Exchanges of groundwater and surface-water are fundamental to a wide range of water-supply and water-quality management issues but challenging to map beyond the reach scale. Waterborne gradient self-potential (SP) measurements are directly sensitive to water flow through riverbed sediments and can be used to infer exchange locations, direction (gain versus loss), scale, and relative changes, but to date applications to river corridor hydrology are limited. Numerical modeling and field experiments were therefore performed herein, each emphasizing waterborne gradient SP logging for identifying and locating focused vertical groundwater discharge (surface-water gain) and recharge (surface-water loss) in a river. Two and three-dimensional numerical models were constructed to simulate the polarities, appearances, and peak amplitudes of streaming-potential and electric-field anomalies on a riverbed and in the surface-water that were attributable to steady-state vertical fluxes of groundwater through high-permeability conduits in the riverbed. Effects of varied hydraulic length-scale of exchange and surface-water depth were tested through numerical modeling. Modeling results aided in data acquisition and interpretation for three repeated field experiments performed along a 1.5–2.0 km reach of the Quashnet River in Cape Cod, Massachusetts, where focused, meter-scale groundwater discharges occur at discrete locations within otherwise ubiquitous and more diffuse groundwater upwelling conditions. Strong gradient SP anomalies were repeatedly measured in the Quashnet River at previously confirmed locations of focused groundwater discharge, showing the efficacy of waterborne gradient SP logging in identifying and characterizing groundwater/surface water exchange dynamics at multiple river network scales.
","language":"English","publisher":"EEGS","doi":"10.32389/JEEG20-066","usgsCitation":"Ikard, S., Briggs, M., and Lane, J.W., 2021, Investigation of scale-dependent groundwater/surface-water exchange in rivers by gradient self-potential logging: Numerical modeling and field experiments: Journal of Environmental and Engineering Geophysics, v. 26, no. 2, 181 p., https://doi.org/10.32389/JEEG20-066.","productDescription":"181 p.","ipdsId":"IP-126186","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":387675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Quashnet River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.51935195922852,\n 41.57115075028995\n ],\n [\n -70.5057907104492,\n 41.57115075028995\n ],\n [\n -70.5057907104492,\n 41.59400643013302\n ],\n [\n -70.51935195922852,\n 41.59400643013302\n ],\n [\n -70.51935195922852,\n 41.57115075028995\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ikard, Scott 0000-0002-8304-4935","orcid":"https://orcid.org/0000-0002-8304-4935","contributorId":201775,"corporation":false,"usgs":true,"family":"Ikard","given":"Scott","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":820534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lane, John W. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":219742,"corporation":false,"usgs":true,"family":"Lane","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":820535,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222423,"text":"70222423 - 2021 - Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument","interactions":[],"lastModifiedDate":"2021-07-28T12:01:05.400154","indexId":"70222423","displayToPublicDate":"2021-07-08T06:56:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9115,"text":"Environmental Software & Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument","docAbstract":"Worldwide, billions of people rely on fresh groundwater reserves for their domestic, agricultural and industrial water use. Extreme droughts and excessive groundwater pumping put pressure on water authorities in maintaining sustainable water usage. High-resolution integrated models are valuable assets in supporting them. The Netherlands Hydrological Instrument (NHI) provides the Dutch water authorities with open source modeling software and data. However, NHI integrated groundwater models often require long run times and large memory usage, therefore strongly limiting their application. As a solution, we present a distributed memory parallelization, focusing on the National Hydrological Model. Depending on the level of integration, we show that significant speedups can be obtained up to two orders of magnitude. As far as we know, this is the first reported integrated groundwater parallelization of an operational hydrological model used for national-scale integrated water management and policy making. The parallel model code and data are freely available.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105092","usgsCitation":"Verkaik, J., Hughes, J.D., Walsum, V., Oude Essink, G., Lin, H., and Bierkens, M., 2021, Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument: Environmental Software & Modelling, v. 143, 105092, 15 p., https://doi.org/10.1016/j.envsoft.2021.105092.","productDescription":"105092, 15 p.","ipdsId":"IP-129864","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":451594,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105092","text":"Publisher Index Page"},{"id":387499,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"143","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Verkaik, Jarno 0000-0001-7420-8304","orcid":"https://orcid.org/0000-0001-7420-8304","contributorId":261418,"corporation":false,"usgs":false,"family":"Verkaik","given":"Jarno","email":"","affiliations":[{"id":52847,"text":"Deltares and Utrecht University","active":true,"usgs":false}],"preferred":false,"id":819993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hughes, Joseph D. 0000-0003-1311-2354 jdhughes@usgs.gov","orcid":"https://orcid.org/0000-0003-1311-2354","contributorId":2492,"corporation":false,"usgs":true,"family":"Hughes","given":"Joseph","email":"jdhughes@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":819994,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsum, van","contributorId":261419,"corporation":false,"usgs":false,"family":"Walsum","given":"van","email":"","affiliations":[{"id":52848,"text":"Wageningen Environmental Research","active":true,"usgs":false}],"preferred":false,"id":819995,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oude Essink, G.H.P. 0000-0003-0931-6944","orcid":"https://orcid.org/0000-0003-0931-6944","contributorId":261420,"corporation":false,"usgs":false,"family":"Oude Essink","given":"G.H.P.","email":"","affiliations":[{"id":52847,"text":"Deltares and Utrecht University","active":true,"usgs":false}],"preferred":false,"id":819996,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lin, H.X.","contributorId":261421,"corporation":false,"usgs":false,"family":"Lin","given":"H.X.","email":"","affiliations":[{"id":52849,"text":"Delft Institute of Applied Mathematics and Leiden University","active":true,"usgs":false}],"preferred":false,"id":819997,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bierkens, M.F.P. 0000-0002-7411-6562","orcid":"https://orcid.org/0000-0002-7411-6562","contributorId":261422,"corporation":false,"usgs":false,"family":"Bierkens","given":"M.F.P.","affiliations":[{"id":52850,"text":"Utrecht University and Deltares","active":true,"usgs":false}],"preferred":false,"id":819998,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228931,"text":"70228931 - 2021 - Juvenile moose stress and nutrition dynamics related to winter ticks, landscape characteristics, climate-mediated factors and survival","interactions":[],"lastModifiedDate":"2022-02-24T17:14:48.527888","indexId":"70228931","displayToPublicDate":"2021-07-07T11:08:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3919,"text":"Conservation Physiology","onlineIssn":"2051-1434","active":true,"publicationSubtype":{"id":10}},"title":"Juvenile moose stress and nutrition dynamics related to winter ticks, landscape characteristics, climate-mediated factors and survival","docAbstract":"Moose populations in the northeastern United States have declined over the past 15 years, primarily due to the impacts of winter ticks. Research efforts have focused on the effects of winter tick infestation on moose survival and reproduction, but stress and nutritional responses to ticks and other stressors remain understudied. We examined the influence of several environmental factors on moose calf stress hormone metabolite concentrations and nutritional restriction in Vermont, USA. We collected 407 fecal and 461 snow urine samples from 84 radio-collared moose calves in the winters of 2017–2019 (January–April) to measure fecal glucocorticoid metabolites (fGCM) concentrations and urea nitrogen:creatinine (UN:C) ratios. We used generalized mixed-effects models to evaluate the influence of individual condition, winter ticks, habitat, climate and human development on stress and nutrition in calf moose. We then used these physiological data to build generalized linear models to predict calf winter survival. Calf fGCM concentrations increased with nutritional restriction and snow depth during adult winter tick engorgement. Calf UN:C ratios increased in calves with lighter weights and higher tick loads in early winter. Calf UN:C ratios also increased in individuals with home ranges composed of little deciduous forests during adult winter tick engorgement. Our predictive models estimated that winter survival was negatively related to UN:C ratios and positively related to fGCM concentrations, particularly in early winter. By late March, as winter ticks are having their greatest toll and endogenous resources become depleted, we estimated a curvilinear relationship between fGCM concentrations and survival. Our results provide novel evidence linking moose calf stress and nutrition, a problematic parasite and challenging environment and winter survival. Our findings provide a baseline to support the development of non-invasive physiological monitoring for assessing environmental impacts on moose populations.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coab048","usgsCitation":"Rosenblatt, E., Debow, J., Blouin, J., Donovan, T.M., Murdoch, J., Creel, S., Rogers, W., Gieder, K., Fortin, N., and Alexander, C., 2021, Juvenile moose stress and nutrition dynamics related to winter ticks, landscape characteristics, climate-mediated factors and survival: Conservation Physiology, v. 9, no. 1, coab048, 20 p., https://doi.org/10.1093/conphys/coab048.","productDescription":"coab048, 20 p.","ipdsId":"IP-123406","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451599,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coab048","text":"Publisher Index Page"},{"id":396433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.212890625,\n 43.6599240747891\n ],\n [\n -71.7626953125,\n 43.6599240747891\n ],\n [\n -71.7626953125,\n 44.98811302615805\n ],\n [\n -73.212890625,\n 44.98811302615805\n ],\n [\n -73.212890625,\n 43.6599240747891\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenblatt, Elias","contributorId":276324,"corporation":false,"usgs":false,"family":"Rosenblatt","given":"Elias","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":835945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Debow, Jacob","contributorId":276321,"corporation":false,"usgs":false,"family":"Debow","given":"Jacob","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":835946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blouin, Joshua","contributorId":276322,"corporation":false,"usgs":false,"family":"Blouin","given":"Joshua","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":835947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":835944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murdoch, James","contributorId":276325,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":835948,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Creel, Scott","contributorId":15089,"corporation":false,"usgs":true,"family":"Creel","given":"Scott","affiliations":[],"preferred":false,"id":835949,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rogers, Will","contributorId":280055,"corporation":false,"usgs":false,"family":"Rogers","given":"Will","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":835950,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gieder, Katherina","contributorId":280056,"corporation":false,"usgs":false,"family":"Gieder","given":"Katherina","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":835951,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fortin, Nick","contributorId":280057,"corporation":false,"usgs":false,"family":"Fortin","given":"Nick","email":"","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":835952,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Alexander, Cedric","contributorId":280058,"corporation":false,"usgs":false,"family":"Alexander","given":"Cedric","email":"","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":835953,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70224961,"text":"70224961 - 2021 - Individual and seasonal variation in the movement behavior of two tropical nectarivorous birds","interactions":[],"lastModifiedDate":"2021-10-08T11:53:19.499027","indexId":"70224961","displayToPublicDate":"2021-07-07T06:49:18","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Individual and seasonal variation in the movement behavior of two tropical nectarivorous birds","docAbstract":"Movement of animals directly affects individual fitness, yet fine spatial and temporal resolution movement behavior has been studied in relatively few small species, particularly in the tropics. Nectarivorous Hawaiian honeycreepers are believed to be highly mobile throughout the year, but their fine-scale movement patterns remain unknown. The movement behavior of these crucial pollinators has important implications for forest ecology, and for mortality from avian malaria (Plasmodium relictum), an introduced disease that does not occur in high-elevation forests where Hawaiian honeycreepers primarily breed.
We used an automated radio telemetry network to track the movement of two Hawaiian honeycreeper species, the ʻapapane (Himatione sanguinea) and ʻiʻiwi (Drepanis coccinea). We collected high temporal and spatial resolution data across the annual cycle. We identified movement strategies using a multivariate analysis of movement metrics and assessed seasonal changes in movement behavior.
Both species exhibited multiple movement strategies including sedentary, central place foraging, commuting, and nomadism , and these movement strategies occurred simultaneously across the population. We observed a high degree of intraspecific variability at the individual and population level. The timing of the movement strategies corresponded well with regional bloom patterns of ‘ōhi‘a (Metrosideros polymorpha) the primary nectar source for the focal species. Birds made long-distance flights, including multi-day forays outside the tracking array, but exhibited a high degree of fidelity to a core use area, even in the non-breeding period. Both species visited elevations where avian malaria can occur but exhibited little seasonal change in elevation (< 150 m) and regularly returned to high-elevation roosts at night.
This study demonstrates the power of automated telemetry to study complex and fine-scale movement behaviors in rugged tropical environments. Our work reveals a system in which birds can track shifting resources using a diverse set of movement behaviors and can facultatively respond to environmental change. Importantly, fidelity to high-elevation roosting sites minimizes nocturnal exposure to avian malaria for far-ranging individuals and is thus a beneficial behavior that may be under high selection pressure.
","language":"English","publisher":"Springer","doi":"10.1186/s40462-021-00275-5","usgsCitation":"Smetzer, J.R., Paxton, K.L., and Paxton, E.H., 2021, Individual and seasonal variation in the movement behavior of two tropical nectarivorous birds: Movement Ecology, v. 9, 36, 15 p., https://doi.org/10.1186/s40462-021-00275-5.","productDescription":"36, 15 p.","ipdsId":"IP-127576","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":451613,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-021-00275-5","text":"Publisher Index Page"},{"id":436283,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92GS2TR","text":"USGS data release","linkHelpText":"Hawai'i Island locations of 'Apapane and 'I'iwi from automated radio telemetry tracking system 2014 to 2016"},{"id":390327,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.379638671875,\n 19.694314241825747\n ],\n [\n -155.15441894531247,\n 19.694314241825747\n ],\n [\n -155.15441894531247,\n 19.89072302399691\n ],\n [\n -155.379638671875,\n 19.89072302399691\n ],\n [\n -155.379638671875,\n 19.694314241825747\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-07-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Smetzer, Jennifer R","contributorId":255352,"corporation":false,"usgs":false,"family":"Smetzer","given":"Jennifer","email":"","middleInitial":"R","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":824870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paxton, Kristina L. 0000-0003-2321-5090","orcid":"https://orcid.org/0000-0003-2321-5090","contributorId":41917,"corporation":false,"usgs":false,"family":"Paxton","given":"Kristina","email":"","middleInitial":"L.","affiliations":[{"id":6977,"text":"University of Hawai`i at Hilo","active":true,"usgs":false},{"id":12981,"text":"Department of Biological Sciences, University of Southern Mississippi","active":true,"usgs":false}],"preferred":false,"id":824871,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paxton, Eben H. 0000-0001-5578-7689","orcid":"https://orcid.org/0000-0001-5578-7689","contributorId":19640,"corporation":false,"usgs":true,"family":"Paxton","given":"Eben","email":"","middleInitial":"H.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":824872,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220893,"text":"ofr20211037 - 2021 - Optimization of salt marsh management at the Edwin B. Forsythe National Wildlife Refuge, New Jersey, through use of structured decision making","interactions":[],"lastModifiedDate":"2021-07-06T18:16:43.818555","indexId":"ofr20211037","displayToPublicDate":"2021-07-06T14:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1037","displayTitle":"Optimization of Salt Marsh Management at the Edwin B. Forsythe National Wildlife Refuge, New Jersey, Through Use of Structured Decision Making","title":"Optimization of salt marsh management at the Edwin B. Forsythe National Wildlife Refuge, New Jersey, through use of structured decision making","docAbstract":"Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Edwin B. Forsythe National Wildlife Refuge in New Jersey. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of 23 marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to about \\$980,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than \\$980,000 included applying sediment to the marsh surface to increase elevation in five marsh management units, digging runnels on the marsh surface to improve drainage in five marsh management units, and breaching roads and berms to improve tidal flow in five marsh management units. The potential management benefits were derived from expected reduction in the duration of surface flooding, improved capacity for marsh elevation to keep pace with sea-level rise and increases in numbers of spiders (as an indicator of trophic health), tidal marsh obligate birds, and wintering American black ducks. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Edwin B. Forsythe National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211037","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Castelli, P.M., and Rettig, V., 2021, Optimization of salt marsh management at the Edwin B. Forsythe National Wildlife Refuge, New Jersey, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1037, 41 p., https://doi.org/10.3133/ofr20211037.","productDescription":"vi, 41 p.","ipdsId":"IP-120822","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":386007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1037/coverthb.jpg"},{"id":386008,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1037/ofr20211037.pdf","text":"Report","size":"7.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1037"},{"id":386009,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1037/images"}],"country":"United States","state":"New Jersey","otherGeospatial":"Edwin B. Forsythe National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.41967010498045,\n 39.388182633584485\n ],\n [\n -74.36851501464844,\n 39.40967202224426\n ],\n [\n -74.36817169189453,\n 39.433011014927224\n ],\n [\n -74.33040618896484,\n 39.45395640766923\n ],\n [\n -74.31255340576172,\n 39.48125549646666\n ],\n [\n -74.3276596069336,\n 39.50059690888215\n ],\n [\n -74.4107437133789,\n 39.51807903374736\n ],\n [\n -74.43305969238281,\n 39.519138415094176\n ],\n [\n -74.4601821899414,\n 39.51198727745152\n ],\n [\n -74.4275665283203,\n 39.49397374330326\n ],\n [\n -74.45743560791016,\n 39.46959506012395\n ],\n [\n -74.44267272949219,\n 39.45766759232811\n ],\n [\n -74.41967010498045,\n 39.388182633584485\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
To investigate whether the cumulative years of treatment had an impact on vireo reproductive effort, we looked at the effects of the Treatment Index on reproductive parameters. Results from generalized linear models indicated that treatment did not have an effect on vireo nesting effort or the number of vireo fledglings per pair produced in 2020.
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Animal migrations influence ecosystem structure, dynamics and persistence of predator and prey populations. The theory of migratory coupling postulates that aggregations of migrant prey can induce large-scale synchronized movements in predators, and this coupling is consequential for the dynamics of ecological communities. The degree to which humans influence these interactions remains largely unknown. We tested whether creation of large resource pulses by humans such as seasonal herding of reindeer Rangifer tarandus and hunting of moose, Alces alces, can induce migratory coupling with Golden Eagles, Aquila chrysaetos, and whether these lead to demographic consequences for the eagles.
Fennoscandia.
We used movement data from 32 tracked Golden Eagles spanning 125 annual migratory cycles over 8 years. We obtained reindeer distribution data through collaboration with reindeer herders based on satellite tracking of reindeer, and moose harvest data from the national hunting statistics for Sweden. We assessed demographic consequences for eagles from ingesting lead from ammunition fragments in moose carcasses through survival estimates and their links with lead concentrations in eagles' blood.
In spring, eagles migrated hundreds of kilometres to be spatially and temporally coupled with calving reindeer, whereas in autumn, eagles matched their distribution with the location and timing of moose hunt. Juveniles were more likely to couple with reindeer calving, whereas adults were particularly drawn to areas of higher moose harvest. Due to this coupling, eagles ingested lead from spent ammunition in moose offal and carcasses and the resulting lead toxicity increased the risk of mortality by 3.4 times.
We show how migratory coupling connects landscape processes and that human actions can influence migratory coupling over large spatial scales and increase demographic risks for predators. We provide vital knowledge towards resolving human–wildlife conflicts and the conservation of protected species over a large spatial and temporal scale.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13373","usgsCitation":"Singh, N.J., Ecke, F., Katzner, T., Bagchi, S., Sandstrom, P., and Hornfeldt, B., 2021, Consequences of migratory coupling of predators and prey when mediated by human actions: Diversity and Distributions, v. 27, no. 9, p. 1848-1860, https://doi.org/10.1111/ddi.13373.","productDescription":"13 p.","startPage":"1848","endPage":"1860","ipdsId":"IP-074750","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":451636,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13373","text":"Publisher Index Page"},{"id":432753,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Sweden","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[22.18317,65.72374],[21.21352,65.02601],[21.36963,64.41359],[19.77888,63.60955],[17.84778,62.7494],[17.11955,61.34117],[17.83135,60.63658],[18.78772,60.08191],[17.86922,58.95377],[16.82919,58.71983],[16.44771,57.04112],[15.87979,56.1043],[14.66668,56.20089],[14.10072,55.40778],[12.94291,55.36174],[12.6251,56.30708],[11.78794,57.44182],[11.02737,58.85615],[11.46827,59.43239],[12.30037,60.11793],[12.63115,61.29357],[11.99206,61.80036],[11.93057,63.12832],[12.57994,64.06622],[13.57192,64.04911],[13.91991,64.44542],[13.55569,64.78703],[15.10841,66.19387],[16.10871,67.30246],[16.76888,68.01394],[17.72918,68.01055],[17.99387,68.56739],[19.87856,68.40719],[20.02527,69.06514],[20.64559,69.10625],[21.97853,68.61685],[23.53947,67.93601],[23.56588,66.39605],[23.90338,66.00693],[22.18317,65.72374]]]},\"properties\":{\"name\":\"Sweden\"}}]}","volume":"27","issue":"9","noUsgsAuthors":false,"publicationDate":"2021-07-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Singh, Navinder J.","contributorId":342307,"corporation":false,"usgs":false,"family":"Singh","given":"Navinder","email":"","middleInitial":"J.","affiliations":[{"id":81856,"text":"Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden","active":true,"usgs":false}],"preferred":false,"id":909987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ecke, Fraucke","contributorId":342308,"corporation":false,"usgs":false,"family":"Ecke","given":"Fraucke","email":"","affiliations":[{"id":81856,"text":"Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden","active":true,"usgs":false}],"preferred":false,"id":909988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":909989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bagchi, Sumanta","contributorId":210387,"corporation":false,"usgs":false,"family":"Bagchi","given":"Sumanta","email":"","affiliations":[],"preferred":false,"id":909990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sandstrom, Per","contributorId":342309,"corporation":false,"usgs":false,"family":"Sandstrom","given":"Per","email":"","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":909991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hornfeldt, Birger","contributorId":342310,"corporation":false,"usgs":false,"family":"Hornfeldt","given":"Birger","email":"","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":909992,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222587,"text":"70222587 - 2021 - Elk monitoring in Mount Rainier and Olympic National Parks: 2008-2017 synthesis report","interactions":[],"lastModifiedDate":"2021-08-06T21:56:24.549864","indexId":"70222587","displayToPublicDate":"2021-07-01T16:45:02","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NCCN/NRR-2021/2284","title":"Elk monitoring in Mount Rainier and Olympic National Parks: 2008-2017 synthesis report","docAbstract":"In 2008, the U.S. Geological Survey (USGS) began collaborating with the National Park Service (NPS)-North Coast and Cascades Network (NCCN), the Muckleshoot Indian Tribe (MIT), Puyallup Tribe of Indians (PTOI), and Washington Department of Fish and Wildlife (WDFW) to develop a standard survey protocol for monitoring long-term changes in the abundance, distribution, and population composition of elk on key summer ranges within Mount Rainier National Park (MORA) and Olympic National Park (OLYM). In MORA, surveys were conducted in two trend count areas (TCAs) that correspond with primary summer ranges used by the North Rainier Herd, which winters outside the park to the North, and the South Rainier Herd, which winters outside the park primarily to the South. In OLYM, we defined five TCAs including an Olympic Core TCA (hereafter, Core TCA) that encompasses summer ranges on the flanks of Mount Olympus, and four TCAs that encompass other primary summer ranges throughout the park. \nThe standard protocol allows for estimating aerial survey detection biases and adjusting raw survey counts to account for elk that were likely present but not seen during surveys. Previously, we developed a suite of aerial-bias-correction models for use in estimating aerial detection biases and adjusting raw counts of elk in MORA based on sighting conditions related to elk group size, vegetation density, lighting conditions, elk movement, as well as combinations of these and other factors. The models were based on independent sighting records of elk groups by front-seat and back-seat observer pairs in a helicopter, including detection records of some radio-collared elk groups. \nHere, we analyze results of the first 10 years of elk monitoring in MORA (2008-2017) and 8 years in OLYM (2008-2015). In a previous report covering surveys conducted from 2008-2011, data were not sufficient to model detection biases of aerial surveys conducted in OLYM; hence, analyses of elk population trends were based on counts adjusted for detection biases in MORA, whereas trends in OLYM were based on raw, unadjusted counts (Griffin et al. 2013, Jenkins et al. 2015). \nOur objectives for the current summary were to:\n(1) incorporate additional data to update aerial-bias-correction models previously developed for use in MORA to include corrections for aerial detection bias in both MORA and OLYM,\n(2) examine trends in elk abundance, distribution, and population composition estimates for subalpine summer ranges within MORA and OLYM, and\n(3) estimate effects of seasonal variation and weather on elk abundance and population composition estimates for subalpine summer ranges in both parks.","language":"English","publisher":"National Park Service","usgsCitation":"Jenkins, K., Lubow, B., Happe, P.J., Braun, K., Boetsch, J., Baccus, W., Chestnut, T., Vales, D.J., Moeller, B.J., Tirhi, M., Holman, E., and Griffin, P.C., 2021, Elk monitoring in Mount Rainier and Olympic National Parks: 2008-2017 synthesis report: Natural Resource Report NPS/NCCN/NRR-2021/2284, xiii, 77 p.","productDescription":"xiii, 77 p.","ipdsId":"IP-123180","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":387745,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387744,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://irma.nps.gov/DataStore/DownloadFile/662550"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Ranier and Olympic National Parks","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6680908203125,\n 46.592843997427416\n ],\n [\n -121.5582275390625,\n 46.69843486113957\n ],\n [\n -121.44287109374999,\n 46.694667307773116\n ],\n [\n -121.39892578125,\n 46.751153008636884\n ],\n [\n -121.53625488281249,\n 47.010225655683485\n ],\n [\n -121.9207763671875,\n 47.010225655683485\n ],\n [\n -121.8988037109375,\n 47.08508535995386\n ],\n [\n -122.16247558593751,\n 47.07386310181414\n ],\n [\n -122.31628417968749,\n 46.87145819560722\n ],\n [\n -122.03613281249999,\n 46.78501604269254\n ],\n [\n -121.9647216796875,\n 46.751153008636884\n ],\n [\n -121.9647216796875,\n 46.57774276255591\n ],\n [\n -121.6680908203125,\n 46.592843997427416\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.28857421875,\n 47.31648293428332\n ],\n [\n -122.904052734375,\n 47.70976154266637\n ],\n [\n -122.9754638671875,\n 47.989921667414194\n ],\n [\n -123.8818359375,\n 48.122101028190805\n ],\n [\n -124.32678222656249,\n 48.158757304569235\n ],\n [\n -124.4091796875,\n 47.912660308427654\n ],\n [\n -124.20043945312499,\n 47.76886840424207\n ],\n [\n -124.398193359375,\n 47.70606513569572\n ],\n [\n -124.112548828125,\n 47.506069781910846\n ],\n [\n -123.8323974609375,\n 47.47266286861342\n ],\n [\n -123.99719238281249,\n 47.342545069660225\n ],\n [\n -123.28857421875,\n 47.31648293428332\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Kurt 0000-0003-1415-6607","orcid":"https://orcid.org/0000-0003-1415-6607","contributorId":221472,"corporation":false,"usgs":true,"family":"Jenkins","given":"Kurt","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":820652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lubow, B. C.","contributorId":64603,"corporation":false,"usgs":false,"family":"Lubow","given":"B. C.","affiliations":[],"preferred":false,"id":820667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Happe, P. J.","contributorId":219686,"corporation":false,"usgs":false,"family":"Happe","given":"P.","email":"","middleInitial":"J.","affiliations":[{"id":16133,"text":"National Park Service, Olympic National Park","active":true,"usgs":false}],"preferred":false,"id":820668,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Braun, K.","contributorId":261796,"corporation":false,"usgs":false,"family":"Braun","given":"K.","email":"","affiliations":[],"preferred":false,"id":820669,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boetsch, J.","contributorId":213934,"corporation":false,"usgs":false,"family":"Boetsch","given":"J.","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":820670,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baccus, W.","contributorId":261797,"corporation":false,"usgs":false,"family":"Baccus","given":"W.","affiliations":[],"preferred":false,"id":820671,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chestnut, T.","contributorId":261798,"corporation":false,"usgs":false,"family":"Chestnut","given":"T.","email":"","affiliations":[],"preferred":false,"id":820672,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Vales, D. J.","contributorId":261799,"corporation":false,"usgs":false,"family":"Vales","given":"D.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":820673,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moeller, B. J.","contributorId":261800,"corporation":false,"usgs":false,"family":"Moeller","given":"B.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":820674,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tirhi, M.","contributorId":261801,"corporation":false,"usgs":false,"family":"Tirhi","given":"M.","affiliations":[],"preferred":false,"id":820675,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Holman, E.","contributorId":261802,"corporation":false,"usgs":false,"family":"Holman","given":"E.","email":"","affiliations":[],"preferred":false,"id":820676,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Griffin, P. C.","contributorId":69499,"corporation":false,"usgs":false,"family":"Griffin","given":"P.","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":820677,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70221862,"text":"70221862 - 2021 - What is the effect of poaching activity on wildlife species?","interactions":[],"lastModifiedDate":"2021-10-06T15:13:10.834057","indexId":"70221862","displayToPublicDate":"2021-07-01T12:04:24","publicationYear":"2021","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":"What is the effect of poaching activity on wildlife species?","docAbstract":"Poaching is a pervasive threat to wildlife, yet quantifying the direct effect of poaching on wildlife is rarely possible because both wildlife and threat data are infrequently collected concurrently. In this study, we used poaching data collected through the Management Information System (MIST) and wildlife camera trap data collected by the Tropical Ecology Assessment and Monitoring (TEAM) network from 2014 to 2017 in Volcanoes National Park, Rwanda. We implemented co-occurrence multi-season occupancy models that accounted for imperfect detection to investigate the effect of poaching on initial occupancy, colonization, and extinction of 5 mammal species. Specifically, we focused on 2 species of conservation concern (mountain gorilla (Gorilla beringei beringei) and golden money (Cercopithecus mitis kandti)), and 3 species targeted by poachers (black-fronted duiker (Cephalophus nigrifrons), bushbuck (Tragelaphus scriptus), and African buffalo (Syncerus caffer)). We found that the probability of local extinction was highest in sites with poaching activity for golden monkey and bushbuck. In addition, the probability of initial occupancy for golden monkey was highest in sites without poaching activity. We only found weak evidence of effects of poaching on parameters governing the occupancy dynamics of the other species. All species showed evidence of poaching presence affecting the probability of detection of the wildlife species. This is the first study to our knowledge to combine direct threat observations from ranger-based monitoring data with camera trap wildlife observations to quantify the effect of poaching on wildlife. Given the widespread collection of ranger-based monitoring and camera trap data, our approach is broadly applicable to numerous protected areas and has the potential to significantly improve conservation management. Specifically, the relationship between poaching activity and wildlife population dynamics (this paper) can be combined with information on the relationship between ranger patrols and poaching activity (Moore et al. 2017) to develop models useful for making wise decisions about ranger patrol deployment.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2397","usgsCitation":"Moore, J.F., Uzabaho, E., Musana, A., Uwingell, P., Hines, J.E., and Nichols, J.D., 2021, What is the effect of poaching activity on wildlife species?: Ecological Applications, v. 31, no. 7, e02397, 12 p., https://doi.org/10.1002/eap.2397.","productDescription":"e02397, 12 p.","ipdsId":"IP-118381","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Rwanda","otherGeospatial":"Volcanoes National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 29.702911376953125,\n -1.3587440869100178\n ],\n [\n 29.663772583007812,\n -1.3882613601346867\n ],\n [\n 29.63081359863281,\n -1.3951257897508238\n ],\n [\n 29.59304809570312,\n -1.3882613601346867\n ],\n [\n 29.561462402343746,\n -1.384829137846475\n ],\n [\n 29.50721740722656,\n -1.4218968729661605\n ],\n [\n 29.49073791503906,\n -1.4383712317629698\n ],\n [\n 29.4927978515625,\n -1.4630825465188169\n ],\n [\n 29.480438232421875,\n -1.4692603328543323\n ],\n [\n 29.485244750976562,\n -1.4788701887242113\n ],\n [\n 29.461898803710938,\n -1.491912069367617\n ],\n [\n 29.439926147460934,\n -1.5269188384985064\n ],\n [\n 29.39804077148437,\n -1.5324100450044358\n ],\n [\n 29.442672729492188,\n -1.568788930117857\n ],\n [\n 29.481124877929688,\n -1.5660433757691457\n ],\n [\n 29.514770507812496,\n -1.5358420419244077\n ],\n [\n 29.519577026367188,\n -1.4891664166873633\n ],\n [\n 29.50996398925781,\n -1.4493540716333067\n ],\n [\n 29.540176391601562,\n -1.422583306939631\n ],\n [\n 29.54635620117188,\n -1.4184647000387454\n ],\n [\n 29.560775756835934,\n -1.408854588797322\n ],\n [\n 29.58549499511719,\n -1.4177782648419572\n ],\n [\n 29.641799926757812,\n -1.4232697407088846\n ],\n [\n 29.671325683593754,\n -1.412973212770802\n ],\n [\n 29.69467163085938,\n -1.4157189580307432\n ],\n [\n 29.70497131347656,\n -1.3875749160752702\n ],\n [\n 29.702911376953125,\n -1.3587440869100178\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Moore, Jennifer F.","contributorId":189122,"corporation":false,"usgs":false,"family":"Moore","given":"Jennifer","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":819048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Uzabaho, Eustrate","contributorId":260880,"corporation":false,"usgs":false,"family":"Uzabaho","given":"Eustrate","email":"","affiliations":[{"id":52699,"text":"Intl. Gorilla Conservation Programme, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Musana, Abel","contributorId":260881,"corporation":false,"usgs":false,"family":"Musana","given":"Abel","email":"","affiliations":[{"id":52700,"text":"Rwanda Development Board, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Uwingell, Prosper","contributorId":260882,"corporation":false,"usgs":false,"family":"Uwingell","given":"Prosper","email":"","affiliations":[{"id":52700,"text":"Rwanda Development Board, Rwanda","active":true,"usgs":false}],"preferred":false,"id":819051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hines, James E. 0000-0001-5478-7230 jhines@usgs.gov","orcid":"https://orcid.org/0000-0001-5478-7230","contributorId":146530,"corporation":false,"usgs":true,"family":"Hines","given":"James","email":"jhines@usgs.gov","middleInitial":"E.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819052,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":200533,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819053,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224605,"text":"70224605 - 2021 - Wetlands","interactions":[],"lastModifiedDate":"2021-09-29T17:05:36.934197","indexId":"70224605","displayToPublicDate":"2021-07-01T11:54:58","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Wetlands","docAbstract":"During the last decades, soil organic carbon (SOC) attracted the attention of a much wider array of specialists beyond agriculture and soil science, as it was proven to be one of the most crucial components of the earth’s climate system, which has a great potential to be managed by humans. Soils as a carbon pool are one of the key factors in several Sustainable Development Goals, in particular Goal 15, “Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification and halt and reverse land degradation and halt biodiversity loss” with the SOC stock being explicitly cited in Indicator 15.3.1.
This technical manual is the first attempt to gather, in a standardized format, the existing data on the impacts of the main soil management practices on SOC content in a wide array of environments, including the advantages, drawbacks, and constraints. This manual presents different sustainable soil management (SSM) practices at different scales and in different contexts, supported by case studies that have been shown with quantitative data to have a positive effect on SOC stocks and successful experiences of SOC sequestration in practical field applications
Volume 2 includes a description of hot spots of SOC stocks. This manual defines hot spots of SOC as areas that represent a proportionally little of the global land surface but on which SOC storage is highly effective; bright spots as large land areas with low SOC stocks per km2 that represent a potential for further carbon sequestration.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Recarbonizing global soils – A technical manual of recommended management practices","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Food and Agriculture Organization of the United Nations","usgsCitation":"Tangen, B., and Bansal, S., 2021, Wetlands, 19 p.","productDescription":"19 p.","startPage":"36","endPage":"54","ipdsId":"IP-121703","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":389967,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fao.org/documents/card/en/c/cb6378en/"},{"id":389968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":824243,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":824244,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237360,"text":"70237360 - 2021 - Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning","interactions":[],"lastModifiedDate":"2022-10-11T16:24:49.683141","indexId":"70237360","displayToPublicDate":"2021-07-01T11:21:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning","docAbstract":"Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB-MTL yielded a median RMSE of 2.43°C; PGDL-MTL yielded 2.16°C; and a PGDL-MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.","language":"English","publisher":"Wiley","doi":"10.1029/2021WR029579","usgsCitation":"Willard, J., Read, J., Appling, A.P., Oliver, S.K., Jia, X., and Kumar, V., 2021, Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning: Water Resources Research, v. 57, no. 7, e2021WR029579, 20 p., https://doi.org/10.1029/2021WR029579.","productDescription":"e2021WR029579, 20 p.","ipdsId":"IP-119147","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":451661,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr029579","text":"Publisher Index Page"},{"id":436285,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9I00WFR","text":"USGS data release","linkHelpText":"Data release: Predicting Water Temperature Dynamics of Unmonitored Lakes with Meta Transfer Learning (Provisional Data Release)"},{"id":408165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854261,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":854262,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":854263,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":854264,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854265,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":854266,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248236,"text":"70248236 - 2021 - Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","interactions":[],"lastModifiedDate":"2023-09-05T15:18:59.650382","indexId":"70248236","displayToPublicDate":"2021-07-01T10:09:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","title":"Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure","docAbstract":"The Queen Charlotte-Fairweather Fault (QC-FF) system off the coast of British Columbia and southeast Alaska is a highly active dextral strike-slip plate boundary that accommodates ∼50 mm/yr of relative motion between the Pacific and North America plates. Nine MW ≥ 6.7 earthquakes have occurred along the QC-FF system since 1910, including a MS(G-R)8.1 event in 1949. Two recent earthquakes, the October 28, 2012 Haida Gwaii (MW7.8) and January 5, 2013 Craig, Alaska (MW7.5) events, produced postseismic transient deformation that was recorded in the motions of 25 nearby continuous Global Positioning System (cGPS) stations. Here, we use 5+ yr of cGPS measurements to characterize the underlying mechanisms of postseismic deformation and to constrain the viscosity structure of the upper mantle surrounding the QC-FF. We construct forward models of viscoelastic deformation driven by coseismic stress changes from these two earthquakes and explore a large set of laterally heterogeneous viscosity structures that incorporate a relatively weak back-arc domain; we then evaluate each model based on its fit to the postseismic signals in our cGPS data. In determining best-fit model structures, we additionally incorporate the effects of afterslip following the 2012 event. Our results indicate the occurrence of a combination of temporally decaying afterslip and vigorous viscoelastic relaxation of the mantle asthenosphere. In addition, our best-fit viscosity structure (transient viscosity of 1.4–2.0 × 1018 Pa s; steady-state viscosity of 1019 Pa s) is consistent with the range of upper mantle viscosities determined in previous studies of glacial isostatic rebound and postseismic deformation.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB021891","usgsCitation":"Guns, K.A., Pollitz, F., Lay, T., and Yue, H., 2021, Exploring GPS observations of postseismic deformation following the 2012 MW7.8 Haida Gwaii and 2013 MW7.5 Craig, Alaska Earthquakes: Implications for viscoelastic Earth structure: Journal of Geophysical Research B: Solid Earth, v. 126, no. 7, e2021JB021891, 20 p., https://doi.org/10.1029/2021JB021891.","productDescription":"e2021JB021891, 20 p.","ipdsId":"IP-127502","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":420483,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","city":"Craig","otherGeospatial":"Haida Gwaii Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -134.04582537099918,\n 55.778120902444044\n ],\n [\n -134.04582537099918,\n 55.08543087867997\n ],\n [\n -132.168788545775,\n 55.08543087867997\n ],\n [\n -132.168788545775,\n 55.778120902444044\n ],\n [\n -134.04582537099918,\n 55.778120902444044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -132.70446739512911,\n 53.158925565323756\n ],\n [\n -132.34949768033022,\n 52.76369069159804\n ],\n [\n -131.7793948050469,\n 52.469804491078975\n ],\n [\n -131.66107156678072,\n 52.29252484773727\n ],\n [\n -130.97264545323122,\n 51.88274035718925\n ],\n [\n -130.75751229274715,\n 51.94245878634544\n ],\n [\n -131.7471248309743,\n 53.33271351252088\n ],\n [\n -132.30647104823325,\n 53.16537474581318\n ],\n [\n -132.70446739512911,\n 53.158925565323756\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-07-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Guns, Katherine A.","contributorId":329359,"corporation":false,"usgs":false,"family":"Guns","given":"Katherine","email":"","middleInitial":"A.","affiliations":[{"id":16619,"text":"UCSD","active":true,"usgs":false}],"preferred":false,"id":882060,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":882061,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lay, Thorne","contributorId":328838,"corporation":false,"usgs":false,"family":"Lay","given":"Thorne","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":882062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yue, Han","contributorId":329362,"corporation":false,"usgs":false,"family":"Yue","given":"Han","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":882063,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223240,"text":"70223240 - 2021 - National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project","interactions":[],"lastModifiedDate":"2021-08-19T15:13:17.0885","indexId":"70223240","displayToPublicDate":"2021-07-01T10:01:38","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"2021/2285","title":"National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project","docAbstract":"The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation communities in national park units throughout the United States. The NPS VMI Program is managed by the NPS Natural Resource Stewardship and Science Inventory and Monitoring Program and provides baseline vegetation information to natural resource managers, researchers, and ecologists. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, and NPS Great Smoky Mountains National Park (GRSM, also referred to as the “Park”) have completed vegetation classification and mapping of GRSM, including the Foothills Parkway, for the NPS VMI Program.
Mappers, ecologists, and botanists collaborated to affirm vegetation types of GRSM and to determine how best to map the vegetation types by using aerial imagery. A vegetation classification developed in 2003 by NatureServe and the NPS served as a foundation to further classify and map the vegetation types of the Park. Data from an additional 10 vegetation plots supported vegetation types either rare or not documented in the 2003 classification. Data from 203 verification sites were collected to test the field key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 972 accuracy assessment (AA) sites were collected (of which 966 were used to test accuracy of the vegetation map layer). This GRSM vegetation mapping project identified 112 vegetation types consisting of 105 association types in the U.S. National Vegetation Classification (USNVC), 2 “park-special” types, 1 “map-special” type, and 4 cultural types in the USNVC.
To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type.
A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines.
Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway.
A summary report, generated from the vegetation map layer, concludes that the 46 map classes representing natural (including ruderal) vegetation types apply to 99.2% of polygons (33,797 polygons; average size of 6.2 ha) and cover 98.6% of the Park (207,971.4 ha). Further broken down, map classes representing natural vegetation types indicate that the Park is 97.7% forest and woodland (205,882.5 ha), 0.6% shrubland (1,174.6 ha), and 0.4% herbaceous (914.3 ha). Map classes representing cultural vegetation types apply to 0.8% of polygons (259 polygons; average size of 4.9 ha) and cover 0.6% of the Park (1,277.4 ha). Map classes representing nonvegetation open and flowing water and unvegetated rock apply to 0.08% of polygons (28 polygons; average size of 58.1 ha) and cover 0.8% of the Park (1,625.9 ha).
A thematic AA study was completed of map classes representing the natural (including ruderal) vegetation types of the Park. Initial AA results were discussed with NPS staff from the Park. Following input from NPS staff on how to handle map classes that fell below accuracy standards, adjustments were made to the vegetation map layer. Final results indicate an overall accuracy of 80.64% (kappa index of 79.96% for chance agreements) based on data from 966 of the 972 AA sites. Most individual map-class themes exceed the NPS VMI Program standard of 80% with a 90% confidence interval.
The GRSM vegetation mapping project delivers many geospatial and vegetation data products, including an in-depth project report discussing methods and results, which includes map classification and map-class descriptions. This suite of products also includes descriptions and a field key to vegetation types; a database of vegetation plots, verification sites, and AA sites; digital images of field sites; field data sheets; digital aerial imagery; hardcopy and digital maps; a geodatabase of vegetation and land cover (map layer), field sites (vegetation plots, verification sites, and AA sites), aerial imagery index, project boundary, and metadata; and a contingency table listing AA results. Geospatial products are projected in the Universal Transverse Mercator, Zone 17 North, by using the North American Datum of 1983. Information on the NPS VMI Program and completed mapping projects are on the internet at https://www.nps.gov/im/vegetation-inventory.htm.
","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2286888","usgsCitation":"Hop, K.D., Strassman, A.C., Sattler, S., White, R., Pyne, M., Govus, T., and Dieck, J., 2021, National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project: Natural Resource Report 2021/2285, 220 p., https://doi.org/10.36967/nrr-2286888.","productDescription":"220 p.","ipdsId":"IP-120204","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.023681640625,\n 35.594785665487244\n ],\n [\n -83.09783935546875,\n 35.806676609227054\n ],\n [\n -83.40545654296875,\n 35.762114795721\n ],\n [\n -83.88336181640625,\n 35.68853320738875\n ],\n [\n -84.034423828125,\n 35.545635932499415\n ],\n [\n -83.90808105468749,\n 35.43605776486772\n ],\n [\n -83.5565185546875,\n 35.39800594715108\n ],\n [\n -83.30657958984375,\n 35.47409160773029\n ],\n [\n -83.023681640625,\n 35.594785665487244\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hop, Kevin D. 0000-0002-9928-4773 khop@usgs.gov","orcid":"https://orcid.org/0000-0002-9928-4773","contributorId":1438,"corporation":false,"usgs":true,"family":"Hop","given":"Kevin","email":"khop@usgs.gov","middleInitial":"D.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strassman, Andrew C. 0000-0002-9792-7181 astrassman@usgs.gov","orcid":"https://orcid.org/0000-0002-9792-7181","contributorId":4575,"corporation":false,"usgs":true,"family":"Strassman","given":"Andrew","email":"astrassman@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sattler, Stephanie 0000-0003-4417-2480 ssattler@usgs.gov","orcid":"https://orcid.org/0000-0003-4417-2480","contributorId":191016,"corporation":false,"usgs":true,"family":"Sattler","given":"Stephanie","email":"ssattler@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Rickie","contributorId":201063,"corporation":false,"usgs":false,"family":"White","given":"Rickie","email":"","affiliations":[],"preferred":false,"id":821498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyne, Milo","contributorId":201061,"corporation":false,"usgs":false,"family":"Pyne","given":"Milo","email":"","affiliations":[],"preferred":false,"id":821499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Govus, Tom","contributorId":264417,"corporation":false,"usgs":false,"family":"Govus","given":"Tom","email":"","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":821500,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dieck, Jennifer 0000-0002-4388-4534 jdieck@usgs.gov","orcid":"https://orcid.org/0000-0002-4388-4534","contributorId":149647,"corporation":false,"usgs":true,"family":"Dieck","given":"Jennifer","email":"jdieck@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821501,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70216757,"text":"70216757 - 2021 - Economic geology and environmental characteristics of antimony deposits","interactions":[],"lastModifiedDate":"2021-10-01T14:40:56.858064","indexId":"70216757","displayToPublicDate":"2021-07-01T09:40:27","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"3","title":"Economic geology and environmental characteristics of antimony deposits","docAbstract":"Antimony is commonly listed as a critical mineral, particularly in the United States and European Union [1]. Its criticality, or supply risk, is derived from a combination of economic vulnerability, disruption potential of supply, and trade exposure [2].Disruption potential relates a country’s ability and willingness to supply a commodity. Commodities for which supply is concentrated in the fewest countries have the greatest potential for supply disruption, trade exposure, and economic vulnerability. In 2018, approximately 61% of the world production of antimony was mined from China, followed by Russia (20%) and Tajikistan (10%) [3] (Fig. 3.1). Several reviews of antimony as a critical mineral have been recently published [4, 5].
The uses of antimony can be divided into three main categories: metal products, non-metal uses, and flame retardants [5]. Most metallic antimony use is inlead-acid batteries. Antimony trioxide (Sb2O3) combined with halogenated com-pounds is used as a fire retardant in plastics, fabrics, and other applications. Other non-metallic uses include as a catalyst for plastics, and in the glass industry. Emerging uses include data storage and novel photovoltaic cells. Recycling of batteries represents an important reuse of antimony, but other uses of antimony do not lend themselves to recycling.
This chapter describes mineral deposit types that are primary sources of antimony, and environmental effects related to their mining. Antimony can be re-covered as either a primary commodity from some deposits or as a by product commodity from some gold or silver deposits. Environmental risks associated with antimony mining include those related to mining in general, such as the acid-generating potential of solid mine waste, and some issues specific to antimony, as described below.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Antimony","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"De Gruyter","doi":"10.1515/9783110668711-003","usgsCitation":"Seal,, R., 2021, Economic geology and environmental characteristics of antimony deposits, chap. 3 of Antimony, p. 49-72, https://doi.org/10.1515/9783110668711-003.","productDescription":"24 p.","startPage":"49","endPage":"72","ipdsId":"IP-123217","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":390119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":806092,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222561,"text":"70222561 - 2021 - Climate change scenario planning for resource stewardship at Wind Cave National Park","interactions":[],"lastModifiedDate":"2021-08-05T14:49:49.415903","indexId":"70222561","displayToPublicDate":"2021-07-01T09:38:32","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/NRR—2021/2274","title":"Climate change scenario planning for resource stewardship at Wind Cave National Park","docAbstract":"This report explains scenario planning as a climate change adaptation tool in general, then describes how it was applied to Wind Cave National Park as the second part of a pilot project to dovetail climate change scenario planning with National Park Service (NPS) Resource Stewardship Strategy development.
In the orientation phase, Park and regional NPS staff, other subject-matter experts, natural and cultural resource planners, and the climate change core team who led the scenario planning project identified priority resource management topics and associated climate sensitivities. Next, the climate change core team used this information to create a set of four divergent climate futures—summaries of relevant climate data from individual climate projections—to encompass the range of ways climate could change in coming decades in the park. Participants in the scenario planning workshop then developed climate futures into robust climate-resource scenarios that considered expert-elicited resource impacts and identified potential management responses. Finally, the scenario-based resource responses identified by park staff and subject matter experts were used to integrate climate-informed adaptations into resource stewardship goals and activities for the park's Resource Stewardship Strategy. This process of engaging resource managers in climate change scenario planning ensures that their management and planning decisions are informed by assessments of critical future climate uncertainties.
","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2286672","usgsCitation":"Runyon, A., Schuurman, G.W., Miller, B.W., Symstad, A., and Hardy, A., 2021, Climate change scenario planning for resource stewardship at Wind Cave National Park: Natural Resource Report NPS/NRSS/NRR—2021/2274, ix, 114 p., https://doi.org/10.36967/nrr-2286672.","productDescription":"ix, 114 p.","ipdsId":"IP-122369","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":40927,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":387718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","otherGeospatial":"Wind Cave National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.49327087402344,\n 43.54008705264584\n ],\n [\n -103.41293334960938,\n 43.54008705264584\n ],\n [\n -103.41293334960938,\n 43.61544814581711\n ],\n [\n -103.49327087402344,\n 43.61544814581711\n ],\n [\n -103.49327087402344,\n 43.54008705264584\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-07-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Runyon, Amber N. 0000-0002-7282-1217","orcid":"https://orcid.org/0000-0002-7282-1217","contributorId":261745,"corporation":false,"usgs":false,"family":"Runyon","given":"Amber N.","affiliations":[{"id":52985,"text":"National Park Service Climate Change Response Program","active":true,"usgs":false}],"preferred":false,"id":820555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schuurman, Gregor W. 0000-0002-9304-7742","orcid":"https://orcid.org/0000-0002-9304-7742","contributorId":147698,"corporation":false,"usgs":false,"family":"Schuurman","given":"Gregor","email":"","middleInitial":"W.","affiliations":[{"id":16909,"text":"U.S. National Park Service, Natural Resource Stewardship and Science, Fort Collins, CO, 80525, USA","active":true,"usgs":false}],"preferred":false,"id":820556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Brian W. 0000-0003-1716-1161","orcid":"https://orcid.org/0000-0003-1716-1161","contributorId":196603,"corporation":false,"usgs":true,"family":"Miller","given":"Brian","email":"","middleInitial":"W.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":820557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Symstad, Amy 0000-0003-4231-2873 asymstad@usgs.gov","orcid":"https://orcid.org/0000-0003-4231-2873","contributorId":201095,"corporation":false,"usgs":true,"family":"Symstad","given":"Amy","email":"asymstad@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":820558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hardy, Amanda","contributorId":261746,"corporation":false,"usgs":false,"family":"Hardy","given":"Amanda","email":"","affiliations":[{"id":52985,"text":"National Park Service Climate Change Response Program","active":true,"usgs":false}],"preferred":false,"id":820559,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223671,"text":"70223671 - 2021 - Tools and technologies for quantifying spread and impacts of invasive species","interactions":[],"lastModifiedDate":"2022-04-13T20:13:00.752506","indexId":"70223671","displayToPublicDate":"2021-07-01T08:53:56","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"11","title":"Tools and technologies for quantifying spread and impacts of invasive species","docAbstract":"The need for tools and technologies for understanding and quantifying invasive species has never been greater. Rates of infestation vary on the species or organism being examined across the United States, and notable examples can be found. For example, from 2001 to 2003 alone, ash (Fraxinus spp.) mortality progressed at a rate of 12.97 km year −1 (Siegert et al. 2014), and cheatgrass (Bromus tectorum) is expected to increase dominance on 14% of Great Basin rangelands (Boyte et al. 2016). The magnitude and scope of problems that invasive species present suggest novel approaches for detection and management are needed, especially those that enable more cost-effective solutions. The advantages of using technologically advanced approaches and tools are numerous, and the quality and quantity of available information can be significantly enhanced by their use. They can also play a key role in development of decision-support systems; they are meant to be integrated with other systems, such as inventory and monitoring, because often the tools are applied after a species of interest has been detected and a threat has been identified. In addition, the inventory systems mentioned in Chap. 10 are regularly used in calibrating and validating models and decision-support systems. For forested areas, Forest Inventory and Analysis (FIA) data are most commonly used (e.g., Václavík et al. 2015) given the long history of the program. In non-forested systems, national inventory datasets have not been around as long (see Chap. 10), but use of these data to calibrate and validate spatial models is growing. These inventory datasets include the National Resources Inventory (NRI) (e.g., Duniway et al. 2012) and the Assessment Inventory and Monitoring program (AIM) (e.g., McCord et al. 2017). Similarly, use of the Nonindigenous Aquatic Species (NAS) database is growing as well (e.g., Evangelista et al. 2017). The consistent protocols employed by these programs prove valuable for developing better tools, but the data they afford are generally limited for some tools because the sampling intensity is too low.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Invasive species in forests and rangelands of the United States: A comprehensive science synthesis for the United States Forest Sector","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"U.S. Forest Service","doi":"10.1007/978-3-030-45367-1_11","collaboration":"U.S. Forest Service","usgsCitation":"Reeves, M., Ibanez, I., Blumenthal, D., Chen, G., Guo, Q., Jarnevich, C.S., Koch, J., Sapio, F., Schwartz, M.D., Meentemeyer, R.K., Wylie, B., and Boyte, S.P., 2021, Tools and technologies for quantifying spread and impacts of invasive species, chap. 11 of Invasive species in forests and rangelands of the United States: A comprehensive science synthesis for the United States Forest Sector, p. 243-265, https://doi.org/10.1007/978-3-030-45367-1_11.","productDescription":"23 p.","startPage":"243","endPage":"265","ipdsId":"IP-082001","costCenters":[{"id":222,"text":"Earth Resources Observation and Science 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Dana","contributorId":70686,"corporation":false,"usgs":true,"family":"Blumenthal","given":"Dana","affiliations":[],"preferred":false,"id":822330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Gang","contributorId":265128,"corporation":false,"usgs":false,"family":"Chen","given":"Gang","email":"","affiliations":[{"id":54601,"text":"UNCC","active":true,"usgs":false}],"preferred":false,"id":822271,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Guo, Qinfeng","contributorId":214263,"corporation":false,"usgs":false,"family":"Guo","given":"Qinfeng","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":822275,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822276,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Koch, Jennifer","contributorId":210475,"corporation":false,"usgs":false,"family":"Koch","given":"Jennifer","email":"","affiliations":[{"id":38113,"text":"The University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":822277,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sapio, Frank","contributorId":265129,"corporation":false,"usgs":false,"family":"Sapio","given":"Frank","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":822272,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schwartz, Michael D.","contributorId":174566,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":822278,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meentemeyer, Ross K.","contributorId":179341,"corporation":false,"usgs":false,"family":"Meentemeyer","given":"Ross","email":"","middleInitial":"K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":822268,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wylie, Bruce 0000-0002-7374-1083","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":201929,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":822273,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Boyte, Stephen P. 0000-0002-5462-3225 sboyte@usgs.gov","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":139238,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen","email":"sboyte@usgs.gov","middleInitial":"P.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":822331,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70238599,"text":"70238599 - 2021 - Mineral Mapping of the Battle Mountain District, Nevada, USA, Using AVIRIS-Classic and SpecTIR Inc. AisaFENIX 1K Imaging Spectrometer Datasets","interactions":[],"lastModifiedDate":"2022-12-01T14:47:41.476167","indexId":"70238599","displayToPublicDate":"2021-07-01T08:39:03","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mineral Mapping of the Battle Mountain District, Nevada, USA, Using AVIRIS-Classic and SpecTIR Inc. AisaFENIX 1K Imaging Spectrometer Datasets","docAbstract":"Urban-rural gradients are useful tools when examining the influence of human disturbances on ecological, social and coupled systems, yet the most commonly used gradient definitions are based on single broad measures such as housing density or percent forest cover that fail to capture landscape patterns important for conservation.
We present an approach to defining urban–rural gradients that integrates multiple landscape pattern metrics related to ecosystem processes important for natural resources and wildlife sustainability.
We develop a set of land cover composition and configuration metrics and then use them as inputs to a cluster analysis process that, in addition to grouping towns with similar attributes, identifies exemplar towns for each group. We compare the outcome of the cluster-based urban-rural gradient typology to outcomes for four commonly-used rule-based typologies and discuss implications for resource management and conservation.
The resulting cluster-based typology defines five town types (urban, suburban, exurban, rural, and agricultural) and notably identifies a bifurcation along the gradient distinguishing among rural forested and agricultural towns. Landscape patterns (e.g., core and islet forests) influence where individual towns fall along the gradient. Designations of town type differ substantially among the five different typologies, particularly along the middle of the gradient.
Understanding where a town occurs along the urban-rural gradient could aid local decision-makers in prioritizing and balancing between development and conservation scenarios. Variations in outcomes among the different urban-rural gradient typologies raise concerns that broad-measure classifications do not adequately account for important landscape patterns. We suggest future urban-rural gradient studies utilize more robust classification approaches.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01287-7","usgsCitation":"Kaminski, A.R., Bauer, D.M., Bell, K., Loftin, C., and Nelson, E., 2021, Using landscape metrics to characterize towns along an urban-rural gradient: Landscape Ecology, v. 36, p. 2937-2956, https://doi.org/10.1007/s10980-021-01287-7.","productDescription":"20 p.","startPage":"2937","endPage":"2956","ipdsId":"IP-105928","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":451686,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-021-01287-7","text":"Publisher Index Page"},{"id":390516,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut. 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\"}}]}","volume":"36","noUsgsAuthors":false,"publicationDate":"2021-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Kaminski, Abigail R.","contributorId":267710,"corporation":false,"usgs":false,"family":"Kaminski","given":"Abigail","email":"","middleInitial":"R.","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":825174,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bauer, Dana Marie","contributorId":267711,"corporation":false,"usgs":false,"family":"Bauer","given":"Dana","email":"","middleInitial":"Marie","affiliations":[{"id":24788,"text":"Clark University","active":true,"usgs":false}],"preferred":false,"id":825175,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bell, Kathleen P.","contributorId":267712,"corporation":false,"usgs":false,"family":"Bell","given":"Kathleen P.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":825176,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":825173,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, Erik","contributorId":267713,"corporation":false,"usgs":false,"family":"Nelson","given":"Erik","affiliations":[{"id":33315,"text":"Bowdoin College","active":true,"usgs":false}],"preferred":false,"id":825177,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228563,"text":"70228563 - 2021 - Fragmentation and streamflow metrics drive prairie chub (Macrhybopsis australis) occurrence in the upper Red River basin","interactions":[],"lastModifiedDate":"2022-02-15T11:58:08.094683","indexId":"70228563","displayToPublicDate":"2021-06-30T16:23:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":862,"text":"Aquatic Conservation: Marine and Freshwater Ecosystems","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fragmentation and streamflow metrics drive prairie chub (Macrhybopsis australis ) occurrence in the upper Red River basin","title":"Fragmentation and streamflow metrics drive prairie chub (Macrhybopsis australis) occurrence in the upper Red River basin","docAbstract":"From 2014 to 2017, the U.S. Geological Survey’s Florence Bascom Geoscience Center (FBGC) entered into an inter-agency agreement with the Federal Highway Administration’s Turner-Fairbank Highway Research Center (TFHRC) to assist in field site selection and auger drilling fieldwork. The TFHRC was developing a device to measure the erosional properties of clay-rich sediments to be used for in situ testing at locations of bridge pier construction. FBGC scientists selected 15 drilling locations at 14 different field sites across Northern Virginia and Southern Maryland for the investigation of near-surface sediment properties and the development and testing of the TFHRC’s in situ scour testing device (ISTD). This report provides information about the project and summarizes the data collected during fieldwork including sediment descriptions of the borehole cores and the methods used during fieldwork.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211038","collaboration":"Prepared in cooperation with the U.S. Department of Transportation Federal Highway Administration","usgsCitation":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2021, Borehole sampling of surficial sediments in Northern Virginia and Southern Maryland: U.S. Geological Survey Open-File Report 2021–1038, 27 p., https://doi.org/10.3133/ofr20211038.","productDescription":"Report: vi, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","ipdsId":"IP-120037","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":386418,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1038/ofr20211038.pdf","text":"Report","size":"6.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1038"},{"id":386421,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A8G5LQ","text":"USGS Data Release","linkHelpText":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2021, Datasheets associated with borehole sampling of surficial sediments in Northern Virginia and Southern Maryland conducted by the U.S. Geological Survey for the Federal Highways Administration Turner-Fairbanks Research Center In Situ Scour Testing Device: U.S. Geological Survey data release"},{"id":386417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1038/coverthb.jpg"}],"country":"United States","state":"Maryland, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.06884765624999,\n 37.77071473849609\n ],\n [\n -75.509033203125,\n 37.77071473849609\n ],\n [\n -75.509033203125,\n 39.257778150283364\n ],\n [\n -78.06884765624999,\n 39.257778150283364\n ],\n [\n -78.06884765624999,\n 37.77071473849609\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 21092
Nonnative crayfish are an immediate and pervasive threat to aquatic environments and their biodiversity. Crayfish control can be achieved by physical methods, water chemistry modification, biological methods, biocidal application, and application of crayfish physiology modifiers. The purpose of this report is to identify suitable candidates for potential control of nonnative crayfish through a comprehensive literature review. This review focuses on control methods, specifically on the available data to support registration of a crayfish pesticide. The literature search resulted in 28,058 documents, which were searched to determine if they contained information on physical, chemical, biological, and (or) biocidal approaches to control crayfish. Pesticides directly toxic to crayfish in this literature review include: pyrethroids (natural pyrethrins and synthetic), fipronil, mirex, antimycin-A, and rotenone. Some chemicals, such as diflubenzuron and emamectin benzoate, alter crayfish physiology resulting in a lower pesticide dose needed to control crayfish. Environmental damage, application rate, exposure duration, nontarget effects, environmental persistence, and registration data gaps were used as criteria to define which pesticides are potentially selective to crayfish, along with which have the greatest amount of data to support registration by the U.S. Environmental Protection Agency.
Synthetic pyrethroids were identified as the most likely candidate to be developed into a crayfish pesticide. A type-2 synthetic pyrethroid, cyfluthrin, has the greatest potential for eradicating nonnative crayfish. Although other invertebrate species will be negatively affected at the concentrations required for crayfish control, compared with other pyrethroids and other potential control chemicals, cyfluthrin offers rapid ecosystem recovery due to being more selective, having fewer effects on native fish, and having a short aquatic persistence. Cyfluthrin also has few data gaps for U.S. Environmental Protection Agency registration purposes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211048","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Schueller, J.R., Smerud, J.R., Fredricks, K.T., and Putnam, J.G., 2021, Literature review for candidate chemical control agents for nonnative crayfish: U.S. Geological Survey Open-File Report 2021–1048, 32 p., https://doi.org/10.3133/ofr20211048.","productDescription":"vii, 32 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-115061","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":386879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1048/ofr20211048.pdf","text":"Report","size":"2.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1048"},{"id":386878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1048/coverthb.jpg"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
The Newfoundland Mountains map area (Newfoundland Mountains and adjacent part of Wells 30' x 60' quadrangles) is located in Box Elder County, northwestern Utah. The map encompasses broad expanses of the Great Salt Lake Desert as well as several picturesque mountain ranges (figures 1, 2, and 3). The geology of the area was last mapped and summarized by Doelling (1980). Since that landmark study, much of the area has been mapped in greater detail and new paleontologic, geochronologic, and structural data provide for an updated view of the geology. In addition, new geophysical studies (Langenheim and others, 2013; Langenheim, 2016) provide key data for improved interpretation of subsurface geology.
The geologic map (plate 1) was compiled from fifteen 7.5' quadrangles mapped at a scale of 1:24,000 (mostly in the western part of the area), one map covering the Newfoundland Mountains at a scale of 1:31,680 (Allmendinger and Jordan, 1989), unpublished geologic mapping at scales from 1:24,000 to 1:50,000 (most by Miller; Bovine Mountain by T.E. Jordan), and reconnaissance mapping and aerial photo interpretation in intervening areas by Miller. Some published maps were remapped or reinterpreted by the authors in light of more recent studies north of the map area. Geologic mapping conducted as part of several theses/dissertations and a few published papers also were used (plate 2, index to geologic mapping). Concealed faults under valley bottoms were interpreted from gravity and aeromagnetic data.
Our approach for this map was to integrate across the main themes of the mapped geology by generalizing units, structures, and polygons. This has the aim of illustrating the principal tectonic and stratigraphic packages, as well as illustrating the patterns of surficial units and geomorphology. Cross sections (plate 2) were constructed to coincide with representative cross sections for several detailed geologic maps. This approach required large bends across valleys. Basin geometry shown in the cross sections was constrained by gravity data and a seismic line since few deep drill holes are available.
","language":"English","publisher":"Utah Geological Survey","usgsCitation":"Miller, D., Felger, T.J., and Langenheim, V., 2021, Geologic and geophysical maps of the Newfoundland Mountains and part of the adjacent Wells 30' x 60' quadrangles, Box Elder County, Utah: Utah Geological Survey Miscellaneous Publication MP-173DM, 34 p.","productDescription":"34 p.","ipdsId":"IP-021940","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":387633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387618,"type":{"id":11,"text":"Document"},"url":"https://ugspub.nr.utah.gov/publications/misc_pubs/mp-173/mp-173.pdf"}],"country":"United States","state":"Utah","county":"Box Elder County","otherGeospatial":"Newfoundland Mountains, Wells 30' x 60' 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Center","active":true,"usgs":true}],"preferred":true,"id":820418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":206978,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":820419,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223832,"text":"70223832 - 2021 - Toward improved decision-support tools for Delta Smelt management actions","interactions":[],"lastModifiedDate":"2021-09-09T16:00:10.030009","indexId":"70223832","displayToPublicDate":"2021-06-30T10:48:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":419,"text":"White Paper","active":false,"publicationSubtype":{"id":9}},"title":"Toward improved decision-support tools for Delta Smelt management actions","docAbstract":"The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level.
Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions.
Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance.
Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement.
• DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions.
• DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth.
• DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt.
There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address.
For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models.
Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time.
Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires:
- Engagement of a committed group of technical experts with appropriate expertise.
- The development of a coordinated workplan including appropriate project management and tracking.
- Dialog between potential users (i.e., managers and policy makers) and technical experts.
- Resources to pursue DST development including personnel and computational resources.
This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.
","language":"English","publisher":"Collaborative Adaptive Management Team","usgsCitation":"Reed, D., Acuna, S., Ateljevich, E., Brown, L.R., Geske, B., Gross, E., Hobbs, J., Kimmerer, W.J., Lucas, L., Nobriga, M., and Rose, K.A., 2021, Toward improved decision-support tools for Delta Smelt management actions: White Paper, v, 34 p.","productDescription":"v, 34 p.","ipdsId":"IP-127826","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389004,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.baydeltalive.com/CSAMP/docs/24756"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Denise","contributorId":215697,"corporation":false,"usgs":false,"family":"Reed","given":"Denise","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":822849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acuna, Shawn","contributorId":257756,"corporation":false,"usgs":false,"family":"Acuna","given":"Shawn","email":"","affiliations":[{"id":52106,"text":"Metropolitan Water District of Southern California","active":true,"usgs":false}],"preferred":false,"id":822850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ateljevich, Eli","contributorId":187437,"corporation":false,"usgs":false,"family":"Ateljevich","given":"Eli","email":"","affiliations":[],"preferred":false,"id":822851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geske, Ben","contributorId":265520,"corporation":false,"usgs":false,"family":"Geske","given":"Ben","email":"","affiliations":[{"id":54715,"text":"Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":822853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gross, Edward","contributorId":264402,"corporation":false,"usgs":false,"family":"Gross","given":"Edward","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":822854,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hobbs, Jim","contributorId":200389,"corporation":false,"usgs":false,"family":"Hobbs","given":"Jim","email":"","affiliations":[],"preferred":false,"id":822855,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kimmerer, Wim J.","contributorId":59169,"corporation":false,"usgs":false,"family":"Kimmerer","given":"Wim","email":"","middleInitial":"J.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":822856,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":822857,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nobriga, Matthew","contributorId":139247,"corporation":false,"usgs":false,"family":"Nobriga","given":"Matthew","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":822858,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Kenneth A","contributorId":147274,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","email":"","middleInitial":"A","affiliations":[{"id":16815,"text":"Dept. of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge","active":true,"usgs":false}],"preferred":false,"id":822859,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70223729,"text":"70223729 - 2021 - Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","interactions":[],"lastModifiedDate":"2021-09-16T15:12:14.688708","indexId":"70223729","displayToPublicDate":"2021-06-30T09:26:46","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","docAbstract":"Green Lake is the deepest natural inland lake in Wisconsin, USA, with a maximum depth of about 72 meters (m). In the early 1900’s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity), with low dissolved oxygen (DO) concentrations only in the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake increased, which led to increased algal production and low DO concentrations not only occurring in its deepest areas but also in the middle of the water column (metalimnion). Routine monitoring of the lake and its tributaries has been conducted by the U.S. Geological Survey since 2004 and 1988, respectively. Results from this monitoring led to the Wisconsin Department of Natural Resources (WDNR) listing the lake as impaired because of low DO concentrations in the metalimnion, with elevated TP concentrations identified as the cause of impairment.
As part of this study, comprehensive sampling of the lake and its tributaries was conducted in 2017–2018 to augment ongoing monitoring and further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water quality models were then used to determine the causes of the low DO concentrations and the magnitude of P load reductions needed to improve the water quality of the lake to meet multiple water-quality goals, including the WDNR criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900’s, but since the late 1970s have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–2018 (baseline period for this study), the near-surface geometric-mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were below the 0.015 mg/L WDNR criterion for the lake), and the minimum metalimnetic DO concentrations measured in August ranged from 1.0 to 4.7 mg/L. It was believed that the degradation in water quality was caused by excessive P inputs to the lake; therefore, the total P inputs to the lake were estimated. The mean annual external P load during 2014–2018 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. At fall turnover, internal sediment recycling contributed an additional 7,040 kg that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there is a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, indicated by Secchi disk depths, had a larger response to decreases in P loading than to increases in loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration on the east side of the lake, with higher TP concentrations than the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr equates to a 46-percent reduction in the potentially controllable external P sources (all external sources except precipitation, atmospheric deposition, and waterfowl) from that measured during water years (WYs) 2014–2018. The total external P loading would need to be decreased to 7,680 kg/yr (17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to be decreased to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June-September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM-AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that metalimnetic DO minima are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM-AED results indicated that settling of organic material during summer may be slowed by the colder, denser, and more viscous water in the metalimnion and increase DO consumption. Based on empirical evidence comparing minimum metalimnetic DO concentrations with various meteorological, hydrologic, water quality, and in-lake physical factors, lower metalimnetic DO concentrations occurred when there was warmer metalimnetic water temperatures, higher near-surface Chl-a and TP concentrations, and lower Secchi depths during summer. GLM-AED results indicated that the external P load would need to be reduced to about 4,010 kg/yr, a 57-percent reduction from that measured in 2014–2018, to eliminate the occurrence of metalimnetic DO minima of less than 5 mg/L in over 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake. However, it may take several years for the full effects of the external load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
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Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading, vii, 115 p.","productDescription":"vii, 115 p.","ipdsId":"IP-129488","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":389346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388824,"type":{"id":15,"text":"Index Page"},"url":"https://www.greenlakeassociation.org/research/"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07920837402344,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.75894467245554\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siebers, Benjamin J. 0000-0002-2900-5169","orcid":"https://orcid.org/0000-0002-2900-5169","contributorId":206518,"corporation":false,"usgs":true,"family":"Siebers","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladwig, Robert","contributorId":265278,"corporation":false,"usgs":false,"family":"Ladwig","given":"Robert","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":822505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":822506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reneau, Paul 0000-0002-1335-7573","orcid":"https://orcid.org/0000-0002-1335-7573","contributorId":217293,"corporation":false,"usgs":true,"family":"Reneau","given":"Paul","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Cory P. 0000-0002-1208-8471","orcid":"https://orcid.org/0000-0002-1208-8471","contributorId":261754,"corporation":false,"usgs":false,"family":"McDonald","given":"Cory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":822508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prellwitz, Stephanie","contributorId":265281,"corporation":false,"usgs":false,"family":"Prellwitz","given":"Stephanie","email":"","affiliations":[{"id":54642,"text":"Green Lake Association","active":true,"usgs":false}],"preferred":false,"id":822509,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lathrop, Richard C","contributorId":172075,"corporation":false,"usgs":false,"family":"Lathrop","given":"Richard","email":"","middleInitial":"C","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822510,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221867,"text":"70221867 - 2021 - Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups","interactions":[],"lastModifiedDate":"2022-04-13T20:17:41.090462","indexId":"70221867","displayToPublicDate":"2021-06-30T09:14:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups","docAbstract":"Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1 & 2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have >17% of their extent in Status 1 & 2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.
","language":"English","publisher":"MDPI","doi":"10.3390/f12070864","usgsCitation":"McKerrow, A., Davidson, A., Rubino, M., Faber-Langendoen, D., and Dockter, D., 2021, Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups: Forests, v. 12, no. 7, 864, 15 p., https://doi.org/10.3390/f12070864.","productDescription":"864, 15 p.","ipdsId":"IP-129762","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":451704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f12070864","text":"Publisher Index Page"},{"id":387107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":819084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davidson, Anne","contributorId":197967,"corporation":false,"usgs":false,"family":"Davidson","given":"Anne","email":"","affiliations":[],"preferred":false,"id":819085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rubino, Matthew J. 0000-0003-0651-3053","orcid":"https://orcid.org/0000-0003-0651-3053","contributorId":215500,"corporation":false,"usgs":false,"family":"Rubino","given":"Matthew J.","affiliations":[{"id":39268,"text":"North Carolina State University, NC Cooperative Fish & Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":819086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faber-Langendoen, Don","contributorId":260895,"corporation":false,"usgs":false,"family":"Faber-Langendoen","given":"Don","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":819087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216814,"corporation":false,"usgs":true,"family":"Dockter","given":"Daryn","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":819088,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}