The sagebrush biome within the western United States has been reshaped by disturbances, management, and changing environmental conditions. As a result, sagebrush cover and configuration have varied over space and time, influencing processes and species that rely on contiguous, connected sagebrush. Previous studies have documented changes in sagebrush cover, but we know little about how the connectivity of sagebrush has changed over time and across the sagebrush biome. We investigated temporal connectivity patterns for sagebrush using a time series (1985–2020) of fractional sagebrush cover and used an omnidirectional circuit algorithm to assess the density of connections among areas with abundant sagebrush. By comparing connectivity patterns over time, we found that most of the biome experienced moderate change; the amount and type of change varied spatially, indicating that areas differ in the trend direction and magnitude of change. Two different types of designated areas of conservation and management interest had relatively high proportions of stable, high-connectivity patterns over time and stable connectivity trends on average. These results provide ecological information on sagebrush connectivity persistence across spatial and temporal scales that can support targeted actions to address changing structural connectivity and to maintain functioning, connected ecosystems.
","language":"English","publisher":"MDPI","doi":"10.3390/land12061176","usgsCitation":"Buchholtz, E.K., O’Donnell, M.S., Heinrichs, J., and Aldridge, C.L., 2023, Temporal patterns of structural sagebrush connectivity from 1985 to 2020: Land, v. 12, no. 6, 1176, 13 p., https://doi.org/10.3390/land12061176.","productDescription":"1176, 13 p.","ipdsId":"IP-149528","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":443212,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/land12061176","text":"Publisher Index Page"},{"id":435296,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ED3OHH","text":"USGS data release","linkHelpText":"Sagebrush structural connectivity yearly and temporal trends based on RCMAP sagebrush products, biome-wide from 1985 to 2020"},{"id":420238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.96080474168178,\n 38.705700643079126\n ],\n [\n -115.78518256667087,\n 34.921896948006165\n ],\n [\n -114.61161113478016,\n 36.338264497018244\n ],\n [\n -113.767664676413,\n 35.05005751349394\n ],\n [\n -112.65446218611822,\n 34.49061043886182\n ],\n [\n -112.06315236527561,\n 35.73094134678898\n ],\n [\n -109.54598209325795,\n 34.19549893805224\n ],\n [\n -106.89665758360098,\n 32.69494633725995\n ],\n [\n -104.71617700706355,\n 36.11077455150861\n ],\n [\n -104.55021627544596,\n 39.39791391643246\n ],\n [\n -102.74204247363139,\n 41.178395117310885\n ],\n [\n -101.91586719976846,\n 44.15607703673757\n ],\n [\n -102.57953583556366,\n 47.8714554984052\n ],\n [\n -104.41540160422653,\n 47.87078532552721\n ],\n [\n -105.32574816853759,\n 47.678443090909866\n ],\n [\n -105.58191871342933,\n 48.88758293756308\n ],\n [\n -111.59184312773306,\n 49.007633669026006\n ],\n [\n -111.43229696003331,\n 46.72141563224213\n ],\n [\n -113.85176301374159,\n 49.08872019311082\n ],\n [\n -115.1516486551431,\n 46.36810451906729\n ],\n [\n -115.50149965627025,\n 44.04779080867547\n ],\n [\n -118.81497419811586,\n 48.948530483008625\n ],\n [\n -120.40779607271287,\n 48.96193708211112\n ],\n [\n -121.56614731481,\n 44.386012657400386\n ],\n [\n -122.69235641752775,\n 42.28566289188129\n ],\n [\n -122.1452674395685,\n 39.40569110412574\n ],\n [\n -120.96080474168178,\n 38.705700643079126\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Buchholtz, Erin K. 0000-0002-1985-9531","orcid":"https://orcid.org/0000-0002-1985-9531","contributorId":300162,"corporation":false,"usgs":true,"family":"Buchholtz","given":"Erin","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881230,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":140876,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881231,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":881232,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":881233,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70244058,"text":"ofr20231023 - 2023 - Calibration of the Trinity River Stream Salmonid Simulator (S3) with extension to the Klamath River, California, 2006–17","interactions":[],"lastModifiedDate":"2023-09-18T19:50:01.76388","indexId":"ofr20231023","displayToPublicDate":"2023-06-02T06:56:34","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1023","displayTitle":"Calibration of the Trinity River Stream Salmonid Simulator (S3) with Extension to the Klamath River, California, 2006–17","title":"Calibration of the Trinity River Stream Salmonid Simulator (S3) with extension to the Klamath River, California, 2006–17","docAbstract":"The Trinity River is managed in two sections: (1) the upper 64-kilometer (km) “restoration reach” downstream from Lewiston Dam and (2) the 120-km lower Trinity River downstream from the restoration reach. The Stream Salmonid Simulator (S3) has been previously constructed and calibrated for the restoration reach. In this report, we extended and parameterized S3 for the 120-km section of the lower Trinity River to the confluence with the Klamath River and then to the Pacific Ocean in northern California.
S3 is a deterministic life-stage structured-population model that tracks daily growth, movement, and survival of juvenile 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 (1) survival and growth within each habitat unit and (2) movement of fish among habitat units.
The physical template of the Trinity River was formed by classifying the river into 910 meso-habitat units that were designated into runs, riffles, or 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 timeseries 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 key movement and survival parameters by calibrating the model to 12 years (2007–18) of weekly juvenile abundance estimates from two rotary screw traps: (1) the Pear Tree trap near the downstream end of the restoration reach and (2) the Willow Creek trap site is about 40.2 km upriver from the Trinity River’s confluence with the Klamath River. The calibration consisted of replicating historical conditions as closely as possible (for example: flow, temperature, spawner abundance, spawning location and timing, and hatchery releases), and then running the model to predict weekly abundance passing the trap location. We also evaluated four alternative model structures that included either no density-dependence, density-independent movement and survival, density-dependent survival, or density-dependent movement. Akaike information criterion model selection was used to evaluate the strength of evidence for alternative model structures to simulate the observed abundance estimates.
Model selection supported the conclusion that the fully density-dependent model and density-dependent survival model was better supported by the data than the no density-dependence or density-dependent movement model. Because density-dependent movement was favored in past evaluations, we focus on the results from the fully density-dependent model. Parameter estimates from this model indicated that fry were less likely than parr to move downstream and that fry moved slower. Fry had a lower daily survival probability than parr. In contrast, hatchery fish had the highest probability of movement and the lowest daily survival probability.
Fitting the model to both traps individually enabled us to independently compare the fit and performance of S3 at simulating fish abundance, timing, and growth of juvenile salmon in the upper restoration reach and lower Trinity River. We obtained a better fit to the data at the Willow Creek trap site than we obtained at the Pear Tree trap site, regardless of whether we fit the model to the abundances at the Pear Tree trap or Willow Creek trap. This better fit was surprising given that the S3 input data for the upper restoration reach required fewer assumptions than fitting to the Willow Creek trap site that is farther down river. Fitting S3 to weekly abundances at the Willow Creek trap site required making assumptions about (1) extrapolating capacity-flow relationships to unmeasured habitat units; (2) spatially allocating spawners within the lower Trinity River; and (3) approximating the abundance, timing, and size of juveniles entering from tributaries. The model provided better fit to the data at the Willow Creek trap site. In the weekly abundance estimates, in relation to the S3 simulated abundances, several migration years’ (2011, 2015–17) weekly abundance estimates appeared truncated and were near or at peak annual abundances in January, suggesting that a large fraction of juveniles was migrating as early as December at the Pear Tree trap site. Some early life dynamics may not be currently incorporated into S3. For example, the estimation of abundance at the Pear Tree trap may be biased because of size selectivity. Knowing about selectivity at the Pear Tree trap could greatly improve S3’s ability to predict weekly and peak abundances each year.
Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.
Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of “pegmatitic” (AUC = 0.962) and “alkalic” (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials.
Amplifications of seismic waves traveling upward through a continuous, interface‐free velocity profile are consistently smaller when computed using the square‐root‐impedance (SRI) method than when computed using full‐resonance (FR) calculations. This was found for a wide range of velocity profiles. For realistic profiles, for which the gradient of velocity decreases with depth, the differences are not large, with the ratio of FR/SRI amplifications ranging from about 1.05 to 1.3. Comparisons of the amplifications from a continuous velocity profile with those from approximations to that profile using a stack of constant‐velocity layers give some support to the hypothesis that the difference between FR and SRI amplifications for gradient profiles is because the former is controlled by the ratio of seismic impedances, whereas the latter is based on the square root of the seismic impedance ratios. This implies that gradient profiles will always have FR amplifications greater than SRI amplifications. A model‐independent, easy‐to‐implement modification of the SRI amplifications is proposed that shows promise in bringing the SRI amplifications closer to the FR amplifications.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220197","usgsCitation":"Boore, D., and Abrahamson, N., 2023, On the ratio of full‐resonance to square‐root‐impedance amplifications for shear‐wave velocity profiles that are a continuous function of depth: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1192-1207, https://doi.org/10.1785/0120220197.","productDescription":"16 p.","startPage":"1192","endPage":"1207","ipdsId":"IP-145432","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":481876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Boore, David 0000-0002-8605-9673 boore@usgs.gov","orcid":"https://orcid.org/0000-0002-8605-9673","contributorId":140502,"corporation":false,"usgs":true,"family":"Boore","given":"David","email":"boore@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Abrahamson, Norm A","contributorId":195307,"corporation":false,"usgs":false,"family":"Abrahamson","given":"Norm A","affiliations":[],"preferred":false,"id":926871,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241067,"text":"sir20215142 - 2023 - Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models","interactions":[],"lastModifiedDate":"2026-02-23T18:29:12.962569","indexId":"sir20215142","displayToPublicDate":"2023-06-01T13:55:00","publicationYear":"2023","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":"2021-5142","displayTitle":"Groundwater Residence Times in Glacial Aquifers—A New General Simulation-Model Approach Compared to Conventional Inset Models","title":"Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models","docAbstract":"Groundwater is important as a drinking-water source and for maintaining base flow in rivers, streams, and lakes. Groundwater quality can be predicted, in part, by its residence time in the subsurface, but the residence-time distribution cannot be measured directly and must be inferred from models. This report compares residence-time distributions from four areas where groundwater flow and travel time were simulated with conventional simulation-inset models (IMs) and with a new automated model-construction method called general simulation models (GSMs). The comparison provides an opportunity to explore controls on travel time and improve the methods used in the creation of GSMs. These models can be useful for three main-use cases: (1) rapid testing of relationships that govern groundwater flow and age, (2) generation of consistent examples for training a machine-learning metamodel, and (3) serving as a starting point for more detailed models.
Comparison of the GSMs to IMs indicated a qualified pattern of agreement for residence-time distributions as indicated by the Nash-Sutcliffe efficiency and Spearman’s correlation coefficient. The agreement was best for the median values of the simulated residence times in young fractions of groundwater (defined as the fractions of groundwater in samples less than 65 years old) at the scale of the eight-digit hydrologic-unit code. Generally, the median values of the young fractions in the IMs were correlated with the median values from the GSMs. The relative trends across the four areas also were similar for the other residence-time metrics. The medians of residence-time metrics at finer scales show a fair degree of scatter. The GSM results compared most poorly for median travel times in the older fraction of groundwater (older than 65 years).
The GSM approach is intended as a flexible framework for developing models that can be useful individually as screening tools or collectively to support projects in statistical learning. Although one set of GSM algorithms was presented here, the approach can accommodate many types of data and also different categories of prior information. Comparison of GSMs and IMs suggests ways in which the GSMs, while remaining easy to construct and calibrate, can be improved for estimating groundwater travel times. IMs do not yield exact travel times, and matching GSMs to IMs does not guarantee an improvement; however, IMs provide a convenient benchmark against which to explore relations between physical characteristics of watersheds and the distribution of travel times within them.
This effort was undertaken as part of the National Water Quality Program of the U.S. Geological Survey to assist in determining the susceptibility of groundwater in glacial aquifers to a variety of natural and anthropogenic contaminants.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215142","programNote":"National Water Quality Program","usgsCitation":"Starn, J.J., Kauffman, L.J., and Feinstein, D.T., 2023, Groundwater residence times in glacial aquifers—A new general simulation-model approach compared to conventional inset models: U.S. Geological Survey Scientific Investigations Report 2021–5142, 37 p., https://doi.org/10.3133/sir20215142.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112499","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":500447,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114759.htm","linkFileType":{"id":5,"text":"html"}},{"id":413862,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5142/images/"},{"id":413858,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5142/coverthb.jpg"},{"id":413859,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5142/sir20215142.pdf","text":"Report","size":"6.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5142"},{"id":413861,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5142/sir20215142.XML"},{"id":413863,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HS83JL","text":"USGS data release","linkHelpText":"MODPATH-NWT and MODPATH6 models used to compare a new general simulation model approach with a conventional inset model approach for groundwater residence time in glacial aquifers"},{"id":413860,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215142/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5142"}],"country":"United States","state":"Illinois, Indiana, Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.5,\n 46\n ],\n [\n -89,\n 46\n ],\n [\n -89,\n 41.5\n ],\n [\n -84.5,\n 41.5\n ],\n [\n -84.5,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Digital flood-inundation maps for an 8-mile reach of Papillion Creek near Offutt Air Force Base, Nebraska, were created by the U.S. Geological Survey (USGS) in cooperation with the U.S. Air Force, Offutt Air Force Base. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Program website at https://www.usgs.gov/mission-areas/water-resources/science/flood-inundation-mapping-fim-program, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgages Papillion Creek at Fort Crook, Nebr. (station 06610795), and Papillion Creek at Harlan Lewis Road near La Platte, Nebr. (station 06610798). Near-real-time stages at these streamgages may be obtained from the USGS National Water Information System database at https://doi.org/10.5066/F7P55KJN or from the National Weather Service Advanced Hydrologic Prediction Service at https://water.weather.gov/ahps/.
Flood profiles were computed for the 8-mile stream reach by means of a one-dimensional step-backwater model. The model was calibrated by adjusting roughness coefficients to best represent the current (2022) stage-streamflow relation at the Papillion Creek at Fort Crook (station 06610795) streamgage.
The hydraulic model then was used to compute water-surface profiles for 157 scenarios using a combination of stage values in 1-foot (ft) stage intervals that ranged from 27 to 39 ft at the Papillion Creek at Fort Crook (station 06610795) streamgage and from 13.9 to 30.9 ft at the Papillion Creek at Harlan Lewis Road near La Platte (station 06610798) streamgage, as referenced to the local datums. The simulated water-surface profiles then were combined by a geographic information system with a digital elevation model, which had a 3.281-ft grid to delineate the area flooded and water depths at each stage. The availability of these flood-inundation maps, along with information regarding current stage from the USGS streamgages, can provide emergency management personnel and residents with information that is critical for flood response activities and postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235054","collaboration":"Prepared in cooperation with the U.S. Air Force, Offutt Air Force Base","usgsCitation":"Strauch, K.R., and Hobza, C.M., 2023, Flood-inundation maps for an 8-mile reach of Papillion Creek near Offutt Air Force Base, Nebraska, 2022: U.S. Geological Survey Scientific Investigations Report 2023–5054, 12 p., https://doi.org/10.3133/sir20235054.","productDescription":"Report: vi, 12 p.; Data Release; Dataset","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-135851","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":417490,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5054/images"},{"id":417652,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235054/full"},{"id":417492,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XQIXMN","text":"USGS data release","linkHelpText":"Flood inundation geospatial datasets for Papillion Creek near Offutt Air Force Base, Nebraska"},{"id":417489,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5054/sir20235054.XML"},{"id":417491,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":417486,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5054/sir20235054.pdf","text":"Report","size":"3.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5054"},{"id":417485,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5054/coverthb.jpg"},{"id":500933,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114763.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nebraska","otherGeospatial":"Offutt Air Force Base, Papillion Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.966667,\n 41.15\n ],\n [\n -95.966667,\n 41.05\n ],\n [\n -95.8667,\n 41.05\n ],\n [\n -95.8667,\n 41.15\n ],\n [\n -95.966667,\n 41.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Many coastal estuaries have experienced declines in pH over the past few decades due to coastal acidification. However, mean monthly water column pH values (collected during daylight hours) have increased in Tampa Bay, Florida over recent decades concurrent with seagrass recovery. We measured changes in carbonate system and water quality variables in Tampa Bay and the near-coastal Gulf of Mexico environment to quantify diurnal to seasonal trends, drivers, and controls of carbonate chemistry; identify exposure periods to low pH conditions; and to examine the potential for seagrasses to buffer acidification in Tampa Bay. Autonomous sensor packages deployed in Tampa Bay and the Gulf of Mexico from December 2017 to June 2020 recorded hourly measurements of seawater temperature, salinity, pressure, pHT (total scale), carbon dioxide (pCO2), dissolved oxygen (DO), and photosynthetically active radiation. Results indicated strong temperature and biological influence on DO, pHT, and pCO2 in Tampa Bay during the dry season, and only weak to moderate correlation of these variables with temperature and salinity during the wet season. Strong influence from biological processes during the wet season was coincident with spring-to-summer periods of maximum seagrass growth rates. Gulf of Mexico results indicated higher pHT and DO, and lower pCO2 than in Tampa Bay, with similar but attenuated seasonal variation. Results suggest potential benefits from seagrass photosynthesis increasing pHT, DO, and decreasing pCO2 in Tampa Bay, and delivery of high pHT, low pCO2 Gulf of Mexico water to Tampa Bay during flood tides. Approximately 30% of pHT and pCO2 data records collected in Tampa Bay were below pHT 7.900 and above pCO2 of 600 μatm, primarily during the wet season, indicating potential for dissolution of carbonate sediments that may also help buffer acidification conditions in Tampa.
","language":"English","publisher":"Florida Academy of Sciences","usgsCitation":"Yates, K.K., Moore, C., Lemon, M.K., Moyer, R.P., Tomasko, D.A., Masserini, R., and Sherwood, E.T., 2023, Coastal acidification trends and controls in a subtropical estuary, Tampa Bay, Florida USA: Florida Scientist, v. 86, no. 2, p. 214-228.","productDescription":"15 p.","startPage":"214","endPage":"228","ipdsId":"IP-122626","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":420720,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Gulf of Mexico, Tampa Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.34603301937987,\n 28.090560342743913\n ],\n [\n -83.31697141493213,\n 28.090560342743913\n ],\n [\n -83.31697141493213,\n 27.425867242036304\n ],\n [\n -82.34603301937987,\n 27.425867242036304\n ],\n [\n -82.34603301937987,\n 28.090560342743913\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yates, Kimberly K. 0000-0001-8764-0358","orcid":"https://orcid.org/0000-0001-8764-0358","contributorId":214349,"corporation":false,"usgs":true,"family":"Yates","given":"Kimberly","email":"","middleInitial":"K.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":882816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moore, Christopher 0000-0003-3210-4878 csmoore@usgs.gov","orcid":"https://orcid.org/0000-0003-3210-4878","contributorId":149727,"corporation":false,"usgs":true,"family":"Moore","given":"Christopher","email":"csmoore@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":882884,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lemon, Mitchell K","contributorId":329645,"corporation":false,"usgs":false,"family":"Lemon","given":"Mitchell","email":"","middleInitial":"K","affiliations":[{"id":33877,"text":"CNTS","active":true,"usgs":false}],"preferred":false,"id":882885,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moyer, Ryan P.","contributorId":198993,"corporation":false,"usgs":false,"family":"Moyer","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":13560,"text":"Florida Fish and Wildlife Conservation Commission, Eustis, FL","active":true,"usgs":false}],"preferred":false,"id":882817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tomasko, David A.","contributorId":172728,"corporation":false,"usgs":false,"family":"Tomasko","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":882818,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Masserini, R. 0000-0002-2841-1819","orcid":"https://orcid.org/0000-0002-2841-1819","contributorId":329644,"corporation":false,"usgs":false,"family":"Masserini","given":"R.","email":"","affiliations":[{"id":78677,"text":"University of Tampa","active":true,"usgs":false}],"preferred":false,"id":882819,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sherwood, Edward T. 0000-0001-5330-302X","orcid":"https://orcid.org/0000-0001-5330-302X","contributorId":150472,"corporation":false,"usgs":false,"family":"Sherwood","given":"Edward","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":882820,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70260395,"text":"70260395 - 2023 - Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska","interactions":[],"lastModifiedDate":"2024-10-31T13:49:21.623893","indexId":"70260395","displayToPublicDate":"2023-06-01T08:39:58","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"title":"Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska","docAbstract":"Freshwater ecosystems of the Alaskan Arctic and Subarctic provide resources that are culturally, ecologically, and economically invaluable. Presently, these regions are relatively free of the impacts from invasive species compared to southern latitudes. To date, there have been relatively few verified introductions of aquatic invasive species (AIS) to freshwater ecosystems in Alaska. The expanding list and distribution of AIS has led to significant negative ecological and economic impacts (e.g., waterweed Elodea nuttalli; E. canadensis and northern pike Esox Lucius introduced outside its native range in Alaska). Escalating human activity across Alaskan lands and waters, coupled with rapidly shifting environmental conditions, increases the potential for new species introductions and subsequent establishment. Creating a proactive framework for well-informed decision-making and action can improve the effectiveness of prevention efforts and bolster decision support tools that help resource managers direct limited resources. Prioritizing AIS that may be introduced and become established, as well as the locations at highest risk of invasion, is foundational to building a proactive invasive species management framework in Alaska.
This project sought to identify and prioritize AIS known to be invasive in the contiguous United States, evaluate current and future habitat suitability for AIS in Alaska, and assess potential for AIS to be transported to habitats across Alaska, utilizing similar assessment methods as implemented for Bering Sea marine invasive species and non-native plants in Alaska. To accomplish this goal, the objectives of the project were to: 1) develop a formal ranked list of potential AIS to freshwater systems of Alaska; 2) assess the level of establishment risk for potential AIS by developing habitat suitability models for waterbodies across Alaska; and 3), identify potential pathways and specific vectors for high-risk AIS to invade Alaska and develop a framework for how vector analysis will be completed to understand transport risk. Overall, our goal is horizon scanning which is defined by Roy et al. (2019) as “a systematic examination of potential threats and opportunities, within a given context, and likely future developments, which are at the margin of current thinking and planning.” The scans include pathway analyses and risk screening of species present at pathway origin points, with a focus on identifying species at high risk of being introduced, becoming established, spreading, and causing harm.
We refined a list of 28 AIS from a list of hundreds based on characterizations of species’ invasiveness and species’ proximity to Alaska (USGS 2020; GBIF 2022). Next, we evaluated the relative invasiveness of individual species to create an initial AIS ranking. We sought to characterize habitat suitability of AIS by selecting variables that were continental in scale, covering North America to include Alaska as well as the lower 48 states comparing natural discharge, sub-basin average terrain slope (degrees), average silt fraction, average organic carbon, lithological class, and human footprint in sub-basin in 2009. We estimated AIS habitat suitability across the entire state of Alaska using the physiological tolerances of the AIS (Appendix 2). We also evaluated pathways and vectors for the introduction of AIS (Appendix 2). Many pathways and vectors considered did not meet the criteria for Alaska or freshwater systems.
Of the 28 ranked species that we categorized as very high, high, and moderate levels of invasiveness; all three risk groups included fish and mollusks (Appendix 2). One commonality of the very high-invasiveness-ranked species was the availability of Ecological Risk Screening Summary documents (USFWS, 2022) produced by U.S. Fish and Wildlife Service (USFWS), except for the goldfish (Carassius auratus) and the New Zealand mudsnail (Potamopyrgus antipodarum). The Ecological Risk Screening Summary is now available for New Zealand mudsnails. In general, fish species often ranked very high or high in invasiveness and included sportfish and aquarium fish, suggesting the importance of pathways such as aquarium trade, fishing industry, intentional (but illegal) introductions of sportfishes and aquarium fishes for establishment. The technique we used for habitat suitability models necessitated aquatic environmental datasets that were continental in scale, which was often interpolated from very coarse resolution source data layers, particularly in Alaska. Better spatial data representing aquatic environments would likely improve this approach. While the lack of introductions in Alaska and nearby provinces and states is encouraging, the lack of occurrence data for the focal species also created complications for habitat suitability modeling. Despite the challenges, the habitat suitability models indicated limited suitability for warmwater species while some species, such as Brook trout (Salvelinus fontinalis), have high habitat suitability across Alaska no matter what threshold approach is taken. Some environmental predictors were more important than others. Specifically, the most important predictor variable, ‘frost free days,’ was critical for 15 out of 28 species as expected due to harsh winter conditions in Arctic and Subarctic regions. The second most important predictor was ‘subbasin land surface runoff’, a variable that indicates the amount of discharge and runoff, while the third most important predictor was ‘snow cover’ another indication of winter conditions.
Overall, the ability to understand the effect of future climate scenarios on the establishment of AIS was challenging. A detailed dataset of freshwater temperatures and water chemistry (e.g., pH, calcium) would greatly improve the ability to predict invasiveness of freshwater species to Alaska’s ecosystems on a regional basis. Future studies may benefit from a more focused geographic scope examining a group of subbasins or a regional basin rather than the entire state. These drainages could be selected based upon the mostly likely locations of introduction pathways. The two most prevalent pathway risks for AIS are in-state transfer and stowaways/contaminants. Although there are examples of introductions from other pathways, the risk is somewhat mitigated by Alaska’s climate and regulations. However, variable application of protocols for inspection and cleaning of fishing gear, watercraft, and other similar items while traveling into Alaska as well as transferring from waterbody to waterbody within the state creates a substantial risk in introducing invasive species. We plot cumulative invasive vulnerability for all subbasins and for the top 10% of subbasins (Appendix 3).
","language":"English","publisher":"Alaska Center for Conservation Science, University of Alaska Anchorage","usgsCitation":"Geist, M., Jarnevich, C.S., Steer, A., Osnas, J., Carey, M.P., Martin, A., Davis, T., and Kelty, R., 2023, Modeling, mapping, and measuring the risk of freshwater invasive species across Alaska, 236 p.","productDescription":"236 p.","ipdsId":"IP-140719","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":463468,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://accs.uaa.alaska.edu/publications/"},{"id":463483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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in coastal marine environments. Within coastal watersheds, environmental managers are particularly interested in climate impacts on terrestrial processes, which may undermine the efficacy of management actions designed to reduce eutrophication and consequent low-oxygen conditions in receiving coastal waters. However, substantial uncertainty accompanies the application of Earth system model (ESM) projections to a regional modeling framework when quantifying future changes to estuarine hypoxia due to climate change. In this study, two downscaling methods are applied to multiple ESMs and used to force two independent watershed models for Chesapeake Bay, a large coastal-plain estuary of the eastern United States. The projected watershed changes are then used to force a coupled 3-D hydrodynamic–biogeochemical estuarine model to project climate impacts on hypoxia, with particular emphasis on projection uncertainties. Results indicate that all three factors (ESM, downscaling method, and watershed model) are found to contribute substantially to the uncertainty associated with future hypoxia, with the choice of ESM being the largest contributor. Overall, in the absence of management actions, there is a high likelihood that climate change impacts on the watershed will expand low-oxygen conditions by 2050 relative to a 1990s baseline period; however, the projected increase in hypoxia is quite small (4 %) because only climate-induced changes in watershed inputs are considered and not those on the estuary itself. Results also demonstrate that the attainment of established nutrient reduction targets will reduce annual hypoxia by about 50 % compared to the 1990s. Given these estimates, it is virtually certain that fully implemented management actions reducing excess nutrient loadings will outweigh hypoxia increases driven by climate-induced changes in terrestrial runoff.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-20-1937-2023","usgsCitation":"Hinson, K.E., Friedrichs, M.A., Najjar, R.G., Herrmann, M., Bian, Z., Bhatt, G., St-Laurent, P., Tian, H., and Shenk, G.W., 2023, Impacts and uncertainties of climate-induced changes in watershed inputs on estuarine hypoxia: Biogeosciences, v. 20, no. 10, p. 1937-1961, https://doi.org/10.5194/bg-20-1937-2023.","productDescription":"25 p.","startPage":"1937","endPage":"1961","ipdsId":"IP-151711","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":443237,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-20-1937-2023","text":"Publisher Index Page"},{"id":417643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay, James River, Potomac River, Susquehanna River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.02714093628992,\n 36.82353526720044\n ],\n [\n -75.9679722985874,\n 37.107188810688925\n ],\n [\n -75.9613980055088,\n 37.27478056969166\n ],\n [\n -75.81018926471236,\n 37.51504137048339\n ],\n [\n -75.52749466235369,\n 37.90511682250509\n ],\n [\n -75.67870340315011,\n 38.4632102094632\n ],\n [\n -75.79704067855599,\n 39.71333987986998\n ],\n [\n -76.07316098783689,\n 39.69310779262992\n ],\n [\n -77.52607975810103,\n 39.01191023782434\n ],\n [\n -77.6707142058197,\n 38.2931354196956\n ],\n [\n -77.756180015835,\n 37.50982650176154\n ],\n [\n -77.41431677577297,\n 37.42111800113112\n ],\n [\n -77.37487101730382,\n 37.31662036563128\n ],\n [\n -77.02643348416402,\n 37.211977253147836\n ],\n [\n -76.81605610566426,\n 37.143881287547615\n ],\n [\n -76.71086741641476,\n 37.09670197901805\n ],\n [\n -76.6779959510242,\n 36.95498773192651\n ],\n [\n -76.5793815548517,\n 36.86562468091324\n ],\n [\n -76.67142165794561,\n 36.74982306374538\n ],\n [\n -76.40187564174332,\n 36.71821041760633\n ],\n [\n -76.02714093628992,\n 36.82353526720044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"20","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Hinson, Kyle E. 0000-0002-2737-2379","orcid":"https://orcid.org/0000-0002-2737-2379","contributorId":306024,"corporation":false,"usgs":false,"family":"Hinson","given":"Kyle","email":"","middleInitial":"E.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":874433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedrichs, Marjorie A. M. 0000-0003-2828-7595","orcid":"https://orcid.org/0000-0003-2828-7595","contributorId":222588,"corporation":false,"usgs":false,"family":"Friedrichs","given":"Marjorie","email":"","middleInitial":"A. M.","affiliations":[{"id":40564,"text":"Virginia Institute of Marine Science, William & Mary","active":true,"usgs":false}],"preferred":false,"id":874434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Najjar, Raymond G. 0000-0002-3770-2300","orcid":"https://orcid.org/0000-0002-3770-2300","contributorId":261280,"corporation":false,"usgs":false,"family":"Najjar","given":"Raymond","email":"","middleInitial":"G.","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":874435,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Herrmann, Maria","contributorId":198519,"corporation":false,"usgs":false,"family":"Herrmann","given":"Maria","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":874436,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bian, Zihao","contributorId":306026,"corporation":false,"usgs":false,"family":"Bian","given":"Zihao","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":874437,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bhatt, Gopal 0000-0002-6627-793X","orcid":"https://orcid.org/0000-0002-6627-793X","contributorId":252963,"corporation":false,"usgs":false,"family":"Bhatt","given":"Gopal","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":874438,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"St-Laurent, Pierre 0000-0002-1700-9509","orcid":"https://orcid.org/0000-0002-1700-9509","contributorId":261288,"corporation":false,"usgs":false,"family":"St-Laurent","given":"Pierre","email":"","affiliations":[{"id":40564,"text":"Virginia Institute of Marine Science, William & Mary","active":true,"usgs":false}],"preferred":false,"id":874439,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tian, Hanqin","contributorId":296449,"corporation":false,"usgs":false,"family":"Tian","given":"Hanqin","affiliations":[{"id":64042,"text":"Schiller Institute for Integrated Science and Society, Department of Earth and Environmental Sciences, Boston College, Chestnut Hill, MA 02467, United States","active":true,"usgs":false}],"preferred":false,"id":874440,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874441,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70244060,"text":"70244060 - 2023 - Abiotic and biotic factors reduce the viability of a high-elevation salamander in its native range","interactions":[],"lastModifiedDate":"2023-08-08T13:57:10.373732","indexId":"70244060","displayToPublicDate":"2023-06-01T07:35:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Abiotic and biotic factors reduce the viability of a high-elevation salamander in its native range","docAbstract":"Whitebark pine (Pinus albicaulis Engelm.) has experienced rapid population declines and is listed as threatened under the Endangered Species Act in the United States. Whitebark pine in the Sierra Nevada of California represents the southernmost end of the species' distribution and, like other portions of its range, faces threats from an introduced pathogen, native bark beetles, and a rapidly warming climate. Beyond these chronic stressors, there is also concern about how this species will respond to acute stressors, such as drought. We present patterns of stem growth from 766 large (average diameter at breast height >25 cm), disease-free whitebark pine across the Sierra Nevada before and during a recent period of drought. We contextualize growth patterns using population genomic diversity and structure from a subset of 327 trees. Sampled whitebark pine generally had positive to neutral stem growth trends from 1970 to 2011, which was positively correlated with minimum temperature and precipitation. Indices of stem growth during drought years (2012 to 2015) relative to a predrought interval were mostly positive to neutral at our sampled sites. Individual tree growth response phenotypes appeared to be linked to genotypic variation in climate-associated loci, suggesting that some genotypes can take better advantage of local climatic conditions than others. We speculate that reduced snowpack during the 2012 to 2015 drought years may have lengthened the growing season while retaining sufficient moisture to maintain growth at most study sites. Growth responses may differ under future warming, however, particularly if drought severity increases and modifies interactions with pests and pathogens.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10072","usgsCitation":"van Mantgem, P., Milano, E.R., Dudney, J., Nesmith, J., Vandergast, A.G., and Zald, H.S., 2023, Growth, drought response, and climate-associated genomic structure in whitebark pine in the Sierra Nevada of California: Ecology and Evolution, v. 13, no. 5, e10072, 20 p., https://doi.org/10.1002/ece3.10072.","productDescription":"e10072, 20 p.","ipdsId":"IP-128278","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443243,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10072","text":"Publisher Index Page"},{"id":435298,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EKZR4C","text":"USGS data release","linkHelpText":"Growth, Drought Response, and Genomic Structure Data for Whitebark Pine in the Sierra Nevada of California (ver. 2.0, May 2023)"},{"id":417641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.90787138940058,\n 40.75842259785122\n ],\n [\n -122.22647490502563,\n 40.27400725576794\n ],\n [\n -121.76504912377561,\n 39.786096804298865\n ],\n [\n -121.51236357690067,\n 39.32870262476027\n ],\n [\n -120.86417021752547,\n 38.310125593418576\n ],\n [\n -120.83121123315044,\n 38.08564366930443\n ],\n [\n -119.28213896752547,\n 36.71547588003058\n ],\n [\n -119.01846709252573,\n 36.265018388250496\n ],\n [\n -118.87564482690041,\n 35.70496447203716\n ],\n [\n -118.80972685815075,\n 35.322580152836636\n ],\n [\n -118.79874053002564,\n 35.04422375724022\n ],\n [\n -118.32632842065081,\n 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0000-0003-3986-065X","orcid":"https://orcid.org/0000-0003-3986-065X","contributorId":305558,"corporation":false,"usgs":false,"family":"Dudney","given":"Joan","email":"","affiliations":[{"id":66253,"text":"Environmental Studies Program, Santa Barbara, California, USA","active":true,"usgs":false}],"preferred":false,"id":874444,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nesmith, Jonathan 0000-0002-8930-9105","orcid":"https://orcid.org/0000-0002-8930-9105","contributorId":306029,"corporation":false,"usgs":false,"family":"Nesmith","given":"Jonathan","email":"","affiliations":[{"id":66353,"text":"USDA FS","active":true,"usgs":false}],"preferred":false,"id":874445,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vandergast, Amy G. 0000-0002-7835-6571","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":97617,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":874446,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zald, Harold S.J. 0000-0002-6505-8233","orcid":"https://orcid.org/0000-0002-6505-8233","contributorId":306030,"corporation":false,"usgs":false,"family":"Zald","given":"Harold","email":"","middleInitial":"S.J.","affiliations":[{"id":66353,"text":"USDA FS","active":true,"usgs":false}],"preferred":false,"id":874447,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70244124,"text":"70244124 - 2023 - Estimating streamflow permanence with the watershed erosion prediction project model: Implications for surface water presence modeling and data collection","interactions":[],"lastModifiedDate":"2023-06-09T15:27:22.153327","indexId":"70244124","displayToPublicDate":"2023-06-01T07:01:39","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating streamflow permanence with the watershed erosion prediction project model: Implications for surface water presence modeling and data collection","docAbstract":"Many data collection efforts and modeling studies have focused on providing accurate estimates of streamflow while fewer efforts have sought to identify when and where surface water is present and the duration of surface water presence in stream channels, hereafter referred to as streamflow permanence. While physically-based hydrological models are frequently used to explore how water quantity may be influenced by various climatic and basin characteristics at local, regional, national, and global extents they are less often used to explore streamflow permanence. Herein, the Watershed Erosion Prediction Project (WEPP) hydrological model is applied to watersheds in the humid H. J. Andrews Experimental Forest (HJA) and watersheds of the arid Willow and Whitehorse creeks (WW), both in Oregon, to simulate daily (WW) and annual (HJA and WW) streamflow permanence. One thousand parameter combinations were tested to calibrate WEPP to observed streamflow in the HJA watersheds and one hundred parameter combinations were tested to calibrate WEPP to observed surface water presence time series data in WW watersheds. When calibrated to observed streamflow, WEPP correctly classified annual streamflow permanence for 83% of HJA stream reaches. In the WW, WEPP simulations correctly classified 63–93% of daily streamflow permanence observations and 59-87% of annual streamflow permanence classifications. Inclusion of a dry-day threshold (the maximum number of days a stream reach could be modeled ‘dry’ but still classified as permanent for the year) improved annual accuracy in three WW watersheds from 2-10%. Parameter sets that produced the best daily accuracies in WW resulted in poor annual accuracies. Results highlight the importance of evaluating physically-based streamflow permanence models on both permanent and nonpermanent streams at daily and annual time scales to ensure evaluation metrics are appropriate for interpretation purposes. Additionally, results suggest that strategic collection of surface water presence observations and streamflow observations may support robust calibration of physically based models to simulate streamflow permanence moving forward.
Two decades of drought in the southwestern USA are spurring concerns about increases in wind erosion, dust emissions, and associated impacts on ecosystems, agriculture, human health, and water supply. Different avenues of investigation into primary drivers of wind erosion and dust have yielded mixed results depending on the spatial and temporal sensitivity of the evidence. We monitored passive aeolian sediment traps from 2017 to 2020 across eighty-one sites near Moab UT to understand patterns of sediment flux. At measurement sites we collated climate, soil, topography and vegetation spatial layers to better understand the context of wind erosion and then combine these data with field observations of land use in models to characterize the influence of cattle grazing, oil and gas well pads, and vehicle/heavy equipment disturbance that potentially drive both exposure of bare soil and increases in erodible sediment supply that increase vulnerability to erosion. Disturbed areas with low soil calcium carbonate content yielded high sediment transport in dry years, but notably areas with little disturbance and low bare soil exposure had much less activity. Cattle grazing had the largest land use association with erosional activity with analyses suggesting that both herbivory and trampling from cattle could be drivers. The amount and distribution of bare soil exposure from new sub-annual fractional cover remote sensing products proved very helpful in mapping erosion, and new predictive maps informed by field data are presented to help depict spatial patterns of wind erosion activity. Our results suggest that despite the magnitude of current droughts, minimizing surface disturbance in vulnerable soils can mitigate a large portion of dust emissions. Results can help managers identify eroding areas where disturbance reduction and soil surface protection measures can be prioritized.
Long-term and accurate wave hindcast databases are often required in different coastal engineering projects. The assessment of the nearshore wave climate is often accomplished by using downscaling techniques to translate offshore waves to coastal areas. However, dynamical downscaling approaches may incur huge computational cost. Additionally, the common use of bulk parameterizations are often not accurate for multidimensional waves. To overcome these limitations, we present a hybrid downscaling approach that combines mathematical algorithms (statistical downscaling) and numerical modeling (dynamical downscaling) over the individual spectral partitions. Every wave partition is downscaled and aggregated afterward by using principles of wave linear theory. By assuming linearity in the propagation of the wave celerity, the application of the method is limited from offshore to intermediate water depths. In addition, the method proposed uses a technique to simplify the spectral boundary conditions in complex domains. The methodology has been applied and validated in the island states of Samoa, American Samoa, Majuro, and Kwajalein, showing good skill at reproducing the spectral hourly time series of significant wave height, peak period, and peak direction. Moreover, an accurate representation of the observed energy spectrum was achieved. This study provides insight into the numerical approximation of the combined sea-swell states while improving the quality of fast spectral forecasting and early warning systems.
1. Conversion of land for settlements and agriculture is increasing globally and can influence wildlife space use. However, there is limited research to identify the thresholds of land-use change that incur wildlife avoidance and how these thresh-olds might vary across levels of selection.
2. We evaluated multi-level avoidance thresholds of elk Cervus canadensis impacted by residential development and irrigated agriculture across the Greater Yellowstone Ecosystem in Idaho, Montana and Wyoming. Using GPS data from765 elk in 21 herds, we estimated habitat selection in relation to development and agriculture at three levels (home range selection, within home range selection and movement path selection). Next, using individual selection covariates and as-sociated measures of land-use availability, we used functional-response models to evaluate how selection varied based on availability, and in turn, to estimate avoidance thresholds.
3. We found individual and level-specific variation in elk responses to environmental factors. Elk exhibited stronger responses (either selection or avoidance) when selecting home range locations (i.e. second-order selection) than when selecting areas within home ranges (i.e. third-order selection) or selecting movement paths (i.e. fourth-order selection). Importantly, elk avoidance of development and agriculture changed as the amount of land in these categories changed. Across all levels of selection elk exhibited neutral selection for human development at low levels of availability (<1.1%–2.2% developed) but avoided areas that were >1.1%–2.2% developed. Conversely, elk selected positively for irrigated agriculture at low to moderate levels of availability (<52.0%–66.2% agriculture) but exhibited neutral selection in areas that were >52.0%–66.2% agriculture.
4. Synthesis and applications. Elk avoidance of low levels of human development suggests conservation efforts such as restrictions on future development or conservation easements could focus on areas that are still below 2% developed. Additionally, because elk selection was strongest at the landscape scale, conservation actions that are based on information about the overall landscape structure may be most impactful. Our results highlight the importance of under-standing variability in wildlife habitat selection at multiple levels, particularly in relation to land-use change, and highlight how functional response modelling can help inform landscape conservation.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14401","usgsCitation":"Gigliotti, L., Atwood, M., Cole, E.K., Courtemanche, A., Dewey, S., Gude, J., Hurley, M., Kauffman, M., Kroetz, K., Leonard, B., MacNulty, D., Maichak, E., McWhirter, D., Mong, T., Proffitt, K., Scurlock, B., Stahler, D., and Middleton, A., 2023, Multi-level thresholds of residential and agricultural land use for elk avoidance across the Greater Yellowstone Ecosystem: Journal of Applied Ecology, v. 60, no. 6, p. 1089-1099, https://doi.org/10.1111/1365-2664.14401.","productDescription":"11 p.","startPage":"1089","endPage":"1099","ipdsId":"IP-145333","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467109,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.14401","text":"Publisher Index Page"},{"id":466426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.49834976413001,\n 45.26023090682858\n ],\n [\n -111.49834976413001,\n 43.83379000138524\n ],\n [\n -108.63921958902552,\n 43.83379000138524\n ],\n [\n -108.63921958902552,\n 45.26023090682858\n ],\n [\n -111.49834976413001,\n 45.26023090682858\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Laura Christine 0000-0002-6390-4133","orcid":"https://orcid.org/0000-0002-6390-4133","contributorId":348259,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Laura Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atwood, M. Paul","contributorId":348260,"corporation":false,"usgs":false,"family":"Atwood","given":"M. Paul","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":923314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cole, Eric K. 0000-0002-2229-5853 eric_cole@fws.gov","orcid":"https://orcid.org/0000-0002-2229-5853","contributorId":348261,"corporation":false,"usgs":true,"family":"Cole","given":"Eric","email":"eric_cole@fws.gov","middleInitial":"K.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":true,"id":923315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Courtemanche, Alyson","contributorId":348262,"corporation":false,"usgs":false,"family":"Courtemanche","given":"Alyson","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":923316,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dewey, Sarah","contributorId":348263,"corporation":false,"usgs":false,"family":"Dewey","given":"Sarah","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":923317,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gude, Justin A.","contributorId":348264,"corporation":false,"usgs":false,"family":"Gude","given":"Justin A.","affiliations":[{"id":81193,"text":"Montana Department of Fish","active":true,"usgs":false}],"preferred":false,"id":923318,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hurley, Mark","contributorId":348265,"corporation":false,"usgs":false,"family":"Hurley","given":"Mark","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":923319,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":923320,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kroetz, Kailin","contributorId":348267,"corporation":false,"usgs":false,"family":"Kroetz","given":"Kailin","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":923321,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Leonard, Bryan","contributorId":348270,"corporation":false,"usgs":false,"family":"Leonard","given":"Bryan","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":923322,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"MacNulty, Daniel R.","contributorId":179179,"corporation":false,"usgs":false,"family":"MacNulty","given":"Daniel R.","affiliations":[],"preferred":false,"id":923323,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Maichak, Eric","contributorId":348277,"corporation":false,"usgs":false,"family":"Maichak","given":"Eric","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":923324,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"McWhirter, Douglas","contributorId":348281,"corporation":false,"usgs":false,"family":"McWhirter","given":"Douglas","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":923325,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mong, Tony W.","contributorId":348286,"corporation":false,"usgs":false,"family":"Mong","given":"Tony W.","affiliations":[{"id":83329,"text":"Wyoming Game and Fish Department, Cody, WY 82414","active":true,"usgs":false}],"preferred":false,"id":923326,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Proffitt, Kelly","contributorId":348289,"corporation":false,"usgs":false,"family":"Proffitt","given":"Kelly","affiliations":[{"id":81193,"text":"Montana Department of Fish","active":true,"usgs":false}],"preferred":false,"id":923327,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Scurlock, Brandon","contributorId":348292,"corporation":false,"usgs":false,"family":"Scurlock","given":"Brandon","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":923328,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Stahler, Daniel","contributorId":348295,"corporation":false,"usgs":false,"family":"Stahler","given":"Daniel","affiliations":[{"id":79152,"text":"Yellowstone Center for Resources","active":true,"usgs":false}],"preferred":false,"id":923329,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Middleton, Arthur D.","contributorId":348297,"corporation":false,"usgs":false,"family":"Middleton","given":"Arthur D.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":923330,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70274657,"text":"70274657 - 2023 - A review of N-mixture models","interactions":[],"lastModifiedDate":"2026-04-02T15:48:25.149913","indexId":"70274657","displayToPublicDate":"2023-06-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":23780,"text":"WIREs Computational Statistics","active":true,"publicationSubtype":{"id":10}},"title":"A review of N-mixture models","docAbstract":"N-mixture models were born in 2004 of the necessity to model animal population size from point counts with imperfect detection of individuals, where capture-recapture methods are infeasible. Initially developed for applications where population size was assumed constant, N-mixture models were extended in 2011 to include population dynamics, allowing application to populations whose size fluctuates during the study. A further extension in 2014 accommodates populations with multiple “states” such as age class or sex. More recent extensions model spatial movement of animals among habitat patches or the spatial spread of infectious disease in a human population. The core idea underlying this class of models is a hierarchical structure, where the observation model is defined conditional on the model for true abundance. This hierarchy allows researchers to incorporate information about observation and abundance processes, while permitting distinct inferences about elements affecting detection and those affecting abundance. Another benefit of the hierarchical approach is the ability to accommodate many existing sampling protocols such as removal sampling and distance sampling. One drawback to N-mixture models is that since they estimate both abundance and detection from replicated but unmarked counts, model parameters may not be clearly identifiable. A second drawback is that when observed counts are large, calculating the N-mixture likelihood is computationally infeasible. This difficulty motivated an approximate likelihood based on the normal approximation to the binomial. The normal approximation provides a diagnostic of parameter estimability based on the closed-form expression of the Fisher information matrix for a multivariate normal likelihood.
","language":"English","publisher":"Wiley","doi":"10.1002/wics.1625","usgsCitation":"Madsen, L., and Royle, J., 2023, A review of N-mixture models: WIREs Computational Statistics, v. 15, no. 6, e1625, 15 p., https://doi.org/10.1002/wics.1625.","productDescription":"e1625, 15 p.","ipdsId":"IP-147184","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":502082,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wics.1625","text":"Publisher Index Page"},{"id":502005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Madsen, Lisa","contributorId":369194,"corporation":false,"usgs":false,"family":"Madsen","given":"Lisa","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":958596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":958597,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244057,"text":"sir20235056 - 2023 - Source contributions to suspended sediment and particulate selenium export from the Loutsenhizer Arroyo and Sunflower Drain watersheds in Colorado","interactions":[],"lastModifiedDate":"2026-03-09T16:27:43.171892","indexId":"sir20235056","displayToPublicDate":"2023-05-31T17:25:00","publicationYear":"2023","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":"2023-5056","displayTitle":"Source Contributions to Suspended Sediment and Particulate Selenium Export from the Loutsenhizer Arroyo and Sunflower Drain Watersheds in Colorado","title":"Source contributions to suspended sediment and particulate selenium export from the Loutsenhizer Arroyo and Sunflower Drain watersheds in Colorado","docAbstract":"Selenium in aquatic ecosystems of the lower Gunnison River Basin in Colorado is affecting the recovery of populations of endangered, native fish species. Dietary exposure is the primary pathway for bioaccumulation of selenium in fish, and particulate selenium can be consumed directly by fish or by the invertebrates on which fish feed. Although selenium can be incorporated into particulate matter via biogeochemical processes, particulate selenium can also enter aquatic ecosystems of the lower Gunnison River Basin from sediments derived from the selenium-rich Mancos Shale. The U.S. Geological Survey, in cooperation with the Colorado Water Conservation Board, conducted this study during 2018–19 to identify sources of selenium-rich suspended sediments from two watersheds underlain by Mancos Shale: Loutsenhizer Arroyo and Sunflower Drain, which is a locally known agricultural drainage near the municipality of Delta, Colorado.
A multipronged approach (fieldwork, laboratory work, and computer modeling) referred to as “sediment fingerprinting” was used to evaluate sources of suspended sediments in the streams flowing out of the two studied watersheds. Four potential source types for suspended sediments were identified and sampled (using soil plugs) within the watersheds: rangelands, agricultural fields, arroyo walls, and streambanks. The sediment fingerprinting approach used elemental concentrations and naturally occurring fallout radionuclides as tracers to apportion percent contributions from the four source types of suspended sediments found in streamflow from both watersheds.
To determine the dominant sources of suspended sediment in streamflow from both watersheds, a mathematical “unmixing” model was used. Unmixing models apportion source percentages to samples of material in which those sources are mixed. These models used elemental and isotopic data in the suspended sediments to unmix them into proportional contributions from source types. The results indicated that arroyo walls and streambanks generally dominated as sources of the suspended sediment. Arroyo walls and streambanks were channel-adjacent sources, with sediments mobilized by water flowing within the stream channel. These sources accounted for greater than 50 percent of suspended sediment in all but one sample and accounted for 100 percent of suspended sediment in 5 of the 11 samples collected. Rangeland and agricultural field sources were located in uplands outside of stream channels and were detected more often during the non-irrigation season. Rangeland and agricultural field sources each were found in 5 of the 11 samples collected. Concentrations of selenium in sediment-source samples were comparatively greater in streambanks and lower in rangelands, with agricultural fields and arroyo walls being intermediate. As a result, source apportionments for particulate selenium skewed towards sources adjacent to stream channels more than for suspended sediments. Water imports for irrigation have changed the hydrology of the watersheds, and a notable fraction of imported water passes through the watersheds rapidly. The rapid flowthrough water during the irrigation season likely contributes heavily to sediment erosion and transport in Loutsenhizer Arroyo and Sunflower Drain, particularly from channel-adjacent sources of sediment. Decreases in irrigation season streamflow, at least in Loutsenhizer Arroyo, may have decreased sediment erosion and transport during the 2018–20 irrigation seasons compared to the 2015–17 seasons.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235056","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board","usgsCitation":"Bern, C.R., Williams, C.A., and Smith, C.G., 2023, Source contributions to suspended sediment and particulate selenium export from the Loutsenhizer Arroyo and Sunflower Drain watersheds in Colorado: U.S. Geological Survey Scientific Investigations Report 2023–5056, 32 p., https://doi.org/10.3133/sir20235056.","productDescription":"Report: vii, 32 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-132717","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":485917,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114745.htm","linkFileType":{"id":5,"text":"html"}},{"id":417883,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235056/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5056"},{"id":417619,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5056/sir20235056.xml"},{"id":417618,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5056/images"},{"id":417612,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99EZZJK","text":"USGS data release","linkHelpText":"Geochemical and fallout radionuclide data for sediment source fingerprinting studies of the Loutsenhizer Arroyo and Sunflower Drain watersheds in western Colorado"},{"id":417611,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5056/sir20235056.pdf","text":"Report","size":"3.49 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5056"},{"id":417610,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5056/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Loutsenhizer Arroyo Watershed, Sunflower Drain Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.2032088457371,\n 38.871468271392075\n ],\n [\n -108.2032088457371,\n 38.38029358037457\n ],\n [\n -107.27351004163626,\n 38.38029358037457\n ],\n [\n -107.27351004163626,\n 38.871468271392075\n ],\n [\n -108.2032088457371,\n 38.871468271392075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Planets are expected to conclude their growth through a series of giant impacts: energetic, global events that significantly alter planetary composition and evolution. Computer models and theory have elucidated the diverse outcomes of giant impacts in detail, improving our ability to interpret collision conditions from observations of their remnants. However, many open questions remain, as even the formation of the Moon—a widely suspected giant-impact product for which we have the most information—is still debated. We review giant-impact theory, the diverse nature of giant-impact outcomes, and the governing physical processes. We discuss the importance of computer simulations, informed by experiments, for accurately modeling the impact process. Finally, we outline how the application of probability theory and computational advancements can assist in inferring collision histories from observations, and we identify promising opportunities for advancing giant-impact theory in the future.
River corridors integrate the active channels, geomorphic floodplain and riparian areas, and hyporheic zone while receiving inputs from the uplands and groundwater and exchanging mass and energy with the atmosphere. Here, we trace the development of the contemporary understanding of river corridors from the perspectives of geomorphology, hydrology, ecology, and biogeochemistry. We then summarize contemporary models of the river corridor along multiple axes including dimensions of space and time, disturbance regimes, connectivity, hydrochemical exchange flows, and legacy effects of humans. We explore how river corridor science can be advanced with a critical zone framework by moving beyond a primary focus on discharge-based controls toward multi-factor models that identify dominant processes and thresholds that make predictions that serve society. We then identify opportunities to investigate relationships between large-scale spatial gradients and local-scale processes, embrace that riverine processes are temporally variable and interacting, acknowledge that river corridor processes and services do not respect disciplinary boundaries and increasingly need integrated multidisciplinary investigations, and explicitly integrate humans and their management actions as part of the river corridor. We intend our review to stimulate cross-disciplinary research while recognizing that river corridors occupy a unique position on the Earth's surface.
Both biotic and abiotic factors can influence the survival and growth of age-0 salmonids. Diseases, such as whirling disease, can also affect salmonid demographics and population dynamics. Here, we conducted a supplementary analysis and evaluated specific stream characteristics that may have been responsible for the differences in growth and survival of two whirling disease resistant Rainbow Trout Oncorhynchus mykiss strains observed by Avila et al. (2018). We used regression modeling to analyze the influence of the biotic and abiotic characteristics of nine streams on the short-term apparent survival and growth of two Rainbow Trout strains, 5,000 German Rainbow Trout and 5,000 German Rainbow Trout × Colorado River Rainbow Trout in each stream. Akaike's information criterion (AICc) model selection was used to identify the factors that most affected short-term survival and growth. Average stream temperature had the largest (positive) effect, βtemp = 0.060, on short-term survival. Rainbow Trout strain, average stream temperature (βtemp = 1.55), competitor biomass (βcompetitor biomass = −0.002), and predator number (βpredator number = 0.01) additively affected short-term growth. Our results indicate that both biotic and abiotic factors are important short-term determinants of Rainbow Trout poststocking performance and may account for the differences in survival and growth that we observed among stocking locations.
As scientific understanding of barrier morphodynamics has improved, so has the ability to reproduce observed phenomena and predict future barrier states using mathematical models. To use existing models effectively and improve them, it is important to understand the current state of morphodynamic modeling and the progress that has been made in the field. This manuscript offers a review of the literature regarding advancements in morphodynamic modeling of coastal barrier systems and summarizes current modeling abilities and limitations. Broadly, this review covers both event-scale and long-term morphodynamics. Each of these sections begins with an overview of commonly modeled phenomena and processes, followed by a review of modeling developments. After summarizing the advancements toward the stated modeling goals, we identify research gaps and suggestions for future research under the broad categories of improving our abilities to acquire and access data, furthering our scientific understanding of relevant processes, and advancing our modeling frameworks and approaches.
Missing data are typical yet must be addressed for proper inferences or expanding datasets to guide our limnological understanding and management of aquatic systems. Interpolation methods (i.e., estimating missing values using known values within the dataset) can alleviate data gaps and common problems. We compared seven popular interpolation methods for predicting substantial missingness in a long-term water quality dataset from the Upper Mississippi River, U.S.A. The dataset included 80,000 sampling sites collected over 30 yr that had substantial missingness for total nitrogen (TN), total phosphorus (TP), and water velocity. For all three interpolated water quality variables, random forests had very high prediction accuracy and outperformed the methods of ordinary kriging, polynomial regressions, regression trees, and inverse distance weighting. TP had a mean absolute error (MAE) of 0.03 mg (L-TP)−1, TN had a MAE of 0.39 mg (L-TN)−1, and water velocity had a MAE of 0.10 m s−1. The random forests' error rates were mapped and showed low spatiotemporal variability across the riverscape, indicating high model performance across many habitat types and large spatial scales. In the current era of “big data,” interpolation becomes an imperative step prior to ecological analyses yet remains unfamiliar and underutilized. Our research briefly describes the importance of addressing missingness and provides a roadmap to conduct model intercomparisons of other big datasets. We also share adaptable data analysis scripts, which allows others to readily conduct interpolation comparisons for many limnology applications and contexts.
Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, “littoral-cell”-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise (SLR), and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,760 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water-level-corrected, satellite-derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in situ surveyed shorelines (e.g., mean sea-level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20-year hindcast period (1995–2015) provides nearly equivalent model forecast accuracy during a validation period (2015–2020) compared to model calibration with monthly in situ observations at Ocean Beach. When comparing model-predicted shoreline positions to satellite-derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi-decadal simulations of shoreline change due to projected wave and sea-level conditions, while holding the model parameters fixed. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 m, respectively. The satellite-data-assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006936","usgsCitation":"Vitousek, S., Vos, K., Splinter, K., Erikson, L.H., and Barnard, P.L., 2023, A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions: JGR Earth Surface, v. 128, no. 7, e2022JF006936, 47 p., https://doi.org/10.1029/2022JF006936.","productDescription":"e2022JF006936, 47 p.","ipdsId":"IP-146968","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443298,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006936","text":"Publisher Index Page"},{"id":435306,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95T9188","text":"USGS data release","linkHelpText":"CoSMoS-COAST: The Coastal, One-line, Assimilated, Simulation Tool of the Coastal Storm Modeling System"},{"id":435305,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CJMB2H","text":"USGS data release","linkHelpText":"Projections of shoreline change for California due to 21st century sea-level rise"},{"id":419397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"128","issue":"7","noUsgsAuthors":false,"publicationDate":"2023-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Vitousek, Sean 0000-0002-3369-4673 svitousek@usgs.gov","orcid":"https://orcid.org/0000-0002-3369-4673","contributorId":149065,"corporation":false,"usgs":true,"family":"Vitousek","given":"Sean","email":"svitousek@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vos, Kilian","contributorId":317755,"corporation":false,"usgs":false,"family":"Vos","given":"Kilian","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":879266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Splinter, Kristen D.","contributorId":317757,"corporation":false,"usgs":false,"family":"Splinter","given":"Kristen D.","affiliations":[{"id":65517,"text":"University of New South Wales - Sydney","active":true,"usgs":false}],"preferred":false,"id":879267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":879268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":879269,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}