The Trinity River is managed in two sections: (1) from the upper 64-kilometer “restoration reach” downstream from Lewiston Dam to the confluence with the North Fork Trinity River, and (2) the 120-kilometer lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously applied to these reaches and the Klamath River. To estimate fish growth, past S3 calibration efforts in the Trinity and Klamath Rivers used maximum likelihood methods that considered only the abundance of juvenile Chinook salmon (Oncorhynchus tshawytscha) passing a fish trap to estimate survival and movement parameters, but not fish consumption. Previous calibrations did not estimate the average proportion of maximum consumption (Cy) when estimating survival (Sy) and movement (M0y) parameters across years (y) of data, but because no other information was available in the literature a fixed value of Cy=0.66 was assumed. Therefore, the goal of this report is to present an alternative approach that calibrates the S3 model to multivariate data (that is, abundance and size), enabling the estimation of the average proportion of maximum consumption, in conjunction with survival and movement parameters for a particular migration year. We fit the S3 model to individual years of weekly trap abundance estimates and mean fish sizes (fork length) at the Pear Tree Gulch (hereafter referred to as Pear Tree) fish trap representing the restoration reach. We used the Earth Mover’s Distance (EMD) as the objective value to be minimized in parameter optimization. This approach estimated survival, movement, and consumption parameters for each migration year. Because we had information on the abundance of natural and hatchery produced juvenile salmon at the fish traps, we estimated survival and movement for natural and hatchery fish.
S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile Chinook Salmon. A key theme of the model is that river discharge affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive survival and growth within each habitat unit and movement of fish among habitat units.
The physical template of the restoration reach of the Trinity River was classified into 356 meso-habitat units comprised of runs, riffles, and pools. For each habitat unit, we developed a timeseries of daily discharge, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity time series were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg maturation and survival, and in turn, juvenile movement, growth, and survival.
We estimated movement, survival, and consumption parameters by calibrating the model to 12 years of weekly juvenile abundance estimates and fish sizes at the Pear Tree fish trap near the downstream end of the restoration reach. We estimated parameters for 12 years that included a wide range of female spawner abundances (1,414–11,494) and water year types (critically dry–extremely wet). We contrast the estimated parameters to the corresponding number of female spawners and the total annual volume of water discharged for the Trinity River (Trinity River Restoration Program; https://www.trrp.net/restoration/flows/summary/).
The calibration consisted of replicating historical conditions as closely as possible (for example, discharge; temperature; spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location from each brood year of adults and subsequent migration year of their juvenile progeny. Because density-dependent movement was favored in past evaluations, we estimated S3 parameters based on density-independent survival and density-dependent movement. Likewise, each year’s estimated survival parameter for natural (SNy) and hatchery (SHy) fish may be interpreted as the mean daily survival probability from emergence or hatchery release to the Pear Tree fish trap. Under density dependence, the estimated movement parameter for natural (M0Ny) and hatchery (M0Hy) fish represents the intercept of the Beverton-Holt model; the probability of remaining in a habitat at near-zero abundance.
We estimated Cy by using EMD and incorporating abundance and fish size into model calibration. Average daily proportions of maximum consumption, , across the years were generally high (=0.640; standard deviation (SD) SD=0.176), suggesting that fish were feeding at about two-thirds of expected maximum consumption rates. This average proportion of maximum consumption,is very similar to what has been assumed (=0.66) in previous Trinity and Klamath River S3 calibration and simulation efforts. In 2017, we estimated the lowest Cy, suggesting lower average consumption for juvenile salmon in high-discharge water years. When this high discharge year was excluded, there was no apparent trend in Cy with annual water volume. Estimates of survival showed little trend over the range in spawner abundances, but a trend towards higher natural and hatchery fish survival with higher annual volumes of water was apparent. Over the 12 years, the average survival of hatchery fish was =0.888 (SD=0.079) and the average survival natural fish was=0.969 (SD=0.01).
With respect to fish movement, we estimated higher M0Ny and M0Hy with higher annual volumes of water in the Trinity River. Higher MN0y or MH0y suggest greater probability of remaining in a habitat at low fish densities, with potential for density-dependent processes in movement to occur. The highest M0Ny = 0.676 was estimated during brood year 2012, and the overall average for natural fish was =0.276 (SD=0.188) and for hatchery fish was=0.467 (SD=0.235). Under the Beverton-Holt model, as M0Ny or M0Hy approach zero, there is less capacity for change in fish movement as fish density increases.
The S3 model was initialized with only the spatiotemporal distribution of spawners, so it performed well at capturing the essential outmigration features that are ultimately governed by rates of growth, movement, and mortality. We used a new optimization method that could accommodate multivariate data on abundance and fish size collected at the Pear Tree fish trap, enabling the calibration of S3 to estimate five parameters for 12 separate years of data. Incorporating weekly fish size data for each year in our parameter optimization process made the estimation of Cy possible and represents a step forward in the fitting of the S3 model to fish trap data for the purposes of parameter calibration and the estimation of growth parameters with respect to annual conditions. We identified lack of fit and adding important effects into the S3 model may improve the S3 estimation and simulation of water scenarios.
The Trinity River Restoration Program (TRRP) Science Advisory Board recommended that the TRRP focus on developing core elements of a decision support system (DSS; Buffington and others, 2014). Toward that end, the habitat and S3 models described in this report are both core elements of the DSS. The structure of S3 makes it a particularly useful fish production model for the DSS because population dynamics are sensitive to (1) water temperature, (2) daily discharge management, and (3) habitat quality and quantity. Each of these variables are key management parameters under consideration in the TRRP. As such, the S3 model may provide valuable insights into the potentially variable effects of different management decisions on the Trinity River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241070","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., and De Juilio, K., 2025, Calibration of the Stream Salmonid Simulator (S3) model to estimate annual survival, movement, and food consumption by juvenile Chinook salmon (Oncorhynchus tshawytscha) in the restoration reach of the Trinity River, California, 2006–18: U.S. Geological Survey Open-File Report 2024–1070, 21 p., https://doi.org/10.3133/ofr20241070.","productDescription":"vii, 22 p.","onlineOnly":"Y","ipdsId":"IP-156648","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":485969,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1070/images"},{"id":485968,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241070/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1070"},{"id":485967,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1070/ofr20241070.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1070"},{"id":485966,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1070/coverthb.jpg"},{"id":485970,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1070/ofr20241070.XML"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.1406578585185,\n 40.785753827016435\n ],\n [\n -123.1406578585185,\n 40.69151845163566\n ],\n [\n -122.79146166523228,\n 40.69151845163566\n ],\n [\n -122.79146166523228,\n 40.785753827016435\n ],\n [\n -123.1406578585185,\n 40.785753827016435\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Lampreys (Petromyzontiformes) are an ancient group of fishes with complex life histories. We created a life cycle model that includes an R Shiny interactive web application interface to simulate abundance by life stage. This will allow scientists and managers to connect available demographic information in a framework that can be applied to questions regarding lamprey biology and conservation. We used Pacific lamprey (Entosphenus tridentatus) as a case study to highlight the utility of this model. We applied a global sensitivity analysis to explore the importance of individual life stage parameters to overall population size, and to better understand the implications of existing gaps in knowledge. We also provided example analyses of selected management scenarios (dam passage, fish translocations, and hatchery additions) influencing Pacific lamprey in fresh water. These applications illustrate how the model can be applied to inform conservation efforts. This tool will provide new capabilities for users to explore their own questions about lamprey biology and conservation. Simulations can hone hypotheses and predictions, which can then be empirically tested in the real world.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0323408","usgsCitation":"Gomes, D.G., Benjamin, J.R., Clemens, B.J., Lampman, R., and Dunham, J., 2025, New technology for an ancient fish: A lamprey life cycle modeling tool with an R Shiny application: PLoS ONE, v. 20, no. 5, e0323408, 25 p., https://doi.org/10.1371/journal.pone.0323408.","productDescription":"e0323408, 25 p.","ipdsId":"IP-172919","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":489170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0323408","text":"Publisher Index Page"},{"id":486169,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomes, Dylan Gerald-Everett 0000-0002-2642-3728","orcid":"https://orcid.org/0000-0002-2642-3728","contributorId":346160,"corporation":false,"usgs":true,"family":"Gomes","given":"Dylan","email":"","middleInitial":"Gerald-Everett","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":937518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":937519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clemens, Benjamin J.","contributorId":195098,"corporation":false,"usgs":false,"family":"Clemens","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":937520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lampman, Ralph","contributorId":215233,"corporation":false,"usgs":false,"family":"Lampman","given":"Ralph","email":"","affiliations":[],"preferred":true,"id":937521,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":937522,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267437,"text":"70267437 - 2025 - Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends","interactions":[],"lastModifiedDate":"2025-05-23T15:07:59.713754","indexId":"70267437","displayToPublicDate":"2025-05-14T10:05:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10778,"text":"Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends","docAbstract":"Standard flood-frequency analysis methods rely on an assumption of stationarity, but because of growing understanding of climatic persistence and concern regarding the effects of climate change, the need for methods to detect and model nonstationary flood frequency has become widely recognized. In this study, a regional statistical method for estimating the effects of climate variations on annual maximum (peak) flows that allows for the effect to vary by quantile is presented and applied. The method uses a panel–quantile regression framework based on a location-scale model with two fixed effects per basin. The model was fitted to 330 selected gauged basins in the midwestern United States, filtered to remove basins affected by reservoir regulation and urbanization. Precipitation and discharge simulated using a water-balance model at daily and annual time scales were tested as climate variables. Annual maximum daily discharge was found to be the best predictor of peak flows, and the quantile regression coefficients were found to depend monotonically on annual exceedance probability. Application of the models to gauged basins is demonstrated by estimating the peak-flow distributions at the end of the study period (2018) and, using the panel model, to the study basins as-if-ungauged by using leave-one-out cross validation, estimating the fixed effects using static basin characteristics, and parameterizing the water-balance model discharge using median parameters. The errors of the quantiles predicted as-if-ungauged approximately doubled compared to the errors of the fitted panel model.
","language":"English","publisher":"MDPI","doi":"10.3390/hydrology12050119","usgsCitation":"Over, T.M., Marti, M.K., and Podzorski, H.L., 2025, Regional analysis of the dependence of peak-flow quantiles on climate with application to adjustment to climate trends: Hydrology, v. 12, no. 5, 119, 43 p., https://doi.org/10.3390/hydrology12050119.","productDescription":"119, 43 p.","ipdsId":"IP-167316","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":487957,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/hydrology12050119","text":"Publisher Index Page"},{"id":486509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938195,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marti, Mackenzie K. 0000-0001-8817-4969 mmarti@usgs.gov","orcid":"https://orcid.org/0000-0001-8817-4969","contributorId":289738,"corporation":false,"usgs":true,"family":"Marti","given":"Mackenzie","email":"mmarti@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938196,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Podzorski, Hannah Lee 0000-0001-5204-2606 hpodzorski@usgs.gov","orcid":"https://orcid.org/0000-0001-5204-2606","contributorId":333626,"corporation":false,"usgs":true,"family":"Podzorski","given":"Hannah","email":"hpodzorski@usgs.gov","middleInitial":"Lee","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938197,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70271181,"text":"70271181 - 2025 - The Hardscrabble Creek complex: A newly discovered, mostly buried, Mesoproterozoic mafic-ultramafic pluton in the Wet Mountains, Colorado, USA","interactions":[],"lastModifiedDate":"2025-09-02T14:49:24.55281","indexId":"70271181","displayToPublicDate":"2025-05-14T09:44:44","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"The Hardscrabble Creek complex: A newly discovered, mostly buried, Mesoproterozoic mafic-ultramafic pluton in the Wet Mountains, Colorado, USA","docAbstract":"The origin of prolific ca. 1.4 Ga ferroan magmatism between the southwestern USA and eastern Canada is enigmatic and has been explained by various models, including extensional, mantle plume, and convergent plate-margin models. Rare mafic plutons are associated with the ferroan plutons, which may help constrain their mantle source and tectonic setting. In the southwestern USA, only two such mafic plutons are known to exist. We present the first evidence for a third, mostly buried, potentially layered, mafic-ultramafic Mesoproterozoic pluton, informally referred to as the Hardscrabble Creek complex, in the central Wet Mountains of Colorado, USA. Recent geophysical data show an elliptical magnetic and gravity high spatially coincident with local gabbroic outcrops. New field and petrographic analyses of these exposed rocks reveal that they consist of ultramafic to mafic cumulates, including orthopyroxenite, olivine norite, norite, and anorthosite. High-precision U-Pb dating of zircon from orthopyroxenite and norite yield weighted mean 206Pb/238U dates of 1352.36 ± 1.60 Ma and 1352.37 ± 1.71 Ma, respectively. These dates indicate that the complex formed over a narrow timeframe, after the adjacent 1362 ± 7 Ma ferroan San Isabel Granite, and during the waning stages of the regional ca. 1.4 Ga ferroan magmatism. Whole-rock geochemistry and Nd-Sr-Pb isotope compositions of samples from the Hardscrabble Creek complex are similar to those of the San Isabel Granite, suggesting that they were derived from the same or a similar mantle source. The mineral chemistry of the samples is comparable to Proterozoic massif-type anorthosites and related mafic intrusions, indicating that the Hardscrabble Creek complex and San Isabel Granite together represent a rare anorthosite-mangerite-charnockite-granite (AMCG) suite in the southwestern USA. The Hardscrabble Creek complex is unique because it formed ~80 m.y. after the other few mafic plutons in the southwestern USA, and it contains an ultramafic section that is absent from these plutons and rare to the AMCG suite in general. A combination of arc-like whole-rock geochemistry, chondrite uniform reservoir-like Nd-Sr-Pb isotopes, and ocean island basalt (OIB)-like zircon trace element chemistry suggests that the complex was derived from a partial melt of OIB-like mantle and interacted with metasomatically enriched lithospheric mantle. The enriched lithospheric mantle signature, combined with the long ~160 m.y. duration of magmatism in the region, is consistent with a period of protracted convergent tectonism.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B37903.1","usgsCitation":"Magnin, B.P., Brake, S.S., Kuiper, Y., Mohr, M.T., and Hanson, R.E., 2025, The Hardscrabble Creek complex: A newly discovered, mostly buried, Mesoproterozoic mafic-ultramafic pluton in the Wet Mountains, Colorado, USA: GSA Bulletin, v. 137, no. 9-10, p. 4558-4574, https://doi.org/10.1130/B37903.1.","productDescription":"17 p.","startPage":"4558","endPage":"4574","ipdsId":"IP-168096","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":495119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Wet Mountains","volume":"137","issue":"9-10","noUsgsAuthors":false,"publicationDate":"2025-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Magnin, Benjamin Patrick 0000-0001-9951-4404","orcid":"https://orcid.org/0000-0001-9951-4404","contributorId":300679,"corporation":false,"usgs":true,"family":"Magnin","given":"Benjamin","email":"","middleInitial":"Patrick","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":947668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brake, Sandra S.","contributorId":360805,"corporation":false,"usgs":false,"family":"Brake","given":"Sandra","middleInitial":"S.","affiliations":[{"id":17777,"text":"Indiana State University","active":true,"usgs":false}],"preferred":false,"id":947669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kuiper, Yvette 0000-0002-8506-8180","orcid":"https://orcid.org/0000-0002-8506-8180","contributorId":299649,"corporation":false,"usgs":false,"family":"Kuiper","given":"Yvette","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":947670,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mohr, Michael T. 0009-0001-3814-6908","orcid":"https://orcid.org/0009-0001-3814-6908","contributorId":360807,"corporation":false,"usgs":false,"family":"Mohr","given":"Michael","middleInitial":"T.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":947671,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hanson, Richard E.","contributorId":360809,"corporation":false,"usgs":false,"family":"Hanson","given":"Richard","middleInitial":"E.","affiliations":[{"id":25471,"text":"Texas Christian University","active":true,"usgs":false}],"preferred":false,"id":947672,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267215,"text":"70267215 - 2025 - Paleo-scours within the layered sulfate-bearing unit at Gale crater, Mars: Evidence for intense wind erosion","interactions":[],"lastModifiedDate":"2025-05-20T13:19:43.188052","indexId":"70267215","displayToPublicDate":"2025-05-14T08:15:31","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9967,"text":"JGR Planets","active":true,"publicationSubtype":{"id":10}},"title":"Paleo-scours within the layered sulfate-bearing unit at Gale crater, Mars: Evidence for intense wind erosion","docAbstract":"The surface of modern Mars is largely shaped by wind, but the influence of past wind activity is less well constrained. Sedimentary rocks exposed in the lower foothills of Aeolis Mons, the central mound within Gale crater, record a transition from predominantly lacustrine deposition in the Murray formation to aeolian deposition in the Mirador formation. Here, we report a series of enigmatic decameter-wide, concave-up scour-and-fill structures within the Mirador formation and discuss their formation mechanisms. Using panoramic images of stratigraphy exposed in cliff faces acquired by the Curiosity rover, we map the extent, distribution and orientation of the scour-and-fill structures and document the sedimentary facies within and surrounding these structures. The scours are grouped into two classes: (A) scours with a simple, symmetric morphology and light-toned, draping infill; and (B) scours with lateral pinching and dark-toned infill. We find that the scour-enclosing environment is composed of planar, even-in-thickness laminations with a pin-stripe pattern which we interpret as wind-ripple strata formed within an aeolian sandsheet environment. Class B contains cm-scale cross-bedding and a wing-shaped feature making this scour-and-fill structure consistent with fluvial processes. We interpret scour fill of class A as an aeolian infill due to similarities with the surrounding sandsheet strata. The broad morphologies and distribution of class A are also consistent with the geometry of blowout structures formed by localized, enhanced wind deflation. These paleo-blowout structures occur clustered within the same stratigraphic interval, which may imply that they record an interval of intensified wind activity at Gale crater.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024JE008680","usgsCitation":"Roberts, A., Gupta, S., Banhan, S., Cowart, A., Edgar, L.A., Rapin, W., Dietrich, W., Kite, E., Davis, J., Caravaca, G., Mondro, C., Gasda, P., Johnson, J., Le Mouelic, S., Fey, D., Bryk, A., Paar, G., Harris, R., Fraeman, A., and Vasavada, A., 2025, Paleo-scours within the layered sulfate-bearing unit at Gale crater, Mars: Evidence for intense wind erosion: JGR Planets, v. 130, no. 5, e2024JE008680, 32 p., https://doi.org/10.1029/2024JE008680.","productDescription":"e2024JE008680, 32 p.","ipdsId":"IP-169999","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":489183,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index 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S.G.","contributorId":355430,"corporation":false,"usgs":false,"family":"Banhan","given":"S.G.","affiliations":[{"id":84748,"text":"Department of Earth Science & Engineering, Imperial College London","active":true,"usgs":false}],"preferred":false,"id":937308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cowart, A.","contributorId":355431,"corporation":false,"usgs":false,"family":"Cowart","given":"A.","affiliations":[{"id":13179,"text":"Planetary Science Institute","active":true,"usgs":false}],"preferred":false,"id":937309,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science 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Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Fine-resolution satellite remote sensing improves spatially distributed snow modeling to near real time","docAbstract":"Given the highly variable distribution of seasonal snowpacks in complex mountainous environments, the accurate snow modeling of basin-wide snow water equivalent (SWE) requires a spatially distributed approach at a sufficiently fine grid resolution (<500 m) to account for the important processes in the seasonal evolution of a snowpack (e.g., wind redistribution of snow to resolve patchy snow cover in an alpine zone). However, even well-validated snow evolution models, such as SnowModel, are prone to errors when key model inputs, such as the precipitation and wind speed and direction, are inaccurate or only available at coarse spatial resolutions. Incorporating fine-spatial-resolution remotely sensed snow-covered area (SCA) information into spatially distributed snow modeling has the potential to refine and improve fine-resolution snow water equivalent (SWE) estimates. This study developed 30 m resolution SnowModel simulations across the Big Thompson River, Fraser River, Three Lakes, and Willow Creek Basins, a total area of 4212 km2 in Colorado, for the water years 2000–2023, and evaluated the incorporation of a Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat SCA datasets into the model’s development and calibration. The SnowModel was calibrated spatially to the Landsat mean annual snow persistence (SP) and temporally to the MODIS mean basin SCA using a multi-objective calibration procedure executed using Latin hypercube sampling and a stepwise calibration process. The Landsat mean annual SP was also used to further optimize the SnowModel simulations through the development of a spatially variable precipitation correction field. The evaluations of the SnowModel simulations using the Airborne Snow Observatories’ (ASO’s) light detection and ranging (lidar)-derived SWE estimates show that the versions of the SnowModel calibrated to the remotely sensed SCA had an improved performance (mean error ranging from −28 mm to −6 mm) compared with the baseline simulations (mean error ranging from 69 mm to 86 mm), and comparable spatial patterns to those of the ASO, especially at the highest elevations. Furthermore, this study’s results highlight how a regularly updated 30 m resolution SCA could be used to further improve the calibrated SnowModel simulations to near real time (latency of 5 days or less).
","language":"English","publisher":"MDPI","doi":"10.3390/rs17101704","usgsCitation":"Sexstone, G., Akie, G.A., Selkowitz, D.J., Barnhart, T., Rey, D., León-Salazar, C., Carbone, E., and Bearup, L.A., 2025, Fine-resolution satellite remote sensing improves spatially distributed snow modeling to near real time: Remote Sensing, v. 17, no. 10, 1704, 24 p., https://doi.org/10.3390/rs17101704.","productDescription":"1704, 24 p.","ipdsId":"IP-174585","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":490140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs17101704","text":"Publisher Index Page"},{"id":486286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.33,\n 40.85\n ],\n [\n -106.33,\n 39.65\n ],\n [\n -105.17,\n 39.65\n ],\n [\n -105.17,\n 40.85\n ],\n [\n -106.33,\n 40.85\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"10","noUsgsAuthors":false,"publicationDate":"2025-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938011,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akie, Garrett Alexander 0000-0002-6356-7106","orcid":"https://orcid.org/0000-0002-6356-7106","contributorId":290236,"corporation":false,"usgs":true,"family":"Akie","given":"Garrett","email":"","middleInitial":"Alexander","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938012,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Selkowitz, David J. 0000-0003-0824-7051 dselkowitz@usgs.gov","orcid":"https://orcid.org/0000-0003-0824-7051","contributorId":3259,"corporation":false,"usgs":true,"family":"Selkowitz","given":"David","email":"dselkowitz@usgs.gov","middleInitial":"J.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":938013,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":202558,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":938014,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - 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2025 - Shifting baselines of coral-reef species composition from the Late Pleistocene to the present in the Florida Keys","interactions":[],"lastModifiedDate":"2025-06-23T14:20:17.616459","indexId":"70268342","displayToPublicDate":"2025-05-13T09:14:05","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5781,"text":"The Depositional Record","active":true,"publicationSubtype":{"id":10}},"title":"Shifting baselines of coral-reef species composition from the Late Pleistocene to the present in the Florida Keys","docAbstract":"The ongoing global-scale reassembly of modern coral reefs is unprecedented compared with the observed stability of most late Quaternary reef assemblages. One notable exception is the marine isotope stage (MIS) 5e (ca 130–116 thousand years ago [ka]) reefs in the Florida Keys, where the ubiquitous shallow-water coral, Acropora palmata, was near absent. Little is known, however, about reefs that grew during MIS5d–a (ca 116–74 ka), between MIS5e and the Holocene. It is therefore unclear whether Florida's unique MIS5e coral assemblages represent a geologically brief anomaly or a more persistent departure from the western Atlantic coral-reef archetype. We addressed that question by reconstructing the composition of MIS5d–a reefs within 29 coral-reef cores collected throughout the Florida Keys. We then compared the relative composition of corals during MIS5d–a to existing datasets from MIS5e, Holocene and modern (1996 and 2022) reefs to evaluate how far today's reef assemblages have diverged from geological baselines. We show that although the proportion of reef frameworks built by corals was remarkably consistent (ca 38%), species composition changed significantly through time. Acropora palmata was rare throughout MIS5, which we hypothesise was due to greater cold-temperature stress in Florida's subtropical reefs compared with the more climatically stable tropics. In contrast, the massive reef-building coral, Orbicella spp., was regionally dominant throughout the late Quaternary, but has become increasingly rare on modern reefs. By 2022, reefs in the Florida Keys were characterised by a truly novel coral assemblage dominated by Porites astreoides and Siderastrea siderea. In many ways, Florida's reefs defy the concept of a natural baseline; instead, their most persistent characteristic since the Late Pleistocene is their uniqueness. Yet, as reefs are increasingly subjected to unprecedented levels of environmental change, the exceptions to what was normal in the past could, paradoxically, provide the best geological analogues for the future.
","language":"English","publisher":"WIley","doi":"10.1002/dep2.70009","usgsCitation":"Toth, L., Stathakopoulos, A., Hsia, S., and Weinstein, D.A., 2025, Shifting baselines of coral-reef species composition from the Late Pleistocene to the present in the Florida Keys: The Depositional Record, v. 11, no. 3, p. 893-916, https://doi.org/10.1002/dep2.70009.","productDescription":"24 p.","startPage":"893","endPage":"916","ipdsId":"IP-173487","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":491456,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/dep2.70009","text":"Publisher Index Page"},{"id":491097,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Keys","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.17917140030995,\n 25.383351188271973\n ],\n [\n -81.59701661611159,\n 24.90866922937429\n ],\n [\n -83.02465696293855,\n 24.77579446049141\n ],\n [\n -83.06138870428032,\n 24.366054515738625\n ],\n [\n -81.8345485434568,\n 24.395013657422012\n ],\n [\n -80.65178647224371,\n 24.557720553123204\n ],\n [\n -80.17917140030995,\n 25.383351188271973\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940869,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":940870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsia, Scarlette Shan-Hwei 0000-0002-2230-9004","orcid":"https://orcid.org/0000-0002-2230-9004","contributorId":346523,"corporation":false,"usgs":true,"family":"Hsia","given":"Scarlette Shan-Hwei","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":940871,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weinstein, David A.","contributorId":206027,"corporation":false,"usgs":false,"family":"Weinstein","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":940872,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266760,"text":"sir20245133 - 2025 - Using the D-Claw software package to model lahars in the Middle Fork Nooksack River drainage and beyond, Mount Baker, Washington","interactions":[],"lastModifiedDate":"2025-07-03T14:16:54.133347","indexId":"sir20245133","displayToPublicDate":"2025-05-12T15:08:17","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5133","displayTitle":"Using the D-Claw Software Package to Model Lahars in the Middle Fork Nooksack River Drainage and Beyond, Mount Baker, Washington","title":"Using the D-Claw software package to model lahars in the Middle Fork Nooksack River drainage and beyond, Mount Baker, Washington","docAbstract":"Lahars, or volcanic mudflows, are the most hazardous eruption-related phenomena that will affect communities living along rivers that originate on Mount Baker. In the past 15,000 years, the largest lahars from Mount Baker have affected the Middle Fork Nooksack River drainage and beyond. Here we use the physics-based D-Claw software package to model nine lahar scenarios that are initiated as water-saturated landslides between Sherman Crater and the Roman Wall on the Mount Baker edifice and flow down the Middle Fork Nooksack River. The scenarios range in volume from 1 to 260 million cubic meters and have an initial hydraulic permeability from 10−12 to 10−10 meters squared. Model output includes data such as flow depth, velocity, runout distance, area inundated, arrival time, and sediment concentration as well as information that allows scientists to calculate other important hydrologic characteristics such as lahar discharge. These data are important to officials who have the responsibility to plan for, or take mitigation measures against, future Mount Baker lahars. To check the validity of the D-Claw results, we compare the scenarios to known geologic information. We also compare D-Claw results with empirical models that have been used in the past to determine potential inundation areas, runout distances, and arrival times. These comparisons highlight similarities and differences between empirical and physics-based models. We also present D-Claw scenario-based animations to help scientists, officials, and lay people alike to visualize how future lahars could affect communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245133","usgsCitation":"Gardner, C.A., Benage, M.C., Cannon, C., and George, D.L., 2025, Using the D-Claw software package to model lahars in the Middle Fork Nooksack River drainage and beyond, Mount Baker, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5133, 47 p., https://doi.org/10.3133/sir20245133.","productDescription":"Report: vii, 47 p.; 9 Animation Videos; Data Release","numberOfPages":"47","ipdsId":"IP-151680","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":485743,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioC2.mp4","text":"Appendix 4 - Scenario C2","size":"35.9 MB","description":"Scenario C2","linkHelpText":"- Scenario C2"},{"id":485742,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB3.mp4","text":"Appendix 4 - Scenario B3","size":"47 MB","description":"Scenario B3","linkHelpText":"- Scenario B3"},{"id":485741,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB2.mp4","text":"Appendix 4 - Scenario B2","size":"37.6 MB","description":"Scenario B2","linkHelpText":"- Scenario B2"},{"id":485740,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioB1.mp4","text":"Appendix 4 - Scenario B1","size":"25.6 MB","description":"Scenario B1","linkHelpText":"- Scenario B1"},{"id":485739,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA3.mp4","text":"Appendix 4 - Scenario A3","size":"50.4 MB","description":"Scenario A3","linkHelpText":"- Scenario A3"},{"id":485737,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioA1.mp4","text":"Appendix 4 - Scenario A1","size":"35.4 MB","description":"Scenario A1","linkHelpText":"- Scenario A1"},{"id":485736,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1PEX7FS","text":"USGS data release","description":"George, D.L., Cannon, C.M., Benage, M.C., and Gardner, C.A., 2025, Simulated lahar extents and dynamics in the Middle Fork Nooksack River drainage, resulting from hypothetical landslide sources on the western summit of Mount Baker, Washington: U.S. Geological Survey data release, https://doi.org/10.5066/P1PEX7FS.","linkHelpText":"Simulated lahar extents and dynamics in the Middle Fork Nooksack River drainage, resulting from hypothetical landslide sources on the western summit of Mount Baker, Washington"},{"id":485734,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5133/images"},{"id":485733,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133.XML","description":"SIR 2024-5133 XML"},{"id":485731,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5133 PDF"},{"id":485730,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5133/coverthb.jpg"},{"id":485745,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5133/sir20245133_app4_scenarioE2.mp4","text":"Appendix 4 - 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Johnston Cascades Volcano Observatory
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Building 10, Suite 100
Vancouver, WA 98683
Email: askCVO@usgs.gov
","tableOfContents":"Gravity data require submeter elevation accuracy for data processing, and differential global navigation satellite system (dGNSS) equipment is commonly used to acquire three-dimensional positional data to achieve such accuracy. However, lidar (light detection and ranging) data are commonly used to develop digital elevation models (DEMs) of Earth’s surface. Therefore, using elevations from lidar-derived DEMs for gravity-data acquisition and reduction may improve field efficiency and reduce cost. This study examines the feasibility of using DEMs for gravity-data reduction by comparing dGNSS elevation data from 435 gravity stations in Michigan, Wyoming, and Colorado with their respective DEM elevations. The results show that the average difference between DEM and dGNSS elevations is 13 centimeters (cm) and that 93 percent of those differences are less than 50 cm, even in areas with steep terrain. Because an elevation discrepancy of 50 cm corresponds to an error of roughly 0.1 milligals (mGal) in the simple Bouguer gravity anomaly, the results suggest that lidar-derived DEMs are a viable source for acquiring the elevation data needed to process gravity data, thus improving both the cost and efficiency of data collection for regional surveys where an accuracy of less than 1.0 mGal is desired.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20251019","programNote":"Mineral Resources Program","usgsCitation":"Murchek, J.T., Drenth, B.J., Reitman, J.J., Anderson, E.D., Magnin, B.P., and DeGraff, J.M., 2025, The feasibility of using lidar-derived digital elevation models for gravity data reduction (ver. 1.1, July 2025): U.S. Geological Survey Open-File Report 2025–1019, 33 p., https://doi.org/10.3133/ofr20251019.","productDescription":"vii, 33 p.","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-163043","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":491565,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2025/1019/coverthb2.jpg"},{"id":491633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_118572.htm"},{"id":491634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2025-1019 PDF"},{"id":491636,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2025/1019/ofr20251019.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2025-1019 XML"},{"id":491635,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20251019/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2025-1019 HTML"},{"id":491638,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2025/1019/versionHist.txt","size":"654 B","linkFileType":{"id":2,"text":"txt"}},{"id":491637,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2025/1019/images/"}],"edition":"Version 1.0: May 12, 2025; Version 1.1: July 1, 2025","contact":"Director, Energy and Minerals Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192-0002
The San Luis Valley in Colorado, USA, an agriculturally dominated stopover area, is used by the Rocky Mountain population of Antigone canadensis tabida (Greater Sandhill Crane) and some midcontinental individuals of A. c. canadensis (Lesser Sandhill Crane) during migration. While the numbers of both subspecies are stable, the effects of continued water scarcity and declines in grain output on the energetics of cranes in the San Luis Valley are unclear. We conducted roadside counts of A. c. tabida and A. c. canadensis on agricultural fields to determine the effects of crop type, roost distance, and tillage intensity on their selection and abundance on crop fields. Antigone canadensis varied in their use of the San Luis Valley for foraging. In autumn, both subspecies selected barley and other grains over other crop types. In spring, cranes preferred to forage in barley fields, and selection declined as distance to roosts increased. Both subspecies also selected barley fields that were lightly or not tilled. We modeled covariates on abundance for A. c. tabida only and found that more cranes were found close to roosts early in the season in autumn. As the season progressed, the number of A. c. tabida increased as roost distance increased. In spring, abundance was influenced by an interaction between time and crop, with the highest numbers found on barley and pasture around mid-March. Our results suggest that A. canadensis may switch to other crop types as resources are depleted near roosts but appear to prefer to fly farther for grains. Grains that are left idle or moderately tilled and are located near roosts will help ensure A. canadensis are able to maintain adequate nutrient reserves at agriculturally dominated stopover areas during migration.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duaf027","collaboration":"U. S. Fish and Wildlife Service","usgsCitation":"Vanausdall, R., Kendall, W.L., and Collins, D., 2025, Antigone canadensis (Sandhill Crane) foraging patterns influenced by crop type, roost distance, and tillage intensity during spring and autumn migration at a primary stopover area: Ornithological Applications, v. 127, duaf027, 17 p., https://doi.org/10.1093/ornithapp/duaf027.","productDescription":"duaf027, 17 p.","ipdsId":"IP-170214","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490643,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duaf027","text":"Publisher Index Page"},{"id":489266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"San Luis Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.19980544186409,\n 37.483870494045064\n ],\n [\n -106.19980544186409,\n 36.996256317805006\n ],\n [\n -105.66654470738654,\n 36.996256317805006\n ],\n [\n -105.66654470738654,\n 37.483870494045064\n ],\n [\n -106.19980544186409,\n 37.483870494045064\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","noUsgsAuthors":false,"publicationDate":"2025-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Vanausdall, Rachel A.","contributorId":356156,"corporation":false,"usgs":false,"family":"Vanausdall","given":"Rachel A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":938810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Daniel P.","contributorId":356157,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938812,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266804,"text":"70266804 - 2025 - Nature-based solutions extend the lifespan of a regional levee system under climate change","interactions":[],"lastModifiedDate":"2025-05-13T15:40:10.266447","indexId":"70266804","displayToPublicDate":"2025-05-09T10:36:34","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7146,"text":"Nature Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Nature-based solutions extend the lifespan of a regional levee system under climate change","docAbstract":"Nature-based solutions are receiving increasing attention as a cost-effective climate adaptation strategy. Horizontal levees are nature-based adaptation solutions that include a sloping wetland habitat buffer fronting a levee. They can offer a hybrid solution to reinforce traditional levees in estuarine areas—plants on the horizontal levee can provide wave attenuation benefits as well as habitat benefits, but how the design of horizontal levees influences risk of levee failure remains unquantified. We use a hydrodynamic model, XBeach non-hydrostatic (XB-NH), to assess the stability and sustainability of existing levees and determine how hybrid nature-based climate adaptation measures can reduce the risk of overtopping on levees in San Francisco Bay. We compare overtopping rates in the existing levee system and in a variety of nature-based adaptation scenarios using a range of widths and slopes of horizontal levees to assess how horizontal levees perform in reducing risk of flooding, both with present day conditions and sea level rise. We show that climate change will challenge existing levee flood defenses in San Francisco Bay and increase the risk of overtopping, and that the nature-based solution of horizontal levees can meaningfully reduce risk of overtopping while simultaneously supporting marsh habitat. Flood risk reduction and habitat provision are both maximized with more gradually sloping and wider horizontal levee designs. Results show that the risk of overtopping can be reduced by up to 30% with horizontal levees. This analysis provides insight into horizontal levee design considerations and a methodological approach to adapt levees to prepare for climate change in urban wave-exposed estuaries. We show that horizontal levees can support preparation for the projected impacts of sea level rise (SLR) while simultaneously providing new intertidal wetland habitat.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-025-99762-7","usgsCitation":"Taylor-Burns, R.M., Reguero, B.G., Barnard, P.L., and Beck, M.W., 2025, Nature-based solutions extend the lifespan of a regional levee system under climate change: Nature Scientific Reports, v. 15, no. 1, 16218, 11 p., https://doi.org/10.1038/s41598-025-99762-7.","productDescription":"16218, 11 p.","ipdsId":"IP-162956","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-025-99762-7","text":"Publisher Index Page"},{"id":485822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.52930652577294,\n 37.836817243666715\n ],\n [\n -122.52930652577294,\n 37.38775011750823\n ],\n [\n -121.90448236358971,\n 37.38775011750823\n ],\n [\n -121.90448236358971,\n 37.836817243666715\n ],\n [\n -122.52930652577294,\n 37.836817243666715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2025-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor-Burns, Rae M. 0000-0003-4963-6643","orcid":"https://orcid.org/0000-0003-4963-6643","contributorId":312507,"corporation":false,"usgs":false,"family":"Taylor-Burns","given":"Rae","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":936805,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reguero, Borja G. 0000-0001-5526-7157","orcid":"https://orcid.org/0000-0001-5526-7157","contributorId":193831,"corporation":false,"usgs":false,"family":"Reguero","given":"Borja","email":"","middleInitial":"G.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":936807,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":936806,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beck, Michael W.","contributorId":259298,"corporation":false,"usgs":false,"family":"Beck","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":true,"id":936808,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70271374,"text":"70271374 - 2025 - Global methane budget 2000-2020","interactions":[],"lastModifiedDate":"2025-09-10T14:32:19.040545","indexId":"70271374","displayToPublicDate":"2025-05-09T09:24:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1426,"text":"Earth System Science Data","active":true,"publicationSubtype":{"id":10}},"title":"Global methane budget 2000-2020","docAbstract":"Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. CH4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2), and both emissions and atmospheric concentrations of CH4 have continued to increase since 2007 after a temporary pause. The relative importance of CH4 emissions compared to those of CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020.
The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH4 yr−1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr−1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches.
For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr−1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr−1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr−1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr−1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH4 yr−1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr−1) larger global emissions (669 Tg CH4 yr−1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr−1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters.
The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH4 yr−1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty (∼ 25 %). The total sink of CH4 is estimated at 633 [507–796] Tg CH4 yr−1 by the bottom-up approaches and at 554 [550–567] Tg CH4 yr−1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified.
For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH4 yr−1 in the top-down budget and 211 [195–231] Tg CH4 yr−1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH4 yr−1 in the top-down budget and 120 [117–125] Tg CH4 yr−1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH4 yr−1 in the top-down budget and 28 [21–39] Tg CH4 yr−1 in the bottom-up budget.
We identify five major priorities for improving the CH4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH4 observations at local (e.g. FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.
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State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":948309,"contributorType":{"id":1,"text":"Authors"},"rank":66},{"text":"Zhu, Qing","contributorId":260547,"corporation":false,"usgs":false,"family":"Zhu","given":"Qing","affiliations":[],"preferred":false,"id":948310,"contributorType":{"id":1,"text":"Authors"},"rank":67},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false},{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false}],"preferred":false,"id":948311,"contributorType":{"id":1,"text":"Authors"},"rank":68},{"text":"Zhuang, Qianlai","contributorId":207137,"corporation":false,"usgs":false,"family":"Zhuang","given":"Qianlai","email":"","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":948312,"contributorType":{"id":1,"text":"Authors"},"rank":69}]}} ,{"id":70267494,"text":"70267494 - 2025 - Small waterbodies of large conservation concern: Towards an integrated approach to more accurately measuring surface water dynamics","interactions":[],"lastModifiedDate":"2025-05-27T14:15:43.711544","indexId":"70267494","displayToPublicDate":"2025-05-09T09:04:33","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Small waterbodies of large conservation concern: Towards an integrated approach to more accurately measuring surface water dynamics","docAbstract":"Millions of small waterbodies are dispersed throughout the middle of the North American continent, and billions of dollars have been invested to conserve, restore, and manage these waterbodies in the 20th and 21st centuries. Small waterbody conservation has been supported by different stakeholders aiming at improving water quality, enhancing floodwater storage, and supporting migratory bird breeding habitat. Conservation agencies are using hydrological and biological monitoring, modeling, and mapping to adaptively manage small waterbodies in the face of stressors such as invasive species and climate change. As remote sensing estimates of small waterbody surface water extent have become easier to access, understanding the capabilities and limitations of using remote sensing, especially in areas lacking surface water monitoring, is important for conservation decision making. Here, we used in situ monitoring and process-based hydrological modeling to explore remote sensing accuracy, especially related to waterbody size, emergent aquatic vegetation cover, and climatic conditions. Overall, we found that the accuracy of satellite and aerial imagery surface water mapping approaches vastly decreased for waterbodies smaller than 2 ha. We also found emergent vegetation could be masking surface water in waterbodies larger than 2 ha and that accuracy of some remote sensing estimates may decrease during wetter climatic periods. These results indicate that sensors commonly used for surface water applications alone may not be able to accurately detect small waterbody surface water, which supports the need for combining monitoring and modeling to understand how small waterbodies may respond to future changes in climate and land use.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2025.113525","usgsCitation":"McKenna, O.P., Lothspeich, A., Vacek, S., MacDonald, D., Eash, J., Vanderhoof, M.K., McCulloch, E., Ross, C., Sabrina, S., and Knight, J., 2025, Small waterbodies of large conservation concern: Towards an integrated approach to more accurately measuring surface water dynamics: Ecological Indicators, v. 175, 113525, 13 p., https://doi.org/10.1016/j.ecolind.2025.113525.","productDescription":"113525, 13 p.","ipdsId":"IP-156979","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488103,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2025.113525","text":"Publisher Index Page"},{"id":486573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Nelson Lake Waterfowl Protection Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.298889,\n 45.492\n ],\n [\n -95.298889,\n 45.4889\n ],\n [\n -95.295,\n 45.4889\n ],\n [\n -95.295,\n 45.492\n ],\n [\n -95.298889,\n 45.492\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"175","noUsgsAuthors":false,"publicationDate":"2025-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":938404,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lothspeich, Audrey Claire 0000-0002-5460-6142","orcid":"https://orcid.org/0000-0002-5460-6142","contributorId":355935,"corporation":false,"usgs":true,"family":"Lothspeich","given":"Audrey Claire","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":938405,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vacek, Sara","contributorId":178445,"corporation":false,"usgs":false,"family":"Vacek","given":"Sara","email":"","affiliations":[],"preferred":false,"id":938406,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MacDonald, Dawn","contributorId":355936,"corporation":false,"usgs":false,"family":"MacDonald","given":"Dawn","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":938407,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eash, Josh D.","contributorId":267175,"corporation":false,"usgs":false,"family":"Eash","given":"Josh D.","affiliations":[{"id":55428,"text":"U.S. Fish and Wildlife Service, 5600 American Blvd. W., Bloomington, MN","active":true,"usgs":false}],"preferred":false,"id":938408,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":938409,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McCulloch, Elyssa C.","contributorId":355940,"corporation":false,"usgs":false,"family":"McCulloch","given":"Elyssa C.","affiliations":[{"id":84869,"text":"Formerly - USGS NPWRC","active":true,"usgs":false}],"preferred":false,"id":938410,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ross, Caryn D.","contributorId":355942,"corporation":false,"usgs":false,"family":"Ross","given":"Caryn D.","affiliations":[{"id":84869,"text":"Formerly - USGS NPWRC","active":true,"usgs":false}],"preferred":false,"id":938411,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sabrina, Sadia","contributorId":355943,"corporation":false,"usgs":false,"family":"Sabrina","given":"Sadia","affiliations":[{"id":84869,"text":"Formerly - USGS NPWRC","active":true,"usgs":false}],"preferred":false,"id":938412,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Knight, Joseph F.","contributorId":355944,"corporation":false,"usgs":false,"family":"Knight","given":"Joseph F.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":938413,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70266225,"text":"tm7C29 - 2025 - Bayesian mapping of regionally grouped, sparse, univariate earth science data","interactions":[],"lastModifiedDate":"2025-05-12T15:26:22.543681","indexId":"tm7C29","displayToPublicDate":"2025-05-08T12:05:00","publicationYear":"2025","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C29","displayTitle":"Bayesian Mapping of Regionally Grouped, Sparse, Univariate Earth Science Data","title":"Bayesian mapping of regionally grouped, sparse, univariate earth science data","docAbstract":"Some earth science data are naturally grouped by region, and it is often desirable to map these data by region. However, if there are only a few samples within each region, then the map should be smoothed in an appropriate way to mitigate the problems that arise from having only a few samples. A smoothing algorithm based on a Bayesian hierarchical model is developed and presented in this report. This algorithm has several features that make it especially suitable for mapping earth science data: it can account for measurements that are censored, it can process multiple datasets with different measurement errors and different censoring thresholds, and it can calculate the uncertainty in any statistic that is mapped. The algorithm is demonstrated by mapping gold concentrations that are measured in streambed sediments in the Taylor Mountains quadrangle in southwestern Alaska.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section C: Computer programs in Book 7: Bayesian Mapping of Regionally Grouped, Sparse, Univariate Earth Science Data","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/tm7C29","programNote":"Mineral Resources Program","usgsCitation":"Ellefsen, K.J., Wang, B., and Goldman, M.A., 2025, Bayesian mapping of regionally grouped, sparse, univariate earth science data: U.S. Geological Survey Techniques and Methods, book 7, chap. C29, 20 p., https://doi.org/10.3133/tm7C29.","productDescription":"iv, 20 p.","onlineOnly":"Y","ipdsId":"IP-148248","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":485233,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c29/coverthb2.jpg"},{"id":485714,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/tm7C29/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"T and M 7C29"},{"id":485567,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/07/c29/tm7c29.xml"},{"id":485566,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/07/c29/images"},{"id":485235,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P14X4CKG","text":"USGS software release","linkHelpText":"Software for Bayesian mapping of regionally grouped, sparse, univariate earth science data (program BMRGSU)"},{"id":485234,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c29/tm7c29.pdf","text":"Report","size":"9.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7C29"}],"country":"United States","state":"Alaska","otherGeospatial":"Taylor Mountains quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159,\n 61\n ],\n [\n -159,\n 60\n ],\n [\n -156,\n 60\n ],\n [\n -156,\n 61\n ],\n [\n -159,\n 61\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
Emerging wildlife pathogens often display geographic variability due to landscape heterogeneity. Modeling approaches capable of learning complex, non-linear spatial dynamics of diseases are needed to rigorously assess and mitigate the effects of pathogens on wildlife health and biodiversity. We propose a novel machine learning (ML)-guided approach that leverages prior physical knowledge of ecological systems, using partial differential equations. We present our approach, taking advantage of the universal function approximation property of neural networks for flexible representation of the underlying dynamics of the geographic spread and growth of wildlife diseases. We demonstrate the benefits of our approach by comparing its forecasting power with commonly used methods and highlighting the obtained insights on disease dynamics. Additionally, we show the theoretical guarantees for the approximation error of our model. We illustrate the implementation of our ML-guided approach using data from white-nose syndrome (WNS) outbreaks in bat populations across the US. WNS is an infectious fungal disease responsible for significant declines in bat populations. Our results on WNS are useful for disease surveillance and bat conservation efforts. Our methods can be broadly used to assess the effects of environmental and anthropogenic drivers impacting wildlife health and biodiversity.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/eds.2025.3","usgsCitation":"Reyes, J., Oh, G., McGahan, I., Ma, T., Russell, R., Walsh, D.P., and Zhu, J., 2025, Learning complex spatial dynamics of wildlife diseases with machine learning-guided partial differential equations: Environmental Data Science, v. 4, e28, 23 p., https://doi.org/10.1017/eds.2025.3.","productDescription":"e28, 23 p.","ipdsId":"IP-160182","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490167,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/eds.2025.3","text":"Publisher Index Page"},{"id":489410,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":356170,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oh, Gina","contributorId":333634,"corporation":false,"usgs":false,"family":"Oh","given":"Gina","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian","contributorId":333637,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian","email":"","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ma, Ting Fung","contributorId":356257,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":938923,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Russell, Robin 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":333621,"corporation":false,"usgs":false,"family":"Russell","given":"Robin","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938924,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":938925,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zhu, Jun","contributorId":356177,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":938926,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266550,"text":"70266550 - 2025 - A partner-driven decision support model to inform the reintroduction of bull trout","interactions":[],"lastModifiedDate":"2025-05-09T15:23:24.336355","indexId":"70266550","displayToPublicDate":"2025-05-08T10:13:24","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"A partner-driven decision support model to inform the reintroduction of bull trout","docAbstract":"Assessments of species reintroductions involve a series of complex decisions that include human perspectives and ecological contexts. Here, we present a reintroduction assessment involving bull trout (Salvelinus confluentus) using a structured decision-making process. We approached this assessment by engaging partners representing public utilities, government agencies, and Tribes with shared interests in a potential reintroduction. These individuals identified objectives, decision alternatives, and ecological scenarios that were incorporated into a co-produced simulation-based model of potential reintroduction outcomes. The model included mathematical representations of habitat availability, life history expression, and assumptions regarding constraints on potential bull trout populations. Within each recipient stream, partners chose to explore a wide range of decision alternatives and simulated scenarios affecting reintroduction success. Results suggested that 1) reintroductions using eggs or adults were most optimal, 2) adding more individuals resulted in diminishing returns, 3) access to migratory habitat could improve success, and 4) the diversity of opportunities for life history expression led to improved reintroduction opportunities. In addition, modeled scenarios indicated some recipient streams consistently produced lower abundance of reintroduced bull trout. This work contributes a novel example to a growing portfolio of reintroduction assessments that may inform future conservation for bull trout and many other species facing similar challenges.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0323427","usgsCitation":"Benjamin, J.R., Neibauer, J., Anthony, H., Vazquez, J., Rawhouser, A., and Dunham, J., 2025, A partner-driven decision support model to inform the reintroduction of bull trout: PLoS ONE, v. 20, no. 5, e0323427, 17 p., https://doi.org/10.1371/journal.pone.0323427.","productDescription":"e0323427, 17 p.","ipdsId":"IP-172949","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":490112,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0323427","text":"Publisher Index Page"},{"id":485650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Chelan watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.49036241599774,\n 48.04634063295097\n ],\n [\n -120.37067822007936,\n 48.16552254778642\n ],\n [\n -120.75794320314404,\n 48.538515779248854\n ],\n [\n -120.80213597240349,\n 48.5303354671282\n ],\n [\n -121.11319724267128,\n 48.54549059352783\n ],\n [\n -121.18989609066836,\n 48.38742171862049\n ],\n [\n -121.0025710146728,\n 48.255137103181255\n ],\n [\n -120.49036241599774,\n 48.04634063295097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":936553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neibauer, Judith","contributorId":354836,"corporation":false,"usgs":false,"family":"Neibauer","given":"Judith","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":936554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anthony, Hugh","contributorId":354839,"corporation":false,"usgs":false,"family":"Anthony","given":"Hugh","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":936555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vazquez, Jose","contributorId":354841,"corporation":false,"usgs":false,"family":"Vazquez","given":"Jose","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":936556,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rawhouser, Ashley","contributorId":243429,"corporation":false,"usgs":false,"family":"Rawhouser","given":"Ashley","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":936557,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dunham, Jason 0000-0002-6268-0633","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":220078,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":936558,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70266306,"text":"70266306 - 2025 - Variability in hydrologic response to wildfire between snow zones in forested headwaters","interactions":[],"lastModifiedDate":"2025-05-15T15:08:04.001368","indexId":"70266306","displayToPublicDate":"2025-05-08T10:02:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Variability in hydrologic response to wildfire between snow zones in forested headwaters","docAbstract":"Rising temperatures and shifting fire regimes in the western United States are pushing fires upslope into areas of deep winter snowpack, where we have little knowledge of the likely hydrologic impacts of wildfire. We quantified differences in the timing and magnitude of stormflow responses to summer rainstorms among six catchments of varying levels of burn severity and seasonal snowpack cover for years 1–3 after the 2020 Cameron Peak fire. Our objectives were to (1) examine whether responsiveness, magnitude, and timing of stormflow responses to rainfall vary between burned and unburned catchments and between snow zones, and (2) identify the factors that affect these responses. We evaluated whether differences in storm hydrograph peak flow, total flow, stage rise, and lag to peak time differed by snow zone and burn category using generalised linear models. Additional predictors in these models are the maximum 60-min rainfall intensity for each storm, the cumulative potential water deficit prior to the storm, and the year post-fire. These models showed that the high snow zone (HSZ) has higher total stormflow than the low snow zone (LSZ), likely due to the higher soil moisture content in that area. In both snow zones, the biggest driver of the magnitude of the stormflow response was MI60. Burn category did not have a clear impact on stormflow response in the HSZ, but it did impact stage rise at the severely burned catchment in the LSZ. This was the only site that had widespread overland flow post-fire. These results demonstrate that the stormflow responses to fire vary between snow zones, indicating a need to account for elevation and snow persistence in post-fire risk assessments.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.70151","usgsCitation":"Miller, Q., Barnard, D.M., Sears, M., Hammond, J., and Kampf, S., 2025, Variability in hydrologic response to wildfire between snow zones in forested headwaters: Hydrological Processes, v. 39, no. 5, e70151, 16 p., https://doi.org/10.1002/hyp.70151.","productDescription":"e70151, 16 p.","ipdsId":"IP-172047","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":490124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.70151","text":"Publisher Index Page"},{"id":485996,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106,\n 41\n ],\n [\n -106,\n 40.333\n ],\n [\n -105,\n 40.333\n ],\n [\n -105,\n 41\n ],\n [\n -106,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Quinn","contributorId":354373,"corporation":false,"usgs":false,"family":"Miller","given":"Quinn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, David M 0000-0003-1877-3151","orcid":"https://orcid.org/0000-0003-1877-3151","contributorId":222833,"corporation":false,"usgs":false,"family":"Barnard","given":"David","email":"","middleInitial":"M","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":935515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sears, Megan","contributorId":354374,"corporation":false,"usgs":false,"family":"Sears","given":"Megan","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":935517,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kampf, Stephanie","contributorId":346221,"corporation":false,"usgs":false,"family":"Kampf","given":"Stephanie","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935518,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70267309,"text":"70267309 - 2025 - Leveraging detection uncertainty to estimate Renibacterium salmoninarum infection status among multiple tissues and assays","interactions":[],"lastModifiedDate":"2025-05-20T16:55:57.977539","indexId":"70267309","displayToPublicDate":"2025-05-08T09:45:45","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging detection uncertainty to estimate Renibacterium salmoninarum infection status among multiple tissues and assays","docAbstract":"Effective disease surveillance relies on accurate pathogen testing and robust prevalence estimates. Diagnostic specificity (DSp), the probability that an uninfected animal tests negative, is high when false positives are low. Diagnostic sensitivity (DSe) is the probability an infected animal tests positive; higher DSe means fewer false negatives. However, sensitivity and false negatives are harder to estimate without a \"gold standard\", an assay that can detect between 90 - 100% of true positive infections. Occupancy estimation of infection prevalence offers one solution by allowing for imperfect detection of the pathogen. Testing potentially infected tissues multiple times allows for the use of a Bayesian multistate occupancy model to estimate the probability of pathogen infection in tissues [Formula: see text] and detection probabilities [Formula: see text] for different assays. Using [Formula: see text] and [Formula: see text] from the posterior distribution, the conditional probability of detecting the pathogen can be modeled, allowing for the calculation of DSe. Renibacterium salmoninarum is a bacterial pathogen causing bacterial kidney disease among salmonid species and was the model pathogen we used to train our model. The current testing standard for salmonids combines initial screening for antibodies using direct fluorescent antibody test (DFAT) with polymerase chain reaction (PCR) confirmation to detect R. salmoninarum. However, detection of R. salmoninarum still varies between species, tissues, and assays. Here, a multi-state occupancy model was used to estimate detection probability among individual and dual kidney/liver infections with DFAT and qPCR in fish with an unknown infection status. Both assays produced false negatives, but qPCR had fewer than DFAT and a higher DSe. Infection state was often misclassified, but multiple surveys per individual or combining tissues for testing improved DSe for both assays.
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R.","contributorId":355584,"corporation":false,"usgs":false,"family":"Fetherman","given":"Eric R.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":937689,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huyvaert, Kathryn P.","contributorId":355585,"corporation":false,"usgs":false,"family":"Huyvaert","given":"Kathryn P.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937690,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drennan, John D.","contributorId":355587,"corporation":false,"usgs":false,"family":"Drennan","given":"John D.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":937691,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brock, Rebecca E.","contributorId":355589,"corporation":false,"usgs":false,"family":"Brock","given":"Rebecca E.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937692,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yeatts, Brooke","contributorId":355591,"corporation":false,"usgs":false,"family":"Yeatts","given":"Brooke","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":937693,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937694,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266525,"text":"70266525 - 2025 - Marginalizing time in habitat selection and species distribution models improves inference","interactions":[],"lastModifiedDate":"2025-05-09T15:11:57.95249","indexId":"70266525","displayToPublicDate":"2025-05-08T08:01:21","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Marginalizing time in habitat selection and species distribution models improves inference","docAbstract":"Aim
Recent methodological advances for studying how animals move and use space with telemetry data have focused on fine-scale, more mechanistic inference. However, in many cases, researchers and managers remain interested in larger scale questions regarding species distribution and habitat use across study areas, landscapes, or seasonal ranges. Point processes offer a unified framework for many methods applied in studies of species distribution and resource selection; however, challenges remain in terms of dealing with temporal autocorrelation common in many types of telemetry data collected from animal locations.
Innovation
Space–time point processes (STPPs) have a unique property, in that marginalising time offers a connection between individual animal movement and broader point processes, yet this property has seen little attention in both statistical and applied research. In this paper, we first present some of the details of this marginalisation property and methods for applying marginalised STPPs (mSTTPs) to autocorrelated telemetry data and then apply a mSTTP in a case study on the summer space use and habitat selection of female caribou (Rangifer tarandus) in Denali National Park and Preserve, Alaska.
Main Conclusions
The case study demonstrated that an mSTPP approach can improve inference over other commonly used methods in terms of its ability to account for temporal autocorrelation and offers greater precision in parameter estimates and improved predictions of space use. As this method fits conveniently into the existing point process frameworks, it offers a practical solution to dealing with temporal autocorrelation inherent to many types of telemetry data when research questions center around broader scale patterns of animal habitat selection and space use.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.70028","usgsCitation":"Eisaguirre, J.M., Adams, L., Borg, B., and Johnson, H.E., 2025, Marginalizing time in habitat selection and species distribution models improves inference: Diversity and Distributions, v. 31, no. 5, e70028, 9 p., https://doi.org/10.1111/ddi.70028.","productDescription":"e70028, 9 p.","ipdsId":"IP-170546","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":488294,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.70028","text":"Publisher Index Page"},{"id":485648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Denali National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -151.66979451782657,\n 64.15039809953939\n ],\n [\n -151.66979451782657,\n 63.132768852129516\n ],\n [\n -147.1767819339525,\n 63.132768852129516\n ],\n [\n -147.1767819339525,\n 64.15039809953939\n ],\n [\n -151.66979451782657,\n 64.15039809953939\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Eisaguirre, Joseph Michael 0000-0002-0450-8472","orcid":"https://orcid.org/0000-0002-0450-8472","contributorId":301980,"corporation":false,"usgs":true,"family":"Eisaguirre","given":"Joseph","email":"","middleInitial":"Michael","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":936464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Layne G. 0000-0001-6212-2896 ladams@usgs.gov","orcid":"https://orcid.org/0000-0001-6212-2896","contributorId":2776,"corporation":false,"usgs":true,"family":"Adams","given":"Layne G.","email":"ladams@usgs.gov","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":936465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Borg, Bridget","contributorId":173862,"corporation":false,"usgs":false,"family":"Borg","given":"Bridget","affiliations":[{"id":27306,"text":"Denali Natil Park and Preserve","active":true,"usgs":false}],"preferred":false,"id":936466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":936467,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266848,"text":"70266848 - 2025 - Organic matter composition versus microbial source: Controls on carbon loss from fen wetland and permafrost soils","interactions":[],"lastModifiedDate":"2025-05-13T15:35:55.063862","indexId":"70266848","displayToPublicDate":"2025-05-07T10:26:06","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9326,"text":"JGR Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Organic matter composition versus microbial source: Controls on carbon loss from fen wetland and permafrost soils","docAbstract":"Wetland and permafrost soils contain some of Earth's largest reservoirs of organic carbon, and these stores are threatened by rapid warming across the Arctic. Nearly half of northern wetlands are affected by permafrost. As these ecosystems warm, the cycling of dissolved organic matter (DOM) and the opportunities for microbial degradation are changing. This is particularly evident as the relationship between wetland and permafrost DOM dynamics evolves, especially with the introduction of permafrost-derived DOM into wetland environments. Thus, understanding the interplay of DOM composition and microbial communities from wetlands and permafrost is critical to predicting the impact of released carbon on global carbon cycling. As little is understood about the interactions between wetland active layer and permafrost-derived sources as they intermingle, we conducted experimental bioincubations of mixtures of DOM and microbial communities from two fen wetland depths (shallow: 0–15 cm, and deep: 15–30 cm) and two ages of permafrost soil (Holocene and Pleistocene). We found that the source of microbial inoculum was not a significant driver of dissolved organic carbon (DOC) degradation across treatments; rather, DOM source and specifically, DOM molecular composition, controlled the rate of DOC loss over 100 days of bioincubations. DOC loss across all treatments was negatively correlated with modified aromaticity index, O/C, and the relative abundance of condensed aromatic and polyphenolic formula, and positively correlated with H/C and the relative abundance of aliphatic and peptide-like formula. Pleistocene permafrost-derived DOC exhibited ∼70% loss during the bioincubation driven by its initial molecular-level composition, highlighting its high bioavailability irrespective of microbial source.
","language":"English","publisher":"Wiley","doi":"10.1029/2024JG008445","usgsCitation":"Starr, S., Wickland, K., Kellerman, A.M., McKenna, A.M., Kurek, M., Miller, A., Karsaras, A., Douglas, T.A., Mackelprang, R., Shade, A., and Spencer, R., 2025, Organic matter composition versus microbial source: Controls on carbon loss from fen wetland and permafrost soils: JGR Biogeosciences, v. 130, no. 5, e2024JG008445, 17 p., https://doi.org/10.1029/2024JG008445.","productDescription":"e2024JG008445, 17 p.","ipdsId":"IP-162667","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":488194,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jg008445","text":"Publisher Index Page"},{"id":485821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Fairbanks","otherGeospatial":"Big Trail Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.87084295958078,\n 64.9276059180502\n ],\n [\n -147.87084295958078,\n 64.9051043539906\n ],\n [\n -147.7757330370365,\n 64.9051043539906\n ],\n [\n -147.7757330370365,\n 64.9276059180502\n ],\n [\n -147.87084295958078,\n 64.9276059180502\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"130","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Starr, Sommer F.","contributorId":355129,"corporation":false,"usgs":false,"family":"Starr","given":"Sommer F.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":936901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wickland, Kimberly 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University","active":true,"usgs":false}],"preferred":false,"id":936904,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kurek, Martin M.","contributorId":355131,"corporation":false,"usgs":false,"family":"Kurek","given":"Martin M.","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":936905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Aubrey","contributorId":355134,"corporation":false,"usgs":false,"family":"Miller","given":"Aubrey","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":936906,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karsaras, Ariana","contributorId":355137,"corporation":false,"usgs":false,"family":"Karsaras","given":"Ariana","affiliations":[{"id":7092,"text":"Florida State University","active":true,"usgs":false}],"preferred":false,"id":936907,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Douglas, Thomas A. 0000-0003-1314-1905","orcid":"https://orcid.org/0000-0003-1314-1905","contributorId":64553,"corporation":false,"usgs":false,"family":"Douglas","given":"Thomas","email":"","middleInitial":"A.","affiliations":[{"id":33087,"text":"Cold Regions Research and Engineering Laboratory","active":true,"usgs":false}],"preferred":true,"id":936908,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mackelprang, Rachel","contributorId":200882,"corporation":false,"usgs":false,"family":"Mackelprang","given":"Rachel","email":"","affiliations":[{"id":7080,"text":"California State University, Northridge","active":true,"usgs":false}],"preferred":false,"id":936909,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shade, Ashley L.","contributorId":355140,"corporation":false,"usgs":false,"family":"Shade","given":"Ashley 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margin-spanning megasplay fault at the Cascadia Subduction Zone","docAbstract":"It has been previously proposed that a megasplay fault within the Cascadia accretionary wedge, spanning from offshore Vancouver Island to Oregon, has the potential to slip during a future Cascadia subduction zone earthquake. This hypothetical fault has major implications for tsunami size and arrival times and is included in disaster-planning scenarios currently in use in the region. This hypothesis is evaluated in this study using CASIE21 deep-penetrating and U.S. Geological Survey high-resolution seismic reflection profiles. We map changes in wedge structural style and seismic character to identify the inner-outer wedge transition zone where a megasplay fault has been previously hypothesized to exist and evaluate evidence for active faulting within this zone. Our results indicate that there is not an active, through-going megasplay fault in Cascadia, but instead, the structure and activity of faulting at the inner-outer wedge transition zone is highly variable and segmented along strike, consistent with the segmentation of other physical and mechanical properties in Cascadia. Wedge sedimentation, plate dip, and subducting topography are proposed to play a major role in controlling megasplay fault development and evolution. Incorporating updated megasplay fault location, geometry, and activity into modeling of Cascadia earthquakes and tsunamis could help better constrain associated hazards.
","language":"English","publisher":"McGill University Libraries","doi":"10.26443/seismica.v2i4.1477","usgsCitation":"Lucas, M.C., Ledeczi, A., Tobin, H., Carbotte, S.M., Watt, J., Han, S., Boston, B., and Jiang, D., 2025, No evidence for an active margin-spanning megasplay fault at the Cascadia Subduction Zone: Seismica, v. 2, no. 4, 1477, 28 p., https://doi.org/10.26443/seismica.v2i4.1477.","productDescription":"1477, 28 p.","ipdsId":"IP-171867","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488293,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.26443/seismica.v2i4.1477","text":"Publisher Index Page"},{"id":485645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, Oregon, Washington","otherGeospatial":"Cascadia subduction zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -129.8333,\n 49.5\n ],\n [\n -129.8333,\n 42\n ],\n [\n -123,\n 42\n ],\n [\n -123,\n 49.5\n ],\n [\n -129.8333,\n 49.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"4","noUsgsAuthors":false,"publicationDate":"2025-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Lucas, Madeleine C.","contributorId":263451,"corporation":false,"usgs":false,"family":"Lucas","given":"Madeleine","email":"","middleInitial":"C.","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":936468,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ledeczi, Anna M.","contributorId":354806,"corporation":false,"usgs":false,"family":"Ledeczi","given":"Anna M.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":936469,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tobin, Harold J.","contributorId":354808,"corporation":false,"usgs":false,"family":"Tobin","given":"Harold J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":936470,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carbotte, Suzanne M.","contributorId":339692,"corporation":false,"usgs":false,"family":"Carbotte","given":"Suzanne","email":"","middleInitial":"M.","affiliations":[{"id":28041,"text":"Lamont-Doherty Earth Observatory, Columbia University","active":true,"usgs":false}],"preferred":false,"id":936471,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Watt, Janet 0000-0002-4759-3814","orcid":"https://orcid.org/0000-0002-4759-3814","contributorId":221271,"corporation":false,"usgs":true,"family":"Watt","given":"Janet","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":936472,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Han, Shuoshuo","contributorId":339693,"corporation":false,"usgs":false,"family":"Han","given":"Shuoshuo","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":936473,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boston, Brian","contributorId":252937,"corporation":false,"usgs":false,"family":"Boston","given":"Brian","email":"","affiliations":[{"id":40272,"text":"Japan Agency for Marine-Earth Science and Technology","active":true,"usgs":false}],"preferred":false,"id":936474,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jiang, D.","contributorId":354811,"corporation":false,"usgs":false,"family":"Jiang","given":"D.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":936475,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70270858,"text":"70270858 - 2025 - Lessons in business recovery following the 2023 Kahramanmaraş earthquake sequence, Türkiye informed by women entrepreneurs","interactions":[],"lastModifiedDate":"2025-08-26T15:34:04.782936","indexId":"70270858","displayToPublicDate":"2025-05-07T08:27:10","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Lessons in business recovery following the 2023 Kahramanmaraş earthquake sequence, Türkiye informed by women entrepreneurs","docAbstract":"On 6 February 2023, Southern Türkiye was hit by devastating earthquakes, directly affecting over 14 million people in 11 cities, causing more than 50,000 deaths and the destruction of more than 800,000 buildings. This article goes beyond the physical damage imposed by the catastrophe to discuss the effects of the earthquakes on the operations of women-owned businesses. The mixed-method study with entrepreneurs belonging to a women’s business association operating in a moderately disrupted part of the region explores their struggles and recovery expectations. Thirty-five questionnaires were analyzed to identify the reasons for business closure, challenges, and needs faced in the post-disaster period and their recovery strategies. In addition, 23 entrepreneurs participated in roundtable discussions to provide a broader context to their responses to survey topics as well as lessons learned. Across both the survey and roundtables, while many respondents reported minor physical damage to their building, they also experienced financial and personal challenges from disruption to equipment, infrastructure, services, supply chains, institutional decisions, employee well-being, and customer base. Many used their business resources and personal savings to assist employees and others in the community. The women entrepreneurs often felt their recovery needs were ignored by government and private relief organizations and encountered barriers to receiving assistance from public and private institutions. Organizing together as women in business, even informally, provided mutual support during the crisis and recovery periods and catalyzed their role in support of their communities. The results illuminate functional community recovery as a balance of recovery of built infrastructure functionality and recovery of the broader social and economic fabric of the community.
","language":"English","publisher":"Sage","doi":"10.1177/87552930251330921","usgsCitation":"Orhan, E., Wein, A., Kroll, C., and Fung, J., 2025, Lessons in business recovery following the 2023 Kahramanmaraş earthquake sequence, Türkiye informed by women entrepreneurs: Earthquake Spectra, v. 41, no. 3, p. 1910-1940, https://doi.org/10.1177/87552930251330921.","productDescription":"31 p.","startPage":"1910","endPage":"1940","ipdsId":"IP-166964","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":494904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","otherGeospatial":"southern Turkey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 34.331004781765444,\n 37.920957650366006\n ],\n [\n 34.331004781765444,\n 36.747538006336825\n ],\n [\n 40.125738345242354,\n 36.747538006336825\n ],\n [\n 40.125738345242354,\n 37.920957650366006\n ],\n [\n 34.331004781765444,\n 37.920957650366006\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2025-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Orhan, Ezgi","contributorId":360575,"corporation":false,"usgs":false,"family":"Orhan","given":"Ezgi","affiliations":[{"id":86042,"text":"Cankaya University, Turkiye","active":true,"usgs":false}],"preferred":false,"id":947222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wein, Anne 0000-0002-5516-3697 awein@usgs.gov","orcid":"https://orcid.org/0000-0002-5516-3697","contributorId":589,"corporation":false,"usgs":true,"family":"Wein","given":"Anne","email":"awein@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":947223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kroll, Cynthia","contributorId":360576,"corporation":false,"usgs":false,"family":"Kroll","given":"Cynthia","affiliations":[{"id":66280,"text":"Cynthia Kroll Consulting","active":true,"usgs":false}],"preferred":false,"id":947224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fung, Juan","contributorId":360577,"corporation":false,"usgs":false,"family":"Fung","given":"Juan","affiliations":[{"id":25356,"text":"National Institute of Standards and Technology","active":true,"usgs":false}],"preferred":false,"id":947225,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70267683,"text":"70267683 - 2025 - Interpreting a sudden population decline in a long-lived species (Malaclemys terrapin rhizophorarum)","interactions":[],"lastModifiedDate":"2025-05-29T15:03:15.907361","indexId":"70267683","displayToPublicDate":"2025-05-07T07:57:28","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Interpreting a sudden population decline in a long-lived species (Malaclemys terrapin rhizophorarum)","docAbstract":"Long-term ecological studies are critical for providing insight into population dynamics and detecting population declines, particularly for species of conservation concern. However, spatiotemporal variation and logistical challenges make the identification of sudden population declines difficult. We conducted an in-water capture-mark-recapture study of mangrove diamond-backed terrapins (Malaclemys terrapin rhizophorarum) within Big Sable Creek, in Everglades National Park, Florida. We used an 18-year dataset (2001 to 2019) incorporating year, sex, hurricane occurrence, and sampling effort to estimate survival using Cormack–Jolly–Seber (CJS) models in Program Mark. Annual survivorship estimates were high from 2001 to 2003 for both sexes (91%–96%) and variable from 2006 to 2014 (77%–92%). Beginning in 2015, survival estimates exhibited a steeper decline (females: 65%, males 75%), and dropped to below 36% by 2018. Because the driver of this apparent population decline is unknown, we created a population projection matrix and used model-estimated annual survival to simulate annual terrapin population size. We then generated competing scenarios of low survival at various age classes to attempt to reproduce a simulated decline mirroring what we observed from our capture data. A scenario of low adult survival (75%–85%) from 2012 to 2018, possibly in conjunction with no reproduction after 2010, provides estimates of abundance that appear to match simulated annual population size and may indicate that adult emigration/human removal or a drastic drop in recruitment could be responsible for the apparent decline in survival. We explore reasons for this apparent decline and highlight difficulties common to long-term studies that may influence how declines are interpreted.
","language":"English","publisher":"British Ecological Society","doi":"10.1002/ece3.71347","usgsCitation":"Guzy, J.C., Smith, B., Denton, M., Cherkiss, M., Roche, D., Crowder, A., and Hart, K., 2025, Interpreting a sudden population decline in a long-lived species (Malaclemys terrapin rhizophorarum): Ecology and Evolution, v. 15, no. 5, e71347, 16 p., https://doi.org/10.1002/ece3.71347.","productDescription":"e71347, 16 p.","ipdsId":"IP-168394","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":488447,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.71347","text":"Publisher Index Page"},{"id":486731,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Cape Sable, Everglades National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.29674951955056,\n 25.648529217591914\n ],\n [\n -81.29674951955056,\n 25.09922085696259\n ],\n [\n -80.79518175403075,\n 25.09922085696259\n ],\n [\n -80.79518175403075,\n 25.648529217591914\n ],\n [\n -81.29674951955056,\n 25.648529217591914\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"5","noUsgsAuthors":false,"publicationDate":"2025-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Guzy, Jacquelyn C. 0000-0003-2648-398X","orcid":"https://orcid.org/0000-0003-2648-398X","contributorId":288520,"corporation":false,"usgs":true,"family":"Guzy","given":"Jacquelyn","email":"","middleInitial":"C.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Brian J. 0000-0002-0531-0492","orcid":"https://orcid.org/0000-0002-0531-0492","contributorId":139672,"corporation":false,"usgs":false,"family":"Smith","given":"Brian J.","affiliations":[{"id":12876,"text":"Cherokee Nation Technology Solutions","active":true,"usgs":false}],"preferred":false,"id":938537,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Denton, Mathew 0000-0002-1024-3722","orcid":"https://orcid.org/0000-0002-1024-3722","contributorId":210504,"corporation":false,"usgs":true,"family":"Denton","given":"Mathew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938538,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cherkiss, Michael 0000-0002-7802-6791","orcid":"https://orcid.org/0000-0002-7802-6791","contributorId":218466,"corporation":false,"usgs":true,"family":"Cherkiss","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938539,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roche, David 0000-0002-3329-2746 droche@usgs.gov","orcid":"https://orcid.org/0000-0002-3329-2746","contributorId":204332,"corporation":false,"usgs":true,"family":"Roche","given":"David","email":"droche@usgs.gov","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938540,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crowder, Andrew G.","contributorId":355985,"corporation":false,"usgs":false,"family":"Crowder","given":"Andrew G.","affiliations":[{"id":84891,"text":"Xylem Analytics","active":true,"usgs":false}],"preferred":false,"id":938541,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":218324,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":938542,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70266883,"text":"70266883 - 2025 - Long-term patterns in growth of White Sturgeon in the Sacramento-San Joaquin River basin, California.","interactions":[],"lastModifiedDate":"2025-05-15T13:11:35.40062","indexId":"70266883","displayToPublicDate":"2025-05-06T09:26:29","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18328,"text":"Frontiers in Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Long-term patterns in growth of White Sturgeon in the Sacramento-San Joaquin River basin, California.","docAbstract":"Introduction: The Sacramento-San Joaquin River system (SSJ) of California includes both riverine, delta, and estuarine habitats and is among the most modified aquatic ecosystems in the United States. Water development projects in the system are associated with declines of many native species, including White Sturgeon Acipenser transmontanus.
Methods: We used White Sturgeon pectoral fin rays collected from 1983 to 2016 throughout the SSJ to assess long-term changes in growth and associations with thermal and hydrological conditions (i.e., temperature, discharge, salinity). Age and growth were estimated from 1,897 White Sturgeon varying in fork length from 25 to 210 cm and from age 0 to 33.
Results: Age structure varied through time with the oldest fish generally sampled during the mid-1980s. Growth of White Sturgeon in 1951–1970 was slower than growth of fish in 1971–1990 and 1991–2012. Growth of White Sturgeon during 1991–2012 was ~10% higher than during other time periods.
Discussion: Little variation in growth was explained by environmental covariates, suggesting that annual growth was likely influenced by factors not measured in our study. Alternatively, population structure and movement behavior of White Sturgeon in the SSJ may be such that the scale (i.e., spatial or temporal) of available habitat covariates was mismatched to the scale at which growth of White Sturgeon responds. Increased growth in recent times may be partly due to density-dependent processes in association with substantial declines in White Sturgeon population abundance over the last several decades. This research provides important information on long-term patterns in growth that contributes to the conservation and management of White Sturgeon in the SSJ and beyond.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/ffwsc.2025.1577065","usgsCitation":"Quist, M.C., Blackburn, S., Ulaski, M., and Jackson, Z., 2025, Long-term patterns in growth of White Sturgeon in the Sacramento-San Joaquin River basin, California.: Frontiers in Freshwater Science, v. 3, 1577065, 10 p., https://doi.org/10.3389/ffwsc.2025.1577065.","productDescription":"1577065, 10 p.","ipdsId":"IP-175717","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488907,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/ffwsc.2025.1577065","text":"Publisher Index Page"},{"id":485931,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.79850686101548,\n 38.755876798924476\n ],\n [\n -122.64621142922209,\n 38.755876798924476\n ],\n [\n -122.64621142922209,\n 37.31156292678925\n ],\n [\n -120.79850686101548,\n 37.31156292678925\n ],\n [\n -120.79850686101548,\n 38.755876798924476\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","noUsgsAuthors":false,"publicationDate":"2025-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":937030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blackburn, Shannon","contributorId":338596,"corporation":false,"usgs":false,"family":"Blackburn","given":"Shannon","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":937031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ulaski, Marta","contributorId":280108,"corporation":false,"usgs":false,"family":"Ulaski","given":"Marta","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":937032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Zachary","contributorId":338597,"corporation":false,"usgs":false,"family":"Jackson","given":"Zachary","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":937033,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266492,"text":"70266492 - 2025 - Using long-term ecological datasets to unravel the impacts of short-term meteorological disturbances on phytoplankton communities","interactions":[],"lastModifiedDate":"2025-05-08T14:12:51.567682","indexId":"70266492","displayToPublicDate":"2025-05-06T09:07:04","publicationYear":"2025","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Using long-term ecological datasets to unravel the impacts of short-term meteorological disturbances on phytoplankton communities","docAbstract":"Extreme meteorological events such as storms are increasing in frequency and intensity, but our knowledge of their impacts on aquatic ecosystems and emergent system properties is limited. Understanding the ecological impacts of storms on the dynamics of primary producers remains a challenge that needs to be addressed to assess the vulnerability of freshwater ecosystems to extreme weather conditions and climate change.
One promising approach to gain insights into storm impacts on phytoplankton community dynamics is to analyse long-term monitoring datasets. However, such an approach requires disentangling the impacts of short-term meteorological disturbances from the effects of the seasonal trajectories of meteorological conditions. To this end, we applied boosted regression tree models to phytoplankton time series from eight relatively large lakes on four continents, coupled with a procedure adapted to detect and quantify rare events.
Overall, the patterns and potential drivers we identified provide important insights into the responses of lakes to short-term meteorological events and highlight differences in the response of phytoplankton communities according to lake morphological characteristics. Our results indicated that deepened thermoclines and lake-specific combinations of drivers describing altered thermal structures caused deviations from the typical trajectories of seasonal phytoplankton succession. For shallow polymictic lakes, shifts in phytoplankton succession also depended on changes in light availability.
Overall, our study highlights the value of long-term monitoring to improve our understanding of phytoplankton sensitivity to short-term meteorological disturbances.