Improved simulations of streamflow and base flow for selected sites within and adjacent to the Mississippi River Alluvial Plain area are important for modeling groundwater flow because surface-water flows have a substantial effect on groundwater levels. One method for simulating streamflow and base flow, random forest (RF) models, was developed from the data at gaged sites and, in turn, was used to make monthly mean streamflow and base-flow predictions at 162 ungaged sites in the study area. Daily streamflow observations and computed base flow from 247 streamgages were used as the basis for the development of these RF models. RF models were constructed from basin and climatic characteristics and related to observed monthly mean streamflow values; models were used to compute monthly base-flow estimates from selected streamgages in and adjacent to the Mississippi River Alluvial Plain extent, which includes streamflows from parts of Alabama, Arkansas, Colorado, Florida, Illinois, Indiana, Kansas, Kentucky, Louisiana, Mississippi, Missouri, New Mexico, Tennessee, and Texas. The explanatory variables for the models were selected to represent physical characteristics and climatic time series for the contributing drainage basins to the streamgages and ungaged locations of interest. The Nash-Sutcliffe efficiency between observed and simulated monthly mean streamflow was greater than 0.80 for 155 of the 247 streamgages, with a median Nash-Sutcliffe efficiency value of 0.83. The streamflow and base-flow simulations can be used to improve inflow values and to verify the Mississippi River Alluvial Plain groundwater flow model. The statistical model, input data, and response data (simulated monthly mean streamflows) are available as a U.S. Geological Survey software release and a U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225079","programNote":"Water Availability and Use Science Program","usgsCitation":"Dietsch, B.J., Asquith, W.H., Breaker, B.K., Westenbroek, S.M., and Kress, W.H., 2023, Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018: U.S. Geological Survey Scientific Investigations Report 2022–5079, 17 p., https://doi.org/10.3133/sir20225079.","productDescription":"Report: v, 17 p.; Tables: 4; Data Release; Dataset; Software Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105480","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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Species distribution models enable resource managers to avoid and mitigate impacts to, or enhance habitat of, target species at the landscape level. Persistent declines of northern long-eared bats (Myotis septentrionalis) due to white-nose syndrome have made acquisition of contemporary data difficult. Therefore, use of legacy data may be necessary for creation of species distribution models. We used historical roost and capture records, both individually and in combination, to assess the distribution and availability of northern long-eared bat habitat across the 670,000-ha Monongahela Na- tional Forest (MNF), West Virginia, USA. We created random forest presence/pseudo-absence models to examine influences of various biotic and abi- otic predictors on both roosting and foraging presence locations of northern long-eared bats. Predicted northern long-eared bat habitat was abundant (43.1% of the MNF) and widely dispersed. Generally, all models suggested that northern long-eared bat habitat was characterized by interior forests containing linear edge features. We observed only 3.4% spatial overlap of habitat based on complete model agreement, but 38.5% of all habitat areas resulted from agreement between capture-only and combination models. Our models provide important assessments of habitat availability necessary for addressing state and federal conservation requirements on the MNF and adjacent eastern West Virginia mountains.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"De La Cruz, J., Ford, W., Jones, S.B., Johnson, J., and Silvis, A., 2023, Distribution of northern long-eared bat summer-habitat derived from historical data collected on the Monongahela National Forest, West Virginia, USA: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 114-124.","productDescription":"11 p.","startPage":"114","endPage":"124","ipdsId":"IP-144150","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481130,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/distribution-northern-long-eared-bat-summer-habitat-monongahela-national-forest-west"},{"id":481153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Monongahela National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.92561978482631,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.52966246222408\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"De La Cruz, J.L.","contributorId":349847,"corporation":false,"usgs":false,"family":"De La Cruz","given":"J.L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":924990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, S. 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At the time of writing this report, Hilcorp Alaska LLC has received BOEM approval for an oil and gas Development and Production Plan (DPP) that includes the construction of the Liberty Drilling Island (LDI) in Foggy Island Bay, situated within Stefansson Sound circa 30 km east of Prudhoe Bay (Figure 1.1). The aim of this study is to investigate how longer periods of open water (defined as < 15% ice cover), decreased sea ice cover, and changes in ocean and atmospheric conditions might affect wave and storm surge conditions, sediment transport patterns, and coastal erosion rates within Foggy Island Bay as well as the modeled influence of the offshore artificial island on sediment transport patterns.","language":"English","publisher":"Bureau of Ocean and Energy Management (BOEM)","usgsCitation":"Erikson, L.H., Nederhoff, C.M., Engelstad, A.C., Kasper, J., and Bieniek, P.A., 2023, Central Beaufort Sea Wave and Hydrodynamic Modeling Study; Report 2: Modeled waves, hydrodynamics, and sediment transport within Foggy Island Bay: OCS Study BOEM 2022-079, 64 p.","productDescription":"64 p.","ipdsId":"IP-147575","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":491591,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://espis.boem.gov/final%20reports/BOEM_2022-079.pdf","linkFileType":{"id":1,"text":"pdf"}},{"id":491739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Foggy Island Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.9566685211152,\n 70.37889977965969\n ],\n [\n -147.9566685211152,\n 70.1704960051022\n ],\n [\n -147.22144502523884,\n 70.1704960051022\n ],\n [\n -147.22144502523884,\n 70.37889977965969\n ],\n [\n -147.9566685211152,\n 70.37889977965969\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941725,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":941726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Engelstad, Anita C 0000-0002-0211-4189","orcid":"https://orcid.org/0000-0002-0211-4189","contributorId":268303,"corporation":false,"usgs":true,"family":"Engelstad","given":"Anita","email":"","middleInitial":"C","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":941727,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kasper, Jeremy L. 0000-0003-0975-6114","orcid":"https://orcid.org/0000-0003-0975-6114","contributorId":208630,"corporation":false,"usgs":false,"family":"Kasper","given":"Jeremy L.","affiliations":[{"id":37850,"text":"University of Alaska Fairbanks, Fairbanks, Alaska, UNITED STATES","active":true,"usgs":false}],"preferred":false,"id":941728,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bieniek, Peter A.","contributorId":210907,"corporation":false,"usgs":false,"family":"Bieniek","given":"Peter","email":"","middleInitial":"A.","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":941729,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245590,"text":"70245590 - 2023 - Results of validation exercise for Marine Benthic Index","interactions":[],"lastModifiedDate":"2023-06-26T14:21:41.468174","indexId":"70245590","displayToPublicDate":"2023-03-01T08:59:30","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesNumber":"23-03-009","title":"Results of validation exercise for Marine Benthic Index","docAbstract":"Marine benthic invertebrates (benthos) are key components of the Puget Sound ecosystem. Because of their direct association living in, and sometimes consuming, sediments, benthos can be valuable sentinels of ecosystem health. Therefore, indicators of benthic invertebrate community health can serve as direct measures of sediment and water quality.
In 2021, the Puget Sound Partnership funded development of a Marine Benthic Index. The Marine Benthic Index thus developed uses a novel approach that accounts for habitat preferences of the benthic invertebrate species. This report describes the design and results of the exercise conducted to validate the Marine Benthic Index.
The goals of the validation exercise were to determine (a) how well the Marine Benthic Index matches more standard ways of assessing community health and (b) how finely it is possible to distinguish between levels of disturbance. A controlled experiment was devised in which simulated benthic communities were generated to correspond to predetermined levels of disturbance, and experts in benthic ecology determined which communities reflected the more-disturbed conditions. In this way, the index was directly compared to traditional methods of assessing benthic communities.
The results provide strong evidence that the “latent disturbance” model used to derive the Marine Benthic Index is identifying effects that benthic experts recognize as disturbance. Not only did the model agree with the experts overall, but also the probability of agreement strongly increased with increasing difference in disturbance level.
The validation exercise results indicate that the Marine Benthic Index is a reliable method of determining disturbance without the necessity of assuming a priori knowledge of the disturbance. Furthermore, the numerical approach embodied in the Marine Benthic Index has the advantage of being able to find patterns beyond the capability of individual experts to know the effects of human disturbances for all species under all environmental conditions.
","language":"English","publisher":"Washington State Department of Ecology","usgsCitation":"Partridge, V., and Schoolmaster, D., 2023, Results of validation exercise for Marine Benthic Index, 20 p.","productDescription":"20 p.","ipdsId":"IP-150986","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":418462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418440,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://apps.ecology.wa.gov/publications/SummaryPages/2303009.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.68802842788664,\n 48.373846480343786\n ],\n [\n -123.81913490967645,\n 48.1115756888573\n ],\n [\n -123.14402339912499,\n 48.1115756888573\n ],\n [\n -122.95024139146665,\n 48.0781752174214\n ],\n [\n -122.80646764384926,\n 48.06564444677369\n ],\n [\n -122.7127021562724,\n 47.86893056996965\n ],\n [\n -122.90648416393074,\n 47.8437642242728\n ],\n [\n -123.16902752914547,\n 47.32939769868071\n ],\n [\n -123.05650894405355,\n 47.07883242923262\n ],\n [\n -122.88773106641568,\n 46.99788994223897\n ],\n [\n -122.5251711811191,\n 47.104367705928325\n ],\n [\n -122.26262781590475,\n 47.38868015989351\n ],\n [\n -122.18136439333833,\n 47.604074545611496\n ],\n [\n -122.25012575089434,\n 47.759788176494226\n ],\n [\n -122.2376236858843,\n 48.00294481937837\n ],\n [\n -122.46266085606811,\n 48.69257458713301\n ],\n [\n -122.76271041631334,\n 48.99289388766516\n ],\n [\n -123.27529508173234,\n 49.005197262284895\n ],\n [\n -123.03150481403307,\n 48.80386145797766\n ],\n [\n -123.21278475668099,\n 48.68432130986798\n ],\n [\n -123.20653372417596,\n 48.41120398521281\n ],\n [\n -123.51283431692622,\n 48.26160937223193\n ],\n [\n -124.73803668792758,\n 48.5810411917262\n ],\n [\n -124.68802842788664,\n 48.373846480343786\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Partridge, Valerie","contributorId":312466,"corporation":false,"usgs":false,"family":"Partridge","given":"Valerie","affiliations":[{"id":67683,"text":"Department of Ecology, State of Washington","active":true,"usgs":false}],"preferred":false,"id":876181,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schoolmaster, Donald 0000-0003-0910-4458","orcid":"https://orcid.org/0000-0003-0910-4458","contributorId":202356,"corporation":false,"usgs":true,"family":"Schoolmaster","given":"Donald","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":876182,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241940,"text":"70241940 - 2023 - Decision science as a framework for combining geomorphological and ecological modeling for the management of coastal systems","interactions":[],"lastModifiedDate":"2023-06-08T14:50:49.582397","indexId":"70241940","displayToPublicDate":"2023-03-01T08:50:12","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Decision science as a framework for combining geomorphological and ecological modeling for the management of coastal systems","docAbstract":"The loss of ecosystem services due to climate change and coastal development is projected to have significant impacts on local economies and conservation of natural resources. Consequently, there has been an increase in coastal management activities such as living shorelines, oyster reef restoration, marsh restoration, beach and dune nourishment, and revegetation projects. Coastal management decisions are complex and include challenging trade-offs. Decision science offers a useful framework to address such complex problems. Here, we provide a synthesis about how decision science can help to integrate research from multiple disciplines (physical and life sciences) with management of coastal and marine systems. Specifically, we discuss the importance of considering concepts and techniques from ecology, coastal geology, geomorphology, climate science, oceanography, and decision analysis when developing conservation plans for coastal restoration. We illustrate the process with several coastal restoration studies. Our capstone example is based on a recent barrier island restoration assessment project at Dauphin Island, Alabama, which included the development of geomorphological and ecological models. We show how decision science can be used as a framework to combine geomorphological and ecological modeling to help inform management decisions while considering uncertainty about system changes and risk tolerance. We also build on our examples through a review of recently developed techniques for spatial conservation planning for land acquisition decisions and the application of adaptive management for sequential decisions.
","language":"English","publisher":"The Resilience Alliance","doi":"10.5751/ES-13696-280150","usgsCitation":"Martin, J., Richardson, M.S., Passeri, D., Enwright, N., Yurek, S., Flocks, J., Eaton, M.J., Zeigler, S., Charkhgard, H., Udell, B.J., and Irwin, E.R., 2023, Decision science as a framework for combining geomorphological and ecological modeling for the management of coastal systems: Ecology and Society, v. 28, no. 1, 50, 42 p.; Data Release, https://doi.org/10.5751/ES-13696-280150.","productDescription":"50, 42 p.; Data Release","ipdsId":"IP-131634","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science 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0000-0002-6209-7915","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":216733,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":868291,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flocks, James 0000-0002-6177-7433","orcid":"https://orcid.org/0000-0002-6177-7433","contributorId":221107,"corporation":false,"usgs":true,"family":"Flocks","given":"James","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":868292,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":213526,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science 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0000-0001-5225-4959","orcid":"https://orcid.org/0000-0001-5225-4959","contributorId":271174,"corporation":false,"usgs":true,"family":"Udell","given":"Bradley","email":"","middleInitial":"James","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":868296,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Irwin, Elise R. 0000-0002-6866-4976 eirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-6866-4976","contributorId":2588,"corporation":false,"usgs":true,"family":"Irwin","given":"Elise","email":"eirwin@usgs.gov","middleInitial":"R.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":868297,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70244090,"text":"70244090 - 2023 - Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin","interactions":[],"lastModifiedDate":"2023-06-01T14:08:40.055086","indexId":"70244090","displayToPublicDate":"2023-03-01T08:44:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin","docAbstract":"Quantifying the effects of streamflow alteration on assemblage response is central to understanding the role humans play in shaping aquatic environments. These changes represent a level of complexity that impedes developing quantitative links between flow and ecological response because stream hydrology is strongly intertwined with natural and anthropogenic factors. Better management outcomes require disentangling these linkages. Benthic macroinvertebrate data were combined with GIS-derived natural and anthropogenic basin characteristics to identify factors associated with changes in flow processes and assemblage characteristics. Models linking streamflow metrics and macroinvertebrate response at basin and subregion scales were developed using boosted regression tree (BRT) analysis. Basin-scale BRT analyses revealed that links between macroinvertebrate response and flow metrics were often obscured, whereas more homogeneous subregions were better able to discern relations with flow. Urban land cover was the primary factor accounting for changes in flow characteristics. Elevation, land cover, and high flow frequency were the principal variables driving changes in assemblage structure within subregions. Assemblage metrics and traits were equally useful for building response models and were affected similarly by streamflow alteration. Results indicate that response models should be developed based on upland and coastal subregions. However, when defining subregions, care should be taken to maintain data sufficiency. Developing practical flow-protection standards that support a balance between human water requirements and ecological integrity requires models that reduce uncertainty and identify management-relevant drivers. However, effective management often differs by location and models developed at the subregion level may be more applicable to management and stakeholder interests.","language":"English","publisher":"Wiley","doi":"10.1002/eco.2508","usgsCitation":"Kennen, J., and Cuffney, T.F., 2023, Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin: Ecohydrology, v. 16, no. 2, e2508, 41 p., https://doi.org/10.1002/eco.2508.","productDescription":"e2508, 41 p.","ipdsId":"IP-128360","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":498861,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2508","text":"Publisher Index Page"},{"id":417646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.07997541667648,\n 38.70906787639316\n ],\n [\n -74.80531721355018,\n 39.00778043156808\n ],\n [\n -74.33839826823872,\n 40.4450386444411\n ],\n [\n -73.72865705730113,\n 40.994591290300974\n ],\n [\n -73.7835886979261,\n 42.478350475454334\n ],\n [\n -75.40956526042564,\n 42.295772510663625\n ],\n [\n -75.42055158855078,\n 41.8349594674406\n ],\n [\n -76.34340315105082,\n 40.43667721449637\n ],\n [\n -75.78859358073888,\n 39.713504216020766\n ],\n [\n -75.76662092448927,\n 39.578152174338356\n ],\n [\n -75.66225080730156,\n 39.41283383409595\n ],\n [\n -75.50294904948888,\n 39.22584914314203\n ],\n [\n -75.47548322917642,\n 39.042631522344635\n ],\n [\n -75.33266096355176,\n 38.846103881559685\n ],\n [\n -75.07997541667648,\n 38.70906787639316\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Kennen, Jonathan G. 0000-0002-5426-4445 jgkennen@usgs.gov","orcid":"https://orcid.org/0000-0002-5426-4445","contributorId":574,"corporation":false,"usgs":true,"family":"Kennen","given":"Jonathan G.","email":"jgkennen@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cuffney, Thomas F. 0000-0003-1164-5560 tcuffney@usgs.gov","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":517,"corporation":false,"usgs":true,"family":"Cuffney","given":"Thomas","email":"tcuffney@usgs.gov","middleInitial":"F.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874462,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241112,"text":"70241112 - 2023 - Large increases in methane emissions expected from North America’s largest wetland complex","interactions":[],"lastModifiedDate":"2023-03-10T15:10:35.729452","indexId":"70241112","displayToPublicDate":"2023-03-01T08:33:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Large increases in methane emissions expected from North America’s largest wetland complex","docAbstract":"Natural methane (CH4) emissions from aquatic ecosystems may rise because of human-induced climate warming, although the magnitude of increase is highly uncertain. Using an exceptionally large CH4 flux dataset (~19,000 chamber measurements) and remotely sensed information, we modeled plot- and landscape-scale wetland CH4 emissions from the Prairie Pothole Region (PPR), North America’s largest wetland complex. Plot-scale CH4 emissions were driven by hydrology, temperature, vegetation, and wetland size. Historically, landscape-scale PPR wetland CH4 emissions were largely dependent on total wetland extent. However, regardless of future wetland extent, PPR CH4 emissions are predicted to increase by two- or threefold by 2100 under moderate or severe warming scenarios, respectively. Our findings suggest that international efforts to decrease atmospheric CH4 concentrations should jointly account for anthropogenic and natural emissions to maintain climate mitigation targets to the end of the century.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.ade1112","usgsCitation":"Bansal, S., Post van der Burg, M., Fern, R., Jones, J., Lo, R., McKenna, O.P., Tangen, B., Zhang, Z., and Gleason, R.A., 2023, Large increases in methane emissions expected from North America’s largest wetland complex: Science Advances, v. 9, no. 9, eade1112, 14 p., https://doi.org/10.1126/sciadv.ade1112.","productDescription":"eade1112, 14 p.","ipdsId":"IP-137112","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":444325,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.ade1112","text":"Publisher Index Page"},{"id":435429,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PKI29C","text":"USGS data release","linkHelpText":"Methane flux model for wetlands of the Prairie Pothole Region of North America: Model input data and programming code"},{"id":413952,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.103,\n 47.104\n ],\n [\n -99.103,\n 47.096\n ],\n [\n -99.091,\n 47.096\n ],\n [\n -99.091,\n 47.104\n ],\n [\n -99.103,\n 47.104\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":866116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Post van der Burg, Max 0000-0002-3943-4194","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":219400,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":866117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fern, Rachel","contributorId":302984,"corporation":false,"usgs":false,"family":"Fern","given":"Rachel","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":866118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":866119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lo, Rachel 0000-0003-1014-7076","orcid":"https://orcid.org/0000-0003-1014-7076","contributorId":303000,"corporation":false,"usgs":true,"family":"Lo","given":"Rachel","email":"","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":866151,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":866121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":866122,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhang, Zhen 0000-0003-0899-1139","orcid":"https://orcid.org/0000-0003-0899-1139","contributorId":149173,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":866123,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gleason, Robert A. 0000-0001-5308-8657 rgleason@usgs.gov","orcid":"https://orcid.org/0000-0001-5308-8657","contributorId":2402,"corporation":false,"usgs":true,"family":"Gleason","given":"Robert","email":"rgleason@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":866124,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70245363,"text":"70245363 - 2023 - Magnetotelluric monitoring of the Geysers Steam Field, northern California: Phase 2","interactions":[],"lastModifiedDate":"2024-10-30T15:06:07.469944","indexId":"70245363","displayToPublicDate":"2023-03-01T08:29:20","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Magnetotelluric monitoring of the Geysers Steam Field, northern California: Phase 2","docAbstract":"An original magnetotelluric (MT) survey collected in 2017 included 42 MT stations mainly in the northwestern part of The Geysers geothermal field in northern California. These data were modeled in 3D and imaged the electrically conductive cover, the electrically resistive steam field, and the electrically resistive Geysers plutonic complex (Peacock et al., 2020; Peacock et al. 2020a). Success of the original survey initiated collaboration between the U.S. Geological Survey and Lawrence Berkeley National Labs to monitor the steam field with MT and passive seismic to create a joint 4D model over the course of three years. This project, funded by the California Energy Commission, began in 2020. Two repeated MT surveys were collected at The Geysers, one in April 2021 (Peacock et al., 2022) and the second in April 2022 that extend further south adding 13 stations to the original 2017 survey. Reported here are observations comparing the 2017 data with the 2022 data. Preliminary results indicate the steam field changed in differently across the steam field, similar to changes observed between the 2017 and 2021 data.","conferenceTitle":"2023 Stanford Geothermal Workshop","conferenceDate":"February 6-8, 2023","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Peacock, J., Alumbaugh, D., Mitchell, M., and Hartline, C., 2023, Magnetotelluric monitoring of the Geysers Steam Field, northern California: Phase 2, 2023 Stanford Geothermal Workshop, Stanford, CA, February 6-8, 2023, 5 p.","productDescription":"5 p.","ipdsId":"IP-148922","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":418359,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418357,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=35652"}],"country":"United States","state":"California","otherGeospatial":"Geysers Steam Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.88115533829513,\n 38.78981141077904\n ],\n [\n -122.88115533829513,\n 38.66481841554048\n ],\n [\n -122.64292684707536,\n 38.66481841554048\n ],\n [\n -122.64292684707536,\n 38.78981141077904\n ],\n [\n -122.88115533829513,\n 38.78981141077904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":875896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alumbaugh, David 0000-0002-6975-7197","orcid":"https://orcid.org/0000-0002-6975-7197","contributorId":299109,"corporation":false,"usgs":false,"family":"Alumbaugh","given":"David","email":"","affiliations":[{"id":64775,"text":"Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":875897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Michael Albert 0000-0001-5070-8793","orcid":"https://orcid.org/0000-0001-5070-8793","contributorId":311096,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael Albert","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":875898,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartline, Craig","contributorId":213429,"corporation":false,"usgs":false,"family":"Hartline","given":"Craig","email":"","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":875899,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248778,"text":"70248778 - 2023 - Indicators of the effects of climate change on freshwater ecosystems","interactions":[],"lastModifiedDate":"2023-09-21T12:06:36.333278","indexId":"70248778","displayToPublicDate":"2023-03-01T07:03:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1246,"text":"Climate Change","onlineIssn":"1573-1480","printIssn":"0165-0009","active":true,"publicationSubtype":{"id":10}},"title":"Indicators of the effects of climate change on freshwater ecosystems","docAbstract":"Freshwater ecosystems, including lakes, streams, and wetlands, are responsive to climate change and other natural and anthropogenic stresses. These ecosystems are frequently hydrologically and ecologically connected with one another and their surrounding landscapes, thereby integrating changes throughout their watersheds. The responses of any given freshwater ecosystem to climate change depend on the magnitude of climate forcing, interactions with other anthropogenic and natural changes, and the characteristics of the ecosystem itself. Therefore, the magnitude and manner in which freshwater ecosystems respond to climate change are difficult to predict a priori. We present a conceptual model to elucidate how freshwater ecosystems are altered by climate change. We identify eleven indicators that describe the response of freshwater ecosystems to climate change, discuss their potential value and limitations, and describe supporting measurements. Indicators are organized in three interrelated categories: hydrologic, water quality, and ecosystem structure and function. The indicators are supported by data sets with a wide range of temporal and spatial coverage, and they inform important scientific and management needs. Together, these indicators improve the understanding and management of the effects of climate change on freshwater ecosystems.
Many pressing conservation issues are complex problems caused by multiple social and environmental drivers; their resolution is aided by interdisciplinary teams of scientists, decision makers, and stakeholders working together. In these situations, how do we generate science to effectively guide conservation (resource management and policy) decisions? This paper describes elements of successful big-team science in conservation, as well as shortcomings and lessons learned, based on our work with the monarch butterfly (Danaus plexippus) in North America. We summarize literature on effective science teams, extracting information about elements of success, effective implementation approaches, and barriers or pitfalls. We then describe recent and ongoing conservation science for the monarch butterfly in North America. We focus primarily on the activities of the Monarch Conservation Science Partnership–an international collaboration of interdisciplinary scientists, policy experts and natural resource managers spanning government, non-governmental and academic institutions—which developed science to inform imperilment status, recovery options, and monitoring strategies. We couch these science efforts in the adaptative management framework of Strategic Habitat Conservation, the business model for conservation employed by the US Fish and Wildlife Service to inform decision-making needs identified by stakeholders from Canada, the United States, and Mexico. We conclude with elements critical to effective big-team conservation science, discuss why science teams focused on applied conservation problems are unique relative to science teams focusing on traditional or theoretical research, and list benefits of big team science in conservation.
Long waves play an important role in coastal inundation and shoreline and dune erosion, requiring a detailed understanding of their evolution in nearshore regions and interaction with shorelines. While their generation and dissipation mechanisms are relatively well understood, there are fewer studies describing how reflection processes govern their propagation in the nearshore. We propose a new approach, accounting for partial reflections, which leads to an analytical solution to the free wave linear shallow-water equations at the wave-group scale over general varying bathymetry. The approach, supported by numerical modeling, agrees with the classic Bessel standing solution for a plane sloping beach but extends the solution to arbitrary alongshore uniform bathymetry profiles and decomposes it into incoming and outgoing wave components, which are a combination of successively partially reflected waves lagging each other. The phase lags introduced by partial reflections modify the wave amplitude and explain why Green’s law, which describes the wave growth of free waves with decreasing depth, breaks down in very shallow water. This reveals that the wave amplitude at the shoreline is highly dependent on partial reflections. Consistent with laboratory and field observations, our analytical model predicts a reflection coefficient that increases and is highly correlated with the normalized bed slope (bed slope relative to wave frequency). Our approach shows that partial reflections occurring due to depth variations in the nearshore are responsible for the relationship between the normalized bed slope and the amplitude of long waves in the nearshore, with direct implications for determining long-wave amplitudes at the shoreline and wave runup.
The Quaternary Clear Lake Volcanic Field (CLVF) in the Northern California Coast Range is the youngest of a string of northward-younging volcanic centers in the state. The CLVF is located within the broad San Andreas Transform Fault System and has been active intermittently for ∼2 million years. Heat beneath the CLVF supports The Geysers, one of the largest producing geothermal fields in the world.
Previous geophysical studies proposed the existence of a magma reservoir beneath Mount Hannah, which is northeast of The Geysers, near the geographic center of the CLVF. The lateral extent, depth, and presence of melt within this reservoir are poorly constrained, as is the relationship between this body and the broader magmatic plumbing of the CLVF. To gain a clearer and more comprehensive picture of the CLVF magma source region, a gravity dataset was compiled and the first 3-D gravity inversions of the CLVF were completed.
Field and synthetic model inversions from the current study both indicate that the gravity low roughly centered on Mount Hannah is not accurately explained by a 5–7 km thick lens of Mesozoic Great Valley Sequence (
The prolonged eruption history of the CLVF, coupled with the compositional variation of erupted rocks over time and space, is consistent with the existence of several, potentially ephemeral, melt-bearing bodies as opposed to one large melt body. Given the density and location of the recovered anomaly, rhyolite-MELTS thermodynamic modeling suggests the existence of 10–30% rhyodacitic melt within the proposed silicic magma reservoir at about 700 °C and 8 km depth (210 MPa). Independent petrologic, geochemical, and seismic evidence indicates that this silicic partial melt zone is underlain by basaltic melt in the lower to middle crust (13 to 21 km depth), which is fed by a mantle source.
Eruptions in the past ∼8.5–13.5 thousand years; high regional heat flow; 3He enrichment of hydrothermal fluids; and our modeling, which suggests the presence of a mid-crustal, silicic partial melt zone, point to a still-active CLVF. The relatively low estimates of partial melt (10–30%) predicted by thermodynamic modeling indicates that an injection of new magma into the imaged partial melt zone is needed to generate sufficient melt to incite future eruptions. Despite the low percent melt estimates within the proposed silicic partial melt zone the potential for future volcanic eruption remains. Due to the proximity of the CLVF to cities surrounding Clear Lake and the densely populated San Francisco Bay Area, continued research and monitoring of the volcanic field are warranted. The geophysical and petrologic modeling presented here improves our understanding of the CLVF magma plumbing system and allows us to better characterize its associated volcanic hazards.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2023.107758","usgsCitation":"Mitchell, M.A., Peacock, J., and Burgess, S.D., 2023, Imaging the magmatic plumbing of the Clear Lake Volcanic Field using 3-D gravity inversions: Journal of Volcanology and Geothermal Research, v. 435, 107758, 41 p., https://doi.org/10.1016/j.jvolgeores.2023.107758.","productDescription":"107758, 41 p.","ipdsId":"IP-137378","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":444342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2023.107758","text":"Publisher Index Page"},{"id":415702,"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 -123.25068446644411,\n 39.211549683768425\n ],\n [\n -123.25068446644411,\n 38.316770047400155\n ],\n [\n -122.29803029534756,\n 38.316770047400155\n ],\n [\n -122.29803029534756,\n 39.211549683768425\n ],\n [\n -123.25068446644411,\n 39.211549683768425\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"435","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mitchell, Michael Albert 0000-0001-5070-8793","orcid":"https://orcid.org/0000-0001-5070-8793","contributorId":299110,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"","middleInitial":"Albert","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":869389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":869390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgess, Seth D. 0000-0002-4238-3797 sburgess@usgs.gov","orcid":"https://orcid.org/0000-0002-4238-3797","contributorId":200371,"corporation":false,"usgs":true,"family":"Burgess","given":"Seth","email":"sburgess@usgs.gov","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":869391,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256611,"text":"70256611 - 2023 - A review of lethal thermal tolerance among freshwater mussels (Bivalvia: Unionida) within the North American faunal region","interactions":[],"lastModifiedDate":"2024-08-07T11:10:46.111024","indexId":"70256611","displayToPublicDate":"2023-03-01T06:08:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5056,"text":"Environmental Reviews","active":true,"publicationSubtype":{"id":10}},"title":"A review of lethal thermal tolerance among freshwater mussels (Bivalvia: Unionida) within the North American faunal region","docAbstract":"The gray bat (Myotis grisescens) is a cave-obligate species that has been listed as federally endangered since 1976, following population declines from human disturbance at hibernation and maternity caves. However, with cave protection, most gray bat populations have increased. As part of a project examining bat use of transportation structures as day-roosts, we continuously acoustically monitored 12 riparian sites within the Clinch River Watershed of southwest Virginia from March through November, 2018–2020. We used 15 different landscape and weather-related variables in generalized linear mixed models to determine factors influencing gray bat presence and activity. Seasonal activity patterns were similar among years, but the number of nightly gray bat calls increased with each passing year, consistent with positive population trends observed at winter hibernacula. Year and average nightly temperatures were positively correlated with gray bat activity, as was, unexpectedly, average nightly wind speed. Total nightly precipitation, distance to the nearest hibernaculum in Tennessee, percent forested area within 2 km of a detector, mean elevation within 2 km of a detector, detector type, and amount of urban development within 2 km of a detector were negatively correlated with gray bat activity. Our findings show where and when gray bat presence is likely in southwest Virginia, thereby helping managers avoid negative impacts from activities such as bridge repair or replacement and planning of future monitoring to track population trends.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Taylor, H., Powers, K., Orndorff, W., Reynolds, R., Hallerman, E.M., and Ford, W., 2023, Sources of yearly variation in gray bat activity in the Clinch River watershed, Virginia: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 107-113.","productDescription":"7 p.","startPage":"107","endPage":"113","ipdsId":"IP-142692","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481149,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/sources-yearly-variation-gray-bat-activity-clinch-river-watershed-virginia","linkFileType":{"id":5,"text":"html"}},{"id":481151,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Clinch River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.6898822712355,\n 37.244895334911206\n ],\n [\n -82.48049233355506,\n 36.59887550386596\n ],\n [\n -81.6898822712355,\n 36.59053043712693\n ],\n [\n -81.38353788654133,\n 36.697772476555386\n ],\n [\n -81.0769253267201,\n 37.244895334911206\n ],\n [\n -81.6898822712355,\n 37.244895334911206\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, H.","contributorId":195324,"corporation":false,"usgs":false,"family":"Taylor","given":"H.","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":924942,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Powers, K.","contributorId":349843,"corporation":false,"usgs":false,"family":"Powers","given":"K.","affiliations":[{"id":34752,"text":"Radford University","active":true,"usgs":false}],"preferred":false,"id":924943,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orndorff, W.","contributorId":349845,"corporation":false,"usgs":false,"family":"Orndorff","given":"W.","affiliations":[{"id":56188,"text":"Virginia Department of Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":924944,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reynolds, Rick","contributorId":267215,"corporation":false,"usgs":false,"family":"Reynolds","given":"Rick","email":"","affiliations":[{"id":55446,"text":"Virginia Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":925006,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hallerman, E. M.","contributorId":280251,"corporation":false,"usgs":false,"family":"Hallerman","given":"E.","email":"","middleInitial":"M.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":924945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":924946,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262832,"text":"70262832 - 2023 - Distribution of summer-habitat for the Indiana bat on the Monongahela National Forest, West Virginia","interactions":[],"lastModifiedDate":"2025-01-24T17:17:27.70657","indexId":"70262832","displayToPublicDate":"2023-03-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Distribution of summer-habitat for the Indiana bat on the Monongahela National Forest, West Virginia","docAbstract":"Hierarchical conservation and management of Indiana bat (Myotis sodalis) habitat may benefit from use of species distribution models. White-nose syndrome has caused additional declines for this endangered bat, requiring use of historical presence locations for habitat-related analy- ses. We created random forest presence/pseudo-absence models to assess the distribution and availability of Indiana bat habitat across the 670,000-ha Monongahela National Forest (MNF), West Virginia, USA. We collated historical roost and capture locations, both individually and in combination, to examine impacts of various biotic and abiotic predictors on roosting and foraging habitat of Indiana bats. Our final concordance map suggests that Indiana bat habitat was abundant (37.2% of the MNF) but localized, with predicted suitable areas often associated with edges of dry-calcareous forests. We observed significant variation between models, with the capture-only model independently identifying the greatest amount of potential habitat (47.8%). However, 21.9% of all potential Indiana bat habitat was identified by complete inter-model agreement. Our SDM outputs may assist land managers in identifying avoidance areas and new survey sites (i.e., capture and acoustic sampling) to support forest management activities.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"De La Cruz, J., Ford, W., Jones, S.B., Johnson, J., and Silvis, A., 2023, Distribution of summer-habitat for the Indiana bat on the Monongahela National Forest, West Virginia: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 125-134.","productDescription":"10 p.","startPage":"125","endPage":"134","ipdsId":"IP-142588","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481122,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/distribution-summer-habitat-indiana-bat-monongahela-national-forest-west-virginia"},{"id":481155,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Viginia","otherGeospatial":"Monongahela National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.92561978482631,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.52966246222408\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"De La Cruz, J.L.","contributorId":349847,"corporation":false,"usgs":false,"family":"De La Cruz","given":"J.L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":924947,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":924948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, S. Beaux","contributorId":346278,"corporation":false,"usgs":false,"family":"Jones","given":"S.","email":"","middleInitial":"Beaux","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":924949,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, J.R.","contributorId":349849,"corporation":false,"usgs":false,"family":"Johnson","given":"J.R.","affiliations":[{"id":32872,"text":"John Hopkins University, Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":924950,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silvis, A.","contributorId":349851,"corporation":false,"usgs":false,"family":"Silvis","given":"A.","affiliations":[{"id":40299,"text":"West Virginia Division of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924951,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262166,"text":"70262166 - 2023 - Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir","interactions":[],"lastModifiedDate":"2025-01-15T20:01:12.656734","indexId":"70262166","displayToPublicDate":"2023-03-01T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir","docAbstract":"Understanding walleye (Sander vitreus) spawning behavior is important for managing walleye fisheries, but such information is limited for Appalachian reservoirs. We assessed spawning movements and spawning locations for a reestablished walleye population in Cheat Lake, West Virginia. We tagged fifty-two walleye with acoustic telemetry transmitters to evaluate environmental correlates associated with pre-spawn movements and to deter- mine spawning locations. Using an information-theoretic approach, we compared candidate logistic regression models to determine which environmental variables best explained upstream movements to spawning areas. The two models with the most support both included additive effects of year and water temperature, with sex also included in the second of these models. Water temperature had a significant positive relationship with pre-spawn movements in each model. Other environmental covariates such as river discharge and water elevation were not significant predictors of upstream pre-spawn move- ments. Walleye made pre-spawn upstream movements in late winter/early spring to spawning areas in the headwaters of Cheat Lake during periods of el- evated water temperatures (75 % of movement events occurred at water temperatures >4.1 C) where spawning occurred in shallow (<1.5 m), rocky habitat. Male walleye generally made upstream pre-spawn movements earlier than females. Our results also suggested the timing of walleye spawning with respect to water-level fluctuations could influence reproductive success due to stranding of eggs or reducing suitable spawning habitat. Knowledge of pre-spawn movement patterns and spawning locations could aid management of this recovering population. Benefits to management may include the prediction of spawning timing and locations for broodstock surveys and influences of water-level fluctuations and other environmental stressors on spawning success.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Smith, D., Welsh, S.A., and Hilling, C.D., 2023, Environmental correlates of walleye spawning movements in an Appalachian hydropower reservoir: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 36-44.","productDescription":"9 p.","startPage":"36","endPage":"44","ipdsId":"IP-145288","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466454,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":466453,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/environmental-correlates-walleye-spawning-movements-appalachian-hydropower-reservoir"}],"country":"United States","state":"Pennsylvania, West Virginia","otherGeospatial":"Cheat Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.90059403320035,\n 39.7348398274502\n ],\n [\n -79.90059403320035,\n 39.665062724725885\n ],\n [\n -79.82498808610146,\n 39.665062724725885\n ],\n [\n -79.82498808610146,\n 39.7348398274502\n ],\n [\n -79.90059403320035,\n 39.7348398274502\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Dustin M.","contributorId":272979,"corporation":false,"usgs":false,"family":"Smith","given":"Dustin M.","affiliations":[{"id":56173,"text":"West Virginia DNR","active":true,"usgs":false}],"preferred":false,"id":923331,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welsh, Stuart A. 0000-0003-0362-054X swelsh@usgs.gov","orcid":"https://orcid.org/0000-0003-0362-054X","contributorId":1483,"corporation":false,"usgs":true,"family":"Welsh","given":"Stuart","email":"swelsh@usgs.gov","middleInitial":"A.","affiliations":[{"id":205,"text":"Cooperative Research Units","active":false,"usgs":true}],"preferred":false,"id":923332,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":923333,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240897,"text":"fs20233001 - 2023 - Flood warning toolset for the Sabinal River near Utopia, Texas","interactions":[],"lastModifiedDate":"2026-02-05T14:42:08.024436","indexId":"fs20233001","displayToPublicDate":"2023-02-28T12:30:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3001","displayTitle":"Flood Warning Toolset for the Sabinal River Near Utopia, Texas","title":"Flood warning toolset for the Sabinal River near Utopia, Texas","docAbstract":"Floods are one of the most frequent and expensive natural disasters that occur across the United States. Rapid, high-water events that occur in local areas—flash floods—are especially difficult for emergency managers to predict and provide advance warning to the public, and insufficient data can hamper postflood recovery efforts. Central Texas is hilly, and it is known as a “flash flood alley” because of its high-intensity rains, shallow soils, and steep terrain, all of which combined can result in loss of life and property damage. For example, the flash flood event during July 2002 claimed 12 lives in central Texas, including 1 in the town of Utopia, which is on the east bank of the Sabinal River in a flash-flood-prone area along the Balcones Escarpment. During the flood event, the peak discharge recorded on July 5, 2002, at U.S. Geological Survey (USGS) streamgage 08198000 Sabinal River near Sabinal, Tex. (hereinafter referred to as the “Sabinal gage”), was 108,000 cubic feet per second (corresponding to a stream stage [also called gage height] of 33.74 feet). To put the 2002 flood into context, during a typical year the median daily discharge in the Sabinal River at the Sabinal gage is only about 23 cubic feet per second. In 2021, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood warning toolset for the Sabinal River near Utopia. This study builds on earlier USGS flood work on the Medina River in Bandera County. The newly developed toolset consists of a newly installed USGS streamgage to collect continuous stream stage data (streamgage 08197970 Sabinal River at Utopia, Tex.; hereinafter referred to as the “Utopia gage”) 13 miles upstream from the Sabinal gage, a hydraulic model developed for the Sabinal River near Utopia, and an online library of digital flood-inundation maps referenced to the stream stage at the Utopia gage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233001","issn":"2327-6932 (online)","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., 2023, Flood warning toolset for the Sabinal River near Utopia, Texas: U.S. Geological Survey Fact Sheet 2023–3001 (ver. 2.0, September 2023), 4 p., https://doi.org/10.3133/fs20233001.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136337","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":421082,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2023/3001/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":499562,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114429.htm","linkFileType":{"id":5,"text":"html"}},{"id":421081,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20235001","text":"Scientific Investigations Report 2023–5001","description":"SIR 2023-5001","linkHelpText":"- Flood-Inundation Maps Created Using a Synthetic Rating Curve for a 10-Mile Reach of the Sabinal River and a 7-Mile Reach of the West Sabinal River Near Utopia, Texas, 2021"},{"id":421080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3001/fs20233001.pdf","text":"Report","size":"1.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3001"},{"id":414773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3001/coverthb.jpg"}],"country":"United States","state":"Texas","city":"Utopia","otherGeospatial":"Sabinal River, West Sabinal River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.6333,\n 29.75\n ],\n [\n -99.6333,\n 29.6\n ],\n [\n -99.5,\n 29.6\n ],\n [\n -99.5,\n 29.75\n ],\n [\n -99.6333,\n 29.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 2023; Version 2.0: September 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Overview
Creation of Flood Warning Toolset
References Cited
Coastal resources are increasingly affected by erosion, extreme weather events, sea level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying effects on coastal landscapes because of the compounding of geologic, oceanographic, ecologic, and socioeconomic factors that exist at a given location. An assessment framework is introduced in this report that synthesizes existing datasets that cover the variability of the landscape, and hazards that may act on the landscape, to evaluate the likelihood of coastal change along the U.S. coastline on a decadal scale. The pilot study that aided in the development of the framework was run in the northeastern United States (from Maine to Virginia) and consists of datasets derived from a variety of Federal, State, and local sources.
First, a decision-tree-based dataset was built that describes the resistance or integrity of the coastal landscape (called the fabric dataset for the purposes of this report) and includes land cover, elevation, slope, long-term (more than 50 years) shoreline change, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (for example, flooding, wave power, and probability of storm overwash) and persistent or perpetual hazards (for example, relative sea level rise rate, short-term [about 30-year] shoreline erosion rate, and storm recurrence interval). The fabric dataset was then merged with the coastal hazards databases, and a model training dataset made up of hundreds of polygons was generated from these combined data to support machine learning.
The pilot study resulted in location-specific, 10-meter-resolution data classified into five raster datasets that include intrinsic characteristics of the coast used to determine the resistance of the landscape to change, the persistent and event hazards that act on the coast, the machine learning output (coastal change likelihood) based on the cumulative effects of the fabric and hazards datasets, and an estimate of the hazard type (event or persistent) that is the most likely to influence coastal change. Final outcomes are intended to be used as a first-order planning tool to determine which areas of the coast are more likely to change in response to future potential coastal hazards and to examine elements and drivers that make change in a location more likely.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1169","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Pendleton, E.A., Lentz, E.E., Sterne, T.K., and Henderson, R.E., 2023, Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia: U.S. Geological Survey Data Report 1169, 56 p., https://doi.org/10.3133/dr1169.","productDescription":"Report: viii, 56 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-141482","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":413447,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96A2Q5X","text":"USGS data release","linkHelpText":"Coastal change likelihood in the U.S. northeast region—Maine to Virginia"},{"id":413449,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1169/images/"},{"id":499552,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114426.htm","linkFileType":{"id":5,"text":"html"}},{"id":413448,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1169/dr1169.XML"},{"id":413446,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1169/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1169"},{"id":413445,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1169/dr1169.pdf","text":"Report","size":"26.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1169"},{"id":413444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1169/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.69060371478722,\n 36.642989353191695\n ],\n [\n -75.63687331767586,\n 36.61725767144067\n ],\n [\n -73.59796884586575,\n 40.01648840470193\n ],\n [\n -70.80274185094413,\n 41.01068598755887\n ],\n [\n -69.57806852142825,\n 41.099580604299234\n ],\n [\n -69.90622192003855,\n 42.11383028198776\n ],\n [\n -70.57455282622757,\n 43.02711001288796\n ],\n [\n -67.01522918769722,\n 44.713652472389384\n ],\n [\n -67.51934985246183,\n 45.183064414796405\n ],\n [\n -71.15470019361379,\n 43.73883250370727\n ],\n [\n -71.3079883194934,\n 41.86654548530123\n ],\n [\n -73.98393265041173,\n 41.230559521628294\n ],\n [\n -76.3263141198747,\n 39.710722533017474\n ],\n [\n -77.25368632549163,\n 38.78457338969852\n ],\n [\n -76.72175177570705,\n 36.72418776523644\n ],\n [\n -75.97037108334082,\n 36.81887134243617\n ],\n [\n -75.94893824149692,\n 36.817996199366135\n ],\n [\n -76.69060371478722,\n 36.642989353191695\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543-1598
The U.S. Army Corps of Engineers (USACE) is considering changes to the management of water surface elevation in four lakes in the Mahoning River Basin. These changes would affect the timing and amounts of water released to the Mahoning River and could affect the water quality of those releases. To provide information on possible water-quality effects from these operational changes, flow and water-quality models were constructed for Berlin Lake, Lake Milton, Michael J Kirwan Reservoir, Mosquito Creek Lake, Mosquito Creek, and the Mahoning River from the dams downstream to Lowellville, Ohio.
The models were calibrated for two calendar years each, with model years selected depending on the availability of water-quality data. Models were developed with CE-QUAL-W2 version 4.2 (Wells, S.A., 2020, CE-QUAL-W2—A two-dimensional, laterally averaged, hydrodynamic and water quality model [version 4.2]: Portland State University, variously paged), a two-dimensional, laterally averaged hydrodynamic and water-quality model. Modeled constituents included flow, velocity, ice cover, water temperature, total dissolved solids (TDS), sulfate, chloride, inorganic suspended sediment, nitrate, ammonia, total Kjeldahl nitrogen, orthophosphate, total phosphorus, dissolved and particulate organic matter, algae, and dissolved oxygen. Iron was included for the lake models, but not the river.
A whole-basin model, with the four lake models and river model, was used to run model scenarios to examine the effects of altered lake water surface elevations on flow and water quality in the lakes, the lake outflows, and the Mahoning River. The initial whole-basin model, with calendar year 2013 hydrology and measured or typical water quality, was designated as scenario 0. Mahoning River flows for calendar year 2013 were close to a 20-year median flow. Four additional scenarios were constructed based on reservoir operations model (RES-SIM) model water surface elevations for the four lakes as provided by USACE. Scenario 1 was the RES-SIM base case, scenario 2 kept Berlin Lake water surface elevations higher in summer, scenario 3 allowed 25 percent of summer flood storage to extend the guide curve, and scenario 4 allowed more flexibility in lake management by removing any downstream Mahoning River minimum flow requirements. The Mahoning River model was not changed in any scenarios but received altered flows from the lakes. Significant findings from this study include the following:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
Bigheaded carp Hypophthalmichthys spp. are invasive species native to Asia expanding in the Mississippi River Basin in North America. An understanding of spatiotemporal distribution and aggregation of invasive carp is key to establishing when and where to focus surveillance designed to monitor expansion, and to managing harvest programs designed to curb population densities. We applied a two-stage hurdle model to assess three aspects of bigheaded carp ecology: distribution, relative abundance, and aggregation. Stage 1 was a binary 0/1 model that represented fish presence (p), and stage 2 was a truncated count distribution that had no zeros and included counts ≥ 1 only (C). Estimates of p and C varied temporally and spatially, but not in harmony and sometimes in opposing directions, indicating temporal and spatial swings in fish distributions and aggregations. Intense fish aggregations in channels in spring shown by low p’s and high C’s, eventually scattered by summer and fall as shown by high p’s and low C’s. An alternative but complementary interpretation of our observations is that p indexes incidence of aggregations and C indexes size of aggregations. Partitioning catch into its zero and nonzero components provided insight into population ecology that can inform development of monitoring and management of harvesting programs targeted at lessening potential effects of the invasion.
","language":"English","publisher":"Invasives.net","doi":"10.3391/mbi.2023.14.2.12","usgsCitation":"Miranda, L.E., Tompkins, J., Dunn, C.G., Morris, J.L., and Combs, M.C., 2023, Patterns of zero and nonzero counts suggest spatiotemporal distributions, aggregation, and dispersion of invasive carp: Management of Biological Invasions, v. 14, no. 2, p. 363-377, https://doi.org/10.3391/mbi.2023.14.2.12.","productDescription":"15 p.","startPage":"363","endPage":"377","ipdsId":"IP-130231","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2023.14.2.12","text":"Publisher Index Page"},{"id":433157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, Kentucky, Mississippi, North Carolina, Tennessee","otherGeospatial":"Cumberland River basin, Kentucky Lake, Lake Barkley, Tennessee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.6579330031289,\n 36.91383372177731\n ],\n [\n -89.3365987950857,\n 36.48519895640891\n ],\n [\n -90.06194179414129,\n 35.00234638850921\n ],\n [\n -88.19060222341875,\n 34.1587395703324\n ],\n [\n -84.99319129439488,\n 34.42656666413427\n ],\n [\n -82.51455380820883,\n 35.573734179364564\n ],\n [\n -81.53907530047877,\n 36.20934135136643\n ],\n [\n -83.66465109105143,\n 36.726966972925695\n ],\n [\n -86.6579330031289,\n 36.91383372177731\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tompkins, J.","contributorId":341343,"corporation":false,"usgs":false,"family":"Tompkins","given":"J.","email":"","affiliations":[{"id":53972,"text":"Kentucky Department of Fish and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":908268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morris, J. L.","contributorId":255439,"corporation":false,"usgs":false,"family":"Morris","given":"J.","email":"","middleInitial":"L.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":908267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Combs, Matthew C.","contributorId":343671,"corporation":false,"usgs":false,"family":"Combs","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":911638,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240990,"text":"70240990 - 2023 - Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA","interactions":[],"lastModifiedDate":"2023-05-12T14:54:16.678942","indexId":"70240990","displayToPublicDate":"2023-02-27T08:19:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2746,"text":"Mineralium Deposita","active":true,"publicationSubtype":{"id":10}},"title":"Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA","docAbstract":"Graphite Creek is an unusual flake graphite deposit located on the Seward Peninsula, Alaska, USA. We present field observations, uranium-lead (U–Pb) monazite and titanite geochronology, carbon (C) and sulfur (S) stable isotope geochemistry, and graphite Raman spectroscopy data from this deposit that support a new model of flake graphite ore genesis in high-grade metamorphic environments. The Graphite Creek deposit is within the second sillimanite metamorphic zone of the Kigluaik Mountains gneiss dome. Flake graphite, hosted in sillimanite-gneiss and quartz-biotite paragneiss, occurs as disseminations and in sets of very high grade (up to 50 wt.% graphite), semi-massive to massive graphite lenses 0.2 to 1 m wide containing quartz, sillimanite, inclusions of garnet porphyroblasts, K-feldspar, and tourmaline. Restitic garnet, sillimanite, graphite, and biotite accumulations indicate a high degree of anatexis and melt loss. Strong yttrium depletion in monazite, high europium ratios (Eu/Eu*), and excursions of high strontium and thorium concentrations are consistent with biotite dehydration melting. Monazite and titanite U–Pb ages record peak metamorphism from ~ 97 to 92 million years ago (Ma) and a retrograde event at ~ 85 Ma. Raman spectroscopy confirms the presence of carbonaceous material and highly ordered, crystalline graphite. Graphite δ13CVPDB values of − 30 to − 12‰ and pyrrhotite δ34SVCDT values of − 14 to 10‰ are consistent with derivation from organic carbon and sulfur in sedimentary rocks, respectively. These data collectively suggest that formation of massive graphite lenses occurred approximately synchronously with high-temperature metamorphism and anatexis of a highly carbonaceous pelitic protolith. Melt extraction and fluid release associated with anatexis were likely crucial for concentrating graphite. High-temperature, graphitic migmatite sequences within high-strain shear zones may be favorable for the occurrence of high-grade flake graphite deposits.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-023-01161-3","usgsCitation":"Case, G.N., Karl, S.M., Regan, S., Johnson, C.A., Ellison, E.T., Caine, J., Holm-Denoma, C., Pianowski, L., and Benowitz, J.A., 2023, Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA: Mineralium Deposita, v. 58, p. 939-962, https://doi.org/10.1007/s00126-023-01161-3.","productDescription":"24 p.","startPage":"939","endPage":"962","ipdsId":"IP-135671","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":444354,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00126-023-01161-3","text":"Publisher Index Page"},{"id":435431,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J50EKX","text":"USGS data release","linkHelpText":"Data for Uranium-Lead Geochronology, Carbon and Sulfur Stable Isotopes, and Raman Spectroscopy from Graphite Creek, Alaska"},{"id":413658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Graphite Creek graphite deposit, Seward Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.1974510679629,\n 65.1\n ],\n [\n -166.1974510679629,\n 64.7052203056632\n ],\n [\n -164.43596050024277,\n 64.7052203056632\n ],\n [\n -164.43596050024277,\n 65.1\n ],\n [\n -166.1974510679629,\n 65.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","noUsgsAuthors":false,"publicationDate":"2023-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Case, George N.D. 0000-0001-9826-5661 gcase@usgs.gov","orcid":"https://orcid.org/0000-0001-9826-5661","contributorId":224941,"corporation":false,"usgs":true,"family":"Case","given":"George","email":"gcase@usgs.gov","middleInitial":"N.D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":865622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Susan M. 0000-0003-1559-7826 skarl@usgs.gov","orcid":"https://orcid.org/0000-0003-1559-7826","contributorId":502,"corporation":false,"usgs":true,"family":"Karl","given":"Susan","email":"skarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":865623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Regan, Sean P.","contributorId":219815,"corporation":false,"usgs":false,"family":"Regan","given":"Sean P.","affiliations":[{"id":13599,"text":"University of Alaska - Fairbanks","active":true,"usgs":false}],"preferred":false,"id":865624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Craig A. 0000-0002-1334-2996 cjohnso@usgs.gov","orcid":"https://orcid.org/0000-0002-1334-2996","contributorId":909,"corporation":false,"usgs":true,"family":"Johnson","given":"Craig","email":"cjohnso@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":865625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellison, Eric T 0000-0002-6761-1397","orcid":"https://orcid.org/0000-0002-6761-1397","contributorId":302853,"corporation":false,"usgs":false,"family":"Ellison","given":"Eric","email":"","middleInitial":"T","affiliations":[{"id":52978,"text":"Department of Geological Sciences, University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":865626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":865627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":865628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pianowski, Laura 0000-0002-5346-8251","orcid":"https://orcid.org/0000-0002-5346-8251","contributorId":218817,"corporation":false,"usgs":true,"family":"Pianowski","given":"Laura","email":"","affiliations":[],"preferred":true,"id":865629,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Benowitz, Jeff A. 0000-0003-2294-9172","orcid":"https://orcid.org/0000-0003-2294-9172","contributorId":229570,"corporation":false,"usgs":false,"family":"Benowitz","given":"Jeff","email":"","middleInitial":"A.","affiliations":[{"id":41671,"text":"Geophysical Institute and Geochronology Laboratory, University of Alaska–Fairbanks","active":true,"usgs":false}],"preferred":false,"id":865630,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262037,"text":"70262037 - 2023 - A big data–model integration approach for predicting epizootics and population recovery in a keystone species","interactions":[],"lastModifiedDate":"2025-01-10T14:56:44.15352","indexId":"70262037","displayToPublicDate":"2023-02-27T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"A big data–model integration approach for predicting epizootics and population recovery in a keystone species","docAbstract":"Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host–pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2827","usgsCitation":"Barrile, G., Augustine, D.J., Porensky, L., Duchardt, C., Shoemaker, K., Hartway, C., Derner, J.D., Hunter, E.A., and Davidson, A.D., 2023, A big data–model integration approach for predicting epizootics and population recovery in a keystone species: Ecological Applications, v. 33, no. 4, e2827, 23 p., https://doi.org/10.1002/eap.2827.","productDescription":"e2827, 23 p.","ipdsId":"IP-142779","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2827","text":"Publisher Index Page"},{"id":465980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.72151393816772,\n 49.06842429079816\n ],\n [\n -106.74407678276975,\n 44.935250564946244\n ],\n [\n -105.02441274593369,\n 40.78587530801761\n ],\n [\n -105.18708495048385,\n 35.23907808129579\n ],\n [\n -110.74352872625728,\n 31.51616164533567\n ],\n [\n -101.12343743446304,\n 28.915776651344572\n ],\n [\n -100.73547916940133,\n 38.76206177537206\n ],\n [\n -100.82267130820097,\n 44.32291601997803\n ],\n [\n -101.89899715272732,\n 47.61230649548153\n ],\n [\n -111.72151393816772,\n 49.06842429079816\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":288734,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel M.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":922769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, David J.","contributorId":189957,"corporation":false,"usgs":false,"family":"Augustine","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":922770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":922771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duchardt, Courtney J.","contributorId":347959,"corporation":false,"usgs":false,"family":"Duchardt","given":"Courtney J.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":922772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shoemaker, Kevin T.","contributorId":288541,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":61793,"text":"University of Nevada – Reno","active":true,"usgs":false}],"preferred":false,"id":922773,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartway, Cynthia R.","contributorId":347961,"corporation":false,"usgs":false,"family":"Hartway","given":"Cynthia R.","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":922774,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Derner, Justin D.","contributorId":195928,"corporation":false,"usgs":false,"family":"Derner","given":"Justin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":922775,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922776,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davidson, Ana D. 0000-0003-4701-5923","orcid":"https://orcid.org/0000-0003-4701-5923","contributorId":304176,"corporation":false,"usgs":false,"family":"Davidson","given":"Ana","email":"","middleInitial":"D.","affiliations":[{"id":65991,"text":"CNHP","active":true,"usgs":false}],"preferred":false,"id":922777,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241408,"text":"70241408 - 2023 - Poleward amplification, seasonal rainfall and forest heterogeneity in the Miocene of the eastern USA","interactions":[],"lastModifiedDate":"2023-03-17T11:42:04.586294","indexId":"70241408","displayToPublicDate":"2023-02-26T06:39:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Poleward amplification, seasonal rainfall and forest heterogeneity in the Miocene of the eastern USA","docAbstract":"Paleoclimate reconstructions can provide a window into the environmental conditions in Earth history when atmospheric carbon dioxide concentrations were higher than today. In the eastern USA, paleoclimate reconstructions are sparse, because terrestrial sedimentary deposits are rare. Despite this, the eastern USA has the largest population and population density in North America, and understanding the effects of current and future climate change is of vital importance. Here, we provide terrestrial paleoclimate reconstructions of the eastern USA from Miocene fossil floras. Additionally, we compare proxy paleoclimate reconstructions from the warmest period in the Miocene, the Miocene Climatic Optimum (MCO), to those of an MCO Earth System Model. Reconstructed Miocene temperatures and precipitation north of 35°N are higher than modern. In contrast, south of 35°N, temperatures and precipitation are similar to today, suggesting a poleward amplification effect in eastern North America. Reconstructed Miocene rainfall seasonality was predominantly higher than modern, regardless of latitude, indicating greater variability in intra-annual moisture transport. Reconstructed climates are almost uniformly in the temperate seasonal forest biome, but heterogeneity of specific forest types is evident. Reconstructed Miocene terrestrial temperatures from the eastern USA are lower than modeled temperatures and coeval Atlantic sea surface temperatures. However, reconstructed rainfall is consistent with modeled rainfall. Our results show that during the Miocene, climate was most different from modern in the northeastern states, and may suggest a drastic reduction in the meridional temperature gradient along the North American east coast compared to today.
Fish that exhibit high foraging activity or bold behavior can be particularly vulnerable to angling. If these traits are heritable, selection through harvest can drive phenotypic change, eventually rendering a target population less vulnerable to angling and consequently impacting the quality of the fishery. In this study, we used parental-based tags to investigate whether vulnerability to angling might be heritable in steelhead trout (Oncorhynchus mykiss) spawned at a hatchery in western Oregon, USA. We found modest evidence to support the hypothesis that vulnerability to angling is a heritable trait in steelhead. However, our data unexpectedly revealed that steelhead collected with in-river traps produced nearly twice as many adult offspring as steelhead collected by anglers. This difference in adult-to-adult production is explained in part through lower egg-to-fry survival of steelhead produced with angler-caught broodstock, possibly related to collection stress and greater time in captivity experienced by angler-caught broodstock. Our findings suggest that managers could improve broodstock fitness and program efficiencies by preferentially spawning fish collected with traps, and limiting use of broodstock collected by anglers. Additional research is needed to identify mechanisms contributing to higher juvenile mortality of steelhead produced with angler-caught broodstock.