Studying conflict and coping in recreation is important because some coping strategies may provoke distress, while others may lead to positive emotional changes. Building on applications of the transactional stress coping model to park visitors, anglers, and other recreation participants, we explored how Minnesota grouse hunters responded to interference by all-terrain vehicle (ATV)/off-highway vehicle (OHV) users, deer hunters, other grouse/bird hunters, general other hunters, hikers, and bear hunters. We examined relationships among interference, coping, and satisfaction for grouse hunters, and examined how hunter beliefs about ATV use related to perceptions of conflict with ATV users. Encounters with ATV users, deer hunters, other grouse hunters, and hikers lead to problem- and emotion-focused coping, including displacement, confrontive coping, and psychological distancing. Interpersonal conflict with ATVs was positively related to perceptions of interference from ATV users, while no/limited conflict was negatively related. Conflict and coping had a minimal effect on satisfaction.
Although reported levels of interference from other user groups was relatively low, grouse hunters reported moderate interference from ATV users and the use of confrontive coping in response to interactions with ATV riders. Confrontive coping has been associated with increased distress, and deserves attention. However, interference from ATV users was low to moderate and most grouse hunters in the study personally use ATVs for recreation, so there is limited need for management to address conflicts between grouse hunters and ATV riders. Nevertheless, zoning for ATV-free grouse hunting could be tested in areas with reported conflicts between hunters and ATV riders.
1.
Theoretical models suggest that ecosystems can be found in one of several possible alternative stable states, and a shift in structural stable state (SSS) can trigger a change in functional stable state (FSS). But we still lack the empirical evidence to confirm these states and transitions, and to inform the rates of change. Here, a 30-yr dataset from long-term ungrazed and grazed temperate grasslands was analyzed to determine whether abrupt transitions of SSS and FSS can occur. We found that the long-term ungrazed grassland experienced abrupt transitions in the dominant plant functional type (shift in SSS) that was followed by a transition between carbon sink and source 1–2 year later (shift in FSS). A directional shift in precipitation and temperature accounted for 40% of the variation in the SSS transition, while the SSS transition explained 20% of the variation in the FSS transition. In contrast, no abrupt transitions for SSS and FSS were observed in the long-term moderately grazed grassland. These findings provide important insight into the interacting effects of climate change and livestock grazing on ecosystem transitions in temperate grasslands. Moderate utilization of production in ecosystems that have co-evolved with herbivores can offset structural and functional transitions induced by climate change.
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These factors vary with scale from interspecific interactions to global climatic cycles. Wetlands are important biodiversity hotspots and contributors of ecosystem services, but the association between proportional wetland cover and species richness has shown mixed results. It is not well known as to what extent there is a relationship between proportional wetland cover and species richness, especially at the sub-continental scale. We used the National Wetlands Inventory to model wetland cover for the conterminous United States and the National Land Cover Database to estimate wetland change between 2001 and 2011. We used a Bayesian spatial Poisson model to estimate a spatially varying coefficient surface describing the effect of proportional wetland cover on the distribution of amphibians, birds, mammals, and reptiles and the cumulative distribution of terrestrial endemic species. 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F.","contributorId":96415,"corporation":false,"usgs":true,"family":"Baldwin","given":"Robert","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":835489,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228397,"text":"70228397 - 2020 - Using the Delphi process to gather information from a Bald Eagle expert panel","interactions":[],"lastModifiedDate":"2022-02-10T14:49:41.948517","indexId":"70228397","displayToPublicDate":"2020-05-01T08:44:45","publicationYear":"2020","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/SWAN.NRR-2020/2128","title":"Using the Delphi process to gather information from a Bald Eagle expert panel","docAbstract":"Bald eagle (Haliaeetus leucocephalus) populations are classified by the Southwest Alaska Network (SWAN) of the National Park Service as a vital sign of biological integrity, largely because of their importance as an indicator species for environmental contaminants and human disturbance. Though Bald Eagles are plentiful in Alaska, it is still imperative to have a monitoring plan that allows for the estimation of population sizes and detection of significant changes in populations. Currently, Bald Eagles are monitored in Kenai Fjords National Park, Katmai National Park and Preserve, Lake Clark National Park and Preserve, and Wrangell – St. Elias National Park, but each park uses different monitoring procedures and evaluation criteria. This makes it difficult for scientists and managers to compare data, detect changes in overall populations, and make effective management decisions. Our research is using a formal structured decision-making process to ensure that the Bald Eagle monitoring conducted by the parks is standardized and meets programmatic goals and objectives. We implemented a Delphi process, which is an iterative survey technique that is used to gather expert opinion. We used online questionnaires to gather information and opinions from National Park Service scientists and managers, eagle experts, and other interested parties. We identified important stressors and feasible monitoring metrics, which were tied to the means objectives for the Bald Eagle monitoring program: minimize cost, minimize effort, maximize ability to detect change in populations, and maximize accurate information about Bald Eagles. We will also analyze monitoring metrics using a consequence table, which determines the performance of each objective in terms of the means objectives chosen by expert panelists. This information will help to create a more accurate conceptual model of the system to guide development of a Bald Eagle monitoring program that can be standardized among Southwest Alaska National Parks.
","language":"English","publisher":"National Park Service","usgsCitation":"Kolstrom, R., Wilson, T., and Gigliotti, L.M., 2020, Using the Delphi process to gather information from a Bald Eagle expert panel: Natural Resource Report NPS/SWAN.NRR-2020/2128, vii, 57 p.","productDescription":"vii, 57 p.","ipdsId":"IP-111246","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395767,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":395766,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://irma.nps.gov/DataStore/DownloadFile/640030"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.65429687499997,\n 54.54657953840501\n ],\n [\n -134.736328125,\n 54.54657953840501\n ],\n [\n -134.736328125,\n 61.501734289732326\n ],\n [\n -155.65429687499997,\n 61.501734289732326\n ],\n [\n -155.65429687499997,\n 54.54657953840501\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kolstrom, Rebecca","contributorId":275658,"corporation":false,"usgs":false,"family":"Kolstrom","given":"Rebecca","email":"","affiliations":[],"preferred":false,"id":834198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Tammy L.","contributorId":275659,"corporation":false,"usgs":false,"family":"Wilson","given":"Tammy L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":834199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gigliotti, Larry M. 0000-0002-1693-5113 lgigliotti@usgs.gov","orcid":"https://orcid.org/0000-0002-1693-5113","contributorId":3906,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Larry","email":"lgigliotti@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834200,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210149,"text":"70210149 - 2020 - Individual and population fitness consequences associated with large carnivore use of residential development","interactions":[],"lastModifiedDate":"2020-05-18T12:15:44.247337","indexId":"70210149","displayToPublicDate":"2020-05-01T07:11:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Individual and population fitness consequences associated with large carnivore use of residential development","docAbstract":"Large carnivores are negotiating increasingly developed landscapes, but little is known about how such behavioral plasticity influences their demographic rates and population trends. Some investigators have suggested that the ability of carnivores to behaviorally adapt to human development will enable their persistence, and yet, others have suggested that such landscapes are likely to serve as population sinks or ecological traps. To understand how plasticity in black bear (Ursus americanus) use of residential development influences their population dynamics, we conducted a 6 year study near Durango, Colorado, USA. Using space-use data on individual bears, we examined the influence of use of residential development on annual measures of bear body fat, cub productivity, cub survival and adult female survival, after accounting for variation in natural food availability and individual attributes (e.g., age). We then used our field-based vital rate estimates to parameterize a matrix model that simulated asymptotic population growth for bears using residential development to different degrees. We found that bear use of residential development was highly variable within and across years, with bears increasing their foraging within development when natural foods were scarce. Increased bear use of development was associated with increased body fat and cub productivity, but reduced cub and adult survival. When these effects were simultaneously incorporated into a matrix model we found that the population was projected to decline as bear use of development increased, given that the costs of reduced survival outweighed the benefits of enhanced productivity. Our results provide a mechanistic understanding of how black bear use of residential development exerts opposing effects on different bear fitness traits and a negative effect on population growth, with the magnitude of those effects mediated by variation in environmental conditions. They also highlight the importance of monitoring bear population dynamics, particularly as shifts in bear behavior are likely to drive increases in human-bear conflicts and the perception of growing bear populations. Finally, our work emphasizes the need to consider the demographic viability of large carnivore populations when promoting the coexistence of people and carnivores on shared landscapes.","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3098","collaboration":"","usgsCitation":"Johnson, H.E., Lewis, D., and Breck, S., 2020, Individual and population fitness consequences associated with large carnivore use of residential development: Ecosphere, v. 11, no. 5, e03098, 23 p., https://doi.org/10.1002/ecs2.3098.","productDescription":"e03098, 23 p.","ipdsId":"IP-111185","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":456893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3098","text":"Publisher Index Page"},{"id":374882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Durango","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.11370849609375,\n 37.1165261849112\n ],\n [\n -107.66876220703125,\n 37.1165261849112\n ],\n [\n -107.66876220703125,\n 37.398528132728615\n ],\n [\n -108.11370849609375,\n 37.398528132728615\n ],\n [\n -108.11370849609375,\n 37.1165261849112\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lewis, David Bruce","contributorId":156433,"corporation":false,"usgs":false,"family":"Lewis","given":"David Bruce","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":789315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breck, Stewart","contributorId":199403,"corporation":false,"usgs":false,"family":"Breck","given":"Stewart","affiliations":[],"preferred":false,"id":789316,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222593,"text":"70222593 - 2020 - Risk-targeted alternatives to deterministic ground motion caps in U.S. seismic provisions","interactions":[],"lastModifiedDate":"2021-08-09T12:02:34.873846","indexId":"70222593","displayToPublicDate":"2020-05-01T06:57:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Risk-targeted alternatives to deterministic ground motion caps in U.S. seismic provisions","docAbstract":"Since their inception over 20 years ago, the maximum considered earthquake ground motion maps in U.S. building codes have capped probabilistic values with deterministic ground motions from characteristic earthquakes on known active faults. This practice has increasingly been called into question both because of spatially non-uniform risk levels that are produced (risk being higher mainly in coastal California) and practical difficulties in defining characteristic earthquakes from recent earthquake rupture forecast models. We describe two proposals developed to enable phase-out of deterministic caps. One approach modestly increases collapse risk targets nationwide based on recent information on return periods of characteristic earthquakes on major central and eastern U.S. seismic sources; adoption of this approach would remove the perceived need for caps in California. The second approach uses geographically varying collapse risk targets, being higher near the highly active faults in California and unchanged elsewhere. Neither approach was adopted for the 2020 National Earthquake Hazards Reduction Program recommended seismic Provisions for new building structures, but they are described in a Part 3 document to accompany the Provisions and Commentary.
Urban growth and climate change together complicate planning efforts meant to adapt to increasingly scarce water supplies. Several studies have independently examined the impacts of urban planning and climate change on water demand, but little attention has been given to their combined impact. Here we forecast urban water demand using a Geographically Weighted Regression model informed by socio-economic, environmental and landscape pattern metrics. The purpose of our study is to evaluate how future scenarios of population densities and climate warming will jointly affect water demand across two rapidly growing U.S. states (North Carolina and South Carolina). Our forecasts indicate that regional water demand by 2065 will increase by 37%–383% relative to the baseline in 2010, across all scenarios of change. Our results show future water demand will increase under rising temperatures, but could be ameliorated by policies that promote higher density development and urban infill. These water-efficient land use policies show a 5% regional reduction in water demand and up to 25% reduction locally for counties with the highest expected population growth by 2065. For rural counties experiencing depopulation, the land use policies we considered are insufficient to significantly reduce water demand. For expanding communities seeking to increase their adaptive capacity to changing socio-environmental conditions, our framework can assist in developing sustainable solutions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.139050","usgsCitation":"Sanchez, G., Terando, A., Smith, J.W., Garcia, A.M., Wagner, C., and Meentemeyer, R.K., 2020, Forecasting water demand across a rapidly urbanizing region: Science of the Total Environment, v. 730, 139050, 13 p., https://doi.org/10.1016/j.scitotenv.2020.139050.","productDescription":"139050, 13 p.","ipdsId":"IP-108370","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":456898,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.139050","text":"Publisher Index Page"},{"id":437009,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95PTP5G","text":"USGS data release","linkHelpText":"Land-use and water demand projections (2012 to 2065) under different scenarios of environmental change for North Carolina, South Carolina, and coastal Georgia"},{"id":377357,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.5419921875,\n 36.491973470593685\n ],\n [\n -81.6064453125,\n 36.66841891894786\n ],\n [\n -84.1552734375,\n 35.06597313798418\n ],\n [\n -82.5732421875,\n 34.08906131584994\n ],\n [\n -80.7275390625,\n 31.87755764334002\n ],\n [\n -77.47558593749999,\n 34.488447837809304\n ],\n [\n -76.2451171875,\n 34.74161249883172\n ],\n [\n -75.0146484375,\n 35.92464453144099\n ],\n [\n -75.5419921875,\n 36.491973470593685\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"730","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sanchez, Georgina M. 0000-0002-2365-6200","orcid":"https://orcid.org/0000-0002-2365-6200","contributorId":210477,"corporation":false,"usgs":false,"family":"Sanchez","given":"Georgina M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":795791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam J. 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":216875,"corporation":false,"usgs":true,"family":"Terando","given":"Adam J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":795792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Jordan W.","contributorId":177326,"corporation":false,"usgs":false,"family":"Smith","given":"Jordan","email":"","middleInitial":"W.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":795793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garcia, Ana Maria 0000-0002-5388-1281 agarcia@usgs.gov","orcid":"https://orcid.org/0000-0002-5388-1281","contributorId":2035,"corporation":false,"usgs":true,"family":"Garcia","given":"Ana","email":"agarcia@usgs.gov","middleInitial":"Maria","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wagner, Chad R. 0000-0002-9602-7413 cwagner@usgs.gov","orcid":"https://orcid.org/0000-0002-9602-7413","contributorId":1530,"corporation":false,"usgs":true,"family":"Wagner","given":"Chad R.","email":"cwagner@usgs.gov","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true},{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true}],"preferred":false,"id":795795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":795796,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212036,"text":"70212036 - 2020 - Effects of flow diversion on Snake Creek and its riparian cottonwood forest, Great Basin National Park","interactions":[],"lastModifiedDate":"2020-08-13T14:59:57.567569","indexId":"70212036","displayToPublicDate":"2020-04-30T09:53:53","publicationYear":"2020","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/GRBA/NRR-2020/2104","title":"Effects of flow diversion on Snake Creek and its riparian cottonwood forest, Great Basin National Park","docAbstract":"Snake Creek flows east from the southern Snake Range in Nevada over complex lithology before leaving Great Basin National Park. The river travels over a section of karst limestone where some surface water naturally recharges the groundwater flow system. In 1961 a water diversion pipeline was constructed by downstream water users to transport surface water through the groundwater recharge zone to reduce potential water losses. The diversion pipeline dewaters a 5-km reach for most of the year by transporting water past the recharge zone then returning it to the channel downstream. Snake Creek was incorporated into the newly established Great Basin National Park in 1986, and today park managers and visitors are concerned that the diversion has destabilized Snake Creek’s riparian ecosystem in this arid region where it has high ecological value. The objectives of this study were to 1) document riparian cottonwood forest conditions in the pipeline-dewatered (DW) reach, 2) evaluate Snake Creek water availability and whether it can support a healthy riparian ecosystem, and 3) determine if dewatering has shifted the fluvial system into an unnatural and poorly functioning state.
We pursued these ecohydrological study objectives in 11 research investigations of Snake Creek’s DW reach and nearby reference reaches. The research investigations analyzed: 1) riparian forest condition, tree age, growth, and death; 2) tree ring chronologies through time and space; 3) hydroclimatic drivers of tree growth; 4) stable carbon isotopes extracted from tree rings; 5) cottonwood ecophysiology related to water transport and water stress; 6) historical aerial photography; 7) stand-level riparian forest production; 8) groundwater availability as related to surface water and plant rooting zones; 9) near-surface geophysics using electrical resistivity imaging; 10) channel and valley geomorphology; and 11) in-channel wood jams caused by fallen trees. Integrating these diverse research topics provided a full perspective of historical and modern conditions along Snake Creek.
We found that modern hydrological conditions in Snake Creek’s DW reach could not maintain the drought-sensitive ecosystem. The riparian cottonwoods (Populus angustifolia and P. angustifolia x P. trichocarpa) have experienced significant dieback. Tree mortality was 2.4 times higher in the DW reach than in reference reaches, and surviving trees supported only 60% of the live canopy compared to trees in reference reaches. Changes in the DW reach forest began in the 1960s and became more severe during the last two decades. Stable carbon isotope ratios and branch dieback analyses both demonstrated initial forest adjustments related to water stress beginning in the early 1960s. Tree ring width chronologies indicated two periods of growth decline in the DW relative to control reaches. The first decline in the 1960s represented an immediate adjustment to the modified flow regime, and the second decline in the 2000s demonstrated reduced resilience to atmospheric drought. Aerial photos and stand-level forest production calculations indicated that substantial riparian forest decline occurred in the 1990s–2010s in the DW reach compared to reference reaches. Stable carbon isotope ratios and leaf water potentials revealed that trees in the DW reach experienced greater drought stress than those in reference reaches. Monitoring wells and electrical resistivity surveys both showed riparian water tables to be largely supported by in-channel surface water flow, indicating that the flow diversion removed water that recharges alluvial groundwater and sustains riparian plants. Areas of widespread tree mortality in the DW reach also corresponded to a larger and more unstable channel with a high instream wood load from fallen trees. Modern conditions of Snake Creek in the DW reach robustly suggest that dewatering the river and its associated riparian corridor adversely affected the riparian ecosystem. The degraded condition is likely to persist and intensify unless water is returned to the channel. As we documented during the wet 1980s and the scientific literature suggest, a partial recovery of the riparian ecosystem is likely possible with restored flows.
","language":"English","publisher":"National Park Service","usgsCitation":"Schook, D.M., Cooper, D.J., Friedman, J.M., Rice, S.E., Hoover, J.D., and Thaxton, R.D., 2020, Effects of flow diversion on Snake Creek and its riparian cottonwood forest, Great Basin National Park: Natural Resource Report NPS/GRBA/NRR-2020/2104, xv, 159 p.","productDescription":"xv, 159 p.","ipdsId":"IP-114048","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":377493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377489,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/DownloadFile/637892"}],"country":"United States","state":"Nevada","otherGeospatial":"Great Basin National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.3951416015625,\n 38.66406704456943\n ],\n [\n -114.114990234375,\n 38.66406704456943\n ],\n [\n -114.114990234375,\n 39.08956785484934\n ],\n [\n -114.3951416015625,\n 39.08956785484934\n ],\n [\n -114.3951416015625,\n 38.66406704456943\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schook, Derek M.","contributorId":178325,"corporation":false,"usgs":false,"family":"Schook","given":"Derek","email":"","middleInitial":"M.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":796163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cooper, David J.","contributorId":53309,"corporation":false,"usgs":true,"family":"Cooper","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":796164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":796165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rice, Steven E.","contributorId":238179,"corporation":false,"usgs":false,"family":"Rice","given":"Steven","email":"","middleInitial":"E.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":796166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hoover, Jamie D.","contributorId":238180,"corporation":false,"usgs":false,"family":"Hoover","given":"Jamie","email":"","middleInitial":"D.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":796167,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Thaxton, Richard D.","contributorId":238181,"corporation":false,"usgs":false,"family":"Thaxton","given":"Richard","email":"","middleInitial":"D.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":796168,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210935,"text":"70210935 - 2020 - 7700-year persistence of an isolated, free-living coral assemblage in the Galápagos Islands: A model for coral refugia?","interactions":[],"lastModifiedDate":"2020-07-07T14:09:51.779689","indexId":"70210935","displayToPublicDate":"2020-04-30T09:05:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"title":"7700-year persistence of an isolated, free-living coral assemblage in the Galápagos Islands: A model for coral refugia?","docAbstract":"In an eastern-Pacific coral assemblage at Devil’s Crown, Galápagos Islands, Ecuador, two coral species, Psammocora stellata and Cycloseris (Diaseris) distorta, form dense populations of unattached colonies on sand and rubble substrata. In the Galápagos, living C. (D.) distorta is found only at this single site, whereas populations of P. stellata are found throughout the archipelago. Six cores dating to 7700 yBP showed P. stellata to be dominant throughout the history of this isolated community, but C. (D.) distorta increased in abundance from ~2200 yBP and reached peak abundance between 1471 yBP and the present. The relative frequency of the two coral species may be linked to millennial-scale climatic variability, and this site may represent a refuge for C. (D.) distorta from unfavorable climatic fluctuations on millennial timescales. Our results demonstrate that some corals can persist in isolated populations for millennia.","language":"English","publisher":"Springer","doi":"10.1007/s00338-020-01935-5","usgsCitation":"Feingold, J., Reigl, B., Hendrickson, K., Toth, L., Cheng, H., Edwards, R.L., and Aronson, R.B., 2020, 7700-year persistence of an isolated, free-living coral assemblage in the Galápagos Islands: A model for coral refugia?: Coral Reefs, v. 39, p. 639-647, https://doi.org/10.1007/s00338-020-01935-5.","productDescription":"9 p.","startPage":"639","endPage":"647","ipdsId":"IP-111819","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":376148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Galápagos Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.0654296875,\n -1.8124417945380265\n ],\n [\n -88.868408203125,\n -1.8124417945380265\n ],\n [\n -88.868408203125,\n 0.5163504323777589\n ],\n [\n -92.0654296875,\n 0.5163504323777589\n ],\n [\n -92.0654296875,\n -1.8124417945380265\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","noUsgsAuthors":false,"publicationDate":"2020-04-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Feingold, Joshua","contributorId":228835,"corporation":false,"usgs":false,"family":"Feingold","given":"Joshua","email":"","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":792215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reigl, Bernhard","contributorId":228836,"corporation":false,"usgs":false,"family":"Reigl","given":"Bernhard","email":"","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":792216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hendrickson, Katie","contributorId":228837,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Katie","email":"","affiliations":[{"id":13165,"text":"Nova Southeastern University","active":true,"usgs":false}],"preferred":false,"id":792217,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":792218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cheng, Hai 0000-0002-5305-9458","orcid":"https://orcid.org/0000-0002-5305-9458","contributorId":223142,"corporation":false,"usgs":false,"family":"Cheng","given":"Hai","email":"","affiliations":[{"id":40680,"text":"Xi'an Jiaotong University","active":true,"usgs":false}],"preferred":false,"id":792219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, R. Lawrence","contributorId":69760,"corporation":false,"usgs":true,"family":"Edwards","given":"R.","email":"","middleInitial":"Lawrence","affiliations":[],"preferred":false,"id":792245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Aronson, Richard B. 0000-0003-0383-3844","orcid":"https://orcid.org/0000-0003-0383-3844","contributorId":212695,"corporation":false,"usgs":false,"family":"Aronson","given":"Richard","email":"","middleInitial":"B.","affiliations":[{"id":17748,"text":"Florida Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":792220,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219907,"text":"70219907 - 2020 - Fisheries research and monitoring activities of the Lake Erie Biological Station, 2019","interactions":[],"lastModifiedDate":"2021-04-16T13:31:57.728158","indexId":"70219907","displayToPublicDate":"2020-04-30T08:29:44","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":8434,"text":"Lake Erie Biological Station Annual Report","active":true,"publicationSubtype":{"id":4}},"title":"Fisheries research and monitoring activities of the Lake Erie Biological Station, 2019","docAbstract":"A comprehensive understanding of fish populations and their interactions is the cornerstone of modern fishery management and the basis for Fish Community Goals and Objectives for Lake Erie (Ryan et al. 2003). This report is responsive to U.S. Geological Survey (USGS) obligations via Memorandum of Understanding (MOU) with the Great Lakes Council of Lake Committees (CLC) to provide scientific information in support of fishery management. Goals for the USGS Great Lakes Deepwater Fish Assessment and Ecological Studies in 2019 were to monitor long-term changes in the fish community and population dynamics of key fishes of interest to management agencies. Specific to Lake Erie, expectations of this agreement were sustained investigations of native percids, forage (prey) fish populations, and Lake Trout.
Our 2019 deepwater program operations began in April and concluded in December, and utilized trawl, gillnet, hydroacoustic, lower trophic sampling, and telemetry methods. This work resulted in 88 bottom trawls covering 65 ha of lake-bottom and catching 24,140 fish totaling 3,622 kg during three separate trawl surveys in the West and Central basins of Lake Erie. Overnight gillnet sets (n=44) for cold water species were performed at 42 unique locations in the West and East basins of Lake Erie. A total of 8.0 km of gillnet was deployed during these surveys, which caught 286 fish, 114 of which were native coldwater species: Lake Trout, Burbot, and Lake Whitefish. USGS hydroacoustic surveys in 2019 produced 240 km of transects, and lower trophic sampling provided data from zooplankton samples (n=21) and water quality profiles (n=21) to populate a database maintained by the Ontario Ministry of Natural Resources and Forestry (OMNRF), Ohio Division of Natural Resources (ODNR), Michigan Division of Natural Resources (MDNR), Pennsylvania Fish and Boat Commission (PFBC), and New York State Department of Environmental Conservation (NYSDEC). USGS also assisted CLC member agencies with deployment and maintenance of the Great Lakes Acoustic Telemetry Observation System (GLATOS) throughout all three Lake Erie sub-basins, supporting multiple coordinated telemetry investigations.
In 2019, Lake Trout investigations included annual gill net surveys and acoustic telemetry of spawning migration and habitat use in coordination with OMNRF, NYSDEC, and PFBC. Results from Lake Trout investigations were reported in the Coldwater Task Group annual report to the Great Lakes Fishery Commission (GLFC) and the CLC (Coldwater Task Group 2020). Likewise, interagency forage fish assessments conducted with hydroacoustics were summarized and reported in the Forage Task Group annual report (Forage Task Group 2020).
This report presents biomass-based summaries of fish communities in western Lake Erie derived from USGS bottom trawl surveys conducted from 2013 to 2019 during June and September. The survey design provided temporal and spatial coverage that did not exist in the historic interagency trawl database, and thus complemented the August ODNR-OMNRF effort to reinforce stock assessments with more robust data. Analyses herein evaluated trends in: total biomass, abundance of dominant predator and forage species, non-native species composition, biodiversity and community structure. Data from this effort can be explored interactively online (https://lebs.shinyapps.io/western-basin/), and are accessible for download (https://doi.org/10.5066/P9LL6YOR, Keretz et al. 2020). Annual survey data are added to these sources as the data become available.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Keretz, K.R., Kocovsky, P., Kraus, R., and Schmitt, J., 2020, Fisheries research and monitoring activities of the Lake Erie Biological Station, 2019: Lake Erie Biological Station Annual Report, 12 p.","productDescription":"12 p.","ipdsId":"IP-116726","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385155,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/lake-erie-committee.php"}],"country":"Canada, United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.4796142578125,\n 41.701627343789205\n ],\n [\n -83.056640625,\n 41.58668835697237\n ],\n [\n -82.9742431640625,\n 41.52091689636249\n ],\n [\n -83.0401611328125,\n 41.422134246213616\n ],\n [\n -82.9083251953125,\n 41.40153558289846\n ],\n [\n -82.79296874999999,\n 41.463311976686235\n ],\n [\n -82.4908447265625,\n 41.380930388318\n ],\n [\n -81.990966796875,\n 41.50446357504803\n ],\n [\n -81.7108154296875,\n 41.48800607185427\n ],\n [\n -81.3372802734375,\n 41.72623044860004\n ],\n [\n -80.48583984375,\n 41.983994270935625\n ],\n [\n -79.46411132812499,\n 42.407234661551875\n ],\n [\n -79.200439453125,\n 42.54093947168063\n ],\n [\n -78.848876953125,\n 42.79943131987838\n ],\n [\n -78.9202880859375,\n 42.879989517714826\n ],\n [\n -79.332275390625,\n 42.871938424448466\n ],\n [\n -79.4696044921875,\n 42.871938424448466\n ],\n [\n -79.7222900390625,\n 42.84375132629021\n ],\n [\n -80.22766113281249,\n 42.783307077249624\n ],\n [\n -80.46936035156249,\n 42.577354839557856\n ],\n [\n -80.85937499999999,\n 42.65012181368022\n ],\n [\n -81.30432128906249,\n 42.66628070564928\n ],\n [\n -81.5185546875,\n 42.5733097370664\n ],\n [\n -81.8096923828125,\n 42.382894009614034\n ],\n [\n -81.9140625,\n 42.285437007491545\n ],\n [\n -81.9305419921875,\n 42.256983603767466\n ],\n [\n -82.02392578125,\n 42.256983603767466\n ],\n [\n -82.430419921875,\n 42.11859868281563\n ],\n [\n -82.50732421875,\n 42.004407212963585\n ],\n [\n -82.5238037109375,\n 41.92271616673922\n ],\n [\n -82.60620117187499,\n 42.02481360781777\n ],\n [\n -82.72705078125,\n 42.02889410108475\n ],\n [\n -82.9248046875,\n 41.98807738309159\n ],\n [\n -83.045654296875,\n 42.049292638686836\n ],\n [\n -83.12805175781249,\n 42.1104489601222\n ],\n [\n -83.155517578125,\n 42.15118709351198\n ],\n [\n -83.22143554687499,\n 42.05337156043361\n ],\n [\n -83.177490234375,\n 42.004407212963585\n ],\n [\n -83.25439453125,\n 41.95540515378059\n ],\n [\n -83.4356689453125,\n 41.828642001860544\n ],\n [\n -83.4906005859375,\n 41.72623044860004\n ],\n [\n -83.4796142578125,\n 41.701627343789205\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Keretz, Kevin R. 0000-0002-4808-8350 kkeretz@usgs.gov","orcid":"https://orcid.org/0000-0002-4808-8350","contributorId":5859,"corporation":false,"usgs":true,"family":"Keretz","given":"Kevin","email":"kkeretz@usgs.gov","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":814367,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kocovsky, Patrick 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":150837,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":814369,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210026,"text":"70210026 - 2020 - Parsing complex terrain controls on mountain glacier response to climate forcing","interactions":[],"lastModifiedDate":"2020-08-06T19:14:26.872296","indexId":"70210026","displayToPublicDate":"2020-04-30T07:41:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Parsing complex terrain controls on mountain glacier response to climate forcing","docAbstract":"Glaciers are a key indicator of changing climate in the high mountain landscape.\nGlacier variations across a mountain range are ultimately driven by regional climate\nforcing. However, changes also reflect local, topographically driven processes such as\nsnow avalanching, snow wind-drifting, and radiation shading as well as the initial\nglacier conditions such as hypsometry and ice thickness. Here we assess the role of\nthese various terrain influences on change to Little Ice Age (LIA) glaciers in Glacier\nNational Park, U.S.A . With available data for LIA and modern glacier areas, we\nestimate glacier volumes using simple ice flow assumptions, and topographically\ndriven processes using terrain proxies. At the LIA glacial maxima there were 82\nglaciers larger than 0.1 km 2 ranging from 0.11 to 4.97 km 2 . Over the course of the\n20 th century, every single LIA glacier decreased in area and 60% (49 glaciers)\ndiminished to below the 0.1 km 2 threshold. Glaciers with large initial area (>1.5 km\n2 ) at the end of LIA persisted. Within the intermediate size class (0.5 km 2 < area <\n1.5 km 2 ), LIA glacier persistence is poorly explained by initial glacier volume, ice\nthickness, or elevation. Instead, wind exposure is an important explanatory factor.\nOur analysis demonstrates the complex response of cirque glaciers to post-LIA climate\nchange in this region: individual glaciers have not necessarily undergone equivalent\nand synchronous change. Nevertheless, that all glaciers in this mountain range\nexperienced retreat demonstrates that local processes mediated adjustments of some\nglaciers, but completely decoupled none from the regional climate forcing.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloplacha.2020.103209","usgsCitation":"Florentine, C., Harper, J.T., and Fagre, D., 2020, Parsing complex terrain controls on mountain glacier response to climate forcing: Global and Planetary Change, v. 191, 103209, 13 p., https://doi.org/10.1016/j.gloplacha.2020.103209.","productDescription":"103209, 13 p.","ipdsId":"IP-112133","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":456906,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gloplacha.2020.103209","text":"Publisher Index Page"},{"id":374649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.620361328125,\n 48.28319289548349\n ],\n [\n -112.96142578125,\n 48.28319289548349\n ],\n [\n -112.96142578125,\n 49.005447494058096\n ],\n [\n -114.620361328125,\n 49.005447494058096\n ],\n [\n -114.620361328125,\n 48.28319289548349\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Florentine, Caitlyn Elizabeth 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":224631,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn Elizabeth","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":788858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harper, Joel T.","contributorId":173392,"corporation":false,"usgs":false,"family":"Harper","given":"Joel","email":"","middleInitial":"T.","affiliations":[{"id":16951,"text":"Department of Geosciences, University of Montana, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":788859,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fagre, Daniel B. 0000-0001-8552-9461","orcid":"https://orcid.org/0000-0001-8552-9461","contributorId":224632,"corporation":false,"usgs":true,"family":"Fagre","given":"Daniel B.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":788860,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70219005,"text":"70219005 - 2020 - Automated location correction and spot height generation for named summits in the coterminous United States","interactions":[],"lastModifiedDate":"2021-03-19T12:31:02.918089","indexId":"70219005","displayToPublicDate":"2020-04-30T07:27:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2035,"text":"International Journal of Digital Earth","active":true,"publicationSubtype":{"id":10}},"title":"Automated location correction and spot height generation for named summits in the coterminous United States","docAbstract":"Spot elevations published on historical U.S. Geological Survey topographic maps were established as needed to enhance information imparted by the quadrangle’s contours. In addition to other features, labels were routinely placed on mountain summits. While some elevations were established through field survey triangulation, many were computed during photogrammetric stereo-compilation. Today, Global Navigation Satellite System (GNSS) receivers have replaced expensive triangulation methods. However, since GNSS measurements require visiting the feature location, a national dataset containing high-accuracy spot elevations has not yet been created. Consequently, modern U.S. Topo maps are devoid of mountain peak or other spot elevations. Still, topographic map users continue to demand the display of spot heights. Therefore, a pilot study was conducted to evaluate the feasibility of automatically generating elevation values at named U.S. summits using available elevation data. The devised method uses an uphill stepping technique to find the most likely highest point in subsequently higher-resolution elevation models. Resulting elevation values are compared to other published sources. Results from 196 summits indicate that values derived from lidar are generally higher, whereas those populated from the one-third arc-second USGS Seamless 3DEP elevation dataset are generally lower. A thorough understanding of these relationships require the evaluation of more points.
Understanding trends in abundance is important to fisheries conservation, but techniques for estimating streamwide abundance of cryptic fishes with strong habitat–abundance relationships are not well established and need further development. We developed techniques for addressing this need using the Harlequin Darter Etheostoma histrio, a small, cryptic freshwater fish associated with submerged wood in streams. Our objectives were to (1) determine how Harlequin Darter abundance and the amount of submerged wood were related at sampled sites and (2) use this relationship to estimate Harlequin Darter abundance at unsampled sites and extrapolate Harlequin Darter abundance estimates and associated uncertainty streamwide. We conducted a mark–recapture study to estimate abundance of Harlequin Darters in 25‐m stream reaches at 24 sites in Big Escambia Creek (BEC) and 18 sites in Pine Barren Creek (PBC) (Escambia River tributaries in northwestern Florida). The number of wood pieces (submerged wood ≥1.5 m long and ≥0.25 m in circumference) in both creeks was counted and mapped using side‐scan sonar and a geographic information system. Harlequin Darter and wood data were used in a Bayesian multinomial mixture model to estimate site abundance of Harlequin Darters, to determine the relationship between wood and Harlequin Darter abundance, and to extrapolate Harlequin Darter abundance streamwide. We found a positive relationship between wood and Harlequin Darter abundance in both creeks, and there were more wood pieces in PBC than in BEC. Streamwide abundance of Harlequin Darters was greater in PBC than in BEC. The extrapolated streamwide abundance estimates were 9,369 Harlequin Darters (95% credible interval = 6,668–13,402) in PBC and 7,439 Harlequin Darters (95% credible interval = 4,493–11,226) in BEC. Our methods effectively estimated abundance of a small, cryptic fish that uses complex wood habitat. In addition, our findings may assist in the conservation of the Harlequin Darter.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10231","usgsCitation":"Holcomb, K.M., Schueller, P., Jelks, H.L., Knight, J.R., and Allen, M., 2020, Use of strong habitat–abundance relationships in assessing population status of cryptic fishes: An example using the Harlequin Darter: Transactions of the American Fisheries Society, v. 149, no. 3, p. 320-334, https://doi.org/10.1002/tafs.10231.","productDescription":"15 p.","startPage":"320","endPage":"334","ipdsId":"IP-107850","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":377944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Big Escambia Creek, Escambia River, Pine Barren Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.37220764160156,\n 30.537425073997134\n ],\n [\n -87.09548950195312,\n 30.537425073997134\n ],\n [\n -87.09548950195312,\n 30.994680105042487\n ],\n [\n -87.37220764160156,\n 30.994680105042487\n ],\n [\n -87.37220764160156,\n 30.537425073997134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Holcomb, Kathryn M","contributorId":239617,"corporation":false,"usgs":false,"family":"Holcomb","given":"Kathryn","email":"","middleInitial":"M","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":797405,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schueller, Paul","contributorId":181829,"corporation":false,"usgs":false,"family":"Schueller","given":"Paul","email":"","affiliations":[],"preferred":false,"id":797406,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jelks, Howard L. 0000-0002-0672-6297 hjelks@usgs.gov","orcid":"https://orcid.org/0000-0002-0672-6297","contributorId":168997,"corporation":false,"usgs":true,"family":"Jelks","given":"Howard","email":"hjelks@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":797407,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, John R","contributorId":239619,"corporation":false,"usgs":false,"family":"Knight","given":"John","email":"","middleInitial":"R","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":797408,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Allen, Micheal S","contributorId":239622,"corporation":false,"usgs":false,"family":"Allen","given":"Micheal S","affiliations":[{"id":47938,"text":"Fisheries and Aquatic Sciences Program, University of Florida","active":true,"usgs":false}],"preferred":false,"id":797409,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70214673,"text":"70214673 - 2020 - Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing","interactions":[],"lastModifiedDate":"2020-10-02T13:24:58.347144","indexId":"70214673","displayToPublicDate":"2020-04-29T08:21:42","publicationYear":"2020","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":"Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing","docAbstract":"Californias Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 20072017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n=68, R2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha 2,524 ha (95% C.I.), and 13,012 ha 1,384 ha for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffes test, p<0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z-scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2153","usgsCitation":"Byrd, K.B., Lorenz, A., Anderson, J., Wallace, C., Kara Moore-O'Leary, Isola, J., Ortega, R., and Reiter, M., 2020, Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing: Ecological Applications, v. 30, no. 7, e02153, 20 p., https://doi.org/10.1002/eap.2153.","productDescription":"e02153, 20 p.","ipdsId":"IP-112842","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":378986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.16796875,\n 40.48038142908172\n ],\n [\n -122.431640625,\n 40.713955826286046\n ],\n [\n -123.00292968749999,\n 40.34654412118006\n ],\n [\n -122.958984375,\n 39.26628442213066\n ],\n [\n -122.431640625,\n 38.58252615935333\n ],\n [\n -121.9482421875,\n 37.33522435930639\n ],\n [\n -120.5419921875,\n 36.06686213257888\n ],\n [\n -119.4873046875,\n 35.02999636902566\n ],\n [\n -119.00390625,\n 34.994003757575776\n ],\n [\n -118.564453125,\n 35.209721645221386\n ],\n [\n -118.95996093749999,\n 36.35052700542763\n ],\n [\n -120.0146484375,\n 37.055177106660814\n ],\n [\n -121.201171875,\n 38.89103282648846\n ],\n [\n -122.16796875,\n 40.48038142908172\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800393,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lorenz, Austen 0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, James","contributorId":242025,"corporation":false,"usgs":false,"family":"Anderson","given":"James","affiliations":[{"id":40562,"text":"Golder Associates","active":true,"usgs":false}],"preferred":false,"id":800395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wallace, Cynthia 0000-0003-0001-8828 cwallace@usgs.gov","orcid":"https://orcid.org/0000-0003-0001-8828","contributorId":149179,"corporation":false,"usgs":true,"family":"Wallace","given":"Cynthia","email":"cwallace@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":800396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kara Moore-O'Leary","contributorId":242031,"corporation":false,"usgs":false,"family":"Kara Moore-O'Leary","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800397,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Isola, Jennifer","contributorId":242027,"corporation":false,"usgs":false,"family":"Isola","given":"Jennifer","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":800398,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ortega, Ricardo","contributorId":242028,"corporation":false,"usgs":false,"family":"Ortega","given":"Ricardo","email":"","affiliations":[{"id":48476,"text":"Grassland Water District","active":true,"usgs":false}],"preferred":false,"id":800399,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Reiter, Matt","contributorId":242029,"corporation":false,"usgs":false,"family":"Reiter","given":"Matt","email":"","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":800400,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70206720,"text":"tm7C24 - 2020 - Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences","interactions":[],"lastModifiedDate":"2020-04-29T12:04:07.712559","indexId":"tm7C24","displayToPublicDate":"2020-04-28T15:10:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C24","displayTitle":"Bayesian Modeling of Non-Stationary, Univariate, Spatial Data for the Earth Sciences","title":"Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences","docAbstract":"Some Earth science data, such as geochemical measurements of element concentrations, are non-stationary—the mean and the standard deviation vary spatially. It is important to estimate the spatial variations in both statistics because such information is indicative of geological and other Earth processes. To this end, an estimation method is formulated as a Bayesian hierarchical model. The method represents the spatially varying mean and the spatially varying standard deviation with basis functions; this formulation implicitly accounts for a spatially varying covariance function. A unique advantage of this method is that it can map the mean, the standard deviation, quantiles, and exceedance probabilities. The method is demonstrated by mapping titanium concentrations, which are measured in the coastal plain of the southeastern United States. Various checks demonstrate that the model fits the data and that the estimated statistics are geologically plausible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C24","usgsCitation":"Ellefsen, K.J., and Van Gosen, B.S., 2020, Bayesian modeling of non-stationary, univariate, spatial data for the Earth sciences: U.S. Geological Survey Techniques and Methods, book 7, chap. C24, 20 p., https://doi.org/10.3133/tm7C24.","productDescription":"Report: iii, 20 p.; Companion File","onlineOnly":"Y","ipdsId":"IP-098004","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":374242,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c24/tm7c24.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7 C-24"},{"id":374257,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c24/supplementary_materials.zip","text":"Supplementary Materials","size":"12.0 kB","linkFileType":{"id":6,"text":"zip"},"description":"T and M 7 C-24 Supplementary Materials"},{"id":374241,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c24/coverthb.jpg"},{"id":374243,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C20","text":"Techniques and Methods 7-C20—","linkHelpText":"User Guide to Bayesian Modeling of Non-Stationary, Univariate, Spatial Data Using R-Language Package BMNUS"}],"contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
Bayesian modeling of non-stationary, univariate, spatial data is performed using the R-language package BMNUS. A unique advantage of this package is that it can map the mean, standard deviation, quantiles, and probability of exceeding a specified value. The package includes several R-language classes that prepare the data for the modeling, help select suitable model parameters, and help analyze the results. This user guide describes the BMNUS package and presents step-by-step instructions to model data that accompany the package.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C20","collaboration":"","usgsCitation":"Ellefsen, K.J, Goldman, M.A., and Van Gosen, B.S., 2020, User guide to the bayesian modeling of non-stationary, univariate, spatial data using R language package BMNUS: U.S. Geological Survey Techniques and Methods, book 7, chap. 20, 27 p., https://doi.org/10.3133/tm7C20.","productDescription":"Report: iv, 27 p.; 6 Companion Files","onlineOnly":"Y","ipdsId":"IP-096956","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":374236,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c20/coverthb.jpg"},{"id":374237,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c20/tm7c20.pdf","text":"Report","size":"1.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 7 C-20"},{"id":374281,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/ScriptsInUsersGuide.R","text":"Scripts in Users Guide","size":"24.0 kB","description":"T & M 7-C20 Scripts in Users Guide"},{"id":374238,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C24","text":"Techniques and Methods 7-C24—","linkHelpText":"Bayesian Modeling of Non-Stationary, Univariate, Spatial Data for the Earth Sciences"},{"id":374282,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/BMNUS_1.0.0.tar.gz","text":"BMNUS Software Package","size":"308.kB","description":"T & M 7-C20 BMNUS Software Package"},{"id":374286,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/RepeatedMeasurements_1.0.0.tar.gz","text":"RepeatedMeasurements Software Package","size":"28.0 kB","description":"T & M 7-C20 RepeatedMeasurements Software Package"},{"id":374283,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/BasicCodaFunctions_1.0.0.tar.gz","text":"BasicCodaFunctions Software Package","size":"16.0 kB","description":"T & M 7-C20 BasicCodaFunctions Software Package"},{"id":374285,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/PairedMeasurements_1.0.0.tar.gz","text":"PairedMeasurements Software Package","size":"16.0 kB","description":"T & M 7-C20 PairedMeasurements Software Package"},{"id":374284,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c20/MappingUtilities_1.0.0.tar.gz","text":"MapUtilities Software Package","size":"8.0 kB","description":"T & M 7-C20 MapUtilities Software Package"}],"contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, Mail Stop 973
Denver, CO 80225
Decades of research has concluded that the percent of impervious surface cover in a watershed is strongly linked to negative impacts on urban stream health. Recently, there has been a push by municipalities to offset these effects by installing structural stormwater control measures (SCMs), which are landscape features designed to retain and reduce runoff to mitigate the effects of urbanisation on event hydrology. The goal of this study is to build generalisable relationships between the level of SCM implementation in urban watersheds and resulting changes to hydrology. A literature review of 185 peer‐reviewed studies of watershed‐scale SCM implementation across the globe was used to identify 52 modelling studies suitable for a meta‐analysis to build statistical relationships between SCM implementation and hydrologic change. Hydrologic change is quantified as the percent reduction in storm event runoff volume and peak flow between a watershed with SCMs relative to a (near) identical control watershed without SCMs. Results show that for each additional 1% of SCM‐mitigated impervious area in a watershed, there is an additional 0.43% reduction in runoff and a 0.60% reduction in peak flow. Values of SCM implementation required to produce a change in water quantity metrics were identified at varying levels of probability. For example, there is a 90% probability (high confidence) of at least a 1% reduction in peak flow with mitigation of 33% of impervious surfaces. However, as the reduction target increases or mitigated impervious surface decreases, the probability of reaching the reduction target also decreases. These relationships can be used by managers to plan SCM implementation at the watershed scale.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13784","usgsCitation":"Bell, C.D., Wolfand, J.M., Panos, C.L., Bhaskar, A.S., Gilliom, R.L., Hogue, T.S., Hopkins, K.G., and Jefferson, A.J., 2020, Stormwater control impacts on runoff volume and peak flow: A meta-analysis of watershed modelling studies: Hydrological Processes, v. 34, no. 14, p. 3134-3152, https://doi.org/10.1002/hyp.13784.","productDescription":"19 p.","startPage":"3134","endPage":"3152","ipdsId":"IP-114115","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":456920,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13784","text":"Publisher Index Page"},{"id":375409,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"14","noUsgsAuthors":false,"publicationDate":"2020-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Bell, Colin D.","contributorId":215502,"corporation":false,"usgs":false,"family":"Bell","given":"Colin","email":"","middleInitial":"D.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfand, Jordyn M.","contributorId":225130,"corporation":false,"usgs":false,"family":"Wolfand","given":"Jordyn","email":"","middleInitial":"M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Panos, Chelsea L.","contributorId":225131,"corporation":false,"usgs":false,"family":"Panos","given":"Chelsea","email":"","middleInitial":"L.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bhaskar, Aditi S.","contributorId":199824,"corporation":false,"usgs":false,"family":"Bhaskar","given":"Aditi","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":790477,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gilliom, Ryan L.","contributorId":225132,"corporation":false,"usgs":false,"family":"Gilliom","given":"Ryan","email":"","middleInitial":"L.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790478,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":790479,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790480,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jefferson, Anne J.","contributorId":199823,"corporation":false,"usgs":false,"family":"Jefferson","given":"Anne","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":790481,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210775,"text":"70210775 - 2020 - Polymeric nanofiber-carbon nanotube composite mats as fast-equilibrium passive samplers for polar organic pollutants","interactions":[],"lastModifiedDate":"2020-06-24T13:26:07.192535","indexId":"70210775","displayToPublicDate":"2020-04-28T08:21:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Polymeric nanofiber-carbon nanotube composite mats as fast-equilibrium passive samplers for polar organic pollutants","docAbstract":"To improve the performance of polymeric electrospun nanofiber mats (ENMs) for equilibrium passive sampling applications in water, we integrated two types of multiwalled carbon nanotubes (CNTs; with and without surface carboxyl groups) into polyacrylonitrile (PAN) and polystyrene (PS) ENMs. For 11 polar and moderately hydrophobic compounds (−0.07 ≤ logKOW ≤ 3.13), 90% of equilibrium uptake was achieved in under 0.8 days (t90% values) in nonmixed ENM-CNT systems. Sorption capacity of ENM-CNTs was between 2- and 50-fold greater than pure polymer ENMs, with equilibrium partition coefficients (KENM-W values) ranging from 1.4 to 3.1 log units (L/kg) depending on polymer type (hydrophilic PAN or hydrophobic PS), CNT loading (i.e., values increased with weight percent (wt %) of CNTs), and CNT type (i.e., greater uptake with carboxylated CNTs composites). During field deployment at Muddy Creek in North Liberty, Iowa, optimal ENM-CNTs (PAN with 20 wt % carboxylated CNTs) yielded atrazine concentrations in surface water with a 40% difference relative to analysis of a same-day grab sample. We also observed a mean percent difference of 30 (±20)% when comparing ENM-CNT sampler results to grab sample data collected within 1 week of deployment. With their rapid, high capacity uptake and small material footprint, ENM-CNT equilibrium passive samplers represent a promising alternative to complement traditional integrative passive samplers while offering convenience over large volume grab sampling.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c00609","usgsCitation":"Qian, J., Martinez, A., Marek, R.F., Nagorzanski, M.R., Zhi, H., Furlong, E., Kolpin, D., LeFevre, G.H., and Cwiertny, D.M., 2020, Polymeric nanofiber-carbon nanotube composite mats as fast-equilibrium passive samplers for polar organic pollutants: Environmental Science & Technology, v. 54, no. 11, p. 6703-6712, https://doi.org/10.1021/acs.est.0c00609.","productDescription":"10 p.","startPage":"6703","endPage":"6712","ipdsId":"IP-114860","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":456924,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7665838","text":"External Repository"},{"id":375847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Qian, Jiajie","contributorId":225499,"corporation":false,"usgs":false,"family":"Qian","given":"Jiajie","email":"","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martinez, Andres","contributorId":225500,"corporation":false,"usgs":false,"family":"Martinez","given":"Andres","email":"","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marek, Rachel F","contributorId":225501,"corporation":false,"usgs":false,"family":"Marek","given":"Rachel","email":"","middleInitial":"F","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nagorzanski, Matthew R.","contributorId":211881,"corporation":false,"usgs":false,"family":"Nagorzanski","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhi, Hui","contributorId":225502,"corporation":false,"usgs":false,"family":"Zhi","given":"Hui","email":"","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":791358,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Furlong, Edward 0000-0002-7305-4603","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":213730,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":791359,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":205652,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791360,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LeFevre, Gregory H.","contributorId":211880,"corporation":false,"usgs":false,"family":"LeFevre","given":"Gregory","email":"","middleInitial":"H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":true,"id":791361,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cwiertny, David M.","contributorId":190557,"corporation":false,"usgs":false,"family":"Cwiertny","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":791362,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209877,"text":"70209877 - 2020 - Economic, land use, and ecosystem services impacts of Rwanda's Green Growth Strategy: An application of the IEEM+ESM platform","interactions":[],"lastModifiedDate":"2020-05-05T13:12:46.436522","indexId":"70209877","displayToPublicDate":"2020-04-28T08:08:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Economic, land use, and ecosystem services impacts of Rwanda's Green Growth Strategy: An application of the IEEM+ESM platform","docAbstract":"We develop and link the Integrated Economic-Environmental Modeling (IEEM) Platform to ecosystem services modeling (ESM). The IEEM+ESM Platform is an innovative decision-making framework for exploring complex public policy goals and elucidating synergies and trade-offs between alternative policy portfolios. The IEEM+ESM approach is powerful in its ability to shed light on (i) change in land use and ecosystem services driven by public policy and the supply and demand responses of businesses and households; and (ii) impacts on standard economic indicators of concern to Ministries of Finance such as gross domestic product and employment, as well as changes in wealth and ecosystem services. The IEEM+ESM approach is being adopted rapidly and by the end of 2020, IEEM+ESM Platforms will be implemented for about 25 countries. To demonstrate the insights generated by the IEEM+ESM approach, we apply it to the analysis of alternative green growth strategies in Rwanda, a country that has made strong progress in reducing poverty and enhancing economic growth in the last 15 years. The case of Rwanda is particularly compelling as it faces intense pressure on its natural capital base and ecosystem services, already with the highest population density in Africa, which is projected to double by 2050. In applying IEEM+ESM and comparing the outcomes of Rwanda’s green growth policies, increasing fertilization of agricultural crops shows the largest economic gains but also trade-offs in environmental quality reflected through higher nutrient export and reduced water quality. Combining crop fertilization with forest plantations better balances critical ecosystem services and their role in underpinning economic development as Rwanda progresses toward its target of middle-income status by 2035. This application to Rwanda’s green growth strategy demonstrates the value-added of the IEEM+ESM approach in generating results that speak to both economic outcomes and impacts on market and non-market ecosystem services.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.138779","collaboration":"","usgsCitation":"Banerjee, O., Bagstad, K.J., Cicowiecz, M., Dudek, S., Horridge, M., Alavalapati, J., Masozera, M.K., Rukundo, E., and Rutebuka, E., 2020, Economic, land use, and ecosystem services impacts of Rwanda's Green Growth Strategy: An application of the IEEM+ESM platform: Science of the Total Environment, v. 729, no. , https://doi.org/10.1016/j.scitotenv.2020.138779.","productDescription":"138779, 21 p.","startPage":"","ipdsId":"IP-110054","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":456930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.138779","text":"Publisher Index Page"},{"id":374453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Rwanda","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[30.4191,-1.13466],[30.81613,-1.69891],[30.75831,-2.28725],[30.4697,-2.41386],[29.93836,-2.34849],[29.63218,-2.91786],[29.02493,-2.83926],[29.11748,-2.29221],[29.25483,-2.21511],[29.29189,-1.62006],[29.57947,-1.34131],[29.82152,-1.44332],[30.4191,-1.13466]]]},\"properties\":{\"name\":\"Rwanda\"}}]}","volume":"729","issue":"","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Banerjee, Onil","contributorId":224437,"corporation":false,"usgs":false,"family":"Banerjee","given":"Onil","email":"","affiliations":[{"id":40887,"text":"Inter-American Development Bank","active":true,"usgs":false}],"preferred":false,"id":788365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":788366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cicowiecz, Martin","contributorId":224438,"corporation":false,"usgs":false,"family":"Cicowiecz","given":"Martin","email":"","affiliations":[{"id":40888,"text":"Universidad Nacional de la Plata","active":true,"usgs":false}],"preferred":false,"id":788367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dudek, Sebastian","contributorId":224439,"corporation":false,"usgs":false,"family":"Dudek","given":"Sebastian","email":"","affiliations":[{"id":34928,"text":"Independent Researcher","active":true,"usgs":false}],"preferred":false,"id":788368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horridge, Mark 0000-0002-1070-5763","orcid":"https://orcid.org/0000-0002-1070-5763","contributorId":224440,"corporation":false,"usgs":false,"family":"Horridge","given":"Mark","email":"","affiliations":[{"id":27874,"text":"Victoria University","active":true,"usgs":false}],"preferred":false,"id":788369,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alavalapati, Janaki","contributorId":224441,"corporation":false,"usgs":false,"family":"Alavalapati","given":"Janaki","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":788370,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Masozera, Michel K.","contributorId":201300,"corporation":false,"usgs":false,"family":"Masozera","given":"Michel","email":"","middleInitial":"K.","affiliations":[{"id":35968,"text":"Wildlife Conservation Society, Rwanda Program","active":true,"usgs":false}],"preferred":false,"id":788371,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rukundo, Emmanuel 0000-0002-3220-3422","orcid":"https://orcid.org/0000-0002-3220-3422","contributorId":222903,"corporation":false,"usgs":false,"family":"Rukundo","given":"Emmanuel","email":"","affiliations":[{"id":16866,"text":"Beijing Normal University","active":true,"usgs":false}],"preferred":false,"id":788372,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rutebuka, Evariste 0000-0001-9267-3349","orcid":"https://orcid.org/0000-0001-9267-3349","contributorId":222904,"corporation":false,"usgs":false,"family":"Rutebuka","given":"Evariste","email":"","affiliations":[{"id":40626,"text":"University of Ibadan","active":true,"usgs":false}],"preferred":false,"id":788373,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209785,"text":"70209785 - 2020 - Longitudinal, lateral, vertical, and temporal thermal heterogeneity in a large impounded river: Implications for cold-water refuges","interactions":[],"lastModifiedDate":"2020-04-29T13:06:52.904537","indexId":"70209785","displayToPublicDate":"2020-04-28T08:04:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Longitudinal, lateral, vertical, and temporal thermal heterogeneity in a large impounded river: Implications for cold-water refuges","docAbstract":"Dam operations can affect mixing of the water column, thereby influencing thermal heterogeneity spatially and temporally. This occurs by restricting or eliminating connectivity in longitudinal, lateral, vertical, and temporal dimensions. We examined thermal heterogeneity across space and time and identified potential cold-water refuges for salmonids in a large impounded river in inland northwestern USA. To describe these patterns, we used thermal infrared (TIR) imagery, in situ thermographs, and high-resolution, 3-D hydraulic mapping. We explained the median water temperature and probability of occurrence of cool-water areas using generalized additive models (GAMs) at reach and subcatchment scales, and we evaluated potential cold-water refuge occurrence in relation to these patterns. We demonstrated that (1) lateral contributions from tributaries dominated thermal heterogeneity, (2) thermal variability at confluences was approximately an order of magnitude greater than of the main stem, (3) potential cold-water refuges were mostly found at confluences, and (4) the probability of occurrence of cool areas and median water temperature were associated with channel geomorphology and distance from dam. These findings highlight the importance of using multiple approaches to describe thermal heterogeneity in large, impounded rivers and the need to incorporate these types of rivers in the understanding of thermal riverscapes because of their limited representation in the literature.","language":"English","publisher":"MDPI","doi":"10.3390/rs12091386","collaboration":"","usgsCitation":"Mejia, F.H., Torgersen, C.E., Berntsen, E.K., Maroney, J.R., Connor, J., Fullerton, A.H., Ebersole, J.L., and Lorang, M.L., 2020, Longitudinal, lateral, vertical, and temporal thermal heterogeneity in a large impounded river: Implications for cold-water refuges: Remote Sensing, v. 12, no. 9, https://doi.org/10.3390/rs12091386.","productDescription":"1386, 29 p.","startPage":"","ipdsId":"IP-116596","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":456932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12091386","text":"Publisher Index Page"},{"id":374347,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-04-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Mejia, Francine H. 0000-0003-4447-231X","orcid":"https://orcid.org/0000-0003-4447-231X","contributorId":214345,"corporation":false,"usgs":true,"family":"Mejia","given":"Francine","email":"","middleInitial":"H.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":788002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":788003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berntsen, Eric K","contributorId":214885,"corporation":false,"usgs":false,"family":"Berntsen","given":"Eric","email":"","middleInitial":"K","affiliations":[{"id":39131,"text":"Kalispel Tribe of Indians","active":true,"usgs":false}],"preferred":false,"id":788004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maroney, Joseph R","contributorId":224367,"corporation":false,"usgs":false,"family":"Maroney","given":"Joseph","email":"","middleInitial":"R","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":788005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Connor, J M","contributorId":224368,"corporation":false,"usgs":false,"family":"Connor","given":"J M","affiliations":[{"id":40867,"text":"Kalispel Tribe Natural Resources Department","active":true,"usgs":false}],"preferred":false,"id":788006,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fullerton, Aimee H.","contributorId":146936,"corporation":false,"usgs":false,"family":"Fullerton","given":"Aimee","email":"","middleInitial":"H.","affiliations":[{"id":12641,"text":"NOAA NMFS","active":true,"usgs":false}],"preferred":false,"id":788007,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebersole, Joseph L.","contributorId":146938,"corporation":false,"usgs":false,"family":"Ebersole","given":"Joseph","email":"","middleInitial":"L.","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":788008,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lorang, Mark L","contributorId":224369,"corporation":false,"usgs":false,"family":"Lorang","given":"Mark","email":"","middleInitial":"L","affiliations":[{"id":40868,"text":"FreshwaterMap","active":true,"usgs":false}],"preferred":false,"id":788009,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211590,"text":"70211590 - 2020 - Time-dependent accumulation of Cd, Co, Cu, Ni, and Zn in mayfly and caddisfly larvae in experimental streams: Metal sensitivity, uptake pathways, and mixture toxicity","interactions":[],"lastModifiedDate":"2020-08-04T13:01:40.624742","indexId":"70211590","displayToPublicDate":"2020-04-28T07:56:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Time-dependent accumulation of Cd, Co, Cu, Ni, and Zn in mayfly and caddisfly larvae in experimental streams: Metal sensitivity, uptake pathways, and mixture toxicity","docAbstract":"Conceptual and quantitative models were developed to assess time-dependent processes in four sequential experimental stream studies that determined abundances of natural communities of mayfly and caddisfly larvae dosed with single metals (Cd, Co, Cu, Ni, Zn) or multiple metals (Cd+Zn, Co+Cu, Cu+Ni, Cu+Zn, Ni+Zn, Cd+Cu+Zn, Co+Cu+Ni, Cu+Ni+Zn). Metal mixtures contained environmentally relevant metal ratios found in mine drainage. Free metal ion concentrations, accumulation of metals by periphyton, and metal uptake by four families of aquatic insect larvae were either measured (Brachycentridae) or predicted (Ephemerellidae, Heptageniidae, Hydropsychidae) using equilibrium and biodynamic models. Toxicity functions, which included metal accumulations by larvae and metal potencies, were linked to abundances of the insect families. Model results indicated that mayflies accumulated more metal than caddisflies and the relative importance of metal uptake by larvae via dissolved or dietary pathways highly depended on metal uptake rate constants for each insect family and concentrations of metals in food and water. For solution compositions in the experimental streams, accumulations of Cd, Cu, and Zn in larvae occurred primarily through dietary uptake, whereas uptake of dissolved metal was more important for Co and Ni accumulations. Cd, Cu, and Ni were major contributors to toxicity in metal mixtures and for metal ratios examined. Our conceptual approach and quantitative results should aid in designing laboratory experiments and field studies that evaluate metal uptake pathways and metal mixture toxicity to aquatic biota.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.139011","usgsCitation":"Balistrieri, L.S., Mebane, C.A., and Schmidt, T., 2020, Time-dependent accumulation of Cd, Co, Cu, Ni, and Zn in mayfly and caddisfly larvae in experimental streams: Metal sensitivity, uptake pathways, and mixture toxicity: Science of the Total Environment, v. 732, 139011, 16 p., https://doi.org/10.1016/j.scitotenv.2020.139011.","productDescription":"139011, 16 p.","ipdsId":"IP-112332","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":456935,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.139011","text":"Publisher Index Page"},{"id":377004,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"732","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Balistrieri, Laurie S. 0000-0002-6359-3849 balistri@usgs.gov","orcid":"https://orcid.org/0000-0002-6359-3849","contributorId":1406,"corporation":false,"usgs":true,"family":"Balistrieri","given":"Laurie","email":"balistri@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":794740,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794742,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}