Coproduction represents an inclusive approach for developing decision-support resources because it seeks to integrate scientific knowledge and end-user needs. Unfortunately, spatial decision support systems (SDSS) coproduction has sometimes resulted in limited utility for end-users, partially due to scarce SDSS coproduction guidance. To initiate coproduction, we held a series of workshops to co-design a spatial conservation prioritization tool for sagebrush ecosystems in the western United States. We share four themes derived from participant feedback and our reflections to guide future SDSS codesign efforts. We found end-user confidence in data inputs and transparency regarding SDSS assumptions generated trust. Workshop participants noted our virtual format, with smaller break-out groups, effectively facilitated discussions. Ultimately, end-users appreciated the conservation context provided by regional-scale SDSS but preferred local-scale prioritization efforts for site-level planning. Therefore, we are shifting ongoing co-design efforts to consider local-scale tool development, which can scale up to larger geographic extents.
Director, Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Coastal California Gnatcatcher (Polioptila californica californica), a federally threatened species, is a flagship species for regional conservation planning in southern California (USA). An inhabitant of coastal sage scrub vegetation, the gnatcatcher has declined in response to habitat loss and fragmentation, exacerbated by catastrophic wildfires. We documented the status of gnatcatchers throughout their California range and examined post-fire recovery of gnatcatchers and their habitat. We used GIS to develop a habitat suitability model for Coastal California Gnatcatchers using climate and topography covariates and selected over 700 sampling points in a spatially balanced manner. Bird and vegetation data were collected at each point between March and May in 2015 and 2016. Presence/absence of gnatcatchers was determined during three visits to points, using area searches within 150 x 150 m plots. We used an occupancy framework to generate Percent Area Occupied (PAO) by gnatcatchers, and analyzed PAO as a function of time since fire. At the regional scale in 2016, 23% of the points surveyed were occupied by gnatcatchers, reflecting the effect of massive wildfires in the last 15 years. Similarly, PAO in the post-fire subset of points was 24%, with the highest occupancy in unburned (last fire <2002) habitat. Positive predictors of occupancy included percent cover of California sagebrush (Artemisia californica), California buckwheat (Eriogonom fasciculatum), and sunflowers (Encelia spp., Bahiopsis laciniata), while negative predictors included laurel sumac (Malosma laurina) and total herbaceous cover; in particular, non-native grasses. Our findings indicate that recovery from wildfire may take decades, and provide information to speed up recovery through habitat restoration.
The National Water Model (NWM) provides critical analyses and projections of streamflow that support water management decisions. However, the NWM performs poorly in lower-elevation rivers of the western United States (US). The accuracy of the NWM depends on the fidelity of the model inputs and the representation and calibration of model processes and water sources. To evaluate the NWM performance in the western US, we compared observations of river water isotope ratios (18O
Parturition timing has long been a topic of interest in ungulate research. However, few studies have examined parturition timing at fine scale (e.g., <1 day). Predator activity and environmental conditions can vary considerably with diel timing, which may result in selective pressure for parturition to occur during diel times that maximize the likelihood of neonate survival. We monitored parturition events and early-life survival of elk (Cervus canadensis) and mule deer (Odocoileus hemionus) in Utah, USA to better understand diel timing of parturition in temperate ungulates. Diel timing of parturition was moderately synchronous among conspecifics and influenced by environmental variables on the date of parturition. For elk, parturition events were most common during the morning crepuscular period and generally occurred later (i.e., closer to 12:00) when a relatively large proportion of the moon was illuminated. For mule deer, parturition events were most common during the diurnal period and generally occurred later (i.e., closer to 15:00) on cold, wet dates. Diel timing of parturition did not influence neonate survival, but larger datasets may be required to verify the apparent lack of influence. Although additional work could evaluate alternative variables that might affect parturition timing, our data provide an improved and finer scale understanding of reproductive ecology and phenology in ungulates.
The Coastal Science Navigator is an online gateway to a wide variety of U.S. Geological Survey (USGS) coastal change hazards-related information, data, and tools relevant to stakeholders’ scientific and decision-making needs. The products within the Coastal Science Navigator provide data related to past, present, and future threats to our coastlines. The filter search allows users to see all available products and identify relevant options by time scale, geographic scope, coastal hazard theme, and other filters. The guided search suggests products based on users’ answers to a short series of questions. A comprehensive summary is available for each product.
The idea for the Coastal Science Navigator arose in 2020 in response to stakeholder feedback identifying the need for a central source for USGS coastal science information. It was published in July 2023 and initially included 55 products. Regular updates are planned to integrate other existing and new products.
This guide introduces some of the many coastal change hazards-related products available through the USGS. In it, we showcase the products included in the Coastal Science Navigator’s initial publication in July 2023. While it is not representative of all the information, tools, and data available, we hope it serves as a compelling snapshot of what the USGS has to offer and encourages you to explore the Coastal Science Navigator to discover more of the products you need.
To navigate this guide, the products have been organized by the time scale they are best suited for—past, present, or future—although many products cover multiple time scales. An additional section features software, one of the many product types available as filters within the Coastal Science Navigator. Other products include downloadable data, websites, and geonarratives (web pages that combine text, images, and interactive maps into narratives you can scroll through). Featured geographic scopes are also highlighted within this guide, detailing some of the many regions in which the USGS conducts research and illustrating another way to filter products within the Coastal Science Navigator.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1523","usgsCitation":"Anderberg, M., and Ernst, S., 2024, Coastal Science Navigator companion guide—Discover the U.S. Geological Survey coastal science products you need: U.S. Geological Survey Circular 1523, 31 p., https://doi.org/10.3133/cir1523.","productDescription":"iv, 31 p.","numberOfPages":"31","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-159018","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499076,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117116.htm","linkFileType":{"id":5,"text":"html"}},{"id":430649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1523/coverthb.jpg"},{"id":430650,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1523/cir1523.pdf","text":"Report","size":"15.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1523"},{"id":430680,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/apps/coastalsciencenavigator/index.html","text":"Coastal Science Navigator"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
Estimates of nitrogen loading from nonpoint sources on Long Island, New York, at or just below the land surface, are essential for assessing the current and future effects of nitrogen on the island’s drinking water and fresh and marine surface receiving waters. Annual estimates of nitrogen loading for the 120 years from 1900 to 2019 for major nonpoint nitrogen sources—septic systems, residential fertilizer, agricultural fertilizer, livestock waste, pet waste, and atmospheric deposition—were made by using a geographic information system to analyze, visualize, and process data sources, and format output data. This analysis provided spatial and temporal estimates of nitrogen loading derived from each nonpoint source at a 500- by 500-foot gridded resolution and represents the total mass of nitrogen applied on, or just below, the land surface annually from 1900 to 2019. These mass estimates are considered unattenuated as they do not reflect the various mechanisms of nitrogen loss, such as plant uptake, overland runoff, and chemical transformations in the soil and unsaturated zones that likely reduce the amount of nitrogen that reaches the water table.
Island-wide and countywide summaries of the estimated nitrogen loading were analyzed to describe the long-term average nitrogen totals and the contributions from the six nonpoint sources. The island-wide average annual nitrogen load from 1900 to 2019 was 14.92 million kilograms (Mkg) nitrogen, which represents the aggregate of individual contributions from septic systems (4.15 Mkg), residential fertilizer (3.28 Mkg), agricultural fertilizer (3.19 Mkg), livestock waste (1.22 Mkg), pet waste (0.98 Mkg), and atmospheric deposition (2.10 Mkg). The island-wide average annual nitrogen load, normalized by area, was 4,100 kilograms nitrogen per square kilometer (kg N/km2), which represents the aggregate of individual contributions from septic systems (1,100 kg N/km2), residential fertilizer (910 kg N/km2), agricultural fertilizer (880 kg N/km2), livestock waste (340 kg N/km2), pet waste (270 kg N/km2), and atmospheric deposition (580 kg N/km2).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245047","programNote":"National Water Quality Program","usgsCitation":"Monti, J., Jr., Walter, D.A., and Jahn, K.L., 2024, Nitrogen load estimates from six nonpoint sources on Long Island, New York, from 1900 to 2019: U.S. Geological Survey Scientific Investigations Report 2024–5047, 40 p., https://doi.org/10.3133/sir20245047.","productDescription":"Report: vi, 40 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-113797","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":430660,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5047/coverthb.jpg"},{"id":430665,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MC2LAJ","text":"USGS data release","linkHelpText":"Annual nitrogen load estimates from six nonpoint sources on Long Island, New York, from 1900 to 2019"},{"id":499468,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117117.htm","linkFileType":{"id":5,"text":"html"}},{"id":430664,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5047/images/"},{"id":430663,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5047/sir20245047.XML"},{"id":430662,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5047 HTML"},{"id":430661,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5047/sir20245047.pdf","text":"Report","size":"4.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5047 PDF"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.02511928852329,\n 40.68218751210432\n ],\n [\n -74.04241711735317,\n 40.61984146636081\n ],\n [\n -73.96024588897663,\n 40.51470305180803\n ],\n [\n -73.57529563875842,\n 40.61328064665312\n ],\n [\n -73.00872461020937,\n 40.711699206964624\n ],\n [\n -72.82706852535102,\n 40.72809246976527\n ],\n [\n -72.51134253267655,\n 40.803437312254374\n ],\n [\n -71.82364810847187,\n 41.03873449309313\n ],\n [\n -71.89284965601844,\n 41.113723998451945\n ],\n [\n -72.21290681342221,\n 41.217914881287896\n ],\n [\n -72.6627165098169,\n 41.01915680925052\n ],\n [\n -73.13847751185946,\n 41.002839709762156\n ],\n [\n -73.18604492083178,\n 40.95059491778545\n ],\n [\n -73.31145759896312,\n 40.94732919735483\n ],\n [\n -73.41095044220684,\n 40.97019137918906\n ],\n [\n -73.65314825931426,\n 40.94406140051893\n ],\n [\n -73.78288230142525,\n 40.84925526234707\n ],\n [\n -73.79584578341924,\n 40.79361766159383\n ],\n [\n -73.87367687293711,\n 40.79362055072983\n ],\n [\n -73.90903980024608,\n 40.79462167829007\n ],\n [\n -73.92389759495337,\n 40.78249751209276\n ],\n [\n -73.93657311063014,\n 40.77857350644817\n ],\n [\n -73.93928570688715,\n 40.77163712685007\n ],\n [\n -73.94979738187155,\n 40.758534591172804\n ],\n [\n -73.9630315224905,\n 40.743113924810274\n ],\n [\n -73.96778515613994,\n 40.72948789268287\n ],\n [\n -73.97083994930827,\n 40.71380180805102\n ],\n [\n -73.97999494183452,\n 40.70814705453665\n ],\n [\n -74.00883757884912,\n 40.7017148459239\n ],\n [\n -74.01188750615336,\n 40.68756571016257\n ],\n [\n -74.02511928852329,\n 40.68218751210432\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The U.S. Geological Survey, in cooperation with the Ohio Department of Natural Resources, performed a two-part study to (1) assess water use and temporal trends and changes in streamflow, and to (2) characterize 2021–23 baseline water quality, as they relate to oil and gas extraction activities in selected eastern Ohio watersheds. Between calendar years 2010 and 2019, hydraulic fracturing water withdrawals totaling about 27,168 million gallons were reported at 643 locations in Ohio. In 2021, wells developed with hydraulic fracturing were the source of most of the oil and gas produced in Ohio.
Daily streamflow time-series data from seven study gages and two reference gages were used to assess temporal trends and changes in streamflow. The study gages were in basins with reported water withdrawals for hydraulic fracturing. The reference gages, which have long periods of record and were subject to minimal streamflow regulation, were in nearby basins with no hydraulic fracturing water withdrawals.
Trend slopes for the period of record annual minimum and median daily streamflows and for annual daily streamflow nonexceedance probabilities less than 0.9 were all uniformly positive at the study and reference gages. This trend indicates a consistently increasing pattern over the periods of record, except for high flows. In addition, analyses of annual streamflow statistics showed no general indication that low flows or extreme low flows at the reference or study gages have lowered, become more frequent, or lengthened in duration since 2010, when records for hydraulic fracturing water withdrawals began in Ohio. In fact, in almost all cases, the opposite was indicated.
Nonexceedance percentiles of daily streamflows were compared between the full and pre-2012 periods of record for reference and study gages. The streamflows associated with nonexceedance percentiles in the lower quartile of daily streamflows determined for the full period of record were larger than or equal to those determined for the pre-2012 period of record for all study and reference gages. This indicates that low flows did not decrease during the post-2011 period of record when water was withdrawn for hydraulic fracturing.
Water-quality data were collected eight times at each of eight sampling sites (six of which were colocated with the study gages). Sampling was done during a variety of flow conditions to assess baseline water quality. In 2021, the 8 sampling sites had drainage basins that were wholly or partially within 7 of the 10 most active counties in Ohio for oil and gas development. As part of the record of baseline conditions, water-quality data were used to assess (1) water types based on major-ion chemistry; (2) sources of salinity to streams; (3) exceedances of aquatic life use criteria; and (4) the correlations between water chemistry and drainage-basin characteristics, such as density of oil and gas wells, density of wastewater treatment plants, or the percentage of different types of land cover (agriculture, developed, forest).
Seven of the water-quality sampling sites were designated as coal-mine impacted based on criteria developed for assessing mine-drainage impacts in Ohio. Mine drainage from historical coal mining in the region likely affected the quality of these streams and complicated the use of some constituents typically used as indicators of oil and gas influence. Based on major-ion chemistry, three main types of water were in the study area―sulfate (three sites), calcium-bicarbonate (one site), and mixed bicarbonate-chloride (four sites) type waters. One site had samples with a higher proportion of sodium and chloride ions than other stream samples, indicating potential contamination with oil-field brine or road salt. Binary mixing curves revealed that 11 samples from 4 of the sampling sites likely contained a component of brine. The results of the baseline assessment of surface-water quality in the study area showed no exceedances of Ohio Environmental Protection Agency aquatic life use criteria. Spearman’s rank correlation coefficients indicated no significant positive correlations with the density of vertical or horizontal oil and gas wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245045","collaboration":"Prepared in cooperation with the Ohio Department of Natural Resources","usgsCitation":"Covert, S.A., and Koltun, G.F., 2024, Analysis of water use associated with hydraulic fracturing and determination of baseline water quality in watersheds within the shale play of eastern Ohio, 2021–23: U.S. Geological Survey Scientific Investigations Report 2024–5045, 61 p., https://doi.org/10.3133/sir20245045.","productDescription":"Report: viii, 61 p.; 2 Data Releases","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-159681","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":430672,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1G2W3JQ","text":"USGS data release","linkHelpText":"Annual streamflow statistics for selected streamgages in and near the shale play area of eastern Ohio (through water year 2021)"},{"id":499467,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117118.htm","linkFileType":{"id":5,"text":"html"}},{"id":430670,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5045/images/"},{"id":430668,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245045/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5045 HTML"},{"id":430667,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5045/sir20245045.pdf","text":"Report","size":"28.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5045 PDF"},{"id":430669,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5045/sir20245045.XML","description":"SIR 2024-5045 XML"},{"id":430666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5045/coverthb.jpg"},{"id":430671,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1EDHXB9","text":"USGS data release","linkHelpText":"Data from quality-control equipment blanks, field blanks, and field replicates for baseline water quality in watersheds within the shale play of eastern Ohio, 2021–23"}],"country":"United States","state":"Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.333,\n 41\n ],\n [\n -82.333,\n 39.125\n ],\n [\n -80.666,\n 39.125\n ],\n [\n -80.666,\n 41\n ],\n [\n -82.333,\n 41\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
Managers invest substantial resources to promote recovery of declining anadromous fish stocks. Recovery strategies are manifold and often include management actions intended to stimulate somatic growth, increase in-river survival, and motivate juvenile outmigration during favorable environmental conditions. Evaluating the efficacy of these management actions is difficult, however, because monitoring data that explicitly track individuals from egg deposition to juvenile outmigration are typically lacking. We developed an integrated population model that links two different and often collected types of anadromous fish monitoring data: spawning ground surveys and rotary screw trap juvenile catch data. The integrated model accounts for incomplete detection and uses the two sources of data to estimate juvenile demographic parameters in a multistate framework. We evaluated the model's performance using simulated data under a range of conditions typically encountered in similar surveys. Simulation results indicated that the model estimated juvenile survival, growth, and movement with no-to-minimal bias (i.e., ≥ 50 % of simulations ± 0–0.05). As an example case study, we fit the model to empirical fall-run Chinook Salmon (Oncorhynchus tshawytscha) monitoring data collected in California's Central Valley, U.S.A. In doing so, we evaluated the influence of environmental conditions (e.g., discharge, water temperature) and habitat availability on juvenile demographic rates. We demonstrated that through our integrated approach we could estimate state transition probabilities that are typically inestimable for naturally produced, unmarked juvenile fish when using traditional statistical approaches to analyze these types of monitoring data. Furthermore, the structure of our model can serve as a useful foundation for decision-support models within adaptive management programs by directly linking management actions, decision-support-model predictions, and monitoring.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2024.110780","usgsCitation":"Wohner, P.J., Duarte, A., and Peterson, J., 2024, An integrated analysis for estimation of survival, growth, and movement of unmarked juvenile anadromous fish: Ecological Modelling, v. 495, 110780, 9 p., https://doi.org/10.1016/j.ecolmodel.2024.110780.","productDescription":"110780, 9 p.","ipdsId":"IP-165440","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488126,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2024.110780","text":"Publisher Index Page"},{"id":485452,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5906056592707,\n 40.63419131375724\n ],\n [\n -122.5906056592707,\n 40.47941759385591\n ],\n [\n -122.33253791183354,\n 40.47941759385591\n ],\n [\n -122.33253791183354,\n 40.63419131375724\n ],\n [\n -122.5906056592707,\n 40.63419131375724\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"495","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wohner, Patti J.","contributorId":338233,"corporation":false,"usgs":false,"family":"Wohner","given":"Patti","email":"","middleInitial":"J.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":935785,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":339254,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":935786,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935787,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70269686,"text":"70269686 - 2024 - A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards","interactions":[],"lastModifiedDate":"2025-07-30T14:53:21.858355","indexId":"70269686","displayToPublicDate":"2024-07-03T09:49:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":681,"text":"Agricultural and Forest Meteorology","active":true,"publicationSubtype":{"id":10}},"title":"A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards","docAbstract":"The almond industry in California faces water management challenges that are being exacerbated by droughts, climate change, and groundwater sustainability legislation. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to explore opportunities to improve precision irrigation management for woody perennial cropping systems. Almond orchards in the California Central Valley were equipped with eddy covariance flux measurements to evaluate satellite remote sensing-based evapotranspiration (RSET) models. OpenET provides high-resolution (30-m spatial and daily temporal) RSET data, synthesizing decades of research for practical water management. This study provides an evaluation of OpenET performance at six almond sites covering a large range in soils, age, and variety. It also compares OpenET ensemble evapotranspiration (ET) data with applied irrigation and precipitation records over an additional 148 almond orchards located in the Central Valley of California. Results show OpenET models, including the ensemble ET value, produced reasonable and actionable ET values, with overall coefficient of determination (R2) and mean absolute error values of 0.73- and 0.95-mm d−1 at the daily time step, respectively. However, given the temporal sampling of Landsat (8-day revisit) and the interpolation methods used, the assessed ET models had difficulty in capturing short-term variability in almond ET; for example, the rapid decline in measured ET observed as a response to lack of irrigation preceding and during almond harvest. The study also drew attention to the spatial complexity in scenarios where irrigated orchards are surrounded by hot/dry areas, causing discrepancies between measured and modeled ET values. In comparison with irrigation records, OpenET ensemble ET was capable of quantifying water input (applied irrigation + precipitation) in almond orchards to within 13 % when evaluating monthly data. Initial results presented here reinforce the idea that RSET models, such as in OpenET, are powerful tools, yet their application requires nuanced understanding and careful consideration of local conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2024.110146","usgsCitation":"Knipper, K., Anderson, M., Bambach, N., Melton, F., Ellis, Z., Yang, Y., Volk, J.M., McElrone, A., Kustas, W.P., Roby, M., Carrara, W., Castro, S., Kilic, A., Fisher, J.B., Ruhoff, A., Senay, G.B., Morton, C., Saa, S., and Allen, R., 2024, A comparative analysis of OpenET for evaluating evapotranspiration in California almond orchards: Agricultural and Forest Meteorology, v. 355, 110146, 18 p., https://doi.org/10.1016/j.agrformet.2024.110146.","productDescription":"110146, 18 p.","ipdsId":"IP-167142","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493302,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.agrformet.2024.110146","text":"Publisher Index Page"},{"id":493185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.24346411280976,\n 35.36581404018972\n ],\n [\n -119.04389955946334,\n 35.98229970980546\n ],\n [\n -119.09665048217278,\n 36.489548538637436\n ],\n [\n -120.48964025685427,\n 37.8956809895726\n ],\n [\n -121.30837256179902,\n 39.0131951853744\n ],\n [\n -121.62152001465728,\n 39.13926422978071\n ],\n [\n -122.21612821856195,\n 38.932254764781106\n ],\n [\n -121.71286410491697,\n 37.92123720196997\n ],\n [\n -120.92135482117862,\n 37.048426115873994\n ],\n [\n -120.01788903644467,\n 36.065623999918515\n ],\n [\n -119.49460792137992,\n 35.23527037396478\n ],\n [\n -119.24346411280976,\n 35.36581404018972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"355","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Knipper, Kyle","contributorId":333373,"corporation":false,"usgs":false,"family":"Knipper","given":"Kyle","email":"","affiliations":[{"id":79855,"text":"USDA Agriculture Research Service","active":true,"usgs":false}],"preferred":false,"id":944430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":269899,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":944431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bambach, Nicolas","contributorId":358904,"corporation":false,"usgs":false,"family":"Bambach","given":"Nicolas","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":944432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melton, Forrest","contributorId":223919,"corporation":false,"usgs":false,"family":"Melton","given":"Forrest","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":944433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellis, Zac","contributorId":358905,"corporation":false,"usgs":false,"family":"Ellis","given":"Zac","affiliations":[{"id":85705,"text":"Olan Food Ingredients","active":true,"usgs":false}],"preferred":false,"id":944434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Yun","contributorId":333379,"corporation":false,"usgs":false,"family":"Yang","given":"Yun","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":944435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Volk, J. 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Presently, measures of the in-situ distribution of fault damage remain limited and along-strike studies are rare. This study focuses on an offshore section Palos Verdes Fault damage zone that spans 28 km, near Los Angeles, California. To investigate the previously unresolved shallow (∼400 m below the seafloor) fault damage zone we use densely spaced (∼500 m line separation) newly collected sparker multichannel seismic lines and sub-bottom profiles. The combination of high-resolution acquisition methods and specialized seismic processing workflows provide improved imaging of shallow faulting. We apply a multi-trace similarity technique to identify discontinuities in the seismic data that may be attributed to faults and fractures. This fault detection approach reveals diverse fault damage patterns on adjacent seismic profiles. However, a discernible damage zone pattern emerges by stacking multiple damage detection profiles along strike. We find that peak damage identified in this way corresponds to the active main fault strand, confirmed in this study, and thus the technique may be useful for identifying active fault strands elsewhere. Additionally, we observe that the variable width of the damage zone along strike is controlled by fault obliquity. Furthermore, our observations reveal a correlation between fault damage and seafloor fluid seeps visible in the water column, suggesting that damage plays a role in controlling fluid flow around the fault.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023AV001155","usgsCitation":"Alongi, T., Brodsky, E., Kluesner, J., and Brothers, D., 2024, Characteristics of the fault damage zone From high-resolution seismic imaging along the Palos Verdes Fault, California: AGU Advances, v. 5, no. 4, e2023AV001155, 20 p., https://doi.org/10.1029/2023AV001155.","productDescription":"e2023AV001155, 20 p.","ipdsId":"IP-160708","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":488271,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023av001155","text":"Publisher Index Page"},{"id":484677,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Palos Verdes Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.4,\n 33.75\n ],\n [\n -118.4,\n 33.375\n ],\n [\n -118,\n 33.375\n ],\n [\n -118,\n 33.75\n ],\n [\n -118.4,\n 33.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-07-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Alongi, Travis Vincent 0000-0002-0865-8064","orcid":"https://orcid.org/0000-0002-0865-8064","contributorId":335029,"corporation":false,"usgs":true,"family":"Alongi","given":"Travis Vincent","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brodsky, Emily","contributorId":299735,"corporation":false,"usgs":false,"family":"Brodsky","given":"Emily","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":933745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kluesner, Jared W. 0000-0003-1701-8832","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":206367,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brothers, Daniel S. 0000-0001-7702-157X","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":210199,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":933747,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256509,"text":"70256509 - 2024 - Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay","interactions":[],"lastModifiedDate":"2024-08-12T16:08:21.662133","indexId":"70256509","displayToPublicDate":"2024-07-02T10:55:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2680,"text":"Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science","active":true,"publicationSubtype":{"id":10}},"title":"Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay","docAbstract":"Once thought to be extirpated from the Chesapeake Bay, fall spawning runs of Atlantic Sturgeon Acipenser oxyrinchus have been rediscovered in the Marshyhope Creek (MC)–Nanticoke River (NR) system of Maryland, United States. High recapture rates in past telemetry surveys suggested a small population in the two connected tributaries. This study aims to generate estimates of abundance and understand within system connectivity for spawning runs in 2020 and 2021.
Data from mobile side-scan sonar surveys and detections of acoustically tagged adults on stationary telemetry receivers were analyzed in an integrated model to estimate spawning season abundance and examine run timing and system connectivity for this population. An array of acoustic receivers was deployed throughout the MC–NR system to monitor the movement of tagged fish during the spawning run period from mid-August to late October. Side-scan sonar surveys were conducted weekly in September in an area of high spawner aggregation to generate count data on spawning run abundance.
In 2020 and 2021, 32 (95% credible interval [CRI] = 23–47) and 70 (95% CRI = 49–105) Atlantic Sturgeon, respectively, used the MC–NR system. The lower estimate for 2020 coincided with an earlier end to the spawning run related to cooler September temperatures in that year.
In both years, high spawning run connectivity between MC and the upper NR was observed. Overall, run estimates supported previous hypotheses that the MC–NR system supports a very small population and that both MC and the upper NR serve as important areas for spawning activity.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/mcf2.10292","usgsCitation":"Coleman, N., Fox, D., Horne, A., Hostetter, N.J., Madsen, J., O’Brien, M., Park, I., Stence, C., and Secor, D., 2024, Spawning run estimates and phenology for an extremely small population of Atlantic Sturgeon in the Marshyhope Creek–Nanticoke River system, Chesapeake Bay: Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, v. 16, no. 3, e10292, 16 p., https://doi.org/10.1002/mcf2.10292.","productDescription":"e10292, 16 p.","ipdsId":"IP-152527","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":439304,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/mcf2.10292","text":"Publisher Index Page"},{"id":432489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay, Marshyhope Creek–Nanticoke River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.89424670173172,\n 38.22898121612636\n ],\n [\n -75.80356837182107,\n 38.3806027695843\n ],\n [\n -75.65092235600899,\n 38.5420516415999\n ],\n [\n -75.51423724108903,\n 38.538485023124736\n ],\n [\n -75.51310206970021,\n 38.701340540846786\n ],\n [\n -75.85480590117533,\n 38.71288976149938\n ],\n [\n -75.86506053032485,\n 38.57767393758033\n ],\n [\n -75.88558745668757,\n 38.40381670546191\n ],\n [\n -75.94442936459689,\n 38.33482586063296\n ],\n [\n -75.96657879053116,\n 38.253499920059284\n ],\n [\n -75.89424670173172,\n 38.22898121612636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Coleman, Nicholas","contributorId":340953,"corporation":false,"usgs":false,"family":"Coleman","given":"Nicholas","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907728,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fox, Dewayne","contributorId":340954,"corporation":false,"usgs":false,"family":"Fox","given":"Dewayne","affiliations":[{"id":37219,"text":"Delaware State University","active":true,"usgs":false}],"preferred":false,"id":907729,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horne, Ashlee","contributorId":340955,"corporation":false,"usgs":false,"family":"Horne","given":"Ashlee","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907730,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":907731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Madsen, John","contributorId":340956,"corporation":false,"usgs":false,"family":"Madsen","given":"John","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907732,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Brien, Michael","contributorId":340957,"corporation":false,"usgs":false,"family":"O’Brien","given":"Michael","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907733,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Park, Ian","contributorId":340958,"corporation":false,"usgs":false,"family":"Park","given":"Ian","affiliations":[{"id":36379,"text":"Delaware Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":907734,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Stence, Chuck","contributorId":340959,"corporation":false,"usgs":false,"family":"Stence","given":"Chuck","email":"","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":907735,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Secor, David","contributorId":340960,"corporation":false,"usgs":false,"family":"Secor","given":"David","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":907736,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255701,"text":"ofr20241041 - 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The African region, which comprises the independent nations that make up the African continent and its associated islands and dependencies, consists of a total of 58 mineral producing countries. The primary objective of this effort was to create a fully attributed Geographic Information System (GIS) portraying existing mining infrastructure, resources, and development capacity across Africa along with the related infrastructure capable of supporting current (for the reference year 2018) and future extractive industry operations in the region. The compiled GIS geodatabase with supporting documentation including comprehensive metadata was published as a USGS data release titled \"Compilation of Geospatial Data (GIS) for the Mineral Industries and Related Infrastructure of Africa.\"
This georeferenced portable document format (GeoPDF) map sheet presents a new geographic information product containing a partial representation of the GIS data. This GeoPDF map provides a visual comparison of the distribution of mineral industry and related infrastructure GIS data, which contributes to a deeper understanding of the intersections and complexities of the extractive industries within Africa.
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12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Ixodid (hard) ticks play important ecosystem roles and have significant impacts on animal and human health via tick-borne diseases and physiological stress from parasitism. Tick occurrence, abundance, activity, and key life-history traits are highly influenced by host availability, weather, microclimate, and landscape features. As such, changes in the environment can have profound impacts on ticks, their hosts, and the spread of diseases. Researchers recognize that spatial and temporal factors influence activity and abundance and attempt to account for both by conducting replicate sampling bouts spread over the tick questing period. However, common field methods notoriously underestimate abundance, and it is unclear how (or if) tick studies model the confounding effects of factors influencing activity and abundance. This step is critical as unaccounted variance in detection can lead to biased estimates of occurrence and abundance. We performed a descriptive review to evaluate the extent to which studies account for the detection process while modeling tick data. We also categorized the types of analyses that are commonly used to model tick data. We used hierarchical models (HMs) that account for imperfect detection to analyze simulated and empirical tick data, demonstrating that inference is muddled when detection probability is not accounted for in the modeling process. Our review indicates that only 5 of 412 (1 %) papers explicitly accounted for imperfect detection while modeling ticks. By comparing HMs with the most common approaches used for modeling tick data (e.g., ANOVA), we show that population estimates are biased low for simulated and empirical data when using non-HMs, and that confounding occurs due to not explicitly modeling factors that influenced both detection and abundance. Our review and analysis of simulated and empirical data shows that it is important to account for our ability to detect ticks using field methods with imperfect detection. Not doing so leads to biased estimates of occurrence and abundance which could complicate our understanding of parasite-host relationships and the spread of tick-borne diseases. We highlight the resources available for learning HM approaches and applying them to analyzing tick data.
","language":"English","publisher":"ScienceDirect","doi":"10.1016/j.ttbdis.2024.102342","usgsCitation":"Siren, A.P., Berube, J., Clarfeld, L.A., Sullivan, C.F., Simpson, B., and Wilson, T.L., 2024, Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies: Ticks and Tick-borne Diseases, v. 15, no. 4, 102342, 9 p., https://doi.org/10.1016/j.ttbdis.2024.102342.","productDescription":"102342, 9 p.","ipdsId":"IP-157954","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439305,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ttbdis.2024.102342","text":"Publisher Index Page"},{"id":433570,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"northern 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0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910479,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70255685,"text":"sir20245050 - 2024 - Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020","interactions":[],"lastModifiedDate":"2026-02-03T19:34:03.41537","indexId":"sir20245050","displayToPublicDate":"2024-07-02T07:51:23","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5050","displayTitle":"Water-Quality Trends in the Kansas River, Kansas, since Enactment of the Clean Water Act, 1972–2020","title":"Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020","docAbstract":"The Clean Water Act was passed by Congress in 1972 to regulate pollution within the waters of the United States. The U.S. Geological Survey (USGS), in cooperation with the Kansas Department of Health and Environment (KDHE), the Kansas Water Office, the Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, compiled and analyzed historical streamflow and water-quality data collected by USGS and KDHE to characterize trends in water-quality constituents of interest because of their relation to water supply, drinking-water treatment, and sediment and nutrient transport, among others (total dissolved solids, chloride, ammonia, dissolved inorganic nitrogen [ammonia and nitrate plus nitrite], total nitrogen, orthophosphate, total phosphorus, total suspended solids, and fecal coliform bacteria) during mean- and low-flow conditions in the Kansas River since the passage of the Clean Water Act in 1972 through 2020. Trends in water-quality concentrations, or densities, and loads were analyzed using the Exploration and Graphics for RivER Trends R package and Weighted Regressions on Time, Discharge, and Season (WRTDS) model at upstream (Kansas River at Wamego, Kansas; USGS station 06887500) and downstream (Kansas River at De Soto, Kansas; USGS station 06892350) locations along the Kansas River using streamflow and water-quality data collected by the USGS and KDHE during 1972 through 2020. The Exploration and Graphics for RivER Trends Confidence Intervals R package and WRTDS bootstrap test estimated direction, uncertainty, and likelihood of trends in concentration and loads for each water-quality constituent of interest.
Downward trends in concentration and load were observed for 5 of the 9 water-quality constituents at both sites during mean-flow conditions during the study period. During low-flow conditions, 7 of the 9 constituents exhibited downward trends, possibly reflecting reductions in point-source contributions to the Kansas River. Downward trends in ammonia, dissolved inorganic nitrogen, and total nitrogen during mean- and low-flow conditions were observed at both Kansas River sites, which were similar to patterns observed nationally. Upward trends were generally observed for orthophosphate and total phosphorus, which were similar to patterns observed at sites in the Mississippi River Basin. Downward trends, or no trend, were observed for chloride. Upward and downward trends were observed for total dissolved solids. Downward trends in total suspended solids and fecal coliform bacteria were observed at both sites, which were also similar to patterns observed nationally. The long-term trend analyses in this report are an essential step to understanding how water-quality conditions have changed in the Kansas River since the passage of the Clean Water Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245050","collaboration":"Prepared in cooperation with the Kansas Water Office, the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy","usgsCitation":"Williams, T.J., Klager, B.J., and Stiles, T.C., 2024, Water-quality trends in the Kansas River, Kansas, since enactment of the Clean Water Act, 1972–2020: U.S. Geological Survey Scientific Investigations Report 2024–5050, 29 p., https://doi.org/10.3133/sir20245050.","productDescription":"Report: viii, 29 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-158483","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":430605,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WVZ8X1","text":"USGS data release","linkHelpText":"Water-quality data and computed flow-normalized and low-flow concentrations and loads in the Kansas River, Kansas, 1972–2020"},{"id":430602,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5050/images/"},{"id":430601,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5050/sir20245050.XML"},{"id":430599,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5050/coverthb.jpg"},{"id":430600,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5050/sir20245050.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5050"},{"id":430731,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245050/full"},{"id":499470,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117100.htm","linkFileType":{"id":5,"text":"html"}},{"id":430604,"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"}],"country":"United States","state":"Kansas","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -97.5,\n 40\n ],\n [\n -97.5,\n 38.75\n ],\n [\n -94.5,\n 38.75\n ],\n [\n -94.5,\n 40\n ],\n [\n -97.5,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The scarcity of historically recorded near-fault ground motions poses a challenge to systematically understanding the influence of near-fault effects on various types of seismic demands for engineering purposes. In particular, the current state of knowledge of the influence of ground-shaking intensity on soil liquefaction and its consequences does not specifically account for the effects of near-fault ground motion characteristics. In this study, the influence of near-fault ground motions on liquefaction triggering and lateral spreading are investigated using non-linear modeling of a hypothetical liquefiable soil column in the finite-element computational platform OpenSees subjected to simulated ground motion time series that represent strong earthquake shaking in the near field. The simulated ground motion time series and resulting datasets are based on a parametric stochastic model and are developed for a range of source and path parameters to represent a realistic variability of ground motion characteristics. Dependencies between ground motion intensity measures (IMs) and liquefaction demand parameters are investigated for near-fault pulse and nonpulse-like ground motion sets. Evolutionary IMs, such as cumulative absolute velocity (CAV) and the time-varying magnitude-adjusted peak ground acceleration (PGAM), are considered in developing liquefaction triggering probability density functions. Post-liquefaction triggering responses such as lateral spreading displacements are examined in relation to PGAM and CAV. The ground motion simulations are validated by comparing their liquefaction-capacity PGAM fragilities and post-triggering CAV vulnerability relationships to historical records from the 1994 Northridge earthquake in California, USA. Finally, a path forward for future studies that includes finding systematic differences in the IM-liquefaction demand relationships between near-fault and far-field stochastic ground motion sets is outlined.
","conferenceTitle":"18th World Conference on Earthquake Engineering","conferenceDate":"June 30-July 5., 2024","conferenceLocation":"Milan, Italy","language":"English","publisher":"International Association of Earthquake Engineering","usgsCitation":"Makdisi, A.J., Dabaghi, M., Brito Silveira, L., Rezaeian, S., Haynie, K.L., and Mason, H., 2024, Effects of stochastically-simulated near-fault ground motions on soil liquefaction, 18th World Conference on Earthquake Engineering, Milan, Italy, June 30-July 5., 2024, 12 p.","productDescription":"12 p.","ipdsId":"IP-159292","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":486140,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://proceedings-wcee.org/view.html?id=25504&conference=18WCEE","linkFileType":{"id":5,"text":"html"}},{"id":486213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Makdisi, Andrew James 0000-0002-8239-0692","orcid":"https://orcid.org/0000-0002-8239-0692","contributorId":267917,"corporation":false,"usgs":true,"family":"Makdisi","given":"Andrew","email":"","middleInitial":"James","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dabaghi, Mayssa","contributorId":221888,"corporation":false,"usgs":false,"family":"Dabaghi","given":"Mayssa","email":"","affiliations":[{"id":40455,"text":"American University of Beirut","active":true,"usgs":false}],"preferred":false,"id":937513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brito Silveira, Lianne 0000-0002-8331-7104","orcid":"https://orcid.org/0000-0002-8331-7104","contributorId":355508,"corporation":false,"usgs":true,"family":"Brito Silveira","given":"Lianne","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":937514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rezaeian, Sanaz 0000-0001-7589-7893","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":238513,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haynie, Kirstie Lafon 0000-0001-9930-6736","orcid":"https://orcid.org/0000-0001-9930-6736","contributorId":289894,"corporation":false,"usgs":true,"family":"Haynie","given":"Kirstie","email":"","middleInitial":"Lafon","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mason, Henry 0000-0003-4279-2854","orcid":"https://orcid.org/0000-0003-4279-2854","contributorId":293188,"corporation":false,"usgs":true,"family":"Mason","given":"Henry","email":"","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":937517,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261254,"text":"70261254 - 2024 - 2024 Crustal Deformation Modeling Workshop report","interactions":[],"lastModifiedDate":"2024-12-04T15:17:20.396096","indexId":"70261254","displayToPublicDate":"2024-07-01T09:15:56","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"2024 Crustal Deformation Modeling Workshop report","docAbstract":"The 2024 Crustal Deformation Modeling Workshop was held June 10–14 at the Colorado School of Mines. The workshop included two days of tutorials on using PyLith for crustal deformation modeling, followed by three days of science talks and discussions. The workshop focused on four primary themes:
● Constraining long-term fault slip rates and their uncertainties using geodetic and geologic data;
● Earthquake cycle modeling with a focus on constraining models using seismic and geodetic data;
● Interaction of fluids and faulting; and
● Separating contributions of surface loading and tectonic loading in crustal deformation.
The complete agenda is available on the CIG website.
","conferenceTitle":"2024 Crustal Deformation Modeling Workshop","conferenceDate":"June 10-14, 2024","conferenceLocation":"Golden, CO","language":"English","publisher":"Computational Infrastructure for Geodynamics","usgsCitation":"Aagaard, B.T., Knepley, M., Lindsey, E., Materna, K.Z., Martens, H.R., and Williams, C., 2024, 2024 Crustal Deformation Modeling Workshop report, 2024 Crustal Deformation Modeling Workshop, Golden, CO, June 10-14, 2024, 4 p.","productDescription":"4 p.","ipdsId":"IP-172275","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":464748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":464724,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://geodynamics.org/resources/2113/supportingdocs"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aagaard, Brad T. 0000-0002-8795-9833 baagaard@usgs.gov","orcid":"https://orcid.org/0000-0002-8795-9833","contributorId":192869,"corporation":false,"usgs":true,"family":"Aagaard","given":"Brad","email":"baagaard@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":920126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knepley, Matthew","contributorId":304241,"corporation":false,"usgs":false,"family":"Knepley","given":"Matthew","affiliations":[{"id":37334,"text":"University at Buffalo","active":true,"usgs":false}],"preferred":false,"id":920127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lindsey, Eric","contributorId":261913,"corporation":false,"usgs":false,"family":"Lindsey","given":"Eric","email":"","affiliations":[{"id":48937,"text":"Earth Observatory of Singapore, Nanyang Technological University, Singapore","active":true,"usgs":false}],"preferred":false,"id":920128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Materna, Kathryn Z. 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":209697,"corporation":false,"usgs":false,"family":"Materna","given":"Kathryn","middleInitial":"Z.","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":920129,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martens, Hilary R","contributorId":215383,"corporation":false,"usgs":false,"family":"Martens","given":"Hilary","email":"","middleInitial":"R","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":920130,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, Charles 0000-0001-7435-9196","orcid":"https://orcid.org/0000-0001-7435-9196","contributorId":243027,"corporation":false,"usgs":false,"family":"Williams","given":"Charles","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":920131,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257116,"text":"70257116 - 2024 - Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin","interactions":[],"lastModifiedDate":"2024-08-12T13:52:05.405983","indexId":"70257116","displayToPublicDate":"2024-07-01T08:31:44","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5134,"text":"NOAA Technical Memorandum","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NOS NCCOS 332","title":"Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin","docAbstract":"As part of the 2014 Chesapeake Bay Watershed Agreement, all Bay States and the District of Columbia have committed to improving the condition of the Bay, which includes a goal to achieve sustainable fisheries. One outcome under that broad goal is improved effectiveness of fish habitat conservation and preservation efforts. In support of that outcome, the U.S. Geological Survey Eastern Ecological Science Center (USGS-EESC) and the National Oceanic and Atmospheric Association’s National Centers for Coastal Ocean Science (NOAA-NCCOS) are actively developing datasets, methods, and analyses to conduct fish habitat assessments in the Chesapeake Bay watershed, guided by recommendations from a regional stakeholder workshop held by the Chesapeake Bay Program’s (CBP) Fish Habitat Action Team (FHAT) in 2018. The joint USGS and NOAA team has been collaborating on methods for conducting inland and estuarine assessments and exploring whether a seamless headwater to estuary assessment could be developed. The goals of this assessment are to benefit both State and Federal fisheries managers, help advance fisheries science, and provide beneficial information for the public. While past national and regional assessments (e.g. the National Fish Habitat Partnership National Assessment) treated inland and estuarine fish habitat conditions separately due to differences in environments, GIS data representation, and data availability, a seamless habitat assessment could be of value for a broad range of stakeholders as many fish species, several of which are invasive or under federal jurisdiction, use habitats across both inland and estuarine waters. This project developed a pilot framework, explored and tested methods necessary for a finer scale, seamless assessment across both inland and estuarine waters, and demonstrated its use.
Although there was interest by the CBP FHAT for the generation of a Baywide fish habitat assessment that spanned tidal salt, tidal fresh, warm non-tidal and cold non-tidal waters, there are a myriad of implementation details and considerations around conducting a Baywide assessment across all four of these general habitat areas. Therefore, the practical need to conduct a tributary-specific pilot assessment arose. At the beginning of this pilot process, members of the FHAT were presented with a decision matrix to choose a study basin using factors such as data availability and tributary size. FHAT members chose the Patuxent River basin, which has been relatively well sampled and studied. Several spatial frameworks were considered before selection of an inclusive gridded framework for summary and analysis that represented inland drainage networks and landscape influences as well as estuarine bathymetry. A suite of landscape and in-water stressor variables were summarized into the framework and were largely generalized over time. In order to assess the viability of the framework, we chose to use species distribution modeling for each of the species to test the framework’s ability to predict habitat use of non-tidal resident, estuarine resident, and migratory species. Tessellated darter (Etheostoma olmstedi), American eel (Anguilla rostrata), and white perch (Morone americana) were chosen as illustrative fish species based on data availability, and differences in life history and habitat use. A nested modeling approach, which involved successive model runs at multiple scales (1000m, 100m, and 10m raster grids) was developed to examine differences in variable importance at different spatial scales and to enhance modeling efficiency. For white perch, a complementary modeling analysis was performed for variables available only in estuarine waters. For all testing, an ensemble modeling approach was conducted, using a suite of potential statistical techniques driven by model strength and variable predictive power. The statistical testing that we conducted was intended only to test the framework and modeling approach, and not to definitively predict all habitats where specific fish species might be present. The modeling we conducted to test the framework did have some limitations. For example, the spatial distribution of favorable habitat areas for white perch was likely influenced by the predominance of fish survey locations near the center channel of the river and the use of generalized in-water conditions. For all species, the use of juvenile and adult fish survey data limits the estimation of habitat use to those life stages. Despite such limitations of the data inputs and modeling approach, we found the framework could seamlessly predict fish habitat distribution across freshwater and tidal environments and integrate the influence of landscape stressors with local in-water factors. The developed framework presented to the Sustainable Fisheries Goal Implementation Team (GIT) and FHAT is informative and could potentially be used for other modeling applications in the Chesapeake Bay watershed and elsewhere. In particular the framework and modeling approach lend themselves to evaluating living resource distributions and underlying habitat conditions in shallow tidal waters and beyond, as recommended by the recent Comprehensive Evaluation of System Response (CESR) report from the Chesapeake Bay Program.
","language":"English","publisher":"NOAA","doi":"10.25923/4jqw-mw29","usgsCitation":"Nisonson, H., Kiser, A.H., Gressler, B.P., Leight, A., and Young, J.A., 2024, Pilot framework for fish habitat assessments across tidal and non tidal waters in the Patuxent River Basin: NOAA Technical Memorandum NOS NCCOS 332, vi, 41 p., https://doi.org/10.25923/4jqw-mw29.","productDescription":"vi, 41 p.","ipdsId":"IP-163665","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":432484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Patuxent River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.5870885061828,\n 38.29653642168418\n ],\n [\n -76.39765972760698,\n 38.2523876845089\n ],\n [\n -76.39469990294168,\n 38.39635283845547\n ],\n [\n -76.5219723635473,\n 38.51224538633858\n ],\n [\n -76.5930081555134,\n 38.75962947245472\n ],\n [\n -76.57524920752218,\n 38.93252018914461\n ],\n [\n -76.82683430406816,\n 39.192214789667304\n ],\n [\n -77.06635333905636,\n 39.45429197245687\n ],\n [\n -77.25054644375115,\n 39.48452671490274\n ],\n [\n -77.41561427713579,\n 39.40769859848646\n ],\n [\n -77.04882238288116,\n 39.139433495010024\n ],\n [\n -76.96002764292388,\n 39.04065023841653\n ],\n [\n -76.82979535765293,\n 38.90718862957951\n ],\n [\n -76.82091588365701,\n 38.66493779010759\n ],\n [\n -76.74100152907371,\n 38.412588059675414\n ],\n [\n -76.5870885061828,\n 38.29653642168418\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nisonson, H","contributorId":342011,"corporation":false,"usgs":false,"family":"Nisonson","given":"H","affiliations":[{"id":81821,"text":"Cooperative Oxford Lab","active":true,"usgs":false}],"preferred":false,"id":909477,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiser, Alexander H. 0000-0002-2871-0640","orcid":"https://orcid.org/0000-0002-2871-0640","contributorId":342012,"corporation":false,"usgs":true,"family":"Kiser","given":"Alexander","middleInitial":"H.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":909478,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gressler, Benjamin P. 0000-0001-6639-8558","orcid":"https://orcid.org/0000-0001-6639-8558","contributorId":270167,"corporation":false,"usgs":true,"family":"Gressler","given":"Benjamin","middleInitial":"P.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":909479,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Leight, A","contributorId":342013,"corporation":false,"usgs":false,"family":"Leight","given":"A","email":"","affiliations":[{"id":81821,"text":"Cooperative Oxford Lab","active":true,"usgs":false}],"preferred":false,"id":909480,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":909481,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255704,"text":"70255704 - 2024 - Reduction of large vessel traffic improves water quality and alters fish habitat-use throughout a large river","interactions":[],"lastModifiedDate":"2024-07-02T11:59:32.798901","indexId":"70255704","displayToPublicDate":"2024-07-01T06:57:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Reduction of large vessel traffic improves water quality and alters fish habitat-use throughout a large river","docAbstract":"Rivers are increasingly used as superhighways for the continental-scale transportation of freight goods, but the ecological impact of large vessel traffic on river ecosystems is difficult to study. Recently, the temporary maintenance closure of lock and dam systems on the Illinois Waterway (USA) brought commercial vessel traffic to a halt along the river's length, offering a rare opportunity to study the response of the ecosystem before, during, and after an extended pause of this persistent anthropogenic disturbance. We observed improvements in main- and side-channel water quality and a redistribution of fish habitat-use during a months-long, near-complete reduction of large vessel traffic. Over 3600 water quality and 1300 fish community samples indicate that large vessel traffic reduction coincided with a 33 % reduction in turbidity as well as increased use of sampling strata near vessel navigation corridors by sound-sensitive and rheophilic fishes. Gizzard shad (Dorosoma cepedianum), the most abundant species in the system, also expanded their use of these ‘impact’ areas. Though inland waterway transport is an economically- and climate-friendly alternative to trucking and rail for the shipment of freight, our data suggest that intense vessel traffic may have profound physical and biological impacts across a large river. Monitoring and mitigation of ecological impacts of the ongoing expansion of inland waterway transport around the world will be critical to balancing large rivers as both useful navigation corridors and functional ecosystems.
Access to high-quality food is critical for long-distance migrants to provide energy for migration and arrival at breeding grounds in good condition. We studied effects of changing abundance and availability of a marine food, common eelgrass (Zostera marina L.), on an arctic-breeding, migratory goose, black brant (Brant bernicla nigricans Lawrence 1846), at a key non-breeding site, Bahía San Quintín, Mexico. Eelgrass, the primary food of brant, is consumed when exposed by the tide or within reach from the water's surface. Using an individual-based model, we predicted effects of observed changes (1991–2013) in parameters influencing food abundance and availability: eelgrass biomass (abundance), eelgrass shoot length (availability, as longer shoots more within reach), brant population size (availability, as competition greater with more birds), and sea level (availability, as less food within reach when sea level higher). The model predicted that the ability to gain enough energy to migrate was most strongly influenced by eelgrass biomass (threshold January biomass for migration = 60 g m−2 dry mass). Conversely, annual variation in population size (except for 1998), was relatively low, and variation in eelgrass shoot length and sea level were not strongly related to ability to migrate. We used observed data on brant body mass at Bahía San Quintín and annual survival to test for effects of eelgrass biomass in the real system. The lowest observed values of body mass and survival were in years when biomass was below 60 g m−2, although in some years of low biomass body mass and/or survival was higher. This suggests that the real birds may have some capacity to compensate to meet their energy demands when eelgrass biomass is low. We discuss consequences for brant population trends and conservation.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11619","usgsCitation":"Stillman, R.A., Rivers, E., Gilkerson, W., Wood, K.A., Clausen, P., Deane, C., and Ward, D.H., 2024, Predicting the response of a long-distance migrant to changing environmental conditions in winter: Ecology and Evolution, v. 14, no. 7, e11619, 15 p., https://doi.org/10.1002/ece3.11619.","productDescription":"e11619, 15 p.","ipdsId":"IP-160623","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":439315,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11619","text":"Publisher Index Page"},{"id":434933,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7T43R88","text":"USGS data release","linkHelpText":"Data from Black Brant (Branta bernicla nigricans) Overwintering in Three Lagoons Along the Baja California Peninsula, Mexico"},{"id":432860,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Baja California","otherGeospatial":"Bahía San Quintín","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.06430118019601,\n 30.523772316473426\n ],\n [\n -116.06430118019601,\n 30.37195862378512\n ],\n [\n -115.92021973295668,\n 30.37195862378512\n ],\n [\n -115.92021973295668,\n 30.523772316473426\n ],\n [\n -116.06430118019601,\n 30.523772316473426\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Stillman, Richard A.","contributorId":151661,"corporation":false,"usgs":false,"family":"Stillman","given":"Richard","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":910500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rivers, E.M.","contributorId":245657,"corporation":false,"usgs":false,"family":"Rivers","given":"E.M.","email":"","affiliations":[{"id":49249,"text":"Merkel & Associates, Inc.","active":true,"usgs":false}],"preferred":false,"id":910501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilkerson, W.","contributorId":245658,"corporation":false,"usgs":false,"family":"Gilkerson","given":"W.","affiliations":[{"id":49250,"text":"Wildfowl & Wetlands Trust","active":true,"usgs":false}],"preferred":false,"id":910502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wood, K. A.","contributorId":167726,"corporation":false,"usgs":false,"family":"Wood","given":"K.","email":"","middleInitial":"A.","affiliations":[{"id":24818,"text":"Department of Life and Environmental Sciences, Bournemouth University, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":910503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clausen, P.","contributorId":245661,"corporation":false,"usgs":false,"family":"Clausen","given":"P.","email":"","affiliations":[{"id":49252,"text":"Department of Bioscience – Wildlife Ecology, Aarhus University","active":true,"usgs":false}],"preferred":false,"id":910505,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deane, C.","contributorId":342932,"corporation":false,"usgs":false,"family":"Deane","given":"C.","email":"","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":910506,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":910507,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ]}