We sampled juvenile wild Steelhead Trout Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize population attributes and investigate life-history metrics, particularly migratory patterns, and early life-stage survival. We used passive integrated transponder (PIT) tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles and adults. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead Trout have been released in the Wind River subbasin since 1997, and hatchery adults are estimated at less than one percent of spawners in most years. Over twenty years of Steelhead Trout status and trend monitoring and research in the subbasin is contributing to understanding of population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, which had an outdated adult ladder and contributed to increased water temperatures reducing performance of juvenile Steelhead Trout.
Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research, Monitoring, and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (https://www.cbfish.org/ProgramStrategy.mvc/Index). Specifically, this work addresses the substrategies of 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and Uncertainties Research regarding differing life histories of a wild Steelhead Trout population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives.
During summer and fall 2020, we PIT-tagged 1,415 Steelhead parr (age-0 and age-1) in the Trout Creek and upper Wind River watersheds. Recaptures and detections of PIT-tagged Steelhead Trout parr happened through repeat headwater sampling, smolt trap operations, and instream PTISs and Columbia River PIT-tag detection infrastructure. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead Trout parr, smolts, and adults, providing data to population assessments, and life-cycle research and modeling.
Detection data from PIT-tagged adult Steelhead Trout at PTISs allow assessment of adult escapement to tributary watersheds within the Wind River subbasin. Adult Steelhead Trout detection efficiency estimates at our primary PTIS in Trout Creek have been greater than 92 percent during eight of the past nine years and have exceeded 90% at our primary PTIS in the Wind River the past three years. Adult escapement estimates to tributary watersheds are helping evaluate the efficacy of the 2009 removal of Hemlock Dam from rkm 2.0 of Trout Creek. The dam had potential negative effects on Steelhead Trout populations in Trout Creek due to hydrologic impairment, increased temperatures, and adult passage issues. Hemlock Dam was laddered for adult passage, but not to modern standards, which likely resulted in avoidance by some adult Steelhead Trout.
We continue to improve our PTISs in the Wind River subbasin. The improvements in siting and addition of grid power to the upper Wind River PTIS (site code WRU, rkm 27.6) during 2016 and 2017, and the addition of the Mine Reach site (site code MIN, rkm 36.0) have much improved PIT-tagged fish monitoring in the upper Wind River watershed. The paired PTIS design in the upper Wind River watershed (sites WRU and MIN), matches that in the Trout Creek watershed (sites TRC and TC4) and will allow comparisons of Steelhead Trout population metrics between the two watersheds as response to Hemlock Dam removal continues and future restoration efforts occur in Trout Creek. We installed two new PTISs during 2020. Both were installed downstream of our primary interrogation sites on Trout Creek and in the mainstem Wind River. We hope the two new sites will provide interrogations information that will allow us to better estimate detection efficiencies of downstream moving juvenile Steelhead Trout at the primary interrogation sites. The additional interrogations will be particularly important for those fish tagged with 9-mm PIT tags as less information from downstream locations is available from them. These sites and other status and trend data will allow evaluation of further planned restoration within the watershed, particularly that proposed for the headwaters of Trout Creek.
Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. We have estimated that from 15 to 51% of parr tagged as age-0 fish in headwater areas make downstream migrations at age 1 for additional rearing during both spring and fall. We have estimated that up to 27% of Steelhead Trout parr, tagged as age-1 fish, make downstream migrations during fall. These findings raise questions about where parr most successfully rear and whether migrations are density or habitat quality driven. Broader monitoring programs would give a more comprehensive understanding of juvenile Steelhead Trout production and rearing and productivity contribution.
Repeat sampling at consistent locations in the subbasin has enabled assessment of juvenile Steelhead Trout growth patterns. Growth rates (relative change in weight) of age-0 PITtagged parr during summer were similar across the subbasin but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly relative growth for parr tagged at age-0 is similar across the subbasin.
Non-native Brook Trout Salvelinus confluentus are present in the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Mean percent-of-catch that is Brook Trout, at four sample sites in Trout Creek, has declined from the period 1998 – 2003 to the period 2011 – 2020. Percent-of-catch and number of Brook Trout at the Trout Creek sites from 2011 through 2020 declined, though both metrics increased in 2018.
Evaluation and planning of restoration efforts are critical to ensure efficient use of resources. Assessing Steelhead Trout life history variation in the Wind River subbasin will inform research and tracking of many populations and help inform habitat restoration and water allocation planning. Movement of Steelhead Trout parr from natal areas to other rearing areas raises questions regarding juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead Trout parr allow investigation of such questions. Increasingly detailed viable salmonid population information, such as that provided by PIT-tagging and instream PTIS networks like those in the Wind River can provide data to inform fisheries policy and management and understand life-history strategies and limiting factors. Such efforts also provide assessment of long-term effects of habitat restoration actions such as the removal of Hemlock Dam on Trout Creek, and the proposed Stage-0 restoration effort for upper Trout Creek, which would be a large-scale effort to reset sections of stream within their floodplain, restoring connectivity and interaction with surrounding landscape.
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However, estimating\ndesired ranges of environmental flows across large, diverse landscapes is challenging. To advance protections of environmental flows for streams in California, USA, we developed a statewide modeling approach focused on functional components of the natural flow regime. Functional flow components in California streams—fall pulse flows, wet season peak flows and base flows, the spring flow recession, and dry season baseflows—support essential physical and ecological processes in riverine ecosystems. These functional flow components can be represented by functional flow metrics (FFMs) and quantified by their magnitude, timing, frequency, duration, and rate-of-change from daily streamflow records. After quantifying FFMs at reference-quality streamflow gages in California, we used machine-learning methods to estimate their natural range of values for all stream reaches in the state based on physical watershed characteristics and climatic factors. We found that the models performed well in predicting FFMs in streams across a diversity of landscape and climate contexts, according to several model performance criteria. Using the predicted FFM values, we established initial estimates of ecological flows that are expected to support critical functions and are broadly protective of ecosystem health. Modeling functional flows statewide offers a pathway for increasing the pace and scale of environmental flow protections in California and beyond.","language":"English","publisher":"Frontiers in Environmental Science","doi":"10.3389/fenvs.2022.787473","usgsCitation":"Grantham, T.E., Carlisle, D.M., Howard, J., Lane, B., Lusardi, R., Obester, A., Sandoval-Solis, S., Stanford, B., Stein, E.D., Taniguchi-Quan, K.T., Yarnell, S.M., and Zimmerman, J.K., 2022, Modeling functional flows in California rivers: Frontiers in Environmental Science, v. 10, 787473, 11 p., https://doi.org/10.3389/fenvs.2022.787473.","productDescription":"787473, 11 p.","ipdsId":"IP-132706","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":448653,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2022.787473","text":"Publisher Index Page"},{"id":435941,"rank":0,"type":{"id":30,"text":"Data 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Here, we used a data-driven approach to identify regional patterns and exogenous controls on P–Q interactions. We applied an information flow analysis, which quantifies uncertainty reduction, to a daily time series of P and Q from 671 watersheds across the conterminous United States. We first demonstrated that information transfer from P to Q primarily reflects the quickflow component of water-budgets, based on a watershed model. Readily quantifiable information flows show a functional relationship with model parameters, suggesting utility for model calibration. Second, applied to real watersheds, P–Q information flows exhibit seasonally varying behavior within regions in a manner consistent with dominant runoff generation mechanisms. However, the timing and the magnitude of information flows also reflect considerable subregional heterogeneity, likely attributable to differences in watershed size, baseflow contributions, and variation in aerial coverage of preferential flow paths. A regression analysis showed that a combination of climate and watershed characteristics are predictive of P–Q information flows. Though information flows cannot, in most cases, uniquely determine dominant runoff mechanisms, they provide a means to quantify the heterogeneous outcomes of those mechanisms within regions, thereby serving as a benchmarking tool for models developed at the regional scale. Last, information flows characterize regionally specific ways in which catchment connectivity changes from the wet to dry season.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR030186","usgsCitation":"Moges, E., Ruddell, B., Zhang, L., Driscoll, J.M., and Larsen, L., 2022, Strength and memory of precipitation's control over streamflow across the conterminous United States: Water Resources Research, v. 58, no. 3, e2021WR030186, 20 p., https://doi.org/10.1029/2021WR030186.","productDescription":"e2021WR030186, 20 p.","ipdsId":"IP-128702","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":448657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr030186","text":"Publisher Index Page"},{"id":397106,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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Lubinski, 2022, Sea duck joint venture key site atlas entries: Garden Peninsula; Green Bay and Bay de Nocs; Southeast Lake Michigan; Sleeping Bear Dunes, 17 p.","productDescription":"17 p.","startPage":"220","endPage":"236","ipdsId":"IP-114292","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":430757,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":430748,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seaduckjv.org/science-resources/sea-duck-key-habitat-sites-atlas-narratives/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.91430692706854,\n 44.545847128351284\n ],\n [\n -86.91076064018311,\n 45.27916001921463\n ],\n [\n -85.81335111497998,\n 45.75373969429327\n ],\n [\n -86.07367240567777,\n 46.0583102775486\n ],\n [\n -86.42400798803507,\n 46.03002448149968\n ],\n [\n -87.0386937617094,\n 45.88323197539174\n ],\n [\n -87.85778302953462,\n 45.089776419672745\n ],\n [\n -88.1864395714017,\n 44.53318465218152\n ],\n [\n -87.91430692706854,\n 44.545847128351284\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.35887197898197,\n 44.6308406271734\n ],\n [\n -86.08385417049092,\n 44.59892444932581\n ],\n [\n -85.5144965008045,\n 45.1158421818501\n ],\n [\n -85.49528923970595,\n 45.35010261013042\n ],\n [\n -85.75757183508726,\n 45.502751167959985\n ],\n [\n -85.93989022455948,\n 45.47135726785672\n ],\n [\n -86.33011554939455,\n 45.05034668879057\n ],\n [\n -86.48684539297616,\n 44.73992431917307\n ],\n [\n -86.35887197898197,\n 44.6308406271734\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.03323269109094,\n 41.84867540740291\n ],\n [\n -86.62083227900176,\n 41.775476248813646\n ],\n [\n -85.96040360802044,\n 42.39498184086449\n ],\n [\n -85.96971487128852,\n 43.12466745581105\n ],\n [\n -86.41561405330732,\n 43.86573959042772\n ],\n [\n -86.89029065548527,\n 43.81594788150977\n ],\n [\n -86.82323881021209,\n 43.149777594261366\n ],\n [\n -86.7213725702679,\n 42.473248830322774\n ],\n [\n -87.03323269109094,\n 41.84867540740291\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kenow, Kevin P. 0000-0002-3062-5197 kkenow@usgs.gov","orcid":"https://orcid.org/0000-0002-3062-5197","contributorId":3339,"corporation":false,"usgs":true,"family":"Kenow","given":"Kevin","email":"kkenow@usgs.gov","middleInitial":"P.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":905586,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fara, Luke J. 0000-0002-1143-4395","orcid":"https://orcid.org/0000-0002-1143-4395","contributorId":202973,"corporation":false,"usgs":true,"family":"Fara","given":"Luke J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":905584,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Houdek, Steven C. 0000-0001-9452-6596 shoudek@usgs.gov","orcid":"https://orcid.org/0000-0001-9452-6596","contributorId":4423,"corporation":false,"usgs":true,"family":"Houdek","given":"Steven","email":"shoudek@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":905585,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brian R. Lubinski","contributorId":339916,"corporation":false,"usgs":false,"family":"Brian R. Lubinski","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":905587,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230685,"text":"70230685 - 2022 - Natural and anthropogenic influences on benthic cyanobacteria in streams of the northeastern United States","interactions":[],"lastModifiedDate":"2022-05-13T15:16:03.306357","indexId":"70230685","displayToPublicDate":"2022-03-01T06:53:35","publicationYear":"2022","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":"Natural and anthropogenic influences on benthic cyanobacteria in streams of the northeastern United States","docAbstract":"Benthic cyanobacteria are widespread in streams and rivers and have the potential to release toxins. In large numbers, these microorganisms and their toxins present a risk to human health. Cyanobacterial abundance in stream biofilms is typically related to single or a limited set of environmental factors, mainly light availability, water temperature, and nutrient concentrations. However, these factors may act synergistically with watershed characteristics and other stressors, such as anthropogenic pollutants, to affect cyanobacteria. We investigated the influence of multiple regional and local variables on the abundance of benthic cyanobacterial genera in streams using all subsets generalized additive modeling. We examined watershed factors (topography, geology, and climate) alongside in-stream factors (geomorphology, hydrology, pH, specific conductance, nutrients, organic contaminants, and dissolved metals) from 76 sites along an urban gradient in the northeast United States. Each genus responded to a distinct combination of environmental variables, demonstrating strong intergeneric variation in environmental selection of realized niches. Four of the 7 potentially toxigenic genera that we modeled were positively influenced by water temperature or nutrients. Nonetheless, watershed characteristics, streamflow, and/or other water quality pollutants were equally or more influential for the potentially toxigenic genera. Additionally, the relationships between cyanobacterial abundance and environmental factors varied in shape and direction across many genera. In particular, with increasing concentrations of herbicides, polychlorinated biphenyls, or metals, the abundance of roughly half of the affected genera decreased, while the others increased. These results likely demonstrate novel toxic effects of the pollutants on cyanobacterial genera in the environment, while indicating that unmeasured biotic interactions may lead to positive responses for other genera. Our results emphasize the need to consider variables beyond those that are most frequently measured or implicated (e.g., water temperature and nutrients) to more fully understand the environmental conditions that influence the distributions and abundance of potentially harmful cyanobacteria.
As human populations grow, decisions regarding use of the world's finite land base become increasingly complex. We adopted a land use–conflict scenario involving renewable energy to illustrate one potential cause of these conflicts and resulting tradeoff decisions. Renewable energy industries wishing to expand operations in the United States are limited by multijurisdictional regulations in finding developable land. Interest groups entreat industries to avoid land for various reasons, including avoidance of prime wildlife habitat in accordance with an “avoidance-first” mitigation strategy. By applying a uniform set of rules for renewable energy facilities to the Prairie Pothole Region and portions of the Northern Great Plains, we evaluated the effects of regulations and avoidance of prime wildlife habitat on the amount of land available for development. In our scenario, existing regulations excluded 39% of the project area from potential development, with human infrastructure accounting for 30% (10–66% among states), whereas federally protected species accounted for < 1% at project area and state levels. Unregulated lands accounted for 61% of the project area, with conservation areas predicted as high-quality sites for breeding grassland birds and waterfowl and for migrating whooping cranes Grus americana accounting for 19% within the project area (6–27% among states). This model demonstrated a limited land base available for new development when accounting for regulations and concerns of a subset of societal interest groups. Additional interest groups likely will have different and competing concerns, further emphasizing the complexity of future land-use decisions as the available land base for development diminishes.
","language":"English","publisher":"U.S. Fish and Wildlife Service","doi":"10.3996/JFWM-21-036","usgsCitation":"Shaffer, J.A., Niemuth, N.D., Loesch, C.R., Derby, C.E., Pearse, A.T., Barnes, K.W., Shaffer, T.L., and Ryba, A.J., 2022, Limited land base and competing land uses force societal tradeoffs when siting energy development: Journal of Fish and Wildlife Management, v. 13, no. 1, p. 106-123, https://doi.org/10.3996/JFWM-21-036.","productDescription":"18 p.","startPage":"106","endPage":"123","ipdsId":"IP-122448","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":448664,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-036","text":"Publisher Index Page"},{"id":405160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Jill A. 0000-0003-3172-0708 jshaffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":3184,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill","email":"jshaffer@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Niemuth, Neal D. 0009-0006-9637-5588","orcid":"https://orcid.org/0009-0006-9637-5588","contributorId":204334,"corporation":false,"usgs":false,"family":"Niemuth","given":"Neal","email":"","middleInitial":"D.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":848974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loesch, Charles R. 0000-0003-3090-1566","orcid":"https://orcid.org/0000-0003-3090-1566","contributorId":213437,"corporation":false,"usgs":false,"family":"Loesch","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":848975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Derby, Clayton E.","contributorId":295253,"corporation":false,"usgs":false,"family":"Derby","given":"Clayton","email":"","middleInitial":"E.","affiliations":[{"id":38051,"text":"Western EcoSystems Technology, Inc.","active":true,"usgs":false}],"preferred":false,"id":848976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnes, Kevin W.","contributorId":295254,"corporation":false,"usgs":false,"family":"Barnes","given":"Kevin","email":"","middleInitial":"W.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":848978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shaffer, Terry L. 0000-0001-6950-8951 tshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-6950-8951","contributorId":3192,"corporation":false,"usgs":true,"family":"Shaffer","given":"Terry","email":"tshaffer@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":848979,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ryba, Adam J.","contributorId":204335,"corporation":false,"usgs":false,"family":"Ryba","given":"Adam","email":"","middleInitial":"J.","affiliations":[{"id":36919,"text":"U.S. Fish and Wildlife Service Habitat and Population Evaluation Team","active":true,"usgs":false}],"preferred":false,"id":848980,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70229031,"text":"ofr20221015 - 2022 - Floods of June 21–July 1, 2018, in the Floyd River and Little Sioux River Basins, northwestern Iowa","interactions":[],"lastModifiedDate":"2026-03-27T19:52:19.500576","indexId":"ofr20221015","displayToPublicDate":"2022-02-28T13:24:18","publicationYear":"2022","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":"2022-1015","displayTitle":"Floods of June 21–July 1, 2018, in the Floyd River and Little Sioux River Basins, Northwestern Iowa","title":"Floods of June 21–July 1, 2018, in the Floyd River and Little Sioux River Basins, northwestern Iowa","docAbstract":"The Floyd River and Little Sioux River Basins in northwestern Iowa flooded on June 21–July 1, 2018, after sustained rainfall on June 14–27, 2018. Within the Floyd River Basin, rainfall totals from June 14 to 21 preceding flooding were 3.01 inches (in.) at Le Mars, 4.50 in. at Orange City, and 7.44 in. at Sheldon. Within the Little Sioux River Basin, rainfall amounts for the 2-week period from June 14 to 27 preceding flooding were 11.29 in. at Lake Park, 12.95 in. at Milford, 5.56 in. at Spencer, 7.71 in. at Sioux Rapids, and 6.13 in. at Cherokee. Flooding in the Floyd River Basin resulted in a recorded maximum peak discharge of 14,300 cubic feet per second (ft3/s; annual exceedance probability [AEP] estimate between 4 and 10 percent) at the U.S. Geological Survey (USGS) streamgage Floyd River at Alton, Iowa (06600100), and a recorded maximum peak discharge of 9,180 ft3/s (AEP estimate greater than 10 percent) at the USGS streamgage Floyd River at James, Iowa (06600500). Flooding in the Little Sioux River Basin resulted in a recorded maximum peak discharge of 16,300 ft3/s (AEP estimate between 4 and 10 percent) at the USGS streamgage Little Sioux River at Linn Grove, Iowa (06605850), and maximum peak discharges of 18,700 ft3/s (AEP estimate greater than 10 percent) and 20,000 ft3/s (AEP estimate greater than 10 percent) were recorded at the USGS streamgages Little Sioux River at Correctionville, Iowa (06606600), and Little Sioux River near Turin, Iowa (06607500), respectively. High-water mark elevations were surveyed at 19 locations along the Floyd River and 22 locations along the Little Sioux River to develop 2 flood profiles: a 52.5-mile profile along the Floyd River from State Highway 3 at Le Mars to U.S. Highway 18 at Sheldon that includes the USGS streamgage Floyd River at Alton and a 101-mile profile along the Little Sioux River from U.S. Highway 59 at Cherokee to U.S. Highway 18 north of Spencer that includes the USGS streamgage Little Sioux River at Linn Grove.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221015","collaboration":"Prepared in cooperation with the Iowa Department of Transportation and the Iowa Highway Research Board (Project HR–140)","usgsCitation":"O’Shea, P.S., Wilson, J.L., Vegrzyn, J.C., and Barnes, K.K., 2022, Floods of June 21–July 1, 2018, in the Floyd River and Little Sioux River Basins, northwestern Iowa: U.S. Geological Survey Open-File Report 2022–1015, 35 p., https://doi.org/10.3133/ofr20221015.","productDescription":"Report: ix, 35 p.; 2 Data Releases; Dataset","numberOfPages":"48","onlineOnly":"N","ipdsId":"IP-111461","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396505,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1015/coverthb.jpg"},{"id":396510,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V8NO0H","text":"USGS data release","linkHelpText":"Peak-flow frequency analysis for seven selected U.S. Geological Survey streamgages in the Floyd and Little Sioux River Basins, Iowa, based on data through water year 2019"},{"id":396508,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1015/images"},{"id":396507,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1015/ofr20221015.XML","size":"189 kB","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2022–1015 XML"},{"id":396506,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1015/ofr20221015.pdf","text":"Report","size":"9.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1015"},{"id":501760,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112527.htm","linkFileType":{"id":5,"text":"html"}},{"id":396512,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":396511,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9W7VP64","text":"USGS data release","linkHelpText":"Peak-flow frequency analysis for three selected streamgages in the Cedar and Little Sioux River Basins, Iowa, based on data through water year 2019"}],"country":"United States","state":"Iowa","otherGeospatial":"Floyd River and Little Sioux River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.6192626953125,\n 42.407234661551875\n ],\n [\n -95.701904296875,\n 42.407234661551875\n ],\n [\n -95.701904296875,\n 43.5326204268101\n ],\n [\n -96.6192626953125,\n 43.5326204268101\n ],\n [\n -96.6192626953125,\n 42.407234661551875\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Populations of bears in Asia are vulnerable to extinction and effective monitoring is critical to measure and direct conservation efforts. Population abundance (local density) or growth (λ) are the most sensitive metrics to change. We discuss the value in implementing spatially explicit capture-recapture (SCR), the current gold standard for density estimation, and open population SCR (OPSCR) to monitor changes in density over time. We provide guidance for designing studies to provide estimates with sufficient power to detect changes. Because of the wide availability of camera traps and interest in their use, we consider six density estimation methods and their extensions developed for use with camera traps, with specific consideration of assumptions and applications for monitoring Asian bears. We conducted a power analysis to calculate the precision in estimates needed to detect changes in populations with reference to IUCN Red List criteria. We performed a systematic review of empirical studies implementing camera trap abundance estimation methods and considered sample sizes, effort, and model assumptions required to achieve adequate precision for population monitoring. We found SCR and OPSCR, reliant on “marked” individuals, are currently the only methods with enough power to reliably detect even moderate to major (20–80%) declines. Camera trap methods with unmarked individuals rarely achieved precision sufficient to detect even large declines (80–90%), although with some exceptions (e.g., situations with moderate population densities, large number of sampling sites, or inclusion of ancillary local telemetry data. We describe additional estimation options including line transects, direct observations, monitoring age-specific survival and reproductive rates, and hybrid/integrated methodologies that may have potential to work for some Asian bear populations. We conclude monitoring changes in abundance or density is possible for most Asian bear populations but will require collaboration among researchers over broad spatial extents and extensive financial investment to overcome biological and logistical constraints. We strongly encourage practitioners to consider study design and sampling effort required to meet objectives by conducting simulations, power analyses, and assumption checks prior to implementing monitoring efforts, and reporting standardized dispersion measures such as coefficients of variation to allow for assessment of precision. Our guidance is relevant to other low-density and wide-ranging species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02058","usgsCitation":"Morin, D., Boulanger, J., Bischof, R., Lee, D., Ngoprasert, D., Fuller, A.K., McLellan, B., Steinmetz, R., Sharma, S., Garshelis, D., Gopalaswamy, A.M., Nawaz, M.A., and Karanth, U., 2022, Comparison of methods for estimating density and population trends for low-density Asian bears: Global Ecology and Conservation, e02058, 21 p., https://doi.org/10.1016/j.gecco.2022.e02058.","productDescription":"e02058, 21 p.","ipdsId":"IP-135458","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467198,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02058","text":"Publisher Index Page"},{"id":466442,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Morin, Dana 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Condors (Gymnogyps californianus)","docAbstract":"Flight behavior of soaring birds depends on a complex array of physiological, social, demographic, and environmental factors. California Condors (Gymnogyps californianus) rely on thermal and orographic updrafts to subsidize extended bouts of soaring flight, and their soaring flight performance is expected to vary in response to environmental variation and, potentially, with experience. We collected 6298 flight tracks described by high-frequency GPS telemetry data from five birds ranging in age from 1 to 19 yr old and followed over 32 d in summer 2016. Using these data, we tested the hypothesis that climb rate, an indicator of flight performance, would be related to the topographic and meteorological variables the bird experienced, and also to its age. Climb rate was greater when condors were flying in faster winds and during environmental conditions that were conducive to updraft development. However, we found no effect of age on climb rate. Although many of these relationships were expected based on flight theory, the lack of an effect of age was unexpected. Our work expands understanding of the relationship condors have with the environment, and it also suggests the potential for as-yet unexplored complexity to this relationship. As such, this study provides insight into avian flight behavior and, because flight performance influences bird behavior and exposure to anthropogenic risk, it has potential consequences for development of conservation management plans.
","language":"English","publisher":"Raptor Research Foundation","doi":"10.3356/JRR-20-94","usgsCitation":"Bonner, S.R., Poessel, S.A., Brandt, J.C., Astell, M.T., Belthoff, J.R., and Katzner, T.E., 2022, Drivers of flight performance of California Condors (Gymnogyps californianus): Journal of Raptor Research, v. 56, no. 1, p. 17-27, https://doi.org/10.3356/JRR-20-94.","productDescription":"11 p.","startPage":"17","endPage":"27","ipdsId":"IP-120507","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448665,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3356/jrr-20-94","text":"Publisher Index Page"},{"id":397109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Bitter Creek and Hopper Mountain National Wildlife Refuges","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.5,\n 34\n ],\n [\n -118.4,\n 34\n ],\n [\n -118.4,\n 36\n ],\n [\n -119.5,\n 36\n ],\n [\n -119.5,\n 34\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bonner, Sophie R.","contributorId":288473,"corporation":false,"usgs":false,"family":"Bonner","given":"Sophie","email":"","middleInitial":"R.","affiliations":[{"id":61767,"text":"Department of Geography, University of Texas at Austin, Austin, TX","active":true,"usgs":false}],"preferred":false,"id":837974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":837975,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, Joseph C.","contributorId":288474,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","email":"","middleInitial":"C.","affiliations":[{"id":61768,"text":"U.S. Fish and Wildlife Service, Hopper Mountain National Wildlife Refuge Complex, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":837976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Astell, Molly T.","contributorId":288475,"corporation":false,"usgs":false,"family":"Astell","given":"Molly","email":"","middleInitial":"T.","affiliations":[{"id":61768,"text":"U.S. Fish and Wildlife Service, Hopper Mountain National Wildlife Refuge Complex, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":837977,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Belthoff, James R. 0000-0002-6051-2353","orcid":"https://orcid.org/0000-0002-6051-2353","contributorId":190592,"corporation":false,"usgs":false,"family":"Belthoff","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":837978,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191909,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd","email":"tkatzner@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":838016,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229057,"text":"70229057 - 2022 - Water chemistry, exposure routes and metal forms determine the bioaccumulation dynamics of silver (ionic and nanoparticulate) in Daphnia magna","interactions":[],"lastModifiedDate":"2022-02-28T15:04:24.769878","indexId":"70229057","displayToPublicDate":"2022-02-28T08:49:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Water chemistry, exposure routes and metal forms determine the bioaccumulation dynamics of silver (ionic and nanoparticulate) in Daphnia magna","title":"Water chemistry, exposure routes and metal forms determine the bioaccumulation dynamics of silver (ionic and nanoparticulate) in Daphnia magna","docAbstract":"Treatment wetlands utilize various physical and biological processes to reduce levels of organic contaminants, metals, bacteria, and suspended solids. Silver nanoparticles (AgNPs) are one type of contaminant that can enter treatment wetlands and impact the overall treatment efficacy. Grazing by filter-feeding zooplankton, such as Daphnia magna, is critical to treatment wetland functioning; but the effects of AgNPs on zooplankton are not fully understood, especially at environmentally relevant concentrations. We characterized the bioaccumulation kinetics of dissolved and nanoparticulate (citrate-coated) 109Ag in D. magna exposed to environmentally relevant 109Ag concentrations (i.e., 0.2–23 nmol L−1 Ag) using a stable isotope as a tracer of Ag. Both aqueous and nanoparticulate forms of 109Ag were bioavailable to D. magna after exposure. Water chemistry affected 109Ag influx from 109AgNP but not from 109AgNO3. Silver retention was greater for citrate-coated 109AgNP than dissolved 109Ag, indicating a greater potential for bioaccumulation from nanoparticulate Ag. Feeding inhibition was observed at higher dietary 109Ag concentrations, which could lead to reduced treatment wetland performance. Our results illustrate the importance of using environmentally relevant concentrations and media compositions when predicting Ag bioaccumulation and provide insight into potential effects on filter feeders critical to the function of treatment wetlands.
","language":"English","publisher":"ACS Publications","doi":"10.1002/etc.5271","usgsCitation":"Lesser, E., Sheikh, F.N., Sikder, M., Croteau, M.N., Franklin, N., Baalousha, M., and Ismail, N.S., 2022, Water chemistry, exposure routes and metal forms determine the bioaccumulation dynamics of silver (ionic and nanoparticulate) in Daphnia magna: Environmental Toxicology and Chemistry, v. 41, no. 3, p. 726-738, https://doi.org/10.1002/etc.5271.","productDescription":"13 p.","startPage":"726","endPage":"738","ipdsId":"IP-131554","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":396548,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Lesser, Emma","contributorId":286941,"corporation":false,"usgs":false,"family":"Lesser","given":"Emma","email":"","affiliations":[{"id":47946,"text":"Smith College","active":true,"usgs":false}],"preferred":false,"id":836370,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sheikh, Fatima Noor","contributorId":286942,"corporation":false,"usgs":false,"family":"Sheikh","given":"Fatima","email":"","middleInitial":"Noor","affiliations":[{"id":47946,"text":"Smith College","active":true,"usgs":false}],"preferred":false,"id":836371,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sikder, Mithun 0000-0002-6295-0939","orcid":"https://orcid.org/0000-0002-6295-0939","contributorId":255449,"corporation":false,"usgs":false,"family":"Sikder","given":"Mithun","email":"","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":836372,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":836373,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Franklin, Natasha","contributorId":286944,"corporation":false,"usgs":false,"family":"Franklin","given":"Natasha","email":"","affiliations":[],"preferred":false,"id":836374,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baalousha, Mohammed","contributorId":239642,"corporation":false,"usgs":false,"family":"Baalousha","given":"Mohammed","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":836375,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ismail, Niveen S.","contributorId":286947,"corporation":false,"usgs":false,"family":"Ismail","given":"Niveen","email":"","middleInitial":"S.","affiliations":[{"id":47946,"text":"Smith College","active":true,"usgs":false}],"preferred":false,"id":836376,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70231191,"text":"70231191 - 2022 - Geology & mineralogy of the Old Mine Park area Trumbull Connecticut","interactions":[],"lastModifiedDate":"2022-05-03T13:59:36.642522","indexId":"70231191","displayToPublicDate":"2022-02-28T08:46:16","publicationYear":"2022","noYear":false,"publicationType":{"id":4,"text":"Book"},"publicationSubtype":{"id":15,"text":"Monograph"},"title":"Geology & mineralogy of the Old Mine Park area Trumbull Connecticut","docAbstract":"Old Mine Park, in the northern Trumbull area (also known as Long Hill) of southwestern Connecticut, is a recreation area encompassing the mineral-rich hill of “Saganawamps” and owned by the Town of Trumbull. Most of its 72 acres are wooded, rocky and undeveloped but it is surrounded by dense infrastructure and transportation, residential, retail, and commercial development (Figure 1). It preserves the first tungsten mine operated east of the Mississippi and the first topaz locality identified in the USA (Hitchcock and Silliman, 1825), as well as the type locality for the mineral tungstite (WO3·H2O). Hiking and biking trails cross the property and continue beyond the park along the former New Haven railroad line that parallels the Pequonnock River, which flows through the southern part of the park. Access is from the south via Old Mine Road, or from the north via Corporate Drive. During the mining era and for many decades after its creation the park was a famous source of mineral specimens. In 2016 the Trumbull Parks and Recreation Commission suspended collecting of any kind.
","language":"English","publisher":"State Geological and Natural History Survey of Connecticut, Department of Energy and Environmental Protection in cooperation with The Geological Society of Connecticut","usgsCitation":"Moritz, H., Wintsch, R.P., Devlin, B., McAleer, R.J., Lee, S., Kim, S., and Yi, K., 2022, Geology & mineralogy of the Old Mine Park area Trumbull Connecticut, 76 p.","productDescription":"76 p.","ipdsId":"IP-134706","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":400047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":400045,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://portal.ct.gov/DEEP/Geology/Bedrock-Geologic-Map-of-Old-Mine-Park"}],"country":"United States","state":"Connecticut","city":"Trumbull","otherGeospatial":"Old Mine Park area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.2322883605957,\n 41.28461122720116\n ],\n [\n -73.22082996368408,\n 41.28461122720116\n ],\n [\n -73.22082996368408,\n 41.293446681007\n ],\n [\n -73.2322883605957,\n 41.293446681007\n ],\n [\n -73.2322883605957,\n 41.28461122720116\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moritz, Harold","contributorId":291251,"corporation":false,"usgs":false,"family":"Moritz","given":"Harold","email":"","affiliations":[{"id":37275,"text":"none","active":true,"usgs":false}],"preferred":false,"id":841905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wintsch, Robert P.","contributorId":192913,"corporation":false,"usgs":false,"family":"Wintsch","given":"Robert","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":841906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Devlin, Bill","contributorId":291252,"corporation":false,"usgs":false,"family":"Devlin","given":"Bill","email":"","affiliations":[{"id":62640,"text":"Rock Bottom Research","active":true,"usgs":false}],"preferred":false,"id":841908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":841907,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lee, Shinae","contributorId":291332,"corporation":false,"usgs":false,"family":"Lee","given":"Shinae","email":"","affiliations":[],"preferred":false,"id":842078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kim, SookJu","contributorId":291333,"corporation":false,"usgs":false,"family":"Kim","given":"SookJu","email":"","affiliations":[],"preferred":false,"id":842079,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yi, Keewook","contributorId":198725,"corporation":false,"usgs":false,"family":"Yi","given":"Keewook","email":"","affiliations":[],"preferred":false,"id":842080,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230066,"text":"70230066 - 2022 - Synthetic aperture radar volcanic flow maps (SAR VFMs): A simple method for rapid identification and mapping of volcanic mass flows","interactions":[],"lastModifiedDate":"2022-03-28T13:33:05.673737","indexId":"70230066","displayToPublicDate":"2022-02-28T08:30:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Synthetic aperture radar volcanic flow maps (SAR VFMs): A simple method for rapid identification and mapping of volcanic mass flows","docAbstract":"Volcanic mass flows, including lava, pyroclastic density currents, and lahars, account for the bulk of fatalities and infrastructure damage caused by volcanic eruptions. Mapping these flows soon after their emplacement is vital to understanding their impact and to forecasting the likely behavior of potential future flows. Synthetic aperture radar (SAR) can provide useful information about surface properties and changes regardless of environmental conditions or time of day, but no individual SAR product can unambiguously detect and map surface mass flows in all conditions. Combining SAR products, however, can capitalize on the strengths and compensate for the weaknesses of individual data types. SAR volcanic flow maps (SAR VFMs) merge cross-polarized amplitude imagery from two different dates with interferometric coherence spanning those dates. The combination of amplitude change with coherence provides a means of detecting volcanic mass flows regardless of surface conditions, and data collected by satellite provide the spatial coverage needed to detect changes over broad areas. Application to eruptions of Kīlauea (Hawaiʻi), Nyiragongo (Democratic Republic of Congo), Sinabung (Indonesia), and Fuego (Guatemala) demonstrate the value of SAR VFMs for monitoring hazardous volcanic activity, and the importance of acquiring cross-polarized satellite SAR imagery for volcano applications. The ever-growing number of public and private satellite SAR missions will provide for improved temporal resolution in SAR VFMs in the future, and the technique may be suitable for automated analysis that is capable of timely identification of changes due to volcanic activity, even in areas that are otherwise unmonitored.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01539-7","usgsCitation":"Poland, M., 2022, Synthetic aperture radar volcanic flow maps (SAR VFMs): A simple method for rapid identification and mapping of volcanic mass flows: Bulletin of Volcanology, v. 84, no. 3, 32, 11 p., https://doi.org/10.1007/s00445-022-01539-7.","productDescription":"32, 11 p.","ipdsId":"IP-132741","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":397696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.511474609375,\n 19.21780295966795\n ],\n [\n -154.92095947265622,\n 19.21780295966795\n ],\n [\n -154.92095947265622,\n 19.6\n ],\n [\n -155.511474609375,\n 19.6\n ],\n [\n -155.511474609375,\n 19.21780295966795\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Poland, Michael 0000-0001-5240-6123","orcid":"https://orcid.org/0000-0001-5240-6123","contributorId":49920,"corporation":false,"usgs":true,"family":"Poland","given":"Michael","affiliations":[{"id":336,"text":"Hawaiian Volcano Observatory","active":false,"usgs":true}],"preferred":true,"id":838941,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230407,"text":"70230407 - 2022 - Rotenone use and subsequent prey loss lowers Osprey fledging rates via brood reduction","interactions":[],"lastModifiedDate":"2022-04-12T12:18:38.286639","indexId":"70230407","displayToPublicDate":"2022-02-28T07:16:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2442,"text":"Journal of Raptor Research","active":true,"publicationSubtype":{"id":10}},"title":"Rotenone use and subsequent prey loss lowers Osprey fledging rates via brood reduction","docAbstract":"Fisheries managers used the fish toxicant rotenone to eradicate an undesirable brown bullhead (Ameiurus nebulosus) population and all other fish species at Hyatt Reservoir, Oregon, on 12 October 1989. This 4-yr study (1988–1990, 1992) compared effects of that rotenone project on Ospreys (Pandion haliaetus) nesting at Hyatt Reservoir and nearby Howard Prairie Reservoir (untreated reference)—the latter a reservoir where both brown bullheads and hatchery-released rainbow trout (Oncorhynchus mykiss) prospered. Because Hyatt Reservoir was treated after Osprey fall migration in 1989, the first 2 yr (1988 and 1989) yielded pretreatment information: number of Osprey pairs was unchanged and reproductive rates were similar and consistent at the two reservoirs. Yearling fish (200–250 mm) were restocked at Hyatt Reservoir in the spring of 1990 and Ospreys returned each year following rotenone treatment, with no decline in the number of occupied or active nests. The negative effect of the rotenone treatment on Ospreys was short-term, resulting in reduced reproductive rates (young/occupied nest, young/active nest, and young/successful nest) during the first nesting season posttreatment, although hatching rates were not affected. Osprey dive success and prey delivery rates declined sharply in 1990, leading to competition for food among siblings and brood reduction. Osprey reproductive rates and prey delivery rates at Hyatt Reservoir in both 1990 and 1992 remained below the extremely high pretreatment rates, but within the range required for population stability. Serious adverse effects of the fish loss on Osprey reproduction were minimized by: (1) the delay of the rotenone application until after breeding season, (2) the restocking of the treated reservoir in the following spring with some larger (yearling) fish (though the timing was late), (3) the maintenance of a supplemental feeding program for a nesting pair of Bald Eagles (Haliaeetus leucocephalus), which minimized kleptoparasitism on Ospreys, and perhaps most important (4) the presence of nearby water bodies, where Osprey obtained some fish in the 1990 and 1992 breeding seasons.
High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments, including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-m resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for snow water equivalent, recharge, and runoff.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-21-0045.1","usgsCitation":"Stern, M.A., Flint, L.E., Flint, A.L., Boynton, R.M., Stewart, J.A., Wright, J.W., and Thorne, J.H., 2022, Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions: Journal of Hydrometeorology, v. 23, no. 3, p. 293-308, https://doi.org/10.1175/JHM-D-21-0045.1.","productDescription":"16 p.","startPage":"293","endPage":"308","ipdsId":"IP-127192","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":448670,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jhm-d-21-0045.1","text":"Publisher Index Page"},{"id":414886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stern, Michelle A. 0000-0003-3030-7065 mstern@usgs.gov","orcid":"https://orcid.org/0000-0003-3030-7065","contributorId":4244,"corporation":false,"usgs":true,"family":"Stern","given":"Michelle","email":"mstern@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":868014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":867968,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boynton, Ryan M 0000-0002-3952-2573","orcid":"https://orcid.org/0000-0002-3952-2573","contributorId":303743,"corporation":false,"usgs":false,"family":"Boynton","given":"Ryan","email":"","middleInitial":"M","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":867969,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stewart, Joseph A E","contributorId":247751,"corporation":false,"usgs":false,"family":"Stewart","given":"Joseph","email":"","middleInitial":"A E","affiliations":[{"id":49638,"text":"USGS WERC & UC Davis","active":true,"usgs":false}],"preferred":false,"id":867970,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wright, Jessica W","contributorId":303744,"corporation":false,"usgs":false,"family":"Wright","given":"Jessica","email":"","middleInitial":"W","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":867971,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thorne, James H.","contributorId":139144,"corporation":false,"usgs":false,"family":"Thorne","given":"James","email":"","middleInitial":"H.","affiliations":[{"id":12659,"text":"U C Davis","active":true,"usgs":false}],"preferred":false,"id":867972,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229414,"text":"70229414 - 2022 - State of stress in areas of active unconventional oil and gas development in North America","interactions":[],"lastModifiedDate":"2022-03-07T11:58:56.628602","indexId":"70229414","displayToPublicDate":"2022-02-28T05:54:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"State of stress in areas of active unconventional oil and gas development in North America","docAbstract":"In this paper, we present comprehensive data on stress orientation and relative magnitude in areas throughout North America where unconventional oil and gas are currently being developed. We find excellent agreement between maximum horizontal principal stress (SHmax) orientations over a wide range of depths, using multiple methods. In all basins studied, we observed coherent stress fields that in some cases vary systematically from one part of a basin to another. In the Appalachian Basin in the eastern United States, SHmax is oriented northeast–southwest to east-northeast–west-southwest and the style of faulting is compressive, transitioning from reverse faulting in eastern Pennsylvania and New York to principally strike-slip faulting in western Pennsylvania, Ohio, and West Virginia. In the midcontinent, central Oklahoma is characterized by an approximately east–west SHmax direction and strike-slip faulting. The Fort Worth Basin in northeastern Texas is characterized by normal–strike-slip faulting and a north-northeast–south-southwest SHmax direction. In the Midland subbasin of western Texas, SHmax is consistently approximately east–west and normal–strike-slip faulting is observed. Farther west, the Delaware subbasin of western Texas and southeastern New Mexico is characterized by normal faulting and SHmax rotates ∼150° clockwise from north to south. Marked changes in SHmax direction also occur across the Raton Basin of southern Colorado and northern New Mexico, the Denver-Julesburg Basin in northern Colorado, and the Uinta Basin in northeastern Utah, likely associated with their location near the margins of extensional provinces. The new data sets we present help improve operational efficiency by constraining absolute stress magnitudes and the ideal azimuth to drill horizontal wells (i.e., perpendicular to the local SHmax orientation) and make it possible to predict which fractures and faults are likely to be activated during hydraulic stimulation.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/08102120151","usgsCitation":"Lundstern, J., and Zoback, M., 2022, State of stress in areas of active unconventional oil and gas development in North America: AAPG Bulletin, v. 106, no. 2, p. 355-385, https://doi.org/10.1306/08102120151.","productDescription":"31 p.","startPage":"355","endPage":"385","ipdsId":"IP-120371","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":435943,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90LS6QF","text":"USGS data release","linkHelpText":"Maximum horizontal stress orientation and relative stress magnitude (faulting regime) data throughout North America"},{"id":396771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"106","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lundstern, Jens-Erik 0000-0003-0000-8013","orcid":"https://orcid.org/0000-0003-0000-8013","contributorId":264189,"corporation":false,"usgs":true,"family":"Lundstern","given":"Jens-Erik","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":837336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zoback, Mark D. 0000-0002-8851-2099","orcid":"https://orcid.org/0000-0002-8851-2099","contributorId":288082,"corporation":false,"usgs":false,"family":"Zoback","given":"Mark D.","affiliations":[{"id":61706,"text":"Stanford University Department of Geophysics","active":true,"usgs":false}],"preferred":false,"id":837337,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231893,"text":"70231893 - 2022 - Simple relationships between residence time and annual nutrient retention, export, and loading for estuaries","interactions":[],"lastModifiedDate":"2022-06-01T11:46:06.234257","indexId":"70231893","displayToPublicDate":"2022-02-27T06:42:37","publicationYear":"2022","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":"Simple relationships between residence time and annual nutrient retention, export, and loading for estuaries","docAbstract":"Simple mathematical models are derived from mass balances for water and transported substance to provide insight into the relationships between import, export, transport, and internal removal for nonconservative substances in an estuary. Extending previous work, our models explicitly include water and substance inputs from the ocean and are expressed in terms of timescales (i.e., mean residence time and the timescale for net removal). Steady-state, timescale-based expressions for ratios of export to import, retention to import, and net export to loading, as well as for loading and annually averaged concentration, are provided. The net export:loading model explains the underlying mechanisms for a well-known empirical relationship between fractional net export and residence time derived by other authors. Although our simplified models are first-order approximations, the relative importance of physical and biochemical processes influencing export or retention of a substance can be assessed using mean residence time and the timescale for net removal. Assumptions employed in deriving the simplified models (e.g., well-mixed, dynamic steady state) may not be met for real estuaries. However, model application to Chesapeake Bay for 1985–2012 demonstrates that interannual variations in total nitrogen (TN) net export:loading can be evaluated, and annual nutrient loadings can be well estimated using numerically modeled time-varying mean residence time, observation-based mean concentration, freshwater inflow, and an appropriately estimated removal timescale. Our model shows that net fractional export of TN loading ranges from 0.3 to 0.5 over the 28-yr period. The models can be employed for other substances and water bodies if the underlying assumptions are applicable.
Deltaic floodplains are highly vulnerable to relative sea level rise (RSLR) depending on the sediment supply from river channels that provides elevation capital as adaptation mechanism. In river channels where levees have restricted sediment supply to coastal deltaic floodplains, river sediment diversions have been proposed as a restoration strategy to increase elevation allowing for marshes to establish and cope with RSLR. The response of coastal wetlands to surface elevation has been well-defined for estuarine marshes, but models for coastal deltaic floodplain marshes have not been resolved. Here we coupled field observations from biomass plots and a mesocosm experiment (‘marsh organ’) with remote sensing techniques to assess biomass allocation of tidal freshwater marsh species in response to gradients in hydroperiod in Wax Lake Delta (WLD), coastal Louisiana, U.S.A.. We found that, contrary to salt-tolerant species, Colocasia esculenta aboveground biomass (AGB) is strongly positively correlated with percent inundated time (R2 = 0.79, P < 0.001), increasing from (mean ± 1SE) 186 ± 69 g/m2 in the supratidal zone to 1422 ± 148 g/m2 beyond its natural occurrence range in the lower intertidal zone. Belowground biomass consistently exceeded AGB at 2363 ± 294 g/m2 on average across elevation treatments. We also found that C. esculenta expanded its surface coverage area by 31% in five years consistent with the growth and emergence of WLD's subaqueous platforms, reflecting this species ability to cope with higher inundation time. In contrast to earlier studies conducted in brackish and saline settings, where longer hydroperiods had negative effects on biomass accumulation, our data suggest that tidal freshwater marshes can cope with longer hydroperiods caused by river sediment diversions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2022.107784","usgsCitation":"Rovai, A.S., Twilley, R.R., Christiensen, A., McCall, A., Jensen, D.J., Snedden, G., Morris, J.T., and Cavell, J.A., 2022, Biomass allocation of tidal freshwater marsh species in response to natural and manipulated hydroperiod in coastal deltaic floodplains: Estuarine, Coastal and Shelf Science, v. 268, 107784, 12 p., https://doi.org/10.1016/j.ecss.2022.107784.","productDescription":"107784, 12 p.","ipdsId":"IP-125365","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":448675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.lsu.edu/oceanography_coastal_pubs/1218","text":"Publisher Index Page"},{"id":396773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"268","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rovai, Andre S.","contributorId":167671,"corporation":false,"usgs":false,"family":"Rovai","given":"Andre","email":"","middleInitial":"S.","affiliations":[{"id":24801,"text":"Federal University of Santa Catarina, Dept. Ecology and Zoology, Brazil","active":true,"usgs":false}],"preferred":false,"id":837318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Twilley, Robert R.","contributorId":34585,"corporation":false,"usgs":false,"family":"Twilley","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christiensen, Alexandra","contributorId":288065,"corporation":false,"usgs":false,"family":"Christiensen","given":"Alexandra","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCall, Annabeth","contributorId":288067,"corporation":false,"usgs":false,"family":"McCall","given":"Annabeth","email":"","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837321,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jensen, Daniel J.","contributorId":288071,"corporation":false,"usgs":false,"family":"Jensen","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":36392,"text":"Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":837322,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Snedden, Gregg 0000-0001-7821-3709","orcid":"https://orcid.org/0000-0001-7821-3709","contributorId":213411,"corporation":false,"usgs":true,"family":"Snedden","given":"Gregg","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":837323,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morris, James T.","contributorId":288074,"corporation":false,"usgs":false,"family":"Morris","given":"James","email":"","middleInitial":"T.","affiliations":[{"id":61699,"text":"Belle W. Baruch Institute for Marine and Coastal Sciences, University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":837324,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cavell, John A.","contributorId":288075,"corporation":false,"usgs":false,"family":"Cavell","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":837325,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70256704,"text":"70256704 - 2022 - Bright spots for inland fish and fisheries to guide future hydropower development","interactions":[],"lastModifiedDate":"2024-09-03T15:00:17.796476","indexId":"70256704","displayToPublicDate":"2022-02-26T09:48:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17103,"text":"Water Biology and Security","active":true,"publicationSubtype":{"id":10}},"title":"Bright spots for inland fish and fisheries to guide future hydropower development","docAbstract":"Hydropower production is one of the greatest threats to fluvial ecosystems and freshwater biodiversity. Now that we have entered the Anthropocene, there is an opportunity to reflect on what might constitute a ‘sustainable’ Anthropocene in the context of hydropower and riverine fish populations. Considering elements of existing practices that promote favorable social-ecological outcomes (i.e., ‘bright spots’) is timely given that there are plans to expand hydropower capacity in previously undammed rivers, intensify dam development in some of the world's largest river systems, and re-license existing facilities. We approach this from a pragmatic perspective: for the foreseeable future, hydropower will likely remain an important source of renewable electricity. To offer support for moving toward a more ‘sustainable’ Anthropocene, we provide syntheses of best practices during the siting, design, construction, operation, and compensation phases of hydropower development to minimize impacts on inland fish. For each phase, we offer positive examples (or what might be considered ‘bright spots’) pertaining to some of the approaches described within our syntheses, acknowledging that these projects may not be viewed as without ecological and (or) societal detriment by all stakeholders. Our findings underscore the importance of protecting critical habitat and free-flowing river reaches through careful site selection and basin-scale planning, infrastructure designs that minimize reservoir effects and facilitate safe passage of fish, construction of hydropower plants using best practices that minimize long-term damage, operating guidelines that mimic natural flow conditions, and compensation that is lasting, effective, inclusive, and locally relevant. Learning from these ‘bright spots’ may require engagement of diverse stakeholders, professionals, and governments at scales that extend well beyond a given site, river, or even basin. Indeed, environmental planning that integrates hydropower development into broader discussions of conserving regional biodiversity and ecosystem services will be of utmost importance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watbs.2022.100009","usgsCitation":"Twardek, W., Cowx, I., Lapointe, N.W., Paukert, C.P., Beard, T., Bennett, E., Browne, D., Carlson, A., Clarke, K.D., Hogan, Z., Lorenzen, K., Lynch, A., McIntyre, P.B., Pompeu, P.S., Rogers, M.W., Sakas, A., Taylor, W., Ward, T.D., Basher, Z., and Cooke, S., 2022, Bright spots for inland fish and fisheries to guide future hydropower development: Water Biology and Security, v. 1, no. 1, 100009, 19 p., https://doi.org/10.1016/j.watbs.2022.100009.","productDescription":"100009, 19 p.","ipdsId":"IP-134471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":448679,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watbs.2022.100009","text":"Publisher Index Page"},{"id":433405,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Twardek, William M.","contributorId":341625,"corporation":false,"usgs":false,"family":"Twardek","given":"William M.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":908713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cowx, Ian G.","contributorId":341626,"corporation":false,"usgs":false,"family":"Cowx","given":"Ian G.","affiliations":[{"id":81763,"text":"Fisheries Institute at the University of Hull","active":true,"usgs":false}],"preferred":false,"id":908714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lapointe, Nicolas W.R.","contributorId":341627,"corporation":false,"usgs":false,"family":"Lapointe","given":"Nicolas","email":"","middleInitial":"W.R.","affiliations":[{"id":54575,"text":"Canadian Wildlife Federation","active":true,"usgs":false}],"preferred":false,"id":908715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908712,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Beard, T. 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