Coastal resilience has become a pressing global issue due to the growing vulnerability of coastlines to the effects of climate change. Nature-based solutions have emerged as a promising approach to coastal protection to not only enhance coastal resilience, but also restore critical ecosystems. Coral reef restoration has the potential to provide ecosystem services benefits; however, there are still key uncertainties in linking restoration design to reductions in coastal flood hazard under current and future climate conditions. In this study, we applied one-dimensional and two-dimensional numerical coastal engineering models, calibrated and validated using field data, to evaluate the effectiveness of coral restoration scenarios on coastal waves, water levels, and flooding along the coast of San Juan, Puerto Rico, U.S.A. Model results indicate a small reduction in maximum water levels under the proposed restoration scenarios. This underscores the importance of these endeavors, not only for ecological preservation but also for preventing further reef deterioration. Such preservation is essential for mitigating the increased coastal risks anticipated in the future. Results from this study provide information to guide policymakers and coastal managers in making informed decisions on viable restoration project design options. By systematically evaluating how restoration location impacts coastal flood hazards, communities can develop and implement proactive strategies to mitigate flood-related risk. In addition, by restoring coral reefs, communities can contribute to environmental preservation while ensuring sustainable development and protection of coastal environments.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2025.1528460","usgsCitation":"Familkhalili, R., Storlazzi, C.D., Nemeth, M., and Viehman, S., 2025, Assessing the effect of coral reef restoration location on coastal flood hazard along the San Juan Coastline, Puerto Rico: Frontiers in Marine Science, v. 12, 1528460, 11 p., https://doi.org/10.3389/fmars.2025.1528460.","productDescription":"1528460, 11 p.","ipdsId":"IP-169548","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488566,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2025.1528460","text":"Publisher Index Page"},{"id":484246,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico, San Juan coastline","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.08782395132914,\n 18.480815260981046\n ],\n [\n -66.09096383189711,\n 18.45217867401871\n ],\n [\n -65.96850848974445,\n 18.42720367145678\n ],\n [\n -65.96005496513799,\n 18.468673931779904\n ],\n [\n -66.08782395132914,\n 18.480815260981046\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2025-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Familkhalili, Ramin","contributorId":353052,"corporation":false,"usgs":false,"family":"Familkhalili","given":"Ramin","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":932770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932772,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nemeth, Michael","contributorId":353056,"corporation":false,"usgs":false,"family":"Nemeth","given":"Michael","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":932773,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Viehman, Shay","contributorId":353053,"corporation":false,"usgs":false,"family":"Viehman","given":"Shay","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":932771,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70265884,"text":"70265884 - 2025 - Pathways for potential exposure to onshore oil and gas wastewater: What we need to know to protect human health","interactions":[],"lastModifiedDate":"2025-04-18T14:52:58.475289","indexId":"70265884","displayToPublicDate":"2025-04-03T07:49:20","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1542,"text":"Environmental Health Perspectives","active":true,"publicationSubtype":{"id":10}},"title":"Pathways for potential exposure to onshore oil and gas wastewater: What we need to know to protect human health","docAbstract":"Produced water is a chemically complex waste stream generated during oil and gas development. Roughly four trillion liters were generated onshore in the United States in 2021 (ALL Consulting, 2022, https://www.gwpc.org/wp-content/uploads/2021/09/2021_Produced_Water_Volumes.pdf). Efforts are underway to expand historic uses of produced water to offset freshwater needs in water-stressed regions, avoid induced seismic activity associated with its disposal, and extract commodities. Understanding the potential exposures from current and proposed produced water uses and management practices can help to inform health-protective practices. This review summarizes what is known about potential human exposure to produced water from onshore oil and gas development in the United States. We synthesize 236 publications to create a conceptual model of potential human exposure that illustrates the current state of scientific inquiry and knowledge. Exposure to produced water can occur following its release to the environment through spills or leaks during its handling and management. Exposure can also arise from authorized releases, including permitted discharges to surface water, crop irrigation, and road treatment. Knowledge gaps include understanding the variable composition and toxicity of produced water released to the environment, the performance of treatment methods, migration pathways through the environment that can result in human exposure, and the significance of the exposures for human and ecosystem health. Reducing these uncertainties may help in realizing the benefits of produced water use while simultaneously protecting human health.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GH001263","usgsCitation":"Ariana, A., Cozzarelli, I.M., Danforth, C., McDevitt, B., Rosofsky, A., and Vorhees, D., 2025, Pathways for potential exposure to onshore oil and gas wastewater: What we need to know to protect human health: Environmental Health Perspectives, v. 9, no. 4, e2024GH001263, 21 p., https://doi.org/10.1029/2024GH001263.","productDescription":"e2024GH001263, 21 p.","ipdsId":"IP-166041","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":490099,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gh001263","text":"Publisher Index Page"},{"id":484765,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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States\"}}]}","volume":"9","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Ariana, Ayusha","contributorId":353543,"corporation":false,"usgs":false,"family":"Ariana","given":"Ayusha","affiliations":[{"id":84430,"text":"Health Effects Institute Energy, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":933805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cozzarelli, Isabelle M. 0000-0002-5123-1007 icozzare@usgs.gov","orcid":"https://orcid.org/0000-0002-5123-1007","contributorId":1693,"corporation":false,"usgs":true,"family":"Cozzarelli","given":"Isabelle","email":"icozzare@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":933806,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danforth, Cloelle","contributorId":353544,"corporation":false,"usgs":false,"family":"Danforth","given":"Cloelle","affiliations":[{"id":84430,"text":"Health Effects Institute Energy, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":933807,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McDevitt, Bonnie 0000-0001-8390-0028","orcid":"https://orcid.org/0000-0001-8390-0028","contributorId":291246,"corporation":false,"usgs":true,"family":"McDevitt","given":"Bonnie","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":933808,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosofsky, Anna","contributorId":353545,"corporation":false,"usgs":false,"family":"Rosofsky","given":"Anna","affiliations":[{"id":84430,"text":"Health Effects Institute Energy, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":933809,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vorhees, Donna","contributorId":353546,"corporation":false,"usgs":false,"family":"Vorhees","given":"Donna","affiliations":[{"id":84430,"text":"Health Effects Institute Energy, Boston, MA, USA","active":true,"usgs":false}],"preferred":false,"id":933810,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266184,"text":"70266184 - 2025 - Behavioral plasticity in detection height of an invasive, arboreal snake based on size, condition, and prey","interactions":[],"lastModifiedDate":"2025-04-29T14:18:46.307912","indexId":"70266184","displayToPublicDate":"2025-04-01T09:14:20","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3777,"text":"Wildlife Research","active":true,"publicationSubtype":{"id":10}},"title":"Behavioral plasticity in detection height of an invasive, arboreal snake based on size, condition, and prey","docAbstract":"Animals may adjust their behavior in predictable ways to balance tradeoffs between resource acquisition and survival or fecundity. Microhabitat selection based on individual traits or environmental conditions is one measure of risk–reward tradeoffs by individuals.
We used data from observational and manipulative studies to investigate whether an arboreal snake (brown treesnake, Boiga irregularis) had context-dependent behavior based on the relationship between estimated prey availability, body condition, size, and detection height (microhabitat use) in two Mariana Islands.
We used observational data collected in four study sites and data from a manipulative study that we collected over a 5-year period. The observational data focused on four sites with different counts of three prey types, including lizards, birds, and small mammals. During the manipulative study we removed snakes, which resulted in increased prey counts over time. Using these two approaches, we tested whether prey counts predicted body condition and then evaluated how prey counts, snake size, and snake condition interactively predicted the detection height of captured individuals.
We found that body condition was greater at sites or in years with greater prey counts across both the observational and manipulative studies. We also found that snakes displayed differential microhabitat use based on both their condition and size. Larger snakes tended to be detected lower than smaller snakes, but only at sites or during years with few bird or small mammal counts. Snakes at sites with greater mammal and bird counts had a positive relationship between size and detection height. Snakes with greater condition scores tended to be detected higher irrespective of size, but this was also dependent on prey counts. At sites with low bird counts, snakes that were in better condition tended to be closer to the ground.
Brown treesnakes modified microhabitat use based on their condition, size, and the number or type of prey available. Our findings were consistent with a hypothesis that they optimized habitat use to secure food resources and maximize survival.
Context-dependent behavioral plasticity may be an important consideration for management of reptiles for population control or growth.
Basaltic lava flows can be highly destructive. Forecasting the future path and/or behavior of an active lava flow is challenging because topography is often poorly constrained and lava has a complex rheology and emplacement history. Preserved lavas are an important source of information which, combined with observations of active flows, underpins conceptual models of lava flow emplacement. However, the value of preserved lavas is limited because pre-eruptive topography and, thus, syn-eruptive lava flow geometry are usually not known. Here, we use tree-mold data to constrain pre-eruptive topography and syn-eruptive lava flow geometry of the July 1974 flow of Kīlauea (USA). Tree molds, which are formed after advancing lava encloses standing trees, preserve the lava inundation height and the final preserved thickness of lava. We used data from 282 tree molds to reconstruct the temporal and spatial evolution of the ~ 2.1 km-long July 1974 flow. The tree mold dataset yields a detailed dynamic picture of staged emplacement, separated by intervals of ponding. In some ponded areas, flow depth during emplacement (~ 5 m) was twice the preserved thickness of the final lava (2–3 m). Drainage of the ponds led to episodic surges in flow advancement, decoupled from fluctuations in vent discharge rate. We infer that the final breakout occurred after the cessation of fountaining. Such complex emplacement histories may be common for pāhoehoe lavas at Kīlauea and elsewhere in situations where the terrain is of variable slope, and/or where lava is temporarily perched and stored.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-025-01817-0","usgsCitation":"Biass, S., Houghton, B.F., Llewellin, E.W., Curran, K., Thordarson, T., Orr, T., Parcheta, C., and Mouginis-Mark, P.J., 2025, Complex staged emplacement of a basaltic lava: The example of the July 1974 flow of Kīlauea: Bulletin of Volcanology, v. 87, 30, 14 p., https://doi.org/10.1007/s00445-025-01817-0.","productDescription":"30, 14 p.","ipdsId":"IP-106014","costCenters":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488501,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-025-01817-0","text":"Publisher Index Page"},{"id":484914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilaueau volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.28799286883776,\n 19.435261686847895\n ],\n [\n -155.28799286883776,\n 19.272560860056274\n ],\n [\n -155.1179644435753,\n 19.272560860056274\n ],\n [\n -155.1179644435753,\n 19.435261686847895\n ],\n [\n -155.28799286883776,\n 19.435261686847895\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"87","noUsgsAuthors":false,"publicationDate":"2025-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Biass, Sebastian","contributorId":353667,"corporation":false,"usgs":false,"family":"Biass","given":"Sebastian","affiliations":[{"id":84453,"text":"University of Geneva, Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":934281,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houghton, Bruce F. 0000-0002-7532-9770","orcid":"https://orcid.org/0000-0002-7532-9770","contributorId":140077,"corporation":false,"usgs":false,"family":"Houghton","given":"Bruce","email":"","middleInitial":"F.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":934282,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Llewellin, Edward W.","contributorId":353668,"corporation":false,"usgs":false,"family":"Llewellin","given":"Edward","middleInitial":"W.","affiliations":[{"id":25252,"text":"Durham University","active":true,"usgs":false}],"preferred":false,"id":934283,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Curran, Kristine C","contributorId":353669,"corporation":false,"usgs":false,"family":"Curran","given":"Kristine C","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":934284,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thordarson, Thorvaldur","contributorId":197925,"corporation":false,"usgs":false,"family":"Thordarson","given":"Thorvaldur","email":"","affiliations":[{"id":35089,"text":"Institute of Earth Sciences, Nordvulk, University of Iceland","active":true,"usgs":false}],"preferred":false,"id":934285,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Orr, Tim R. 0000-0003-1157-7588","orcid":"https://orcid.org/0000-0003-1157-7588","contributorId":26365,"corporation":false,"usgs":true,"family":"Orr","given":"Tim R.","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":934286,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Parcheta, Carolyn 0000-0001-6556-4630 cparcheta@usgs.gov","orcid":"https://orcid.org/0000-0001-6556-4630","contributorId":215617,"corporation":false,"usgs":true,"family":"Parcheta","given":"Carolyn","email":"cparcheta@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":934287,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mouginis-Mark, Peter J. 0000-0002-7173-6141","orcid":"https://orcid.org/0000-0002-7173-6141","contributorId":36793,"corporation":false,"usgs":false,"family":"Mouginis-Mark","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":934288,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70270325,"text":"70270325 - 2025 - The effects of breeding status on common raven movement, home range, and habitat selection","interactions":[],"lastModifiedDate":"2025-08-14T14:21:36.529864","indexId":"70270325","displayToPublicDate":"2025-03-31T09:18:29","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":"The effects of breeding status on common raven movement, home range, and habitat selection","docAbstract":"Anthropogenic infrastructure has contributed to increasing common raven (Corvus corax) abundance across the Great Basin region of the United States, particularly in sagebrush ecosystems, where high raven densities are correlated with reduced sage-grouse (Centrocercus urophasianus) nest survival. Our understanding of how raven reproductive behavior affects sage-grouse nest predation is limited, especially considering their overlapping breeding seasons. Understanding differences in space use and resource selection between breeding and non-breeding ravens could help identify high-use areas and corresponding predation risk for sage-grouse nests. We analyzed space use and resource selection of breeding (n = 13) and non-breeding (n = 32) global positioning system (GPS)-marked ravens in Nevada, USA (2017–2022) during the breeding season (1 March–31 June). We compared home-range size, core area size, step lengths, and resource selection within a Bayesian framework with inference made by comparing Bayesian credible intervals (CRI). We generated home range and core area estimates using autocorrelated kernel density methods. We did not find a difference in home range size between breeding (469.33 km2, 95% CRI = 228.79–709.45 km2) and non-breeding (525.26 km2, 95% CRI = 410.71–654.10 km2) ravens. However, breeding ravens had smaller core areas (10.77 km2, 95% CRI = 3.16–35.78 km2) and shorter step lengths (1,160.33 m/hr, 95% CRI = 1,087.78–1,277.17 m/hr) than non-breeding ravens (core area = 279.50 km2, 95% CRI = 206.77–363.72 km2; step length = 1,953.74 m/hr, 95% CRI = 1,898.42–2,009.56 m/hr). Ravens in both breeding classes selected high normalized difference vegetation index (NDVI) and low annual grass and shrub cover, but non-breeding ravens showed stronger selection for low annual grass and shrub cover areas. We found strong differences in selection between breeding classes for 6 of our 9 covariates: distance to road, solar radiation, distance to natural water, distance to forest edge, percent annual grass cover, and percent shrub cover. Non-breeding ravens concentrated activity near forest edges, natural water sources, and anthropogenic features, whereas breeding ravens focused activity close to their nests. Our findings suggest that raven management could be more effective if it targeted areas with high NDVI and low annual grass and shrub cover, especially in anthropogenically modified landscapes and near forest edges, and prevented raven nest establishment near prey populations of concern.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70004","usgsCitation":"Brockman, J.C., Coates, P., Tull, J.C., Jackson, P.J., O’Neil, S.T., and Williams, P.J., 2025, The effects of breeding status on common raven movement, home range, and habitat selection: Journal of Wildlife Management, v. 89, e70004, 20 p., https://doi.org/10.1002/jwmg.70004.","productDescription":"e70004, 20 p.","ipdsId":"IP-166811","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":498236,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.70004","text":"Publisher Index Page"},{"id":494091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.99706101795434,\n 41.99257312862932\n ],\n [\n -119.99706101795434,\n 38.66664669711224\n ],\n [\n -114.0973231285738,\n 38.66664669711224\n ],\n [\n -114.0973231285738,\n 41.99257312862932\n ],\n [\n -119.99706101795434,\n 41.99257312862932\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"89","noUsgsAuthors":false,"publicationDate":"2025-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Brockman, Julia C.","contributorId":359680,"corporation":false,"usgs":false,"family":"Brockman","given":"Julia","middleInitial":"C.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":946036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":352181,"corporation":false,"usgs":true,"family":"Coates","given":"Peter S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":946037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tull, John C.","contributorId":359682,"corporation":false,"usgs":false,"family":"Tull","given":"John","middleInitial":"C.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":946038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Pat J.","contributorId":359685,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","middleInitial":"J.","affiliations":[{"id":85566,"text":"NDOW","active":true,"usgs":false}],"preferred":false,"id":946039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":946040,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Perry J.","contributorId":359688,"corporation":false,"usgs":false,"family":"Williams","given":"Perry","middleInitial":"J.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":946041,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267980,"text":"70267980 - 2025 - Topographic controls on landslide mobility: Modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico","interactions":[],"lastModifiedDate":"2025-06-10T14:20:41.696971","indexId":"70267980","displayToPublicDate":"2025-03-31T09:12:49","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Topographic controls on landslide mobility: Modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico","docAbstract":"In 2017, Hurricane Maria triggered more than 70 000 landslides in Puerto Rico. After initiation, these predominantly shallow landslides were mobilized to varying extents – some landslides only traveled partway downslope, whereas others reached drainage channels and were mobilized into long-traveled debris flows that could severely impact roads and infrastructure. Thus, forecasting potential landslide runout and inundation zones is critical for estimating landslide and debris-flow hazards. Here we conduct an in-depth topographic analysis of landslide-affected areas from nine study areas and apply a linked modeling technique to estimate locations susceptible to varying degrees of landslide runout in the Lares, Utuado, and Naranjito municipalities.
We find that the longest runout lengths are observed on high-relief escarpments, although highly mobile long-runout debris flows also occurred in lower-relief dissected uplands. These topographic differences indicate that landslides that are initiated under similar conditions and possess equal potential to be mobilized as debris flows may not travel the same distances or affect the same areal extent. Our modeling approach allows the local topography to automatically control the implementation of two runout methods: (1) H/L runout zones are assigned directly downslope of landslide source zones, and (2) debris-flow inundation zones are estimated in the presence of a channel network. Debris-flow volumes are calculated as a function of area-integrated growth factors, estimated as a function of the upstream areas susceptible to shallow landslides. Applying our empirical modeling scheme over an area of 560 km2, our results highlight the efficacy of our methods for the assessment of the potential for landslide runout and debris-flow inundation over diverse terrains with varied susceptibility.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/nhess-25-1229-2025","usgsCitation":"Brien, D.L., Reid, M.E., Cronkite-Ratcliff, C., and Perkins, J.P., 2025, Topographic controls on landslide mobility: Modeling hurricane-induced landslide runout and debris-flow inundation in Puerto Rico: Natural Hazards and Earth System Sciences, v. 25, no. 3, p. 1229-1253, https://doi.org/10.5194/nhess-25-1229-2025.","productDescription":"25 p.","startPage":"1229","endPage":"1253","ipdsId":"IP-147641","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":490625,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-25-1229-2025","text":"Publisher Index Page"},{"id":490306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.96551536620369,\n 18.33797102243001\n ],\n [\n -66.96551536620369,\n 18.13229474686122\n ],\n [\n -66.18238268856022,\n 18.13229474686122\n ],\n [\n -66.18238268856022,\n 18.33797102243001\n ],\n [\n -66.96551536620369,\n 18.33797102243001\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Brien, Dianne L. 0000-0003-3227-7963 dbrien@usgs.gov","orcid":"https://orcid.org/0000-0003-3227-7963","contributorId":229851,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":939854,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reid, Mark E. 0000-0002-5595-1503 mreid@usgs.gov","orcid":"https://orcid.org/0000-0002-5595-1503","contributorId":1167,"corporation":false,"usgs":true,"family":"Reid","given":"Mark","email":"mreid@usgs.gov","middleInitial":"E.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":939855,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cronkite-Ratcliff, Collin 0000-0001-5485-3832 ccronkite-ratcliff@usgs.gov","orcid":"https://orcid.org/0000-0001-5485-3832","contributorId":203951,"corporation":false,"usgs":true,"family":"Cronkite-Ratcliff","given":"Collin","email":"ccronkite-ratcliff@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":939856,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, Jonathan P. 0000-0002-6113-338X","orcid":"https://orcid.org/0000-0002-6113-338X","contributorId":237053,"corporation":false,"usgs":true,"family":"Perkins","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":939857,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266021,"text":"70266021 - 2025 - The GorDAS Distributed Acoustic Sensing experiment above the Cascadia locked zone and subducted Gorda Slab","interactions":[],"lastModifiedDate":"2025-07-09T15:59:50.150051","indexId":"70266021","displayToPublicDate":"2025-03-31T09:00:41","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":"The GorDAS Distributed Acoustic Sensing experiment above the Cascadia locked zone and subducted Gorda Slab","docAbstract":"The southernmost portion of the Cascadia Subduction zone in Northern California produces high rates of moderate and large earthquakes owing to subduction of the Gorda slab and deformation associated with the Mendocino Triple Junction. Distributed Acoustic Sensing (DAS) is rapidly advancing as a method for detecting earthquakes and imaging crustal structure. We have begun a long-term DAS monitoring experiment on buried telecom fiber in Arcata, California, with the goal of increasing the available recordings of moderate to large earthquakes as well as imaging seismogenic structures. We have recorded over a year's worth of data, including most aftershocks of the 2022 Mw6.4 Ferndale earthquake, though not the mainshock itself. The dataset includes numerous magnitude 3.5 and larger earthquakes including the 2023/01/01 Mw5.4 Rio Dell earthquake. Here we present initial results comparing an earthquake detection algorithm, run in real-time on the processing unit of the interrogator system, with both the ShakeAlert earthquake early warning system as well as a post-processed earthquake catalog developed with deep-learning phase-picker algorithms. The rapid onboard processing of the detector demonstrates the potential utility of DAS-based edge computing for earthquake early warning. We also verify the quality of the strain waveforms both in terms of peak amplitudes and waveform similarity using about five months of nodal seismometer data. These instruments were deployed roughly every 300 m along the ~15km long cable and validate large variations in peak strain over short distances that are seen in the DAS data. All data from time windows surrounding both the local and teleseismic earthquakes are publicly available, which will improve our understanding of both the performance of DAS systems in moderate earthquakes and earthquake hazards associated with the Gorda subduction zone.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240415","usgsCitation":"McGuire, J., Barbour, A.J., Stewart, C., Yartsev, V., Karrenbach, M., Hemphill-Haley, M., McPherson, R.C., Stockdale, K., Yoon, C., and Sawi, T., 2025, The GorDAS Distributed Acoustic Sensing experiment above the Cascadia locked zone and subducted Gorda Slab: Seismological Research Letters, v. 96, no. 4, p. 2489-2503, https://doi.org/10.1785/0220240415.","productDescription":"15 p.","startPage":"2489","endPage":"2503","ipdsId":"IP-171005","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":484911,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Cascadia locked zone, Gorda slab","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -126,\n 42\n ],\n [\n -126,\n 39.5\n ],\n [\n -122,\n 39.5\n ],\n [\n -122,\n 42\n ],\n [\n -126,\n 42\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"96","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-03-31","publicationStatus":"PW","contributors":{"authors":[{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":934338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbour, Andrew J. 0000-0002-6890-2452","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":215339,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":934339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Connie","contributorId":222103,"corporation":false,"usgs":false,"family":"Stewart","given":"Connie","email":"","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":934340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yartsev, Victor","contributorId":350345,"corporation":false,"usgs":false,"family":"Yartsev","given":"Victor","affiliations":[{"id":83720,"text":"Luna, Inc","active":true,"usgs":false}],"preferred":false,"id":934341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karrenbach, Martin","contributorId":353682,"corporation":false,"usgs":false,"family":"Karrenbach","given":"Martin","affiliations":[{"id":84462,"text":"Seismics Unuusal","active":true,"usgs":false}],"preferred":false,"id":934342,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hemphill-Haley, Mark","contributorId":295931,"corporation":false,"usgs":false,"family":"Hemphill-Haley","given":"Mark","affiliations":[{"id":63943,"text":"Cal Poly Humboldt","active":true,"usgs":false}],"preferred":false,"id":934343,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McPherson, Robert C.","contributorId":350346,"corporation":false,"usgs":false,"family":"McPherson","given":"Robert","middleInitial":"C.","affiliations":[{"id":83721,"text":"Cal Poly Humboldt Univ.","active":true,"usgs":false}],"preferred":false,"id":934344,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stockdale, Kari","contributorId":352201,"corporation":false,"usgs":false,"family":"Stockdale","given":"Kari","affiliations":[{"id":39079,"text":"NYSDEC","active":true,"usgs":false}],"preferred":false,"id":934345,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yoon, Clara 0000-0003-4521-3889","orcid":"https://orcid.org/0000-0003-4521-3889","contributorId":222019,"corporation":false,"usgs":true,"family":"Yoon","given":"Clara","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":934346,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sawi, Theresa Marie 0000-0001-5938-6743","orcid":"https://orcid.org/0000-0001-5938-6743","contributorId":350347,"corporation":false,"usgs":true,"family":"Sawi","given":"Theresa Marie","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":934347,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70265069,"text":"70265069 - 2025 - Assessment of western Oregon debris-flow hazards in burned and unburned environments","interactions":[],"lastModifiedDate":"2025-04-01T15:02:09.946731","indexId":"70265069","displayToPublicDate":"2025-03-30T07:56:10","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of western Oregon debris-flow hazards in burned and unburned environments","docAbstract":"In the steep and mountainous environment of western Oregon, debris flows pose a considerable threat to property, infrastructure and life. Wildfire is commonly known to increase the susceptibility of steep slopes to debris flows, but the extent of this process in the western Cascades is not well understood. The US Geological Survey (USGS) currently estimates postfire debris-flow likelihood and triggering rainfall thresholds using a model calibrated to a southern California inventory of debris flows generated by excess runoff within the first year after fire. Because of a lack of available data, this model has not been tested in western Oregon, or in locations where postfire debris flows initiate via other mechanisms (e.g., shallow landslides or in-channel failures). Using repeat field observations and aerial imagery, we developed two new debris-flow inventories within and adjacent to the perimeters of five 2020 wildfires in western Oregon: Archie Creek, Holiday Farm, Beachie Creek, Lionshead and Riverside. The first inventory focuses on postfire debris flows (2020–2022); the second focuses on debris flows prior to fires (1995–2020). Our inventories of prefire and postfire debris flows were used to document initiation mechanisms in Oregon's western Cascades and to evaluate the effects of wildfire. We found that wildfire changed the distribution of debris-flow initiation mechanisms in the western Cascades. After the wildfires, annual rates of runoff-generated debris flows increased by 22% and the number of shallow landslide-initiated debris flows decreased by 17% relative to before the wildfires. Despite this shift, shallow landsliding was the dominant debris-flow initiation mechanism in both unburned and burned environments. We found the performance of the current USGS debris-flow likelihood model was degraded relative to other previously tested locations across the intermountain western United States. Our results highlight the need for improved postfire hazard assessment in western Oregon based on regional model calibration that is tuned to the dominant debris-flow initiation mechanisms.
","language":"English","publisher":"Wiley","doi":"10.1002/ESP.70045","usgsCitation":"Selander, B., Calhoun, N.C., Burns, W., Kean, J.W., and Rengers, F.K., 2025, Assessment of western Oregon debris-flow hazards in burned and unburned environments: Earth Surface Processes and Landforms, v. 50, no. 4, e70045, 15 p., https://doi.org/10.1002/ESP.70045.","productDescription":"e70045, 15 p.","ipdsId":"IP-170327","costCenters":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"links":[{"id":488659,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.70045","text":"Publisher Index Page"},{"id":484065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"western Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.57278320768603,\n 46.34148488781506\n ],\n [\n -124.57278320768603,\n 42.016342483468776\n ],\n [\n -121.87720015148463,\n 42.016342483468776\n ],\n [\n -121.87720015148463,\n 46.34148488781506\n ],\n [\n -124.57278320768603,\n 46.34148488781506\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Selander, Brittany Danielle 0000-0002-3332-1068","orcid":"https://orcid.org/0000-0002-3332-1068","contributorId":344520,"corporation":false,"usgs":true,"family":"Selander","given":"Brittany Danielle","affiliations":[{"id":78941,"text":"Geologic Hazards Science Center - Landslides / Earthquake Geology","active":true,"usgs":true}],"preferred":true,"id":932462,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calhoun, Nancy C.","contributorId":216331,"corporation":false,"usgs":false,"family":"Calhoun","given":"Nancy","email":"","middleInitial":"C.","affiliations":[{"id":39395,"text":"DOGAMI","active":true,"usgs":false}],"preferred":false,"id":932463,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, William 0000-0002-4379-6198","orcid":"https://orcid.org/0000-0002-4379-6198","contributorId":344522,"corporation":false,"usgs":false,"family":"Burns","given":"William","affiliations":[{"id":32397,"text":"Oregon Department of Geology and Mineral Industries","active":true,"usgs":false}],"preferred":false,"id":932464,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932465,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":932466,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70265697,"text":"70265697 - 2025 - Understanding predator-prey-competitor dynamics between Lower Missouri River Macrhybopsis and Scaphirhynchus using a population—bioenergetics model ensemble","interactions":[],"lastModifiedDate":"2025-04-15T14:57:29.302648","indexId":"70265697","displayToPublicDate":"2025-03-29T07:49:57","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16139,"text":"Ecological Modeling","active":true,"publicationSubtype":{"id":10}},"title":"Understanding predator-prey-competitor dynamics between Lower Missouri River Macrhybopsis and Scaphirhynchus using a population—bioenergetics model ensemble","docAbstract":"The pallid sturgeon Scaphirhynchus albus is a long-lived, endangered fish in the Missouri River. Individuals become piscivorous as adults, so recruitment from stocking or reproduction could reduce populations of prey, including Macrhybopsis chubs. We constructed an individual- and age-based, multi-species, predator-prey-competitor model (IAMP) to represent the benthic community (sturgeons, chubs, and chironomids) of the Lower Missouri River (LMR) to explore scenarios of potential predator-prey-competitor dynamics. Our simulations suggest that chubs alone are unlikely able to support a level of LMR pallid sturgeon similar to historical or current populations. These simulations also suggest that adult pallid sturgeon may need to shift to non-chub prey fish to achieve the greater sizes observed in the Upper Missouri River. When annual hydrologic regimes were included, we found a negative relationship between chub relative abundance and previous year 30-day minimum flows. Inclusion of temporal environmental variability made it clear that large chub populations may be necessary to support LMR pallid sturgeon. When full stochasticity was included in the IAMP, chub population sizes needed to increase further to ensure continued reproduction and recruitment of both chubs and pallid sturgeon. These results support the hypothesis that the pallid sturgeon population in the Lower Missouri River may be food-limited. However, the full extent of this limitation and the management changes needed to address this will require more research on the biology and population dynamics of this fish community, on pallid sturgeon interactions with prey species, and on how sympatric species may be affected during the pallid sturgeon recovery process.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2025.111097","usgsCitation":"Wildhaber, M.L., Albers, J.L., and Green, N., 2025, Understanding predator-prey-competitor dynamics between Lower Missouri River Macrhybopsis and Scaphirhynchus using a population—bioenergetics model ensemble: Ecological Modeling, v. 504, 111097, 28 p., https://doi.org/10.1016/j.ecolmodel.2025.111097.","productDescription":"111097, 28 p.","ipdsId":"IP-164525","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":488248,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2025.111097","text":"Publisher Index Page"},{"id":484578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, Minnesota, Missouri, Montana, Nebraska, North Dakota, South Dakota, Wyoming","otherGeospatial":"Missouri River","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-111.048974,44.474072],[-111.323669,44.724474],[-111.50494,44.635746],[-111.469185,44.552044],[-112.258665,44.569516],[-112.387389,44.448058],[-112.749011,44.491233],[-112.844859,44.358221],[-113.134824,44.752763],[-113.455071,44.865424],[-113.802955,45.592631],[-114.015633,45.696127],[-114.345019,45.459916],[-114.559038,45.565706],[-114.422963,45.855381],[-114.527096,46.146218],[-114.322912,46.642938],[-114.76689,46.696901],[-115.294785,47.220914],[-115.731348,47.433381],[-115.72377,47.696671],[-116.049153,47.999923],[-116.049193,49.000912],[-95.153711,48.998903],[-95.153314,49.384358],[-94.878454,49.333193],[-94.640803,48.741171],[-93.818375,48.534442],[-92.984963,48.623731],[-92.634931,48.542873],[-92.698824,48.494892],[-92.341207,48.23248],[-92.066269,48.359602],[-91.542512,48.053268],[-90.88548,48.245784],[-90.703702,48.096009],[-89.489226,48.014528],[-90.735927,47.624343],[-92.058888,46.809938],[-92.025789,46.710839],[-92.189091,46.717541],[-92.291976,46.503997],[-92.33859,46.050111],[-92.869193,45.717568],[-92.646602,45.441635],[-92.807362,44.758909],[-91.410555,43.970892],[-91.244135,43.774667],[-91.243183,43.540309],[-96.591213,43.500514],[-96.439335,43.113916],[-96.630311,42.770885],[-96.396107,42.484095],[-96.272901,42.047281],[-96.129186,41.965136],[-96.081843,41.580407],[-95.850188,41.184798],[-95.885349,40.721093],[-95.758045,40.613759],[-91.625161,40.5435],[-91.452458,40.375501],[-91.510322,40.127994],[-91.369953,39.745042],[-90.721593,39.23273],[-90.653164,38.916141],[-90.113327,38.849306],[-90.367013,38.250054],[-89.952499,37.883218],[-89.516685,37.692762],[-89.438275,37.161287],[-89.102879,36.9697],[-89.120437,36.782071],[-89.429311,36.481875],[-89.55264,36.577178],[-89.527029,36.341679],[-89.703511,36.243412],[-89.615128,36.113816],[-89.733095,36.000608],[-90.368718,35.995812],[-90.075934,36.281485],[-90.157136,36.484317],[-94.617919,36.499414],[-94.699735,36.998805],[-109.045223,36.999084],[-109.050076,41.000659],[-111.046723,40.997959],[-111.048974,44.474072]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"504","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wildhaber, Mark L. 0000-0002-6538-9083 mwildhaber@usgs.gov","orcid":"https://orcid.org/0000-0002-6538-9083","contributorId":1386,"corporation":false,"usgs":true,"family":"Wildhaber","given":"Mark","email":"mwildhaber@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":933316,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Albers, Janice L. 0000-0002-6312-8269 jalbers@usgs.gov","orcid":"https://orcid.org/0000-0002-6312-8269","contributorId":3972,"corporation":false,"usgs":true,"family":"Albers","given":"Janice","email":"jalbers@usgs.gov","middleInitial":"L.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":933317,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Green, Nicholas S.","contributorId":301918,"corporation":false,"usgs":false,"family":"Green","given":"Nicholas S.","affiliations":[{"id":65362,"text":"Kennesaw State University","active":true,"usgs":false}],"preferred":false,"id":933318,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267348,"text":"70267348 - 2025 - Do watershed conditions or local climate play a larger role in determining regional stream salamander distributions?","interactions":[],"lastModifiedDate":"2025-09-09T14:37:07.042308","indexId":"70267348","displayToPublicDate":"2025-03-28T10:34:43","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Do watershed conditions or local climate play a larger role in determining regional stream salamander distributions?","docAbstract":"Anthropogenic influences like land use and climate variability interact with natural heterogeneity to influence the persistence of stream salamanders. Using occupancy modeling in the southern Appalachian Mountains, we investigated the influence of land use, climate, and physical context (e.g., drainage area, elevation) on stream salamander occupancy, noting species, and life stage specific responses. Our results illustrate that forest loss is a better predictor of salamander occupancy than physical context (elevation) or climate. Across the gradients in this dataset, precipitation did not have a significant influence on salamander occupancy, potentially due to the observed narrow, wet gradient. Temperature had little effect on Eurycea wilderae occupancy; however, temperature negatively affected adult but not larval Desmognathus amphileucus occupancy. Spatial thermal variability in this study was larger than projected increases due to climate change, suggesting that local mechanisms (e.g., behavior or physiological plasticity) may facilitate salamander resilience to climate change. However, the negative effects of forest loss coupled with rising temperatures (e.g., increased solar radiation, warmer stream runoff) underscore the importance of riparian forests in mitigating climate stressors. Preserving forest cover is critical for maintaining stream salamander populations and may offer opportunities for maintaining resilience in the face of additional stressors like rising temperatures or drought.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-025-05848-8","collaboration":"USFWS","usgsCitation":"Cecala, K.K., Halstead, B., McGrory, J., and Maerz, J.C., 2025, Do watershed conditions or local climate play a larger role in determining regional stream salamander distributions?: Hydrobiologia, v. 852, p. 4053-4067, https://doi.org/10.1007/s10750-025-05848-8.","productDescription":"15 p.","startPage":"4053","endPage":"4067","ipdsId":"IP-114382","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":486222,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia, North Carolina","otherGeospatial":"Upper Little Tennessee watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.2386909895219,\n 35.52532859216157\n ],\n [\n -84.25914633148402,\n 34.38251830455587\n ],\n [\n -82.90909376196274,\n 34.37126510334987\n ],\n [\n -82.90909376196274,\n 35.52532859216157\n ],\n [\n -84.2386909895219,\n 35.52532859216157\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"852","noUsgsAuthors":false,"publicationDate":"2025-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Cecala, Kristen K.","contributorId":171762,"corporation":false,"usgs":false,"family":"Cecala","given":"Kristen","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":937824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":937825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGrory, James S.","contributorId":355637,"corporation":false,"usgs":false,"family":"McGrory","given":"James S.","affiliations":[{"id":84785,"text":"University of the South","active":true,"usgs":false}],"preferred":false,"id":937826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maerz, John C.","contributorId":341635,"corporation":false,"usgs":false,"family":"Maerz","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":937827,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70265930,"text":"70265930 - 2025 - A crustal thermal model of the conterminous U.S. constrained by multiple data sets: A Monte-Carlo approach","interactions":[],"lastModifiedDate":"2025-04-22T15:33:21.230287","indexId":"70265930","displayToPublicDate":"2025-03-28T10:31:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"A crustal thermal model of the conterminous U.S. constrained by multiple data sets: A Monte-Carlo approach","docAbstract":"The thermal structure of the continental crust plays a critical role in understanding its elastic and rheologic properties as well as its dynamic processes. Thermal parameter data sets on continental scales have been used to constrain the crustal thermal structure, including both the direct (e.g. temperature, heat flux and heat conductivity measured at the surface) and indirect (e.g. seismically derived Mohorovičić discontinuity (Moho) temperature, geomagnetically derived Curie depth) observations. In this study, we present a new continental scale crustal heat generation model with additional information from seismologically inferred crustal composition. Together with previous direct and indirect thermal parameter data sets in the conterminous United States, we use the new crustal heat generation model to construct a 3-D crustal temperature model under a newly developed Bayesian framework. Specifically, we first derive profiles of crustal heat generation based on an empirical geochemical relationship at 1683 locations where seismologically derived crustal composition information is available. Then for each of these locations, the average heat generation values in the upper, middle and lower crust are combined with other thermal parameters through a Markov Chain Monte-Carlo inversion for a conductive, vertically smooth temperature profile. The results, posterior distributions of temperature profiles, are used to generate a 3-D crustal thermal model with the uncertainties systematically assessed. The new temperature model overall exhibits similar patterns to that from the U.S. Geological Survey National Crustal Model, but also reduces possible biases and the model's dependence on a single thermal parameter.
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beach","interactions":[],"lastModifiedDate":"2025-06-20T15:18:04.082324","indexId":"70268306","displayToPublicDate":"2025-03-28T10:11:39","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating five shoreline change models against 40 years of field survey data at an embayed sandy beach","docAbstract":"Climate change is affecting coastal ecosystems worldwide as water temperatures increase, hydrologic regimes change, and sea levels rise. Consequently, estuaries risk declines in ecosystem functioning due to increasing temperatures and other hydrologic factors. Characterizing and predicting estuarine water temperature are challenging because these systems are highly dynamic. Statistical models have been used to accurately assess air temperature-water temperature relationships in lakes and streams but have not been effectively applied to tidally influenced ecosystems like estuaries. We used 6 years of continuous monitoring data from the Nisqually River Delta in Puget Sound, Washington, U.S.A., to parameterize and run a non-linear statistical model and generate spatially explicit model predictions. Our goal was to examine spatiotemporal patterns in estuarine water temperature and thermal refugia given current estimates of climactic change. The performance of the parameterized model was similar to that of non-linear stream temperature models (NSE = 0.76; RMSE = 2.34 °C). Scenarios incorporating forecasted high-emission air temperatures through the year 2100 (+ 7 °C) predicted a corresponding 3.55 ± 0.63 °C increase in average water temperatures; however, moderate and high rates of sea-level rise offset temperature increases by 3–20% and substantially reduced the amount of time temperatures exceeded the thermal stress threshold of 20 °C for juvenile salmon. These findings demonstrate how the effects of one climate stressor (sea-level rise) may offset another (temperature increases) to maintain thermal refugia for coldwater fishes. Similar exercises may allow managers to explore mitigation options like the planting of riparian vegetation or modified flooding regimes to further offset rising water temperatures.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s12237-025-01510-7","usgsCitation":"Davis, M.J., Woo, I., and De La Cruz, S.E., 2025, Estuarine tidal cycles may preserve thermal refugia as global temperatures increase: Estuaries and Coasts, v. 48, 90, 19 p., https://doi.org/10.1007/s12237-025-01510-7.","productDescription":"90, 19 p.","ipdsId":"IP-129413","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":486525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Nisqually River Delta, Puget Sound, Salish Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.55330830655157,\n 48.98716401827198\n ],\n [\n -123.55330830655157,\n 47.20224465518157\n ],\n [\n -121.87955279406418,\n 47.20224465518157\n ],\n [\n -121.87955279406418,\n 48.98716401827198\n ],\n [\n -123.55330830655157,\n 48.98716401827198\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","noUsgsAuthors":false,"publicationDate":"2025-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Davis, Melanie J. 0000-0003-1734-7177","orcid":"https://orcid.org/0000-0003-1734-7177","contributorId":202773,"corporation":false,"usgs":true,"family":"Davis","given":"Melanie","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":938184,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":938185,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De La Cruz, Susan E.W. 0000-0001-6315-0864","orcid":"https://orcid.org/0000-0001-6315-0864","contributorId":202774,"corporation":false,"usgs":true,"family":"De La Cruz","given":"Susan","email":"","middleInitial":"E.W.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":938186,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70268307,"text":"70268307 - 2025 - Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments","interactions":[],"lastModifiedDate":"2025-06-20T14:02:06.25149","indexId":"70268307","displayToPublicDate":"2025-03-28T08:57:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Wave driven cross shore and alongshore transport reveal more extreme projections of shoreline change in island environments","docAbstract":"Coastal erosion, intensified by sea level rise, poses significant threats to coastal communities in Hawaiʻi and similar island communities. This study projects long-term shoreline change on the Hawaiian Island of O‘ahu using the data-assimilated CoSMoS-COAST shoreline change model. CoSMoS-COAST models four key shoreline processes: (1) Alongshore transport, (2) Recession due to sea level rise, (3) Cross-shore transport due to waves, and (4) Residual processes represented by a linear trend term. This study marks the first application of CoSMoS-COAST for an oceanic equatorial island with narrow beaches and a dynamic wave climate. The model is informed with a novel combination of shoreline data derived from high-resolution imagery from Planet, Sentinel-2, and Landsat satellites, wave-climate hindcasts specific to Hawai‘i, and regional beach-slope surveys. On a dynamic northern Oʻahu beach, the model achieved a root mean square error of 9.4 m between observations and model output. CoSMoS-COAST predicts that 81% of O‘ahu’s sandy beach coastline could experience beach loss by 2100; with 39.8% of this loss happening by 2030. This represents an increase, 43.3%, in net landward shoreline change compared to previous erosion forecasts, for 0.3 m of sea level rise (2050). Additionally, dynamic processes such as cross-shore equilibrium processes and alongshore sediment transport, play a large contribution to gross shoreline change within the next decade, particularly on O ‘ahu’s north and west shores. In the long term, we find that recession due to sea level rise and residual processes dominate, but dynamic, wave-driven processes (longshore and cross-shore transport) still account for 34% of shoreline change between present and 2100. We assert dynamic, wave-driven processes are a crucial addition for accurate modeling of island sandy beach environments. These findings have implications for O‘ahu’s coastal planning and development, suggesting updates to shoreline policies that rely upon erosion forecasting, and highlights the importance of incorporating wave and alongshore transport in erosion models for other Pacific islands.
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2025 - A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model","interactions":[{"subject":{"id":70265535,"text":"70265535 - 2025 - A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model","indexId":"70265535","publicationYear":"2025","noYear":false,"title":"A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model"},"predicate":"SUPERSEDED_BY","object":{"id":70272242,"text":"70272242 - 2025 - Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model","indexId":"70272242","publicationYear":"2025","noYear":false,"title":"Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model"},"id":1}],"supersededBy":{"id":70272242,"text":"70272242 - 2025 - Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model","indexId":"70272242","publicationYear":"2025","noYear":false,"title":"Technical note: A low-cost approach to monitoring relative streamflow dynamics in small headwater streams using time lapse imagery and a deep learning model"},"lastModifiedDate":"2025-11-24T17:09:06.905617","indexId":"70265535","displayToPublicDate":"2025-03-28T08:41:30","publicationYear":"2025","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":20900,"text":"EGUSphere","active":true,"publicationSubtype":{"id":32}},"title":"A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model","docAbstract":"Despite their ubiquity and importance as freshwater habitat, small headwater streams are under monitored by existing stream gage networks. To address this gap, we describe a low-cost, non-contact, and low-effort method that enables organizations to monitor streamflow dynamics in small headwater streams. The method uses a camera to capture repeat images of the stream from a fixed position. A person then annotates pairs of images, in each case indicating which image has more apparent streamflow or indicating equal flow if no difference is discernible. A deep learning modelling framework called Streamflow Rank Estimation (SRE) is then trained on the annotated image pairs and applied to rank all images from highest to lowest apparent streamflow. From this result a relative hydrograph can be derived. We found that our modelled relative hydrograph dynamics matched the observed hydrograph dynamics well for 11 cameras at 8 streamflow sites in western Massachusetts. Higher performance was observed during the annotation period (median Kendall’s Tau rank correlation 0.75 with range 0.6–0.83) than after it (median Kendall’s Tau 0.59 with range 0.34 – 0.74). We found that annotation performance was generally consistent across the eleven camera sites and two individual annotators and was positively correlated with streamflow variability at a site. A scaling simulation determined that model performance improvements were limited after 1,000 annotation pairs. Our model’s estimates of relative flow, while not equivalent to absolute flow, may still be useful for many applications, such as ecological modelling and calculating event-based hydrological statistics (e.g., the number of out-of-bank floods). We anticipate this method will be a valuable tool to extend existing stream monitoring networks and provide new insights on dynamic headwater systems.
","language":"English","publisher":"EGUSphere","doi":"10.5194/egusphere-2025-1186","usgsCitation":"Goodling, P.J., Fair, J.H., Gupta, A., Walker, J.D., Dubreuil, T., Hayden, M.J., and Letcher, B., 2025, A low-cost approach to monitoring streamflow dynamics in small, headwater streams using timelapse imagery and a deep learning model: EGUSphere, preprint posted March 28, 2025, https://doi.org/10.5194/egusphere-2025-1186.","productDescription":"26 p.","ipdsId":"IP-171724","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488204,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/egusphere-2025-1186","text":"Publisher Index Page"},{"id":484489,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2025-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932970,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fair, Jennifer H. 0000-0002-9902-1893","orcid":"https://orcid.org/0000-0002-9902-1893","contributorId":245941,"corporation":false,"usgs":true,"family":"Fair","given":"Jennifer","middleInitial":"H.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":932971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gupta, Amrita 0000-0003-2643-5865","orcid":"https://orcid.org/0000-0003-2643-5865","contributorId":264600,"corporation":false,"usgs":false,"family":"Gupta","given":"Amrita","email":"","affiliations":[{"id":54512,"text":"Georgia Institute of Techniology","active":true,"usgs":false}],"preferred":false,"id":932972,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walker, Jeffrey D. 0000-0003-1923-6550","orcid":"https://orcid.org/0000-0003-1923-6550","contributorId":244114,"corporation":false,"usgs":false,"family":"Walker","given":"Jeffrey","middleInitial":"D.","affiliations":[{"id":48839,"text":"Walker Environmental Research LLC","active":true,"usgs":false}],"preferred":false,"id":932973,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dubreuil, Todd 0000-0003-0189-4336","orcid":"https://orcid.org/0000-0003-0189-4336","contributorId":217872,"corporation":false,"usgs":true,"family":"Dubreuil","given":"Todd","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":932974,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayden, Michael J. 0000-0002-9010-6831","orcid":"https://orcid.org/0000-0002-9010-6831","contributorId":291388,"corporation":false,"usgs":true,"family":"Hayden","given":"Michael","middleInitial":"J.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":932975,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Letcher, Benjamin 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":242666,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":932976,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266766,"text":"70266766 - 2025 - Assessing microplastics, per- and polyfluoroalkyl substances (PFAS), and other contaminants of global concern in wadable agricultural streams","interactions":[],"lastModifiedDate":"2025-05-29T13:10:29.985307","indexId":"70266766","displayToPublicDate":"2025-03-28T07:46:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9161,"text":"Environmental Science: Processes & Impacts","active":true,"publicationSubtype":{"id":10}},"title":"Assessing microplastics, per- and polyfluoroalkyl substances (PFAS), and other contaminants of global concern in wadable agricultural streams","docAbstract":"Microplastics, per- and polyfluoroalkyl substances (PFAS), antibiotic resistance genes (ARGs), pharmaceuticals and personal care products (PPCPs), and pesticides may lead to unintended environmental contamination through many pathways in multiple matrices. This statewide, multi-matrix study of contaminants of global concern (CGCs) in agricultural streams across Iowa (United States) is the first to examine multiple CGCs in water, bed sediment, and fish to understand their occurrence in small streams located in regions of intense agriculture activity. Iowa plays a pivotal role in agriculture, with more than 85% of Iowa’s landscape devoted to agriculture making it an ideal location for determining the prevalence of CGCs to provide critical baseline exposure data. Fifteen sites were sampled across a range of predominant land uses (e.g., poultry, swine); all sites had detections of microplastics in all matrices. Concentrations of PFAS varied but were detected in water and sediment; all fish had detections of perfluorooctanesulfonate (PFOS), a type of PFAS. More than 50% of water and bed sediment samples had detections of ARGs. The most frequently detected PPCP was metformin. No sites had a cumulative exposure activity ratio greater than 1.0 for chemical exposures; 13 sites were above the 0.001 precautionary threshold. Toxicity quotients calculated using Aquatic Life Benchmarks were below the 0.1 moderate risk threshold for chemical exposures for all but one site. For fish, all sites exceeded the moderate and high-risk thresholds proposed for microplastic particles for food dilution (both chronic and acute exposures) and all sites exceeded the microplastic moderate threshold proposed for chronic tissue translocation, and two sites exceeded the threshold for acute tissue translocation.","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D4EM00753K","usgsCitation":"Meppelink, S.M., Kolpin, D., LeFevre, G., Cwiertny, D., Givens, C.E., Green, L., Hubbard, L.E., Iwanowicz, L.R., Lane, R.F., Mianecki, A., O’Shea, P.S., Raines, C.D., Scott, J., Thompson, D., Wilson, M.C., and Gray, J.L., 2025, Assessing microplastics, per- and polyfluoroalkyl substances (PFAS), and other contaminants of global concern in wadable agricultural streams: Environmental Science: Processes & Impacts, v. 27, p. 1401-1422, https://doi.org/10.1039/D4EM00753K.","productDescription":"22 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Center","active":true,"usgs":true}],"preferred":true,"id":936728,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Gray, James L. 0000-0002-0807-5635","orcid":"https://orcid.org/0000-0002-0807-5635","contributorId":205658,"corporation":false,"usgs":true,"family":"Gray","given":"James","email":"","middleInitial":"L.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true}],"preferred":true,"id":936729,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70264905,"text":"fs20253012 - 2025 - A partnership between the USGS and the Klamath Tribes to apply structured decision making for chronic wasting disease management","interactions":[],"lastModifiedDate":"2025-08-07T20:25:45.598265","indexId":"fs20253012","displayToPublicDate":"2025-03-27T14:00:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2025-3012","displayTitle":"A Partnership Between the USGS and the Klamath Tribes to Apply Structured Decision Making for Chronic Wasting Disease Management","title":"A partnership between the USGS and the Klamath Tribes to apply structured decision making for chronic wasting disease management","docAbstract":"Project Overview: The Klamath Tribes (TKT) are the Klamath, Modoc, and Yahooskin Paiute peoples, and are the first peoples of the land, having lived in ancestral lands of Oregon and California since time immemorial. Members of TKT have rights to hunt, fish, trap, and gather, including the harvest of mule deer (Odocoileus hemionus) and elk (Cervus canadensis nelsoni) within the 1.19 million acres of their Reserved Treaty Rights Area.
Anthropogenic changes threaten the well-being of mule deer and elk and of the Tribes that rely on them. Today, these species are a primary protein source for TKT. They are traded within TKT and among other Tribes and provide materials for cultural and sacred items such as regalia. However, mule deer numbers have been declining across the western states for the past several decades because of multiple stressors, including persistent and frequent drought and wildfires, habitat loss and degradation, vehicle mortality, and increasing barriers to migratory movements between summer and winter ranges. The migratory movements of mule deer, which allow deer to access the best available seasonal habitats, put them at risk of another potential stressor—infection with chronic wasting disease (CWD). Chronic wasting disease is a fatal prion disease of deer that has been detected in 36 U.S. states. It was detected in free-ranging mule deer in northern Idaho in 2021, prompting the Tribes to initiate a planning process for CWD surveillance, prevention, and response measures to preserve and protect the deer and elk within the Reserved Treaty Rights Area.
In 2023, the Klamath Tribes Natural Resources Department began to develop their CWD plan by incorporating preliminary input provided by the Klamath Indian Game Commission (KIGC) and working with scientists from the U.S. Geological Survey (USGS). This collaborative effort includes the application of structured decision making and the development of mathematical models to analyze potential CWD management strategies. The result will be a transparent assessment that incorporates TKT values throughout the process and can inform place-based management of the cultural, natural, and physical resources upon which the Tribes depend. In addition, this process may provide opportunities for broader coordination by natural resource management agencies to work together to ensure the long-term health and sustainability of deer and elk populations within the Reserved Treaty Rights Area and throughout the state of Oregon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20253012","collaboration":"Prepared in Cooperation with the Klamath Tribes Natural Resources Department","usgsCitation":"McEachran, M.C., Guntly-Yancey, K.M., Berl, R.E.W., Gentry, D., Runge, M.C., White, C., and Cook, J.D., 2025, A partnership between the USGS and the Klamath Tribes to apply structured decision making for chronic wasting disease management: U.S. Geological Survey Fact Sheet 2025–3012, 4 p., https://doi.org/10.3133/fs20253012.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-169589","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":493738,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118503.htm","linkFileType":{"id":5,"text":"html"}},{"id":483890,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3012/fs20253012.XML","linkFileType":{"id":8,"text":"xml"},"description":"FS 2025-3012 XML"},{"id":483888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3012/fs20253012.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3012 PDF"},{"id":483891,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3012/images/"},{"id":483887,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3012/coverthb.jpg"},{"id":483889,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253012/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3012 HTML"}],"country":"United States","state":"Oregon","otherGeospatial":"Klamath Tribes reserved treaty rights area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.27104164386708,\n 43.25\n ],\n [\n -122.27104164386708,\n 42\n ],\n [\n -120.75,\n 42\n ],\n [\n -120.75,\n 43.25\n ],\n [\n -122.27104164386708,\n 43.25\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Eastern Ecological Science Center
U.S. Geological Survey
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Total phosphorus (TP) concentrations and loads in the Boise River near Parma, Idaho, were examined to identify changes by month over a 19-year period from water year 2003 through water year 2021 and to evaluate the performance of three common water-quality models. Mean annual TP concentrations and loads were estimated to have reduced by approximately 60 percent over the study period. Mean annual TP concentrations were reduced from 0.42 milligrams per liter in 2003 to 0.18 milligrams per liter in 2021. Mean annual TP loads were reduced from 816 kilograms per day in 2003 to 302 kilograms per day in 2021. Mean annual concentrations and loads reduced by approximately 3 percent per year with the largest changes occurring in the non-irrigation season of October through April. The TP load remained highest in May across the model period while peak concentration shifted from January to March.
High-frequency TP data collected with an automated sampler every 49 hours enabled detailed model performance evaluation of the Load Estimator (LOADEST), Weighted Regressions on Time, Discharge, and Season (WRTDS), and WRTDS method with Kalman filtering (WRTDS_K) water-quality models generated with near-monthly data. All three models were generally able to reproduce the observed concentrations, with the largest errors occurring in the spring when observed concentrations were most variable. Annual TP loads varied by up to 27 percent, or approximately 128,000 kilograms, between the three models calibrated on monthly data. In this system with highly variable concentrations, we note that performance metrics for WRTDS_K based on monthly calibration data masked serious errors that were only revealed by comparing results against higher frequency (49-hour) autosampler data. This emphasizes the value of high frequency validation data to quantify uncertainty in water-quality models when applied to systems where concentrations change rapidly. Lastly, we identify that hydraulic routing may be a valuable addition to discharge, season, and time in water-quality modeling for systems with significant human intervention in natural hydro-biogeochemical processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245110","collaboration":"Prepared in cooperation with the City of Boise","programNote":"National Water Quality Program","usgsCitation":"King, T.V., and Yoder, A.M., 2025, A trend analysis and model comparison of total phosphorus concentrations and loads in the Boise River near Parma, southwestern Idaho, water years 2003–21: U.S. Geological Survey Scientific Investigations Report 2024–5110, 41 p., https://doi.org/10.3133/sir20245110.","productDescription":"Report: vi, 41p.; Data Release","onlineOnly":"Y","ipdsId":"IP-140444","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":493739,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118504.htm","linkFileType":{"id":5,"text":"html"}},{"id":483669,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5110/sir20245110.XML"},{"id":483668,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5110/images"},{"id":483667,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98DMTAN","text":"USGS data release","description":"USGS data release","linkHelpText":"Water quality modeling results of total phosphorus for the lower Boise River near Parma, Idaho 2002 - 2021"},{"id":483666,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245110/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5110"},{"id":483665,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5110/sir20245110.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5110"},{"id":483664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5110/coverthb.jpg"}],"country":"United States","state":"Idaho","city":"Parma","otherGeospatial":"Boise River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.25,\n 44\n ],\n [\n -117.25,\n 43\n ],\n [\n -115.75,\n 43\n ],\n [\n -115.75,\n 44\n ],\n [\n -117.25,\n 44\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4250
This report documents the system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-4 (LISS–4) sensor. It is part of a series of system characterization reports produced by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports describe the methodology and procedures used for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Resourcesat-2A was launched in 2016 on the Polar Satellite Launch Vehicle-C36; it is identical to Resourcesat-2, and together, they decrease imaging revisit time from 5 days to 2–3 days, providing data continuity and improved temporal resolution. Resouresat-2 and 2A carry the Advanced Wide Field Sensor, Linear Imaging Self Scanning-3, and LISS–4 medium-resolution imaging sensors, continuing the legacy of the Indian Space Research Organisation’s Indian Remote Sensing-1C/1D/P3 satellite programs. More information about the Indian Space Research Organisation’s satellites and sensors is available through the Joint Agency Commercial Imagery Evaluation Earth Observing Satellites Online Compendium at https://calval.cr.usgs.gov/apps/compendium/ and from the manufacturer at https://www.isro.gov.in/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team assessed the geometric, radiometric, and spatial performances of the Resourcesat-2A LISS–4 sensor. Geometric performance is divided into the interior geometric performance of band-to-band registration and the exterior geometric performance of geolocation accuracy. The interior geometric performance had mean offsets in the range of −0.118 to 0.024 pixel in easting and −0.053 to 0.022 pixel in northing with root mean square error values from 0.067 to 0.230 pixel in easting and from 0.087 to 0.2 pixel in northing. The exterior geometric performance had offsets in the range of 2.55 to 7.85 meters (m) in easting and −6.15 to 11.15 m in northing with root mean square error values in the range of 2.6 to 8.2 m in easting and 6.35 to 11.8 m in northing compared to the U.S. Department of Agriculture National Agriculture Imagery Program and WorldView-3 orthoimages. The measured radiometric performance had offsets from 0.003 to 0.024 and slopes from 0.736 to 0.952, and spatial performance was in the range of 1.633 to 1.903 pixels for the full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.0529 to 0.0952.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030U","usgsCitation":"Shrestha, M., Sampath, A., Kim, M., Park, S., and Clauson, J., 2025, System characterization report on Resourcesat-2A Linear Imaging Self Scanning-4 sensor, chap. U of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 16 p., https://doi.org/10.3133/ofr20211030U.","productDescription":"iv, 16 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-170098","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":483933,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/u/coverthb.jpg"},{"id":483934,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/u/ofr20211030u.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-U"},{"id":483935,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/u/ofr20211030u.XML"},{"id":483936,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/u/images/"},{"id":483938,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20211030U/full"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
The Atlantic Sturgeon Acipenser oxyrinchus is a wide-ranging, long-lived diadromous fish that is endangered in most of its range. Our objective was to develop and apply long-term, detection-corrected indices of relative abundance for juvenile and adult Atlantic Sturgeon in the Hudson River, New York, United States, to support population monitoring and stock assessment.
We used long-term gill-net catches to estimate relative abundances of juvenile and adult Atlantic Sturgeon while accounting for imperfect detection within an N-mixture modeling framework. We validated the model framework using a simulation–estimation framework based on mean parameter estimates from the adult Atlantic Sturgeon relative abundance index.
Simulation testing indicated that absolute abundance estimates may be biased low due to poor characterization of detection probabilities. However, model estimates of relative abundance tracked simulated abundance trends well. Juvenile relative abundance estimates followed similar trends as raw gill-net catches but were less variable among years when corrected for detection probability. Relative abundance of juveniles increased from 2004 to 2015 prior to declining through 2022, with little evidence for change between the start and end of the survey. Detection-corrected indices for adult sturgeon indicated a consistent increase in relative abundance that was not readily apparent in raw catch indices.
Detection-corrected catch indices can provide improved characterization of Atlantic Sturgeon relative abundance dynamics over raw gill-net catches through use of N-mixture models. The approach has broad applicability to data types that are commonly collected for understanding population trends in stock assessment. Estimation of absolute abundance and other population demographics germane to management would benefit from alternative or auxiliary data collected through approaches such as side-scan sonar or acoustic telemetry, which are increasingly common for monitoring sturgeon populations.
Critical minerals are commodities essential to modern industrial and strategic technologies and are highly vulnerable to supply chain disruption. The Critical Minerals Mapping Initiative (CMMI) is a collaboration among the U.S. Geological Survey (USGS), the Geological Survey of Canada, and Geoscience Australia that aims to deepen global understanding of where critical minerals are located. A key output of this initiative is the Critical Minerals in Ores (CMiO) database that is advancing our collective understanding of critical minerals distributions. For instance, publicly available data on the concentrations of many critical minerals are sparse because these commodities can only be produced in small, yet essential, quantities compared to the primary commodities like copper and zinc. The CMiO database helps bridge this gap by offering high-quality, multielement geochemical data from a wide variety of critical mineral-bearing deposits around the world. Importantly, it uses a novel consensus deposit environment, group, and type classification scheme developed by the agencies that allows comparisons among ore deposits from different regions. The CMiO database contains geochemical data for more than 20,000 samples from more than 100 deposit types comprising 10 deposit environments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/fs20253002","collaboration":"Prepared in collaboration with the Geological Survey of Canada and Geoscience Australia","programNote":"Mineral Resources Program","usgsCitation":"Case, G.N.D., Graham, G.E., Lawley, C.J.M., Bastrakov, E., Huston, D.L., Hofstra, A.H., Lisitsin, V., Hawkins, S.G., and Wang, B., 2025, Critical Minerals in Ores (CMiO) database (ver. 1.2, May 2025): U.S. Geological Survey Fact Sheet 2025–3002, 2 p., https://doi.org/10.3133/fs20253002.","productDescription":"Report: 2 p.; Dataset","onlineOnly":"N","ipdsId":"IP-172113","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":493736,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118563.htm","text":"Version 1.1","linkFileType":{"id":5,"text":"html"}},{"id":485219,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2025/3002/versionHist.txt","size":"4.0 KB","linkFileType":{"id":2,"text":"txt"},"description":"FS 2025-3002 version history"},{"id":483999,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20253002/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2025-3002"},{"id":483900,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2025/3002/fs20253002.xml"},{"id":483899,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2025/3002/images"},{"id":483682,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pid.geoscience.gov.au/dataset/ga/145496","text":"Critical Minerals in Ores - geochemistry database"},{"id":483671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2025/3002/fs20253002.pdf","text":"Report","size":"5.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2025-3002"},{"id":483670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2025/3002/coverthb3.jpg"},{"id":498997,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118502.htm","text":"Version 1.0","linkFileType":{"id":5,"text":"html"}}],"edition":"Version 1.0: March 26, 2025; Version 1.1: April 30, 2025; Version 1.2: May 22, 2025","contact":"Program Coordinator, Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192
A previous study evaluating restoration success of Paddlefish Polyodon spathula suggested that excessive turbidity in lakes and rivers may inhibit foraging by juveniles prior to the development of the rostrum. Although a Paddlefish's rostrum, which contains electroreceptors, helps the fish to locate zooplankton prey, the prerostrum stage lacks many of these electroreceptors, potentially affecting foraging in highly turbid waters. To evaluate this hypothesis, we conducted a series of laboratory experiments in small aquaria by varying the level of turbidity measured as Secchi tube depth: >95 (clear tap water), 40, 20, 10, or 5 cm.
For each foraging trial, approximately 300 zooplankton were added to the aquaria, followed by three postlarval Paddlefish, which were allowed to feed on the zooplankton for approximately 18 h. After the 18-h period, Paddlefish were removed and dissected and the zooplankton in the gut were counted to quantify foraging success.
From three experimental trials, we observed statistically significant nonlinear relationships for two trials, which included the largest fish tested. The smallest Paddlefish size-class showed no significant trend in foraging among turbidity treatments. However, the subsequent trials, which contained larger Paddlefish, both showed unimodal responses, with the highest foraging observed at moderate (30–50-cm Secchi tube depth) turbidity levels.
High turbidity and excessively clear water both appear to affect foraging by juvenile Paddlefish before the rostrum is fully developed, depending on fish size. Understanding this relationship can further influence management and restoration projects directed at this species.
Habitat features needed by wildlife can change in composition throughout the year, particularly in temperate ecosystems, leading to distinct seasonal spatial-use patterns. Studies of species-habitat associations therefore often focus on understanding relationships within discrete seasonal periods with common goals of prediction (e.g., habitat mapping) and inference (e.g., interpreting model coefficients). Across the range of the greater sage-grouse (Centrocercus urophasianus) of western North America, the increasing use of high-frequency tracking devices has led to a surge in habitat association studies covering multiple temporal periods and spatial extents. We reviewed the literature for seasonal habitat association studies corresponding to the second and third orders of selection (Johnson 1980). Our objectives were to summarize the methodological approaches used to estimate habitat associations to aid understanding in cross-study comparisons and identify common habitat features reported as selected or avoided within different seasonal periods. We reviewed 71 second- and third-order studies published from 2007–2023 that evaluated covariates collected in a geographic information system (GIS) and modeled probability of selection or intensity of use. The most common study design evaluated a single level of selection (third-order) and was multi-scale (i.e., covariates were measured at varying spatial grains). The most common model approach estimated habitat associations using resource selection functions (RSFs) fit with logistic regression. Studies mostly focused on the breeding periods and winter, but all seasons throughout the annual cycle were covered. There was clear support for selection of sagebrush and avoidance of trees and rugged terrain across seasons, and strong selection of mesic conditions in summer. However, habitat associations for most covariates were mixed, with proportionally equivalent selection and avoidance reported, even within the same seasons. Different factors hampered cross-study comparisons, including variation in study design, but additional contributors likely included important context-dependent habitat associations, such as functional responses to changing habitat availability. We suggest collaborative studies leveraging multiple datasets can help improve seasonal habitat inference by removing the effects of variable study designs.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.70022","usgsCitation":"Wann, G.T., Whipple, A.L., Orning, E.K., McLachlan, M.M., Beck, J.L., Coates, P., Conway, C.J., Dinkins, J.B., Johnston, A.N., Hagen, C., Makela, P., Naugle, D., Schroeder, M.A., Sedinger, J.S., Walker, B.L., Williams, P.J., Inman, R.D., and Aldridge, C.L., 2025, Greater sage-grouse seasonal habitat associations: A review and considerations for interpretation and management applications: Journal of Wildlife Management, v. 89, no. 5, e70022, 33 p., https://doi.org/10.1002/jwmg.70022.","productDescription":"e70022, 33 p.","ipdsId":"IP-154951","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":488629,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.70022","text":"Publisher Index 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,{"id":70268950,"text":"70268950 - 2025 - Social composition of soft‐release groups is correlated with survival of translocated gopher tortoises","interactions":[],"lastModifiedDate":"2025-07-11T15:08:06.152682","indexId":"70268950","displayToPublicDate":"2025-03-26T08:03:07","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16872,"text":"The Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Social composition of soft‐release groups is correlated with survival of translocated gopher tortoises","docAbstract":"The social structure of translocated animal populations can have important effects on the survival and reproduction of translocated individuals for both solitary and social species. The gopher tortoise (Gopherus polyphemus) is a reptile of conservation concern that is currently experiencing high levels of mitigation translocation in Florida, USA. Individuals live in aggregations of burrows with frequent agonistic, courtship, and burrow-sharing interactions between residents. Given that exposure to many unfamiliar individuals may increase the frequency of aggressive interactions and social stress following translocation, we predicted that tortoises with greater numbers of familiar individuals co-translocated from the same origin site would have higher survival after translocation. To test this, we updated a recently published survival analysis of 2,822 translocated tortoises and 502 identified carcasses from a translocation site in the western Florida panhandle from 2006–2022. After controlling for simultaneous effects of soft-release enclosure identity, release season, release density, region of origin, sex, and size, adding the number of potentially familiar individuals improved model fit and showed increasing the number of familiars reduced the probability of being found dead. This effect was modulated by release density, being apparent only when density was high, suggesting a role for social interactions. This effect was also present only in the first few years after release, prior to the removal of soft-release enclosures preventing dispersal, and was similar in magnitude to previously identified effects of density, release season, and region of origin. We suggest that this effect may result from reduced aggressive interactions or social stress for tortoises with a greater number of familiar individuals in their release enclosures but cannot rule out the possibility of reduced novel pathogen exposure for individuals released with a greater number of individuals from the same source site or other factors that may be confounded with the size of translocated groups. Designing and implementing mitigation translocations to account for social composition of gopher tortoise groups could improve survival in release enclosures.
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