Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
To make more informed predictions of host–pathogen interactions under climate change, studies have incorporated the thermal performance of host, vector and pathogen traits into disease models to quantify effects on average transmission rates. However, this body of work has omitted the fact that variation in susceptibility among individual hosts affects disease spread and long-term patterns of host population dynamics. Furthermore, and especially for ectothermic host species, variation in susceptibility is likely to be plastic, influenced by variables such as environmental temperature. For example, as host individuals respond idiosyncratically to temperature, this could affect the population-level variation in susceptibility, such that there may be predictable functional relationships between variation in susceptibility and temperature. Quantifying the relationship between temperature and among-host trait variation will therefore be critical for predicting how climate change and disease will interact to influence host–pathogen population dynamics. Here, we use a model to demonstrate how short-term effects of temperature on the distribution of host susceptibility can drive epidemic characteristics, fluctuations in host population sizes and probabilities of host extinction. Our results emphasize that more research is needed in disease ecology and climate biology to understand the mechanisms that shape individual trait variation, not just trait averages.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2024.2515","usgsCitation":"Mihaljevic, J., and Paez, D.J., 2025, Systematic shifts in the variation among host individuals must be considered in climate-disease theory: Proceedings of the Royal Society B: Biological Sciences, v. 292, 20242515, https://doi.org/10.1098/rspb.2024.2515.","productDescription":"20242515","ipdsId":"IP-153886","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":483130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"292","noUsgsAuthors":false,"publicationDate":"2025-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Mihaljevic, Joseph R.","contributorId":352200,"corporation":false,"usgs":false,"family":"Mihaljevic","given":"Joseph R.","affiliations":[{"id":84130,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ 86011","active":true,"usgs":false}],"preferred":false,"id":930247,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Páez, David James 0000-0001-9035-394X","orcid":"https://orcid.org/0000-0001-9035-394X","contributorId":296751,"corporation":false,"usgs":true,"family":"Páez","given":"David","middleInitial":"James","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":930248,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70267521,"text":"70267521 - 2025 - Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","interactions":[{"subject":{"id":70267521,"text":"70267521 - 2025 - Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","indexId":"70267521","publicationYear":"2025","noYear":false,"title":"Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020"},"predicate":"SUPERSEDED_BY","object":{"id":70274705,"text":"sir20265001 - 2026 - Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","indexId":"sir20265001","publicationYear":"2026","noYear":false,"title":"Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020"},"id":1}],"supersededBy":{"id":70274705,"text":"sir20265001 - 2026 - Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","indexId":"sir20265001","publicationYear":"2026","noYear":false,"title":"Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020"},"lastModifiedDate":"2026-04-08T14:00:49.054189","indexId":"70267521","displayToPublicDate":"2025-02-05T08:48:40","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":19891,"text":"ESS Open Archive","active":true,"publicationSubtype":{"id":32}},"title":"Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020","docAbstract":"The U.S. Geological Survey (USGS) and the Washington State Department of Ecology (Ecology) have developed watershed models of seasonal load estimates of total nitrogen (TN) and total phosphorus (TP) discharging into the Washington waters of the Salish Sea from 2005 through 2020. The modeling approach used was dynamic SPARROW (SPAtially Referenced Regressions On Watershed attributes), a statistical-physical watershed modeling technique, initially applied at large spatial scales to represent long-term average stream loads throughout a stream network, refined here to estimate seasonal TN and TP loads across watersheds to clarify upstream contributions from discernable point and nonpoint sources delivered to marine waters at surface water confluences along the shoreline and quantify when, where, and why they were high or low. Upstream contributing sources included permitted treated wastewater facilities, crop fertilizer, animal feeding operations, septic systems, urban land and stormwater, atmospheric deposition (TN only), nitrogen fixation by Red Alder Alnus rubra trees (TN only), and background geologic material (TP only). Instream load magnitudes and their source compositions varied widely across watersheds, and even within each watershed, yet the largest loads typically occurred in the large rivers during winter and fall when streamflow was highest. Likewise, instream loads were typically lowest in summer during low streamflow, yet the relative instream aquatic decay was highest. The seasonal storage lag component of those nonpoint sources was estimated to contribute a quarter of the seasonal instream load during winter and fall high streamflow and sometimes half of the instream load during summer low streamflow. A key aspect of Ecology’s current Puget Sound Nutrient Source Reduction Project is consideration of upstream watershed contributions of nutrients to their marine-water discharge points. Simulated seasonal loads carried by streams to 63 river mouth marine discharge points 9 ranged by several orders-of-magnitude for both TN and TP due to the spatial and seasonal differences in hydrologic flows, magnitude and timing of contributing sources, and in-stream decay. The Snohomish and Skagit Rivers discharged the largest TN and TP loads, yet the Samish River was shown to have some of the highest TN and TP yields and concentrations. Additionally, a reference scenario was developed to provide an estimate of the pre-industrial local and regional loads.
","language":"English","publisher":"ESS Open Archive","doi":"10.22541/essoar.173878059.92247480/v1","usgsCitation":"Schmadel, N., Figueroa-Kaminsky, C., Wise, D., Wasielewski, J., Johnson, Z., and Black, R.W., 2025, Preprint: Simulated seasonal loads of total nitrogen and total phosphorus by major source from watersheds draining to Washington waters of the Salish Sea, 2005 through 2020: ESS Open Archive, preprint posted February 05, 2025, https://doi.org/10.22541/essoar.173878059.92247480/v1.","productDescription":"110 p.","ipdsId":"IP-174989","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":486634,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmadel, Noah 0000-0002-2046-1694","orcid":"https://orcid.org/0000-0002-2046-1694","contributorId":219105,"corporation":false,"usgs":true,"family":"Schmadel","given":"Noah","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":938477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Figueroa-Kaminsky, Cristiana","contributorId":350514,"corporation":false,"usgs":false,"family":"Figueroa-Kaminsky","given":"Cristiana","affiliations":[{"id":25353,"text":"Washington State Department of Ecology","active":true,"usgs":false}],"preferred":false,"id":938478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wise, Daniel 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","email":"","affiliations":[],"preferred":true,"id":938479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wasielewski, Jamie K. 0009-0005-7497-3344","orcid":"https://orcid.org/0009-0005-7497-3344","contributorId":344993,"corporation":false,"usgs":false,"family":"Wasielewski","given":"Jamie K.","affiliations":[{"id":82458,"text":"Washington Dept. of Ecology","active":true,"usgs":false}],"preferred":false,"id":938480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":938481,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Black, Robert W. 0000-0002-4748-8213 rwblack@usgs.gov","orcid":"https://orcid.org/0000-0002-4748-8213","contributorId":1820,"corporation":false,"usgs":true,"family":"Black","given":"Robert","email":"rwblack@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938482,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70265026,"text":"70265026 - 2025 - Snapshots of mid-to-late Holocene sea-surface temperature variability from a subtropical western Atlantic coral reef","interactions":[],"lastModifiedDate":"2025-03-31T14:16:41.461574","indexId":"70265026","displayToPublicDate":"2025-02-03T09:11:40","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2996,"text":"Palaeogeography, Palaeoclimatology, Palaeoecology","printIssn":"0031-0182","active":true,"publicationSubtype":{"id":10}},"title":"Snapshots of mid-to-late Holocene sea-surface temperature variability from a subtropical western Atlantic coral reef","docAbstract":"Large-scale Holocene climate reconstructions rely heavily on extratropical proxy records. Coral-based temperature reconstructions from the tropical and subtropical oceans therefore fill a critical spatial and temporal data gap, allowing for reconstruction of seasonally resolved temperature variability. We present five new, monthly-resolved sea-surface temperature (SST) reconstructions (between 39 and 57 years in length) from 2 to 7 thousand years ago (ka) based on the strontium-to‑calcium ratio (Sr/Ca) of Orbicella faveolata corals from subtropical reefs in south Florida. Modern calibrations between O. faveolata Sr/Ca and in situ SST from the region allow us to directly compare the mean and variability of SSTs since the mid-Holocene. In contrast to the low climate variability observed in more tropical areas of the western Atlantic during the Holocene, our records from subtropical south Florida exhibit pronounced changes in mean SST and variability. Our records suggest that mid-Holocene SSTs in the Florida Keys were highly variable, with relatively cooler winters driving a cooler mean SST at ∼6.7 ka (23.7 ± 0.6°C at 6.7 ka and 25.0 ± 0.5°C at 6.6 ka), and relatively warmer summers and more variable temperatures by 5.8 ka (27.1 ± 0.4°C, seasonality of 8.7°C). We also analyzed stable oxygen isotopes in two of our corals and those data support our Sr/Ca-based estimate of climatic warming between 6.6 ka and 5.8 ka (−3.6‰ and − 3.9‰). Both winter and summer temperatures were significantly cooler than the other mid-to-late Holocene snapshots at 3.6 ka (21.2 ± 0.5°C) and SST warmed but remained highly variable at 2.6 ka (25.0 ± 0.6°C, seasonality of 7.9°C). These centennial-scale changes in climate variability potentially contributed to the regional shutdown of reef accretion by the late Holocene. Our reconstructions provide a proof-of-concept study that highlights the value of coral-based SST records from highly sensitive, subtropical locations for understanding Holocene climate on seasonal to centennial timescales.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.palaeo.2025.112777","usgsCitation":"Jacobs, J.A., Richey, J.N., Flannery, J., Thiumalai, K., and Toth, L., 2025, Snapshots of mid-to-late Holocene sea-surface temperature variability from a subtropical western Atlantic coral reef: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 663, 112777, 13 p., https://doi.org/10.1016/j.palaeo.2025.112777.","productDescription":"112777, 13 p.","ipdsId":"IP-166418","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488920,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.palaeo.2025.112777","text":"Publisher Index Page"},{"id":484015,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.74479831836513,\n 24.744714178430996\n ],\n [\n -83.00110001597643,\n 24.744714178430996\n ],\n [\n -83.00110001597643,\n 24.55302673998355\n ],\n [\n -82.74479831836513,\n 24.55302673998355\n ],\n [\n -82.74479831836513,\n 24.744714178430996\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"663","noUsgsAuthors":false,"publicationDate":"2025-02-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Jacobs, Jessica A. 0000-0001-5611-2093","orcid":"https://orcid.org/0000-0001-5611-2093","contributorId":333551,"corporation":false,"usgs":true,"family":"Jacobs","given":"Jessica","email":"","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932342,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richey, Julie N. 0000-0002-2319-7980 jrichey@usgs.gov","orcid":"https://orcid.org/0000-0002-2319-7980","contributorId":174046,"corporation":false,"usgs":true,"family":"Richey","given":"Julie","email":"jrichey@usgs.gov","middleInitial":"N.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flannery, Jennifer A. 0000-0002-1692-2662","orcid":"https://orcid.org/0000-0002-1692-2662","contributorId":350413,"corporation":false,"usgs":false,"family":"Flannery","given":"Jennifer A.","affiliations":[{"id":37487,"text":"formerly USGS","active":true,"usgs":false}],"preferred":false,"id":932344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thiumalai, Kaustubh 0000-0002-7875-4182","orcid":"https://orcid.org/0000-0002-7875-4182","contributorId":264344,"corporation":false,"usgs":false,"family":"Thiumalai","given":"Kaustubh","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":932345,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932346,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263302,"text":"70263302 - 2025 - Bait trapping of waterfowl increases the environmental contamination of avian influenza virus (AIV)","interactions":[],"lastModifiedDate":"2025-03-25T15:54:44.911751","indexId":"70263302","displayToPublicDate":"2025-02-03T08:58:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Bait trapping of waterfowl increases the environmental contamination of avian influenza virus (AIV)","docAbstract":"Highly pathogenic avian influenza virus (HPAIV) H5Nx clade 2.3.4.4b has circulated in North America since late 2021, resulting in higher rates of morbidity and mortality in wild birds than observed in this region before. The objective of this study was to determine whether baiting, which is widely conducted in Canada and the United States as part of waterfowl management practices (e.g., duck banding), influences the occurrence of avian influenza virus (AIV) in wetlands. We used a quasi-experimental design, collecting superficial sediment samples (n = 336) and fecal samples (n = 242) from paired baited (treatment) and non-baited (control) sites at 2 wetlands in Saskatchewan, Canada, between August and September 2022. We visited sampling sites 3 times during the sampling period: prior to the commencement of baiting activities (t0), approximately 14 days after t0 (t1), and 24 days after t0 (t2). We screened samples for AIV using real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) targeting the matrix gene and subjected the PCR-positive samples to next-generation sequencing. We used a mixed-effects logistic regression model to estimate the effect of baiting on the odds of AIV positivity in sediment samples, while controlling for clustering by wetland. At control sites, we did not detect evidence for a difference in the odds of AIV detection in sediment at t1 or t2 versus t0; however, at baited sites, the odds of AIV detection at t1 were 5.43 (95% CI = 1.99, 14.79) times the odds at t0 and at t2 the odds of AIV detection were 8.73 (95% CI = 3.29, 23.18) times the odds at t0. We detected HPAIV clade 2.3.4.4b H5N1 in sediment at 1 treatment site following baiting. There was also a trend towards increased fecal AIV positivity and increased fecal and sediment AIV diversity in baited versus non-baited sites; however, there was insufficient power to determine if these findings were statistically significant. Overall, our results indicate that baiting is associated with localized increases in AIV environmental contamination, with baiting potentially creating concentrated areas of AIV accumulation. As such, wetland baiting activities may pose a risk to wildlife population health through the propagation of AIV in wetlands and the waterfowl using those environments and efforts to replace, refine, or reduce this activity may be warranted depending on local ecosystem contexts and cost-benefit analyses.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22720","usgsCitation":"Andrew, C., McPhee, L., Kuchinski, K., Wight, J., Rahman, I., Mansour, S., Angelo Cortez, G., Kalhor, M., Kenmuir, E., Prystajecky, N., Hargan, K., Lang, A., Leafloor, J., Soos, C., Ramey, A.M., and Himsworth, C., 2025, Bait trapping of waterfowl increases the environmental contamination of avian influenza virus (AIV): Journal of Wildlife Management, v. 89, no. 3, e22720, 15 p., https://doi.org/10.1002/jwmg.22720.","productDescription":"e22720, 15 p.","ipdsId":"IP-165980","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":487623,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22720","text":"Publisher Index Page"},{"id":481700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Saskatchewan","otherGeospatial":"Last Mountain Lake, Porter Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.30641020774367,\n 52.21973136392302\n ],\n [\n -106.30641020774367,\n 52.18282025021517\n ],\n [\n -106.27486390840123,\n 52.18282025021517\n ],\n [\n -106.27486390840123,\n 52.21973136392302\n ],\n [\n -106.30641020774367,\n 52.21973136392302\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.34177706546024,\n 51.457521376650305\n ],\n [\n -105.34177706546024,\n 50.69712483371231\n ],\n [\n -104.82171068042709,\n 50.69712483371231\n ],\n [\n -104.82171068042709,\n 51.457521376650305\n ],\n [\n 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Disease Control Public Health Laboratory","active":true,"usgs":false}],"preferred":false,"id":926234,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Prystajecky, Natalie","contributorId":350531,"corporation":false,"usgs":false,"family":"Prystajecky","given":"Natalie","affiliations":[{"id":83760,"text":"British Columbia Center for Disease Control Public Health Laboratory","active":true,"usgs":false}],"preferred":false,"id":926235,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hargan, Kathryn","contributorId":205716,"corporation":false,"usgs":false,"family":"Hargan","given":"Kathryn","email":"","affiliations":[{"id":36943,"text":"Queens University","active":true,"usgs":false}],"preferred":false,"id":926236,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lang, Andrew","contributorId":331075,"corporation":false,"usgs":false,"family":"Lang","given":"Andrew","affiliations":[],"preferred":false,"id":926237,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Leafloor, James","contributorId":350532,"corporation":false,"usgs":false,"family":"Leafloor","given":"James","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":926238,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Soos, Catherine","contributorId":177909,"corporation":false,"usgs":false,"family":"Soos","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":926240,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":926241,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Himsworth, Chelsea","contributorId":350534,"corporation":false,"usgs":false,"family":"Himsworth","given":"Chelsea","affiliations":[{"id":83761,"text":"British Columbia Ministry of Agriculture","active":true,"usgs":false}],"preferred":false,"id":926242,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70263607,"text":"70263607 - 2025 - Development of ‘SedCam’— A close-range remote sensing method of estimating suspended-sediment concentration in small rivers","interactions":[],"lastModifiedDate":"2025-02-24T16:59:51.078508","indexId":"70263607","displayToPublicDate":"2025-02-01T09:18:08","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Development of ‘SedCam’— A close-range remote sensing method of estimating suspended-sediment concentration in small rivers","docAbstract":"The adaptation of suspended-sediment surrogate technologies continues to rapidly expand across geomorphology and fluvial sediment monitoring efforts. Over a decade of research and development shows increased reliability and accuracy of in-situ surrogates with reduced program cost as compared to traditional sample-based methods, but environmental fouling and probe damage can be problematic. The SedCam technique is a unique non-contact close-range remote sensing method to estimate suspended-sediment concentration from multispectral imagery of a river surface. In contrast to typical airborne- or satellite-based platforms, SedCam uses broadband sensors with lower spectral resolution (three bands covering wavelengths of 340 to 1100 nm) but greater spatial resolution (0.5 mm pixel size; equivalent to medium to coarse sand) and temporal resolution (15-min intervals during daylight hours). This paper summarizes lessons learned from two studies, utilizing three consumer-grade digital cameras (each with different spectral signatures) at two different rivers (each with different sediment characteristics). >90,000 images and 174 concurrent physical samples represent a collective period of 26 months. A subset of these data pairs supports the development of four regression models. Statistical diagnostics show model error can be <40 % when surface point samples are used, with coefficients of determination ≥0.90. This novel approach shows similar accuracy to other surrogate methods such as instream turbidity. Results of this study indicate that optimizing spectra based on expected suspended-sediment concentration increases model performance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2025.109642","usgsCitation":"Mosbrucker, A.R., and Wood, M.S., 2025, Development of ‘SedCam’— A close-range remote sensing method of estimating suspended-sediment concentration in small rivers: Geomorphology, v. 476, 109642, 8 p., https://doi.org/10.1016/j.geomorph.2025.109642.","productDescription":"109642, 8 p.","ipdsId":"IP-146303","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":489813,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geomorph.2025.109642","text":"Publisher Index Page"},{"id":482155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"476","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mosbrucker, Adam R. 0000-0003-0298-0324 amosbrucker@usgs.gov","orcid":"https://orcid.org/0000-0003-0298-0324","contributorId":4968,"corporation":false,"usgs":true,"family":"Mosbrucker","given":"Adam","email":"amosbrucker@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":927555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Molly S. 0000-0002-5184-8306 mswood@usgs.gov","orcid":"https://orcid.org/0000-0002-5184-8306","contributorId":788,"corporation":false,"usgs":true,"family":"Wood","given":"Molly","email":"mswood@usgs.gov","middleInitial":"S.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":927556,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70265576,"text":"70265576 - 2025 - Update of the 2008 provisional Enhanced Geothermal Systems (EGS) assessment for the Great Basin, USA","interactions":[],"lastModifiedDate":"2025-04-14T13:51:55.494273","indexId":"70265576","displayToPublicDate":"2025-02-01T08:50:14","publicationYear":"2025","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Update of the 2008 provisional Enhanced Geothermal Systems (EGS) assessment for the Great Basin, USA","docAbstract":"In response to the Energy Act of 2020, the U.S. Geological Survey (USGS) is updating the Enhanced Geothermal Systems (EGS) resource assessment for the Great Basin, USA. The previous 2008 provisional assessment estimated how much electricity could be generated from EGS resources of the western United States using models of electric-grade heat, models of heat extraction over time, and estimates of how much rock might be stimulated to produce viable amounts of heat. Herein, a similar conceptual strategy is applied, using updated models of heat extraction as a function of fracture spacing and well distance. Previously used reservoir heat delivery models are updated to have a dependence on fracture and well spacing, potentially improving future estimates of EGS resources as ongoing research provides a better understanding about the success of reservoir stimulation as a function of geology and location. For a range of well distances (250-1000 m) and fracture spacings (1-50 m), heat extraction efficiency ranges from 25-62%, demonstrating the importance of accounting for the most likely results of proven viable fracturing technologies. Although fracturing is important, the biggest uncertainty by far in estimating the EGS resource for the Great Basin is estimating which geologic units at what depths can be stimulated sufficiently to produce geothermal energy economically and efficiently. Uncertainties in these factors yield estimates that range over two orders of magnitude with an upper limit of ~174 terawatts-thermal (TWth) produced for 30 years from the upper 7 km of the crust. This upper limit would require significant technological advances to access most of the electric-grade resource across the Great Basin. Assuming that 1% of this estimate will be accessible in the next few decades gives a resource estimate similar to that made in the 2008 provisional assessment. These estimated EGS heat extraction rates far exceed ( greater than 100x) the natural geothermal heat production rate, thus geothermal electricity production at these rates might not be sustainable unless heat is also recharged from other sources (e.g., excess solar energy when supply exceeds demand). In addition to assessment maps and cumulative estimates, the new models of fractured reservoirs developed herein can be used to estimate steady power production given a set of fractures and well spacing, and estimates can be made for setback distances to ensure no thermal interference with nearby powerplants.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings 50th Stanford Geothermal Workshop","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Stanford University","usgsCitation":"Burns, E., Zhang, J., Zhan, H., and Williams, C.F., 2025, Update of the 2008 provisional Enhanced Geothermal Systems (EGS) assessment for the Great Basin, USA, in Proceedings 50th Stanford Geothermal Workshop, v. 50, 16 p.","productDescription":"16 p.","ipdsId":"IP-171271","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":484439,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=37983","linkFileType":{"id":5,"text":"html"}},{"id":484490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Idaho, Nevada, Oregon, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.5604124531929,\n 34.31489047686145\n ],\n [\n -113.98504991431244,\n 35.590033331367565\n ],\n [\n -111.56578416852676,\n 37.28632608593861\n ],\n [\n -110.9892619278067,\n 41.67884360099643\n ],\n [\n -111.97575138287687,\n 43.957662400395805\n ],\n [\n -113.578133783738,\n 43.132778243859036\n ],\n [\n -120.24368172899253,\n 43.85862565467747\n ],\n [\n -122.52741271491658,\n 43.93510559055008\n ],\n [\n -122.60852684393157,\n 40.29249889409965\n ],\n [\n -119.78500758001587,\n 37.03990264299661\n ],\n [\n -116.5604124531929,\n 34.31489047686145\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":933047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Junyuan 0009-0007-0763-4742","orcid":"https://orcid.org/0009-0007-0763-4742","contributorId":346117,"corporation":false,"usgs":false,"family":"Zhang","given":"Junyuan","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":933048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhan, Hongbin 0000-0003-2060-4904","orcid":"https://orcid.org/0000-0003-2060-4904","contributorId":192156,"corporation":false,"usgs":false,"family":"Zhan","given":"Hongbin","email":"","affiliations":[],"preferred":false,"id":933049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Colin F. 0000-0003-2196-5496 colin@usgs.gov","orcid":"https://orcid.org/0000-0003-2196-5496","contributorId":274,"corporation":false,"usgs":true,"family":"Williams","given":"Colin","email":"colin@usgs.gov","middleInitial":"F.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":933050,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70270867,"text":"70270867 - 2025 - Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data","interactions":[],"lastModifiedDate":"2025-08-26T15:15:01.880335","indexId":"70270867","displayToPublicDate":"2025-02-01T08:07:35","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3052,"text":"Photogrammetric Engineering and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data","docAbstract":"Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.
","language":"English","publisher":"Ingenta Connect","doi":"10.14358/PERS.24-00072R3","usgsCitation":"McCormick, R.L., Thenkabail, P., Aneece, I., Teluguntla, P., Oliphant, A., and Foley, D., 2025, Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data: Photogrammetric Engineering and Remote Sensing, v. 91, no. 2, p. 91-100, https://doi.org/10.14358/PERS.24-00072R3.","productDescription":"10 p.","startPage":"91","endPage":"100","ipdsId":"IP-165508","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":495060,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.14358/pers.24-00072r3","text":"Publisher Index Page"},{"id":494898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Fresno","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.72184856336573,\n 37.15510709833474\n ],\n [\n -119.70395156455555,\n 36.73462649015923\n ],\n [\n -118.95495213764198,\n 36.73462649015923\n ],\n [\n -118.99091736443204,\n 37.169191590622404\n ],\n [\n -119.72184856336573,\n 37.15510709833474\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"91","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCormick, Richard L. 0009-0002-8208-2136","orcid":"https://orcid.org/0009-0002-8208-2136","contributorId":346504,"corporation":false,"usgs":true,"family":"McCormick","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":947249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":947250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aneece, Itiya 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":211471,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":947251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teluguntla, Pardhasaradhi 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":211780,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":947252,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":947253,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Foley, Daniel 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":208266,"corporation":false,"usgs":true,"family":"Foley","given":"Daniel","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":947254,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267785,"text":"70267785 - 2025 - Integration of Indigenous Research Methodologies, Traditional Ecological Knowledge and molecular scatology in an assessment of mesocarnivore presence, diet and habitat use on Yurok Ancestral Lands.","interactions":[],"lastModifiedDate":"2025-06-02T14:59:49.70948","indexId":"70267785","displayToPublicDate":"2025-02-01T07:50:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Integration of Indigenous Research Methodologies, Traditional Ecological Knowledge and molecular scatology in an assessment of mesocarnivore presence, diet and habitat use on Yurok Ancestral Lands.","docAbstract":"Partnerships between Tribes and researchers in wildlife monitoring and application of Traditional Ecological Knowledge (TEK) have taken a variety of forms, and some scholars have noted a need for culturally sensitive approaches. Guided by Indigenous Research Methodologies, this research is coupled with Yurok TEK, or hlkelonah 'ue-megetohl ('to take care of the earth'), enabling an applied, culturally sensitive approach in partnership with the Yurok Tribe. We present results from a molecular scatology study of wildlife within the ancestral territory of the Yurok Tribe. Scats were collected opportunistically on road transects. All samples (N = 132) were analysed via DNA barcoding and results matched to documented 'Oohl 'we-toh (Yurok language) names to determine the depositor species (N = 8). Though there were four focal mesocarnivore species in our study, only bobcat (Chmuuek; Lynx rufus) and gray fox (Wergers; Urocyon cinereoargenteus) were detected as depositor species. Post hoc analyses were conducted to explore distribution, habitat use and selection in a use-availability context, and food habits of these two species. We found almost complete separation of bobcat and gray fox use of transects, as well as indication of partitioning of vegetation cover types and food. We demonstrate an integrated framework of Western and Indigenous sciences that allows the Indigenous researcher to transcend structured academic disciplinary boundaries. Our approach can be modified for partnerships between Tribes, agencies, academics and students for wildlife monitoring in broader geographic regions in various research applications.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.13963","usgsCitation":"Ramos, S., and Culver, M., 2025, Integration of Indigenous Research Methodologies, Traditional Ecological Knowledge and molecular scatology in an assessment of mesocarnivore presence, diet and habitat use on Yurok Ancestral Lands.: Molecular Ecology Resources, v. 25, no. 2, e13963, 16 p., https://doi.org/10.1111/1755-0998.13963.","productDescription":"e13963, 16 p.","ipdsId":"IP-144218","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":498239,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1755-0998.13963","text":"Publisher Index Page"},{"id":489378,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"northwestern California, Yurok tribal lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.18637554494717,\n 41.06129121851316\n ],\n [\n -123.18637554494717,\n 40.33744763965922\n ],\n [\n -122.12359837800824,\n 40.33744763965922\n ],\n [\n -122.12359837800824,\n 41.06129121851316\n ],\n [\n -123.18637554494717,\n 41.06129121851316\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-05-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos, Seafha C.","contributorId":356207,"corporation":false,"usgs":false,"family":"Ramos","given":"Seafha C.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":938875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Culver, Melanie 0000-0001-5380-3059 mculver@usgs.gov","orcid":"https://orcid.org/0000-0001-5380-3059","contributorId":197693,"corporation":false,"usgs":true,"family":"Culver","given":"Melanie","email":"mculver@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938876,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70263193,"text":"pp1842BB - 2025 - The effects of management practices on grassland birds—Field Sparrow (Spizella pusilla)","interactions":[{"subject":{"id":70263193,"text":"pp1842BB - 2025 - The effects of management practices on grassland birds—Field Sparrow (Spizella pusilla)","indexId":"pp1842BB","publicationYear":"2025","noYear":false,"chapter":"BB","displayTitle":"The Effects of Management Practices on Grassland Birds—Field Sparrow (Spizella pusilla)","title":"The effects of management practices on grassland birds—Field Sparrow (Spizella pusilla)"},"predicate":"IS_PART_OF","object":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"id":1}],"isPartOf":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"lastModifiedDate":"2025-02-03T14:51:35.210208","indexId":"pp1842BB","displayToPublicDate":"2025-01-31T14:16:50","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1842","chapter":"BB","displayTitle":"The Effects of Management Practices on Grassland Birds—Field Sparrow (Spizella pusilla)","title":"The effects of management practices on grassland birds—Field Sparrow (Spizella pusilla)","docAbstract":"Keys to Field Sparrow (Spizella pusilla) management include providing shrub-dominated edge habitat adjacent to grasslands or grasslands with a shrub component (both of which must include dense grass and moderately high litter cover) and avoiding disturbances that eliminate woody vegetation. Field Sparrows have been reported to use habitats with 16–134 centimeters (cm) vegetation height, 20–145 cm visual obstruction reading, 17–90 percent grass cover, 2–45 percent forb cover, less than 63 percent shrub cover, 3–7 percent bare ground, 14–30 percent litter cover, and 1–7 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842BB","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Parkin, B.D., and Euliss, B.R., 2025, The effects of management practices on grassland birds—Field Sparrow (Spizella pusilla), chap. BB of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 35 p., https://doi.org/10.3133/pp1842BB.","productDescription":"vii, 35 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-096451","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":481568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/bb/coverthb.jpg"},{"id":481569,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/bb/pp1842bb.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–BB"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, North Dakota 58401
During an effusive eruption crisis the initial advance of a lava flow is typically the primary focus of model forecasts and hazard management efforts. Flow branching and lateral expansion of lava flows can pose significant dangers within evolving flow fields throughout the duration of an eruption and are an underappreciated hazard. We use field monitoring, infrasound, time lapse imagery, and lidar data collected during the 2018 lower East Rift Zone eruption of Kīlauea (Hawai‘i) to track the origins, progression, and implications of a flow branching event caused by catastrophic levee failure. Our analyses show that surges in effusion rate, rheologic transitions between pāhoehoe and ‘a‘ā flow regimes, slope-breaks, pre-existing topographic highs, and the structure of perched levee walls all played a role in the failure of the levee and subsequent re-routing of the lava flow. Failure of perched lava structures leads to an acutely hazardous situation because lava impounded by the structure can rapidly inundate the landscape. This is the first time a levee failure event has been observed in such detail with numerous monitoring techniques; this unprecedented level of observation provides quantifiable insights into levee failure processes that have important implications for hazard mitigation and an improved understanding of lava flow emplacement dynamics.
","language":"English","publisher":"Presses universitaires de Strasbourg","doi":"10.30909/vol.08.01.6780","usgsCitation":"Gallant, E., Dietterich, H., Patrick, M.R., Hyman, D., Carr, B., Lyons, J.J., and Meredith, E.S., 2025, Catastrophic lava flow levee failure: Precursors, processes, and implications: Volcanica, v. 8, no. 1, p. 67-80, https://doi.org/10.30909/vol.08.01.6780.","productDescription":"14 p.","startPage":"67","endPage":"80","ipdsId":"IP-166006","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":487712,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.30909/vol.08.01.6780","text":"Publisher Index Page"},{"id":482644,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.9167,\n 19.5\n ],\n [\n -154.9167,\n 19.4333\n ],\n [\n -154.8,\n 19.4333\n ],\n [\n -154.8,\n 19.5\n ],\n [\n -154.9167,\n 19.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallant, Elisabeth 0000-0001-6841-3694","orcid":"https://orcid.org/0000-0001-6841-3694","contributorId":339872,"corporation":false,"usgs":false,"family":"Gallant","given":"Elisabeth","affiliations":[{"id":81292,"text":"University of Hawaiʻi at Hilo","active":true,"usgs":false}],"preferred":false,"id":929079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dietterich, Hannah R. 0000-0001-7898-4343","orcid":"https://orcid.org/0000-0001-7898-4343","contributorId":212771,"corporation":false,"usgs":true,"family":"Dietterich","given":"Hannah R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":929080,"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":929081,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hyman, David Matthew 0000-0002-9607-7584","orcid":"https://orcid.org/0000-0002-9607-7584","contributorId":351609,"corporation":false,"usgs":true,"family":"Hyman","given":"David Matthew","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":929082,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carr, Brett B 0000-0002-1033-3082","orcid":"https://orcid.org/0000-0002-1033-3082","contributorId":251755,"corporation":false,"usgs":false,"family":"Carr","given":"Brett B","affiliations":[{"id":17701,"text":"Lamont-Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":929083,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":929084,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meredith, Elinor S. 0000-0002-3869-1180","orcid":"https://orcid.org/0000-0002-3869-1180","contributorId":270269,"corporation":false,"usgs":false,"family":"Meredith","given":"Elinor","email":"","middleInitial":"S.","affiliations":[{"id":56128,"text":"Earth Observatory of Singapore, Singapore","active":true,"usgs":false}],"preferred":false,"id":929085,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70263196,"text":"70263196 - 2025 - Quantifying the effect of petrogenic carbon on SOC turnover for two Rocky Mountain soils: When are petrogenic carbon corrections required?","interactions":[],"lastModifiedDate":"2025-02-03T15:16:10.140922","indexId":"70263196","displayToPublicDate":"2025-01-31T08:10:51","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying the effect of petrogenic carbon on SOC turnover for two Rocky Mountain soils: When are petrogenic carbon corrections required?","docAbstract":"Petrogenic organic carbon (OCpetro), derived from sedimentary rocks, is an often overlooked and poorly quantified source of soil organic carbon (SOC), which may influence measured or modeled SOC composition, age, and stability. In this study, we exploited differences in thermochemical stability between OCpetro and biogenic SOC (OCbio) using stepped elemental analysis to quantify the fractional contribution of OCpetro to total SOC (fpetro), and we conducted a sensitivity analysis to estimate the effects of OCpetro on modeled SOC transit times and system ages. Specifically, we compared the effects of accounting for OCpetro inputs in SOC turnover modeling (using SoilR) for two montane meadow soils that are underlain by Cretaceous Mancos Shale. At these sites, we estimate that OCpetro comprises 7%–9% of the total SOC stock (fpetro = 0.07–0.09). However, accounting for OCpetro as a mixture of inert and passive C or as completely inert C had negligible effects on SOC transit times and system ages, suggesting that there is a threshold of OCpetro content under which there is minimal effect on calculated SOC turnover. Based on our sensitivity analysis, we estimate this threshold to be fpetro = 0.125, further supporting that the accurate calculation of OCpetro remains an important factor in estimating SOC turnover.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007838","usgsCitation":"Williams, E.K., and Lawrence, C., 2025, Quantifying the effect of petrogenic carbon on SOC turnover for two Rocky Mountain soils: When are petrogenic carbon corrections required?: Journal of Geophysical Research: Biogeosciences, v. 130, no. 2, e2023JG007838, 16 p., https://doi.org/10.1029/2023JG007838.","productDescription":"e2023JG007838, 16 p.","ipdsId":"IP-157028","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":487609,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007838","text":"Publisher Index Page"},{"id":481994,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NI8OWJ","text":"USGS data release","linkHelpText":"Petrogenic organic carbon estimates for two Rocky Mountain soils underlain by Cretaceous Mancos Shale"},{"id":481603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Elk Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.32317289244807,\n 39.29883823943584\n ],\n [\n -107.32317289244807,\n 38.80751849901759\n ],\n [\n -106.43771612889955,\n 38.80751849901759\n ],\n [\n -106.43771612889955,\n 39.29883823943584\n ],\n [\n -107.32317289244807,\n 39.29883823943584\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-01-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Elizabeth Kellisha 0000-0002-4863-9204","orcid":"https://orcid.org/0000-0002-4863-9204","contributorId":344970,"corporation":false,"usgs":true,"family":"Williams","given":"Elizabeth","email":"","middleInitial":"Kellisha","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":925888,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawrence, Corey 0000-0001-6143-7781","orcid":"https://orcid.org/0000-0001-6143-7781","contributorId":219251,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":925889,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70263171,"text":"fs20253005 - 2025 - Using machine learning in Minnesota’s StreamStats to predict fluvial sediment","interactions":[],"lastModifiedDate":"2025-07-21T17:54:12.663669","indexId":"fs20253005","displayToPublicDate":"2025-01-30T14:39:06","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":"2025-3005","displayTitle":"Using Machine Learning in Minnesota’s StreamStats to Predict Fluvial Sediment","title":"Using machine learning in Minnesota’s StreamStats to predict fluvial sediment","docAbstract":"A thorough understanding of fluvial sediment transport is essential for addressing key environmental issues such as aquatic habitat degradation, flooding, excess nutrients, and challenges with river restoration. Fluvial sediment samples are valuable for addressing these concerns, but their collection is often impractical across all rivers and timeframes of interest. In addition, previously used analytical and numerical methods have not allowed for the transfer of knowledge from sites that have data to sites that do not have data. To overcome this limitation, the U.S. Geological Survey developed machine learning models to predict suspended-sediment concentrations and bedload transport in Minnesota rivers that lack physical sediment data and integrated them into the U.S. Geological Survey StreamStats web application.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253005","collaboration":"Prepared in cooperation with the Minnesota Pollution Control Agency","usgsCitation":"Groten, J.T., Lund, J.W., Coenen, E.N., Medenblik, A.S., Wavra, H.N., Kennedy, M., and Johnson, G.D., 2025, Using machine learning in Minnesota’s StreamStats to predict fluvial sediment: U.S. Geological Survey Fact Sheet 2025–3005, 4 p., https://doi.org/10.3133/fs20253005.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-171713","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":492671,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118404.htm","linkFileType":{"id":5,"text":"html"}},{"id":481515,"rank":5,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Expanding human enterprise leading to resource subsidies for generalist species has resulted in widespread increases in common raven (Corvus corax) populations across the Western U.S. Ravens are an efficient predator and increased population abundance has led to adverse effects to multiple sensitive prey species. In regions where problematic interactions between ravens and their prey exist, managers seek efficient and effective tools for monitoring and controlling expanding raven populations. We previously developed a Rapid Assessment Function (RAF) for managers to quickly estimate raven population density and assess the need for management actions. We developed the RAF for the Great Basin (GB RAF) by first estimating raven density using robust distance sampling protocols with >30,000 raven point count surveys from sagebrush ecosystems in California, Nevada, Idaho, and Oregon across 131 field sites and years. We then used the relationship between raven density estimates from distance sampling and n ravens observedsite-year/ n surveyssite-year (that is, raven index) at each site-year combination to develop a function that accounts for detection probability and adjusts simple counts to provide a prediction of ‘true’ density. Our function produced reliable density estimates given approximately 50–100 surveys, thereby reducing the field-based and analytical efforts typically needed to estimate raven density, facilitating more efficient raven management in open sagebrush habitats. In this study, we sought to test our original GB RAF using data from sagebrush ecosystems outside of the Great Basin. Using raven point count data from two field site units in Washington state collected from 2016 to 2023, we calculated density estimates from distance sampling methods, comparable to what was done for previous analyses. We then used the GB RAF to generate predictions of density and compared those values to the more robust estimates from distance sampling. Additionally, we developed modified RAFs specifically for Washington data (WA RAFs) to assess how well they predicted raven density compared to the GB RAF. We found the detection curves estimated for the Washington sites largely aligned with those used to generate the original GB RAF. Furthermore, the estimates from the GB RAF exhibited similar or higher correlation with densities calculated from distance models (Pearson’s r = 0.73) than the modified WA RAFs with 1.33 km and 1.25 km truncation distances (Pearson’s r = 0.63 and 0.73, respectively). Producing an equivalently performing modified WA RAF would likely necessitate more data to reduce estimation error and produce more reliable estimates. These results provide evidence for the applicability of our GB RAF for more widespread use within sagebrush ecosystems, possibly negating the need for locally developed RAFs. Continued assessments of the GB RAF outside of the Great Basin would further verify its applicability across the sagebrush biome.
","language":"English","publisher":"BioRxiv","doi":"10.1101/2025.01.27.635125","usgsCitation":"Brussee, B.E., O’Neil, S.T., Atamian, M., Leingang, C., and Coates, P.S., 2025, Evaluation of a rapid assessment function to aid monitoring and management of common ravens (Corvus corax) in Washington state: BioRxiv, https://doi.org/10.1101/2025.01.27.635125.","productDescription":"27 p.","ipdsId":"IP-167957","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":489959,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1101/2025.01.27.635125","text":"Publisher Index Page"},{"id":482499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928683,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928684,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Atamian, Michael T.","contributorId":351491,"corporation":false,"usgs":false,"family":"Atamian","given":"Michael T.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":928685,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leingang, Colin G.","contributorId":351492,"corporation":false,"usgs":false,"family":"Leingang","given":"Colin G.","affiliations":[{"id":83997,"text":"Yakima Training Center","active":true,"usgs":false}],"preferred":false,"id":928686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":928687,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70263339,"text":"70263339 - 2025 - Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning","interactions":[],"lastModifiedDate":"2025-02-06T15:53:28.669956","indexId":"70263339","displayToPublicDate":"2025-01-29T09:49:48","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":"Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning","docAbstract":"Accurate, high-resolution maps of bedrock outcrops can be valuable for applications such as models of land–atmosphere interactions, mineral assessments, ecosystem mapping, and hazard mapping. The increasing availability of high-resolution imagery can be coupled with machine learning techniques to improve regional bedrock outcrop maps. In the United States, the existing 30 m U.S. Geological Survey (USGS) National Land Cover Database (NLCD) tends to misestimate extents of barren land, which includes bedrock outcrops. This impacts many calculations beyond bedrock mapping, including soil carbon storage, hydrologic modeling, and erosion susceptibility. Here, we tested if a machine learning (ML) model could more accurately map exposed bedrock than NLCD across the entire Sierra Nevada Mountains (California, USA). The ML model was trained to identify pixels that are likely bedrock from 0.6 m imagery from the National Agriculture Imagery Program (NAIP). First, we labeled exposed bedrock at twenty sites covering more than 83 km2 (0.13%) of the Sierra Nevada region. These labels were then used to train and test the model, which gave 83% precision and 78% recall, with a 90% overall accuracy of correctly predicting bedrock. We used the trained model to map bedrock outcrops across the entire Sierra Nevada region and compared the ML map with the NLCD map. At the twenty labeled sites, we found the NLCD barren land class, even though it includes more than just bedrock outcrops, accounted for only 41% and 40% of mapped bedrock from our labels and ML predictions, respectively. This substantial difference illustrates that ML bedrock models can have a role in improving land-cover maps, like NLCD, for a range of science applications.
","language":"English","publisher":"MDPI","doi":"10.3390/rs17030457","usgsCitation":"Shastry, A.R., Cerovski-Darriau, C., Coltin, B., and Stock, J.D., 2025, Mapping bedrock outcrops in the Sierra Nevada Mountains (California, USA) using machine learning: Remote Sensing, v. 17, no. 3, 457, 11 p., https://doi.org/10.3390/rs17030457.","productDescription":"457, 11 p.","ipdsId":"IP-153917","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":487628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17030457","text":"Publisher Index Page"},{"id":481746,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.41245311245856,\n 35.20197552578807\n ],\n [\n -117.96299074904582,\n 36.06858120494961\n ],\n [\n -118.86530937366612,\n 37.63884023254646\n ],\n [\n -119.85965473348287,\n 38.80651233617289\n ],\n [\n -120.17114612624695,\n 40.23030133169971\n ],\n [\n -120.73602418336918,\n 40.662012753561754\n ],\n [\n -122.36739903137283,\n 40.400491599532984\n ],\n [\n -120.74692405664294,\n 38.0147515126105\n ],\n [\n -119.36615838296214,\n 35.979191454701876\n ],\n [\n -118.41245311245856,\n 35.20197552578807\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Shastry, Apoorva Ramesh 0000-0002-3996-4857","orcid":"https://orcid.org/0000-0002-3996-4857","contributorId":317867,"corporation":false,"usgs":true,"family":"Shastry","given":"Apoorva","email":"","middleInitial":"Ramesh","affiliations":[{"id":227,"text":"Earth Surface Dynamics Program","active":true,"usgs":true}],"preferred":true,"id":926515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cerovski-Darriau, Corina 0000-0002-0543-0902","orcid":"https://orcid.org/0000-0002-0543-0902","contributorId":221159,"corporation":false,"usgs":true,"family":"Cerovski-Darriau","given":"Corina","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coltin, Brian","contributorId":350636,"corporation":false,"usgs":false,"family":"Coltin","given":"Brian","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":926517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stock, Jonathan D. 0000-0001-8565-3577 jstock@usgs.gov","orcid":"https://orcid.org/0000-0001-8565-3577","contributorId":3648,"corporation":false,"usgs":true,"family":"Stock","given":"Jonathan","email":"jstock@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":926518,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263130,"text":"70263130 - 2025 - Forecasting sea otter recolonization: Insights from isotopic analysis of modern and zooarchaeological populations","interactions":[],"lastModifiedDate":"2025-01-30T15:28:06.452327","indexId":"70263130","displayToPublicDate":"2025-01-29T09:23:00","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18342,"text":"Proceedings of the Royal Society B, Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting sea otter recolonization: Insights from isotopic analysis of modern and zooarchaeological populations","docAbstract":"Retrospective datasets offer essential context for conservation by revealing species’ ecological roles before industrial-era human impacts. We analysed isotopic compositions of pre-industrial and modern sea otters (Enhydra lutris) to reconstruct pre-extirpation ecology and offer insights for management. Our study focuses on southeast Alaska (SEAK), where sea otters are recolonizing, and northern Oregon, where translocations are being considered. We measured bulk bone collagen δ13C and δ15N values and essential amino acid δ13C values of extirpated sea otters from archaeological contexts, and bulk isotopic values from vibrissae of modern SEAK sea otters. We compare these results with published isotopic data of potential prey and additional archaeological datasets. In SEAK, our data show pre-industrial sea otter populations consumed infaunal bivalves and used soft-sediment (33%) and kelp forest habitats (67%), with sub-regional variation. We anticipate current populations will expand into this historical niche, and conflict with regional traditional/subsistence bivalve fisheries will persist. In northern Oregon, isotopic data from extirpated sea otters indicate past consumption of low trophic level invertebrates and a stronger reliance on kelp forests (88%) rather than soft-sediment habitats, highlighting the importance of kelp forests for future translocations. Our work exemplifies the value of historical ecology in informing conservation strategies for recovering species.
","language":"English","publisher":"The Royal Society","doi":"10.1098/rspb.2024.1682","usgsCitation":"Elliott Smith, E.A., Moss, M., Wellman, H., Gill, V., Monson, D., and Newsome, S.D., 2025, Forecasting sea otter recolonization: Insights from isotopic analysis of modern and zooarchaeological populations: Proceedings of the Royal Society B, Biological Sciences, v. 292, no. 2039, 20241682, 12 p., https://doi.org/10.1098/rspb.2024.1682.","productDescription":"20241682, 12 p.","ipdsId":"IP-162071","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":499595,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11775623/","text":"External Repository"},{"id":481502,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.26359877499726,\n 46.308565616179806\n ],\n [\n -124.26359877499726,\n 45.25430222380987\n ],\n [\n -123.48250866767472,\n 45.25430222380987\n ],\n [\n -123.48250866767472,\n 46.308565616179806\n ],\n [\n -124.26359877499726,\n 46.308565616179806\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -134.6258986222449,\n 59.17859339162297\n ],\n [\n -137.37101203559084,\n 58.25218310558745\n ],\n [\n -133.21606440626744,\n 54.52221277170722\n ],\n [\n -131.50965539994962,\n 54.47095791058172\n ],\n [\n -130.7340511065769,\n 55.94266269565489\n ],\n [\n -134.6258986222449,\n 59.17859339162297\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"292","issue":"2039","noUsgsAuthors":false,"publicationDate":"2025-01-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Elliott Smith, Emma A.","contributorId":140743,"corporation":false,"usgs":false,"family":"Elliott Smith","given":"Emma","email":"","middleInitial":"A.","affiliations":[{"id":13339,"text":"University of New Mexico, Albuquerque","active":true,"usgs":false}],"preferred":false,"id":925637,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moss, Madonna L.","contributorId":350305,"corporation":false,"usgs":false,"family":"Moss","given":"Madonna L.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":925638,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wellman, Hannah P.","contributorId":350306,"corporation":false,"usgs":false,"family":"Wellman","given":"Hannah P.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":925639,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gill, Verena A.","contributorId":140658,"corporation":false,"usgs":false,"family":"Gill","given":"Verena A.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":925640,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":925641,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Newsome, Seth D.","contributorId":81640,"corporation":false,"usgs":false,"family":"Newsome","given":"Seth","email":"","middleInitial":"D.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":925642,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263340,"text":"70263340 - 2025 - The effectiveness of wildfire at meeting restoration goals across a fire severity gradient in the Sierra Nevada","interactions":[],"lastModifiedDate":"2025-02-06T15:23:26.879574","indexId":"70263340","displayToPublicDate":"2025-01-29T09:17:15","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"The effectiveness of wildfire at meeting restoration goals across a fire severity gradient in the Sierra Nevada","docAbstract":"As a consequence of both warming temperatures and over a century of fire suppression, wildfires in the historically frequent-fire forests of the western US have increased both in size and intensity, resulting in large patches of high severity fire that are well outside the historic range of variation. Postfire fuels research has often focused on such high severity patches because of the risk of both type conversion and repeated high severity fire. Yet a substantial portion of any given wildfire will likely still have burned at low to moderate severity. These areas generally retain live mature trees and surface fuels, suggesting that wildfire effects may be in keeping with some forest restoration goals. To better understand the range of postfire fuels conditions across severity classes and how well those conditions align with restoration targets, we sampled three wildfires in mixed conifer forests and giant sequoia groves of the southern Sierra Nevada. These wildfires appear to have met short-term restoration goals for surface fuel reduction, with burned areas having 79.5 % less fuels than unburned areas. Fine woody debris and litter and duff declined with severity, while coarse woody debris was more variable. Small tree density targets were roughly met after low and moderate severity fire, but large tree densities tended to be lower than restoration targets, possibly due to high levels of recent tree mortality. For long-term management, restoration plans set targets for the proportions of the landscape that should be in different fuel load categories, reflecting patterns shaped by many frequent and patchy fires. Observed post-wildfire surface fuels outside of groves were overwhelmingly in the lowest fuels category across severity classes, which is in keeping with short-term goals to reduce surface fuels but is not necessarily contributing to the heterogeneity desired at landscape scales. Surface fuels within giant sequoia groves were higher than those outside groves and therefore more closely matched long-term management targets for variation in fuel loads. However, for a highly valued species that has recently seen substantial losses to high severity fire, managers may find that these higher fuel loads are not desirable even in a landscape context. In summary, low and moderate severity wildfire appear to have beneficial effects in terms of meeting several management goals, however, the large amount of standing postfire fuels, the relative dearth of large trees, and the potential lack of postfire fuel heterogeneity may still pose potential management concerns.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2024.122486","usgsCitation":"Das, A., Rosenthal, L., and Shive, K.L., 2025, The effectiveness of wildfire at meeting restoration goals across a fire severity gradient in the Sierra Nevada: Forest Ecology and Management, v. 580, https://doi.org/10.1016/j.foreco.2024.122486.","productDescription":"122486, 14 p.","startPage":"122486","ipdsId":"IP-170573","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":487023,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2024.122486","text":"Publisher Index Page"},{"id":481738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Kings Canyon National Park, Sequoia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.9,\n 36.7\n ],\n [\n -118.9,\n 36.3\n ],\n [\n -118.3,\n 36.3\n ],\n [\n -118.3,\n 36.7\n ],\n [\n -118.9,\n 36.7\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"580","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Das, Adrian 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":201236,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":926519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenthal, Lisa 0000-0003-4030-7587","orcid":"https://orcid.org/0000-0003-4030-7587","contributorId":350637,"corporation":false,"usgs":false,"family":"Rosenthal","given":"Lisa","affiliations":[{"id":37814,"text":"Former USGS","active":true,"usgs":false}],"preferred":false,"id":926520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shive, Kristen L.","contributorId":194877,"corporation":false,"usgs":false,"family":"Shive","given":"Kristen","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":926521,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269526,"text":"70269526 - 2025 - What 25+ years of \"Did You Feel It\" intensities tell us about shaking in California","interactions":[],"lastModifiedDate":"2025-07-25T13:32:19.484813","indexId":"70269526","displayToPublicDate":"2025-01-29T08:28:22","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"What 25+ years of \"Did You Feel It\" intensities tell us about shaking in California","docAbstract":"“When will the Big One happen?” is a question that people often have for earthquake scientists. But while waiting for the “Big One” to occur, people will usually experience frightening or damaging shaking from multiple relatively smaller‐magnitude earthquakes. Given this context, it raises the question: “Where does most of the damage come from?” Could smaller, yet more frequent, earthquakes account for the majority of reported impactful shaking? To explore this question, we consider reports of earthquake damage and felt shaking experiences from a catalog of community‐collected intensity values from the U.S. Geological Survey’s “Did You Feel It?” system. Comparing these intensities to expectations from a ground‐motion model, we find that earthquakes of magnitudes smaller than expected are responsible for most reported intensities of community decimal intensities (CDI) 4.5 and above (moderate and higher shaking intensity levels). (Here “expected value” is meant in its mathematical sense of the mean or equivalently median shaking intensity.) We also present a regional analysis of observed earthquake shaking for specific areas, detailing the maximum intensity experienced within a predetermined area. We identify several instances of M < 4.5 events that generated maximum intensities of CDI > 5 in regions in California surrounding the cities of Eureka, San Francisco, Los Angeles, and San Diego. Our results motivate the need to include smaller‐magnitude earthquakes in communications about earthquake hazard and risk reduction.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240393","usgsCitation":"Chaffeur, J., Saunders, J.K., Minson, S.E., Baltay Sundstrom, A.S., Cochran, E.S., Hough, S., Quitoriano, V., Page, M.T., and Blair, J.L., 2025, What 25+ years of \"Did You Feel It\" intensities tell us about shaking in California: Seismological Research Letters, v. 96, no. 4, p. 2625-2637, https://doi.org/10.1785/0220240393.","productDescription":"13 p.","startPage":"2625","endPage":"2637","ipdsId":"IP-171178","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":492900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":943975,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":350979,"corporation":false,"usgs":true,"family":"Hough","given":"Susan E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":943976,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Quitoriano, Vince 0000-0003-4157-1101 vinceq@usgs.gov","orcid":"https://orcid.org/0000-0003-4157-1101","contributorId":2582,"corporation":false,"usgs":true,"family":"Quitoriano","given":"Vince","email":"vinceq@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943977,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":943978,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blair, James Luke 0000-0002-6980-6446","orcid":"https://orcid.org/0000-0002-6980-6446","contributorId":213724,"corporation":false,"usgs":true,"family":"Blair","given":"James","email":"","middleInitial":"Luke","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":943979,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262822,"text":"sir20245105 - 2025 - Groundwater hydrology, groundwater and surface-water interactions, water quality, and groundwater-flow simulations for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2017–19","interactions":[],"lastModifiedDate":"2025-01-29T14:30:56.495951","indexId":"sir20245105","displayToPublicDate":"2025-01-28T12:40:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5105","displayTitle":"Groundwater Hydrology, Groundwater and Surface-Water Interactions, Water Quality, and Groundwater-Flow Simulations for the Wet Mountain Valley Alluvial Aquifer, Custer and Fremont Counties, Colorado, 2017–19","title":"Groundwater hydrology, groundwater and surface-water interactions, water quality, and groundwater-flow simulations for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2017–19","docAbstract":"In 2017, the U.S. Geological Survey, in cooperation with the Upper Arkansas Water Conservancy District, began a study to provide a comprehensive analysis of the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado. The study included collection of data pertaining to groundwater hydrology, groundwater and surface-water interactions, and water quality in the alluvial aquifer. In addition to providing foundational information on the hydrology of the alluvial aquifer, a numerical groundwater-flow model was developed to estimate the potential effects of additional storage of groundwater in the alluvial aquifer.
Groundwater-level elevation data from 30 wells were used to estimate groundwater-flow directions in the alluvial aquifer, which were generally from the southwest to northeast, away from the Sangre de Cristo Mountains and towards perennial streams in the center of the valley. Although some seasonal variation was apparent in groundwater-level elevation records, no statistically significant seasonal trends were indicated. Statistically significant long-term trends were indicated in groundwater-level elevation records for 8 of the 30 wells, and of these wells with statistically significant trends, all but 1 indicated a negative trend of groundwater-level elevations. Spatial evaluation of wells with statistically significant negative groundwater-level elevation trends showed many are in areas of denser well drilling for domestic or other uses, indicating increasing groundwater use could potentially be causing groundwater-level elevation declines. There were instances of wells with no statistically significant groundwater-level elevation trends also located in areas of greater density of well completions. Additional investigations may be necessary to more fully characterize the processes responsible for negative groundwater-level elevation trends.
Streamflow gain or loss calculations were completed for low flow in 2017–19 and for high flow in 2018 in nine reaches of streams within the study area. Stream reaches of the upper Texas Creek, upper Grape Creek, upper-middle Grape Creek, and Taylor Creek displayed consistent streamflow loss in each period from 2017 to 2019. These stream reaches represent long-term sources of recharge to the alluvial aquifer. Streamflow gain or loss varies through time in other stream reaches (lower Texas Creek, lower-middle Grape Creek, lower Grape Creek below Westcliffe, and lower Grape Creek above DeWeese Reservoir). The temporally variable behavior indicates these stream reaches may be sources of groundwater recharge or areas of groundwater discharge, likely depending on temporal dynamics between the elevation of the water table and the stream.
Water-quality samples were collected from 10 groundwater wells and 10 stream sites during September through November 2019. All groundwater and stream samples were analyzed for major and trace elements and stable isotopes of water. A subset of groundwater samples was also analyzed for the environmental tracers sulfur hexafluoride, tritium, and noble gases. Comparison of water-quality results to U.S. Environmental Protection Agency drinking water-quality standards indicated no constituents exceeded primary standards for human health. Spatial evaluation of water quality indicated the concentrations of various constituents are likely controlled by groundwater and surface-water interactions and by spatial variability in bedrock geology underlying the alluvial aquifer. Specifically, streams shown to gain from groundwater had water chemistry constituent compositions similar to groundwater, whereas streams exiting the Sangre de Cristo Mountains tended to have compositions consistent with snowmelt. Groundwater geochemistry appeared to be partially controlled by oxidation-reduction processes and by proximity to igneous rocks in the Wet Mountains. Environmental tracers used to estimate groundwater age indicated all sampled groundwater contained tracers representing modern recharge (approximately less than 65 years old) but mixing of premodern recharge (approximately more than 65 years old) also occurs. Spatial evaluation of environmental tracers indicated large faults may be conduits for upwelling of older groundwater. No trends were observed in groundwater age with well depth, indicating all sampled wells are located within the zone of active groundwater flow. The presence of modern groundwater in wells with statistically significant negative groundwater-level elevation trends indicates groundwater storage depletions may be partially offset by capture of modern recharge. Repeated sampling of groundwater age would be necessary, however, to determine if any trends in groundwater age exist, which may indicate changing groundwater recharge, storage, or discharge. Additional investigations could also consider quantifying groundwater age in deeper wells to more fully define the depth of active groundwater flow.
A numerical groundwater-flow model was developed to estimate components of the water budget, simulate groundwater and surface-water interactions, and evaluate the potential effects of aquifer storage and recovery. Simulated groundwater-level elevations from the calibrated groundwater-flow model are similar to the observed pattern of groundwater-level elevations with higher elevations in the western part of the study area along the Sangre de Cristo Mountains. Simulated water-budget components indicate most of the recharge to the alluvial aquifer is derived from streamflow losses, which is consistent with observations of losing streams along the mountain front. The largest groundwater discharge component of the alluvial aquifer was to streams in the center of the valley, where observations of stream gain or loss indicated the predominance of gaining conditions. Comparison of groundwater and surface-water interactions between the calibrated groundwater-flow model for 2000-19 (the base-case model) and a simulation including additional recharge, representing potential aquifer storage and recovery operations, indicated the additional recharge distributed throughout the area had minimal effects on streamflow in the nearby Grape Creek. An analysis of subregional groundwater budgets showed approximately 54 percent of the additional recharge flowed back to nearby Grape Creek, and the other 46 percent was distributed laterally into adjacent cells in the alluvial aquifer. The comparison of simulations and subregional water budget show the additional recharge did not substantially alter groundwater-level elevations or basin wide groundwater storage. Although the analysis of additional recharge provided in the numerical groundwater-flow model considers only one of many possible recharge scenarios, the model provides a useful tool that could be modified for various scenarios to understand potential effects of managed aquifer recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20245105","collaboration":"Prepared in cooperation with the Upper Arkansas Water Conservancy District","usgsCitation":"Newman, C.P., Russell, C.A., Kisfalusi, Z.D., and Paschke, S.S., 2025, Groundwater hydrology, groundwater and surface-water interactions, water quality, and groundwater-flow simulations for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2017–19: U.S. Geological Survey Scientific Investigations Report 2024–5105, 62 p., https://doi.org/10.3133/sir20245105.","productDescription":"Report: vii, 62 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-125470","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":481114,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5105/coverthb.jpg"},{"id":481115,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5105/sir20245105.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5105"},{"id":481144,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9342SSP","text":"USGS data release","linkHelpText":"Environmental tracer model for the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado, 2019"},{"id":481145,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AAUGNY","text":"USGS data release","linkHelpText":"Groundwater-flow model of the Wet Mountain Valley alluvial aquifer, Custer and Fremont Counties, Colorado"},{"id":481407,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5105/images"},{"id":481408,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5105/sir20245105.xml"},{"id":481417,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245105/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5105"}],"country":"United States","state":"Colorado","county":"Custer County, Fremont County","otherGeospatial":"Upper Arkansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.1667,\n 38.5\n ],\n [\n -105.1667,\n 37.9167\n ],\n [\n -105.9167,\n 37.9167\n ],\n [\n -105.9167,\n 38.5\n ],\n [\n -105.1667,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, CO 80225
Quantitative fatty acid signature analysis (QFASA) is a common method of estimating the composition of prey species in the diets of consumers from polar and temperate ecosystems in which lipids are an important source of energy. A key characteristic of QFASA is that the large number of fatty acids that typically comprise lipids permits the dietary contributions of a correspondingly large number of prey types to be estimated. Several modifications to the original QFASA methods have been suggested in the literature and a significant extension of the original model published in 2017 allows simultaneous estimation of both diet proportions and calibration coefficients, which are metabolic constants in the model whose values must otherwise be estimated in independent feeding experiments. However, comparisons of diet estimates obtained using different estimation options have been limited. QFASA has been used to estimate the diet composition of several polar bear (Ursus maritimus) subpopulations, including the Southern Beaufort Sea (SBS) subpopulation. Prior QFASA estimates of SBS polar bear diet composition have most often been obtained using variations of the original QFASA model. We investigated the influence of variations in QFASA analytical methods on diet estimates by re-estimating the diet composition of polar bears from the Alaska portion of the SBS using three different methods and found that differences among the three sets of estimates were substantial. Our results illustrate how important the careful and deliberate selection of QFASA methods can be and we provide some guidance on techniques one might use to evaluate options.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0308283","usgsCitation":"Bromaghin, J.F., Atwood, T.C., and Rode, K.D., 2025, The importance of method selection when estimating diet composition with quantitative fatty acid signature analysis: PLoS ONE, v. 20, no. 1, e0308283, 15 p., https://doi.org/10.1371/journal.pone.0308283.","productDescription":"e0308283, 15 p.","ipdsId":"IP-162413","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":487613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0308283","text":"Publisher Index Page"},{"id":481610,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bromaghin, Jeffrey F. 0000-0002-7209-9500 jbromaghin@usgs.gov","orcid":"https://orcid.org/0000-0002-7209-9500","contributorId":139899,"corporation":false,"usgs":true,"family":"Bromaghin","given":"Jeffrey","email":"jbromaghin@usgs.gov","middleInitial":"F.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":925982,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":925983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":925984,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70265699,"text":"70265699 - 2025 - Integrating sea level rise scenarios into Everglades restoration planning","interactions":[],"lastModifiedDate":"2025-04-15T14:06:33.601383","indexId":"70265699","displayToPublicDate":"2025-01-28T09:03:52","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7504,"text":"Final Report","active":true,"publicationSubtype":{"id":1}},"title":"Integrating sea level rise scenarios into Everglades restoration planning","docAbstract":"One of the largest and most expensive restoration efforts in the world is occurring in the Everglades, a sub-tropical freshwater wetland system located in southern Florida. This unique ecosystem supports several endangered species, provides flood control for Florida’s large urban population, and provides water for both agriculture and drinking supply within the state. The Comprehensive Everglades Restoration Plan (CERP), authorized by Congress in 2000, guides federal, state, and local efforts to build the infrastructure necessary to bring more water into the Everglades and restore its ecological integrity, while balancing other water-related needs such as water supply and flood protection in the human environment. The Everglades encompasses the southern coast of Florida and restoration efforts are likely to be impacted by climate-induced sea level rise. However, currently, many project planning studies do not formally incorporate the potential impacts of sea level rise when evaluating restoration plan outcomes. Resource managers and project planners require methods and tools to confidently incorporate scenarios of sea level rise into their evaluations. The U.S. Geological Survey (USGS) and partners from the University of Florida worked with project planners from multiple agencies to identify restoration questions for consideration when addressing sea level rise. In addition, our project team sought to understand the types of sea level rise decision-support tools that would be of interest, and then worked with Everglades restoration managers and project planners to develop those tools. The tools developed by this project can be used by project planners to inform their decision-making abilities when considering multiple restoration plans across the Everglades landscape. Specifically, the novel ways to visualize output information from ecological models that came of this project can help project planners compare alternative restoration plans that include potential sea level rise impacts. This effort demonstrates how incorporating sea level rise scenarios into Everglades restoration project planning can help managers decide whether projects will maintain or improve ecological integrity and evaluate water availability for wildlife and humans.","language":"English","publisher":"Southeast Climate Adaptation Science Center","usgsCitation":"D’Acunto, L., Romanach, S., Castellano, S., and Clarke, M., 2025, Integrating sea level rise scenarios into Everglades restoration planning: Final Report, 15 p.","productDescription":"15 p.","ipdsId":"IP-174419","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":484573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":484519,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/science/everglades-slr/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":933335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Romanach, Stephanie 0000-0003-0271-7825","orcid":"https://orcid.org/0000-0003-0271-7825","contributorId":223479,"corporation":false,"usgs":true,"family":"Romanach","given":"Stephanie","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":933336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castellano, Stephanie","contributorId":353362,"corporation":false,"usgs":false,"family":"Castellano","given":"Stephanie","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":933338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clarke, Mysha","contributorId":353361,"corporation":false,"usgs":false,"family":"Clarke","given":"Mysha","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":933337,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70262914,"text":"70262914 - 2025 - Hotter temperatures alter riparian plant outcomes under regulated river conditions","interactions":[],"lastModifiedDate":"2025-01-28T15:42:49.175773","indexId":"70262914","displayToPublicDate":"2025-01-27T09:39:38","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Hotter temperatures alter riparian plant outcomes under regulated river conditions","docAbstract":"Climate change and river regulation alter environmental controls on riparian plant occurrence and cover worldwide. Simultaneous changes to river flow and air temperature could result in unanticipated plant responses to novel environmental conditions. Increasing temperature could alter riparian plant response to hydrology and other factors, while river regulation may exacerbate environmental stress through novel flows like those resulting from power generation. Further, plant establishment and growth may require differing conditions, which may be decoupled by novel conditions. Using a large dataset that spans a natural 5°C mean annual temperature (MAT) gradient and a Bayesian model that integrates plant occurrence and cover, we address four questions: (1) Does hotter MAT modify plant response to hydrology, substrate composition, topography, and cover of co-occurring plant species? (2) Does the timing of hydropower tides benefit some species over others? (3) Does dam-induced erosion hinder riparian species more than upland species? (4) Do occurrence and cover respond to different environmental variables, allowing for decoupling of life history processes? We addressed these questions with data collected along 364 km of the Colorado River downstream of Glen Canyon Dam, Arizona, United States of America. Occurrence and cover class were recorded in >10,000 plots from 2016 to 2020, along with environmental covariates that repeat across the climate gradient. For 36 species, plant occurrence and cover were modeled with respect to MAT, hydrology, substrate, topography, other plant cover, and their interactions with MAT. There were four key results. (1) Increasing MAT will not only directly influence plants but will mediate their responses to the environment, including greater dependence on stable water supplies. (2) The timing of hydropower tides shapes plant community composition. (3) Dam-related erosion has an outsized effect on riparian species, which could lead to a loss of regionally unique plant species. (4) For all species, the most important covariates driving occurrence differed from those for cover, suggesting the potential for these life stages to be decoupled. Not only will climate change and river regulation independently alter plant distributions, interactions among hotter temperature, dam-controlled flow patterns, and limited fine sediments will determine which species flourish or perish under future conditions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1645","usgsCitation":"Palmquist, E.C., Ogle, K., Butterfield, B.J., Whitham, T.G., Allan, G.J., and Shafroth, P., 2025, Hotter temperatures alter riparian plant outcomes under regulated river conditions: Ecological Monographs, v. 95, no. 1, e1645, 21 p., https://doi.org/10.1002/ecm.1645.","productDescription":"e1645, 21 p.","ipdsId":"IP-159047","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":481415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River downstream of Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.21176601160529,\n 36.979125233919234\n ],\n [\n -113.97825341279814,\n 36.979125233919234\n ],\n [\n -113.97825341279814,\n 35.65153018969767\n ],\n [\n -111.21176601160529,\n 35.65153018969767\n ],\n [\n -111.21176601160529,\n 36.979125233919234\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"95","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":925281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ogle, Kiona","contributorId":248351,"corporation":false,"usgs":false,"family":"Ogle","given":"Kiona","email":"","affiliations":[],"preferred":false,"id":925282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":925283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Whitham, Thomas G.","contributorId":174327,"corporation":false,"usgs":false,"family":"Whitham","given":"Thomas","email":"","middleInitial":"G.","affiliations":[{"id":27416,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Nothern Arizona University, Flagstaff, AZ 86011 USA","active":true,"usgs":false}],"preferred":false,"id":925284,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allan, Gerard J.","contributorId":189075,"corporation":false,"usgs":false,"family":"Allan","given":"Gerard","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":925285,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":925286,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266202,"text":"70266202 - 2025 - Integrated analysis of marked and count data to characterizefine-scale stream fish movement","interactions":[],"lastModifiedDate":"2025-04-30T15:07:09.143764","indexId":"70266202","displayToPublicDate":"2025-01-27T07:51:02","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2932,"text":"Oecologia","active":true,"publicationSubtype":{"id":10}},"title":"Integrated analysis of marked and count data to characterizefine-scale stream fish movement","docAbstract":"Immigration and emigration are key demographic processes of animal population dynamics. However,\n3 we have limited knowledge on how fine-scale movement varies over space and time. We developed a\n4 Bayesian integrated population model using individual mark-recapture and count data to characterize\n5 fine-scale movement of stream fish at 20-m resolution every two months for 28 months. Our study\n6 targeted small-bodied fish, for which imperfect capture was accounted for (bluehead chub Nocomis\n7 leptocephalus, creek chub Semotilus atromaculatus and mottled sculpin Cottus bairdii). Based on\n8 data from 2,021 individuals across all species, we found that proportions of immigrants averaged\n9 30-42% among the study species, but they varied over space and time. Creek chub immigrants\n10 increased during warmer intervals when individuals grew more and transitioned between body size\n11 classes, suggesting that immigration was due to ontogenetic habitat shifts. There was a weak pattern\n12 across the species that individuals were more likely to leave 20-m sections when flow was higher.\n13 Water-column species (bluehead chub and creek chub) were more likely to immigrate into and stay\n14 in deeper sections with more pool area. Across all species and occasions, number of immigrants\n15 to stream sections did not decrease with number of individuals that survived and stayed in the\n16 same sections. Thus, the habitat did not appear saturated, and our data provided no evidence that\n17 intra-specific interactions affected fine-scale movement at our fish densities. In conclusion, high\n18 turnover rates characterized fish movement among stream sections and their variation was associated\n19 with temporal and spatial shifts in abiotic conditions.","language":"English","publisher":"Springer Nature","doi":"10.1007/s00442-024-05639-3","usgsCitation":"Kanno, Y., Pregler, K., and Kim, S., 2025, Integrated analysis of marked and count data to characterizefine-scale stream fish movement: Oecologia, v. 207, 25, 15 p., https://doi.org/10.1007/s00442-024-05639-3.","productDescription":"25, 15 p.","ipdsId":"IP-162172","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485204,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Clemson University Experimental Forest, Indian Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.82048747799679,\n 34.68143669803426\n ],\n [\n -82.82048747799679,\n 34.67911153920382\n ],\n [\n -82.81055950407821,\n 34.67911153920382\n ],\n [\n -82.81055950407821,\n 34.68143669803426\n ],\n [\n -82.82048747799679,\n 34.68143669803426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"207","noUsgsAuthors":false,"publicationDate":"2025-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Kanno, Yoichiro","contributorId":353979,"corporation":false,"usgs":false,"family":"Kanno","given":"Yoichiro","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":934906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pregler, Kasey Celene 0000-0002-0664-9594","orcid":"https://orcid.org/0000-0002-0664-9594","contributorId":353980,"corporation":false,"usgs":true,"family":"Pregler","given":"Kasey Celene","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":934907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Seoghyun","contributorId":353981,"corporation":false,"usgs":false,"family":"Kim","given":"Seoghyun","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":934908,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70263279,"text":"70263279 - 2025 - Perpetuation of avian influenza from molt to fall migration in wild Swan Geese (Anser cygnoides): An agent-based modeling approach","interactions":[],"lastModifiedDate":"2025-02-04T14:46:30.055812","indexId":"70263279","displayToPublicDate":"2025-01-25T08:40:30","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3700,"text":"Viruses","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Perpetuation of avian influenza from molt to fall migration in wild Swan Geese (Anser cygnoides): An agent-based modeling approach","title":"Perpetuation of avian influenza from molt to fall migration in wild Swan Geese (Anser cygnoides): An agent-based modeling approach","docAbstract":"Wild waterfowl are considered to be the reservoir of avian influenza, but their distinct annual life cycle stages and their contribution to disease dynamics are not well understood. Studies of the highly pathogenic avian influenza (HPAI) virus have primarily focused on wintering grounds, where human and poultry densities are high year-round, compared with breeding grounds, where migratory waterfowl are more isolated. Few if any studies of avian influenza have focused on the molting stage where wild waterfowl congregate in a few selected wetlands and undergo the simultaneous molt of wing and tail feathers during a vulnerable flightless period. The molting stage may be one of the most important periods for the perpetuation of the disease in waterfowl, since during this stage, immunologically naïve young birds and adults freely intermix prior to the fall migration. Our study incorporated empirical data from virological field samplings and markings of Swan Geese (Anser cygnoides) on their breeding grounds in Mongolia in an integrated agent-based model (ABM) that included susceptible–exposed–infectious–recovered (SEIR) states. Our ABM results provided unique insights and indicated that individual movements between different molting wetlands and the transmission rate were the key predictors of HPAI perpetuation. While wetland extent was not a significant predictor of HPAI perpetuation, it had a large effect on the number of infections and associated death toll. Our results indicate that conserving undisturbed habitats for wild waterfowl during the molting stage of the breeding season could reduce the risk of HPAI transmission.
","language":"English","publisher":"MDPI","doi":"10.3390/v17020196","usgsCitation":"Takekawa, J., Choi, C., Prosser, D.J., Sullivan, J.D., Batbayar, N., and Xiao, X., 2025, Perpetuation of avian influenza from molt to fall migration in wild Swan Geese (Anser cygnoides): An agent-based modeling approach: Viruses, v. 17, no. 2, 196, 20 p., https://doi.org/10.3390/v17020196.","productDescription":"196, 20 p.","ipdsId":"IP-171183","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":487618,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/v17020196","text":"Publisher Index Page"},{"id":481653,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mongolia, Russia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 114.5,\n 50.33\n ],\n [\n 114.5,\n 49.25\n ],\n [\n 116,\n 49.25\n ],\n [\n 116,\n 50.33\n ],\n [\n 114.5,\n 50.33\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationDate":"2025-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Takekawa, John","contributorId":330942,"corporation":false,"usgs":false,"family":"Takekawa","given":"John","affiliations":[{"id":32931,"text":"USGS - Retired","active":true,"usgs":false}],"preferred":false,"id":926134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Choi, Chang-Yong","contributorId":181784,"corporation":false,"usgs":false,"family":"Choi","given":"Chang-Yong","email":"","affiliations":[],"preferred":false,"id":926135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prosser, Diann J. 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":221167,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":926136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":926137,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Batbayar, Nyambaya","contributorId":181791,"corporation":false,"usgs":false,"family":"Batbayar","given":"Nyambaya","affiliations":[],"preferred":false,"id":926138,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Xiao, Xiangming","contributorId":181792,"corporation":false,"usgs":false,"family":"Xiao","given":"Xiangming","email":"","affiliations":[],"preferred":false,"id":926139,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}