We located 255 den sites and 129 rendezvous sites in Arizona and New Mexico, USA (1998–2023) using tracking collars and site visits. We sampled habitat conditions in wolf-occupied regions of Arizona and New Mexico and fit logistic regressions to these data following a use–available study design to estimate resource selection functions (RSF) for den and rendezvous sites. We hypothesized wolves would select areas that offered greater physical protection, lower human-disturbance, and access to reliable water sources for pup-rearing but that the relative importance of these features would differ between the denning and rendezvous site seasons. Mexican wolves selected den sites at higher elevations in steeper and rougher terrain that were closer to permanent waterbodies but farther from rural roads. Selection of rendezvous sites was also associated with higher elevations and proximity to waterbodies but varied with availability of green leaf biomass on the landscape. While still highly predictive, our rendezvous site model was less predictive than our den model (Spearman's correlation averaged 0.81 [SE = 0.05] vs. 0.90 [SE = 0.03], respectively), possibly because water and green leaf biomass are more spatially diffuse and variable because of monsoonal rains during the rendezvous site season. Our results suggest that terrain features associated with physical protection and access to reliable water were most important in characterizing suitable pup-rearing habitat for Mexican wolves. By predicting suitable den and rendezvous site habitat across portions of the Mexican Wolf Experimental Population Area, our models can help guide future population monitoring by reducing the total search area when surveying for wolves and increase the probability of detecting all members of a pack.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70017","usgsCitation":"Bassing, S., Oakleaf, J., Cain, J.W., Greenleaf, A., Gardner, C., and Ausband, D.E., 2025, Predicting pup-rearing habitat for Mexican wolves: Journal of Wildlife Management, v. 89, no. 5, e70017, 19 p., https://doi.org/10.1002/jwmg.70017.","productDescription":"e70017, 19 p.","ipdsId":"IP-169774","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486238,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":488960,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.70017","text":"Publisher Index Page"}],"country":"United States","state":"Arizona, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.15656068155297,\n 37.15236143729469\n ],\n [\n -114.79334387076136,\n 36.19839033323856\n ],\n [\n -114.82092420356022,\n 32.28431539821898\n ],\n [\n -111.22371663071222,\n 31.529018437535207\n ],\n [\n -108.37781874007106,\n 31.259432273304338\n ],\n [\n -108.00163404937481,\n 31.805280742585737\n ],\n [\n -103.03648352957026,\n 31.93273826397835\n ],\n [\n -103.13422840316065,\n 37.15236143729469\n ],\n [\n -114.15656068155297,\n 37.15236143729469\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Bassing, Sarah B.","contributorId":355638,"corporation":false,"usgs":false,"family":"Bassing","given":"Sarah B.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":937834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oakleaf, John K.","contributorId":355639,"corporation":false,"usgs":false,"family":"Oakleaf","given":"John K.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937836,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Greenleaf, Allison R.","contributorId":355640,"corporation":false,"usgs":false,"family":"Greenleaf","given":"Allison R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937837,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gardner, Colby M.","contributorId":355641,"corporation":false,"usgs":false,"family":"Gardner","given":"Colby M.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937838,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ausband, David Edward 0000-0001-9204-9837","orcid":"https://orcid.org/0000-0001-9204-9837","contributorId":275329,"corporation":false,"usgs":true,"family":"Ausband","given":"David","email":"","middleInitial":"Edward","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937839,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70265779,"text":"70265779 - 2025 - A 700-year rupture sequence of great eastern Aleutian earthquakes from tsunami modeling of stratigraphic records","interactions":[],"lastModifiedDate":"2025-04-15T15:17:05.760533","indexId":"70265779","displayToPublicDate":"2025-03-17T10:11:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"A 700-year rupture sequence of great eastern Aleutian earthquakes from tsunami modeling of stratigraphic records","docAbstract":"Great Aleutian underthrusting earthquakes produced destructive tsunamis impacting Hawaiʻi in 1946 and 1957. Prior modeling of the 1957 tsunami deposit and runup records on eastern Aleutian and Hawaiian Islands jointly with tide-gauge observations across the Pacific Ocean constrained a rupture model with shallow slip up to 26 m along 600 km of the plate boundary. Here we implement this modeling approach to older deposits and show alternating deep and shallow megathrust slip up to 26, 32, and 22 m for great earthquakes along the same segment in the 18th, 15th, and 14th centuries. All three modeled prehistoric Aleutian earthquakes produce tsunami inundation in Hawaiʻi with the most severe, 14th century event having impacts exceeding the 1957 event. The along-dip variability of these four ruptures spanning seven centuries provides insights on earthquake cycles for engineering design and hazard assessment. The 15th century and 1957 rupture models provide evidence for recurrence of tsunami earthquakes, which can produce disproportionately large tsunamis for a given moment magnitude due to reduced rigidity in the shallow megathrust. The 14th and 18th century events likely ruptured deeper regions that did not slip in 1957, suggesting potential for corresponding deeper failure in the next great eastern Aleutian earthquake.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-025-57802-w","usgsCitation":"Yamazaki, Y., Cheung, K.F., Lay, T., La Selle, S., Witter, R., and Jaffe, B., 2025, A 700-year rupture sequence of great eastern Aleutian earthquakes from tsunami modeling of stratigraphic records: Nature Communications, v. 16, 2638, 16 p., https://doi.org/10.1038/s41467-025-57802-w.","productDescription":"2638, 16 p.","ipdsId":"IP-169333","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488253,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-025-57802-w","text":"Publisher Index Page"},{"id":484585,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Hawaii","otherGeospatial":"Aleutian Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -146.41033794940356,\n 60.59031062994006\n ],\n [\n -179.99,\n 60.59031062994006\n ],\n [\n -179.99,\n 18\n ],\n [\n -146.41033794940356,\n 18\n ],\n [\n -146.41033794940356,\n 60.59031062994006\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 172.7792582818936,\n 55.23104605282941\n ],\n [\n 172.7792582818936,\n 48.90879653399284\n ],\n [\n 179.99,\n 48.90879653399284\n ],\n [\n 179.99,\n 55.23104605282941\n ],\n [\n 172.7792582818936,\n 55.23104605282941\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Yamazaki, Yoshiki","contributorId":216792,"corporation":false,"usgs":false,"family":"Yamazaki","given":"Yoshiki","email":"","affiliations":[{"id":39517,"text":"University of Hawaii at Mano","active":true,"usgs":false}],"preferred":false,"id":933512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheung, Kwok Fai","contributorId":329690,"corporation":false,"usgs":false,"family":"Cheung","given":"Kwok","email":"","middleInitial":"Fai","affiliations":[{"id":78685,"text":"University of Hawai'i at Manoa","active":true,"usgs":false}],"preferred":false,"id":933513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lay, Thorne","contributorId":328838,"corporation":false,"usgs":false,"family":"Lay","given":"Thorne","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":933514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"La Selle, SeanPaul 0000-0002-4500-7885 slaselle@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-7885","contributorId":181565,"corporation":false,"usgs":true,"family":"La Selle","given":"SeanPaul","email":"slaselle@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Witter, Robert C. 0000-0002-1721-254X rwitter@usgs.gov","orcid":"https://orcid.org/0000-0002-1721-254X","contributorId":4528,"corporation":false,"usgs":true,"family":"Witter","given":"Robert C.","email":"rwitter@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":933516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jaffe, Bruce E. 0000-0002-8816-5920","orcid":"https://orcid.org/0000-0002-8816-5920","contributorId":335664,"corporation":false,"usgs":false,"family":"Jaffe","given":"Bruce E.","affiliations":[{"id":80462,"text":"former USGS PCMSC employee","active":true,"usgs":false}],"preferred":false,"id":933517,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264717,"text":"70264717 - 2025 - Climate and dispersal ability limit future habitats for Gila monsters in the Mojave Desert","interactions":[],"lastModifiedDate":"2025-03-20T14:58:11.540958","indexId":"70264717","displayToPublicDate":"2025-03-17T09:50:31","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":"Climate and dispersal ability limit future habitats for Gila monsters in the Mojave Desert","docAbstract":"Describing future habitat for sensitive species can be helpful in planning conservation efforts to ensure species persistence under new climatic conditions. The Gila monster (Heloderma suspectum) is an iconic lizard of the southwestern United States. The northernmost range of Gila monsters is the Mojave Desert, an area experiencing rapid human population growth and urban sprawl. To understand current and potential future habitat for Gila monsters in the Mojave Desert, we fit ensemble species distribution models using known locations and current environmental variables known to be important to the species' biology. We then projected future suitable habitat under different climate forecasts based on IPCC emission scenarios. To ensure that Gila monsters would be able to disperse to newly suitable habitat, we fit Brownian Bridge movement models using telemetry data from two locations in Nevada. This model indicated that Gila monsters prefer to move through areas with a moderate slope and higher shrub cover. Modeled current suitable habitat for Gila monsters in Nevada was primarily in rugged bajadas and lower elevations at the bases of mountain ranges. Predictions of potential future habitat suggested that overall habitat suitability through 2082 would remain relatively stable throughout the study area in the lower emissions scenario, but in the high emissions scenario potential habitat is greatly reduced in many lower-elevation areas. Future habitat areas at higher elevations under the high emissions scenario showed moderate increases in suitability, though occupancy would likely be limited by Gila monster dispersal capabilities. Finally, we determined how well the protected area network of our study area encompassed future Gila monster habitat to highlight potential opportunities to protect this important species.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71008","usgsCitation":"Hromada, S.J., Jones, J., Stalker, J., Wood, D.A., Vandergast, A.G., Tracy, C.R., Gienger, C., and Nussear, K.E., 2025, Climate and dispersal ability limit future habitats for Gila monsters in the Mojave Desert: Ecology and Evolution, v. 15, e71008, 15 p., https://doi.org/10.1002/ece3.71008.","productDescription":"e71008, 15 p.","ipdsId":"IP-166958","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":488343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71008","text":"Publisher Index Page"},{"id":483582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Nevada, Utah","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.33194255435204,\n 37.76713352893536\n ],\n [\n -117.13833621353872,\n 37.76713352893536\n ],\n [\n -117.13833621353872,\n 33.75898076102743\n ],\n [\n -113.33194255435204,\n 33.75898076102743\n ],\n [\n -113.33194255435204,\n 37.76713352893536\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2025-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hromada, Steven J.","contributorId":245147,"corporation":false,"usgs":false,"family":"Hromada","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":931420,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Jason L.","contributorId":352480,"corporation":false,"usgs":false,"family":"Jones","given":"Jason L.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":931421,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stalker, Jocelyn B.","contributorId":352484,"corporation":false,"usgs":false,"family":"Stalker","given":"Jocelyn B.","affiliations":[{"id":84237,"text":"Austin Peay State University","active":true,"usgs":false}],"preferred":false,"id":931422,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":931423,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":57201,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":931424,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tracy, C. 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However, the organic content of these rocks has likely suffered from the radiation environment on the surface of Mars to an extent yet to be quantified. For the first time, a 1000 sols long ageing experiment was conducted at the surface of Mars, i.e. under actual Martian conditions, relying on the 100 % organic Ertalyte target carried by Perseverance. White at landing, the Ertalyte target has turned brown with time, while its Raman signal changed, with a modification of the background (its maximum has shifted from 1500 to 2000 cm−1) and a reduction of the contribution of the Raman signal of Ertalyte (by a factor of 5 over the first 500 sols). Given the intrinsic resistance of the Ertalyte to UV exposure, which is not anticipated for most Martian organic materials, these results suggest that exposure at the surface of Mars will make the detection of Martian organic molecules challenging.
","language":"English","publisher":"European Association of Geochemistry","doi":"10.7185/geochemlet.2509","usgsCitation":"Bernard, S., Beyssac, O., Manrique, J., Lopez Reyes, G., Ollila, A., Le Mouelic, S., Beck, P., Pilleri, P., Forni, O., Julve-Gonzales, S., Veneranda, M., Reyes Rodriguez, I., Madariaga Mota, J., Aramenda, J., Castro, K., Clave, E., Royer, C., Fornaro, T., Bousquet, B., Sharma, S., Johnson, J., Cloutis, E., Gabriel, T.S., Meslin, P., Gasnault, O., Cousin, A., Wiens, R., and Maurice, S., 2025, Ageing of organic materials at the surface of Mars: A Raman study aboard Perseverance: Geochemical Perspectives Letters, v. 34, p. 25-30, https://doi.org/10.7185/geochemlet.2509.","productDescription":"6 p.","startPage":"25","endPage":"30","ipdsId":"IP-169947","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":488337,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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velocimetry","interactions":[],"lastModifiedDate":"2025-03-18T16:47:13.564948","indexId":"70264626","displayToPublicDate":"2025-03-16T11:34:04","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":"Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry","docAbstract":"Continuous, high-resolution data for characterizing freshwater habitat conditions can support successful management of endangered salmonids. Uncrewed aircraft systems (UAS) make acquiring such fine-scale data along river channels more feasible, but workflows for quantifying reach-scale salmon habitats are lacking. We evaluated the potential for UAS-based mapping of hydraulic habitats using spectrally based depth retrieval and particle image velocimetry (PIV) by comparing these methods to a more well-established flow modeling approach. Our results indicated that estimates of water depth, depth-averaged velocity, and flow direction derived via remote sensing and modeling techniques were comparable and in good agreement with field measurements. Predictions of spring-run Chinook salmon (Oncorhynchus tshawytscha) juvenile rearing habitat produced from PIV and model output were similar, with small errors relative to direct field observations. Estimates of hydraulic heterogeneity based on kinetic energy gradients in the flow field were generally consistent between PIV and flow modeling, but errors relative to field measurements were larger. PIV results were sensitive to the velocity index (α) used to convert surface velocities to depth-averaged velocities. Sun glint precluded PIV analysis along the margins of some images and a large degree of overlap between frames was thus required to obtain continuous coverage of the reach. Similarly, shadows cast by riparian vegetation caused gaps in spectrally based bathymetric maps. Despite these limitations, our results suggest that for sites with sufficient water surface texture, UAS-based PIV can provide detailed hydraulic habitat information at the reach scale, with accuracies comparable to traditional field methods and multidimensional flow modeling.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR038045","usgsCitation":"Harrison, L.R., Legleiter, C.J., Overstreet, B., and White, J., 2025, Evaluating the potential to quantify salmon habitat via UAS-based particle image velocimetry: Water Resources Research, v. 3, no. 61, e2024WR038045, 21 p., https://doi.org/10.1029/2024WR038045.","productDescription":"e2024WR038045, 21 p.","ipdsId":"IP-163184","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":488333,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr038045","text":"Publisher Index Page"},{"id":483481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"North Santiam River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.01923518970605,\n 44.68226153883313\n ],\n [\n -122.36818028867839,\n 44.68226153883313\n ],\n [\n -122.36818028867839,\n 44.857748774184074\n ],\n [\n -123.01923518970605,\n 44.857748774184074\n ],\n [\n -123.01923518970605,\n 44.68226153883313\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"61","noUsgsAuthors":false,"publicationDate":"2025-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":930994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":930995,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon 0000-0001-7845-6671 boverstreet@usgs.gov","orcid":"https://orcid.org/0000-0001-7845-6671","contributorId":169201,"corporation":false,"usgs":true,"family":"Overstreet","given":"Brandon","email":"boverstreet@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":930996,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, James 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":193492,"corporation":false,"usgs":true,"family":"White","given":"James","email":"jameswhite@usgs.gov","affiliations":[],"preferred":true,"id":930997,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70264651,"text":"70264651 - 2025 - Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York","interactions":[],"lastModifiedDate":"2025-03-18T16:31:27.341468","indexId":"70264651","displayToPublicDate":"2025-03-15T11:10:19","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York","docAbstract":"Estimates of dynamic groundwater volumes supplying baseflow to streams are important for water availability projections during extended periods of drought. The primary goals of this study were to provide dynamic storage volume estimates, inferred from streamflow recession analysis, for baseflow regimes within seven gaged catchments within the Neversink Reservoir Watershed (NRW), a critical municipal water source for New York City. Additionally, geomorphological properties, surficial geology and hydro-meteorological processes were quantified and described in relation to time and spatially variable recession behaviour and storage estimates across the NRW. To explore these relationships, we (1) evaluated seasonal trends in streamflow recession behaviour in relation to modelled potential evapotranspiration (PET) and catchment runoff rates, (2) derived empirical streamflow models for cool-season runoff using both linear and nonlinear reservoir assumptions for baseflow and (3) calculated metrics related to the geology and geomorphology of each catchment and compared these metrics to area normalised baseflow dynamic storage estimates. Results show that baseflow recession behaves as a nonlinear reservoir, and applying linear groundwater reservoir assumptions may underestimate the total dynamic storage volumes compared to what would be predicted for a nonlinear reservoir. Increases in PET caused decreases in storage conditions that resulted in increased recession rates and nonlinearity in streamflow recession during the growing season. Additionally, we found that while no single physical catchment characteristic solely predicted catchment storage dynamics, sediment volume and stream gradients were stronger predictors of normalised storage volumes than catchment surface area or surface topography alone. Within the NRW, catchments with the highest sediment volume exhibited the lowest recession rates and higher dynamic storage volumes, while the smallest catchment, mostly devoid of sediment, had the fastest recession rate and lowest dynamic storage volume.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70106","usgsCitation":"Benton, J., and Doctor, D.H., 2025, Dynamic baseflow storage estimates and the role of topography, geology and evapotranspiration on streamflow recession characteristics in the Neversink Reservoir Watershed, New York: Hydrological Processes, v. 39, no. 3, e70106, 17 p., https://doi.org/10.1002/hyp.70106.","productDescription":"e70106, 17 p.","ipdsId":"IP-163356","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":483480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Neversink Reservoir Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.95860919620127,\n 42.2147005922842\n ],\n [\n -74.56712312527952,\n 42.2147005922842\n ],\n [\n -74.56712312527952,\n 41.956641367777735\n ],\n [\n -73.95860919620127,\n 41.956641367777735\n ],\n [\n -73.95860919620127,\n 42.2147005922842\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Benton, Joshua R. 0000-0002-1698-6455","orcid":"https://orcid.org/0000-0002-1698-6455","contributorId":352387,"corporation":false,"usgs":false,"family":"Benton","given":"Joshua R.","affiliations":[{"id":84197,"text":"**please fill in","active":true,"usgs":false},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":false,"id":931072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":931073,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70264474,"text":"sir20255011 - 2025 - Comparison of hydrologic data and water budgets between 2003–08 and 2018–23 for the eastern part of the Arbuckle-Simpson aquifer, south-central Oklahoma","interactions":[],"lastModifiedDate":"2025-07-23T17:07:13.510916","indexId":"sir20255011","displayToPublicDate":"2025-03-14T15:26:55","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":"2025-5011","displayTitle":"Comparison of Hydrologic Data and Water Budgets Between 2003–08 and 2018–23 for the Eastern Part of the Arbuckle-Simpson Aquifer, South-Central Oklahoma","title":"Comparison of hydrologic data and water budgets between 2003–08 and 2018–23 for the eastern part of the Arbuckle-Simpson aquifer, south-central Oklahoma","docAbstract":"The Arbuckle-Simpson aquifer is divided spatially into three parts (eastern, central, and western). The largest groundwater withdrawals are from the eastern part of the Arbuckle-Simpson aquifer, which provides water to approximately 39,000 people in Ada and Sulphur, Oklahoma, and surrounding areas. The Arbuckle-Simpson aquifer, including the eastern part, is designated a sole source aquifer for its service area. Based primarily on data collected between 2003 and 2008, a series of comprehensive hydrologic studies of the Arbuckle-Simpson aquifer was published to provide the information necessary to perform groundwater-flow model simulations so that the Oklahoma Water Resources Board could determine how much water could be withdrawn from the aquifer while maintaining flow to springs and streams. As part of the Phase 1 studies, an aquifer water budget was developed from a numerical model for the period 2003–08. For this report, Phase 1 refers to the 2003–08 data collection period, although for some of the analyses, data collected prior to 2003 were used to inform model development work. Allocation of water from this aquifer was then established by the Oklahoma Water Resources Board in 2013. Additional well-spacing rules were also established by the Oklahoma Water Resources Board for sensitive sole source groundwater basins. To determine how the water budget for the eastern part of the Arbuckle-Simpson aquifer has changed over time, recently collected hydrologic data (2018–23) were compared to data collected during 2003–08. The analysis of changes in the aquifer water budget from 2003–08 to 2018–23 could help resource managers better understand changes in the overall balance of water in storage and the potential effects on streamflow, changes in groundwater levels, and the effects of different water uses in the aquifer area on available water in the eastern part of the Arbuckle-Simpson aquifer and streams overlying the eastern part of the Arbuckle-Simpson aquifer.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"The mummichog Fundulus heteroclitus is a trophically important fish inhabiting Atlantic coastal salt marshes, with few in situ estimates of overwinter survival throughout the species range. We estimated overwinter apparent survival rates of F. heteroclitus at the approximate mid-latitudinal species range [coastal North Carolina (USA)] in four tidal creeks that experience variable winter water temperatures. To estimate apparent survival, we fitted a Cormack-Jolly-Seber model to daily mark-resight data autonomously obtained from fish marked with passive integrated transponder tags. Creek, year, mean daily water temperature, change in mean daily temperature, fish length and fish condition were considered for effects on the modelled parameters: apparent survival (Φ) (product of true survival and site fidelity) and detection probability (p). Modelling showed that water temperature and fish metrics were not related to Φ. Water temperature was directly related to p, indicating reduced fish activity and thus reduced detection probability or poor antenna detection performance at low temperatures. Creek was related to Φ and p, and the creek most open to its downstream estuary (lacking a culvert) had lower rates than the others. Greater loss (fish mortality plus emigration) in this one creek may more effectively transfer production of F. heteroclitus to larger waterbodies via emigration or predation. Conversely, lower Φ may reflect reduced detection efficiency. The results suggest that F. heteroclitus survival is insensitive to variable winter water temperatures typical of thermal dynamics in shallow estuaries in this region of its range. Median creek-specific overwinter Φ rates (range of median values, 2 × 10−8, 0.04) were roughly equal to previously published rates for these creeks during the growing season (April–October). At these latitudes and with increasingly moderate winters, the results indicate that natural mortality could arise equally or more so from predation during the growing season than mechanisms such as starvation, direct mortality, thermal morbidity and stress-related susceptibility to predation resulting from intermittently low water temperatures during the overwinter season.
","language":"English","publisher":"Wiley","doi":"10.1111/jfb.70020","usgsCitation":"Rudershausen, P.J., and O'Donnell, M.J., 2025, Overwinter survival of an estuarine resident fish (Fundulus heteroclitus) in North Carolina salt marsh creeks: Journal of Fish of Biology, v. 107, no. 1, p. 188-200, https://doi.org/10.1111/jfb.70020.","productDescription":"13 p.","startPage":"188","endPage":"200","ipdsId":"IP-168105","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":488323,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jfb.70020","text":"Publisher Index Page"},{"id":483454,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.61589538108844,\n 34.739737828981234\n ],\n [\n -76.80342045072118,\n 34.74057409854757\n ],\n [\n -76.80219234343787,\n 34.68681714274115\n ],\n [\n -76.61593931817359,\n 34.68548980814643\n ],\n [\n -76.61589538108844,\n 34.739737828981234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"107","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Rudershausen, P. J.","contributorId":352331,"corporation":false,"usgs":false,"family":"Rudershausen","given":"P.","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":930816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Donnell, Matthew J. 0000-0002-9089-2377","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":295467,"corporation":false,"usgs":true,"family":"O'Donnell","given":"Matthew","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":930817,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}