{"pageNumber":"33","pageRowStart":"800","pageSize":"25","recordCount":46619,"records":[{"id":70265786,"text":"70265786 - 2025 - A synthesis of freshwater forested wetland soil organic carbon storage","interactions":[],"lastModifiedDate":"2025-04-16T14:31:18.998298","indexId":"70265786","displayToPublicDate":"2025-04-07T09:27:41","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5860,"text":"Frontiers in Forests and Global Change","active":true,"publicationSubtype":{"id":10}},"title":"A synthesis of freshwater forested wetland soil organic carbon storage","docAbstract":"<p><span>Freshwater forested wetlands account for ~76% (918 M ha) of the total global wetland extent. However, freshwater forested wetlands are difficult to distinguish from upland forest due to canopy coverage, the abundance of wetland-nonwetland mosaics, seasonal hydropatterns, and fewer readily observable connections to large surface water bodies relative to marshes and other emergent habitats. Therefore, freshwater forested wetland ecosystems are often misclassified as upland forests in carbon accounting models, underestimating soil organic carbon (SOC) storage. This study highlights freshwater forested wetland SOC accounting challenges and presents SOC densities/stocks from a global literature synthesis across different freshwater forested wetland types. We reviewed 374 forested wetland articles, compiling and calculating carbon densities by depth from 90 freshwater forested wetland studies to construct a database of 334 study sites including nine countries. The median (± median absolute deviation) SOC stock was 91.2 ± 46.4 Mg C ha</span><sup>−1</sup><span>&nbsp;and 235.3 ± 125.6 Mg C ha</span><sup>−1</sup><span>&nbsp;in the top 30 cm and 100 cm of soil, respectively. The tidal freshwater forested wetland had highest SOC stock (341.6 ± 98.4 Mg C ha</span><sup>−1</sup><span>) in the upper 100 cm soil profile followed by rainforest (285.6 ± 75.8 Mg C ha</span><sup>−1</sup><span>), non-tidal swamps (229.3 ± 120.4 Mg C ha</span><sup>−1</sup><span>), and floodplain forested wetlands (176.6 ± 84 Mg C ha</span><sup>−1</sup><span>). Within the conterminous United States forest type groups, the&nbsp;</span><i>Tsuga</i><span>/</span><i>Picea</i><span>&nbsp;group had the highest median SOC stocks (353.6 ± 82.9 Mg ha</span><sup>−1</sup><span>) in the top 100 cm of soil followed by&nbsp;</span><i>Quercus/Pinus</i><span>&nbsp;(246.6 ± 82.3 Mg ha</span><sup>−1</sup><span>) and&nbsp;</span><i>Quercus/Liquidambar/Taxodium</i><span>&nbsp;(207.9 ± 87.7 Mg ha</span><sup>−1</sup><span>) groups, likely driven by variability in litter degradability, wetland hydroperiod, geomorphic positions, and regional climatic factors. This literature synthesis highlights SOC accounting in freshwater forested wetland carbon pools when estimating carbon stocks and fluxes. Results can be used to improve carbon modeling outcomes, as well as inform regional, national, and global management of wetland carbon resources.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffgc.2025.1528440","usgsCitation":"Sapkota, Y., Berkowitz, J., Stagg, C., and Busby, R., 2025, A synthesis of freshwater forested wetland soil organic carbon storage: Frontiers in Forests and Global Change, v. 8, 1528440, 15 p., https://doi.org/10.3389/ffgc.2025.1528440.","productDescription":"1528440, 15 p.","ipdsId":"IP-176238","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488264,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffgc.2025.1528440","text":"Publisher Index Page"},{"id":484639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2025-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Sapkota, Yadav","contributorId":353419,"corporation":false,"usgs":false,"family":"Sapkota","given":"Yadav","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":933528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berkowitz, Jacob F.","contributorId":353422,"corporation":false,"usgs":false,"family":"Berkowitz","given":"Jacob F.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":933529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":214880,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":933530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Busby, Ryan R.","contributorId":353425,"corporation":false,"usgs":false,"family":"Busby","given":"Ryan R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":933531,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70265447,"text":"70265447 - 2025 - Estimating indicators of cyanobacterial harmful algal blooms in New York State","interactions":[],"lastModifiedDate":"2025-04-07T14:54:27.747567","indexId":"70265447","displayToPublicDate":"2025-04-07T07:50:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Estimating indicators of cyanobacterial harmful algal blooms in New York State","docAbstract":"<p><span>Cyanobacteria harmful algal blooms (cyanoHABs) are a global concern for aquatic ecosystem and human health. Limited funding for monitoring programs and inconsistent determination of cyanoHAB occurrence present challenges for identifying commonly effective variables for characterizing cyanoHABs and the development of generalized models. We compiled a combination of water quality measurements, lake morphology, climatology, remote sensing data, and observations of cyanoHAB occurrence across New York State and used this dataset to develop two sets of predictive models. The first model predicted chlorophyll&nbsp;</span><i>a</i><span>, a common indicator of algal biomass, and assessed the importance of variables for modeled predictions. The most important variables were then used in a second set of models to classify cyanoHAB occurrence. The irradiance attenuation coefficient (</span><i>K<sub>d</sub></i><span>), which was estimated from Secchi depth measurements, and total phosphorus were the two most important variables for predicting chlorophyll&nbsp;</span><i>a</i><span>. The second model examined several variables for their ability to classify cyanoHAB occurrence. Predicted cyanoHAB occurrence based on thresholds of chlorophyll&nbsp;</span><i>a</i><span>,&nbsp;</span><i>K<sub>d</sub></i><span>, or total nitrogen all had moderate agreement and were able to correctly classify approximately 70% of observed cyanoHABs. Our analysis indicated that multiple data types were important for predicting chlorophyll&nbsp;</span><i>a</i><span>&nbsp;statewide and that simple widely available water quality parameters could classify cyanoHABs occurrence with reasonable accuracy. Identifying variables that can be monitored with increased frequency and decreased latency to detect cyanoHAB occurrence will better inform water managers and provide valuable additional data for further refining predictive models of the likelihood of cyanoHABs occurrence.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2025.113403","usgsCitation":"Savoy, P., Gorney, R.M., and Graham, J.L., 2025, Estimating indicators of cyanobacterial harmful algal blooms in New York State: Ecological Indicators, v. 173, 113403, 11 p., https://doi.org/10.1016/j.ecolind.2025.113403.","productDescription":"113403, 11 p.","ipdsId":"IP-171481","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":488558,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2025.113403","text":"Publisher Index Page"},{"id":484244,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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York\",\"nation\":\"USA  \"}}]}","volume":"173","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Savoy, Philip 0000-0002-6075-837X","orcid":"https://orcid.org/0000-0002-6075-837X","contributorId":300288,"corporation":false,"usgs":true,"family":"Savoy","given":"Philip","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":932740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gorney, Rebecca Michelle 0000-0003-4406-261X","orcid":"https://orcid.org/0000-0003-4406-261X","contributorId":317259,"corporation":false,"usgs":true,"family":"Gorney","given":"Rebecca","email":"","middleInitial":"Michelle","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932742,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265471,"text":"70265471 - 2025 - Neural network-based temporal ensembling of water depth estimates derived from SuperDove Images","interactions":[],"lastModifiedDate":"2025-04-07T15:08:27.841513","indexId":"70265471","displayToPublicDate":"2025-04-06T08:01:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Neural network-based temporal ensembling of water depth estimates derived from SuperDove Images","docAbstract":"CubeSats provide a wealth of high-frequency observations at a meter-scale spatial resolution. However, most current methods of inferring water depth from satellite data consider only a single image. This approach is sensitive to the radiometric quality of the data acquired at that particular instant in time, which could be degraded by various confounding factors, such as sun glint or atmospheric effects. Moreover, using single images in isolation fails to exploit recent improvements in the frequency of satellite image acquisition. This study aims to leverage the dense image time series from the SuperDove constellation via an ensembling framework that helps to improve empirical (regression-based) bathymetry retrieval. Unlike previous studies that only ensembled the original spectral data, we introduce a neural network-based method that instead ensembles the water depths derived from multi-temporal imagery, provided the data are acquired under steady flow conditions. We refer to this new approach as NN-depth ensembling. First, every image is treated individually to derive multitemporal depth estimates. Then, we use another NN regressor to ensemble the temporal water depths. This step serves to automatically weight the contribution of the bathymetric estimates from each time instance to the final bathymetry product. Unlike methods that ensemble spectral data, NN-depth ensembling mitigates against propagation of uncertainties in spectral data (e.g., noise due to sun glint) to the final bathymetric product. The proposed NN-depth ensembling is applied to temporal SuperDove imagery of reaches from the American, Potomac, and Colorado rivers with depths of up to 10 m and evaluated against in situ measurements. The proposed method provided more accurate and robust bathymetry retrieval than single-image analyses and other ensembling approaches.","language":"English","publisher":"MDPI","doi":"10.3390/rs17071309","usgsCitation":"Niroumand-Jadidi, M., Legleiter, C.J., and Bovolo, F., 2025, Neural network-based temporal ensembling of water depth estimates derived from SuperDove Images: Remote Sensing, v. 17, no. 7, 1309, 19 p., https://doi.org/10.3390/rs17071309.","productDescription":"1309, 19 p.","ipdsId":"IP-149931","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":488569,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17071309","text":"Publisher Index Page"},{"id":484247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"7","noUsgsAuthors":false,"publicationDate":"2025-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Niroumand-Jadidi, Milad 0000-0002-9432-3032","orcid":"https://orcid.org/0000-0002-9432-3032","contributorId":292943,"corporation":false,"usgs":false,"family":"Niroumand-Jadidi","given":"Milad","email":"","affiliations":[{"id":63082,"text":"Digital Society Center, Fondazione Bruno Kessler, Trento, Italy","active":true,"usgs":false}],"preferred":false,"id":932783,"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":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":932784,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bovolo, Francesca 0000-0003-3104-7656","orcid":"https://orcid.org/0000-0003-3104-7656","contributorId":292944,"corporation":false,"usgs":false,"family":"Bovolo","given":"Francesca","email":"","affiliations":[{"id":63082,"text":"Digital Society Center, Fondazione Bruno Kessler, Trento, Italy","active":true,"usgs":false}],"preferred":false,"id":932785,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70272682,"text":"70272682 - 2025 - Uncertainty quantification of geophysical and hydrologic parameters estimated from borehole nuclear magnetic resonance data","interactions":[],"lastModifiedDate":"2025-12-04T16:46:03.167798","indexId":"70272682","displayToPublicDate":"2025-04-05T10:43:22","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18010,"text":"JGR Machine Learning and Computation","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty quantification of geophysical and hydrologic parameters estimated from borehole nuclear magnetic resonance data","docAbstract":"<p><span>Borehole nuclear magnetic resonance (bNMR) data are typically used to infer in situ hydrologic properties. Partial water content as a function of pore size is estimated by fitting the measured NMR response to a multi-exponential&nbsp;</span><i>T</i><sub>2</sub><span>&nbsp;distribution, and the sum of estimated&nbsp;</span><i>T</i><sub>2</sub><span>&nbsp;amplitudes equals the total volumetric water content. From these estimated parameters, several empirical relationships are commonly used to infer hydraulic conductivity from the NMR-estimated water content and&nbsp;</span><i>T</i><sub>2</sub><span>&nbsp;distribution. Often, parameters are estimated through deterministic inversion methods that produce a single best-fit estimate, but do not reflect uncertainties in model parameters. Here, a Bayesian Markov chain Monte Carlo (McMC) approach for analyzing bNMR data is developed that allows for comprehensive uncertainty quantification of NMR parameters and derived hydrologic properties. The underlying model that describes the&nbsp;</span><i>T</i><sub>2</sub><span>&nbsp;distribution is defined by a set of spline interpolation points. The number of interpolation points is allowed to vary in a trans-dimensional algorithm that naturally favors simple models with fewer interpolation points, allowing the data to inform the necessary level of model complexity. Additionally, data error is estimated as an unknown parameter. Analysis of the ensemble of models output from the McMC algorithm provides useful details on the range of plausible&nbsp;</span><i>T</i><sub>2</sub><span>&nbsp;distributions that can fit a measured bNMR decay curve, as well as uncertainty estimates of total water content. The ensemble of NMR parameters can also be propagated through commonly used relationships to produce uncertainty estimates on derived parameters such as bound/capillary/mobile water content or hydraulic conductivity.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JH000461","usgsCitation":"Minsley, B.J., Phillips, S.N., and James, S.R., 2025, Uncertainty quantification of geophysical and hydrologic parameters estimated from borehole nuclear magnetic resonance data: JGR Machine Learning and Computation, v. 2, no. 2, e2024JH000461, 15 p., https://doi.org/10.1029/2024JH000461.","productDescription":"e2024JH000461, 15 p.","ipdsId":"IP-171454","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":497115,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jh000461","text":"Publisher Index Page"},{"id":497066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-04-05","publicationStatus":"PW","contributors":{"authors":[{"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":951326,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Stephanie N. 0000-0002-2022-7726","orcid":"https://orcid.org/0000-0002-2022-7726","contributorId":214857,"corporation":false,"usgs":true,"family":"Phillips","given":"Stephanie","email":"","middleInitial":"N.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":951327,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"James, Stephanie R. 0000-0001-5715-253X","orcid":"https://orcid.org/0000-0001-5715-253X","contributorId":260620,"corporation":false,"usgs":true,"family":"James","given":"Stephanie","email":"","middleInitial":"R.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":951328,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265808,"text":"70265808 - 2025 - Salinas Valley integrated hydrologic and reservoir operations models, Monterey and San Luis Obispo Counties, California","interactions":[],"lastModifiedDate":"2025-04-16T14:10:07.800114","indexId":"70265808","displayToPublicDate":"2025-04-05T09:01:07","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"Salinas Valley integrated hydrologic and reservoir operations models, Monterey and San Luis Obispo Counties, California","docAbstract":"<p><span>The area surrounding the Salinas Valley groundwater basin in Monterey and San Luis Obispo Counties of California is a highly productive agricultural area, contributes significantly to the local economy, and provides a substantial portion of vegetables and other agricultural commodities to the Nation. This region of California provides about half of the Nation’s lettuce, celery, broccoli, and spinach each year. Thus, this agricultural area provides significant volumes of agricultural products not just for California but the entire United States. Changes in population and increased agricultural development, which includes a shift toward more water-intensive crops, and climate variability, have put increasing demand on both surface water and groundwater resources in the valley. This has resulted in water management challenges in the Salinas Valley that are predominantly related to distribution of water supply throughout the basin. Where and when the water is present in the surface and subsurface does not coincide with where and when the water is needed. To deal with the distribution issue, historically water has been used conjunctively in the valley. Conjunctive use is a water management strategy that coordinates surface water and groundwater use to maximize water availability. Groundwater is used throughout the Salinas Valley to meet water demands when surface water supplies are insufficient. Availability of surface water is constrained by climate. Precipitation and streamflow vary seasonally and year to year. Although there are two reservoirs in the Salinas Valley to capture and store water during wet periods, the only conveyance of reservoir water to coastal agricultural areas is the Salinas River. Increasing demand on groundwater and surface water resources throughout the Salinas Valley has resulted in undesirable effects of unsustainable water use, such as surface water depletion, groundwater level declines, storage depletion in the principal aquifers, and seawater intrusion. To address these escalating issues, local communities, water management agencies, and groundwater sustainability agencies are evaluating how to sustainably manage both their surface water and groundwater resources. To meet water demands and reduce undesirable effects of unsustainable water use, continued conjunctive management of surface water and groundwater would ideally incorporate strategies to deal with increases in demand and a variable climate. To evaluate the challenging water management issues in the Salinas Valley, the U.S. Geological Survey, Monterey County Water Resource Agency, and the Salinas Valley Basin Groundwater Sustainability Agency developed a comprehensive suite of models that represent the Salinas Valley Hydrogeologic system called the Salinas Valley System Model. The Salinas Valley Geologic Framework was developed to characterize the subsurface using various topographic and geologic data sources, including information on hydrogeologic units, their surfaces and extents, geologic structures, lithology, and elevations from borehole data and cross sections, as well as details on faults and existing models. The Salinas Valley Watershed Model simulates the entire Salinas River watershed. Monthly surface water inflows into the integrated hydrologic model domain were simulated using the Salinas Valley Watershed Model. The historical model uses historical climate data, water and land use data, and reservoir releases to simulate agricultural operations, including landscape water demands, diversions, and reclaimed wastewater. The operational model adds an embedded reservoir operations framework to the simulation of the historical model that allows specified operational rules to simulate reservoir releases and changes in reservoir storage. The operational model assumes current reservoir operations and constant land use, which differs from historical conditions. Thus, the operational model is a hypothetical baseline model that can be used by local water managers to evaluate and quantify potential benefits of water supply projects. Together, the geologic framework, watershed, historical, and operational models form a tool that can be used to simulate irrigated agriculture and associated reservoir operations of the integrated hydrologic system of the Salinas Valley.</span></p>","language":"English","publisher":"Eartharxiv","doi":"10.31223/X5ZD9N","usgsCitation":"Henson, W.R., Hanson, R., Boyce, S.E., Hevesi, J.A., and Jachens, E.R., 2025, Salinas Valley integrated hydrologic and reservoir operations models, Monterey and San Luis Obispo Counties, California: EarthArXiv, https://doi.org/10.31223/X5ZD9N.","productDescription":"312 p.","ipdsId":"IP-172765","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":488263,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31223/x5zd9n","text":"Publisher Index Page"},{"id":484636,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933598,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hanson, Randy 0000-0002-9819-7141","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":216356,"corporation":false,"usgs":false,"family":"Hanson","given":"Randy","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":933667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hevesi, Joseph A. 0000-0003-2898-1800 jhevesi@usgs.gov","orcid":"https://orcid.org/0000-0003-2898-1800","contributorId":1507,"corporation":false,"usgs":true,"family":"Hevesi","given":"Joseph","email":"jhevesi@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jachens, Elizabeth Rae 0000-0001-5885-8892","orcid":"https://orcid.org/0000-0001-5885-8892","contributorId":294690,"corporation":false,"usgs":true,"family":"Jachens","given":"Elizabeth","email":"","middleInitial":"Rae","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933670,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70265488,"text":"70265488 - 2025 - Linking tidal-creek sediment fluxes to vertical sediment accretion in a restored salt marsh","interactions":[],"lastModifiedDate":"2026-02-10T13:50:34.127265","indexId":"70265488","displayToPublicDate":"2025-04-05T08:46:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Linking tidal-creek sediment fluxes to vertical sediment accretion in a restored salt marsh","docAbstract":"<p>Despite growing interest and investment in salt-marsh restoration, relatively few marshes subjected to restoration efforts have been systematically monitored to assess physical restoration trajectory or success. In south San Francisco Bay, CA, USA, where 83% of wetlands were lost via human manipulation, the largest wetland restoration effort on the U.S. west coast is currently underway, restoring approximately 6,000 ha of former salt-production ponds to mixed habitats. The Whale’s Tail–Cargill Mitigation salt-marsh complex in south San Francisco Bay has a century-long history of drainage, industrial use as salt-production ponds, and subsequent restoration and recovery. Restoration of the 20-ha Cargill Mitigation Marsh was initiated in the late 1990s when the levee surrounding the subsided, former salt-production pond was breached in two locations, enabling conversion back to salt-marsh habitat in the subsequent decades. Here we present time-series measurements of sediment fluxes in the primary tidal creek entering the salt-marsh complex, which are compared to decadal-scale sedimentation patterns determined from repeat elevation surveys and cores collected at the study site. All three methods show net sediment import to the restored marsh. The greatest equivalent sedimentation rates occurred early in the restoration, with generally decreasing rates through time. The long-term average, as determined from cores and expressed as a vertical sedimentation rate, is approximately 1.8 cm yr <sup>-1</sup>. Rates from the elevation data are between 1.4 and 2.6 cm yr<sup>-1</sup>, with higher rates earlier in the restoration. The most recent estimates, computed from time-series instrument deployments, indicate seasonal variability in sediment import. Annualized rates are lower in winter, approximately 0.1 cm yr<sup>-1</sup>, and higher in summer, approximately 1.7 cm yr<sup>-1</sup>. Although our measured long-term equivalent sedimentation rates are considerably greater than the current local relative sea-level rise (SLR) of 0.3 cm yr<sup>-1</sup>, an increase in SLR or decrease in available suspended sediment would threaten the ability of the marsh to keep pace with SLR and avoid drowning in the future.</p>","language":"English","publisher":"Wiley","doi":"10.1002/esp.70053","usgsCitation":"Nowacki, D.J., Lacy, J.R., and La Selle, S., 2025, Linking tidal-creek sediment fluxes to vertical sediment accretion in a restored salt marsh: Earth Surface Processes and Landforms, v. 50, no. 5, e70053, 15 p., https://doi.org/10.1002/esp.70053.","productDescription":"e70053, 15 p.","ipdsId":"IP-163776","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488630,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.70053","text":"Publisher Index Page"},{"id":484336,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"south San Francisco Bay","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -122.17457846697607,\n              37.64847819573012\n            ],\n            [\n              -122.17457846697607,\n              37.554966892844135\n            ],\n            [\n              -122.09191529974544,\n              37.554966892844135\n            ],\n            [\n              -122.09191529974544,\n              37.64847819573012\n            ],\n            [\n              -122.17457846697607,\n              37.64847819573012\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"50","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Nowacki, Daniel J. 0000-0002-7015-3710 dnowacki@usgs.gov","orcid":"https://orcid.org/0000-0002-7015-3710","contributorId":174586,"corporation":false,"usgs":true,"family":"Nowacki","given":"Daniel","email":"dnowacki@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":932818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lacy, Jessica R. 0000-0002-2797-6172","orcid":"https://orcid.org/0000-0002-2797-6172","contributorId":201703,"corporation":false,"usgs":true,"family":"Lacy","given":"Jessica","email":"","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":932820,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265929,"text":"70265929 - 2025 - Identical sequence types of Yersinia ruckeri associated with lethal disease in wild-caught invasive Blue Catfish and cultured hybrid catfish (Channel Catfish ♀ × Blue Catfish ♂) from disparate aquatic ecosystems","interactions":[],"lastModifiedDate":"2025-05-12T15:45:47.245272","indexId":"70265929","displayToPublicDate":"2025-04-04T11:59:08","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2177,"text":"Journal of Aquatic Animal Health","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Identical sequence types of <i>Yersinia ruckeri</i> associated with lethal disease in wild-caught invasive Blue Catfish and cultured hybrid catfish (Channel Catfish ♀ × Blue Catfish ♂) from disparate aquatic ecosystems","title":"Identical sequence types of Yersinia ruckeri associated with lethal disease in wild-caught invasive Blue Catfish and cultured hybrid catfish (Channel Catfish ♀ × Blue Catfish ♂) from disparate aquatic ecosystems","docAbstract":"<div class=\" sec\"><div class=\"title\">Objective</div><p class=\"chapter-para\">The Blue Catfish<span>&nbsp;</span><i>Ictalurus furcatus</i><span>&nbsp;</span>is commonly raised in warmwater aquaculture in the United States to produce Channel Catfish<span>&nbsp;</span><i>I. punctatus</i><span>&nbsp;</span>× Blue Catfish hybrids. It is also a prominent aquatic invasive species of concern in the mid-Atlantic region of the United States. Here,<span>&nbsp;</span><i>Yersina ruckeri</i><span>&nbsp;</span>was isolated from moribund Blue Catfish and hybrid catfish from disparate regions of the USA. The goal of the research here was to compare these<span>&nbsp;</span><i>Y. ruckeri</i><span>&nbsp;</span>strains to each other and other known strains for which adequate sequence data was available. In addition, we sought to determine if the strain from Blue Catfish was pathogenic to Rainbow Trout<span>&nbsp;</span><i>Oncorhynchus mykiss</i>.</p></div><div class=\" sec\"><div class=\"title\">Methods</div><p class=\"chapter-para\">Moribund hybrid catfish from culture ponds in Mississippi were processed for diagnostic evaluation in March 2016. In April 2022, a moribund Blue Catfish specimen was collected from a tributary of the Nanticoke River in Maryland. Bacterial isolates were identified and characterized using biochemical tests, antimicrobial sensitivity screening, serotyping, and complete or partial genome sequencing. Disease pathology was described via histology. The isolate from Blue Catfish was used in challenge experiments to determine if it was pathogenic to Rainbow Trout. Multilocus sequencing typing was conducted using the PubMLST database.</p></div><div class=\" sec\"><div class=\"title\">Results</div><p class=\"chapter-para\">Biochemical testing was consistent with<span>&nbsp;</span><i>Y. ruckeri</i>. A draft genome of the<span>&nbsp;</span><i>Y. ruckeri</i><span>&nbsp;</span>isolate was assembled based on Oxford Nanopore Technology sequencing and identified a single genomic replicon (3,791,418 bp) consistent in size to other<span>&nbsp;</span><i>Y. ruckeri</i><span>&nbsp;</span>genomes and a pLT plasmid (60, 933 bp). The challenge study demonstrated no significant virulence of this isolate for Rainbow Trout (<i>Y. ruckeri</i>). This isolate was most similar to other strains isolated from ictalurids. Notably, the gyrase B gene from this isolate was identical to that of archived strains isolated from moribund Mississippi hybrid catfish aquaculture during 2016 and these isolates share identical PubMLST sequence type profiles. Similarly, they shared a pLT plasmid that differed by only 6 bp. This plasmid has never been reported from trout isolates and appears to be unique to ictalurids.</p></div><div class=\" sec\"><div class=\"title\">Conclusions</div><p class=\"chapter-para\">Analyses here provide preliminary genetic evidence that geographically distant (Maryland and Mississippi, USA) isolates of<span>&nbsp;</span><i>Y. ruckeri</i><span>&nbsp;</span>from ictalurids are genetically similar to each other and<span>&nbsp;</span><i>Y. ruckeri</i><span>&nbsp;</span>(strain SC09) that infects ictalurids in China. This strain is not a biothreat to Rainbow Trout at typical culture temperatures.</p></div>","language":"English","publisher":"Oxford Academic","doi":"10.1093/jahafs/vsae004","usgsCitation":"Densmore, C., Hendrix, M., Reichley, S., Raines, C.D., Bressman, N., Crum, Z., Khoo, L., Waldbieser, G., Griffin, M., Welch, T., and Iwanowicz, L., 2025, Identical sequence types of Yersinia ruckeri associated with lethal disease in wild-caught invasive Blue Catfish and cultured hybrid catfish (Channel Catfish ♀ × Blue Catfish ♂) from disparate aquatic ecosystems: Journal of Aquatic Animal Health, v. 37, no. 1, p. 21-33, https://doi.org/10.1093/jahafs/vsae004.","productDescription":"13 p.","startPage":"21","endPage":"33","ipdsId":"IP-149482","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":484851,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"37","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Densmore, Christine L. 0000-0001-6440-0781","orcid":"https://orcid.org/0000-0001-6440-0781","contributorId":349917,"corporation":false,"usgs":false,"family":"Densmore","given":"Christine L.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":934042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hendrix, Madeleine","contributorId":353591,"corporation":false,"usgs":false,"family":"Hendrix","given":"Madeleine","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":934043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reichley, Stephen","contributorId":353592,"corporation":false,"usgs":false,"family":"Reichley","given":"Stephen","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":934044,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Raines, Clayton D. 0000-0002-0403-190X","orcid":"https://orcid.org/0000-0002-0403-190X","contributorId":296362,"corporation":false,"usgs":true,"family":"Raines","given":"Clayton","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":934045,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bressman, Noah 0000-0002-2916-3562","orcid":"https://orcid.org/0000-0002-2916-3562","contributorId":349920,"corporation":false,"usgs":false,"family":"Bressman","given":"Noah","affiliations":[{"id":36201,"text":"Salisbury University","active":true,"usgs":false}],"preferred":false,"id":934046,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crum, Zachary","contributorId":349921,"corporation":false,"usgs":false,"family":"Crum","given":"Zachary","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":934047,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Khoo, Lester","contributorId":353593,"corporation":false,"usgs":false,"family":"Khoo","given":"Lester","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":934048,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Waldbieser, Geoffrey","contributorId":353594,"corporation":false,"usgs":false,"family":"Waldbieser","given":"Geoffrey","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":934049,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Griffin, Matt","contributorId":353595,"corporation":false,"usgs":false,"family":"Griffin","given":"Matt","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":934050,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Welch, Timothy J","contributorId":265180,"corporation":false,"usgs":false,"family":"Welch","given":"Timothy J","affiliations":[{"id":54629,"text":"National Center for Cool and Cold Water Aquaculture, Agricultural Research Service, United States Department of Agriculture, Kearneysville, West Virginia","active":true,"usgs":false}],"preferred":false,"id":934051,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Iwanowicz, Luke R. 0000-0002-1197-6178","orcid":"https://orcid.org/0000-0002-1197-6178","contributorId":79382,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":934052,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70264969,"text":"70264969 - 2025 - Nanometer-scale relationships between sedimentary organic matter molecular composition, fluorescence, cathodoluminescence, and reflectance: The importance of oxygen content at low thermal maturities","interactions":[],"lastModifiedDate":"2025-04-07T15:21:16.156202","indexId":"70264969","displayToPublicDate":"2025-04-04T10:19:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2958,"text":"Organic Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Nanometer-scale relationships between sedimentary organic matter molecular composition, fluorescence, cathodoluminescence, and reflectance: The importance of oxygen content at low thermal maturities","docAbstract":"<p><span>Molecular characterization of sedimentary organic matter (SOM), termed macerals, is a common goal when seeking to understand petroleum generation as well as other geologic processes in deep time. However, unambiguous measurement of discrete macerals is challenging due to the small size of organic particles in sedimentary rocks, the proximity of different organic matter types to one another, mineral-organic matter interactions, and maceral mixing that occurs during SOM isolation prior to ex situ analysis. The recent advent of infrared spectrometers capable of nanometer-scale resolution and the application of these technologies to geologic samples has enabled advances in rapid, in situ molecular characterization of SOM allowing for insights into paleoenvironmental processes, such as organic matter productivity and preservation, among others. Here we employ one such technology, optical photothermal infrared (OPTIR) spectroscopy, to map SOM functional group distributions at 500-nm resolution in a sample from the Lower Cretaceous Sunniland Limestone of the South Florida Basin. Examined fields of view include occurrences of amorphous organic matter (AOM), inertinite, micrinite, solid bitumen, telalginite, and vitrinite. OPTIR data from these macerals are compared against traditional organic petrographic data from the same organic grains including fluorescence intensity and white light reflectance as well as against cathodoluminescence response, an emerging organic petrographic approach. Maceral oxygen content (using carbonyl functional group abundance as a proxy) is observed to vary widely between maceral types but correlates strongly with fluorescence and cathodoluminescence intensity as well as against reflectance. These findings highlight the important role that oxygen content plays in determining the optical properties of SOM and further demonstrate the ability of OPTIR to discriminate subtle molecular differences between SOM types.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.orggeochem.2025.104990","usgsCitation":"Jubb, A., Hackley, P.C., McAleer, R.J., and Qu, J., 2025, Nanometer-scale relationships between sedimentary organic matter molecular composition, fluorescence, cathodoluminescence, and reflectance: The importance of oxygen content at low thermal maturities: Organic Geochemistry, v. 204, 104990, 7 p., https://doi.org/10.1016/j.orggeochem.2025.104990.","productDescription":"104990, 7 p.","ipdsId":"IP-173289","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":488599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.orggeochem.2025.104990","text":"Publisher Index Page"},{"id":484251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"204","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":932125,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":932126,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","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":932127,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qu, Jing","contributorId":242671,"corporation":false,"usgs":false,"family":"Qu","given":"Jing","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":932128,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70267524,"text":"70267524 - 2025 - Streamflow response to glacier mass loss varies with basin precipitation across Alaska","interactions":[],"lastModifiedDate":"2025-05-28T14:28:22.882379","indexId":"70267524","displayToPublicDate":"2025-04-04T09:24:54","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":"Streamflow response to glacier mass loss varies with basin precipitation across Alaska","docAbstract":"<p><span>Diminishing glaciers affect streamflow, and given the extent of glaciers in Alaska and adjacent Canada, continued glacier mass loss is likely to have profound effects on ecosystems sensitive to runoff. The effects of glacier mass loss on streamflow are likely to vary across the wide ranges of basin size, glacier cover, and precipitation in this region. In this study, we use U.S. Geological Survey (USGS) streamflow data with satellite-based glacier volume change estimates to quantify how glacier mass loss subsidized streamflow over the 2000–2019 period for 116 glacierized basins. We examine interannual variability in that subsidy at three USGS-monitored glaciers to explore the ability of the subsidy to buffer streamflow derived solely from precipitation. We found the relative importance of percent glacier cover on streamflow magnitude increases in drier basins. In the driest basins, glaciers produced 40 times greater percent glacier mass loss subsidies to streamflow for the percent glacier cover compared to the wettest basins. While the subsidy from glacier mass loss buffers interannual variability in streamflow to varying degrees, it can also increase streamflow variability. Smaller amounts of percent glacier cover are needed to produce summer-melt-dominated seasonal flow regimes in drier basins than in wetter basins. Decreasing glacier cover will eventually decrease summer streamflow, increasing spring streamflow in drier basins, and attenuating seasonality with increasing spring and autumnal streamflow in wetter basins. Quantifying the downstream effects of continued glacier mass loss without the computational expense of a hydrological model is broadly applicable in this changing climate.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024WR037859","usgsCitation":"Curran, J.H., Rick, B., Littell, J., and Sass, L., 2025, Streamflow response to glacier mass loss varies with basin precipitation across Alaska: Water Resources Research, v. 61, no. 4, e2024WR037859, 18 p., https://doi.org/10.1029/2024WR037859.","productDescription":"e2024WR037859, 18 p.","ipdsId":"IP-165191","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":490156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024wr037859","text":"Publisher Index Page"},{"id":486640,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70267440,"text":"70267440 - 2025 - Optimizing control of a freshwater invader in time and space","interactions":[],"lastModifiedDate":"2025-05-23T15:49:02.287668","indexId":"70267440","displayToPublicDate":"2025-04-04T08:37:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing control of a freshwater invader in time and space","docAbstract":"The global spread of invasive species in aquatic ecosystems has prompted population control efforts to mitigate negative impacts on native species and ecosystem functions. Removal programs that optimally allocate removal effort across space and time offer promise for improving invader suppression or eradication, especially given the limited resources available to these programs. However, science-based guidance to inform such programs remains limited. This study leverages two intensive fish removal programs for nonnative green sunfish (Lepomis cyanellus) in intermittent streams of the Bill Williams River basin in Arizona, USA, to explore alternative management strategies involving variable allocation of removal effort in time and space and compare static versus dynamic decision rules. We used Bayesian hierarchical modeling to estimate demographic parameters using existing removal data, with evidence that both removal programs led to at least a 0.39 probability of eradication. Simulated alternative management strategies revealed that population suppression, but not eradication, could be achieved with reduced effort, and that dynamic management practices that respond to species abundance in real time can improve the efficiency of removal efforts. High removal frequency and program duration, including continued monitoring after zero fish were captured, contributed to successful population control. With management efforts struggling to keep pace with the rising spread and impacts of invasive species, this research demonstrates the utility of quantitative removal models to help improve invasive removal programs and robustly evaluate the success of population suppression and eradication.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.70026","usgsCitation":"Diallo, J., Converse, S.J., Chmiel, M., Stites, A., and Olden, J., 2025, Optimizing control of a freshwater invader in time and space: Ecological Applications, v. 35, no. 3, e70026, 17 p., https://doi.org/10.1002/eap.70026.","productDescription":"e70026, 17 p.","ipdsId":"IP-151980","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487965,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.70026","text":"Publisher Index Page"},{"id":486519,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Bill Williams River basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -113.87855871265536,\n              34.69978264689745\n            ],\n            [\n              -113.87855871265536,\n              34.0589425188774\n            ],\n            [\n              -113.23223159100554,\n              34.0589425188774\n            ],\n            [\n              -113.23223159100554,\n              34.69978264689745\n            ],\n            [\n              -113.87855871265536,\n              34.69978264689745\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"35","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Diallo, Jessica O.","contributorId":355825,"corporation":false,"usgs":false,"family":"Diallo","given":"Jessica O.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":938203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":938204,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chmiel, Matthew","contributorId":355827,"corporation":false,"usgs":false,"family":"Chmiel","given":"Matthew","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":938205,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stites, Andy","contributorId":355829,"corporation":false,"usgs":false,"family":"Stites","given":"Andy","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":938206,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olden, Julian D.","contributorId":338326,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[],"preferred":false,"id":938207,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70265931,"text":"70265931 - 2025 - Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery","interactions":[],"lastModifiedDate":"2025-04-22T16:13:44.477123","indexId":"70265931","displayToPublicDate":"2025-04-03T11:10:32","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5012,"text":"Soil and Tillage Research","active":true,"publicationSubtype":{"id":10}},"title":"Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery","docAbstract":"<p><span>Crop residue serves an important role in agricultural systems as high levels of fractional crop residue cover (</span><i>f</i><sub>R</sub><span>) can reduce erosion, preserve soil moisture, and build soil organic carbon. However, the ability to accurately quantify&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;at scale has been limited. In this study we produced annual maps of&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;for farmland in Maryland, USA using WorldView-3 (WV3) imagery paired with on-farm photographs (</span><i>n</i><span> = 895) classified to&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;using SamplePoint software. Univariate linear regressions were used to compare photograph&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;to WV3 crop residue indices including: 1) Shortwave Infrared Normalized Difference Residue Index (SINDRI), 2) Shortwave Infrared Difference Residue Index (SIDRI), 3) Normalized Difference Tillage Index (NDTI), and 4) Shortwave Infrared Angle Index (SWIRA). SINDRI and SIDRI are based on narrow bands capable of measuring lignocellulose absorption features. NDTI and SWIRA are based on Landsat-comparable broad bands. Our findings demonstrated that SINDRI outperformed other indices in&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;estimation in terms of coefficient of determination (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.869) and root mean square error (RMSE = 0.111), when&nbsp;</span><i>R</i><sup>2</sup><span>&nbsp;and RMSE were averaged across six individual years. For a univariate analysis combining five years of high-quality WV3 imagery, SINDRI again exhibited the highest&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;estimation performance (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.795; RMSE = 0.141), suggesting that SINDRI can map&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;accurately with a singular relationship, potentially reducing the need for labor-intensive ground data collection. For broad-band indices, a&nbsp;multiple linear regression&nbsp;analysis that included a Water Index (WI) and Normalized Difference Vegetation Index (NDVI) as additional predictors increased the accuracy of&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;estimation significantly, particularly for SWIRA (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.767; RMSE = 0.144), but also NDTI (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.654; RMSE = 0.174). Our findings suggest that while indices computed from narrow-band imagery are most accurate for&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;estimation, SWIRA has the potential to improve&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;estimation compared to NDTI, especially when used in conjunction with WI and NDVI. An index suite of SWIRA, WI, and NDVI can be computed with Landsat 4–9 imagery, providing a more accurate record of global&nbsp;</span><i>f</i><sub>R</sub><span>&nbsp;dating back to 1982.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.still.2025.106524","usgsCitation":"Lamb, B.T., Hively, W.D., Jennewein, J., Thieme, A., Soroka, A.M., Santos, L., Jones, D., and Mirsky, S., 2025, Multiyear crop residue cover mapping using narrow-band vs. broad-band shortwave infrared satellite imagery: Soil and Tillage Research, v. 251, 106524, 19 p., https://doi.org/10.1016/j.still.2025.106524.","productDescription":"106524, 19 p.","ipdsId":"IP-170664","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia  Water Science Center","active":true,"usgs":true}],"links":[{"id":488482,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.still.2025.106524","text":"Publisher Index Page"},{"id":484843,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"251","noUsgsAuthors":false,"publicationDate":"2025-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Lamb, Brian T. 0000-0001-7957-5488","orcid":"https://orcid.org/0000-0001-7957-5488","contributorId":291893,"corporation":false,"usgs":true,"family":"Lamb","given":"Brian","middleInitial":"T.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jennewein, Jyoti","contributorId":243442,"corporation":false,"usgs":false,"family":"Jennewein","given":"Jyoti","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":934058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thieme, Alison","contributorId":335444,"corporation":false,"usgs":false,"family":"Thieme","given":"Alison","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":934059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soroka, Alexander M. 0000-0002-8002-5229","orcid":"https://orcid.org/0000-0002-8002-5229","contributorId":201664,"corporation":false,"usgs":true,"family":"Soroka","given":"Alexander","email":"","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":934060,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Santos, Leticia","contributorId":353598,"corporation":false,"usgs":false,"family":"Santos","given":"Leticia","affiliations":[{"id":13595,"text":"NCSU","active":true,"usgs":false}],"preferred":false,"id":934061,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Daniela","contributorId":353599,"corporation":false,"usgs":false,"family":"Jones","given":"Daniela","affiliations":[{"id":13595,"text":"NCSU","active":true,"usgs":false}],"preferred":false,"id":934062,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mirsky, Steven","contributorId":292000,"corporation":false,"usgs":false,"family":"Mirsky","given":"Steven","affiliations":[{"id":62785,"text":"USDA-ARS Sustainable Agricultural Systems Laboratory","active":true,"usgs":false}],"preferred":false,"id":934063,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70265463,"text":"70265463 - 2025 - Assessing the effect of coral reef restoration location on coastal flood hazard along the San Juan Coastline, Puerto Rico","interactions":[],"lastModifiedDate":"2025-04-07T15:00:09.063332","indexId":"70265463","displayToPublicDate":"2025-04-03T09:53:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the effect of coral reef restoration location on coastal flood hazard along the San Juan Coastline, Puerto Rico","docAbstract":"<p><span>Coastal resilience has become a pressing global issue due to the growing vulnerability of coastlines to the effects of climate change. Nature-based solutions have emerged as a promising approach to coastal protection to not only enhance coastal resilience, but also restore critical ecosystems. Coral reef restoration has the potential to provide ecosystem services benefits; however, there are still key uncertainties in linking restoration design to reductions in coastal flood hazard under current and future climate conditions. In this study, we applied one-dimensional and two-dimensional numerical coastal engineering models, calibrated and validated using field data, to evaluate the effectiveness of coral restoration scenarios on coastal waves, water levels, and flooding along the coast of San Juan, Puerto Rico, U.S.A. Model results indicate a small reduction in maximum water levels under the proposed restoration scenarios. This underscores the importance of these endeavors, not only for ecological preservation but also for preventing further reef deterioration. Such preservation is essential for mitigating the increased coastal risks anticipated in the future. Results from this study provide information to guide policymakers and coastal managers in making informed decisions on viable restoration project design options. By systematically evaluating how restoration location impacts coastal flood hazards, communities can develop and implement proactive strategies to mitigate flood-related risk. In addition, by restoring coral reefs, communities can contribute to environmental preservation while ensuring sustainable development and protection of coastal environments.</span></p>","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2025.1528460","usgsCitation":"Familkhalili, R., Storlazzi, C.D., Nemeth, M., and Viehman, S., 2025, Assessing the effect of coral reef restoration location on coastal flood hazard along the San Juan Coastline, Puerto Rico: Frontiers in Marine Science, v. 12, 1528460, 11 p., https://doi.org/10.3389/fmars.2025.1528460.","productDescription":"1528460, 11 p.","ipdsId":"IP-169548","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488566,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2025.1528460","text":"Publisher Index Page"},{"id":484246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico, San Juan coastline","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -66.08782395132914,\n              18.480815260981046\n            ],\n            [\n              -66.09096383189711,\n              18.45217867401871\n            ],\n            [\n              -65.96850848974445,\n              18.42720367145678\n            ],\n            [\n              -65.96005496513799,\n              18.468673931779904\n            ],\n            [\n              -66.08782395132914,\n              18.480815260981046\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Familkhalili, Ramin","contributorId":353052,"corporation":false,"usgs":false,"family":"Familkhalili","given":"Ramin","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":932770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nemeth, Michael","contributorId":353056,"corporation":false,"usgs":false,"family":"Nemeth","given":"Michael","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":932773,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Viehman, Shay","contributorId":353053,"corporation":false,"usgs":false,"family":"Viehman","given":"Shay","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":932771,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70265420,"text":"70265420 - 2025 - Temporal and spatial comparison of coal mine ventilation methane emissions and mitigation quantified using PRISMA satellite data and on-site measurements","interactions":[],"lastModifiedDate":"2025-04-04T14:34:41.568218","indexId":"70265420","displayToPublicDate":"2025-04-03T09:24:13","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Temporal and spatial comparison of coal mine ventilation methane emissions and mitigation quantified using PRISMA satellite data and on-site measurements","docAbstract":"<p><span>Emission monitoring at the facility level (bottom-up, BU) is key for accurate reporting of coal mine methane (CMM) emissions. Recent advances in aerial and satellite observations (top-down, TD) indicate that these methods have the potential to support CMM emissions monitoring and reporting of, as well as track the effectiveness of, mitigation actions. That said, studies have shown discrepancies between BU and TD estimations. Performing TD monitoring with concurrent BU measurements at the same mines may help address the observed discrepancies and improve quantification methods to narrow the gap between BU and TD data. This paper presents a comparison of concurrent BU-TD methane emission fluxes monitored from two ventilation shafts at a southwestern Virginia, USA, longwall mine to complement the existing body of studies on satellite-based monitoring of coal mines by incorporating continuous ground-based monitoring with concurrent TD monitoring of methane emission fluxes to address the gap and provide valuable insights into temporal emission patterns. The shafts were monitored on multiple dates between 2020 and 2023. BU monitoring was performed at the exhaust fans, while TD quantifications used PRISMA hyperspectral satellite data and two different wind reanalysis datasets (i.e., GEOS-FP and ERA5). This mine also offered a unique opportunity for BU-TD comparisons before and after ventilation air methane (VAM) oxidizer operation, which was installed at one of the shafts in 2022. The results showed that TD-estimated mean fluxes were generally lower than BU data, which were attributed to quantification difficulties associated with the low albedo caused by heavy vegetation and the terrain of the area. However, despite the discrepancies in mean emissions, both the interquartile range and the data range of the distributions generally overlapped, and the estimates correctly showed the emission trends.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2025.179268","usgsCitation":"Karacan, C.O., Irakulis-Loitxate, I., Field, R., and Warwick, P., 2025, Temporal and spatial comparison of coal mine ventilation methane emissions and mitigation quantified using PRISMA satellite data and on-site measurements: Science of the Total Environment, v. 975, 179268, 15 p., https://doi.org/10.1016/j.scitotenv.2025.179268.","productDescription":"179268, 15 p.","ipdsId":"IP-170217","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":488612,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2025.179268","text":"Publisher Index Page"},{"id":484193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":484182,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencedirect.com/science/article/pii/S0048969725009040"}],"country":"United States","state":"Virginia","county":"Buchanan County","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -81.90238072120187,\n              37.52094325898793\n            ],\n            [\n              -82.24473468408098,\n              37.52094325898793\n            ],\n            [\n              -82.24473468408098,\n              37.186003050018414\n            ],\n            [\n              -81.90238072120187,\n              37.186003050018414\n            ],\n            [\n              -81.90238072120187,\n              37.52094325898793\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"975","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":932647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irakulis-Loitxate, Itziar","contributorId":352980,"corporation":false,"usgs":false,"family":"Irakulis-Loitxate","given":"Itziar","affiliations":[{"id":84319,"text":"UNEP, International Methane Emission Observatory, Universitat Politècnica de València,","active":true,"usgs":false}],"preferred":false,"id":932648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Field, Robert A.","contributorId":352981,"corporation":false,"usgs":false,"family":"Field","given":"Robert A.","affiliations":[{"id":82714,"text":"UNEP, International Methane Emission Observatory","active":true,"usgs":false}],"preferred":false,"id":932649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warwick, Peter D. 0000-0002-3152-7783","orcid":"https://orcid.org/0000-0002-3152-7783","contributorId":207248,"corporation":false,"usgs":true,"family":"Warwick","given":"Peter D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":932650,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70265248,"text":"70265248 - 2025 - Scent detection dogs detect a species of hard tick, Dermacentor albipictus, with comparable accuracy and efficiency to traditional tick drag surveys","interactions":[],"lastModifiedDate":"2025-04-04T14:16:10.572872","indexId":"70265248","displayToPublicDate":"2025-04-02T15:51:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20749,"text":"Parasites and Vectors","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Scent detection dogs detect a species of hard tick, <i>Dermacentor albipictus</i>, with comparable accuracy and efficiency to traditional tick drag surveys","title":"Scent detection dogs detect a species of hard tick, Dermacentor albipictus, with comparable accuracy and efficiency to traditional tick drag surveys","docAbstract":"<h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Background</h3><p>Accurate surveillance data are critical for addressing tick and tick-borne pathogen risk to human and animal health. Current surveillance methods for detecting invading or expanding tick species are limited in their ability to scale efficiently to state or national levels. In this study we explored the potential use of scent detection dogs to assist field surveys for a hard tick species:<span>&nbsp;</span><i>Dermacentor albipictus</i>.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Methods</h3><p>We used a series of indoor and in situ training simulations to teach scent detection dogs to recognize<span>&nbsp;</span><i>D. albipictus</i><span>&nbsp;</span>scent, distinguish tick scent from associated vegetation, and develop a cautious search pattern. After training, we deployed both a scent detection dog survey team and a human-only survey team on transect and surveillance plot surveys then compared the detection rates and efficiency of both methods.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Results</h3><p>Scent detection dogs required more time and money to train on field surveys but were comparable to traditional tick drags when accounting for cost per unit area surveyed. There was a lack of agreement on positive (ticks present) versus negative (ticks not present) sites between the two methods, implying that neither method is particularly reliable at detecting<span>&nbsp;</span><i>D. albipictus</i>.</p><h3 class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Conclusions</h3><p>Estimating detection bias and false negative rates for tick surveillance methods such as tick drags will be important for accurately evaluating tick-borne disease risk across space and into the future. We found scent detection dogs to be a reasonable alternative sampling approach to consider when ticks are at low abundance or patchily distributed such as during tick range expansion or novel invasions. Scent detection dogs may also be useful for sampling for ticks in areas or along surfaces that are difficult to sample with the traditional tick drag technique like at ports of entry or livestock competitions.</p>","language":"English","publisher":"Springer Nature","doi":"10.1186/s13071-024-06519-8","usgsCitation":"Koser, T., Hurt, A., Thompson, L., , C., Wise, B., and Cross, P., 2025, Scent detection dogs detect a species of hard tick, Dermacentor albipictus, with comparable accuracy and efficiency to traditional tick drag surveys: Parasites and Vectors, v. 18, no. 1, 126, 10 p., https://doi.org/10.1186/s13071-024-06519-8.","productDescription":"126, 10 p.","ipdsId":"IP-168190","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":488608,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13071-024-06519-8","text":"Publisher Index Page"},{"id":484191,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"northwestern Wyoming","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -111.04102386062645,\n              44.992117493072584\n            ],\n            [\n              -111.04102386062645,\n              42.90928827965368\n            ],\n            [\n              -108.00191469467029,\n              42.90928827965368\n            ],\n            [\n              -108.00191469467029,\n              44.992117493072584\n            ],\n            [\n              -111.04102386062645,\n              44.992117493072584\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"18","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Koser, Troy","contributorId":344812,"corporation":false,"usgs":false,"family":"Koser","given":"Troy","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":932600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurt, Aimee","contributorId":219932,"corporation":false,"usgs":false,"family":"Hurt","given":"Aimee","affiliations":[],"preferred":false,"id":932601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":207364,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":932602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":" Courtemanch","contributorId":204813,"corporation":false,"usgs":false,"given":"Courtemanch","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":932603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wise, Benjamin","contributorId":189800,"corporation":false,"usgs":false,"family":"Wise","given":"Benjamin","affiliations":[],"preferred":false,"id":932604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":932605,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70265438,"text":"70265438 - 2025 - High-precision U-Pb data and reference age for Emerald Lake apatite","interactions":[],"lastModifiedDate":"2025-04-07T14:33:41.213661","indexId":"70265438","displayToPublicDate":"2025-04-02T09:25:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5898,"text":"Data in Brief","onlineIssn":"2352-3409","active":true,"publicationSubtype":{"id":10}},"title":"High-precision U-Pb data and reference age for Emerald Lake apatite","docAbstract":"<p><span>New isotope dilution thermal ionization mass spectrometry U-Pb data for Emerald Lake apatite demonstrate its potential as a reference material for geochronology. A three-dimensional&nbsp;</span><sup>238</sup><span>U/</span><sup>206</sup><span>Pb-</span><sup>207</sup><span>Pb/</span><sup>206</sup><span>Pb-</span><sup>204</sup><span>Pb/</span><sup>206</sup><span>Pb isochron produces a 95.2 ± 1.1 Ma date with an initial Pb isotopic composition of&nbsp;</span><sup>206</sup><span>Pb/</span><sup>204</sup><span>Pb = 18.85 ± 0.19 and&nbsp;</span><sup>207</sup><span>Pb/</span><sup>204</sup><span>Pb = 15.68 ± 0.10 (n = 5, MSWD = 9.5). These data yield a weighted mean initial Pb-corrected&nbsp;</span><sup>206</sup><span>Pb/</span><sup>238</sup><span>U date of 95.18 ± 0.10 Ma (n = 5, MSWD = 1.5) and a weighted mean initial Pb-corrected&nbsp;</span><sup>207</sup><span>Pb/</span><sup>235</sup><span>U date of 95.20 ± 0.17 Ma (n = 5, MSWD = 0.5). The new high-precision U-Pb age of Emerald Lake apatite further enables its utility as a reference material for&nbsp;</span><i>in situ</i><span>&nbsp;U-Pb apatite geochronology. Aliquots of Emerald Lake apatite are available for distribution for use in future studies.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.dib.2025.111464","usgsCitation":"Apen, F., Gaynor, S.P., and Schoene, B., 2025, High-precision U-Pb data and reference age for Emerald Lake apatite: Data in Brief, v. 60, 111464, 7 p., https://doi.org/10.1016/j.dib.2025.111464.","productDescription":"111464, 7 p.","ipdsId":"IP-175496","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":488556,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dib.2025.111464","text":"Publisher Index Page"},{"id":484241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","noUsgsAuthors":false,"publicationDate":"0202-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Apen, Francisco","contributorId":353004,"corporation":false,"usgs":false,"family":"Apen","given":"Francisco","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":932730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaynor, Sean Patrick 0000-0002-8353-511X","orcid":"https://orcid.org/0000-0002-8353-511X","contributorId":346264,"corporation":false,"usgs":true,"family":"Gaynor","given":"Sean","email":"","middleInitial":"Patrick","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":932731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schoene, Blair","contributorId":353005,"corporation":false,"usgs":false,"family":"Schoene","given":"Blair","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":932732,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265044,"text":"fs20253017 - 2025 - U.S. Geological Survey global seabed mineral resources","interactions":[],"lastModifiedDate":"2025-08-07T20:38:53.667421","indexId":"fs20253017","displayToPublicDate":"2025-04-01T12:30:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3017","displayTitle":"U.S. Geological Survey Global Seabed Mineral Resources","title":"U.S. Geological Survey global seabed mineral resources","docAbstract":"<p>The U.S. Geological Survey (USGS) provides science and data on seabed mineral resources and ecosystems, as well as on the potential hazards associated with extraction. The Nation relies on minerals for infrastructure, technology, manufacturing, and energy production. Critical minerals are essential to the economic and national security of the United States and have a supply chain vulnerable to disruption. </p><p>For decades, USGS scientific innovation has contributed to the delineation of seabed mineral resources, the mechanisms of seabed mineral formation, and the environmental impacts of resource extraction. Since 1962, the USGS has also led scientific inquiries into the potential for deep sea mining. By providing impartial science on seabed minerals and their environmental setting in the deep oceans, the USGS enables decision-makers to evaluate the best practices for mineral resource development.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253017","usgsCitation":"U.S. Geological Survey, 2025, U.S. Geological Survey global seabed mineral resources (ver. 1.1, April 8, 2025): U.S. Geological Survey Fact Sheet 2025–3017, 4 p., https://doi.org/10.3133/fs20253017.","productDescription":"4 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-173468","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":484322,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2025/3017/versionHist.txt","size":"680 KB","linkFileType":{"id":2,"text":"txt"}},{"id":484142,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3017/fs20253017.XML","description":"FS 2025-3017 XML"},{"id":493746,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118515.htm","linkFileType":{"id":5,"text":"html"}},{"id":484143,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3017/images/"},{"id":484141,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253017/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3017 HTML"},{"id":484014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3017/fs20253017.pdf","text":"Report","size":"4.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3017 PDF"},{"id":484013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3017/coverthb2.jpg"}],"edition":"Version 1.0: April 1, 2025; Version 1.1: April 8, 2025","contact":"<p>Lead, <a href=\"https://www.usgs.gov/centers/pcmsc/science/global-seabed-mineral-resources\" data-mce-href=\"https://www.usgs.gov/centers/pcmsc/science/global-seabed-mineral-resources\">Global Seabed Minerals Resources Project</a><br>Coordinator, <a href=\"https://www.usgs.gov/programs/cmhrp\" data-mce-href=\"https://www.usgs.gov/programs/cmhrp\">Coastal and Marine Hazards and Resources Program</a><br>Coordinator, <a href=\"https://www.usgs.gov/programs/mineral-resources-program\" data-mce-href=\"https://www.usgs.gov/programs/mineral-resources-program\">Mineral Resources Program</a><br>Coordinator, <a href=\"https://www.usgs.gov/programs/land-management-research-program\" data-mce-href=\"https://www.usgs.gov/programs/land-management-research-program\">Land Management Research Program</a></p><p><a href=\"../contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2025-04-01","revisedDate":"2025-04-08","noUsgsAuthors":false,"publicationDate":"2025-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"U.S. Geological Survey","contributorId":152492,"corporation":true,"usgs":false,"organization":"U.S. Geological Survey","id":932401,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70271166,"text":"70271166 - 2025 - Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Version 4 product user guide","interactions":[],"lastModifiedDate":"2025-11-26T18:00:37.184663","indexId":"70271166","displayToPublicDate":"2025-04-01T11:55:49","publicationYear":"2025","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Version 4 product user guide","docAbstract":"<p>The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a multispectral imager that was launched on board the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra Platform on December 18, 1999, and has been observing and collecting Earth observations for over 25 years. ASTER covers a wide spectral region from visible to thermal infrared, including 14 spectral bands with high spatial, spectral, and radiometric resolution. The spectral band passes are shown in Table 1.1. The wide spectral region is covered by three telescopes, (1) three Visible and Near Infrared Radiometer (VNIR) bands with a spatial resolution of 15 meters (m), (2) six Short Wave Infrared Radiometer (SWIR) bands with a spatial resolution of 30 m and (3) five Thermal Infrared Radiometer (TIR) bands with a spatial resolution of 90 m. Each of the three subsystems has a nadir-pointing telescope, and the VNIR subsystem has an additional backward pointing telescope that is used to see backward in the near infrared spectral band (band 3B) to obtain stereo coverage. Each ASTER acquisition (scene) covers an area of 60 x 60 km. ASTER is a partnership between NASA, Japan's Ministry of Economy, Trade and Industry (METI), the National Institute of Advanced Industrial Science and Technology (AIST) in Japan, and Japan Space Systems (J-spacesystems). </p><p>The Land Processes Distributed Active Archive Center (LP DAAC) ingests, archives, processes, and distributes ASTER data.</p>","language":"English","publisher":"NASA","usgsCitation":"Yuan, Y.L., and Krehbiel, C., 2025, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Version 4 product user guide, 13 p.","productDescription":"13 p.","ipdsId":"IP-175643","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":496910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":496909,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://lpdaac.usgs.gov/documents/2243/ASTER_User_Guide_V4.pdf","linkFileType":{"id":1,"text":"pdf"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yuan, Ying Li 0000-0002-3957-3098","orcid":"https://orcid.org/0000-0002-3957-3098","contributorId":360751,"corporation":false,"usgs":true,"family":"Yuan","given":"Ying","middleInitial":"Li","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":947631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krehbiel, Cole 0000-0003-2558-6952 cole.krehbiel.ctr@usgs.gov","orcid":"https://orcid.org/0000-0003-2558-6952","contributorId":198822,"corporation":false,"usgs":true,"family":"Krehbiel","given":"Cole","email":"cole.krehbiel.ctr@usgs.gov","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":951048,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70266184,"text":"70266184 - 2025 - Behavioral plasticity in detection height of an invasive, arboreal snake based on size, condition, and prey","interactions":[],"lastModifiedDate":"2025-04-29T14:18:46.307912","indexId":"70266184","displayToPublicDate":"2025-04-01T09:14:20","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral plasticity in detection height of an invasive, arboreal snake based on size, condition, and prey","docAbstract":"<div class=\"section\"><strong>Context</strong><p id=\"d6e276\">Animals may adjust their behavior in predictable ways to balance tradeoffs between resource acquisition and survival or fecundity. Microhabitat selection based on individual traits or environmental conditions is one measure of risk–reward tradeoffs by individuals.</p></div><div class=\"section\"><strong>Aims</strong><p id=\"d6e281\">We used data from observational and manipulative studies to investigate whether an arboreal snake (brown treesnake,<span>&nbsp;</span><i>Boiga irregularis</i>) had context-dependent behavior based on the relationship between estimated prey availability, body condition, size, and detection height (microhabitat use) in two Mariana Islands.</p></div><div class=\"section\"><strong>Methods</strong><p id=\"d6e289\">We used observational data collected in four study sites and data from a manipulative study that we collected over a 5-year period. The observational data focused on four sites with different counts of three prey types, including lizards, birds, and small mammals. During the manipulative study we removed snakes, which resulted in increased prey counts over time. Using these two approaches, we tested whether prey counts predicted body condition and then evaluated how prey counts, snake size, and snake condition interactively predicted the detection height of captured individuals.</p></div><div class=\"section\"><strong>Key results</strong><p id=\"d6e294\">We found that body condition was greater at sites or in years with greater prey counts across both the observational and manipulative studies. We also found that snakes displayed differential microhabitat use based on both their condition and size. Larger snakes tended to be detected lower than smaller snakes, but only at sites or during years with few bird or small mammal counts. Snakes at sites with greater mammal and bird counts had a positive relationship between size and detection height. Snakes with greater condition scores tended to be detected higher irrespective of size, but this was also dependent on prey counts. At sites with low bird counts, snakes that were in better condition tended to be closer to the ground.</p></div><div class=\"section\"><strong>Conclusions</strong><p id=\"d6e299\">Brown treesnakes modified microhabitat use based on their condition, size, and the number or type of prey available. Our findings were consistent with a hypothesis that they optimized habitat use to secure food resources and maximize survival.</p></div><div class=\"section\"><strong>Implications</strong><p id=\"d6e304\">Context-dependent behavioral plasticity may be an important consideration for management of reptiles for population control or growth.</p></div>","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/WR24085","usgsCitation":"Nafus, M.G., Gray, L., Klug, P.E., Rodda, G.H., and Goetz, S.M., 2025, Behavioral plasticity in detection height of an invasive, arboreal snake based on size, condition, and prey: Wildlife Research, v. 52, WR24085, 13 p., https://doi.org/10.1071/WR24085.","productDescription":"WR24085, 13 p.","ipdsId":"IP-148796","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":496370,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr24085","text":"Publisher Index Page"},{"id":485129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Cocos Island, Guam","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              144.9880503743945,\n              13.69255187982374\n            ],\n            [\n              144.51411886644297,\n              13.69255187982374\n            ],\n            [\n              144.51411886644297,\n              13.15444039485007\n            ],\n            [\n              144.9880503743945,\n              13.15444039485007\n            ],\n            [\n              144.9880503743945,\n              13.69255187982374\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"52","noUsgsAuthors":false,"publicationDate":"2025-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Nafus, Melia G. 0000-0002-7325-3055 mnafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":197462,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia","email":"mnafus@usgs.gov","middleInitial":"G.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Levi","contributorId":351880,"corporation":false,"usgs":false,"family":"Gray","given":"Levi","affiliations":[{"id":51975,"text":"USGS Fort Collins Science Center (formerly)","active":true,"usgs":false}],"preferred":false,"id":934827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klug, Page E. pklug@usgs.gov","contributorId":5545,"corporation":false,"usgs":true,"family":"Klug","given":"Page","email":"pklug@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rodda, Gordon H. 0000-0002-6696-7308 roddag@usgs.gov","orcid":"https://orcid.org/0000-0002-6696-7308","contributorId":210066,"corporation":false,"usgs":true,"family":"Rodda","given":"Gordon","email":"roddag@usgs.gov","middleInitial":"H.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934829,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goetz, Scott Michael 0000-0002-8705-5316","orcid":"https://orcid.org/0000-0002-8705-5316","contributorId":228868,"corporation":false,"usgs":true,"family":"Goetz","given":"Scott","email":"","middleInitial":"Michael","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":934830,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70266187,"text":"70266187 - 2025 - Fossil diatoms in Arctic marine surface sediments","interactions":[],"lastModifiedDate":"2025-04-29T13:58:41.800521","indexId":"70266187","displayToPublicDate":"2025-04-01T08:57:24","publicationYear":"2025","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":21216,"text":"Pages Magazine","active":true,"publicationSubtype":{"id":30}},"title":"Fossil diatoms in Arctic marine surface sediments","docAbstract":"<p><span>Diatoms are one of the main constituents of marine phytoplankton in the Arctic, and thanks to their siliceous skeletons, diatom fossils are relatively well preserved in sediments. Due to their species-specific sensitivity to different ocean conditions, their abundance and assemblages in sediments are routinely used by paleoceanographers to reconstruct the state of the surface ocean in the past (Koç 2007). By using statistical methods, we can obtain valuable knowledge about their ecological preferences (Oksman et al. 2019) and generate quantitative reconstructions of various parameters, such as sea-surface temperature and sea-ice concentration through time (Krawczyk et al. 2021; Sha et al. 2014). The Marine Arctic Diatoms (MARDI) working group (WG) (</span><span class=\"Hyperlink\">pastglobalchanges.org/mardi</span><span>) aims to advance knowledge on marine-diatom ecology and diatom-based reconstructions by compiling and harmonizing data from surface-sediment samples across the Arctic.</span></p>","language":"English","publisher":"PAGES Past Global Changes","doi":"10.22498/pages.33.1.40","usgsCitation":"Pearce, C., Caissie, B.E., Carter-Champion, A., Limoges, A., Luostarinen, T., Simpson, G.L., and Weckstrom, K., 2025, Fossil diatoms in Arctic marine surface sediments: Pages Magazine, v. 33, no. 1, https://doi.org/10.22498/pages.33.1.40.","productDescription":"1 p.","startPage":"40","ipdsId":"IP-173865","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":487828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.22498/pages.33.1.40","text":"Publisher Index Page"},{"id":485126,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Pearce, Christof","contributorId":197126,"corporation":false,"usgs":false,"family":"Pearce","given":"Christof","email":"","affiliations":[{"id":25421,"text":"Department of Geological Sciences, Stockholm University, Sweden","active":true,"usgs":false}],"preferred":false,"id":934838,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caissie, Beth Elaine 0000-0001-9587-1842","orcid":"https://orcid.org/0000-0001-9587-1842","contributorId":292500,"corporation":false,"usgs":true,"family":"Caissie","given":"Beth","email":"","middleInitial":"Elaine","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":934839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carter-Champion, Alice","contributorId":353962,"corporation":false,"usgs":false,"family":"Carter-Champion","given":"Alice","affiliations":[{"id":84535,"text":"Royal Holloway, University of London, UK","active":true,"usgs":false}],"preferred":false,"id":934840,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Limoges, Audrey","contributorId":353963,"corporation":false,"usgs":false,"family":"Limoges","given":"Audrey","affiliations":[{"id":24781,"text":"University of New Brunswick, Canada","active":true,"usgs":false}],"preferred":false,"id":934841,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Luostarinen, Tiia","contributorId":353964,"corporation":false,"usgs":false,"family":"Luostarinen","given":"Tiia","affiliations":[{"id":29870,"text":"University of Helsinki, Finland","active":true,"usgs":false}],"preferred":false,"id":934842,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Simpson, Gavin L.","contributorId":178139,"corporation":false,"usgs":false,"family":"Simpson","given":"Gavin","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":934843,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weckstrom, Kaarina","contributorId":209733,"corporation":false,"usgs":false,"family":"Weckstrom","given":"Kaarina","email":"","affiliations":[],"preferred":false,"id":934844,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70267298,"text":"70267298 - 2025 - Balancing monitoring and management in the adaptive management of an invasive species","interactions":[],"lastModifiedDate":"2025-05-20T15:19:56.182679","indexId":"70267298","displayToPublicDate":"2025-04-01T08:13:05","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":"Balancing monitoring and management in the adaptive management of an invasive species","docAbstract":"<p><span>Efficient allocation of managers' limited resources is necessary to effectively control invasive species, but determining how to allocate effort between monitoring and management over space and time remains a challenge. In an adaptive management context, monitoring data are key for gaining knowledge and iteratively improving management, but monitoring costs money. Community science or other opportunistic monitoring data present an opportunity for managers to gain critical knowledge without a substantial reduction in management funds. We designed a management strategy evaluation to investigate optimal spatial allocation of resources to monitoring and management, while also exploring the potential for community science data to improve decision-making, using adaptive management of invasive flowering rush (</span><i>Butomus umbellatus</i><span>) in the Columbia River, USA, as a case study. We evaluated management and monitoring alternatives under two invasion conditions, a well-established invasion and an emerging invasion, for both risk-neutral and risk-averse decision makers. Simulations revealed that regardless of invasion condition or managers' risk tolerance, allocating effort outward from the estimated center of invasion (</span><i>Epicenter</i><span>&nbsp;prioritization) resulted in the lowest overall level of infestation at the end of management. This allocation outperformed alternatives in which management occurred in fixed areas (</span><i>Linear</i><span>&nbsp;prioritization) and alternatives that targeted patchily distributed areas with the highest estimated infestation level of the invasive species (</span><i>High invasion</i><span>&nbsp;prioritization). Additionally, management outcomes improved when more resources were allocated toward removal effort than monitoring effort, and the addition of community science data improved outcomes only under certain scenarios. Finally, actions that led to the best outcomes often did not produce the most accurate and precise estimates of parameters describing system function, emphasizing the importance of using value of information principles to guide monitoring. Our adaptive management approach is adaptable to many invasive species management contexts in which ongoing monitoring allows management strategies to be updated over time.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.71176","usgsCitation":"Thompson, B., Olden, J., and Converse, S.J., 2025, Balancing monitoring and management in the adaptive management of an invasive species: Ecology and Evolution, v. 15, no. 4, e71176, 18 p., https://doi.org/10.1002/ece3.71176.","productDescription":"e71176, 18 p.","ipdsId":"IP-174988","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489759,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71176","text":"Publisher Index Page"},{"id":486218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Columbia River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121.29861281933483,\n              45.86074969037472\n            ],\n            [\n              -121.29861281933483,\n              45.533549912375776\n            ],\n            [\n              -120.10221677965575,\n              45.533549912375776\n            ],\n            [\n              -120.10221677965575,\n              45.86074969037472\n            ],\n            [\n              -121.29861281933483,\n              45.86074969037472\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Brielle K.","contributorId":355570,"corporation":false,"usgs":false,"family":"Thompson","given":"Brielle K.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":937664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olden, Julien","contributorId":355571,"corporation":false,"usgs":false,"family":"Olden","given":"Julien","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":937665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":937666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70265056,"text":"70265056 - 2025 - Uncertainty reduction for subaerial landslide-tsunami hazards","interactions":[],"lastModifiedDate":"2025-04-01T15:08:00.852625","indexId":"70265056","displayToPublicDate":"2025-04-01T08:03:52","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty reduction for subaerial landslide-tsunami hazards","docAbstract":"<p><span>Subaerial rock slopes may generate a tsunami by rapidly moving into the water. Large uncertainty in landslide characteristics propagates into large uncertainty in tsunami hazard, making hazard assessment more difficult for land and emergency managers. Once a potentially tsunamigenic landslide is identified, it may not be clear which landslide characteristics contribute most significantly to uncertainty in the tsunami hazard. Our aim is to document the relative worth of different landslide characteristics (e.g., size, material properties) for reducing uncertainty in landslide-tsunami hazard assessments. Isolating the relative importance of specific landslide characteristics may inform prioritization of data collection and improve efficiency in understanding hazard. To accomplish this, we generated a set of 288 landslide-tsunami simulations in which we systematically varied the size and material properties of possible failure extents at the Barry Arm landslide complex in northwestern Prince William Sound, Alaska, USA. We find that for landslides smaller than the receiving waterbody, the landslide volume has the strongest effect on resulting wave characteristics and thus the highest leverage on reducing uncertainty in tsunami hazard. In contrast, for landslides substantially larger than the waterbody, the duration of rapid movement of the landslide has the highest leverage. Based on our results, we propose a classification scheme for subaerial landslides based on the relative size of the landslide and waterbody. Additionally, our results support the generation of a tsunami height transfer function between existing tide gages and a nearby coastal city. These results have direct implications for the practice of operational early warning.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JF007906","usgsCitation":"Barnhart, K.R., George, D.L., Collins, A.L., Schaefer, L.N., and Staley, D.M., 2025, Uncertainty reduction for subaerial landslide-tsunami hazards: Journal of Geophysical Research: Earth Surface, v. 130, no. 4, e2024JF007906, 33 p., https://doi.org/10.1029/2024JF007906.","productDescription":"e2024JF007906, 33 p.","ipdsId":"IP-167063","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":488660,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jf007906","text":"Publisher Index Page"},{"id":484066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prince William Sound","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -148.7784819894945,\n              61.26414641973446\n            ],\n            [\n              -148.7784819894945,\n              59.85768506370988\n            ],\n            [\n              -145.67415454216552,\n              59.85768506370988\n            ],\n            [\n              -145.67415454216552,\n              61.26414641973446\n            ],\n            [\n              -148.7784819894945,\n              61.26414641973446\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"130","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932432,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932433,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Andrew L. 0000-0003-4751-7333","orcid":"https://orcid.org/0000-0003-4751-7333","contributorId":332093,"corporation":false,"usgs":true,"family":"Collins","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932434,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schaefer, Lauren N. 0000-0003-3216-7983","orcid":"https://orcid.org/0000-0003-3216-7983","contributorId":241997,"corporation":false,"usgs":true,"family":"Schaefer","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932435,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932436,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70261844,"text":"70261844 - 2025 - Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States","interactions":[],"lastModifiedDate":"2024-12-30T15:09:17.858395","indexId":"70261844","displayToPublicDate":"2025-04-01T08:00:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States","docAbstract":"Hydrologic research and operations make extensive use of streamflow time series. In most applications, these time series are estimated from rating curves, which relate flow to some easy-to-measure surrogate, typically stage. The conventional stage-discharge rating takes the form of a segmented power law, with one segment for each hydrologic control at the stream gauge. However, these ratings are notoriously difficult to estimate with numerical methods, so that most are still developed manually. A few automated algorithms have emerged, but their use is sporadic, and their relative merits have not been rigorously assessed. One recently developed approach, the generalized power-law, avoids the segmenting problem by representing the power-law exponent as a Gaussian process. On the one hand, this representation is more flexible and easier to fit, but its flexibility might allow unrealistic solutions, so it needs to be tested under a range of conditions to assess its operational viability. This study evaluates the generalized power-law rating curve model by applying it to observations from 180 streams in Iceland, Sweden, and the United States. Overall, the model proved flexible and computationally robust, generating convincing rating curves across a range of geographic settings and was comparable to curves generated by a segmented rating model. Lastly, we propose a model-selection algorithm based on information theory to help identify the best rating curve model for a particular stream gauge.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.132537","usgsCitation":"Vias, R., Hrafnkelsson, B., Hodson, T.O., Rögnvaldsson, S., Jansson, A., and Gardarsson, S., 2025, Evaluating the applicability of the generalized power-law rating curve model: With applications to paired discharge-stage data from Iceland, Sweden, and the United States: Journal of Hydrology, v. 651, 132537, 19 p., https://doi.org/10.1016/j.jhydrol.2024.132537.","productDescription":"132537, 19 p.","ipdsId":"IP-167791","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":488042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.132537","text":"Publisher Index Page"},{"id":465527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iceland, Sweden, United 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Center","active":true,"usgs":true}],"preferred":true,"id":922011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rögnvaldsson, Sölvi","contributorId":347574,"corporation":false,"usgs":false,"family":"Rögnvaldsson","given":"Sölvi","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":922012,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jansson, Axel Örn","contributorId":347575,"corporation":false,"usgs":false,"family":"Jansson","given":"Axel Örn","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":922013,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gardarsson, Sigurdur M.","contributorId":347576,"corporation":false,"usgs":false,"family":"Gardarsson","given":"Sigurdur M.","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":922014,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70268062,"text":"70268062 - 2025 - Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2024","interactions":[],"lastModifiedDate":"2026-03-16T15:52:30.720962","indexId":"70268062","displayToPublicDate":"2025-03-31T10:45:28","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2024","docAbstract":"<p>Fall bottom trawl (fall BT) and lakewide acoustic (AC) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The fall BT survey has been conducted each fall since 1973 using 12-m trawls at depths ranging from 9 to 110 m at fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species [i.e., Alewife (<i>Alosa pseudoharengus</i>), Bloater (<i>Coregonus hoyi</i>), Rainbow Smelt (<i>Osmerus mordax</i>), Deepwater Sculpin (<i>Myoxocephalus thompsonii</i>), Slimy Sculpin (<i>Cottus cognatus</i>), Round Goby (<i>Neogobius melanostomus</i>), Ninespine Stickleback (<i>Pungitius pungitius</i>)] as well as age-0 Yellow Perch (<i>Perca flavescens</i>) and large (&gt; 350 mm) Burbot (<i>Lota lota</i>). In recent years, wild juvenile (&lt;400 mm) Lake Trout (<i>Salvelinus namaycush</i>) have also become more common in the fall bottom trawl. The AC survey has been conducted each late summer/early fall since 2004 (except 2020). The 2024 AC survey consisted of 24 transects [468 km total (291 miles)] covering bottom depths ranging from 16 to 173 m and 38 midwater trawl tows at 4 to 72 m; this survey estimates densities of three prey fish species (i.e., Alewife, Bloater, and Rainbow Smelt). The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks. In spring of 2024, an additional spring bottom trawl survey (spring BT) was implemented across six of the transects sampled in the fall and sites ranged in depth from 9 to 237 m. The goal of the spring BT, conducted annually since 2021 with differing levels of effort, was to explore seasonal differences in biomass density and distributions of key prey species, most notably Alewife. </p><p>Total prey fish biomass density from the spring BT was 5.7 kg/ha. For the AC survey, total biomass density of prey fish equaled 10.8 kg/ha, more than double the long-term average (20042023) of 5.1 kg/ha but 4.0 kg/ha lower than the 2023 estimate. For the fall BT, total biomass density of prey fish equaled 2.1 kg/ha, the lowest value since 2020 and 69% lower than the average from 2004-2023 (6.8 kg/ha). The 2024 fall BT biomass density was only 6.3% of the average over the entirety of the time series (1973-2023; 33.1 kg/ha). Over the period both surveys have been conducted (2004-2024), total biomass density has trended downward in the fall BT (despite a high 2022 estimate) and remained relatively stable in the AC survey. &nbsp;</p><p>Deepwater Sculpin and Bloater were the most common species (by biomass) among prey fishes in the spring BT while the AC survey and fall BT reported co-dominance of Bloater and Alewife. Mean biomass of yearling and older (YAO) Alewife was 1.30 kg/ha in the spring BT, 4.7 kg/ha in the AC survey, and 0.68 kg/ha in the fall BT. Since 2014, annual survey results suggest that the catchability of YAO Alewives for the fall BT is substantially lower than the AC survey. Like previous spring surveys, Alewives were aggregated in deeper habitats, with 93% of biomass collected between 110 and 201 m. Results of the 2024 spring BT align with past spring surveys and do not suggest that spring bottom trawling provides a better index of age-2 and older Alewives than fall bottom trawling, even with adjustments for differences in habitat use. However, the spring BT does appear to index age-1 Alewives more effectively than the fall BT.&nbsp;</p><p>The 2024 AC survey YAO Alewife biomass density estimate was 77% higher than the average from 2004-2023. The Alewife population of Lake Michigan appears to be composed mostly of young fish and the proportion of age-4 and older Alewives was &lt;1.8% in each of the three surveys. Age-0 Alewife numeric density from the AC survey was 510 fish/ha in 2024, slightly higher than the long-term mean (486 fish/ha). Biomass density of large (≥120 mm) Bloater was 5.2 kg/ha in the AC survey and 0.76 kg/ha in the fall BT, while total Bloater biomass in the spring BT was 1.8 kg/ha - all three estimates were much lower than what was estimated by the fall BT between 1981 and 1998. The density of small (&lt;120 mm) Bloater was 456 fish/ha in the AC survey, the second highest value in the time series and potentially reflective of an above average 2024 year-class. &nbsp;Meanwhile, small Bloater density estimated in the fall BT was only 16 fish/ha. Biomass density of large Rainbow Smelt (≥90 mm) was 0.21 kg/ha in the AC survey and 0.03 kg/ha in the fall BT survey, continuing the trend of low large Rainbow Smelt biomass observed since 2001. Numeric density of small (&lt;90 mm) Rainbow Smelt was 31 fish/ha in the AC survey and 143 fish/ha in the fall BT. </p><p>All four prey fish species indexed only by the fall BT had below-average biomass densities regardless of trawling season. Deepwater Sculpin biomass density was 0.26 kg/ha, which makes 14 of the past 15 years with biomass &lt;1 kg/ha. Spring BT Deepwater Sculpin biomass density (2.0 kg/ha) was higher than any fall BT estimate since 2006, likely reflective of including bottom trawls at greater depths in the spring than the fall. Slimy Sculpin was estimated to be &lt; 0.04 kg/ha in the spring and fall BT, an order of magnitude lower than the long-term average from the fall BT. Round Goby biomass density estimates were low and similar across seasons (0.43 kg/ha in the spring and 0.10 kg/ha in the fall). 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,{"id":70265089,"text":"70265089 - 2025 - Monitoring lava lake fluctuations and crater refilling with continuous laser rangefinders","interactions":[],"lastModifiedDate":"2025-04-01T15:19:37.845971","indexId":"70265089","displayToPublicDate":"2025-03-31T10:10:48","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3841,"text":"Journal of Applied Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring lava lake fluctuations and crater refilling with continuous laser rangefinders","docAbstract":"<p><span>The U.S. Geological Survey’s Hawaiian Volcano Observatory (HVO) has developed a new method to continuously monitor lava lake elevations. Since 2018, HVO has stationed a laser rangefinder on Kīlauea’s caldera rim. The instrument automatically measures lava lake elevation each second, with centimeter accuracy. A stream of elevation data flows to HVO’s database and public website, contributing a valuable channel to HVO’s volcano monitoring network. The data display is intuitive for users, providing essential information with a new level of clarity. HVO has used this method to track Kīlauea’s changing lava lake elevations over a series of eruptions, and the time series data show several volcanic processes: crater refilling, gas pistoning, lava lake surface behavior, and endogenous crater floor uplift. This technique is versatile, nimble, and easy to use. Continuous laser rangefinders may also prove useful for tracking lava lakes elsewhere, and for monitoring other hazards such as growing lava domes and debris flows.</span></p>","language":"English","publisher":"Springer Nature","doi":"10.1186/s13617-025-00152-5","usgsCitation":"Younger, E.F., Tollett, W., and Patrick, M.R., 2025, Monitoring lava lake fluctuations and crater refilling with continuous laser rangefinders: Journal of Applied Volcanology, v. 14, 4, 17 p., https://doi.org/10.1186/s13617-025-00152-5.","productDescription":"4, 17 p.","ipdsId":"IP-170275","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13617-025-00152-5","text":"Publisher Index Page"},{"id":484068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea volcano","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -155.31226314039785,\n              19.439663913303676\n            ],\n            [\n              -155.31226314039785,\n              19.385937325516892\n            ],\n            [\n              -155.2364870703903,\n              19.385937325516892\n            ],\n            [\n              -155.2364870703903,\n              19.439663913303676\n            ],\n            [\n              -155.31226314039785,\n              19.439663913303676\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2025-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Younger, Edward F. 0000-0002-1493-3069","orcid":"https://orcid.org/0000-0002-1493-3069","contributorId":215132,"corporation":false,"usgs":true,"family":"Younger","given":"Edward","email":"","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tollett, William 0000-0001-9646-0244","orcid":"https://orcid.org/0000-0001-9646-0244","contributorId":215618,"corporation":false,"usgs":true,"family":"Tollett","given":"William","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932514,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70270410,"text":"70270410 - 2025 - Aggregated space use by soft-released translocated Gopher Tortoises (Gopherus polyphemus)","interactions":[],"lastModifiedDate":"2025-08-19T14:47:20.981012","indexId":"70270410","displayToPublicDate":"2025-03-31T09:31:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1892,"text":"Herpetologica","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Aggregated space use by soft-released translocated Gopher Tortoises (<i>Gopherus polyphemus</i>)","title":"Aggregated space use by soft-released translocated Gopher Tortoises (Gopherus polyphemus)","docAbstract":"<p><span>Translocated herpetofauna can exhibit irregular space use and movement patterns when compared with resident conspecifics. In Florida, USA, Gopher Tortoises (</span><i>Gopherus polyphemus</i><span>) are translocated throughout the state to mitigate habitat loss due to development. The postrelease space use of translocated Gopher Tortoises within soft-release pens can affect population dynamics and population monitoring efficacy, and understanding spatial patterns can aid wildlife managers with population management. We used a combination of time-lapse cameras, animal tracking devices, and burrow distribution surveys to investigate translocated tortoise space use at Eglin Air Force Base, Florida, where tortoises have been translocated since 2015. We investigated 10 soft-release pens that varied in size (4–41 ha) and shape (due to landscape configuration and existing infrastructure). Time-lapse cameras and burrow distribution surveys showed that tortoises used habitat within 20 m of soft-release pens (silt fences) significantly more than the interior of pens. In most pens, the selection of pen-edge habitat resulted in a clustering effect that lessened upon subsequent surveys, after fences were removed. Additionally, our tracking data showed mixed evidence for clustering, where three of the seven tortoises used edge area significantly more than the interior of pens. Such clustering can affect the efficacy of population survey methods while potentially having negative impacts on the health of translocatees by increasing local density.</span></p>","language":"English","publisher":"The Herpetologists' League","doi":"10.1655/herpetologica-d-24-00038.1","usgsCitation":"Jones, M.D., Loope, K.J., Porter, V.H., Walkup, D.K., Ryberg, W.A., Preston, J.R., Johnson, J.T., Hagedorn, B.W., Bilbow, R., Moore, B.M., Bowers, B.C., Lopez, R.R., and Hunter, E.A., 2025, Aggregated space use by soft-released translocated Gopher Tortoises (Gopherus polyphemus): Herpetologica, v. 81, no. 2, p. 141-151, https://doi.org/10.1655/herpetologica-d-24-00038.1.","productDescription":"11 p.","startPage":"141","endPage":"151","ipdsId":"IP-161767","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":494309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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