Director, Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Harmful algal blooms (HABs) are a persistent and increasing problem globally, yet we still have limited knowledge about how they affect wildlife. Although semi-aquatic and aquatic amphibians and reptiles have experienced large declines and occupy environments where HABs are increasingly problematic, their vulnerability to HABs remains unclear. To inform monitoring, management, and future research, we conducted a literature review, synthesized the studies, and report on the mortality events describing effects of cyanotoxins from HABs on freshwater herpetofauna. Our review identified 37 unique studies and 71 endpoints (no-observed-effect and lowest-observed-effect concentrations) involving 11 amphibian and 3 reptile species worldwide. Responses varied widely among studies, species, and exposure concentrations used in experiments. Concentrations causing lethal and sublethal effects in laboratory experiments were generally 1 to 100 µg/L, which contains the mean value of reported HAB events but is 70 times less than the maximum cyanotoxin concentrations reported in the environment. However, one species of amphibian was tolerant to concentrations of 10,000 µg/L, demonstrating potentially immense differences in sensitivities. Most studies focused on microcystin-LR (MC-LR), which can increase systemic inflammation and harm the digestive system, reproductive organs, liver, kidneys, and development. The few studies on other cyanotoxins illustrated that effects resembled those of MC-LR at similar concentrations, but more research is needed to describe effects of other cyanotoxins and mixtures of cyanotoxins that commonly occur in the environment. All experimental studies were on larval and adult amphibians; there were no such studies on reptiles. Experimental work with reptiles and adult amphibians is needed to clarify thresholds of tolerance. Only nine mortality events were reported, mostly for reptiles. Given that amphibians likely decay faster than reptiles, which have tissues that resist decomposition, mass amphibian mortality events from HABs have likely been under-reported. We propose that future efforts should be focused on seven major areas, to enhance our understanding of effects and monitoring of HABs on herpetofauna that fill important roles in freshwater and terrestrial environments. Environ Toxicol Chem 2024;00:1–14. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Deepwater sculpin (Myoxocephalus thompsonii) were considered extirpated from Lake Ontario prior to the 1990s but have since resurged and are now an abundant offshore demersal species. As deepwater sculpin reproduction is poorly described, an investigation of their gonadal development and fecundity was conducted to better understand their reproductive biology. To evaluate spawning period duration and if females spawn multiple times during their spawning period, we compared deepwater sculpin gonadosomatic index (GSI), gonadal development, and fecundity using individuals collected in fall and spring from 2018 to 2021. Our analysis revealed female GSI was greater in fall (8.1 ± 6.2 %) than spring (4.4 ± 4.3 %). Absolute fecundity averaged 763 ± 246 oocytes and relative fecundity averaged 19 ± 6 oocytes per gram of fish. Histological analysis revealed the presence of only one batch of developing oocytes in the ovary (n = 60), indicating group-synchronous ovarian organization. Our findings suggest deepwater sculpin spawn once annually but have a protracted spawning season indicated by prolonged elevated GSI values. Therefore, protracted spawning in deepwater sculpin likely results in an extended period of larval emergence rather than the majority occurring in late spring as previously suggested. A longer timeframe for deepwater sculpin larval emergence may increase reproductive success and contribute to their population’s recovery.
Data from prior research indicate the prepupal stage of the monarch butterfly life cycle is more sensitive to clothianidin exposure than the larval stage. A set of experiments was conducted to determine if the dietary clothianidin exposures that cause prepupal mortality are environmentally relevant. Monarch larvae were raised from egg to pupae on clothianidin-contaminated swamp milkweed plants (Asclepias incarnata). Larval growth as well as larval and prepupal survival were monitored throughout the experiments, in which the exposures ranged from 1.4 to 2793.1 ng/g leaf. Exposures of 5.4 to 46.9 ng/g leaf resulted primarily in prepupal mortality, whereas higher exposures of 1042.4 to 2793.1 ng/g leaf resulted exclusively in larval mortality, indicating the prepupal stage is more sensitive to clothianidin exposure than the larval stage. A median lethal concentration and a 10% lethal concentration of 37 and 6 ng/g leaf, respectively, were estimated for prepupal mortality. Both effect concentrations are within the range of clothianidin concentrations reported in leaves collected from wild milkweed plants, indicating prepupal mortality is an environmentally relevant effect. Environ Toxicol Chem 2024;00:1–6. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
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.
Flow alteration and riparian vegetation encroachment are causing habitat simplification with severe consequences for native fishes. To assess the effectiveness of enhancing simplified habitat in a large dryland river, we experimentally added invasive wood at 19 paired treatment and reference (no wood added) subreaches (50–100 m) within the main channel of the San Juan River. Using a before-after-control-impact design, we sampled fishes and macroinvertebrates, and quantified habitat complexity. After wood addition, total native fish densities were 2.2× higher in treatments compared with references, whereas total nonnative fish densities exhibited no response. Macroinvertebrate densities were 6.8× higher, and habitat complexity increased in treatments. Counts of geomorphic features in treatments increased from 1 to a maximum of 11 following wood addition, while the number of features in references remained unchanged. Wood addition has potential to instigate natural riverine processes, ultimately enhancing native fish habitat by increasing macroinvertebrate densities and habitat complexity in dryland rivers. Water overallocation and increasing aridity will continue to challenge efforts to improve habitat conditions with environmental flows alone, and managers might consider integrating non-flow alternatives like addition of abundant, invasive wood to reduce habitat simplification.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.4334","usgsCitation":"Miller, B., McKinstry, M., Budy, P., and Pennock, C., 2024, Wood you believe it? Experimental addition of nonnative wood enhances instream habitat for native dryland fishes: River Research and Applications, v. 40, no. 8, p. 1512-1526, https://doi.org/10.1002/rra.4334.","productDescription":"15 p.","startPage":"1512","endPage":"1526","ipdsId":"IP-160668","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Utah","otherGeospatial":"San Juan River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.71157434026986,\n 37.5496706864223\n ],\n [\n -109.73904016058236,\n 37.221774800585685\n ],\n [\n -109.11831262152,\n 36.97244274103839\n ],\n [\n -108.71181848089486,\n 36.628638963464745\n ],\n [\n -108.52505090276979,\n 36.58499376537446\n ],\n [\n -108.15151574651973,\n 36.74406318472876\n ],\n [\n -109.34902551214496,\n 37.575793768007784\n ],\n [\n -109.71157434026986,\n 37.5496706864223\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"40","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-07-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Benjamin J.","contributorId":354723,"corporation":false,"usgs":false,"family":"Miller","given":"Benjamin J.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":936240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKinstry, Mark C.","contributorId":354724,"corporation":false,"usgs":false,"family":"McKinstry","given":"Mark C.","affiliations":[{"id":84646,"text":"Salt Lake City","active":true,"usgs":false}],"preferred":false,"id":936241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pennock, Casey A.","contributorId":287044,"corporation":false,"usgs":false,"family":"Pennock","given":"Casey A.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":936243,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255845,"text":"70255845 - 2024 - Isotopic evaluation of the National Water Model reveals missing agricultural irrigation contributions to streamflow across the western United States","interactions":[],"lastModifiedDate":"2024-07-09T12:01:04.051993","indexId":"70255845","displayToPublicDate":"2024-07-04T06:59:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17995,"text":"Hydrology and Earth Systems Science","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic evaluation of the National Water Model reveals missing agricultural irrigation contributions to streamflow across the western United States","docAbstract":"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.
Avian droppings (combination of fecal matter and urates) provide a non-lethal and non-invasive matrix for measuring pesticide exposures. In the field, droppings may be collected days or weeks after excretion and the persistence of pesticide residues in weathered droppings is not known. Thus, we studied the effects of weathering on pesticide residues in droppings. Domestic chicken (Gallus gallus domesticus) hens were used as a representative species for Order Galliformes. We collected droppings from hens before they were exposed to the pesticides (reference or pre-dose droppings ). Thereafter, the hens were orally administered encapsulated wheat seeds coated with Raxil® PRO Shield (containing the active ingredients imidacloprid, prothioconazole, metalaxyl, and tebuconazole) for consecutive 7 days. During this time, their droppings were collected on days 3, 5, and 8 from the start of the exposure period (post-dose droppings ). The pre-dose and post-dose droppings were weathered for up to 30 days in autumn and spring in shrubsteppe habitat. Droppings were analyzed using HPLC coupled to triple quad LC/MS for parent compound and metabolite residues. No pesticide or its metabolite residues were detected in the weathered reference droppings. No parent pesticide compounds were detected in weathered post-dose droppings but imidacloprid metabolites, imidacloprid-5-hydroxy and imidacloprid-olefin, and the prothioconazole metabolite, desthio-prothioconazole, were detected in all post-dose weathered samples from both seasons. The active ingredients metalaxyl and tebuconazole and their metabolites were not detected in any of the samples. Our results suggest that, depending on the pesticide, its concentration, and the environmental conditions, residues of some pesticides can be detected in droppings weathered for at least 30 days. Knowledge of pesticide persistence in weathered droppings can help refine the quality and quantity of fecal samples that are collected for monitoring pesticide exposures to birds.
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
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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":70255996,"text":"70255996 - 2024 - Invertebrate trophic structure on marine ferromanganese and phosphorite hardgrounds","interactions":[],"lastModifiedDate":"2024-07-30T14:55:12.863126","indexId":"70255996","displayToPublicDate":"2024-07-03T06:58:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Invertebrate trophic structure on marine ferromanganese and phosphorite hardgrounds","docAbstract":"The Southern California Borderland hosts a variety of geologic and oceanographic features that allow for diverse habitats to occur in a restricted region with a strong oxygen minimum zone (OMZ) and hard substrates. These include ferromanganese (FeMn) crusts and phosphorites targeted for deep-seabed mining in other regions. Baseline studies regarding hardground macro- (> 0.3 mm) and megafaunal (> 2 cm) invertebrates are lacking, although they contribute to understanding nutrient cycling and resilience of deep-sea communities to ocean deoxygenation, fishing, or mineral extraction. With the goal of understanding how substrate type, depth, and dissolved oxygen concentration influence invertebrate trophic structure, we surveyed δ13C and δ15N values of invertebrates on hard substrates on the Southern California Borderland margin along a depth gradient (120–2400 m) through the OMZ at inshore (< 100 km from shore) and offshore (100–250 km from shore) sites, using generalized additive models and community-level metrics. Macrofaunal isotopic values correlate with substrate type, exhibiting higher trophic diversity on FeMn crusts and specialized communities on phosphorites. Megafaunal isotopic values correlate with proximity to shore; animals offshore seem to depend more on phytoplanktonic production than animals inshore. In general, δ15N increased with decreasing dissolved oxygen and increasing depth, possibly due to remineralization processes within the OMZ and with depth. We discuss how feeding modes and community composition might influence the observed patterns. This study elucidates the importance of the environmental context in shaping invertebrate trophic structure on continental margins and provides baseline knowledge that may be useful in regions where these minerals are targeted for extraction.
Introduction: Habitat loss and degradation pose significant threats to global fish and wildlife populations, prompting substantial investments in habitat creation and restoration efforts. Not all habitats provide equal benefits, leading to challenges in prioritizing restoration actions. For example, juvenile anadromous salmonids require high quality rearing aquatic habitats to achieve the physiological requirements needed to successfully migrate to the ocean. However, there are profound disagreements among anadromous salmon restoration managers whether it is best to focus efforts on restoring in-channel habitats that are available for the entire rearing period or floodplain habitats that, while facilitating greater growth and survival than in-channel habitats, are only available for a few weeks at a time and are typically only activated every two-to-three years.
Methods: We used an existing fall-run Chinook salmon decision-support model to evaluate under what conditions floodplain restoration would provide greater benefits than in-channel habitat restoration. The simulations included a wide range of floodplain inundation frequencies and durations and floodplain benefits in the form of increased survival and growth relative to in-channel habitats.
Results: The simulations results indicated that in-channel habitat restoration was always the best habitat restoration action when there was no existing in-channel habitat despite simulating a wide range of flood frequency, duration, and growth and survival benefits. Floodplain restoration was generally best when there was sufficient in-channel habitat available to successfully rear most of the juveniles produced by the returning adult salmon.
Discussion: We hypothesize that in-channel and floodplain habitats have different roles in salmon population maintenance with in-channel habitats regulating the overall population size and floodplains acting as recurrent resource pulses. Our study provides a quantitative framework to evaluate the benefit of these two habitat types and provides generalizable rulesets that can be used by managers when implementing habitat restoration strategies for species that inhabit both in-channel and floodplain habitats.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcosc.2024.1428697","usgsCitation":"Peterson, J., and Duarte, A., 2024, An evaluation of tradeoffs in restoring ephemeral vs. perennial habitats to conserve animal populations: Frontiers in Conservation Science, v. 5, 1428697, 12 p., https://doi.org/10.3389/fcosc.2024.1428697.","productDescription":"1428697, 12 p.","ipdsId":"IP-165963","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488153,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcosc.2024.1428697","text":"Publisher Index Page"},{"id":485520,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.15324571467127,\n 38.645272222200845\n ],\n [\n -122.15324571467127,\n 35.22532646207205\n ],\n [\n -118.48275085421352,\n 35.22532646207205\n ],\n [\n -118.48275085421352,\n 38.645272222200845\n ],\n [\n -122.15324571467127,\n 38.645272222200845\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-07-02","publicationStatus":"PW","contributors":{"authors":[{"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":935994,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":337608,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":935995,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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 - 2024 - Geospatial PDF map of the compilation of GIS data for the mineral industries and related infrastructure of Africa","interactions":[],"lastModifiedDate":"2024-07-17T15:49:48.797057","indexId":"ofr20241041","displayToPublicDate":"2024-07-02T10:45:00","publicationYear":"2024","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":"2024-1041","displayTitle":"Geospatial PDF Map of the Compilation of GIS Data for the Mineral Industries and Related Infrastructure of Africa","title":"Geospatial PDF map of the compilation of GIS data for the mineral industries and related infrastructure of Africa","docAbstract":"In 2021, the U.S. Geological Survey's (USGS) National Minerals Information Center (NMIC) completed the project titled \"Compilation of geospatial data for the mineral industries and related infrastructure of Africa.\" This project aimed to leverage the expertise and capabilities of the NMIC to collect, synthesize, and interpret geospatial data to inform on the extractive resources of the African region and expand the NMIC's understanding on the impact of mineral industry of African nations in the global economy. 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.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241041","usgsCitation":"Neustaedter, E.R., Kemna, R.F., Padilla, A.J., and Otarod, D., 2024, Geospatial PDF map of the compilation of GIS data for the mineral industries and related infrastructure of Africa: U.S. Geological Survey Open-File Report 2024–1041, 1 geospatial map, scale 1:38,504,000, https://doi.org/10.3133/ofr20241041.","productDescription":"1 Map: 18.00 x 12.00 inches; Data Release","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130230","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":431024,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241041/full"},{"id":431025,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"http://pubs.usgs.gov/of/2024/1041/images/"},{"id":430978,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1041/ofr20241041.XML","description":"OFR 2024-1041 XML"},{"id":430674,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/of/2024/1041/coverthb.jpg"},{"id":430676,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97EQWXP","text":"USGS data release","linkHelpText":"Compilation of geospatial data (GIS) for the mineral industries and related infrastructure of Africa"},{"id":430675,"rank":2,"type":{"id":11,"text":"Document"},"url":"http://pubs.usgs.gov/of/2024/1041/ofr20241041.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1041 PDF"}],"otherGeospatial":"Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -22.324219500055847,\n 41.39808853602034\n ],\n [\n -22.324219500055847,\n -37.83510956130807\n ],\n [\n 53.9648429999433,\n -37.83510956130807\n ],\n [\n 53.9648429999433,\n 41.39808853602034\n ],\n [\n -22.324219500055847,\n 41.39808853602034\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Anadromous river herring populations, collectively alewife (Alosa pseudoharengus) and blueback herring (Alosa aestivalis), have experienced a multi-century decline in abundance and distribution. These declines have been attributed in part to anthropogenic threats in freshwater ecosystems (e.g., habitat fragmentation, overharvest, water pollution, watershed development). An understanding of variability in juvenile productivity and growth is critical to developing restoration approaches. We characterized variability in juvenile river herring growth among 11 freshwater lakes in the northeastern USA. We used age estimates from otoliths and length measurements to calculate growth rates of juvenile river herring (n = 1452). We tested the effects of juvenile river herring densities, zooplankton (biomass and size), habitat area (based on thermocline depth), and water quality (temperature, nutrients, chlorophyll a) on juvenile growth. Mean monthly growth rates ranged from 0.56 to 1.41 mm/d and typically increased throughout the summer. Increased juvenile growth was best predicted by lower juvenile density (β = − 0.104, P < 0.001) and higher zooplankton biomass (β = 0.032, P < 0.05). Combined with information about juvenile densities and mortality, these results broaden the understanding of anadromous juvenile river herring productivity, provide information that can contribute to refining stock assessment and life cycle models, and help to better understand the potential impacts of habitat conservation and restoration decisions.
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Fisheries","active":true,"usgs":false}],"preferred":false,"id":910803,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jordaan, Adrian","contributorId":343340,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":910804,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257461,"text":"70257461 - 2024 - Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies","interactions":[],"lastModifiedDate":"2024-09-06T16:40:27.763678","indexId":"70257461","displayToPublicDate":"2024-07-02T09:29:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5082,"text":"Ticks and Tick-borne Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Accounting for missing ticks: Use (or lack thereof) of hierarchical models in tick ecology studies","docAbstract":"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.
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consumers and growth across climatic conditions in the northern Gulf of Alaska","docAbstract":"Macroalgae and phytoplankton support the base of highly productive nearshore ecosystems in cold-temperate regions. To better understand their relative importance to nearshore food webs, this study considered four regions in the northern Gulf of Alaska where three indicator consumers were collected, filter-feeding mussels (Mytilus trossulus), pelagic-feeding Black Rockfish (Sebastes melanops), and benthic-feeding Kelp Greenling (Hexagrammos decagrammus). The study objectives were to (1) estimate the proportional contributions of macroalgal and phytoplankton organic matter using carbon and nitrogen stable isotopes, (2) determine if macroalgal use affected consumer growth using annual growth rings in shells or otoliths, and (3) describe changes in organic matter use and growth during the Pacific Marine Heatwave (PMH; 2014–2016) in one consumer, mussels. Macroalgae were the major organic matter source (> 60%) to the diet for all three consumers. The relationships between macroalgal contribution and growth were neutral for both fish species and significantly positive for mussels. During the PMH, mussels had a drop (> 10%) in macroalgal contributions and grew 45% less than in other time periods. Simultaneously, the relationship between macroalgal contribution and mussel growth was strongest during the PMH, explaining 48% variation compared to 3–12% before or after the PMH. Collectively, the results suggest that macroalgae is likely more important to cold-temperate nearshore food webs than phytoplankton. Management actions aimed at conserving and expanding macroalgae are likely to benefit nearshore food webs under all climate scenarios and especially during marine heatwaves.
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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
Globally, glaciers and icefields contribute significantly to sea level rise. Here we show that ice loss from Juneau Icefield, a plateau icefield in Alaska, accelerated after 2005 AD. Rates of area shrinkage were 5 times faster from 2015–2019 than from 1979–1990. Glacier volume loss remained fairly consistent (0.65–1.01 km3 a−1) from 1770–1979 AD, rising to 3.08–3.72 km3 a−1 from 1979–2010, and then doubling after 2010 AD, reaching 5.91 ± 0.80 km3 a−1 (2010–2020). Thinning has become pervasive across the icefield plateau since 2005, accompanied by glacier recession and fragmentation. Rising equilibrium line altitudes and increasing ablation across the plateau has driven a series of hypsometrically controlled melt-accelerating feedbacks and resulted in the observed acceleration in mass loss. As glacier thinning on the plateau continues, a mass balance-elevation feedback is likely to inhibit future glacier regrowth, potentially pushing glaciers beyond a dynamic tipping point.